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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1568529210.1371/journal.pbio.0030038Research ArticleBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyPlant ScienceOryzaThe Genomes of Oryza sativa: A History of Duplications The Genomes of Oryza sativaYu Jun [email protected] 1 2 Wang Jun 1 2 Lin Wei 1 Li Songgang 1 3 Li Heng 1 4 Zhou Jun 1 Ni Peixiang 1 Dong Wei 1 Hu Songnian 2 Zeng Changqing 1 Zhang Jianguo 1 Zhang Yong 1 3 Li Ruiqiang 1 Xu Zuyuan 1 Li Shengting 1 Li Xianran 1 Zheng Hongkun 1 Cong Lijuan 1 Lin Liang 1 Yin Jianning 1 Geng Jianing 1 Li Guangyuan 1 Shi Jianping 1 Liu Juan 1 Lv Hong 1 Li Jun 1 Wang Jing 1 3 Deng Yajun 1 Ran Longhua 5 Shi Xiaoli 1 3 Wang Xiyin 1 3 Wu Qingfa 1 Li Changfeng 1 Ren Xiaoyu 1 Wang Jingqiang 1 Wang Xiaoling 1 Li Dawei 1 Liu Dongyuan 1 Zhang Xiaowei 1 Ji Zhendong 1 Zhao Wenming 1 Sun Yongqiao 1 Zhang Zhenpeng 1 Bao Jingyue 1 Han Yujun 1 Dong Lingli 1 Ji Jia 1 Chen Peng 1 Wu Shuming 1 Liu Jinsong 1 Xiao Ying 1 Bu Dongbo 6 Tan Jianlong 6 Yang Li 1 Ye Chen 1 Zhang Jingfen 6 Xu Jingyi 6 Zhou Yan 2 Yu Yingpu 2 Zhang Bing 2 Zhuang Shulin 2 Wei Haibin 2 Liu Bin 1 Lei Meng 1 Yu Hong 2 Li Yuanzhe 1 Xu Hao 2 Wei Shulin 1 He Ximiao 1 Fang Lijun 2 Zhang Zengjin 1 Zhang Yunze 1 Huang Xiangang 1 Su Zhixi 2 Tong Wei 1 Li Jinhong 2 Tong Zongzhong 1 Li Shuangli 1 Ye Jia 2 Wang Lishun 1 Fang Lin 1 Lei Tingting 1 Chen Chen 1 Chen Huan 2 Xu Zhao 1 Li Haihong 1 Huang Haiyan 1 Zhang Feng 1 Xu Huayong 2 Li Na 1 Zhao Caifeng 1 Li Shuting 1 Dong Lijun 1 Huang Yanqing 1 Li Long 1 Xi Yan 1 Qi Qiuhui 1 Li Wenjie 1 Zhang Bo 1 Hu Wei 1 Zhang Yanling 1 Tian Xiangjun 2 Jiao Yongzhi 1 Liang Xiaohu 1 Jin Jiao 1 7 Gao Lei 1 4 Zheng Weimou 1 4 Hao Bailin 1 4 Liu Siqi 1 2 Wang Wen 2 8 Yuan Longping 9 Cao Mengliang 9 McDermott Jason 10 Samudrala Ram 10 Wang Jian [email protected] 1 2 Wong Gane Ka-Shu [email protected] 1 2 11 Yang Huanming [email protected] 1 2 1Beijing Institute of Genomics of the Chinese Academy of Sciences, Beijing Genomics Institute, Beijing Proteomics InstituteBeijingChina2James D. Watson Institute of Genome Sciences of Zhejiang University, Hangzhou Genomics Institute, Key Laboratory of Genomic Bioinformatics of Zhejiang ProvinceHangzhouChina3College of Life Sciences, Peking UniversityBeijingChina4Institute of Theoretical Physics, Chinese Academy of SciencesBeijingChina5Beijing North Computation CenterBeijingChina6BioInformatics Laboratory, Institute of Computing Technology, Chinese Academy of SciencesBeijingChina7Department of Statistics and Financial Mathematics, College of Mathematical Sciences, Beijing Normal UniversityBeijingChina8Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina9National Hybrid Rice R & D CenterChangshaChina10Computational Genomics Group, Department of MicrobiologyUniversity of Washington, Seattle, WashingtonUnited States of America11UW Genome Center, Department of Medicine, University of WashingtonSeattle, WashingtonUnited States of AmericaBennetzen Jeff Academic EditorUniversity of GeorgiaUnited States of America2 2005 1 2 2005 1 2 2005 3 2 e3824 5 2004 23 11 2004 Copyright: © 2005 Yu et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Rice Genome Approaches Completion We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family. Comparative genome sequencing of indica and japonica rice reveals that duplication of genes and genomic regions has played a major part in the evolution of grass genomes ==== Body Introduction The importance of the rice genome is reflected in the fact that rice was the first crop plant to have its genome sequenced; astonishingly, it was sequenced by four independent research teams at Beijing Institute of Genomics [1], Syngenta [2], International Rice Genome Sequencing Project (IRGSP) [3,4,5], and Monsanto. Beijing analyzed the two parental strains, 93–11 and PA64s, for a popular land race of super-hybrid rice, LYP9, and released a 4.2x draft for 93–11, a cultivar of the indica subspecies. This draft was acquired by a whole-genome shotgun (WGS) method [6]. Syngenta and IRGSP worked on Nipponbare, a cultivar of the japonica subspecies. Syngenta also used a WGS method and published a 6x draft. IRGSP used the clone-by-clone method [7] and released a 10x draft that incorporates the Syngenta data. Their publications include the finished version of Chromosomes 1, 4, and 10. These efforts have been widely hailed not only because rice feeds much of the world's population but also because rice is expected, through comparative analyses, to play a major role in understanding the grass family of crop plants [8,9,10,11,12,13]. We will report on an improved version of Beijing indica, which brings the coverage of the 93–11 dataset up to 6.28x. In addition, we improved Syngenta japonica by reassembling their sequence from the raw traces (National Center for Biotechnology Information Trace Archive; http://www.ncbi.nlm.nih.gov/Traces/trace.cgi?) and combining that information with our 93–11 assembly. We achieved almost three orders of magnitude of improvement in long-range contiguity, and put essentially all the genes on the map, by combining the two WGS assemblies in a manner that preserves the single nucleotide polymorphism (SNP) information for indica–japonica differences. Both of these WGS assemblies were constructed independent of the information in IRGSP japonica. Hence, the two japonica assemblies allow us to compare the WGS and clone-by-clone methods objectively. By taking the clone-by-clone assembly as a “gold standard,” we can estimate the intrinsic misassembly rates for our two WGS assemblies—not just the japonica WGS but also the indica WGS, as identical assembly procedures are used and both contain 6x coverage. If we compare IRGSP japonica to Beijing indica, any increases in the discrepancy rate beyond this intrinsic misassembly rate can be attributed to indica–japonica differences. In the same spirit, genes are identified for all three assemblies using the same annotation procedures, to assess gene content differences without the methodological inconsistencies that have plagued previous comparisons. Finally, we introduce a simple method for analyzing gene duplications that resolves the contradictory claims that rice is an ancient aneuploid [14] and an ancient polyploid [15]. In the process, we demonstrate that duplication of individual genes plays a major role in the continuing evolution of the grass genomes. Both WGS sequences, and details of our analyses, are available from our own Web site (Beijing Genomics Institute Rice Information System; http://rise.genomics.org.cn) [16]. The version of IRGSP japonica that we use was downloaded October 5, 2003, from GenBank and DNA Data Bank of Japan according to the guidelines at http://www.genome.arizona.edu/shotgun/rice/status and the physical map at http://rgp.dna.affrc.go.jp/IRGSP/download. Results WGS Assembly of indica and japonica Many legitimate concerns have been raised about the differing qualities of the rice sequences that have been published [17,18] and on the idea that they must be “finished” [19,20]. Higher quality is of course a good thing, but it does come at a cost, and lost in the discussion is the reality that cost–benefit factors have always been important in sequencing. Most notably, all genome projects to date have focused primarily on the euchromatic regions that can be cloned and sequenced, even though important genes are missed as a result. For example, an essential 5.1-Mb fertility gene [21] resides in the heterochromatic Y chromosome of the Drosophila genome. In plant genomes, costs are primarily driven by the intergenic retrotransposon clusters [22] that account for about half of the rice genome, and even more of the larger maize (6x) and wheat (38x) genomes. Hence, our objective is merely to have all the genes assembled in one piece, without fragmentation, and anchored to the maps. A similar objective has been proposed [23,24] for crop genomes in general. Our benchmark is the set of full-length japonica cDNAs from the Knowledge-Based Oryza Molecular-Biological Encyclopedia [25] that contains 19,079 nonredundant cDNAs (nr-KOME). We begin with a few definitions. At the end of any WGS, a substantial fraction of the reads (specifically, those whose sequences are highly repeated across the genome) are invariably left unassembled. The usable reads are assembled into contigs, scaffolds, and super-scaffolds. In a contig, the identity of every base is defined. In contrast, scaffolds and super-scaffolds have gaps (regions of known length but otherwise unknown base content). The difference is that one refers to the sequence before any linking information from indica and japonica sources are combined (scaffold) and the other refers to the sequence after they are combined (super-scaffold). All of the raw data that went into these WGS assemblies are listed in Table S1, and the assembly procedure itself is outlined in Figure 1. Figure 1 Basic Algorithm for Construction of Scaffolds and Super-Scaffolds We start with the smallest plasmids and progressively work our way up to the largest BACs. Only links with two or more pieces of supporting evidence are made. These include 34,190 “anchor points” constructed from a comparison of indica and japonica. Each anchor is a series of high-quality BlastN hits (typically 98.5% identity) put together by a dynamic programming algorithm that allows for small gaps to accommodate the polymorphic intergenic repeats. Typical anchor points contain four BlastN hits at a total size of 9 kb (including gaps). Notice how in the beginning indica and japonica are processed separately, to construct what we called scaffolds. Only at the end do we use data from one subspecies to link scaffolds in the other subspecies, and these are what we called super-scaffolds. Compared with our previous 4.2x assembly of indica, more shotgun reads and a few directed finishing reads were added to increase the coverage to 6.28x. We did not use the older assembly at all. Instead, we went back to the raw reads and reassembled them, with an updated version of RePS [26,27] that incorporates some recent concepts from Phusion [28]. Increasing coverage is essential for reducing single-base error rates. Based on the estimates from RePS, 97.2% and 94.6% of our new assembly has an error rate of better than 10−3 and 10−4, respectively. For the older assembly, the percentages were only 90.8% and 83.5%, respectively. Equally important, and as expected from Poisson sampling statistics [29], increasing coverage improves the scaffold size to a point where, even without additional finishing effort, most of the nr-KOME cDNAs can be aligned in one piece, without fragmentation. All we had to do was find a way to link these scaffolds together to create larger super-scaffolds, which could then be anchored to the physical [30] and genetic [31] maps. Mapped super-scaffolds for Beijing indica have a N50 size (the size above which half of the total length of a sequence dataset is found) of 8.3 Mb, which is a thousand times better than our previous draft, as shown in Table 1. We used an unorthodox method to construct super-scaffolds of megabase size from initial scaffolds of 30-kb size. Most of the increase in long-range contiguity came from combining the two WGS assemblies, not from the bacterial artificial chromosome (BAC) end pairs, which were of limited utility because their insert sizes were too large. Notice that in combining indica and japonica data, we use the alternate subspecies only for order and orientation information, not to fill missing bases. In other words, every base in the indica assembly is from indica. Not one single base is from japonica. Another key point is that Syngenta japonica is our reassembly of their raw data, not the published assembly. By using RePS for both WGS assemblies, we obtain error estimates for every base, which will later be essential for use in polymorphism detection. We would concede that if genes are ordered differently in indica and japonica, there is a small probability that by forcing the two subspecies together, we lose this information. However, there is no evidence of a major reordering of the genes because, if there were, it would have been seen in all these years of genetic mapping. The benefits thus outweigh the risks. Table 1 Summary of Assembled Contigs, Scaffolds, and Super-Scaffolds Each piece can be further subdivided on the basis of whether or not it is mapped and, if not, on the basis of its size. N50 refers to the size above which half of the total length of the sequence set can be found. An equivalent size for the unassembled reads is computed by dividing the number of high-quality Q20 bases (estimated single-base error rate of 10−2) by the effective shotgun coverage The total genome size, including the unassembled reads and the unmapped pieces of all sizes, is 466.3 Mb for Beijing indica and 433.2 Mb for Syngenta japonica. For this estimate, we added up all the pieces at the scaffold level (as opposed to the super-scaffold level, where the gap size estimates are taken from the alternate subspecies and may not be representative of the underlying genome). We believe this difference is real, because the two genome sizes are based on the same procedures and similar WGS datasets. Although many smaller pieces fall between the cracks in the maps, these unmapped pieces turn out to be extremely gene poor. Hence, in our submission to DNA Data Bank of Japan/European Molecular Biology Library/GenBank, we omit unassembled reads and unmapped pieces smaller than 2 kb, which has the advantage of also filtering out nonrice contaminants from inevitable mix-ups in the lab. Physical distance is defined along a pseudo-chromosome where gaps of estimated size larger than 200 kb (a typical BAC) are collapsed to 200 kb. Between adjacent super-scaffolds, where by definition we do not have an estimated gap size, we insert a 5-kb gap. To validate the long-range accuracy of our assemblies, we compared physical and genetic distances, as shown in Figures S1 and S2. We use only those 1,519 markers that can be found in all three rice assemblies by Blastn at E-values of 10−100. There are two classes of discrepancies. First, the marker is on different chromosomes. All three rice assemblies agree with each other but not with the genetic map in 135 of 152 such markers. In the second class, the disagreement is on positions within a chromosome, and all three rice assemblies agree with each other but not with the genetic map in 41 of 60 such markers. Only a small handful of discrepancies are unique to any one assembly. It is highly unlikely that all three rice assemblies will make the same mistake, so we conclude that on the scale of hundreds of kilobases, our WGS data are better than the genetic map. Computed over every five markers, the mean (median) recombination rate is 4.5 (4.2) cM/Mb. We do expect smaller-scale misassemblies in the WGS data, as, for example, in Beijing indica, 98.1%, 71.0%, and 39.3% of the unassembled, assembled-but-unmapped, and mapped pieces, respectively, contain 20-mer repeats that are estimated to occur at least twice in the genome. About half of these 20-mer repeats are recognizable transposable elements (TEs) in RepeatMasker (http://www.repeatmasker.org, and TE compositions in different categories of assembled data are summarized in Table S2. The most problematic misassemblies are those that occur within genes, as these affect our ability to annotate the genome. Hence, we compared the WGS data to gene sequences defined by nr-KOME and excised from IRGSP japonica. We searched for alignment discrepancies of at least 500 bp, consistent with misassembled reads, and interpreted any increase in the discrepancy rate from Syngenta japonica to Beijing indica as being due to polymorphic differences. There are remarkably few genes with discrepancies in coding exons, only 0.23% in Syngenta japonica and 1.44% in Beijing indica. If we include UTR exons and introns, the rates are 0.84% in Syngenta japonica and 5.65% in Beijing indica. Hence, the implication is that WGS misassemblies occur less frequently than polymorphic differences. Table 2 shows the number of nr-KOME cDNAs that are found in each of the three rice assemblies, using the criterion that 95% of the coding region must be alignable in BLAT [32]. Some cDNAs align to multiple pieces of the assembly, but most align to one single piece. Even if we consider only the latter case, all three rice assemblies are at least 91.2% complete. Regardless of the assembly, the gaps seem to be random, as genes that are fragmented in one assembly are often intact in another. Of the cDNAs, 98.1% can be found in one piece in either Beijing indica or Syngenta japonica (if we also insist that they be anchored to the map, this number becomes 97.7%). Combining all three rice assemblies results in 98.6% completeness. Strikingly, only 0.7% of the genes align to the unmapped Beijing indica sequence, despite the fact these unmapped data were 12.3% of the searched sequence. This is the first of many examples that we will provide to support the idea that the unmapped pieces are extremely gene poor. Table 2 Summary of nr-KOME cDNAs with Complete Alignments (Not Including UTRs) in Each of the Three Rice Assemblies We require that 95% of the gene be aligned, but there are two ways to count. “Found in genome” will accept fragmented genes that are aligned in multiple pieces, whereas “aligned in one piece” will not Gene Identification and Classification We used an unorthodox method for gene identification. The conventional method, epitomized by Ensembl [33], uses sequence similarity to known genes and proteins to remove erroneous predictions, which are a serious problem for vertebrates because of the preponderance of large, multiexon genes, some of which can be megabases in size. However, plant genes are only a few kilobases in size, and given that Arabidopsis is still the only other sequenced plant, the Ensembl approach would remove many valid genes in a misguided effort to control a less serious problem. We removed erroneous predictions by relying instead on the fact most of them are actually TEs that are mistakenly called genes. Ultimately, our method is vindicated by whole-genome microarray experiments using 70-mer oligos that are hybridized to mRNA from five different tissue types. One finds that 82% of predicted rice genes with no homologs in Arabidopsis can be detected in this manner, as opposed to 88% of predicted rice genes with homologs (L. Ma, J. Wang, C. Chen, X. Liu, N. Su, et al., unpublished data). For the purpose of discussion, we will classify rice genes as WH (with homolog) or NH (no homolog), based on sequence similarity to Arabidopsis, with the stringency set to a level that is typically found in the literature. Nucleotide sequences are translated into protein sequences, and the Arabidopsis genome is searched in all six reading frames using TBlastN at E-values of 10−7. Putative exons are chained together, and success is declared if we can account for either 50% of the protein or 100 residues. We are not concerned that more sensitive search algorithms might identify homologies that we missed. Even the best algorithms are limited in their ability to identify structural homology by sequence similarity [34]. The main objective is to show how genes that are highly homologous or nonhomologous are sufficiently different as to merit special attention in data analysis, and the simplest way to emphasize this is to draw a dividing line. For methodological consistency, we annotated all three rice assemblies using the same procedures. We use FGENESH [35] for gene prediction because it has been shown to be the best of the available ab initio algorithms for rice [1]. An updated performance assessment is shown in Figure S3. The challenge in removing erroneous predictions resulting from TEs lies in how we compensate for the fact that the database used by RepeatMasker is incomplete. Figure 2 demonstrates how grass genomes are organized as gene islands of low copy number separated by intergenic repeat clusters of high copy number. We set a dividing line at copy number 10, not because there are no TEs below it but because there are few genes above it. Specifically, for genes defined by nr-KOME, 99.4% of the exons and 98.1% of the introns are attributed to 20-mers of copy number under 10. Using the finished sequence of Chromosomes 1 and 10, we show in Figure S4 that the mean (median) sizes are 23.7 kb (9.6 kb) for gene islands and 5.6 kb (3.5 kb) for intergenic repeat clusters. Applying RepeatMasker to these intergenic repeat clusters only identifies 47.6% as TEs, overwhelmingly gypsy and copia. We therefore propose to filter the predictions by removing genes for which 50% of their coding region is attributable to any combination of RepeatMasker TEs or 20-mers of copy number over 10. Figure 2 A Region on Beijing indica Chromosome 2, Showing Three Gene Islands Separated by Two Intergenic Repeat Clusters of High 20-mer Copy Number Transposable elements identified by RepeatMasker are classified based on the nomenclature of Table S2. Depicted genes include both nr-KOME cDNAs and FGENESH predictions. Although this filter might remove some real genes, it removes only a small fraction of them, as demonstrated by the nr-KOME cDNAs, where it eliminates 0.9% of these genes. In contrast, applying this same filter to the FGENESH predictions eliminates 19%–22% of the gene set, as indicated in Table 3. We believe that most of the removed predictions are TEs and that the benefits of removing these artifacts outweigh the risks of removing real genes. After this procedure, the gene counts range from 49,088 (Beijing indica) to 45,824 (Syngenta japonica) to 43,635 (IRGSP japonica). Previous estimates for Chromosomes 1, 4, and 10 made no such correction and found slightly larger numbers. About 45%–47% of predicted genes are NH, in contrast to 34.3% of nr-KOME cDNAs. This discrepancy is due to a combination of prediction errors and the fact that NH genes are difficult to clone because they are poorly expressed (data not shown). Radically different numbers have been given for mean gene size, from 2.6 kb in Chromosome 10 to 4.5 kb in our previous article. As we show in Table 4, much of this discrepancy can be explained by differences in definition. Predicted genes have a mean (median) size of 2.5 kb (1.8 kb). We get the same result for nr-KOME if we exclude UTRs, but we get a size of 3.6 kb (2.9 kb) if we include UTRs. If we restrict the genes to WH genes, this raises the gene size to 4.0 kb (3.4 kb). Table 3 Number of FGENESH Predictions in All Three Rice Assemblies Filtering refers to the process in which we remove predictions where 50% of the coding region is attributable to any combination of RepeatMasker TEs or 20-mers of copy number over ten. EST confirmation requires 100 bp of exact match Table 4 Characteristics of FGENESH Predictions and nr-KOME cDNAs Predicted genes do not included UTRs. Mean (median) are both given Even after removing likely TEs, two particular subclasses warrant caution, as they contain a higher than normal rate of erroneous predictions, which is reflected in a reduced rate of confirmation by ESTs. Overall, we used 200,648 ESTs from indica, japonica, and other rice subspecies. The confirmation rule is exact match over 100 bp. Genes predicted in unmapped sequences are confirmed at much lower rates than genes predicted in mapped sequences—about 11 times lower, even after removing 3.4 times as many unmapped genes as likely TEs. Genes unique to only one assembly also show lower confirmation rates, by a factor of roughly nine, when compared with the 35,052–36,940 genes that are shared by all three assemblies, as summarized in Figure 3. A more detailed analysis is given in Table S3. What is important is that few of these genes are likely to be real. We can use the ratio of the EST confirmation rates to correct our gene count estimates. Beijing indica is computed as [(36,940 × 39.6) + (1967 × 28.1) + (1586 × 20.4) + (8595 × 4.9)]/39.6 = 40,216. Similarly, we get 37,794 for Syngenta japonica and 37,581 for IRGSP japonica. If unique genes are truly expressed at lower levels than shared genes, this procedure might underestimate the gene count. One should thus interpret these numbers as lower bounds. Figure 3 Overlapping FGENESH Predictions in All Three Rice Assemblies Two predictions are shared when 50% of their coding regions can be aligned. Because of imprecision in the predictions and overlap criteria, we get slightly different numbers for each assembly, and these are encoded through multiple color-coded numbers in the Venn diagram. EST confirmation requires 100 bp of exact match. Unlike the genes, we do not bother to show a different number for each assembly, because they are very similar. Using the same EST adjustments, the number of predicted genes in Beijing indica that are not found in either japonica assembly is 1,064. Conversely, Syngenta japonica has 1,517 predicted genes that are not in indica (the number for IRGSP japonica is 1,479). As a fraction of the totals, 2.2% and 3.3% of indica and japonica genes, respectively, are unique to the subspecies, which is plausibly comparable to the amount of sequence that might still be missing. There is little difference in gene content between indica and japonica, but major differences are seen in the intergenic regions. Only 260 Mb (72%) of the mapped sequences can be aligned. This remains true no matter how much we relax the alignment parameters, and despite the fact that we had 34,190 “anchor points” (see Figure 1), which ensure that the indica–japonica comparisons are always made between the same regions of the chromosomes from the two subspecies. This unalignable fraction would be even larger if unmapped and unassembled sequences were included. Notice also that 20-mer repeat content is 59.2% in mapped-but-unaligned regions, as compared to 31.8% in mapped-and-aligned regions. Everything that we see is consistent with the fact that plant intergenic regions are rapidly evolving [36]. As further proof of this fact, Table 5 shows the SNP rates in these alignable regions. The rates vary from as little as 3.0 SNP/kb in coding regions to as much as 27.6 SNP/kb in identifiable TEs. Table 5 Variation between indica and japonica Defined by SNP and Insertion–Deletion (Indel) Rates Variation rates for 5′ UTR, coding, intron, and 3′ UTR refer to gene regions defined by nr-KOME. To demonstrate where the high SNP rates come from, we consider regions of 20-mer copy number under ten and RepeatMasker TEs Biological functions are inferred by and displayed within the Bioverse framework [37,38] by combining more than seven of the latest computational techniques, including profile–profile comparison to well-curated protein families, motif discovery, and structural assignment/prediction. Note that we do not use transitive annotations, as their error propagation rates are too high. We present these results in Gene Ontology (GO) [39] and InterPro [40] formats. Functions are assigned to 60.2% of WH genes and even to 17.5% of NH genes, reflecting the fact that Bioverse uses highly sensitive techniques. Figure 4 shows a couple of our GO comparisons, focused on plant-specific categories in Gramene [41]. From the fraction of the gene set in each category, rice and Arabidopsis are remarkably similar. FGENESH-predicted genes and nr-KOME cDNAs exhibit very similar patterns too, confirming the unbiased nature of these cDNAs. InterPro domain categories tell much the same story, and these data are summarized in Table S4. Figure 4 Functional Classifications from GO, Focused on Plant-Specific Categories Outlined by Gramene (A) compares predicted genes from Arabidopsis and Beijing indica. (B) compares predicted genes from Beijing indica with nr-KOME cDNAs. We ignore categories with less than 0.1% of the genes. Bioverse is distinguished from other annotation pipelines in that it also determines protein–protein interactions. Two proteins are predicted to interact if they are both similar in sequence to proteins involved in known interactions. The known interactions are taken from numerous sources, including Protein Data Bank [42] and the Database of Interacting Proteins (which stores yeast two-hybrid studies, affinity column studies, and literature searches) [43]. The resultant network has 1,879 proteins/nodes with 8,902 unique interactions. Figure 5 highlights a small portion of this network, for defense proteins (i.e., classified as “defense related” under GO molecular function or “defense response” under GO biological process) and their direct neighbors in the network. Many occupy central positions, meaning the network would fall apart if they were removed. Such genes are essential for cell survival [44]. More details can be found at http://bioverse.compbio.washington.edu. Figure 5 A Sample Bioverse-Predicted Interaction Network for Defense Proteins and Their Direct Neighbors The symbols are colored to indicate some of the major GO categories under “molecular function.” We draw a cross over the symbol for an NH gene. Rectangles indicate proteins that are manually classified as being R-genes. They appear on genes that are not colored as defense, because some genes have multiple functions, not because of an annotation error. The white circles with green outline are unannotated genes that might also belong to this network, at a lower confidence. Figure S5 shows that, near the centromeres, there is an increase in TE density (especially for large class I TEs like gypsy and copia) and a decrease in gene density. A more detailed view is given by the pullout figures of Figure S6, right down to the level of individual genes and TEs, to emphasize the excellent level of concordance between the two different WGS assemblies: Beijing indica and Syngenta japonica. Evidence of Whole-Genome Duplication Duplication of individual genes, chromosomal segments, or even entire genomes is an important source of raw materials for gene genesis [45]. In the extreme case of a whole-genome duplication (WGD), convincing examples are difficult to find because of the expected rapid loss of duplicated genes and because the rate of individual gene duplication is high enough to mask any remnants of an ancient WGD [46]. Yeast was the first genome in which a WGD was detected [47]. In plants, the existence issue is not disputed, as polyploidy is common [48,49,50,51,52,53], but even with complete genome sequence, many details remain obscure. For Arabidopsis, the number and timing of these duplication events is still unknown [54,55,56,57,58,59]. For rice, segmental duplications were known [60,61,62] before the rice genome sequence was published. However, detailed analysis of this sequence has resulted in the contradictory assertions that rice is an ancient aneuploid [14] and an ancient polyploid [15]. Here, we resolve this conflict by showing that every conceivable class of duplication that could have happened did in fact happen, including a WGD. We accept that every class of duplication is present in the same genome, and we thus explicitly assign, to every homolog pair, a status as to the class of duplication from which it came. For the sake of discussion, we define three classes: segmental duplication of multiple genes along a chromosome, tandem duplication of individual genes, and a category called background duplications to encompass everything else that cannot be so easily classified. In this conception, a WGD is a collection of segmental duplications that cover a majority of the genome, all of which date back to a common time in evolutionary history. All three rice assemblies give the same result, so we show only Beijing indica. Unlike previous analyses, we avoid predicted genes. Instead, we define a homolog pair to be a single nr-KOME cDNA and one of its potentially many homologs within rice. These homologs are defined by translating the cDNA's coding sequence into protein and searching the rice genome in all six reading frames for putative exons, with TBlastN at E-values of 10−7. Exons in the same order and orientation are linked together, and success is declared if these linked exons can account for 50% of the original protein sequence. This technique has the advantage that the homolog need not be a cDNA or a predicted gene (as neither dataset is likely to be complete). In fact, the homolog might even be a remnant of an ancient duplication that is no longer a functional gene. Complications are found at two extremes. Many cDNAs have no homologs, but many others have too many homologs. In particular, 24.5% of WH genes have no homologs in rice, whereas 64.4% of NH genes have no homologs in rice. Because NH genes are dispersed throughout the genome, sandwiched between WH genes, we cannot adopt a strict colinearity rule in our search for duplicated segments. There would be too many exceptions. Conversely, when there is at least one homolog in rice, the mean (median) number of homologs per cDNA is 40 (5). Rather than deal with the complexities of this situation, we focus first on the cDNAs with one and only one homolog. This reduces the background duplication noise and allows us to identify trend lines indicative of segmental and tandem gene duplications. We can then add back those cDNAs with more than one homolog that we had rejected earlier by using our newly defined trend lines to constrain the choices. The above procedure leaves us with 2,271 homolog pairs (or cDNAs). We adopt a graphical approach, because in the presence of massive background noise, trend lines are often easier to identify by eye than by software. Figure 6 depicts Chromosomes 2 and 6, and Figure S7 depicts all 12 chromosomes. There are 18 pairs of duplicated segments that together cover 65.7% of the length of all the mapped super-scaffolds. The mean (median) number of homolog pairs per segment is 34 (23). The segment sizes are 6.9 Mb (5.4 Mb), and they differ by 43% (42%) within a segment pair, which is not at all unexpected given the rapidly evolving nature of the rice intergenic regions. Instances of multiple duplicated segments on the same chromosomal region are extremely rare, covering only 0.9% of the total length. No additional multilevel duplications are detected if we use cDNAs with up to two homologs, as opposed to those with only one. Notice also that there are duplicated segments on all 12 rice chromosomes, as summarized in Figure 7. Figure 6 Duplicated Segments in the Beijing indica Assembly Depicted here are the plots for Chromosomes 2 (A) and 6 (B). Each data point represents the coordinated genomic positions in a homolog pair, consisting of one nr-KOME cDNA and its one and only TBlastN homolog in rice. Shown on the x-axis is the position of a gene on the indicated chromosome, and shown on the y-axis is the position of its homolog on any of the rice chromosomes, with chromosome number encoded by the colors indicated on the legend at the right. Figure 7 Graphical View of All Duplicated Segments The 12 chromosomes are depicted along the perimeter of a circle, not in order but slightly rearranged so as to untangle the connections between segments. Overall, we cover 65.7% of the genome. One can date the duplications by computing the number of substitutions per silent site (Ks). Multiple substitution corrections are done within K-Estimator [63]. To improve our statistics, we now include the higher-order homologs (those cDNAs with more than one homolog that we had removed before). Table 6 shows that this doubles or triples the number of homolog pairs in every segment and brings the mean (median) to 74 (53). The resultant Ks distribution is shown in Figure 8. One pair of segments on Chromosomes 11 and 12 is more recent in origin and has more homolog pairs per unit length than all the others. It was previously identified in many publications. If we ignore this segment pair, the mean Ks is 0.69, dating the duplication event to 53 million years ago (Mya), assuming a neutral evolutionary rate of 6.5 × 10−9 substitutions per silent site per year [64]. Most of the uncertainties are due to the multiple-substitution corrections for Ks. Another popular algorithm for Ks [65] dates the duplication event to 94 Mya. Figure 8 Distribution of Substitutions per Silent Site (Ks) for Homolog Pairs in Segmental, Tandem, and Background Duplications In (A), contributions from the recent segmental duplication on Chromosomes 11 and 12 are colored in red. The tandem duplication data are shown on two different scales, one to emphasize the magnitude of the zero peak (B) and another to highlight the exponential decay (C). Background duplications are shown in (D). Table 6 Summary of Duplicated Segments in the Beijing indica Assembly We give start and stop positions on the pseudo-chromosome, segment sizes, number of homolog pairs, mean Ks rates, percentage of homolog pairs with Ks < 0.25, and flanking nr-KOME cDNAs. One set of numbers is for the initial analysis of those cDNAs with one and only one homolog. A second is for the analysis of additional cDNAs with higher-order homologs a Computed total and mean omit the recent segmental duplication on Chromosomes 11 and 12 Chr, Chromosome The molecular clock can also vary between genes and between taxa [66,67]. Evidence for the former is seen in the width of the distribution for Ks in Figure 8, which has a standard deviation of 49.8% based on individual homolog pairs (as opposed to 14.5% when based on duplicated segment pairs). We believe that the variation between genes will cancel out, but we cannot remove the systematic error resulting from the multiple substitution corrections or the potential error in the 6.5 × 10−9 evolutionary rate (which was derived from a small number of genes). However, all we really want to know is whether the duplication event occurred before or after the origin of the grasses, 55–70 Mya [68]. To this end, phylogenetic approaches can be used, albeit for a limited number of genes, because so few plants have been fully sequenced. A majority of these phylogenies indicate that the duplication event occurred before this pivotal point in evolution [14]. Almost certainly, the duplication event occurred after the divergence of monocots and eudicots, 170–235 Mya [69]. However, the best evidence for the statement that the duplication event must have predated the origin of the grasses is the fact that there is no other way to reconcile it with the widely observed synteny between different grass genomes [70]. In striking contrast, the Chromosome 11 to 12 duplication dates back to just 21 Mya, which postdates the origins of the grasses by a comfortable margin. If we accept that a WGD occurred before the divergence of maize–rice, and that a duplication in Chromosomes 11 and 12 occurred afterward, we might then expect to find two levels of duplication in this region of rice. We thus extended our analysis to consider cDNAs that map to as many as four loci. No indications of such a multilevel duplication could be found. Undaunted, we decided to try another approach and analyzed the maize–rice synteny, starting from the maize genetic map [71]. The results are given in Figures S8 and S9. We found 35 pairs of syntenic segments covering 71.4% and 52.9% of the maize and rice genomes, respectively. All previously identified segments are confirmed, except for those on Chromosomes 11 and 12 of rice. No synteny is found in the vicinity of this recent duplication. There are many explanations, and they need not contradict our hypothesis, as only 65.7% of the rice genome is in identifiably duplicated segments, and the region from Chromosome 11 to 12 is a minuscule 3.0% of the genome. It is possible that any traces of the WGD had already been lost by the time this recent duplication occurred. The region is also sufficiently small that any synteny with maize would be difficult to detect. It is too early to draw conclusions, especially as maize–rice synteny appears to be much more complicated than previously thought [72]. Given how so much of the rice genome is covered by segmental duplications, and the fact that all but one of our 18 segment pairs date back to the same time, give or take a standard deviation of 14.5%, the simplest interpretation is that a WGD did occur and that it happened before the origin of the grasses. However, it is equally clear that other classes of duplications are also present, and these are worth investigating too. Ongoing Individual Gene Duplications Tandem duplications are represented by the trend along the diagonal, Y = X, that is observed in all chromosomes (see Figures 6 and Figure S7). Segmental duplications within the same chromosome are possible, but their trend would not be along the diagonal, and none were actually seen in our analysis. As an indicator of the prevalence of the three different duplication classes, we use the number of homolog pairs before and after the inclusion of higher-order homologs. Segmental duplications contain 609 and 1,340 pairs, whereas tandem duplications contain 311 and 957 pairs. We can increase the tandem numbers by relaxing our definitions to allow two TBlastN homologs of an nr-KOME cDNA to count as a homolog pair (instead of insisting that one always be a cDNA). This is what we use in the Ks distribution plot of Figure 8, which contains 1,696 homolog pairs. Rather than a maximum in the distribution at some nonzero Ks, we find a big peak at zero Ks, followed afterward by an exponential decay. The implication is that tandem duplication is an ongoing evolutionary process that provides an endless source of raw materials for gene genesis. If we adopt the methods and parameters of the Arabidopsis genome paper, we find that 16.5% of the rice genome is tandemly duplicated, compared to 16.2% of the Arabidopsis genome. Note, however, that the Ks distribution for tandemly duplicated genes in Arabidopsis is highly unusual, in the sense that it does not exhibit the big peak at zero Ks that is seen in virtually every other plant genome [52]. In addition to segmental and tandem duplications, there is a third and last class of duplications that looks like background noise in our figures. The number of homolog pairs is 1,351 and 32,384 before and after higher-order homologs, respectively, although with no trend line to constrain the choice of homologs, that second number is almost certainly an overestimate, since only 4,212 cDNAs are involved. Surprisingly few of these higher-order homologs are the result of processed pseudogenes, as the number of cases in which a multiexon cDNA pairs with a single-exon TBlastN homolog is 9.8%. To demonstrate how overwhelmingly these higher-order homologs contribute to the background noise, Figure 9 depicts what Chromosome 2 would have looked like if we had included them. For simplicity of interpretation, Figure 8 is the Ks distribution of the cDNAs with one and only one homolog. This distribution has characteristics of the distribution for tandem duplications—large peak at zero Ks followed by exponential decay—except that the magnitudes of the Ks are much larger for background duplications. We believe that most of these background duplications were originally tandem duplications that, over time, migrated to other parts of the genome, but we cannot rule out the possibility of direct duplications to remote loci. Some older duplications may even be due to migration of genes from segmental duplications, but these are a small part of the overall picture. However we do the counting, it appears that this combination of recent tandem and background duplications, which we call individual gene duplications, would rival any contribution from the segmental duplications. Figure 9 A View of All Duplications Found on Rice Chromosome 2 In contrast to Figure 6, where we featured those cDNAs with one and only one TBlastN homolog, here we show all detectable TBlastN homologs, up to a maximum of 1,000 per cDNA. Tandem and segmental duplications show markedly different Ka/Ks distributions, a popular test for evolutionary selection, where Ka and Ks refer to the fraction of nonsynonymous and synonymous sites, respectively, that are changed within a homolog pair [73]. Ka/Ks is one under neutrality, below one under purifying selection, and above one under adaptive selection. Tandem duplications tend to have larger Ka/Ks values, as we show in Figure 10. The averages are 0.720 (tandem) and 0.365 (segmental), and more homolog pairs exhibit Ka/Ks > 1 in tandem duplications. This is consistent with the observation that more recent duplications tend to have larger Ka/Ks values [74] and with the idea that, immediately after duplication, one of the two genes undergoes a fast evolving phase [75]. Finally, let us consider again those nr-KOME cDNAs with one and only one homolog. Among the ones assigned to a tandem duplication, 65.3% are NH, but among the ones assigned to a segmental duplication, 23.8% are NH. Hence, there is a marked correlation between NH genes and tandem duplications. Figure 10 Ka/Ks Distribution for Homolog Pairs Ka and Ks are the fraction of the available nonsynonymous and synonymous sites that are changed in the homolog pairs. Ka/Ks > 1 is an indicator of positive selection. Shown is the Ka/Ks distribution for segmental duplications (A) and for tandem duplications (B). Our WGD is in good agreement with the results of Paterson et al. [15], but we can also explain the seemingly contradictory results of Vandepoele et al. [14] First, they did not have a complete genome; about two-thirds of their segmental duplications were interrupted by a break in the assembly. Second, their algorithms were very likely confounded by the many NH genes with no homologs in rice itself and by the many individual gene duplications that in aggregate masked the WGD. In fact, their segmental duplications had a Ks distribution similar to ours, but they only covered 15% of the genome. Then, when they examined the distribution of Ks for all duplicates, what they found was a big peak at zero Ks. This lead them to conclude there was no WGD, when, in fact, almost every class of duplication that had been hypothesized was present, and they needed only to allow for that. Discussion Until recently, Arabidopsis was the only sequenced plant genome. When two rice genomes were first published in draft format, the comparative analyses that could be done were hindered by a lack of long-range contiguity. Now, there are three plant genomes (indica rice, japonica rice, and Arabidopsis) with multimegabase contiguity. In our analyses, we strived to maintain methodological consistency. To assess the accuracy of our assemblies, we first compared IRGSP japonica to Syngenta japonica, so that polymorphic differences would not be a confounding factor. To compare gene content in the three rice assemblies, we annotated them all with the same procedures. Our conclusion is that, even if the WGS method does fall just slightly short of the clone-by-clone method in terms of accuracy and completeness, it comes remarkably close. This is why all the genome-sequencing projects now being funded by the National Human Genome Research Institute (in the United States) are being done with WGS methods (http://www.genome.gov/11007951). Rice is also now one of the few organisms with the luxury of having a complete genome sequence for two important subspecies. Comparisons of indica and japonica reveal strikingly little difference in the gene content, but there are massive intergenic differences. This vindicates our strategy to focus on genic sequences, because if the intergenic sequences are so unstable even between indica and japonica, they are highly unlikely to be functional. Our analysis of the duplication history in rice resolves a simmering dispute and, at the same time, raises some intriguing questions. We find evidence for an ancient WGD, a recent segmental duplication, and massive ongoing individual gene duplications. This last phenomenon can explain certain unexpected findings. Sequencing of orthologous loci between grass genomes has identified many smaller-scale rearrangements that were not seen in the original map-based studies. Many of these exceptions to synteny are due to tandem duplications [76,77,78], which makes sense, given how these duplications are a frequent and ongoing event for grass genome evolution. In addition, the massive ongoing individual gene duplications provide a never-ending source of raw material for gene genesis. We believe that the large number of rice NH genes is a transient effect of this ongoing process. The contrary argument is that any such transients cannot be long-lived, as one of the two genes must decay rapidly to avoid the dosage-doubling problem [79,80]. We believe this is irrelevant when there is a continual injection of new gene duplicates. Additional details must, however, be deferred to a future article, in which we can better address other important issues, such as the critical need to confirm NH genes in proteomics and conservation in the maize genome sequence. Looking toward the future, we would point out that the Chinese Superhybrid Rice Genome Project was designed to include not only a major subspecies of rice, namely, the indica variety represented by93–11, but also the maternal strain of the LYP9 superhybrid, PA64s, which has a complex breeding history incorporating genetic material from indica, japonica, and javanica—all of the major subspecies of cultivated rice. Work on PA64s is continuing at our Beijing center. For the research community, we will be providing DNA microarrays to facilitate the systematic studies of gene expression in different tissues and developmental stages, and under different physiological and environmental conditions. We will develop molecular markers for mapping causative genes in mutant lines and marker-assisted breeding. This publication, and the associated data release, is also a fitting way to celebrate the end of 2004, which the General Assembly of the United Nations declared to be the International Year of Rice (http://www.fao.org/rice2004). Materials and Methods Construction of reference cDNAs: nr-KOME The initial Knowledge-Based Oryza Molecular-Biological Encyclopedia dataset [25] had 28,444 japonica cDNAs with complete open reading frames. These cDNAs were aligned to Syngenta japonica, and when two alignments overlapped by at least 100 bp, the smaller cDNA was removed. A small number of clones could not be aligned—not even partially—to any of our three rice assemblies (Beijing indica, Syngenta japonica, and IRGSP japonica). Removing these as nonrice contaminants gave a set of 19,079 nonredundant cDNAs that we call nr-KOME. Because the sequence quality is so high, we could use the longest open reading frame for the overwhelming majority of these cDNAs, without having to correct for sequencing errors. Minor corrections are applied to 2.5% of these cDNAs, following the methods first developed for GenScan [81]. Repeats and their effects on WGS misassembly The basic procedure for converting sequence reads into contigs and scaffolds was described in our original publication on RePS [26], our WGS assembler. A common source of confusion is the distinction between mathematically defined repeats (MDRs) and biologically defined repeats. What we focus on are MDRs, which refer to 20-mer sequences that are exactly repeated in the genome, without regard to their underlying biological context. In our nomenclature, “depth” refers to the number of times that a 20-mer appears in the unassembled sequence reads and “copy number” refers to the number of times that it appears in the (correctly assembled) genome. “Coverage” is the number of times that the genome is redundantly sampled, and therefore depth = copy number × coverage. Special procedures are used to compute depths efficiently [27]. In a WGS assembly, the problems arise from the MDRs, which are not equivalent to the biologically defined repeats. For example, TEs qualify as biologically defined repeats, and they can be recognized, even after many millions of years of degradation, by specialized programs like RepeatMasker (http://www.repeatmasker.org). However, the degradation makes it trivial to distinguish between two copies of an ancient TE, so these do not cause assembly problems. It is also relatively easy to distinguish between gene duplicates, because their introns and flanking intergenic regions are under fewer evolutionary constraints than their exons. Even for recent TEs and gene duplicates, assembly problems can be avoided, because RePS computes the copy number for every 20-mer in the WGS assembly, and it will refuse to join anything that might be ambiguous. Indeed, the only way a misassembly can occur is if there is a low copy MDR and its copy number is underestimated by RePS. All of our tests show that, although this can happen, it is a rare event. On the usefulness (or not) of BAC end pairs The fundamental challenge was that we had to create super-scaffolds of megabase size from scaffolds of 30-kb size. It is generally thought that BAC end pairs are useful for this purpose, but this is not true when the BAC inserts, typically 122–187 kb, are much bigger than the scaffold sizes. Instead of linking adjacent scaffolds, they link every fourth to sixth scaffold. The fact that the density of BAC ends is 2.3 kb does not help, because there is no way to determine the order and orientation of the overlapping BACs. Fingerprint maps do provide some ordering information, but nothing like 2.3-kb resolution, and orientation information is still missing. The danger in using the BACs at this point is that you end up with a morass of interleaving super-scaffolds [26], with no way to untangle them. We actually did an assembly with only the BACs, and the result was that the super-scaffolds were 87% larger than they should have been. In the mouse project [82], the solution was to use fosmid end pairs, because these inserts are constrained to an almost ideal size of 40 kb. In the case of rice, we did not need to sequence fosmid end pairs, because by combining the indica and japonica WGS assemblies, it is possible to get linking information at the requisite length scales. We did of course use all available BAC end pairs [83] (http://rgp.dna.affrc.go.jp/blast/runblast.html, but they were only useful after the intermediate-range linking that came from combining WGS assemblies. Misassemblies versus polymorphic differences To verify our WGS assemblies on the smaller-length scales that are more characteristic of genes, we compare them with IRGSP japonica, taking the latter as the “gold standard” not because it is perfect but because it more likely to be correct. We focus on gene regions by aligning nr-KOME cDNAs to IRGSP japonica and excising the sequences from the 5′ to 3′ UTRs, including introns and an additional 500 bp at both ends. What we search for are potential misassemblies due to misplaced reads. Given that a typical read is 500 bp, these should appear as segments of 500 bp or more in which the excised gene sequence cannot be aligned with the WGS assembly. Such discrepancies are noted based on where they occur in the context of the gene. Although it is possible to detect more than one discrepancy per gene, we only count the most serious discrepancy in each gene based on the likelihood of it being functional. The prioritization is from coding exon, to UTR exon, to intron. Notice that discrepancies of this nature are not always from misassemblies. In the Beijing indica comparison, they can also be due to polymorphic differences. Although there is no way to tell what any particular discrepancy is, we know the misassembly rate from the Syngenta japonica comparison. Therefore, any increase in the discrepancy rate in the Beijing indica comparison can be attributed to polymorphic differences. Ab initio predictions in WH versus NH genes FGENESH [35] behaves very differently for WH and NH genes, as defined by nr-KOME. Following the methods of our recent review [84], we compute false positive (FP) and false negative (FN) rates. Error rates are given on a per amino acid basis. This means that in addition to correctly identifying the coding bases, we require the reading frame to be correctly determined. WH genes show very low error rates (FP = 0.10 and FN = 0.05). Although NH genes show higher error rates (FP = 0.35 and FN = 0.25), these are not that much worse than human genes (FP = 0.30 and FN = 0.12), and like it or not, error rates like these are the state of the art in ab initio prediction. On closer examination, it is clear that most of the problems in rice are caused by single-exon genes with small coding regions, which are more prevalent among NH genes and form a category that all ab initio algorithms handle poorly. This category of genes does not affect the gene count because FP and FN cancel each other out. We therefore focus on removing TEs that are mistakenly called genes. Comparison of indica-japonica to identify SNPs The sequence alignments for indica and japonica are straightforward, with almost no chance of paralog confusion, because of our 34,190 unique “anchor points” (see Figure 1). We partition the sequence into four nonoverlapping categories called unassembled, assembled-but-unmapped, mapped-but-unaligned, and aligned. The last category is where almost all of the genes are, and where we can get polymorphism data. Detailed sequence alignments are computed with CrossMatch, a Smith-Waterman algorithm that is included in Phrap (http://www.phrap.org). This is preferred to any of the BLAST alignment tools, which, although they are faster, occasionally miss subtle details. To discriminate between polymorphisms and sequencing errors, we use the error probability p attached to every base, and given as Q = −10 × log(p). Following the rules established in the early days of large-scale polymorphism discovery [85], we use thresholds of Q > 23 at the SNP site and Q > 15 for the two flanking 5-bp regions. Experience has taught us that higher thresholds (30 and 22, respectively) are required for the indels. For comparison, an independent analysis [86] reported mean rates of 7.1 SNP/kb and 2.0 indel/kb, with 98% of these SNPs experimentally confirmed. Our SNP rates are two times higher because we aligned more of the intergenic sequence. If we eliminate this factor, say, by restricting our rates to the introns of the genic regions defined by nr-KOME, our rates are 6.1 SNP/kb and 1.3 indel/kb, which are actually lower than the rates from that independent analysis. On the reliability of the p–p interaction data Bioverse annotations in this article are dated July 2003 (FGENESH) and November 2002 (nr-KOME). Two proteins are said to interact if they are similar to two other proteins that are known to interact. Our criterion is that the product of the similarity measures (percentage identity) must exceed 0.15. For example, two proteins with 45% and 30% identity to two other proteins that are experimentally determined to interact would be rejected, as their score is 0.45 × 0.30 = 0.135. The reliability of this approach, especially for transfer of interaction data between organisms, has been demonstrated in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori analyses [87]. As an example of a predicted interaction for rice that has been independently confirmed, Bioverse identification numbers 21736 and 8526 (score 0.21) show an interaction between CDK-activating kinase and H-type cyclins [88]. A general way to verify the predicted interactions is to compare them against known protein complexes in the Protein Data Bank. Unfortunately, there are few Protein Data Bank structures from rice, and even fewer are of protein complexes. Given this dearth of experimentally determined interactions for rice, Bioverse is almost the only source of large-scale interaction data. Details of the duplication and synteny analysis We defined a homolog pair as a single nr-KOME cDNA and its TBlastN homolog, but occasionally that TBlastN homolog will overlap with another cDNA. To avoid double counting, we keep only the larger of these two cDNAs. Segmental duplications identified by visual inspection must have at least five homolog pairs, with no more than 5 Mb between adjacent homolog pairs. We approximate the trend line with a second- or third-order polynomial, and to capture what our eyes indicate should be captured, we accept homolog pairs within a 500-kb radius of this polynomial. Slightly different definitions are used for tandem duplications, depending on application. For Ks, we allow two TBlastN homologs to count as a homolog pair and accept homolog pairs within a 50-kb radius of the diagonal, although the mean (median) center-to-center distance is 6.8 kb (4.7 kb). To compare tandem duplications in rice and Arabidopsis, we use the methods described in the Arabidopsis genome paper and analyze predicted genes with BlastP at E-values of 10−20. To determine the maize–rice synteny, we began with 1,063 maize genetic markers [71] and searched for BlastN alignments to rice of at least 100-bp size and 80% identity. Given the segmental allotetraploid origins of maize [89], many markers are associated with two loci in maize. Each marker aligns to a mean (median) of 1.9 (1) loci in rice. We used only the longest of these alignments and verified in retrospect that using all of them would not have mattered. In the end, there are 35 pairs of syntenic segments, which cover 71.4% and 52.9% of the maize and rice genomes, respectively, and the mean (median) number of markers per syntenic segment is 18 (12). Supporting Information Figure S1 Genetic Versus Physical Map Distance for All 12 Rice Chromosomes, Based on Beijing indica Similar results are seen with the other two assemblies, Syngenta japonica and IRGSP japonica. (1 MB EPS). Click here for additional data file. Figure S2 Number of Discrepant Markers in Comparisons of Genetic and Physical Maps for 1,519 Markers Found in All Three Rice Assemblies We count discrepancies where the markers are found (A) on different chromosomes and (B) in different locations on the same chromosome. (458 KB ZIP). Click here for additional data file. Figure S3 Gene Prediction by FGENESH, Tested against nr-KOME cDNAs Genomic size refers to the unspliced transcript, with introns, but constrained to the region from the start to stop codons. CDS size refers to the spliced transcript, without introns. Predictions are assessed with FP and FN rates, where per-aa (per amino acid) refers to the fact that we check whether the reading frame is correct. (351 KB ZIP). Click here for additional data file. Figure S4 Distribution of Sizes for Gene Islands and Intergenic Repeat Clusters, Based on Complete Sequence of Chromosomes 1 and 10 from IRGSP japonica Intergenic repeat clusters are regions of size larger than 1.5 kb (i.e., between a MITE and a gypsy/copia TE), where most of the 20-mer copy numbers exceed ten. Lower copy number regions are tolerated up to a “maximum gap size,” which defaults to 150 bp. Regions lying between two adjacent intergenic repeat clusters are taken to be gene islands. (233 KB ZIP). Click here for additional data file. Figure S5 Gene and TE Densities for Beijing indica Chromosome 7, as a Percentage of Sequence Length Near the centromeres, there is an increase in TE density (especially for the large, class I TEs such as gypsy and copia) and a decrease in gene density. This is not an artifact of the fact that WGS assemblies underrepresent larger TEs, as much the same effect is observed when we use IRGSP japonica instead (data not shown). (362 KB ZIP). Click here for additional data file. Figure S6 Coordinated Annotation of the Individual Chromosomes for Beijing indica and Syngenta japonica We depict all the genetic markers, nr-KOME cDNAs, FGENESH gene predictions, and transposable elements identified by RepeatMasker. Genes are depicted as WH (colored blue) or NH (colored red) based on their similarity to Arabidopsis. TEs are decomposed into classes I, II, and III. Correspondence between indica and japonica is indicated by drawing a connecting line between the 5′ ends of the nr-KOME cDNAs that clearly align to both assemblies. (9.6 MB ZIP). Click here for additional data file. Figure S7 Duplicated Segments in the Beijing indica Assembly for All 12 Chromosomes, Plotted in the Manner of Figure 6, and with a Total of 12 Panels (507 KB ZIP). Click here for additional data file. Figure S8 Complete Synteny between Maize and Rice I Each point indicates the genomic positions for a maize genetic marker and its highest confidence match in rice. The x-axis shows a specific chromosome for one genome, and the y-axis shows all chromosomes for a second genome, with the chromosome numbers color-coded as per the legend. We show here 12 panels for rice. (311 KB ZIP). Click here for additional data file. Figure S9 Complete Synteny between Maize and Rice II Each point indicates the genomic positions for a maize genetic marker and its highest confidence match in rice. The x-axis shows a specific chromosome for one genome, and the y-axis shows all chromosomes for a second genome, with the chromosome numbers color-coded as per the legend. We show here ten panels for maize. (288 KB ZIP). Click here for additional data file. Table S1 Raw Data for Beijing indica and Syngenta japonica Assemblies Read length is the number of Q20 bases with an error rate of 10−2 or better. Effective coverage is based on the depth of reads in contigs over 5 kb in size, ignoring regions with 20-mer repeats. Clone insert sizes are specified in terms of tenth and 90th percentiles. (16 KB XLS). Click here for additional data file. Table S2 Transposable Elements Identified with RepeatMasker Are Put into Classes I, II, and III As a result of our efforts to identify indica–japonica polymorphisms, the sequence is divided into four nonoverlapping categories: unassembled, assembled-but-unmapped, mapped-but-unaligned, and aligned (with all the SNPs). (28 KB XLS). Click here for additional data file. Table S3 Detailed Analysis of Gene Overlaps from Figure 3 For each region of the Venn diagram, we use BLAT to align the predicted gene to the other assembly (or assemblies) where the gene is supposedly missing. The objective is to determine whether it is the sequence that is missing, or whether the discrepancy is due to the errors in the ab initio predictions. What we find is a bit of both. However, fragmented sequence assemblies are not a problem. If the gene is found at all, it is usually found in one piece. What is striking is that predicted genes that are unique to the two WGS assemblies do tend to be genuinely missing from IRGSP japonica sequence. This supports the idea that the WGS method can sometimes identify genes that are not well represented in the BAC clone libraries. (17 KB XLS). Click here for additional data file. Table S4 Table of InterPro Domain Rankings One table compares predicted genes from Arabidopsis and Beijing indica. The second table compares predicted genes from Beijing indica with nr-KOME cDNAs. (169 KB XLS). Click here for additional data file. Accession Numbers The DNA Data Bank of Japan/European Molecular Biology Laboratory/GenBank (BGI-RIS http://rise.genomics.org.cn [16]) project accession numbers for the WGS sequences discussed in this article are Beijing indica ( AAAA00000000, version AAAA02000000) and Syngenta japonica (AACV00000000, version AACV01000000). Note Added in Proof The idea that TEs are often mistakenly annotated as genes was also suggested in a recent paper by Bennetzen et al. [90]. This project was funded through Chinese Academy of Sciences (grants KSCX1-SW-03, KSCX2-SW-223, and KSCX2-SW-306), Commission for Economy Planning, Ministry of Science and Technology (grants 2001AA225041, 2002AA229021, 2002AA2Z1001, 2002AA104250, 2002AA234011, 2001AA231061, 2001AA231011, 2001AA231101, 2004AA231050, and 2003AA207160), National Natural Science Foundation of China (grants 30399120, 30200159, 30370330, 30370872, 30200163, and 90208019), Beijing Municipal Government, Zhejiang Provincial Government, Hangzhou Municipal Government, Zhejiang University, and China National Grid. Some funding is from the United States National Human Genome Research Institute (grant 1 P50 HG02351), the United States National Science Foundation (grant DBI 0217241), and Searle Scholars Program. Competing interests. The authors have declared that no competing interests exist. The following authors performed the experiments: Jun Yu, Wei Lin, Jun Zhou, Wei Dong, Songnian Hu, Changqing Zeng, Zuyuan Xu, Xianran Li, Liang Lin, Jianning Yin, Jianing Geng, Jianping Shi, Yajun Deng, Qingfa Wu, Changfeng Li, Jingqiang Wang, Dawei Li, Xiaowei Zhang, Yongqiao Sun, Zhenpeng Zhang, Jingyue Bao, Peng Chen, Yingpu Yu, Meng Lei, Jinhong Li, Zongzhong Tong, Shuangli Li, Tingting Lei, Huan Chen, Haiyan Huang, Feng Zhang, Caifeng Zhao, Yanqing Huang, Yan Xi, Qiuhui Qi, Wenjie Li, Bo Zhang, Jian Wang, and Huanming Yang. The following authors analyzed the data: Jun Wang, Songgang Li, Heng Li, Peixiang Ni, Jianguo Zhang, Yong Zhang, Ruiqiang Li, Shengting Li, Hongkun Zheng, Lijuan Cong, Guangyuan Li, Juan Liu, Hong Lv, Jun Li, Jing Wang, Xiaoyu Ren, Xiaoling Wang, Dongyuan Liu, Zhendong Ji, Wenming Zhao, Yujun Han, Lingli Dong, Jia Ji, Jinsong Liu, Ying Xiao, Li Yang, Chen Ye, Yan Zhou, Bing Zhang, Shulin Zhuang, Haibin Wei, Hong Yu, Yuanzhe Li, Hao Xu, Lijun Fang, Zengjin Zhang, Yunze Zhang, Xiangang Huang, Zhixi Su, Wei Tong, Jia Ye, Chen Chen, Huayong Xu, Na Li, Shuting Li, Lijun Dong, Long Li, Wei Hu, Xiangjun Tian, Yongzhi Jiao, Xiaohu Liang, Jason McDermott, Ram Samudrala, and Gane Ka-Shu Wong. The following authors contributed reagents/materials/analysis tools: Jun Wang, Songgang Li, Heng Li, Peixiang Ni, Jianguo Zhang, Yong Zhang, Ruiqiang Li, Shengting Li, Hongkun Zheng, Guangyuan Li, Juan Liu, Longhua Ran, Xiaoli Shi, Xiyin Wang, Xiaoyu Ren, Dongyuan Liu, Wenming Zhao, Yujun Han, Shuming Wu, Jinsong Liu, Dongbo Bu, Jianlong Tan, Chen Ye, Jingfen Zhang, Jingyi Xu, Yan Zhou, Bin Liu, Shulin Wei, Ximiao He, Zengjin Zhang, Xiangang Huang, Lishun Wang, Lin Fang, Zhao Xu, Haihong Li, Lijun Dong, Yanling Zhang, Jiao Jin, Lei Gao, Weimou Zheng, Bailin Hao, Siqi Liu, Wen Wang, Longping Yuan, Mengliang Cao, Jason McDermott, Ram Samudrala, Jian Wang, and Huanming Yang. The following authors wrote the paper: Jun Yu and Gane Ka-Shu Wong. Author contributions. The following authors conceived and designed the experiments: Jun Yu, Jian Wang, Gane Ka-Shu Wong, and Huanming Yang. Citation: Yu J, Wang J, Lin W, Li S, Li H, et al. (2005) The genomes of Oryza sativa: A history of duplications. PLoS Biol 3(2): e38. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1568529310.1371/journal.pbio.0030045Research ArticleEcologyEvolutionMicrobiologyZoologyEubacteriaAging and Death in an Organism That Reproduces by Morphologically Symmetric Division Aging and Symmetric DivisionStewart Eric J [email protected] 1 2 Madden Richard 3 ¤Paul Gregory 1 2 Taddei François 1 2 1Inserm, U571ParisFrance2UniversitéParisFrance3Institut des Hautes Etudes Scientifique, Le Bois-MarieBures-sur-YvetteFranceKirkwood Thomas Academic EditorUniversity of Newcastle upon TyneUnited Kingdom2 2005 1 2 2005 1 2 2005 3 2 e454 8 2004 4 12 2004 Copyright: © 2005 Stewart et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Aging and Death in E. coli In macroscopic organisms, aging is often obvious; in single-celled organisms, where there is the greatest potential to identify the molecular mechanisms involved, identifying and quantifying aging is harder. The primary results in this area have come from organisms that share the traits of a visibly asymmetric division and an identifiable juvenile phase. As reproductive aging must require a differential distribution of aged and young components between parent and offspring, it has been postulated that organisms without these traits do not age, thus exhibiting functional immortality. Through automated time-lapse microscopy, we followed repeated cycles of reproduction by individual cells of the model organism Escherichia coli, which reproduces without a juvenile phase and with an apparently symmetric division. We show that the cell that inherits the old pole exhibits a diminished growth rate, decreased offspring production, and an increased incidence of death. We conclude that the two supposedly identical cells produced during cell division are functionally asymmetric; the old pole cell should be considered an aging parent repeatedly producing rejuvenated offspring. These results suggest that no life strategy is immune to the effects of aging, and therefore immortality may be either too costly or mechanistically impossible in natural organisms. Detailed time lapse photography reveals that organisms that divide symmetrically, such as the bacterium E. coli, can indeed age and consequently that no organism is immune to mortality ==== Body Introduction That populations survive indefinitely while individuals grow old and die requires that young offspring be produced at the expense of old parents, and this has classically been explained in terms of an immortal germ line passing through a transient and disposable soma, or body [1]. However, with the discovery of aging in single-celled organisms with no clear separation of these two constituents, it has been proposed that reproduction by asymmetric division is a prerequisite for aging [2] and that organisms that reproduce without a distinction between parent and offspring do not age, thus exhibiting functional immortality [3,4]. One difficulty with this distinction is that an asymmetry sufficient for aging may not necessarily result in a visible difference during cell division. Therefore, what may be a more useful common feature of the organisms that have currently been found to age is the presence of a juvenile phase (developmental asymmetry) in their life cycle. Juvenile cells are either smaller or undifferentiated and must go through a period of growth or differentiation before becoming capable of reproducing. This is similar to the idea that the critical requirement for aging in unicellular organisms is the presence of a parent cell that provides for a smaller offspring cell [2]. Consistent with this, the primary single-celled organisms that have been shown to age (the yeast Saccharomyces cerevisiae and the bacterium Caulobacter crescentus) share the traits of a visibly asymmetric division and an identifiable juvenile phase [3,5,6]. The report of aging in the binary fission yeast Schizosaccharomyces pombe has reinforced these apparent characteristics of aging organisms. The same study that found indications of aging in this organism also found that, contrary to expectations, its cell division process was visibly asymmetric, resulting in enlarged mother cells after only a few divisions, possibly indicating that S. pombe development is similar to that of budding yeast [7]. Here we look for evidence of aging in Escherichia coli, that is, in cells that do undergo a morphologically symmetrical division and do not exhibit a juvenile phase, in order to determine if such organisms age. Previous studies of senescence in E. coli have mostly focused on the loss of viability over time during nutrient depletion (conditional senescence) [4]. However, these studies do not address aging in terms of parent and offspring (reproductive life span), nor do they address growing cells that are not under conditions of starvation. To identify reproductive life-span effects, it is necessary to follow individual cells as they grow and reproduce over time. The bacterium E. coli grows in the form of a rod, which reproduces by dividing in the middle. One new end (or pole) per progeny cell is produced during this division event (Figure 1). Therefore, one of the ends of each cell has just been created during division (termed the new pole), and one is pre-existing from a previous division (termed the old pole). Old poles can exist for many divisions, and if cells are followed over time, an age in divisions can be assigned to each pole, and hence to each cell. While experiments following the partitioning of cell constituents have found uniform distributions of DNA and lipids in daughter cells [8], it is known that components of the cell wall turn over slowly and are conserved in the poles where they are formed [9]. More generally, any cell constituent with limited diffusion and a long half-life may be expected to accumulate at the old pole, so there may exist a physiological (rather than morphological) asymmetry between the old and new poles. Limited by the manual measurements required, individual cells growing under the microscope have been followed in the past for a small number of divisions [10,11,12]. The only difference between poles was seen in a single experiment that indicated that old pole cells might divide later than their new pole siblings, hinting at a possible inequality between these cells [13]. To determine if E. coli experiences aging related to the inheritance of the old pole, we followed individual exponentially growing cells in an automated fluorescence microscopy system through up to nine generations of growth and reproduction, measuring the physical parameters of each cell over time. We present conclusive evidence for aging in the old pole cell, including cumulatively slowed growth, less offspring biomass production, and an increased probability of death. Figure 1 The Life Cycle of E. coli During cell division, two new poles are formed, one in each of the progeny cells (new poles, shown in blue). The other ends of those cells were formed during a previous division (old poles, shown in red). (A) The number of divisions since each pole was formed is indicated by the number inside the pole. Using the number of divisions since the older pole of each cell was formed, it is possible to assign an age in divisions to that cell, as indicated. Similarly, cells that consecutively divided as a new pole are assigned a new pole age, based on the current, consecutive divisions as a new pole cell. (B) Time-lapse images of growing cells corresponding to the stages in (A). False color has been added to identify the poles. Results Cells were grown from one cell into a monolayer microcolony that contained up to 500 cells, and time-lapse images (see Video S1 for an example film) were analyzed with custom automated software designed for this purpose. We followed 94 such colonies, resulting in the complete record from division to division, of 35,049 cells. As the history and physical parameters of each cell in the microcolony are known, and the identity of each pole is tracked, the complete lineage can be determined. The resulting lineages from each film were averaged by each unique cell position within the lineage. This can be represented as a single, bifurcating tree, where each branch point is an average cell for that position in the lineage, and the length of the lines connecting cells to their progeny are proportional to the growth rate of the cell (Figure 2). At each division in the tree, the cell inheriting the old pole of the progenitor cell is represented on the right branch of the sibling pair (in red), while the cell inheriting the new pole is on the left branch (in blue). Figure 2 Average Lineage Showing Old Pole Effect on Growth Rate The first division in the microcolonies is not represented, as the identity of the poles is not known until after one division (hence each initial cell gives rise to two lineages that are tracked separately, and subsequently combined from all films to create the single average lineage shown here). The lengths of the lines connecting cells to their progeny are proportional to the average growth rate of that cell; a longer line represents a higher growth rate for that cell. At each division, the cell inheriting the old pole is placed on the right side of the division pair, and shown in red, while new poles are placed on the left side of each pair, and shown in blue (note that this choice of orientation is not the same as that of Figure 1, to compare more easily old and new pole lineages). Because the position of the start of the growth line for each new generation is dependent on the generations that preceded it, the difference in growth rates is cumulative. Green lines indicate the point at which the first cell divides in the last four generations. Nine generations from 94 films encompassing 35,049 cells are included in this tree. The average growth rate of all the cells corresponds to a doubling time of 28.2+/−0.1 min. The data used to generate the average lineage are provided in Dataset S1. The pattern of fast and slow growth rates in this average lineage gives striking evidence for reproductive asymmetry between the progeny cells, as the cells that show a cumulatively slowed rate of growth (shorter lines) are those cells that have more often inherited an old pole during their ancestry. To verify that this pattern in the average lineage is actually due to a difference in growth rate between new and old pole cells, we performed a pairwise comparison of every set of sister cells that was produced at the eighth generation in each of the films. As sister cells share temporal and spatial surroundings, this comparison controls for potential environmental variation within the microcolony. The comparison (two-tailed t-test) includes cells of all division ages and shows that the average growth rate of the old pole progeny cell is 2.2% (+/−0.1%) slower than that of the new pole cell. This analysis, performed on 7,953 cell pairs, conclusively demonstrates (p < 0.00001, t = 14.40, df = 7952) that the cell that inherits the old pole grows slower than the new pole cell produced in the same division. Two factors from this same dataset demonstrate the lack of a juvenile phase in E. coli. First, comparison of the progeny cells shows that the new pole cell is slightly larger on average (0.9+/−0.1%; p < 0.00001, t = 5.62, df = 7952) than the old pole cell (the contrary would be expected in the presence of a juvenile phase). Second, the young pole cell is marginally more likely to divide sooner than the old pole cell (in about 15% of the cases cells divide within the same 2-min time point; of those where the two cells divide in different time points, 54% of the time the new pole cell divides first [significant; p < 0.00001, t = 5.02, df = 4812]), which is also not consistent with a phase where the young cell must grow or differentiate before reproduction. These differences are consistent for all generations during steady-state growth (data not shown). Therefore, while a juvenile phase is absent, there is a consistent functional asymmetry between the two progeny cells that is disadvantageous to the old pole. Each cell is defined not just by its preceding division, however, but also by all previously recorded divisions, back to the initial cell in the analysis. Therefore, each old pole cell can be categorized by the number of consecutive, final old pole divisions that occurred to produce the current cell (thus giving the age in divisions of its old pole). Equivalently, each new pole cell can be assigned a number of divisions that it sequentially divided as the new pole cell. Comparing these values with the growth rates of the cells, we find that the older the old pole of a cell is, the slower the growth rate of that cell, while cells with more consecutive new pole divisions exhibit increasing growth rates (Figure 3A). Furthermore, a pairwise comparison shows that the difference in the growth rate between the old pole sibling (the mother cell) and the new pole sibling (daughter cell) increases with the increasing age of the mother (Figure 3B). Therefore, the difference between pairs of progeny cells, as well as the pattern seen in the average lineage, is not only due to a decrease in growth rate of cells that have inherited the old pole, but also to an increase in the growth rate (for at least three divisions; subsequent divisions do not detectably improve) of cells that have repeatedly inherited the new pole. Figure 3 The Effects of Consecutive Divisions as an Old or New Pole on Growth Rate (A) The cellular growth rate, represented on the y-axis, is normalized to the growth rate of all cells from the same generation and geography in each film. On the x-axis consecutive divisions are seen as either a new pole (open circles), showing rejuvenation, or an old pole (closed circles), showing aging. Cells represented at each point: new pole divisions 1–7: 7,730; 3,911; 1,956; 984; 465; 211; 89; old pole divisions 1–7: 4,687; 3,833; 1,933; 956; 465; 213; 75. (B) Pair comparison of the growth rates of sibling cells. The division age of the old pole sibling (the mother cell) is shown on the x-axis. The percentage difference between the growth rate of the new pole sibling (the daughter cell) and this cell is shown on the y-axis. A positive difference corresponds to a faster growth rate for the new pole cell. Cell pairs represented at each point, ages 1–7: 9,722; 4,824; 2,409; 1,202; 601; 282; 127. In both graphs, cells are from all 94 films. The error bars represent the standard error of the mean. The old and new pole growth rates in (A) and the pair differences in (B) are fitted to a line to show the trend; however, the actual progressions may not be linear (R 2 old poles = 0.97, new poles = 0.83, pair difference = 0.94). To determine the longer-term effect of inheriting the old pole, we performed a second pairwise analysis, comparing the total length of offspring produced by sister cells from the fifth generation until the end of tracking (this generation was chosen as each cell has the opportunity to progress through about three divisions). As the bacteria are rod shaped, the total length of cells produced is proportional to the biomass of the offspring. The results show that old pole cells produce less offspring biomass compared to their new pole sisters (3.1+/−0.3% less, p < 0.00001, t = 9.29, df = 1565). Therefore, it appears that the slower growth rate of the old pole cells also results in a longer-term decreased ability to produce offspring biomass. Another long-term effect of aging is the probability of survival of the organism over time. During the growth of the microcolonies, sixteen cells were observed to cease growing; these cells never resumed growth during the course of the experiment. We have defined these cells as potentially dead cells and have analyzed their locations in the lineages. While these apparent deaths may ultimately be due to stochastic events, they show a statistically significant bias (p = 0.01; see Materials and Methods) toward increased divisions spent as an old pole (over the total observation history). This observation is consistent with the hypothesis that aged cells are more susceptible to harmful events and/or less likely to survive them. It is unlikely that these cells represent a growth arrested “persister” state, as it has recently been demonstrated that persister cells that arise during exponential growth occur at a frequency of approximately 1.2 × 10−6 [14]; the appearance of apparently dead cells in our study (about 4.6 × 10−4) is almost 400 times more frequent. Discussion We find that the old pole is a significant marker for multiple phenotypes associated with aging, namely, decreased metabolic efficiency (reduced growth rate), reduced offspring biomass production, and an increased chance of death. Thus, E. coli, an organism with a morphologically symmetrical division, no juvenile phase, and no identified separation between germ line and soma, is susceptible to aging. Unlike the process of clonal senescence [15], where an entire population progressively declines in fitness, here the life potential of the lineage is continually renewed through young offspring cells (the process of rejuvenation) that are produced at the expense of aged parent cells. That the two cells are not equal, despite appearances, indicates a functional asymmetry that may be explained by a number of mechanisms, such as those that result in the polar localization of cell components [16,17]. In the simplest example, any component localized to the cell poles will have more time to accumulate in an old pole than in a young one. However, the pole effect may involve more active processes (such as differential turnover or accumulation) because, in addition to the aging effect of the parent, new pole cells show a concomitant increase in their growth and reproduction over several divisions. This result is not without precedent in aging organisms. In the budding yeast S. cerevisiae, the daughter cell of a young mother cell has a greater reproductive life span compared to the daughter cell of an old mother cell [18]. A likely explanation for this effect in yeast is the loss of segregation control of detrimental cellular components, such as extrachromosomal rDNA circles [19], and possibly damaged mitochondria as well [20]. Additionally, in fruit flies (Drosophila melanogaster), several generations of daughters are required to recover from the effects of chromosome damage [21]. In our study, the new pole cells can apparently benefit from the preferential distribution of cellular components at the expense of the old pole cell for at least three divisions. After further divisions as a new pole cell, it is not clear that there is a continuing benefit to the cell. The observation that an optimum is not reached in a single division implies that the mechanism responsible for an age-related bias in cell component inheritance is not absolute; repeated rounds of sorting continue to improve the condition of new pole cells. On the other hand, the observed rate of decline in old pole cells (about 1% of the initial value per division) would result in a complete cessation of growth after about 100 divisions. While the behavior of cells with more than seven old pole divisions cannot be determined from these data, it is interesting to note that the observed rate of decline in offspring production in C. crescentus (a bacterium with an obviously asymmetric division and a developmentally significant juvenile phase) is of about the same magnitude as the decline in growth rate measured here [3]. In S. cerevisiae, the rate of offspring production also declines, resulting in cell cycle times that are as much as five times longer for mother cells that have divided 30 times than for young cells. This is an accelerating decline, however, and there is no detectable difference in cell cycle time for the first ten divisions [22]. Our results show that a juvenile phase is not required for the process of aging any more than the presence of a germ line or a visibly asymmetric division is. In contrast, we demonstrate the presence of a physiological asymmetry in E. coli, which is essential for the process of aging. The cost of the aging process in lost growth to the population under these conditions is about 2%. In competition, these cells would be rapidly displaced by competitors that did not age, but only if the cost of avoiding senescence were not equal or greater. It has been proposed that one such cost is the rigorous level of maintenance and repair that would be required to prevent the decline and eventual extinction of a perfectly symmetrical organism [23]. The physiological asymmetry during division may therefore represent the disposal by preferential partitioning of cellular damage that is expensive or impossible to repair. Concerning the evolutionary origin of the aging process seen here, it is possible that an asymmetry of division existed before aging appeared as a life history trait in these cells, and that such an inequality may have therefore allowed (or forced) aging to occur. However, as we have detected this asymmetry solely through phenotypes that can be linked to aging, it is equally possible that the necessity of aging is itself the evolutionary cause of the asymmetry. If this latter explanation is found to be correct, then the occurrence of aging in a single-celled organism that is apparently meticulously symmetric otherwise may indicate that it is either not cost effective in general to produce an immortal life form, or it is impossible to achieve perfect molecular maintenance through natural selection. The use of the model organism E. coli provides an excellent genetic platform for studying the fundamental mechanisms of cellular aging and may provide insight into the costs and evolutionary roots of repair, maintenance, and longevity. Simple model organisms such as yeast and nematodes have already proven their value in identifying evolutionarily conserved pathways that regulate life span in higher organisms [24,25]. As has been seen with many fundamental life functions, it may be that the primary processes involved in aging in E. coli will also be conserved in other forms of life. Materials and Methods Strain and growth conditions The sequenced wild-type strain of E. coli, MG1655 [26], was modified to express the gene encoding yellow fluorescent protein under the control of the lactose operon repressor and the Pl promoter of lambda phage (gene construct from M. Elowitz [27]). Cells were inoculated onto microscope cavity slides from exponentially growing liquid cultures, such that the colonies and cells grew exponentially in a single plane on the surface of a solid matrix of LB-agarose (NaCl concentration of 5 g/l, supplied by SdS, Peypin, France; other components were DIFCO from Becton Dickinson, Franklin Lakes, New Jersey, United States; agarose from QA-Agarose, Qbiogene, Irvine, California, United States; plus 1 mM IPTG from Qbiogene). The slide cavities were sealed with silicone grease and contained sufficient oxygen and nutrients to allow undiminished growth and fluorescence for the length of the experiment. The slides were incubated in a temperature-controlled (Cube and Box incubation system, Life Imaging Services, Reinach, Switzerland) automated microscope (Zeiss 200M; Zeiss, Jena, Germany) at 30 °C for up to 6 h. The entire microscope was contained within the incubator, eliminating temperature gradients. These conditions resulted in an excess of nutrients and a protected environment without external causes of cell mortality. Microscopy Up to seven fields containing one to four cells each were manually identified at the start of the experiment, and stored in the MetaMorph microscope control software (Universal Imaging, Downingtown, Pennsylvania, United States). Fluorescent images were recorded at each field with time points taken generally every 4 min for the first 160 min, then every 2 min for the remaining time. A subset of six colonies was recorded every 40 s for improved time resolution. The excitation light did not affect the cellular growth rate (data not shown). Images were taken with a CoolSNAP HQ (Princeton Instruments, Trenton, New Jersey, United States) at 100X magnification; the resulting images have a spatial dimension of 0.064 μ per pixel. Excitation light (480 to 520 nm) was limited to 5% of the output of a 100-W Hg vapor lamp, with an exposure of 2 s. Emission wavelengths were 505 to 565 nm (filters from Chroma, Rockingham, Vermont, United States). See Video S1 for a sample film. Image analysis The custom analysis software (BHV) was developed by integration of open-source software under a central shell scripted in Python [28]. For pixel-intensive operations, C routines were written and linked to the Python shell. To segment the cells in the images, local minima were identified to outline the cells, then eroded to remove remaining connections, and dilated back to size. The measurement process calculated the second moments of the cell's pixel intensity/location distribution and matched it to a rectangle with the same parameters. Long, curved cells that could not be approximated to a rectangle were measured manually, using tools built into the software. Frames were then compared at successive time points, and cells were identified from frame to frame by their overlap with the previous frame, taking into account predicted cell movement due to growth of the colony. This process tracked 80% of the cells into the ninth generation without manual intervention, measuring approximations to their length, width, fluorescence intensity, orientation, geographical location within the colony, and the complete lineage history of each cell. The rate of change of each of these parameters was calculated, with the growth rate being represented by an exponential fit to length over time. Length is proportional to cell mass, as cells did not increase in width during growth. The limits to the cell-tracking process were expansion of the colony beyond the image frame and the formation of multiple layers of cells. Manual measurements were used to correct tracking errors, which allowed us to produce datasets for colonies that are up to 100% tracked. Therefore, the complete history of each cell, including how many times it divided and its relationship with the other cells in the lineage, was unambiguously determined. Statistical analysis We verified that our results are not the effect of bias unrelated to pole age in two ways. First, we compared pairs of daughters of the same cell using a pairwise t-test. Since the sibling cells occupy similar points in space and time, this eliminates influences coming from overall changes in the colony growth rate or potential variations in environment across the colony. Second, we created control datasets (data not shown) from the colonies in which all the properties of the cells and lineages are preserved, with the exception of pole identity, which is randomly assigned at each division. In this case, we tested the null hypothesis that the pole age of a cell is not a factor in cell physiology by comparing the observed result with the expected normal binomial distribution derived from the null hypothesis. In both cases, we determined from these tests the probability that the old pole effect is due to random fluctuations, expressed as a p-value. We determined if the apparently dead cells were biased toward divisions as old pole cells by examining their complete recorded division histories, which describe how many times each cell divided as an old pole or new pole cell. We compared the average number of old pole divisions of the population of 16 dead cells (mean = 3.44) with the distribution of averages (mean = 2.75, SD = 0.29) from 1,000,000 randomly generated sets of 16 cells with the same number of total divisions (total divisions = 6, 6, 4, 4, 7, 4, 5, 7, 8, 3, 6, 6, 7, 7, 6, 2). This yielded the one-tailed probability (the hypothesis was that these cells would be enriched in old pole divisions, based on our observations of growth rates) of such an old pole bias arising by chance. All error ranges in the text and figures represent the standard error of the mean. The values for Figure 3A were normalized by finding the mean growth rate of all cells that shared similar conditions and dividing the individual growth rates of each cell by this mean. Similar conditions were defined as being in the same colony, with the same number of generations since the start of the film, and either in the interior of the colony or on the border (border cells exhibited a slight artifact in size measurement due to light spread in the optics; both interior and exterior populations showed a similar old pole effect). Supporting Information Dataset S1 Table of Data for the Average Lineage Shown in Figure 2 The lineage is represented as a series of letters, where “O” indicates a division as an old pole cell, and “N” a division as a new pole cell. The first cell in the tree is labeled “–.” The number of cells indicates how many individual cell growth rates comprise the average rate. At early points in the lineage, there are many more than 94 cells (the number of films), as each initial cell gives rise to two lineages (as it is not possible to assign old/new pole status to the initial cell), and some films start with more than one cell. The average growth rates approximately correspond to microns per minute. (52 KB XLS). Click here for additional data file. Video S1 Film of Growing Microcolony This film shows 305 min (114 frames) of the growth of a microcolony condensed to 7 s. For the first 40 frames (approximately 3 s), images were taken every 4 min; for the remaining frames, images were taken every 2 min. The complete lineage history of the entire microcolony from the single initial cell in frame 1 to all 505 cells in frame 114 has been tracked and recorded, allowing pole ages to be assigned to every cell. (286 KB WMV). Click here for additional data file. We thank R. D'Ari, M. Fox, M. Gromov, and A. Carbone for valuable discussions, M. Elowitz for the gene construct and advice, A. Lindner and A. Gordon for help with the manuscript, the imaging facility at Institut Jacques Monod and Agnès Troullier for technical assistance, B. Sorre for measurements of exposure controls, and S. Timmermann for assistance with the figures. The work would not have been possible without the support of M. Radman. EJS was funded by the European Molecular Biology Organization and Fondation pour la Recherche Médicale. Competing interests. The authors have declared that no competing interests exist. Author contributions. EJS and FT conceived and designed the experiments. EJS performed the experiments. EJS, RM, GP, and FT analyzed the data. RM contributed reagents/materials/analysis tools. EJS wrote the paper. RM developed BHV, the custom image and data analysis software. ¤Current address: Département de Microbiologie, Faculté de Médecine, Université de Sherbrooke, Québec, Canada Citation: Stewart EJ, Madden R, Paul G, Taddei F (2005) Aging and death in an organism that reproduces by morphologically symmetric division. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030046SynopsisGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyNeuroscienceDanio (Zebrafish)What Does an Airline Traveler Have in Common with a Glowing Fish? Synopsis2 2005 1 2 2005 1 2 2005 3 2 e46Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Light-Dependent Development of Circadian Gene Expression in Transgenic Zebrafish ==== Body In William Gibson's novel Pattern Recognition, the protagonist posits a theory of jet lag: “Souls can't move that quickly, and are left behind, and must be awaited, upon arrival, like lost luggage.” Science has yet to address the issue of a spiritual speed limit, but it is generally accepted that jet lag actually results from the upset of the body's circadian clock, a biochemical pacemaker that dictates daily rhythms in sleep and wakefulness as well as body temperature and metabolic activity. In humans, the circadian rhythm responds to many factors, but daytime–nighttime (or, more precisely, light–dark) cycles are one major regulator. It is possible to gradually reset an upset circadian clock; if travelers remain in the same place for long enough, their circadian rhythm will eventually adjust to the new time zone and ambient light patterns, and the symptoms of jet lag will disappear. The more scientists know about the workings of the circadian clock, the closer they can come to manipulating it. Much is known about the molecular machinery of the circadian clock in the fruitfly, Drosophila melanogaster. Two circadian proteins, Clock and Cycle, cooperate to induce expression of two other proteins, Per and Tim, and when levels of Per and Tim are high enough, they cooperate to shut off their own expression. This negative feedback loop leads to periodic fluctuations in the level of Per and underlies the circadian rhythm in flies. However, until recently, not much was known about the mechanics of the circadian clock in vertebrates. A fusion gene (period3-luciferase) was used to track circadian rhythms Maki Kaneko and Gregory Cahill have created a new tool for investigating the components of the circadian clock in vertebrates: a zebrafish that luminesces (glows) in sync with the periodicity of its circadian clock. To do this, the researchers created a transgene that places expression of the firefly luciferase gene under the control of the promoter of the zebrafish circadian gene period3 (per3). Each cell of the transgenic fish has one normal copy of the per3 gene and one copy of the period3-luciferase fusion gene (per3-luc). Therefore, whenever per3 expression is normally turned on in a cell, the cell produces Per3 protein and also produces the luciferase protein. While characterizing their transgenic zebrafish, the authors made some interesting findings. First, contrary to earlier studies, the authors found that per3 periodicity is not hardwired into zebrafish embryos; instead, per3 periodicity is entrained by alternating light–dark cycles, which must occur at specific stages in early development. Also, other external factors such as ambient temperature can influence both the level of per3 mRNA expressed in the animal and the magnitude of its protein-level oscillations. Because the establishment of circadian rhythms in the adult animal can be so strongly influenced by conditions experienced by the embryos, the authors suggest using a standardized set of conditions for the culture of transgenic embryos in future experiments involving adult fish. Under these controlled conditions, Kaneko and Cahill anticipate that these transgenic zebrafish will be quite useful in examining the molecular machinery of the vertebrate circadian clock. For example, researchers can use the per3-luc transgenic zebrafish in forward genetic screens (where researchers mutagenize the animal to induce a desired phenotype and then identify the mutated gene responsible for the phenotype). In this case, mutagenized zebrafish could be examined for disruptions of per3-luc periodicity or expression. What is more, luminescence can be measured quickly and noninvasively, making this animal an ideal candidate for high-throughput screening aimed at identifying components of the circadian clock in the zebrafish. Thanks to luminescent fish, scientists may someday gain enough insight to make jet lag a thing of the past.	0	PMC546040	CC BY	2021-01-05 08:28:10	no	PLoS Biol. 2005 Feb 1; 3(2):e46	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030046	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030047SynopsisBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyPlant ScienceOryzaRice Genome Approaches Completion Synopsis2 2005 1 2 2005 1 2 2005 3 2 e47Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Genomes of Oryza sativa: A History of Duplications ==== Body In April 2002, Science published draft genome sequences for the two major subspecies of cultivated rice, Oryza sativa. The release of the rice genome—the first plant crop to be sequenced—was big news. Rice is a staple crop for more than half of the world's population, and it was hoped that the availability of the genome sequence might enable scientists to develop more productive rice strains or strains that are more environmentally friendly. Furthermore, the rice genome might provide the key to understanding the genetics of other major cereal crops, all of which have much larger genomes. But the sequences published in 2002 were only draft genomes, containing many gaps and errors—works-in-progress rather than finished products. Now, a large group of scientists led by the Beijing Institute of Genomics is publishing a much improved, near-complete genome analysis of the indica and japonica subspecies of O. sativa, which are eaten in India and China, and Japan, respectively. Their analysis team, led by Gane Ka-Shu Wong, provides important insights into the evolution of rice. First of all, the team improved their original whole-genome shotgun sequencing of indica by generating significantly more sequence data, and then they used better methods to assemble these data. In whole-genome shotgun sequencing, the entire genome is chopped into random fragments, each fragment is sequenced, and then powerful computer programs search for overlaps and put all these data in order. It's like putting a fiendishly difficult jigsaw puzzle together by looking for patches of matching color. The key to the improvement in the genome sequence analysis is that the researchers have used the combined DNA sequence data from the two subspecies to facilitate the sequence assembly. The result is a nearly 1,000-fold increase in contiguity for the two genome sequences. In other words, while the original draft was very fragmented, in the new version, 97.7% of the genes can be found, in either the indica or the japonica dataset, on one piece of DNA whose position along the chromosomes is well defined. The researchers have also used their improved genome sequence to investigate the evolutionary history of rice. Central to evolution is the development of new functions through mutation of existing genes. But when mutations occur in functional genes, the result is rarely beneficial, so it is thought that evolution is more likely to proceed first by duplicating existing genes and then experimenting on the “backup” copy of the gene. Wong and colleagues report that there is evidence in the rice DNA sequences for a whole-genome duplication event just before the grasses diverged from other flowering plants, about 55–70 million years ago. This genome duplication may have played a role in the origin of the grasses, which then spread rapidly across the world to provide important sources of food that, among other things, possibly influenced human evolution. A bowl of indica (white, long grains) and japonica (brown, short grains) rice Analysis of the rice genomes also indicates that a small chromosomal segment was duplicated about 21 million years ago and that there is massive ongoing duplication of individual genes. These individual gene duplications provide a continuous source of raw material for gene genesis and very likely contribute to the differences between members of the grass family. Now the challenge is to use the rice sequences as a basis for detailed genetic analyses of additional cereal crops and for the development of improved strains of not only rice, but wheat, maize, and other important food crops.	0	PMC546041	CC BY	2021-01-05 08:21:19	no	PLoS Biol. 2005 Feb 1; 3(2):e47	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030047	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030058SynopsisEcologyEvolutionMicrobiologyZoologyEubacteriaAging and Death in E. coli Synopsis2 2005 1 2 2005 1 2 2005 3 2 e58Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Aging and Death in an Organism That Reproduces by Morphologically Symmetric Division ==== Body As human beings, aging and death are an inevitable part of our lives. As we pass through each decade, the concrete signs of aging—greying hair, aches and pains, the gradual failure of one organ system after another—and the realization that we are mortal increasingly occupies our thoughts. All other multicellular animals and plants also show clear signs of aging, as do some single-celled organisms. In the yeast Saccharomyces cerevisiae (baker's yeast), for example, the function of individual cells gradually declines with time, and each yeast cell has a finite life span. In organisms like this, it has been proposed that reproduction by asymmetric division is a prerequisite for aging. In other words, for a unicellular organism to age, when it divides, it must give rise to a “parent” cell and a smaller offspring cell (as in yeast), which then has to go through a juvenile phase of growth or differentiation before it divides. At each cell division, the parent cell becomes older until it reaches its natural life span and dies. A growing microcolony of E. coli But what about organisms that produce two apparently identical cells when they divide? Do such organisms age? The assumption has been for some years that cells that divide symmetrically do not age and are functionally immortal. Eric Stewart and colleagues have now tested this idea by analyzing repeated cycles of reproduction in Escherichia coli, a bacteria that reproduces without a juvenile phase and with an apparently symmetric division. E. coli is a rod-shaped organism that reproduces by dividing in the middle. Each resultant cell inherits an old end or pole and a new pole, which is made during the division. The new and the old pole contain slightly different components, so although they look the same, they are physiologically asymmetrical. At the next division, one cell inherits the old pole again (plus a brand new pole), while the other cell inherits, a not-quite-so-old pole and a new pole. Thus, Stewart and co-workers reasoned, an age in divisions can be assigned to each pole and hence to each cell. The researchers used automated time-lapse microscopy to follow all the cell divisions in 94 colonies, each grown from a single fluorescently labeled E. coli cell. In all, the researchers built up a lineage for 35,049 cells in terms of which pole—old or new—each cell had inherited at each division during its history. They found that the cells inheriting old poles had a reduced growth rate, decreased rate of offspring formation, and increased risk of dying compared with the cells inheriting new poles. Thus, although the cells produced when E. coli divide look identical, they are functionally asymmetric, and the “old pole” cell is effectively an aging parent repeatedly producing rejuvenated offspring. Stewart and his colleagues conclude that no life strategy is immune to the effects of aging and suggest that this may be because immortality is too costly or is mechanistically impossible. This may be bad news for people who had hoped that advances in science might eventually lead to human immortality. Nevertheless, E. coli should now provide an excellent genetic platform for the study of the fundamental mechanisms of cellular aging and so could provide information that might ameliorate some of the unpleasantness of the human aging process.	0	PMC546042	CC BY	2021-01-05 08:28:10	no	PLoS Biol. 2005 Feb 1; 3(2):e58	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030058	oa_comm
==== Front BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-5-11563162410.1186/1471-244X-5-1Research ArticleDuloxetine in the treatment of Major Depressive Disorder: A comparison of efficacy in patients with and without melancholic features Mallinckrodt Craig H [email protected] John G [email protected] Chaofeng [email protected] Madelaine M [email protected] Joel [email protected] Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 462852005 4 1 2005 5 1 1 8 4 2004 4 1 2005 Copyright © 2005 Mallinckrodt et al; licensee BioMed Central Ltd.2005Mallinckrodt et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The most prominent feature of melancholic depression is a near-total loss of the capacity to derive pleasure from activities or other positive stimuli. Additional symptoms can include psychomotor disturbances, anorexia, excessive guilt, and early awakening from sleep. Melancholic patients may exhibit treatment responses and outcomes that differ from those of non-melancholic patients. Pooled data from double-blind, placebo-controlled studies were utilized to compare the efficacy of duloxetine in depressed patients with and without melancholic features. Methods Efficacy data were pooled from 8 double-blind, placebo-controlled clinical trials of duloxetine. The presence of melancholic features (DSM-IV criteria) was determined using results from the Mini International Neuropsychiatric Interview (MINI). Patients (aged ≥ 18 years) meeting DSM-IV criteria for major depressive disorder (MDD) received duloxetine (40–120 mg/d; melancholic, N = 759; non-melancholic, N = 379) or placebo (melancholic, N = 519; non-melancholic, N = 256) for up to 9 weeks. Efficacy measures included the 17-item Hamilton Rating Scale for Depression (HAMD17) total score, HAMD17 subscales (Maier, anxiety, retardation, sleep), the Clinical Global Impression of Severity (CGI-S) and Patient Global Impression of Improvement (PGI-I) scales, and Visual Analog Scales (VAS) for pain. Results In data from all 8 studies, duloxetine's advantage over placebo did not differ significantly between melancholic and non-melancholic patients (treatment-by-melancholic status interactions were not statistically significant). Duloxetine demonstrated significantly greater improvement in depressive symptom severity, compared with placebo, within both melancholic and non-melancholic cohorts (p ≤ .001 for HAMD17 total score, CGI-S and PGI-I). When analyzed by gender, the magnitude of improvement in efficacy outcomes did not differ significantly between duloxetine-treated male and female melancholic patients. In the two studies that assessed duloxetine 60 mg once-daily dosing, duloxetine-treated melancholic patients had significantly greater improvement compared with placebo on HAMD17 total score, CGI-S, PGI-I, 3 of 4 subscales of the HAMD17, and VAS overall pain severity (p < .01). Estimated probabilities of response and remission were significantly greater for melancholic patients receiving duloxetine 60 mg QD compared with placebo (response 74.7% vs. 42.2%, respectively, p < .001; remission 44.4% vs. 24.7%, respectively, p = .002 Conclusions In this analysis of pooled data, the efficacy of duloxetine in patients with melancholic features did not differ significantly from that observed in non-melancholic patients. ==== Body Background Although the first appearance of the term "melancholia" dates back to antiquity[1], its use was in decline until it was re-adopted in 1980 by the authors of the Diagnostic and Statistical Manual of Mental Disorders, 3rdEdition (DSM-III) to describe a subtype of major depressive disorder (MDD). Although revised in DSM-III-R, the current DSM-IV "With Melancholic Features Specifier" represents a return to the older DSM-III definition. According to DSM-IV criteria, the principal diagnostic feature exhibited by patients with melancholic depression is a loss of pleasure in all, or almost all, activities or a lack of reactivity to usually pleasurable stimuli [2]. Additional symptoms include diurnal variation (depression worse in the morning), psychomotor disturbances, anorexia, excessive guilt, and early awakening from sleep. Estimates of the prevalence of melancholic features among patients diagnosed with MDD range from 16% to 53% [3-6], although the prevalence may be as high as 76% among inpatients [6]. Melancholia is encountered equally in both genders [7], but is observed more frequently in older patients [4,8]. While melancholia is associated with an increased comorbidity of anxiety disorders, panic disorder, and phobia [9], melancholic patients have a significantly lower suicide rate than non-melancholics [10]. A considerable body of evidence suggests that biological differences exist between melancholic and non-melancholic depression. The hypercortisolism of melancholia has been described as perhaps the best documented finding in biological psychiatry[11]. Many of the features exhibited by melancholic patients closely resemble those that occur reflexively in non-depressed populations during acutely stressful or threatening situations [12]. Thus, depressed patients with melancholic features consistently demonstrate an activation of the hypothalamic-pituitary-adrenocortical (HPA) axis[13], resulting in laboratory findings of dexamethasone non-suppression. Melancholic patients also have an activated corticotrophin-releasing hormone (CRH) system and may have diminished activities of the growth hormone and reproductive axes[14]. When compared with non-melancholic depressed patients, melancholics have also been shown to exhibit lower concentration of nighttime serum melatonin [15], lower plasma serotonin (5-HT) concentrations [16,17], and an impaired in vivo immune response [18,19]. Given the distinct clinical and physiological features associated with melancholia, it is perhaps not surprising that melancholic patients may exhibit treatment responses and outcomes that differ from those of non-melancholic depressed patients. In reviewing a number of long-term studies, Parker et al concluded that melancholics appear to respond well to treatment of individual episodes but have frequent recurrences, while non-melancholics may respond less rapidly and less completely in any individual episode but show a general pattern of improvement over time [20]. Overall, melancholics appear to have higher rehospitalization rates and greater illness morbidity over an extended period [20]. Melancholic patients are less likely to respond to non-specific supportive therapies, such as cognitive behavior therapy or interpersonal psychotherapy, and more often require pharmacotherapy to achieve a successful treatment outcome [21,22]. Within the context of clinical trials, this apparent lack of response to non-specific therapies is frequently manifested in the form of a markedly lower placebo response rate among melancholic, when compared with non-melancholic, patients [23-27]. Thus, in some studies, antidepressant-treated melancholic patients have demonstrated significantly greater improvements compared with placebo while a non-melancholic cohort has failed to achieve separation from placebo. However, the drug response is usually of similar magnitude in all patients, and the difference in outcome can be traced to significantly different placebo responses within melancholic and non-melancholic patients [26,28]. Although a number of studies have investigated the relative efficacy of specific classes of antidepressant medications in melancholic patients, results have been somewhat inconsistent [26]. While tricyclic antidepressants (TCAs) have demonstrated superiority over selective serotonin reuptake inhibitors (SSRIs) in some studies of melancholic depression [29-32], results from other head-to-head studies have failed to support these findings [33-35]. Thus, the current consensus is that SSRIs and TCAs demonstrate comparable efficacy for the treatment of melancholic depression [13,36]. With regard to safety and tolerability, however, SSRIs offer considerable advantages since they lack the anticholinergic, antihistaminergic, and cardiotoxic effects associated with TCAs [37]. As a result, SSRIs are now recognized as the first-line treatment for melancholia. The antidepressant duloxetine is a balanced and potent dual reuptake inhibitor of 5-HT and norepinephrine (NE). The efficacy of duloxetine in the treatment of MDD has been established in randomized, double-blind, placebo-controlled studies of up to 9 weeks duration [38]. In the present study, pooled data from 8 clinical trials of duloxetine were utilized to compare treatment outcomes in melancholic patients with those in non-melancholic patients. Methods Study Design All 8 studies included in these analyses were randomized, multicenter, double-blind, placebo-controlled clinical trials and represented all of the double-blind studies included in the New Drug Application reviewed by regulatory agencies for duloxetine's indication in MDD. All studies incorporated double-blind, variable-expected duration placebo lead-in periods to blind patients and investigators to the start of active therapy. Six studies also utilized an active comparator – fluoxetine (20 mg QD) in Studies 1 and 2, and paroxetine (20 mg QD) in Studies 3, 4, 7, and 8. Study protocols were reviewed and approved by the ethical review board at each center, in accordance with the principles of the Declaration of Helsinki, and all patients provided written informed consent prior to the administration of any study procedures or study drug. The numbers of patients randomized in each study are summarized in Table 1. Detailed safety and efficacy results from Studies 1 [39], 4 [40], 5 [41], and 6 [42] have been published previously. Table 1 Numbers of randomized patients‡ Study Placebo SSRIa Duloxetine 40 mg/db 60 mg QD 80 mg/dc 120 mg/dd 1 70 (80.0) 33 (72.7) - - - 70 (77.1) 2 74 (73.0) 37 (86.5) - - - 82 (69.5) 3 90 (55.6) 89 (64.0) 91 (62.6) - 84 (60.7) - 4 89 (66.3) 87 (63.2) 86 (72.1) - 91 (69.2) - 5 121 (69.4) - - 122 (63.1) - - 6 139 (62.6) - - 128 (62.5) - - 7 93 (66.7) 86 (68.6) - - 95 (65.3) 93 (66.7) 8 99 (67.7) 97 (69.1) - - 93 (71.0) 103 (66.0) TOTAL 775 (67.0) 429 (68.5) 1138 (66.7) ‡ Data presented in the form T (% M), where T = total number of patients, % M = percentage of patients with melancholic features a. SSRI in Studies 1 and 2 is fluoxetine (20 mg QD). SSRI in Studies 3, 4, 7, and 8 is paroxetine (20 mg QD). b. Administered 20 mg twice-daily (BID) c. Administered 40 mg BID d. Administered 60 mg BID. Studies 1 and 2 incorporated a forced titration from 20 mg BID to 60 mg BID. Patients In all studies, patients were 18 years of age or older and met criteria for MDD as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4thEdition (DSM-IV) [43]. The diagnosis of MDD was confirmed by the Mini International Neuropsychiatric Interview (MINI) [44], a standardized diagnostic interview based on DSM-IV criteria. Patients had a 17-item Hamilton Rating Scale for Depression (HAMD17)[45] total score ≥ 15 and a Clinical Global Impression of Severity (CGI-S)[46] score ≥ 4 at the screening and second study visits. The presence of melancholic features (DSM-IV criteria) was determined using results from the MINI: "Either feature 1 or 2 in Criteria A AND three (or more) features from Criteria B must be present to qualify for melancholic features. A. Either of the following, occurring during the most severe period of the current episode. 1. Loss of pleasure in all, or almost all, activities; 2. Lack of reactivity to usually pleasurable stimuli B. Three (or more) of the following: 1. Distinct quality of depressed mood; 2. Depression regularly worse in the morning; 3. Early morning awakening (at least 2 hours before usual time of awakening); 4. Marked psychomotor retardation or agitation; 5. Significant anorexia or weight loss; 6. Excessive or inappropriate guilt." Patients were excluded for the following reasons: a current and primary Axis I disorder, other than MDD; an Axis II disorder which could interfere with protocol compliance; lack of response of the current depressive episode to two or more adequate courses of antidepressant therapy; serious medical illness; a serious risk of suicide; a history of substance abuse or dependence within the last year, or a positive urine drug screen. Concomitant medications with primarily central nervous system activity were not permitted, with the exception of episodic use of chloral hydrate or zolpidem for insomnia. Chronic use of prescription analgesic medications was not allowed; episodic use was permitted at the discretion of the physician in charge of the study. Use of anti-hypertensive medications was not permitted unless the patient had been on a stable dose for at least 3 months prior to study entry. Data Pooling Strategies Efficacy analyses were performed on three sets of data, obtained using the following pooling strategies: (A) "All Studies" – data from all 8 studies were pooled. Placebo: melancholic (n = 519; 67.0%), non-melancholic (n = 256; 33.0%). Duloxetine (40–120 mg/d): melancholic (n = 759; 66.7%), non-melancholic (n = 379; 33.3%). Duloxetine was compared with placebo in one set of analyses. In a second set of analyses using data from the 6 SSRI-controlled studies, duloxetine was compared with fluoxetine and paroxetine: Placebo: melancholic (n = 348; 67.6%), non-melancholic (n = 167; 32.4%). Duloxetine (40–120 mg/d): melancholic (n = 602; 67.8%), non-melancholic (n = 286; 32.2%). SSRI: melancholic (n = 294; 68.5%), non-melancholic (n = 135; 31.5%); (B) "Positive Studies" – data from placebo- and duloxetine-treated patients were pooled from the 6 studies (1, 4, 5, 6,7, and 8) that demonstrated a significant advantage for duloxetine over placebo on the primary efficacy measure. Placebo: melancholic (n = 415; 67.9%), non-melancholic (n = 196; 32.1%). Duloxetine (40–120 mg/d): melancholic (n = 594; 67.4%), non-melancholic (n = 287; 32.6%); (C) "Focus Studies" – data were pooled from the 2 studies (5 and 6) that compared duloxetine 60 mg once-daily with placebo. Placebo: melancholic (n = 171; 65.8%), non-melancholic (n = 89; 34.2%). Duloxetine (60 mg/d): melancholic (n = 157; 62.8%), non-melancholic (n = 93; 37.2%). Strategy A facilitated assessments of differential efficacy in the largest possible data set. While the inclusion of all available data has obvious advantages, the presence of failed studies could mask differential treatment effects. If a study failed to detect an overall effect it is unlikely to help detect differential subgroup effects. Therefore strategy B essentially served as a robustness check for strategy A. Pooling strategy C facilitated assessments at the recommended target dose. Efficacy Measures In all 8 studies, the primary efficacy outcome was mean change from baseline to endpoint in HAMD17 total score. Other efficacy measures assessed in all studies included HAMD17 subscales: anxiety/somatization (Items 10, 11, 12, 13, 15, and 17), Maier (Items 1, 2, 7, 8, 9, and 10), retardation (Items 1, 7, 8, and 14), and sleep (Items 4, 5, and 6); the CGI-S scale; and the Patient Global Impression of Improvement (PGI-I) scale [46]. Response was defined as a decrease from baseline of at least 50% on the HAMD17 total score. Remission was defined as a HAMD17 total score ≤ 7. In Studies 3 – 8, the severity of painful physical symptoms was assessed using Visual Analog Scales (VAS) for pain [47]. Statistical analyses Patients with missing melancholia status were not included in the analyses. All other patients with a baseline and at least one postbaseline observation were included in the analyses. Mean changes from baseline to last observation (carried forward) in HAMD17 total score, CGI-S, and PGI-I were assessed using an analysis of covariance (ANCOVA) with models that included baseline score, treatment, melancholia status (features present Yes/No), investigator, and the treatment-by-melancholia status interaction as independent variables. Hereafter this analysis is referred to as LOCF mean change. The treatment-by-melancholic status interaction was the main basis upon which differential treatment effects between the subgroups were assessed. Contrasts between duloxetine and placebo within the melancholic and non-melancholic subgroups were used to assess the clinical relevance of treatment effects. Longitudinal mean changes and categorical changes (estimated probabilities) were assessed using a likelihood-based mixed-effects model repeated measures (MMRM) approach. Models for mean changes included treatment, visit, investigator, baseline HAMD17 value, melancholia status, and the two-way and three-way interactions between treatment, visit, and melancholia status. Hereafter this analysis is referred to as MMRM mean change. The categorical longitudinal analyses were similar in concept to the longitudinal mean change analyses, but simplifications were necessary to reduce the computational complexity. The categorical analyses were applied only to patients with melancholic features so that the main effect of melancholic status and its two-way and three-way interactions with visit and treatment could be deleted. Therefore, the model for the categorical analyses included treatment, visit, investigator, baseline HAMD17 value, and the treatment-by-visit interaction. A logit link function and a binomial error structure were included to account for the non-linearity of the response and the non-normality of the data, respectively. Hereafter, this analysis is referred to as categorical MMRM. Remission and response rates at last observation were assessed using Fisher's Exact test. The LOCF mean change analysis of HAMD17, CGI-S and PGI-I was applied to all three databases. The focus for the analyses was on all studies and the positive studies with the primary aim of detecting differential effects of duloxetine in patients with and without melancholic features. The MMRM mean change and categorical MMRM analyses were then applied to a wide variety of outcomes from the focus studies in order to gain an in-depth perspective on the efficacy of duloxetine in patients with melancholic features. In addition, LOCF analyses of data from the focus studies were conducted in order to assess robustness of the MMRM results. Results Patient characteristics Baseline patient demographics are summarized in Table 2. The prevalence of melancholic features in the overall patient population was 67.1% (1572/2342). The melancholic cohort had a significantly higher proportion of females compared with the non-melancholic group (69.5% vs. 60.8%, p < .001). Melancholic patients also had a significantly lower mean body weight than non-melancholics (77.7 kg vs. 81.3 kg, p < .001). Mean age at enrollment did not differ significantly between melancholic and non-melancholic patients. Mean baseline HAMD17 total scores and CGI-S scores were significantly higher (more severe illness) in melancholic patients compared with non-melancholics (HAMD17: 22.3 vs. 20.5, p < .001; CGI-S: 4.41 vs. 4.26, p < .001). There was a marginally significant difference in VAS overall pain severity at baseline (31.7 vs. 31.0 for melancholic and non-melancholic groups, respectively; p = .053), although the clinical relevance of this small difference is questionable. Table 2 Baseline demographics and illness severity (all studies)† Melancholic (N = 1572) Non-melancholic (N = 770) p-value Gender, n (%) Female 1092 (69.5) 468 (60.8) <.001 Age, y Mean (SD) 42.1 (12.2) 43.4 (12.8) .347 Age range Min – Max 18 – 82 18 – 82 - Weight, kg Mean (SD) 77.7 (20.5) 81.3 (21.4) <.001 HAMD17 Total Score Mean (SD) 22.3 (3.9) 20.5 (3.2) <.001 CGI-Severity Mean (SD) 4.41 (0.56) 4.26 (0.48) <.001 VAS Overall Pain Mean (SD) 31.7 (25.3) 31.0 (26.0) .053 † Includes patients from paroxetine and fluoxetine treatment arms (N = 429). Efficacy – All studies and positive studies Analyses of mean changes from baseline to last observation (LOCF mean change) from all eight studies and the six positive studies are summarized in Table 3. In both melancholic and non-melancholic patients, duloxetine demonstrated significant advantages over placebo in HAMD17 total score, CGI-S and PGI-I (p ≤ .001). Treatment-by-melancholic status interactions were not significant for HAMD17 total score, CGI-S, or PGI-I, in either data set (p > .50 for each comparison). Using pooled data from all studies, the effect size for HAMD17 total score was 0.33 in melancholic patients and 0.32 in non-melancholics. In the six positive studies, the effect size for HAMD17 total score was 0.39 in melancholic patients compared with 0.43 in non-melancholics. Table 3 Mean changes from baseline to last observation in all studies and the subset of positive studies Melancholic Status Mean Change (SD) p-valuea p-valueb Duloxetine Placebo HAMD17 Total Score All Studies MEL (n = 1249) -8.97 (7.36) -6.57 (7.24) <.001 NON-MEL (n = 621) -8.25 (6.55) -6.20 (6.22) <.001 .651 Positive Studies MEL (n = 984) -9.83 (7.20) -7.03 (7.06) <.001 NON-MEL (n = 474) -9.15 (6.23) -6.47 (6.27) <.001 .781 CGI-Severity All Studies MEL (n = 1251) -1.52 (1.25) -1.11 (1.21) <.001 NON-MEL (n = 621) -1.47 (1.23) -1.14 (1.19) .001 .511 Positive Studies MEL (n = 986) -1.62 (1.25) -1.16 (1.20) <.001 NON-MEL (n = 474) -1.54 (1.13) -1.15 (1.17) <.001 .679 PGI-Improvement† Mean (SD) All Studies MEL (n = 1249) 2.67 (1.27) 3.07 (1.27) <.001 NON-MEL (n = 621) 2.71 (1.31) 3.17 (1.27) <.001 .865 Positive Studies MEL (n = 984) 2.64 (1.26) 3.09 (1.27) <.001 NON-MEL (n = 474) 2.70 (1.28) 3.18 (1.32) <.001 .912 a. p-value for duloxetine vs. placebo b. p-value for treatment-by-melancholia status interaction † Mean score (SD). Lower mean score indicates greater improvement. Within the subset of melancholic patients, treatment-by-gender interactions for mean change in HAMD17 total score, mean change in CGI-S score, and mean endpoint PGI-I score were not statistically significant, indicating that duloxetine's efficacy was similar in male and female melancholic patients (Table 4). In pooled data from all studies, the effect size for HAMD17 total score was 0.29 in male melancholic patients compared with 0.35 in female melancholics. Effect sizes for CGI-S score were 0.35 vs. 0.33 for male and female melancholic patients, respectively, while PGI-I effect sizes were 0.26 (males) vs. 0.34 (females). Table 4 Mean changes from baseline to last observation by gender in melancholic patients Gender Mean Change (SD) p-valuea p-valueb Duloxetine Placebo HAMD17 Total Score Male (n = 386) -7.99 (7.04) -5.95 (7.13) .003 .932 Female (n = 863) -9.42 (7.46) -6.83 (7.29) <.001 CGI-Severity Male (n = 386) -1.41 (1.28) -0.97 (1.22) .001 .668 Female (n = 865) -1.57 (1.24) -1.17 (1.20) <.001 Mean (SD) PGI-Improvement† Male (n = 385) 2.80 (1.27) 3.13 (1.23) .009 .877 Female (n = 864) 2.61 (1.26) 3.04 (1.29) <.001 a. p-value for duloxetine vs. placebo b. p-value for treatment-by-gender interaction † Mean score (SD). Lower mean score indicates greater improvement. In melancholic patients, treatment-by-age interactions for mean change in HAMD17 total score were not significant at thresholds of age 55 (p = .777), age 60 (p = .387), or age 65 (p = .540), indicating that the efficacy of duloxetine did not differ between older and younger melancholic patients irrespective of the old/young age threshold. In the 6 studies that included an SSRI comparator, there were no significant differences in baseline-to-endpoint changes in efficacy measures between duloxetine and SSRI treatment groups (Table 5). In addition, treatment-by-therapy interactions for mean change in HAMD17 total score, mean change in CGI-S score, and mean endpoint PGI-I score were not statistically significant, indicating that the relative efficacy of duloxetine and SSRIs did not differ significantly between melancholic and non-melancholic patients (Table 5). Table 5 Mean changes from baseline to last observation in active comparator studies Melancholic Status Mean Change (SD) p-valuea p-valueb Duloxetine SSRI HAMD17 Total Score MEL (n = 878) -8.85 (7.39) -8.60 (7.68) .709 .784 NON-MEL (n = 416) -8.18 (6.52) -7.59 (6.70) .897 CGI-Severity MEL (n = 880) -1.53 (1.26) -1.52 (1.33) .979 .865 NON-MEL (n = 416) -1.46 (1.24) -1.40 (1.24) .474 Mean (SD) PGI-Improvement† MEL (n = 877) 2.67 (1.28) 2.64 (1.34) .484 .817 NON-MEL (n = 416) 2.64 (1.29) 2.57 (1.02) .077 a. p-value for duloxetine vs. SSRI b. p-value for treatment-by-melancholia status interaction † Mean score (SD). Lower mean score indicates greater improvement. Efficacy – focus studies Analyses of mean changes from baseline to Week 9 (MMRM mean change) for melancholic patients in the two focus studies (Studies 5 and 6) are summarized in Table 6. Melancholic patients receiving duloxetine had significantly greater improvement in mean HAMD17 total score and HAMD17 Maier subscale compared with those receiving placebo (p < .001). Significant differences from placebo first occurred at Week 1 (Maier subscale, Figure 1) or Week 2 (total score) and were present at all subsequent visits. Significant advantages for duloxetine over placebo for mean changes to Week 9 among melancholic patients were also observed on the HAMD17 retardation and sleep subscales, but not for the anxiety subscale (p = .230). On both clinician-rated (CGI-S) and patient-rated (PGI-I) assessments of global improvement, duloxetine-treated melancholic patients had significantly greater mean improvements compared with melancholics receiving placebo (p < .001). Robustness of the MMRM results was confirmed in that significant differences were also observed in LOCF mean change analyses. Table 6 Mean changes from baseline to week 9 in melancholic patients (focus studies) Mean Change (SE) p-value Duloxetine 60 mg QD (n = 153) Placebo (n = 163) HAMD17 Total Score -11.02 (0.65) -7.51 (0.61) <.001 Subscales Maier -6.16 (0.36) -3.95 (0.34) <.001 Anxiety -2.65 (0.22) -2.29 (0.21) .230 Retardation -4.52 (0.26) -2.67 (0.24) <.001 Sleep -1.78 (0.17) -1.17 (0.16) .007 VAS Overall Paina -6.12 (-22.8) 0.13 (0.48) .002 CGI-Severity -1.84 (0.11) -1.30 (0.10) <.001 PGI-Improvementb 2.47 (0.11) 3.08 (0.11) <.001 CGI = Clinical Global Impression; HAMD = Hamilton Rating Scale for Depression; PGI = Patient Global Impression a. Values are mean change (percentage mean change), and p value is from main effect of treatment b. Mean score (SE). Lower mean score indicates greater improvement. Figure 1 Mean changes in HAMD17 Maier subscale for melancholic patients receiving duloxetine (60 mg QD) or placebo. p < .005 vs. placebo. Mean changes from baseline (MMRM mean change) for VAS overall pain were also assessed. Figure 2 shows a visitwise plot of mean changes in VAS overall pain severity for melancholic patients. For the main effect of treatment (pooling results from all visits – interpreted similar to an area under the curve analysis) duloxetine-treated melancholic patients had significantly greater improvement compared with placebo. Duloxetine's advantage over placebo in treating the painful physical symptoms did not vary substantially between patients with and without melancholic features. However, the response profiles were somewhat different in that response to placebo was generally lower in patients with melancholic features compared with non-melancholic patients. For example, the mean percentage improvement in overall pain among non-melancholic patients receiving placebo was 15.6%, compared with a 0.5% worsening in overall pain among placebo-treated melancholic patients. Figure 2 Mean changes in VAS overall pain severity for melancholic patients receiving duloxetine (60 mg QD) or placebo. p < .005 vs. placebo. Estimated probabilities (categorical MMRM analyses) of remission at Week 9 were significantly higher for melancholic patients treated with duloxetine (60 mg QD) compared with placebo (44.4% vs. 24.7%, respectively; p = .002). The estimated probability of response among melancholic patients was 74.7% for duloxetine compared with 42.2% for placebo (p < .001). The advantage of duloxetine over placebo in remission and response rates was also significant in LOCF analyses (p = .032 and p = .002, respectively). Discussion The magnitude of duloxetine's advantage over placebo was generally similar in melancholic and non-melancholic patients. Treatment-by-melancholic status interactions were not significant for any of the three assessed efficacy measures (HAMD17 total score, CGI-S, PGI-I). These results suggest that duloxetine's efficacy does not differ substantially between melancholic and non-melancholic patients. Furthermore, within the group of melancholic patients, duloxetine's treatment effects were similar in male and female patients (treatment-by-gender interactions were not statistically significant). One exception was noted in the anxiety subscale of the HAMD17. In the two focus studies, duloxetine did not achieve separation from placebo in patients with melancholic features, but demonstrated efficacy in non-melancholic patients. The reason for the disparity in outcomes between melancholic and non-melancholic patients on the HAMD17 anxiety subscale is unclear. In previous studies, the prevalence of melancholic features in populations of depressed outpatients has ranged from 16% to 53%. The substantial variation in the reported rates of melancholia may be a result of the numerous criteria used to define melancholic features over the past two decades, including DSM-III, DSM-III-R, DSM-IV, and the Newcastle 1 Depression Rating Scale (N1) [48]. In the present study of depressed outpatients, the prevalence of melancholic features was substantially higher (67.1%) than previous estimates, and merits further comment. At baseline screening all patients were required to meet DSM-IV criteria for MDD, while the presence of melancholic features (DSM-IV criteria) was determined using results from the MINI (further details are provided in the Methods section). The high prevalence of melancholic features was consistent across all 8 clinical trials regardless of geographic location (two studies were conducted in Eastern Europe, six were conducted in the United States). Given the large number of investigative sites (over 140 sites across the 8 studies), and the fact that melancholic features were not used as an inclusion or exclusion criterion in any of the studies, it is unlikely that raters were artificially inflating the number of patients classified as melancholic. A more likely explanation appears to be that the screening method (i.e. the MINI plus DSM-IV criteria) produced a substantial number of false positive results with regard to melancholic features. It is possible that the use of an alternative melancholia screening tool, such as the CORE scale developed by Parker et al [49], would have identified a smaller and more well-defined group of melancholic patients. Thus, in a study evaluating the CORE measure of melancholia against the DSM-IV construct, the CORE criteria identified patients with neuroendocrine disturbance whereas DSM-IV criteria did not [50]. In a wider context, the fact that such a high proportion of patients in the current study met DSM-IV criteria for melancholia may raise questions regarding the validity of melancholia as a separate clinical entity [50], and whether it should be considered simply as a variant of MDD which differs only in severity [51,52]. Although the current results cannot directly address concerns as to the validity of melancholia, the unusually high proportion of melancholic patients identified at baseline suggests that the use of the MINI together with DSM-IV criteria may well be an inadequate screening tool for such purposes. There was no significant difference in age between melancholic and non-melancholic patient groups, despite the fact that melancholic features are more common in older patients. Patients with melancholic features had significantly lower mean body weight at baseline compared to non-melancholics, with a between-group difference of 3.6 kg (8.0 lbs). Since one of the DSM-IV criteria for melancholic features is "significant anorexia or weight loss", this result does not appear surprising. However, given that the melancholic cohort contained a significantly greater proportion of females when compared with the non-melancholic group, the lower body weight among melancholics may be an artifact of gender rather than of MDD subtype. For example, in a recent study of 1694 depressed patients, Berlin et al reported that the presence of melancholic features was not associated with lower body mass index [53]. As expected, melancholic patients exhibited a greater severity of illness at baseline compared with non-melancholics, with a mean baseline HAMD17 score almost two points higher (approximately one-half standard deviation) in the melancholic group. Melancholic patients also had significantly higher baseline CGI-S scores when compared to those without melancholic features. Although melancholic patients had a slightly higher overall pain severity at baseline (as assessed using the self-rated VAS measure of overall pain), the difference only approached statistical significance and its clinical relevance is questionable. A frequent finding in placebo-controlled antidepressant studies is that placebo responses among melancholic patients are smaller than those observed in non-melancholic patients [23-25,54], although this is not always the case [55]. In the present assessment, mean changes in HAMD17, CGI-S or PGI-I scores among both placebo- and duloxetine-treated melancholic patients were slightly larger than those of non-melancholic patients, resulting in a very similar drug-placebo difference in both patient groups. In measures of pain severity there was evidence of greater placebo response among non-melancholic patients; however, duloxetine's advantage over placebo was similar in melancholic and non-melancholic patients, since patients without melancholic features exhibited more robust responses to both drug and placebo, when compared with melancholic patients. There does not appear to be a clear consensus as to which class of antidepressant medication is most efficacious for depressed patients exhibiting melancholic features. Some studies have demonstrated advantages for TCAs over SSRIs in the treatment of melancholia [29-32], and these results have found support from patient-rated assessments of antidepressant efficacy. When melancholic patients were asked to judge the extent to which previous antidepressant therapies had been effective, 38% of those who had received TCAs rated them as moderately or totally effective, compared with 22% of those who had received SSRIs [56]. However, other studies have found no significant differences in efficacy between SSRIs and TCAs in melancholic patients [33,35], or in some cases superiority of an SSRI (fluvoxamine) over a TCA (imipramine) [34,57]. In the absence of clear evidence for superior efficacy of any one class of medication, the safety and tolerability profile of the SSRIs has led to their emergence as first line treatment for melancholia. The present study found no significant differences in efficacy between duloxetine and SSRI comparators within the subgroup of melancholic patients, although we note that the studies were not powered to detect such differences. These results suggest that pharmacological treatment of patients with melancholic features should be assessed on a case-by-case basis, and emphasize that distinct class effects have yet to be demonstrated consistently within this group of patients. Results from the present investigation must be considered in light of several limitations. Firstly, this was a post-hoc analysis of pooled clinical trial data. Although subgroup analyses of efficacy assessments stratified by melancholic status were pre-specified in each protocol, the pooling strategies and some of the analyses conducted in the present investigation were not pre-specified. Secondly, while melancholic features were evaluated as part of the MINI at study entry, specific melancholic features were not an entry requirement and randomization was not stratified by melancholic status. Furthermore, as discussed previously in some detail, the MINI is not an ideal tool for assessing the presence of melancholic features. Thirdly, the HAMD17 scale has only limited capability to assess improvement in melancholic symptoms. Anhedonia, one of the key features of melancholia, is only assessed indirectly as a part of question 7, while psychomotor agitation is only partially addressed by question 9. Furthermore, items such as diurnal variation in mood are not specifically measured by the HAMD17. Fourthly, the group of patients receiving the recommended target dose of duloxetine (60 mg QD) could not be compared head-to-head with SSRIs, as these two studies did not feature active comparator treatment groups. Finally, although duloxetine demonstrated significant advantage over placebo on a number of efficacy measures, the individual studies were powered on the basis of the primary outcome – mean change in HAMD17 total score. Therefore, results from other efficacy measures should be viewed as secondary in nature. Together, these limitations necessitate that the results from the present investigation cannot be considered confirmatory. A prospectively designed clinical trial will be required to confirm the results suggested by the current set of analyses. Conclusions In these analyses of pooled data, the efficacy of duloxetine in patients with melancholic features did not differ significantly from that observed in non-melancholic patients. Additional prospectively designed clinical trials evaluating duloxetine's efficacy in melancholic patients will be required to substantiate the preliminary findings described here. Competing interests Drs. Mallinckrodt, Watkin, Liu, Wohlreich, and Raskin are employees of Eli Lilly and Company. Authors' contributions CHM proposed the data pooling strategies, designed the statistical analyses, and participated in interpretation of data and drafting of the manuscript. MMW, JR, and JGW participated in interpretation of data and drafting of the manuscript. CL performed the statistical analyses. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements This work was sponsored by Eli Lilly and Company. All of the authors accept full responsibility for the conduct of this trial. 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==== Front BMC GeriatrBMC Geriatrics1471-2318BioMed Central London 1471-2318-5-11562740310.1186/1471-2318-5-1Research ArticleMeasuring depression in nursing home residents with the MDS and GDS: an observational psychometric study Koehler Melissa [email protected] Terry [email protected] John [email protected] Michael [email protected] G Iain [email protected] Brant E [email protected] John N [email protected] Richard N [email protected] New Hanover Regional Medical Center, 2131 S 17th Street, Wilmington, NC 28401, USA2 University of Vermont College of Medicine, Fletcher Allen Health Care, 111 Colchester Avenue, Burlington, VT 05401-1473, USA3 Department of Health Studies and Gerontology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada4 Department of Psychology, Lakehead University, Thunder Bay, Ontario, Canada5 The University of Kent, Canterbury, Center for Health Service Studies, George Allen Wing, Kent, CT2 7NF, UK6 University of Michigan Institute of Gerontology, 300 North Ingalls Ann Arbor, MI 48109-2007, USA7 Research and Training Institute, Hebrew Rehabilitation Center for Aged, Boston, 1200 Centre Street, Massachusetts 02131 USA8 Division of Gerontology, Beth Israel Deaconess Medical Center, Division on Aging, Harvard Medical School, Boston, MA USA2005 1 1 2005 5 1 1 18 8 2004 1 1 2005 Copyright © 2005 Koehler et al; licensee BioMed Central Ltd.2005Koehler et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The objective of this study was to examine the Minimum Data Set (MDS) and Geriatric Depression Scale (GDS) as measures of depression among nursing home residents. Methods The data for this study were baseline, pre-intervention assessment data from a research study involving nine nursing homes and 704 residents in Massachusetts. Trained research nurses assessed residents using the MDS and the GDS 15-item version. Demographic, psychiatric, and cognitive data were obtained using the MDS. Level of depression was operationalized as: (1) a sum of the MDS Depression items; (2) the MDS Depression Rating Scale; (3) the 15-item GDS; and (4) the five-item GDS. We compared missing data, floor effects, means, internal consistency reliability, scale score correlation, and ability to identify residents with conspicuous depression (chart diagnosis or use of antidepressant) across cognitive impairment strata. Results The GDS and MDS Depression scales were uncorrelated. Nevertheless, both MDS and GDS measures demonstrated adequate internal consistency reliability. The MDS suggested greater depression among those with cognitive impairment, whereas the GDS suggested a more severe depression among those with better cognitive functioning. The GDS was limited by missing data; the DRS by a larger floor effect. The DRS was more strongly correlated with conspicuous depression, but only among those with cognitive impairment. Conclusions The MDS Depression items and GDS identify different elements of depression. This may be due to differences in the manifest symptom content and/or the self-report nature of the GDS versus the observer-rated MDS. Our findings suggest that the GDS and the MDS are not interchangeable measures of depression. ==== Body Background Depression is common among residents of nursing homes [1]. Of the many instruments used to identify depression in the elderly, the Geriatric Depression Scale (GDS)[2] is probably the most widely used in research settings. The original version comprises 30 items, whereas subsequent versions have been proposed with 15, 12 and later five items [3-5]. None of the items query somatic complaints, rather, questions inquire about the respondent's perspective on their life over the previous week. The Minimum Data Set (MDS), created in response to the US Omnibus Budget Reconciliation Act of 1987, aims to provide a uniform, standardized, and comprehensive assessment of residents in nursing homes [6]. The MDS has undergone several revisions since its inception, and is currently undergoing another revision process. Section E of MDS version 2.0 assesses 16 depression symptoms that capture verbal and non-verbal indicators of depressed mood or anxiety as perceived by nursing home staff. A summary scale, the Depression Rating Scale (DRS), uses a subset of seven of these symptoms and has been shown to be a reliable and valid measure of depression among nursing home residents [7]. The MDS DRS is therefore, by nature of its ubiquity, potentially the most widely available depression assessment instrument for older adults in nursing home settings. This study compares the measurement properties of two measures of geriatric depression: the GDS and the MDS. The GDS is evaluated via a 15 and five item form. The MDS is evaluated by the items located in Section E in total and as a subset included in the Depression Rating Scale. Methods Participants were nursing home residents living in nine Massachusetts nursing facilities participating in a research study between July 1994 and March 1998. Details of the study are described elsewhere [8]. All data used in this study reflect baseline and pre-intervention observations. The sample is considered representative of persons living in nursing homes in Eastern Massachusetts. Potential participants consented individually or by proxy to participate in a research study, using a protocol approved by the local Institutional Review Board. Exclusion criteria included: 1) a terminal prognosis, 2) a projected stay of less than 90 days, or 3) health complications that prohibited contact. Seven hundred four (n = 704) residents and/or their proxies participated in the baseline assessment, representing 67% of those eligible (14% of screened residents were ineligible). Seventy-seven percent of residents were women, 95% were White, non-Hispanic, and the median age was 86 years (interquartile range, 79–91 years). About half (54%) of residents had a chart diagnosis of dementia. Assessment of depression Residents were evaluated with the Minimum Data Set (MDS) Resident Assessment Instrument version 2.0 [6] as well as a 15-item version of the Geriatric Depression Scale (GDS-15) [2,3,9]. Trained research nurses collected the observations. For this analysis, we also considered a 5-item version of the GDS (GDS-5) [5]. MDS data collected and used in this analysis included demographic and clinical characteristics, level of cognitive and communicative functioning, and symptoms of depression. We used a simple sum of all symptoms in MDS section E1, referred to as E1SUM, as one MDS-based measure of depression. We also used a subset of seven of these MDS symptoms to code the MDS Depression Rating Scale (DRS) [7]. We also examined the depression symptom scales with regard to indicators of clinically recognized depression: chart diagnoses of depression and recent use of antidepressant medication. These data were also obtained by research nurses using structured chart review forms keyed to data elements collected with the MDS. MDS assessors are instructed to note the presence of a disorder related to the resident's current functional, cognitive, and behavioural status, medical treatments, and risk of death [10]. Among the disorders assessed is depression. The MDS manual is not specific with regard to clinical or diagnostic criteria for indicating depression diagnoses. The MDS also includes assessment of psychotropic medication use in the seven days preceding the assessment, including antidepressant use. In our analyses, we compared residents receiving any antidepressant with those receiving no antidepressant. Assessment of cognitive impairment We stratified the sample into two groups based on the severity of cognitive impairment. The impaired group comprised residents who were comatose (MDS 2.0 item B1 = 1), and/or with a short-term memory problem (B2a = 1) and those who only rarely/never make themselves understood (C4 = 3). All other residents were assigned to the "cognitively intact" group. This decision rule matches a screening rule for the MDS versus GDS depression symptom assessment proposed in the US Centers for Medicare & Medicaid Services' (CMS) working draft of the MDS version 3.0 [11]. Analytic approach We compared sample statistics and psychometric properties for each of the four depression scales across strata defined by cognitive impairment. We evaluated missing data by examining the proportion of residents with complete data on all assessment items, and also by the proportion of residents with complete data on a majority of items in the scale. For all other analyses we substituted missing values with the mean of the resident's non-missing items if a majority of the scale items were not missing. We examined means, standard deviations (SD), proportion of residents scoring at the floor, the internal consistency reliability (coefficient alpha [12], examined with and without a row-wise mean substitution rule for missing item responses) and the correlation among the depression scales. Finally, we examined the relationship of the scale scores to clinical indicators of depression: chart diagnoses of depression and a record of antidepressant use. We compared the mean of the scale scores across each of four cells formed by crossing antidepressant use and depression diagnoses. These comparisons were also made within cognitive impairment strata. Within strata, cell means were standardized with respect to the mean and standard deviation of the group of residents that neither received antidepressants nor had a chart diagnosis of depression. In this way, cell mean differences can be interpreted on an effect size metric [13]. All analyses were conducted with STATA software (College Station, Texas). Results Sample statistics and missing data Table 1 presents the sample statistics and psychometric properties for the comparison of the GDS and the MDS depression assessment instruments, stratified by level of cognitive impairment. Approximately 70% of residents were classified as cognitively impaired. Among the cognitively impaired, a majority had missing values for the GDS-derived scales (i.e., the GDS-15 and the GDS-5). A substantial proportion (about one in six) of residents were missing data on all GDS items. However, for the GDS-5, about 1 in 20 of the residents without cognitive impairment were missing data on all GDS items. On the other hand, the presence of missing data on the MDS-derived scales (i.e., E1SUM and DRS) was essentially nil in both cognitive impairment groups. About a third of residents had no missing values on the GDS-15 and more than half had missing data on the GDS-5. Table 1 Sample statistics and psychometric properties of Geriatric Depression Scale and MDS Depression Rating Scale. Sample statistic or psychometric property Cognitively Impaired Group (n = 495) Cognitively Intact Group (n = 209) All Participants (N = 704) Number (%) with missing data on all items GDS-15 81 (16%) 35 (17%) 116 (16%) GDS-5 110 (22%) 5 (2%) 115 (16%)* E1SUM 3 (1%) 2 (1%) 5 (1%) DRS 0 (0%) 0 (0%) 0 (0%) Number (%) with complete data on all items GDS-15 139 (28%) 111 (53%) 250 (36%)* GDS-5 223 (45%) 157 (75%) 380 (54%)* E1SUM 492 (99%) 207 (99%) 699 (99%) DRS 494 (100%) 209 (100%) 703 (100%) Number (%) with majority of scale items not missing GDS-15 357 (72%) 203 (97%) 560 (80%)* GDS-5 352 (71%) 200 (96%) 552 (78%)* E1SUM 495 (100%) 209 (100%) 704 (100%) DRS 495 (100%) 209 (100%) 704 (100%) Mean* (SD) GDS-15 4.7 (3.5) 4.9 (3.4) 4.8 (3.5) GDS-5 2.0 (1.5) 2.1 (1.6) 1.5 (1.6) E1SUM 3.9 (3.8) 2.7 (3.4) 3.6 (3.7)* DRS 1.9 (2.1) 1.8 (2.3) 1.9 (2.2) Proportion at floor GDS-15 0.081 0.053 0.072 GDS-5 0.166 0.167 0.166 E1SUM 0.206 0.349 0.249* DRS 0.356 0.397 0.368 Alpha – internal consistency reliability – row-wise complete cases only GDS-15 0.799 0.781 0.791 GDS-5 0.609 0.597 0.602 E1SUM 0.695 0.738 0.708 DRS 0.542 0.672 0.583* Alpha – internal consistency reliability – row-wise mean substitution for missing data† GDS-15 0.825 0.798 0.814 GDS-5 0.663 0.634 0.651 E1SUM 0.695 0.738 0.708 DRS 0.542 0.672 0.583* Correlation coefficients (GDS-5, GDS-15) 0.858 0.852 0.856 (DRS, GDS-15) 0.073 0.098 0.080 (DRS, GDS-5) 0.065 0.064 0.062 (E1SUM, GDS-15) 0.068 0.096 0.072 (E1SUM, GDS-5) 0.055 0.058 0.049 (E1SUM, DRS) 0.850 0.940 0.865* Abbreviations: GDS-15, 15-item Geriatric Depression Scale; GDS-5, 5-item Geriatric Depression Scale; DRS, Minimum Data Set (MDS) Depression Rating Scale; E1SUM denotes the sum of all mood and anxiety items in section E1 of the MDS; SD, standard deviation * For each respondent, if a majority of the items were not missing, any missing item was replaced with the mean of the non-missing items. † Missing item responses replaced with mean score for all respondents 'non-missing' on that item. On the other hand, about half of the cognitively intact group had no missing GDS-15 scores and three-quarters had no missing GDS-5 scores. Using an item-level missing data mean substitution rule, predicated on a resident having at least a majority of items present, the frequency of missing data for scale scores diminished for the GDS-derived scales. However, the impact of missing data remains an important problem for GDS-derived measures among the cognitively impaired: between one quarter and one third of the cognitively impaired still had missing data. All noted differences in the frequency of missing data describe large effect sizes (in Cohen's effect size taxonomy [13]) and are statistically significant (P < .001). The very high frequency of missing data for the GDS encouraged us to examine the frequency of missing data at the item level. We present this information in Table 2, limiting the sample to those missing at least one but not all GDS items. The item with the greatest frequency of missing data was item 15 ("do you think that most people are better off than you are") for both the cognitively impaired group and those with better cognitive functioning. The item with the fewest missing values was item 5 among the cognitively impaired group ("are you in good spirits most of the time?") and item 1 among those without cognitive impairment ("are you basically satisfied with your life?"). Although the proportion with missing data on each item differed significantly across cognitive impairment strata for only one item (item 9), many of the differences across groups depict medium or larger effect sizes (items 2, 9, 10, 12). Table 2 Proportion of residents with missing data on individual items of the Geriatric Depression Scale. Limited to residents missing at least one but less than all 15 items. Item descriptions Cognitively Impaired Group (n = 251) Cognitively Intact Group (n = 93) All Participants (N = 344) 1. Are you basically satisfied with your life?* 37 (15%) 6 (6%) 43 (13%) 2. Have you dropped many of your activities and interests? 68 (27%) 10 (11%) 78 (23%) 3. Do you feel that your life is empty? 57 (23%) 16 (17%) 73 (21%) 4. Do you often get bored?* 47 (19%) 14 (15%) 61 (18%) 5. Are you in good spirits most of the time? 30 (12%) 9 (10%) 39 (11%) 6. Are you afraid that something bad is going to happen? 44 (18%) 13 (14%) 57 (17%) 7. Do you feel happy most of the time? 35 (14%) 12 (13%) 47 (14%) 8. Do you often feel helpless?* 55 (22%) 20 (22%) 75 (22%) 9. Do you prefer to stay in your room, rather than go out and doing new things?* 88 (35%) 11 (12%) 99 (29%) 10. Do you feel you have more problems with memory than most? 80 (32%) 11 (12%) 91 (26%) 11. Do you think that it is wonderful to be alive? 48 (19%) 14 (15%) 62 (18%) 12. Do you feel pretty worthless they way you are now?* 78 (31%) 14 (15%) 92 (27%) 13. Do you feel full of energy? 64 (25%) 14 (15%) 78 (23%) 14. Do you feel that your situation is hopeless? 74 (29%) 16 (17%) 90 (26%) 15. Do you think that most people are better off than you? 110 (44%) 31 (33%) 141 (41%) * included in GDS-5 Mean level of depression The means for the GDS- and MDS-derived scales were different between the cognitively intact and impaired groups (Table 1). Of note, the cognitively impaired group received higher scores on both the GDS-15 and GDS-5, indicating higher levels of depression. While these differences were small [13] they describe statistically significant differences (P < .05). Conversely, the cognitively intact residents within both the DRS and E1SUM had higher depression ratings. The difference between cognitive impairment groups was trivial[13] and not statistically different for the GDS (P = .79), but of moderate size[13] and statistically significant for E1SUM (P < .001). Floor effect Figure 1 illustrates several characteristics of the GDS-15 and the DRS, including the floor effect. Both instruments produce distributions with a high proportion of respondents scoring zero. For the GDS-derived measures as well as the DRS, the proportion scoring at the floor is similar for both cognitive impairment groups. The difference in the proportion scoring at the floor on the E1SUM measure is moderately different between the cognitively intact and cognitively impaired groups (P < 0.01). Figure 1 Scatter plot of 15-item Geriatric Depression Scale (GDS) and Depression Rating Scale (DRS) scores among 560 Nursing Home Residents in Nine Massachusetts Nursing Homes. Internal consistency reliability The GDS-15 had the highest internal consistency reliability coefficient, and the lowest was observed for the DRS. However, this difference does not substantially exceed that expected due to the greater scale length of the GDS-15. Using the Spearman-Brown prophecy formula [14], the reliability of the DRS for the total sample would be 0.72 if it had 15 similar items (instead of 7), which is closer to that observed for the GDS-15 (for residents with complete data). There was no statistically significant difference in the internal consistency reliability across cognitive impairment groups (using the variance ratio test[15]) for the GDS-derived scales and the E1SUM. However, the differences across cognitive impairment strata for the DRS were statistically significant (P < .01). We also note that the estimated internal consistency reliability can be artificially inflated by using a row-wise (i.e., person-wise) mean substitution rule for missing data. This artificial inflation affects GDS-derived scales but not DRS-derived scales. Correlation of DRS and GDS The correlation between the GDS- and DRS- derived measures were not statistically different from zero. All of the differences in correlation coefficients are similar among the cognitively impaired and cognitively intact and not statistically different, except for the correlation of the E1SUM and DRS (P < .001). Relation of DRS and GDS to indicators of conspicuous depression The comparison of mean depression symptom scale scores among residents that received antidepressants and/or had a chart diagnosis of depression, within and across cognitive impairment strata, is reported in Table 3. For the GDS-15, within both cognitive impairment groups, there are trivial marginal differences in the mean score for either a depression diagnosis or record of antidepressant use. For the GDS-5, the mean score is somewhat higher for residents with a diagnosis or record of antidepressant use. These differences correspond to small to moderate effect size differences [13]. The marginal differences are not statistically significant within cognitive impairment strata, but for the total sample the pattern of means are essentially the same and are statistically significant for a depression diagnosis (P = .02) and approach significance for antidepressant use (P = .05). Table 3 Mean depression scale score as a function of the presence of diagnosis of depression or receiving antidepressants. Means are standardized to the mean and variance of the scale score for the group without a diagnosis and who did not receive an antidepressant. Cognitively Impaired Group (N = 495) Cognitively Intact Group (N = 209) Total (N = 704) Depression Diagnosis Depression Diagnosis Depression Diagnosis GDS-15 No Yes Total No Yes Total No Yes Total Antidepressant Use No 0.00 0.14 0.02 0.00 .47 0.06 0.00 0.26 0.03 (203) (26) (229) (116) (16) (132) (319) (42) (361) Yes 0.07 0.43 0.31 0.29 0.36 0.09 0.16 0.41 0.32 (42) (86) (128) (30) (41) (71) (72) (127) (199) Total 0.01 0.36 0.12 0.06 0.39 0.15 0.03 0.37 0.13 (245) (112) (357) (146) (57) (203) (391) (169) (560) GDS-5 Antidepressant Use No 0.00 0.05 0.01 0.00 0.51 0.06 0.00 0.23 0.03 (199) (23) (222) (115) (16) (131) (314) (39) (353) Yes 0.00 0.31 0.21 0.23 0.28 0.26 0.09 0.30 0.22 (44) (86) (130) (30) (39) (69) (74) (125) (199) Total 0.00 0.26 0.08 0.05 0.35 0.13 0.02 0.29 0.10 (243) (109) (352) (145) (55) (200) (388) (164) (552) E1SUM Antidepressant Use No 0.00 0.43 0.05 0.00 0.23 0.03 0.00 0.37 0.05 (295) (39) (334) (119) (18) (137) (414) (57) (471) Yes 0.60 0.55 0.57 0.48 -.06 0.18 0.55 0.37 0.44 (62) (99) (161) (31) (41) (72) (93) (140) (233) Total 0.11 0.52 0.22 0.10 0.04 0.08 0.10 0.37 0.18 (357) (138) (495) (150) (59) (209) (507) (197) (704) DRS Antidepressant Use No 0.00 0.40 0.05 0.00 0.16 0.02 0.00 0.32 0.04 (294) (39) (333) (119) (18) (137) (413) (57) (470) Yes 0.59 0.59 0.59 0.51 -0.15 0.13 0.56 0.33 0.42 (62) (99) (161) (31) (41) (72) (93) (140) (233) Total 0.10 0.54 0.22 0.11 -0.06 0.06 0.10 0.33 0.17 (356) (138) (494) (150) (59) (209) (506) (197) (703) Abbreviations: GDS-15, 15-item Geriatric Depression Scale; GDS-5, 5-item Geriatric Depression Scale; DRS, Minimum Data Set (MDS) Depression Rating Scale; E1SUM denotes the sum of all mood and anxiety items in section E1 of the MDS For the MDS-derived depression scales, the differences in means associated with antidepressant use and depression diagnoses are much more dramatic than for the GDS-derived scales, but only among the cognitively impaired. For both the E1SUM and DRS, the marginal differences for depression diagnoses and antidepressant use describe moderate to large effect sizes. These differences are statistically significant (both P < .001). On the other hand, among those residents not identified as cognitively impaired, the marginal differences associated with antidepressant use and a depression diagnosis were trivial to small and not statistically significant. Discussion In this study, we found that MDS- and GDS-derived depression measures were not correlated with one another, but were apparently adequately reliable measures of their intended construct. Thus, we infer that the MDS and the GDS measure different aspects of nursing home residents' depression. Each scale has specific strengths and limitations. The practical utility of the GDS is undermined by a very high frequency of missing data. The proportion of GDS responses missing differs greatly by level of cognitive functioning. The floor effect limits both instruments. While the internal consistency reliability is apparently greater for the GDS, this may simply be due to the greater number of items on the GDS. We observed a weak relationship between GDS-5 scale scores and clinical indicators of depression (diagnoses, antidepressant use), but the strong association between MDS-derived scales and clinical indicators was only seen among cognitively impaired residents. Other investigators have found a low correlation between the DRS and the GDS and other instruments for assessing depression, but these findings vary according to data collection strategies. For example, Anderson et al found a low correlation between the MDS DRS abstracted from residents' charts and symptom data collected with the GDS (r = .13) and the Hamilton Depression Rating Scale (HDRS; r = 0.24) using research interviewers [16]. Similarly, Hendrix et al[17] found a lack of correspondence between MDS depression symptoms and depression classified using a cut-point on the Cornell Scale for Depression in Dementia (CSDD). Hendrix and colleagues attributed the low agreement of CSDD and MDS to different data collection strategies. In their study, the CSDD was collected by primary caregivers, while the MDS was abstracted from the chart, and these authors suggest that the nurse administrators that completed the MDS did not consult the primary caregivers and the resident in completing the MDS depression items. Contrast with these findings a recent study by Ruckdeschel and colleagues [18], who converted the MDS items into a self-report assessment device and reported a very high correlation with depression symptom data collected with the GDS (r = 0.71). In our study, the assessment methods followed more closely how they were designed to be used. The GDS was used as a direct interview, and the MDS was used as instructed in the MDS manual [6], and included review of the chart, semi-structured interview with the resident, direct caregivers, and available family members or key informants, in order to arrive at final symptom ratings. MDS- and GDS-derived measures were comparably reliable after adjustments for test length, but were nevertheless not very highly correlated. Therefore, whatever differentiates the dimensions assessed by the two devices, it is probably an influence beyond the level of assessor training and the rigor of the evaluation. The fact that the MDS- and GDS-derived scales do not correlate implies that the two scales evaluate different aspects of a resident's depression. For positive MDS depression symptom ratings, residents must visibly act by making negative statements, be easily angered, and display unrealistic fears to trigger MDS symptoms. The GDS asks residents if they are satisfied with their life, feel helpless or worthless, and are often bored. It is conceivable that GDS-derived measures capture a brooding mental set, reflective of a dysphoric personality trait or adjustment disorder (for example in response to a recent change in living situation) rather than the presence of major depression. The lack of correlation between MDS depression measures has implications for proposed revisions to the MDS. Until more is known about the phenomenology and clinical validity of syndromes measured by these and other depression measures used in long-term care settings, adding self-report of symptoms of depression to the MDS is supported by our findings. We note that both CMS's draft revision of the MDS[11] and new versions of assessment instruments developed for other care settings by interRAI include a provision for self-assessment of depression [19]. However, our findings may have further implications for CMS's revision of the MDS. The current draft of the revision calls for a sub-set of MDS section E items for those who are cognitively impaired, and direct questioning with the GDS-5 for those who are cognitively intact. Our findings suggest a more conservative approach might be to use both for all residents, or the MDS for all residents and the GDS for all residents who can communicate regardless of cognitive level. Two key findings underlie this recommendation. First, we find that the GDS and MDS are not complementary, but orthogonal. Second, we find no evidence for compromised measurement properties of the GDS among those with cognitive impairment. Conclusions The Geriatric Depression Scale and Minimum Data Set mood items measure different aspects of the depression syndrome among nursing home residents. The two measures cannot be treated as exchangeable or equivalent, and each has it's own strengths and limitations. The GDS uses self-report, but as a consequence suffers from a high frequency of missing data. The MDS relies upon informant ratings and therefore provides information about most residents. Although the GDS has higher internal consistency reliability than MDS, this is not beyond that expected due to greater scale length. The MDS measures were more strongly related to antidepressant use and record of a diagnosis of depression than were GDS measures. These results highlight the fact that more psychometric research is needed to better understand and improve the measurement of depression among frail nursing home residents. List of abbreviations used GDS Geriatric Depression Scale MDS Minimum Data Set DRS Depression Rating Scale CSDD Cornell Scale for Depression in Dementia HDRS Hamilton Depression Rating Scale Competing interests No financial competing interests. TR, JH, GIC, BEF and JNM are fellows of InterRAI, a non-profit group dedicated to improving the health care for persons who are elderly, frail or disabled. InterRAI owns the copyright to the MDS for nursing homes and many other care settings. InterRAI offers free licenses for the use of assessment forms of which it owns the copyright to. Therefore, there are no financial competing interests for members of the writing group. For more information please visit . Authors' contributions MK and RNJ conceived of the study. MK drafted the manuscript. RNJ performed the analyses and provided critical review of and completed the manuscript. JNM obtained the data, participated in the analysis and provided critical review of the manuscript. JH, MS, GIC and BEF provided critical review of the analyses and manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements RNJ's work on this project was supported, in part, by the Harvard Medical School Claude D. Pepper Older American's Independence Center grant (5P60AG008812-12, Lewis Lipsitz, Principal Investigator). RNJ and JNM were supported on this project in part by NIH/NIA grant 1R01AG017931-01A1 (John N. Morris, Principal Investigator). Nursing home resident data was collected with support of an NIH/NIA funded research study (5P50AG011719-05, John Morris, Principal Investigator). JNM holds the HRCA Alfred A. and Gilda Slifka Chair in Social Gerontological Research. JH's participation was supported by an Investigator Award from the Canadian Institutes for Health Research. ==== Refs Jones RN Marcantonio EM Rabinowitz T Prevalence and correlates of recognized depression in U.S. nursing homes J Am Geriatr Soc 2003 51 1404 1409 14511160 10.1046/j.1532-5415.2003.51458.x Yesavage JA Brink TL Rose TL Lum O Huang V Adey M Leirer VO Development and validation of a geriatric depression screening scale: a preliminary report J Psychiatr Res 1982 17 37 49 7183759 10.1016/0022-3956(82)90033-4 Sheikh JI Yesavage J Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version Clin Gerontol 1986 5 Sutcliffe C Cordingley L Burns A Mozley CG Bagley H Huxley P Challis D A new version of the geriatric depression scale for nursing and residential home populations: the geriatric depression scale (residential) (GDS-12R) Int Psychogeriatr 2000 12 173 181 10937538 10.1017/S104161020000630X Hoyl MT Alessi CA Harker JO Josephson KR Pietruszka FM Koelfgen M Mervis JR Fitten LJ Rubenstein LZ Development and testing of a five-item version of the Geriatric Depression Scale J Am Geriatr Soc 1999 47 873 878 10404935 Morris JN Bernabei R Ikegami N Gilgen R Frijters D Hirdes JP Fries BE Steel K Carpenter I DuPasquier JN Henrard JC RAI-Home Care (RAI-HC)(C) Assessment Manual for Version 2.0 1999 Washington, DC, InterRAI Corporation Burrows AB Morris JN Simon SE Hirdes JP Phillips C Development of a minimum data set-based depression rating scale for use in nursing homes Age Ageing 2000 29 165 172 10791452 10.1093/ageing/29.2.165 Morris JN Fiatarone M Kiely DK Belleville-Taylor P Murphy K Littlehale S Ooi WL O'Neill E Doyle N Nursing rehabilitation and exercise strategies in the nursing home J Gerontol A Biol Sci Med Sci 1999 54 M494 M500 10568531 Yesavage JA Geriatric Depression Scale Psychopharmacol Bull 1988 24 709 711 3249773 Health Care Financing Administration Long Term Care Resident Assessment Instrument User's Manual Version 2.0 1995 Rockville, MD, Health Care Financing Administration Centers for Medicare and Medicaid Services Draft Version of MDS 3.0 (The Draft MDS 3.0 is a CMS working document) Cronbach LJ Coefficient alpha and the internal structure of tests Psychometrika 1951 16 297 334 Cohen J Statistical power analysis for the behavioral sciences 1988 Hillsdale, New Jersey, Lawrence Erlbaum Associates Guilford JP Psychometric Methods 1936 New York, McGraw Hill Book Company, Inc. Berk RA Handbook of methods for detecting test bias 1982 Baltimore, Johns Hopkins University Press Anderson RL Buckwalter KC Buchanan RJ Maas ML Imhof SL Validity and reliability of the Minimum Data Set Depression Rating Scale (MDSDRS) for older adults in nursing homes Age Ageing 2003 32 435 438 12851189 10.1093/ageing/32.4.435 Hendrix CC Sakauye KM Karabatsos G Daigle D The use of the Minimum Data Set to identify depression in the elderly J Am Med Dir Assoc 2003 4 308 312 14613597 10.1097/01.JAM.0000094065.05310.FB Ruckdeschel K Thompson R Datto CJ Streim JE Katz IR Using the Minimum Data Set 2.0 Mood Disturbance Items as a Self-Report Screening Instrument for Depression in Nursing Home Residents Am J Geriatr Psychiatry 2004 12 43 49 14729558 10.1176/appi.ajgp.12.1.43 InterRAI Corporation MDS/RAI Assessment Instruments WWW Page Accessed September 25, 2002 Last Update August 15, 2001 http://wwwinterraiorg 2002	15627403	PMC546185	CC BY	2021-01-04 16:30:32	no	BMC Geriatr. 2005 Jan 1; 5:1	utf-8	BMC Geriatr	2,005	10.1186/1471-2318-5-1	oa_comm
==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-31563664210.1186/1471-2105-6-3Research ArticleDifferences in codon bias cannot explain differences in translational power among microbes Dethlefsen Les [email protected] Thomas M [email protected] Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA2 Department of Microbiology and Immunology, Stanford University, Palo Alto, California 94304, USA2005 6 1 2005 6 3 3 16 9 2004 6 1 2005 Copyright © 2005 Dethlefsen and Schmidt; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Translational power is the cellular rate of protein synthesis normalized to the biomass invested in translational machinery. Published data suggest a previously unrecognized pattern: translational power is higher among rapidly growing microbes, and lower among slowly growing microbes. One factor known to affect translational power is biased use of synonymous codons. The correlation within an organism between expression level and degree of codon bias among genes of Escherichia coli and other bacteria capable of rapid growth is commonly attributed to selection for high translational power. Conversely, the absence of such a correlation in some slowly growing microbes has been interpreted as the absence of selection for translational power. Because codon bias caused by translational selection varies between rapidly growing and slowly growing microbes, we investigated whether observed differences in translational power among microbes could be explained entirely by differences in the degree of codon bias. Although the data are not available to estimate the effect of codon bias in other species, we developed an empirically-based mathematical model to compare the translation rate of E. coli to the translation rate of a hypothetical strain which differs from E. coli only by lacking codon bias. Results Our reanalysis of data from the scientific literature suggests that translational power can differ by a factor of 5 or more between E. coli and slowly growing microbial species. Using empirical codon-specific in vivo translation rates for 29 codons, and several scenarios for extrapolating from these data to estimates over all codons, we find that codon bias cannot account for more than a doubling of the translation rate in E. coli, even with unrealistic simplifying assumptions that exaggerate the effect of codon bias. With more realistic assumptions, our best estimate is that codon bias accelerates translation in E. coli by no more than 60% in comparison to microbes with very little codon bias. Conclusions While codon bias confers a substantial benefit of faster translation and hence greater translational power, the magnitude of this effect is insufficient to explain observed differences in translational power among bacterial and archaeal species, particularly the differences between slowly growing and rapidly growing species. Hence, large differences in translational power suggest that the translational apparatus itself differs among microbes in ways that influence translational performance. ==== Body Background Translational power is the rate of protein synthesis of a cell or culture, normalized to the amount of biomass invested in the protein synthesis machinery. We are introducing the term 'translational power' to describe precisely the same concept (and the same quantitative parameter, see Methods) that was originally defined as 'ribosome efficiency' [1-3]. In recent years, this concept has more commonly been called 'translational efficiency' [4,5], particularly in discussions of codon usage bias [6-8]. Although we are reluctant to depart from established terminology, we do so to avoid an inconsistency with the meaning of 'efficiency' as it is used in many other areas of science and in common parlance. In the physical sciences and in many areas of biology, the efficiency of a process refers to a comparison of output to input, in particular to the fluxes of useful energy and/or mass (e.g., the efficiency of a heat engine [9], trophic transfer efficiency [10]). These scientific meanings of 'efficiency' are consistent with the common notion that a process obtaining the desired output with little waste is highly efficient. According to these conventions, calculations of efficiency make no direct reference to the rate at which a process occurs. Physicists and engineers use a distinct term, 'power,' to refer to the rate of energy consumption or the rate at which work is performed [11]. The semantic distinction between power (or rate) and efficiency is important, because in many real and idealized physical systems, the laws of thermodynamic result in an inherent tradeoff between power and efficiency [9]. In biology, several attempts to argue for the universality of power-efficiency tradeoffs [12,13] have justifiably been criticized for the misapplication of thermodynamic arguments [14-16]. Nonetheless, many specific tradeoffs have been demonstrated in a wide range of organisms that can be described as evolutionary choices between power (increased rates of biological processes such as resource acquisition, metabolism or organismal growth) and efficiency (increased biological output measured as probability of survival, production of biomass, number of progeny, etc. per unit resource) [17-24]. Among bacteria, comparisons of coexisting species or strains have also provided evidence for power-efficiency tradeoffs [25-28], as have comparisons of engineered mutant strains [29,30]. However, the absence of apparent tradeoffs in some carefully designed studies of bacteria demonstrates that such tradeoffs are not inevitable [31-33]. Even if power-efficiency tradeoffs occur only in some biological contexts, it is valuable to maintain a semantic distinction between power (implying rapid rate) and efficiency (implying low waste). However, the terms 'ribosome efficiency' and 'translational efficiency' blur this distinction, because they refer to a rate – the quantitative measure of ribosome efficiency [1] is expressed in units of (time-1). We prefer the term 'translational power', which refers to the rate of protein synthesis of a cell or culture, normalized to the mass of the translational apparatus, in a manner that is more consistent with the connotations of 'power' and 'efficiency' derived from other areas of science and from colloquial usage. Translation rate (a synonym of 'protein chain growth rate' [3,34], meaning the rate of amino acid polymerization per active ribosome) is one component of translational power, but translational power reflects other properties of the protein synthesis system as well, most notably the fraction of ribosomes that are active (see Methods, also chapter 6 of reference [34]). Intuitively, translational power measures the capacity of the protein synthesis subsystem to drive replication of the cell, the protein-dominated autocatalytic system to which it belongs. The concept and a quantitative metric of translational power were first introduced to facilitate comparisons of translational performance between different growth rates within a single bacterial strain [1]. The initial belief that translational power is nearly constant in a strain across a wide range of growth rates, based both on empirical data and theoretical arguments [2,34], has gradually given way to the current understanding that translational power increases with growth rate, at least in E. coli [3,4,35,36]. The question of whether translational power varies between microbial species has been investigated only rarely, in four studies that each compared a single slowly-growing microbial species to E. coli [37-40]. In each case, translational power was found to be higher in E. coli than in the slowly growing comparison strain. Although each of these studies discusses this unexpected result, only one of them references the same result from another study. In previous work, the consistent association of low translational power with slowly growing microbes appears to have escaped notice; however, our reanalysis of the data from these four studies as well as additional published data (presented in Results) suggests that the association is robust. One factor capable of affecting translational power is the biased usage of synonymous alternative codons. In the standard translational code, 18 of the 20 amino acids are encoded by more than a single codon, but in many microorganisms, synonymous codons are not used with equal frequency. The pattern first found in E. coli and Bacillus subtilis turns out to be common: the majority of genes within an organism show a preference for the same subset of codons, but the degree of bias towards the preferred subset is correlated with the expression level of the gene [41,42]. For some time, the consensus has been that such a pattern reflects selection for translational power [7,8]. Codon bias increases translational power because preferred codons tend to be translated more rapidly than synonymous alternatives [43-45]. This effect can be attributed to the high abundance of tRNAs cognate to the preferred codons, to a canonical base pair interaction at the codon wobble position between preferred codons and their cognate tRNAs, or to both these factors [7,8]. Codon bias resulting from selection for translational power (or for any other translation-dependent benefit) is correlated with gene expression level because the benefit accrues during each instance of translation, so the selective pressure for preferred codons is stronger in more highly expressed genes [7,8]. In contrast to the codon bias caused by translational selection, codon bias that is consistent in both magnitude and direction in genes that vary widely in expression level is explained most easily by mutational bias acting on DNA [8,46]. While the effects of both translational selection and mutational bias are evident in some microbial genomes with moderately biased G+C content [47,48], organisms with strong mutational bias (very high or low G+C content) have been reported to show very little [49] or no [50-52] evidence of translational selection. Theoretical calculations indicate that if the strength of mutational bias exceeds a certain critical threshold, any pre-existing codon preferences that conflict with the mutational bias will be reversed [53]. In this case, codon use is almost entirely determined by the mutational bias, which influences genes equally regardless of expression level. Note that while the degree of codon bias and the gene expression level would not be correlated among genes from such a genome, this does not necessarily imply that deviations from the average (biased) codon usage would be selectively neutral, nor that the fitness effects of any such deviations would be independent of gene expression level. The absence of a correlation between codon usage and gene expression level has also been reported in some organisms with moderate G+C content, in particular the spirochete Treponema pallidum [54] and the proteobacteria Helicobacter pylori [55]. The lack of evidence for translational selection in these organisms requires an explanation, since they lack a strong mutational bias that could obscure such evidence. It has been suggested that rapid exponential growth confers little or no fitness benefit in these strains [8,55], consistent with their slow growth rate and other characteristics of their ecological niche. If so, these organisms would not experience selection for translational power. If variation in the strength of selection for translational power leads to differences in the degree of codon bias between microbes (superimposed on any differences in codon bias that can be attributed to variation in mutational bias), we wondered whether differences in codon bias could in turn explain the observed differences in translational power between microbes. An estimate of the effect of biased codon use on the overall rate of translation would depend on knowledge of absolute or relative translation rates in vivo for each codon. Unfortunately, these data are incomplete even for E. coli, and are not available for other microbes. Therefore, we approach the issue by framing the following question: How much faster is the translation rate of E. coli than the expected translation rate of a hypothetical organism that has the same proteome composition and the same investment in translational machinery as E. coli, but which lacks codon bias? Here we report results from a simple mathematical model developed to address this question. For convenience, we will refer to the hypothetical E. coli-like organism with uniform use of synonymous alternative codons as 'Uni'. By 'same proteome composition', we mean that over a cell generation, each amino acid is incorporated into protein the same number of times in Uni and in E. coli, although for the 18 amino acids specified by multiple codons, the individual codons will differ in frequency. By 'same investment in translational machinery', we mean that the total biomass of the translational apparatus is the same in Uni and in E. coli, although ideally the allocation of that biomass among various components of the apparatus in Uni would be optimized for unbiased codon usage. However, in order to apply empirical codon-specific translation rate data, we will impose a more stringent requirement on Uni, that the abundance of each individual component of the translational apparatus will be unchanged in comparison to E. coli. Due to this restriction, and due to the incomplete data for codon-specific translation rates, we make no claim to be able to answer our question precisely. However, our approximations are adequate to conclude that differences in codon bias alone are unlikely to account for differences in translational power of the magnitude inferred from macromolecular analysis of slowly growing and rapidly growing microbes. Results Comparisons of translational power among microbes We know of 4 studies that have made explicit comparisons of translational power between different microbial species; in each case, the comparison was made between E. coli and a single slowly growing strain [37-40]. One of these studies relied on original measurements of E. coli [38]; the remaining studies made comparisons using the E. coli data of Bremer and Dennis [3]. Although growth rates and translation rates vary with temperature [56], at least 2 of the 4 studies [39,40] compared data from strains grown at different temperatures without compensating for temperature effects. One of 2 studies that made comparisons based on the number of ribosomes per cell volume appears to have assumed that E. coli cell volume is constant over a range of growth rates [39], which is unlikely. We have reanalyzed the data from these studies (as described in Methods) to provide consistent comparisons of translational power between E. coli and other strains. In addition, we applied the same comparative methodology to every microbial species for which we could find the requisite data in the literature. The list of species that could be included is surprisingly short; most studies reporting both the protein and RNA content of microbes growing at known rates have involved E. coli or closely related enteric bacteria. Table 1 summarizes the comparisons of translational power between E. coli and all other species. The comparisons of translational power in Table 1 are based on the fastest growth rate for which data are available for each of the comparison organisms, because at submaximal growth rates, there may be a reduction in the average translation rate [4,57], in the active fraction of ribosomes [35,36], or both. Either of these phenomena would reduce translational power. However, the comparisons to E. coli are not always based the fastest E. coli growth rate, but rather on the growth rate at which E. coli makes a comparable investment in the translational apparatus as the comparison organism. A comparison at similar investment levels reflects the expectation that the selective pressure to maximize translational power increases with the biomass invested in the apparatus [4,58]. If the comparisons had always been made to the fastest E. coli growth rate (i.e., where E. coli translational power is highest), the disparity in translational power would be greater for most of the comparisons shown. Even with the conservative comparisons displayed in Table 1, the published data suggest that translational power varies considerably between strains, particularly for comparisons between microbes adapted to different ranges of growth rates. While translational power is higher in E. coli and other rapidly growing organisms, it is lower in slowly growing organisms, ranging from less than 17% to 42% of the value for E. coli. Hence, if differences in the degree of codon bias are to explain these differences in translational power, we would expect codon bias to be capable of accelerating the rate of translation by 2.5-fold to 6-fold. In summarizing the comparisons of Table 1 as a contrast between slowly growing and rapidly growing microbes, we are not relying on the actual growth rates shown in the third column, especially since chemostat growth rates are necessarily constrained below the maximal growth rate for a strain. Instead, we have relied both on well-recognized growth characteristics for some species (e.g., Sphingopyxis alaskensis and Rickettsia prowazekii are slow growers, Salmonella enterica and Enterobacter aerogenes are rapid growers), and on the number of copies of the ribosomal RNA (rrn) operon per genome. High rrn copy number is an adaptation permitting rapid growth [59,60], while low rrn copy number is characteristic of microbes adapted for slow growth [39,61]. Estimates of the translation rate benefit of codon bias We define the translation rate benefit of codon bias in E. coli as sbias, the fractional increase in the time required to replicate the E. coli proteome if the actual codon bias of E. coli were to be replaced with uniform use of synonymous codons (Equation (10) in Methods). Our estimates of sbias depend on the relative translation rates of individual codons in vivo, and on the frequency with which each codon is used in synthesizing the proteome. The sources we have used for these data, and the details of several adjustments made to the source data, are described in the Methods section. All data used in our estimates of sbias are presented in Table 2. Because the codon-specific translation rate data are incomplete even for E. coli, we have explored 4 different scenarios (described in Methods) for extrapolating from the empirical rate data to obtain an estimate of sbias over all codons. Scenarios 1–4 are increasingly complex, and represent deliberate attempts to assign translation rates to the unmeasured codons in a way that increases sbias while remaining consistent with patterns found in the empirical data. Furthermore, in Scenario 5, we apply a theoretical approach [62] for predicting optimal codon-specific translation rates that does not rely on empirical translation rate measurements at all, but only on codon frequency and tRNA abundance data. Estimates of sbias for all scenarios are presented in Figure 1. The empirical translation rate data used in Scenarios 1–4 reflect ternary complex selection at the ribosomal A-site, but not translocation of the newly-formed peptidyl-tRNA from the A-site to the P-site [45]. Thus, for these scenarios we show two estimates of sbias that are based on different assumptions regarding the relative duration of translocation and ternary complex selection. The white bars of Figure 1 are based on the assumption that the duration of translocation is negligible for all codons in comparison to the duration of ternary complex selection. The cross-hatched bars of Figure 1 are based on the assumption that translocation requires a finite amount of time that is constant for all codons, but short in comparison to the time required for ternary complex selection [63]. In Scenario 5 the duration of translocation is not treated explicitly, but the theoretical rate predictions refer to the entire cycle of translational elongation. Hence, we have grouped the estimate from Scenario 5 with other estimates that account for the duration of translocation. Our estimates of the benefit of codon bias in E. coli relative to the complete absence of codon bias range from 0.6 – 1.4 if translocation time is neglected, or from 0.4 – 1.1 with the more realistic assumption that translocation requires a short amount of time. We have also estimated the benefit of codon bias in E. coli relative to the limited degree of codon bias that might be found in an actual low-bias organism, rather than making a comparison to the biologically unrealistic standard of strictly uniform synonymous codon use. We took T. pallidum as our example of a microbe with limited codon bias, since it is a slowly growing bacterium with little mutational bias (52.7% G+C) that has also been reported to lack translational selection [54]. The T. pallidum genome has the second-most uniform codon use over all predicted genes (assessed as Wright's effective number of codons [64]) among 108 bacterial and archaeal species for which complete genome sequences were available in June, 2003 (data not shown). Our method for generating a set of low bias codon frequencies from T. pallidum genome codon frequencies is described in Methods. Estimates of the translation rate benefit of codon bias for E. coli relative to low bias codon frequencies are shown by the black bars of Figure 1, again assuming a short, invariant duration of translocation. The estimated benefits range from 0.2 – 0.6; as expected, these estimates are smaller than estimates derived from a comparison to strictly uniform codon usage. Because the theoretical estimates of Scenario 5 fall in the middle of the corresponding ranges of empirical estimates from Scenarios 1–4, we are confident that our results are not merely an artifact of unrecognized errors in the empirical rate measurements. The benefit of codon bias calculated for individual amino acids Our definition of sbias can be applied over any subset of codons, in particular, it can be applied to the codons of each amino acid separately. While all amino acids with multiple codons except proline contribute positively to sbias in all scenarios, the magnitude of that contribution is highly variable between amino acids (Figure 2). Codon bias accelerates the translation of most amino acids only slightly in E. coli, because most non-preferred codons are not particularly rare in the E. coli proteome, compared to the preferred synonym. For example, among the 9 amino acids encoded by 2 codons, on average the preferred codon is 2.9-fold more abundant than the non-preferred codon. Of these amino acids, asparagine shows the greatest difference between preferred and non-preferred codon frequencies, with GAC being 5.2-fold more abundant than GAU. Even if the disparity in codon-specific translation rates is unrealistically large, the ratio of the frequencies of preferred to non-preferred codons in E. coli constrains the maximum possible value of sbias. For asparagine, even if the preferred codon were translated instantaneously (i.e., infinitely faster than the non-preferred codon), the difference between using the non-preferred codon at 16% of asparagine residues in E. coli instead of at 50% of asparagine residues in Uni corresponds to only about a 3-fold acceleration of translation (sbias ≈ 2) for this amino acid. With more realistic disparities between the translation rates of preferred and non-preferred codons, the largest estimate of sbias for asparagine in any of our scenarios is less than 0.2. In other words, we estimate that codon bias in E. coli leads to no more than a 20% decrease in the time required to translate all asparagine codons in the proteome (Figure 2). The amino acids with the largest values of sbias are leucine, isoleucine, and arginine (Figure 2). Although these amino acids are not rare, they possess between them the six rarest codons in E. coli, each encoding less than 0.1% of the proteome. (An average codon encodes 1.6% of the proteome.) The frequencies of the most and the least abundant synonyms for leucine, isoleucine and arginine differ by 74-fold, 83-fold, and 1460-fold, respectively. (The higher ratio for arginine reflects the extreme rarity of AGG, which is 17-fold less abundant than the second rarest E. coli codon, AUA encoding isoleucine.) Since the translation rates measured or assumed for the 6 rarest codons are quite slow, their increased abundance in Uni accounts for the much of the additional time required for replicating the Uni proteome. If these six codons remained as rare in Uni as they are in E. coli, while all other synonymous codons were used without bias in Uni, the translation rate benefit estimated under Scenario 4 (the scenario producing the largest estimate of sbias) would be reduced by almost half (data not shown). The influence of these 6 codons is such that the estimate of sbias is quite sensitive to the translation rates assigned to them, in contrast to the relative insensitivity of sbias to the exact translation rates assigned to most codons. Discussion We want to know whether reduced codon bias could account for the lower translational power measured in at least some slowly growing bacteria, in comparison to E. coli. We approach this issue by its converse, calculating how much faster the proteome is replicated in E. coli than it would be in the complete absence of codon bias. If we take our estimates at face value, we would conclude that even during rapid growth when the proteome is most biased and translation is fastest, sbias is unlikely to be much larger than 1 (cross-hatched bars of Figure 1), which corresponds to a 2-fold increase in the average translation rate. An effect of this magnitude approaches the smaller disparities in the comparisons of translational power between E. coli and slowly growing strains shown in Table 1, but could not explain the roughly 5-fold difference in translational power between E. coli and S. alaskensis, R. prowazekii, Halobacterium cutirubrum, or sulfate-reducing strain PT2. However, there are two reasons to think that the benefit of codon bias for E. coli, in comparison to most actual slow-growing organisms, is even less than this estimate. The first reason is that we have prevented our hypothetical Uni from adapting to the codon frequencies we have assigned to it, by keeping the abundance of each component of the translational apparatus fixed. The data do not suggest that maximizing translational power has been the only selective pressure influencing codon use in E. coli [45,65]. If it had been, the codon with the highest rate constant for ternary complex selection among synonymous alternatives would always be the preferred codon, since it would permit faster translation with a lower biomass investment in cognate tRNA. Of 10 amino acids with multiple codons for which codon-specific translation rate measurements exist [44,45], leucine, serine and proline are not consistent with this prediction. On the other hand, it seems clear that selection for rapid translation has exerted some, and perhaps the major influence on the coevolution of codon frequencies and tRNA abundance in E. coli. The codon with the highest rate constant is the preferred codon for 7 of the 10 amino acids for which data are available. Other considerations (possibly including error avoidance [66], interactions between adjacent tRNA anticodons [67], or factors unrelated to translation [68]) may have been more influential than the inherent characteristics of the codon-anticodon interactions for determining the preferred codons encoding leucine, serine and proline. However, the importance of rapid translation remains evident in that E. coli still translates the preferred codons quickly for 2 of these 3 amino acids, albeit with a larger investment in tRNA than would be necessary if the interaction between the preferred codon and its cognate tRNA occurred more readily. At a larger scale, the correlation across all codons between frequency and cognate tRNA abundance [69,70] is best explained as a response to selection for rapid translation, as is the pattern of increased bias towards rapidly translated codons with increased levels of gene expression [45]. Without asserting that the distribution of tRNA abundance in E. coli necessarily produces the fastest possible translation rate for the E. coli codon frequency distribution, it is clear that selection for translational power has been a significant factor in the coevolution of codon frequencies and cognate tRNA abundances in E. coli. Thus, it is very unlikely that we have attained the maximum possible translation rate for Uni by matching the E. coli distribution of tRNA abundance values (in the form of a particular distribution of codon-specific translation rates) to the very different codon frequency distribution of Uni. For this reason, our estimates confound the translation rate benefit of codon bias in E. coli with the penalty of a suboptimal allocation of translational resources in Uni. The second reason that our approach overstates the relative benefit of codon bias for E. coli in comparison to actual slow-growing organisms is that actual microbes are not completely devoid of codon bias. Assessing sbias in E. coli in comparison to a biologically plausible standard for low codon bias, instead of in comparison to the implausible standard of no codon bias whatsoever, reduces the estimated benefit in E. coli by about half (black bars of Figure 1). Only a slight bias in codon use is sufficient to obtain a substantial benefit of faster translation because only a few codons in E. coli are translated much more slowly than the median rate (Table 2). Moderate avoidance of only these few codons can provide a considerable acceleration of the average translation rate without generating a dramatic bias in overall codon use. Our estimate of a biologically plausible standard for low bias codon frequencies is deliberately conservative, underestimating the degree of bias expected in most slowly growing microbes, for two reasons. First, our low bias codon frequencies are based on the genome codon frequencies of T. pallidum, as if all predicted genes in the genome were expressed equally. Correspondence analysis performed at the level of individual genes failed to uncover evidence that codon use varies with expression level in T. pallidum [54]. If this were true, the proteome codon frequencies would indeed be similar to genome codon frequencies, regardless of variability in gene expression levels. However, a more sensitive analysis using codon frequencies summed over a set of putative high expression genes indicates that codon use in such genes is more biased than codon use in the genome as a whole. This conclusion is based on a comparison of Wright's effective number of codons [64] calculated for codon frequencies summed over all predicted genes annotated as ribosomal proteins or translation elongation factors (Nc = 52.7) or calculated for codon frequencies summed over all predicted genes in the genome (Nc = 55.2) [71]. The failure to observe this low level of codon bias in the previous analysis based on individual gene sequences [54] can probably be attributed to high gene-to-gene variability in codon frequency estimates based on the small samples of codons represented by individual genes. Thus, even for T. pallidum, the proteome codon frequencies appropriate for estimating the benefit of codon bias will be more biased than the genome-derived low bias codon frequencies shown in Table 2. The second reason our low bias codon frequencies underestimate the degree of codon bias in most slowly growing microbes is that T. pallidum is essentially free of the influence of mutational bias, with a genome G+C content of 52.7%. In contrast, many slow-growing microbes have more extensive codon bias that can be attributed mostly or entirely to the biased nucleotide composition of the genome (e.g., R. prowazekii [52], H. pylori [55], Borrelia burgdorferi [54], Buchnera aphidicola [72], Mycoplasma genitalium [73], and Chlamydia species [74]). If codon bias derived from mutational bias, like codon bias derived from translational selection, permits more rapid translation for the same investment in translational machinery, the use of low bias codon frequencies derived from T. pallidum will underestimate the translation rate of many slow growing strains. We believe that codon bias derived from mutational bias does, indeed, have the potential to accelerate translation. The translation rate benefit of codon bias depends on matching preferred codons with cognate tRNAs that are abundant and/or that form 3 canonical base pairs [7,8]. Even when codon use is determined by mutational bias in the DNA replication and repair systems [46], not by selection acting simultaneously on codons and their cognate tRNAs via translation-associated effects, selection for translational power can influence the relative abundance and anticodon sequence of tRNA species. Relatively few mutations are sufficient to influence the identity and abundance of tRNA molecules in an organism, in comparison to the number of mutations required to influence proteome codon frequencies. (Consider that 45 mutations could allow a single mutation in the anticodon wobble position or in the regulatory region of many or even all tRNA genes, depending on the organism, while 45 mutations could alter the identity of less than 0.5% of the >9,000 codons in genes encoding ribosomal proteins and translational elongation factors.) Hence, the mutation-selection balance argument invoked to explain diminished codon bias in genes expressed at low levels in many strains [8,75] also suggests that the distribution of tRNAs can be influenced by translational selection that may be too weak to create a dramatic effect on codon usage. In fact, if codon use is biased in the same direction in all genes (as expected if the source of codon bias is mutational bias), instead of being biased only in highly expressed genes, it would increase the selective pressure for adaptation of the tRNA pool. Hence, it would be very surprising if the anticodons and the relative abundances of tRNA molecules in organisms with high or low G+C content did not reflect their biased use of codons. This prediction is confirmed by the only two studies we have found of tRNA abundance in microbes with extreme G+C content, involving Mycoplasma capricolum (25% G+C) [76] and Micrococcus luteus (74% G+C) [77]. M. capricolum, but not M. luteus, can be considered a constitutively slow-growing strain. As expected, cognate tRNA abundance in both organisms is correlated with codon frequency, both across all codons and within synonymous codon families [76,77]. For M. capricolum, this is accomplished largely without the tRNA gene dosage effects that are important for E. coli [70] and B. subtilis [78], since 28 of the 29 M. capricolum tRNA genes are present in only a single copy [76]. These examples indicate that selection for translational power is operative even for organisms in which the codon bias is determined by mutational bias instead of translational selection, and even for slowly growing organisms. Because codon bias from any source can be exploited to obtain higher translational power, the estimates of sbias for E. coli compared to codon frequencies derived from T. pallidum will overstate the benefit that exists for E. coli relative to most other slowly growing microbes that have greater mutational bias. In summary, we believe the translation rate benefit of codon bias in E. coli is likely to be less than 0.6 (see black bars of Figure 1) relative to an actual slow-growing organism that shows limited codon bias, such as T. pallidum, and substantially less than 0.6 relative to a slow-growing organism with more extensive codon bias. We do not mean to suggest that the advantage of translating as much as 60% faster than a competitor is unimportant. Clearly, the benefit of codon bias for E. coli must be substantial, considering that it arises from the aggregate effect of many thousands of preferred codons that are stably maintained in the E. coli genome, despite the randomizing influence of mutation acting at each individual codon. On the other hand, the influence of codon bias on the average translation rate is far smaller than the differences in translational power observed between microbes adapted to different ranges of growth rates. For differences in codon bias to explain the difference in translational power between E. coli and S. alaskensis, sbias would have to be about 5; to explain the difference between E. coli and R. prowazekii, sbias would have to be about 3. Is it possible that the comparisons of translational power presented in Table 1 are flawed? The colorimetric assays used for RNA and protein measurement in these studies are indeed dependent on procedural details, such that comparisons between laboratories and between studies are less reliable than comparisons within a study. Nonetheless, variation between species in the estimates of translational power presented in Table 1 do not appear to result simply from large random errors around a common mean. Estimates of translational power for slowly growing species with few rrn operons cluster around low values; the reverse is true for species capable of rapid growth with higher numbers of rrn operons. In addition, our own measurements of 10 bacterial species (including E. coli, S. alaskensis and 8 recent soil bacterial isolates) reproduce the same pattern; we have found differences in translational power that are comparable in magnitude to those shown in Table 1[79]. Hence, we believe the comparisons in Table 1 are an adequate representation of the differences in translational power between rapidly growing and slowly growing microbes. Conclusions Because codon bias influences translational power, and because the degree of codon bias due to translational selection may differ systematically between rapidly growing and slowly growing strains, we investigated the parsimonious hypothesis that observed differences in translational power between microbial species could be explained by differences in the degree of codon bias. However, based on the analysis reported here, such an explanation is not plausible. Instead, differences in translational power between rapidly growing and slowly growing species suggest that the translational apparatus itself has different performance characteristics in rapidly growing and slowly growing microbes. Methods Translational power, translation rate and the active fraction of ribosomes Conceptually, we define translational power as the rate of protein synthesis in a cell or culture, normalized to the biomass invested in the protein synthesis system. We intend the term to be synonymous with 'translational efficiency' [4,5,8]; our rationale for departing from established terminology is provided in the Introduction. The protein synthesis system is comprised of ribosomes, elongation factors, tRNAs, tRNA synthetases, mRNAs, and numerous other components. Measuring the mass of the entire system is not trivial, because it includes a variable fraction of the cell's protein. However, since the protein synthesis system includes essentially all the cell's RNA, we follow Kjeldgaard and Kurland [1] in using RNA mass (R) as an index of the biomass invested in the entire system. For a culture in balanced, exponential growth, the instantaneous rate of increase of any culture component is dX/dt = μX, where μ is the specific growth rate and X is the mass of the component present in the culture at that moment. Hence, μP is the rate of protein synthesis in a culture containing mass P of protein. Thus, our quantitative measure of translational power is: This quantitative measure of translational power will be consistent with the conceptual definition as long as RNA is a nearly constant fraction of the mass of the entire protein synthesis system. Translational power reflects both the average translation rate and the fraction of active ribosomes in a cell or culture, which we demonstrate as follows, using the approach of chapter 6 of reference [34]. 'Translation rate' refers to the rate of amino acid polymerization of an active ribosome. The average translation rate of a cell or culture is the rate of amino acid polymerization in the entire culture divided by the total number of active ribosomes: We know that the mass rate of protein synthesis in a culture in balanced growth is μP. Units of protein mass can be converted to a number of amino acids by dividing the protein mass by the average mass of an amino acid: number of amino acids polymerized per unit time = μP/(average mass of amino acid) (3) The number of ribosomes in a culture containing a mass R of RNA can be found by multiplying R by the fraction of RNA that is ribosomal, and then dividing by the mass of RNA in a ribosome. However, only a fraction of these ribosomes are active at any given time. Thus: Substituting Equations (3) and (4) into Equation (2) yields: After rearranging terms in Equation (5), we have: where The quantity μP/R in Equation (6) is the quantitative measure of translational power from Equation (1) [1,3]. From Equation (6), it is clear that translational power reflects both the average translation rate and the active fraction of ribosomes in a cell or culture. What of the term we have labeled C, implying a constant? The two quantities in the numerator, the mass of RNA in a ribosome and the average mass of an amino acid, are indeed constant or nearly constant, both within a strain at different growth rates, and across strains. However, despite the constant ribosomal fraction of RNA reported in reference [3], other data indicate that the rRNA fraction decreases from about 85% to about 75% as growth rate declines in E. coli from 1.7 hr-1 to 0.28 hr-1 [70], a result which is expected on theoretical grounds [4,65]. This variation is not dramatic; it would reduce translational power by only 12%, if the average translation rate and active fraction of ribosomes were unchanged. Data are also available from 2 of the 4 studies that have compared translational power between E. coli and a slowly growing strain. The rRNA fraction is reported as 84% for H. cutirubrum at specific growth rates of both 0.10 hr-1 and 0.05 hr-1, after the authors made the deliberately generous assumption that messenger RNA comprises 5% of the total RNA [37]. The rRNA fraction is about 85% for R. prowazekii at a specific growth rate of ~0.07 hr-1, after a correction is made for 2–3% messenger RNA [38]. These data suggest that variation between microbial species in the ribosomal fraction of RNA is limited, even when comparing species that grow at very different rates. Comparisons of translational power based on published data Table 1 summarizes comparisons of translational power between E. coli and all other bacterial and archaeal species for which we could find both the protein content and the RNA content of cultures growing at known rates. Throughout this work, E. coli is represented by the Bremer and Dennis data [3], which are typical of the data reported for E. coli in many other studies. Similarly, comparisons between E. coli and 2 closely related species of enteric bacteria, S. enterica and E. aerogenes, are made using only a single representative study for the latter strains, chosen from among several published reports. For the remaining species, only a single published study was available for comparison, except for one species represented by two studies, both of which are included. For strains not grown at 37°C, we assume that the growth rate, but not the macromolecular content, would be altered by growth in the same medium at a different temperature [80]. The growth rates reported for these strains were adjusted to the growth rates expected at 37°C using the linear range of the relationship reported in reference [56]. (Although this temperature-growth rate relationship was generated with E. coli, the comparison is mathematically identical whether the temperature correction is applied to E. coli or to the comparison strain.) The comparisons in Table 1 use the fastest growth rate for which data are available for the comparison organisms, and use data for E. coli growing at a rate such that it matches the comparison organism for investment in the translational apparatus. (For two of the comparison strains, translational power differed considerably between the fastest growth rates obtained in different culture conditions; both values are reported.) One of three measures was used to gauge the level of investment in the translational apparatus, depending on the quantity measured in the original study. The possible measures were the number of ribosomes per cell volume, the ratio of protein to ribosomal RNA, or the ratio of protein to total RNA. Values of these quantities for E. coli were interpolated between adjacent data points to estimate the growth rate at which E. coli made the same investment in the translational apparatus as the comparison organism. The translational power of the comparison organism at the fastest available growth rate was then expressed as a percentage of the translational power of E. coli at the 'same investment' growth rate. A comparison at similar investment levels reflects the expectation that the selective pressure to maximize translational power increases with the biomass invested in the apparatus [4,58]. If the comparisons had always been made to the fastest E. coli growth rate (i.e., where E. coli translational power is highest), the disparities in translational power would be greater for most of the comparisons shown. Calculation of the translation rate benefit of codon bias Consider a cell in which a total of Ci codons of type i are translated during a single cell generation, so that the sum over all sense codons C = ΣCi is the total number of codons translated during a cell generation. (Hereafter we refer to the translational output over a cell generation as the proteome.) If we define ci = Ci/C as the proportion of all codons of type i in the proteome and ri as the average translation rate of codons of type i, the total time required for replication of the proteome (i.e., the proteome generation time) will be where R# is the average number of ribosomes active in translation over the cell cycle and the sum is over all sense codons. Codon bias in favor of rapidly translated codons will reduce gp in comparison to uniform codon use. If a mutation changes the fitness of an organism from w to w', the benefit of the mutation is typically described as s, where w'/w = 1 + s. By analogy, and considering gp to be inversely related to fitness, we can express the translation rate benefit of codon bias as The protein content (and thus C) is the same in Uni as in E. coli by hypothesis. With the restrictive condition that the abundance of each individual component of the translational apparatus is unchanged in Uni, ribosome content (R#) will be the same also. Hence, the C/R# term of gp in Equation (8) cancels from both the numerator and denominator of Equation (9) for sbias, leading to Since amino acid frequencies are identical in E. coli and Uni, the disparities in translation rates between synonymous codons largely determine the magnitude of the translation rate benefit of codon bias. We will use the same codon-specific translation rates (the ri's) for both Uni and E. coli, again invoking the restrictive stipulation that the abundance of each individual tRNA species is unchanged. If rate constants for the interaction of each codon with each of its cognate tRNA species were known, we could calculate the optimal tRNA abundance distribution for the codon frequencies of Uni, and infer the resulting codon-specific translation rates [62,65]. However, in vivo codon-specific translation rate data are available only as codon averages, including translation from all tRNA species cognate to each codon. Hence, rate constants specific to each codon-cognate tRNA pair cannot be calculated from the available data for the codons translated by multiple tRNA species, and thus we cannot calculate an optimal tRNA abundance distribution for Uni. Instead, we have constrained Uni to maintain the same tRNA distribution and codon-specific translation rates as E. coli. Insofar as the E. coli rates reflect an allocation of tRNA abundance that would be sub-optimal for Uni (as we argue in the Discussion section), our approach will tend to overestimate of the benefit of codon bias in E. coli, a conservative error for our purposes. Data sources All data used in our estimates of sbias are reported in Table 2. For the codon frequencies used in synthesizing the proteome of E. coli, we rely on the data of Dong et al. at a specific growth rate of 1.73 hr-1 [70], compiled from public gene sequence databases and protein abundance data derived from 2D gel electrophoresis studies [81,82]. The absolute codon frequencies shown in Table 2 have been recalculated from [70] with initiation and stop (including selenocysteine) codons removed. As expected, the translation rate benefit of codon bias was found to increase monotonically with growth rate, when calculated by any of the scenarios described below, using the proteome codon frequencies and tRNA abundance data from the range of growth rates reported in reference [70] (data not shown). This increase in sbias reflects simply the increasing bias in both proteome codon usage and relative tRNA abundance with increasing growth rate. Since we are interested in the maximum effect of codon bias, we report results from only the highest growth rate for which data are available. To investigate the importance of low levels of codon bias, we applied Equation (10) either with Uni having strictly uniform use of synonymous codons, or with Uni assigned a set of low bias codon frequencies (Table 2). The low bias frequencies were generated from relative codon frequencies over all predicted genes in the complete genome sequence of T. pallidum [71]. By relative codon frequencies, we mean the absolute frequency of a codon divided by absolute frequency of the amino acid it encodes. The set of T. pallidum relative codon frequencies for a particular amino acid were multiplied by the absolute frequency of that amino acid in the E. coli proteome; the resulting set of absolute codon frequency values were assigned to the codons of that amino acid in the low bias set so as to retain the same rank order of codon frequency among synonyms as exists in the E. coli proteome. For example, the absolute frequency of isoleucine and the identity of the 1st, 2nd and 3rd most common isoleucine codons are the same in the low bias set as in the E. coli proteome. However, the relative frequencies of the 1st, 2nd and 3rd most common isoleucine codons in the low bias set are the same as the relative frequencies of the 1st, 2nd and 3rd most common isoleucine codons in the T. pallidum genome. To represent codon-specific translation rates, we use the relative rate data (the quantity RtRNA/Rshift) of Curran and Yarus [45] for the 29 sense codons beginning with U or C (YNN codons, Y = pyrimidine). Although incomplete, this is by far the largest data set available for in vivo translational kinetics. The original publication transposed values reported for two arginine codons, CGC and CGA [83]; we have corrected this error. We also revised the rate measured for CGA downward, to account for interference from the bulky wobble position inosine-adenine base pair in the P site that results from translation of a CGA codon. Such interference is strongly suggested to slow selection of a ternary complex at the codon subsequent to CGA [83]; such an effect would not have been measured with the experimental system of reference [45], but is appropriate to include as a codon-specific effect of CGA on translation rate. In the absence of more precise data, we reduced the translation rate measured for CGA by a factor of 3, the factor by which CGA reduces read-through of a following stop codon by a suppressor tRNA in comparison to CGC [83]. This adjustment to the CGA rate brings these results into rough agreement with those of Sorensen and Pedersen [84], who used an experimental approach that would have detected a consistent effect of CGA on the translation rate of the subsequent codon, attributing it to slow translation of CGA itself. The relative rates of reference [45], modified as described above, are listed in Table 2. The relative rates reported by Curran and Yarus [45] do not reflect the entire translational cycle, but rather the time required for selecting a cognate ternary complex at an empty, codon-programmed ribosomal A site, which is believed to occupy the majority of the elongational cycle [63]. Although peptide bond formation may be very rapid, the time required for the EF-G-catalyzed translocation of the ribosome to the subsequent codon (and the associated movement of P- and A-site tRNAs) may not be much shorter than the time needed for EF-Tu-catalyzed ternary complex selection [63]. Hence, in addition to calculations made using rates of ternary complex selection to represent an entire cycle of translational elongation (assuming, in effect, that the duration of translocation is negligible), we also made calculations after modifying the reported rates by adding an invariant 'translocation time' to the variable 'ternary complex selection time' for all codons. The duration of translocation per codon was set at 40% of the average time required to select a ternary complex containing tRNAphe at a UUU codon, consistent with the only quantitative measure of translocation rate that has been made in conditions approximating those in vivo [63]. Results from both sets of calculations (white and cross-hatched bars of Figure 1) are presented for each scenario (described below) that is based on these ternary complex selection rates. For convenience, elsewhere in this report we refer to the relative rates of reference [45] as translation rates, rather than using the more accurate but cumbersome expression 'ternary complex selection rates'. To calculate the total abundance of cognate tRNA for each codon, we assign cognate specificity largely according to Björk [85], and use the tRNA abundance data from Dong et al. [70]. We differ from Björk only in assuming that the leucine and glycine tRNAs with uridine in the anticodon wobble position (for which nucleotide modifications have not been characterized) will read codons ending in U, A and G, instead of A and G only. This would be the case if the wobble position U is modified to cmO5U, as is done for each of the other 6 amino acids encoded by a full box of the translational code (i.e., amino acids for which the four XXN codons are synonyms). Following Björk, we assume that 40% of the tRNAs for glutamate, glutamine and lysine with uridine in the anticodon wobble position are modified to mnm5Se2U and thus read codons ending in A or G; the balance of these tRNA species are assumed to have mnm5S2U in the wobble position and read A-ending codons only [85]. The abundance of two pairs of isoaccepting tRNA species (Gln1 + Gln2 and Ile1 + Ile2) were reported as summed values by Dong et al. [70], since these individual species were not separated under the experimental conditions applied. We have resolved the summed values to the abundance of individual species using the ratios of the individual abundance values reported by Ikemura [69]. We show cognate tRNA abundance data in Table 2 as a percentage of total tRNA, omitting initiator and selenocysteine tRNAs; the sum of all values is greater than 100%, reflecting the partially overlapping specificity of many tRNA species. Scenarios for extrapolating from incomplete empirical translation rate data We address the incompleteness of codon-specific translation rate data in several ways. In Scenario 1, we assume that the effects of biased use of YNN codons on translation rate can be used to represent the effects of bias over all codons, without assigning particular translation rates to the unmeasured codons. However, since the YNN codons are almost half of all sense codons but only account for about a third of all expression (Table 2), they must be less highly expressed, on average, than the RNN codons (R = purine). Consequently, selection for translational power may have been weaker among YNN codons than RNN codons. Scenarios 2–4 address this potential deficiency by applying various strategies of assigning translation rates to the unmeasured codons that are consistent with observed patterns, but that could allow the effect of codon bias on translation rate to be greater among RNN codon than YNN codons. Scenario 5 abandons empirical codon-specific translation rate measurements completely, assigning translation rates to all codons on the basis of the proteome codon frequency and cognate tRNA abundance of E. coli, assuming optimality (i.e., maximal translation rate) according to theory developed by Solomovici et al. [62]. Scenario 1 The 29 YNN codons encode 10 amino acids, 9 of which have multiple codons. For 7 of these 9 amino acids, the most common synonym is the codon with the fastest translation rate. One of the remaining amino acids is serine, for which the two fastest-translated codons are the two most abundant, although in reverse order, with relatively small differences between the two in both rate and abundance. Only proline appears to be anomalous; the 2 most abundant codons encode over 90% of all proline residues in the proteome [70], but support ternary complex selection about 3.5-fold more slowly than the 2 least abundant codons [45]. It has been suggested [45] that this anomaly could be adaptive; if proline, because of its unique structure, is found preferentially between protein domains [86] where slow translation may be important to permit cotranslational folding [87,88]. If proline is the only amino acid for which such contrarian selection pressure is more important than selection for translational power, including proline codons in a sample intended to represent all codons will lead to an underestimate of sbias. Hence, in Scenario 1 we apply Equation (10) over YNN codons, with the calculated translation time for non-proline YNN codons weighted by a factor of 3.2, which scales the expression level of these codons to the expression level of all non-proline codons. In other words, we assume the effects of codon bias on translation rate among the 25 non-proline YNN sense codons are representative of the effects of codon bias among all 57 non-proline sense codons, whereas the translation rates measured for proline codons are applied only to themselves. Scenario 2 Curran and Yarus noted that among highly expressed genes, there is a significant tendency for rapidly-translated codons to be used frequently, although the relationship appears to be nonlinear [45]. We observe the same pattern comparing their relative rate data to the proteome codon frequency data of Dong et al. [70] at the highest growth rate. For non-proline YNN codons, the best fit (R2 = 0.56) of a quadratic relationship passing through the origin between the codon frequency and translation rate data of Table 2 is ci = 0.205 ri - 0.522 ri2. We use this equation to predict translation rates from codon frequency for all RNN codons, as shown in Table 2. Since our objective is to obtain a reasonable estimate the codon-specific translation rate for codons which have not been measured, not to defend a particular model of the relationship between codon frequency and translation rate, we make no attempt to justify a quadratic fit in comparison to other possible functional relationships. The predicted rates for RNN codons and the measured rates for YNN codons (Table 2) are used with Equation (10) to estimate the translation rate benefit of codon bias under Scenario 2. Scenario 3 The preceding scenario applied to the YNN codons tends to predict translation rates among synonymous alternatives that are not as disparate as those actually observed. Furthermore, the fit of a functional relationship between codon frequency and translation rate among YNN codons is better when only preferred codons are considered, instead of all codons. Hence, we fit a quadratic relationship passing through the origin to data from 10 preferred non-proline YNN codons, obtaining ci = 0.352 ri - 1.611 ri2 (R2 = 0.81). Among the 10 preferred codons, we include UGG, the sole tryptophan codon, and UUG, the preferred leucine codon within the UUR split box although not the preferred leucine codon overall. We then apply this equation to predict translation rates from codon frequencies for 12 preferred RNN codons, including AUG, the sole methionine codon, and AGG and AGC, the preferred arginine and serine codons within their respective split boxes, although not the preferred codons overall. For non-preferred RNN codons, translation rate is predicted by multiplying the predicted rate for the preferred synonym (within the full or split box) by the ratio of the square roots of the codon frequencies for the non-preferred and preferred codons: This relationship was chosen both because a dependence on the square root of codon frequency has been suggested repeatedly in theoretical investigations of optimal translation rates [62,65,89,90], and because for all non-preferred RNN codons, this relationship leads to a greater disparity of predicted translation rates compared to the preferred synonym than the regression of Scenario 2. (It also predicts a greater translation rate disparity than is observed for the majority of non-preferred YNN codons.) When both the quadratic regression for preferred codons and Equation (11) for non-preferred codons are applied to predict the translation rate of non-proline YNN codons, the correlation of predicted with measured translation rates is comparable to that attained with Scenario 2 (R2 = 0.57). The predicted rates for RNN codons and the measured rates for YNN codons (Table 2) are used with Equation (10) to estimate the translation rate benefit of codon bias under Scenario 3. Scenario 4 This scenario is generated in three steps, with the goal of generating an estimate of the translation rate benefit of codon bias that is consistent with the most extreme empirical observations. First, three rare RNN codons (AGG and AGA for arginine and AUA for isoleucine, all with ci < 0.1%) are assigned the slowest relative translation rate observed among YNN codons (ri = 0.6 for the rare leucine codon CUA). Second, the translation rates for preferred RNN codons within full or split boxes (except AGG) are estimated according to the regression equation described for Scenario 3. Finally, the translation rates for non-preferred codons (except AGA and AUA) are predicted from the preferred synonym using the ratios of the most disparate translation rates observed empirically among synonymous alternatives, treating split boxes and full boxes of the translational code separately. The most extreme ratio observed among translation rates in a split box is 3.375, for glutamate codons in the study of Sorensen and Pedersen [84]. The most extreme ratios observed for translation rates of codons in a full box is 1:1.3:1.6:24 for the CUN leucine codons in the study of Curran and Yarus [45]. (Exploring other rate values 1 ≤ x ≤ y ≤ 24 in ratios of the form 1:x:y:24 failed to find any that greatly increased the estimated benefit beyond that using the leucine ratios, data not shown.) Although this scenario is based on extreme observations, applying these 3 rules to the non-proline YNN codons leads to a correlation of predicted and measured translation rates (R2 = 0.67) somewhat better than that obtained under Scenario 2 or Scenario 3. The predicted rates for RNN codons and the measured rates for YNN codons (Table 2) are used with Equation (10) to estimate the translation rate benefit of codon bias under Scenario 4. Scenario 5 In contrast to the preceding scenarios that extend codon-specific translation rate measurements of YNN codons in various ways to make estimates of the effect of codon bias over all codons, Scenario 5 incorporates a theoretical prediction of the optimal translation rates for all codons based only on codon frequency and cognate tRNA abundance data. While this approach necessarily involves additional assumptions, it has the advantage of drawing on data that is more complete and less likely to be influenced by unrecognized experimental errors. Solomovici et al. [62] assume that selection on synonymous codon frequencies reflects intrinsic differences in rate constants for a cognate tRNA interacting with preferred and non-preferred codons, while the total tRNA abundance and amino acid composition are fixed. They demonstrate that the fastest overall translation rate is obtained when the square roots of synonymous codon frequencies are proportional to the rate constants for cognate tRNA interacting with the codons. They assume further that the rate constants for the interaction of all non-degenerate or preferred codons with their preferred cognate tRNA are identical, so the translation rate for these codons is proportional to cognate tRNA abundance. We modified the approach of reference [62] to reflect greater degeneracy in translation than assumed by the original authors ([85], also the comments earlier in this section), and applied it using the codon frequency and tRNA abundance data of Dong et al. [70], modified as shown in Table 2. The predicted relative translation rates for YNN codons (i.e., the recalculated quantities dij and dim, j of reference [62] for codons with single or multiple cognate tRNAs, respectively) are not in good agreement with observed relative rates of Curran and Yarus [45] (R2 = 0.30). However, the empirical codon frequencies of Dong et al. [70] are correlated more closely with predicted relative rates of Scenario 5 (R2 = 0.70) than with the empirical relative rates of Curran and Yarus [45] (R2 = 0.31). A good correlation between the predicted translation rates and the empirical codon frequencies is expected, since the codon frequencies were used to generate the predictions. However, the poor correlation between predicted and empirical translation rates could reflect the inadequacies in any of 3 areas: 1) the assumptions of Solomovici et al. [62], 2) the rate measurements of Curran and Yarus [45], and/or 3) the codon and tRNA data of Dong et al. [70]. Alternatively, the discrepancy between predicted optimal translation rates and empirical rates may indicate that the phenotype of E. coli is not perfectly optimized for maximal translation rates (as suggested in reference [65]), either because of genetic drift or because of conflicting selection pressures. Nonetheless, the disparity between the relative rates of synonymous preferred and non-preferred codons for most amino acids are greater with the predicted rates of Scenario 5 than with the observed rates. Hence, Scenario 5 will generate a higher estimate of the translation rate benefit of codon bias than would a strict application of the empirical codon-specific translation rates. (In fact, none of our scenarios are strict applications of the empirical rates; Scenarios 1–4 also deliberately extrapolate from the empirical rates in ways that will increase the estimated benefit of codon bias.) The predicted translation rates for all codons (Table 2) are used with Equation (10) to estimate the translation rate benefit of codon bias under Scenario 5. Authors' contributions LD conceived of the project, collected and analyzed the data, developed the mathematical model, and drafted the manuscript. TMS helped plan the project, critiqued the work as it progressed, and edited the manuscript. Acknowledgements This work has benefited from our numerous discussions with J.H. Jackson. We gratefully acknowledge the support of a Center for Biological Modeling/Quantitative Biology Interdisciplinary Research Award for LD. This research was also supported by a grant from the National Science Foundation (IBN 9875254) awarded to TMS. Figures and Tables Figure 1 Translation rate benefit of codon bias in E. coli The estimated translation rate benefit of codon bias in E. coli, according to 5 different scenarios (described in Methods) for extrapolating from incomplete empirical data to obtain an estimate over all codons. White bars: duration of translocation assumed to be negligible in comparison to the duration of ternary complex selection. Cross-hatched bars: duration of translocation assumed to be invariant and short in comparison to the duration of ternary complex selection. Both white and cross-hatched bars: benefit of codon bias in E. coli estimated relative to uniform codon use. Black bars: duration of translocation assumed to be invariant and short in comparison to the duration of ternary complex selection, benefit of codon bias in E. coli estimated relative to a biologically realistic degree of low codon bias (see text). Figure 2 Translation rate benefit of codon bias by amino acid The translation rate benefit of codon bias for each amino acid in E. coli is plotted versus the frequency of the amino acid in the E. coli proteome. Each amino acid is represented by its one-letter abbreviation. Panels a – e represent Scenarios 1 – 5, respectively (described in Methods). For all panels, the duration of translocation is assumed to be negligible and the benefit is estimated in comparison to uniform codon use (corresponding to the white bars of Figure 1). Only a few amino acids encoded by one or more rare codons contribute disproportionately to the total translation rate benefit of codon bias in E. coli. Table 1 Comparisons of translational power Comparison organism rrn copy#a Specific growth rateb (culture)c (hr-1) Actual growth temp.d (cor.)e Compared byf E. coli comp. growth rateg (hr-1) Translational powerh Ref. Sphingopyxis alaskensis 1 0.29 (B) 30°C (1.80) RCi >1.73j <17%j [39] sulfate reducing strain PT2k (2) 0.40 (B) 23°C (3.34) RNA >1.73j <17%j [91] Streptomyces coelicolor 6 0.54 (B) 30°C (1.80) RNA >1.73j <21%j [40] Halobacterium cutirubrum 2 0.10 (B) 37°C rRNA 0.49 22% [37] Rickettsia prowazekii 1 0.09 (B) 34°C (1.28) RCi 0.37 24% [38] Synechococcus sp. 6301 (2) 0.16 (C) 39°C (0.85) RNA 0.36 42% [92] Streptomyces hygroscopicus (6) 0.58 – 0.90l (B, C) 28°C (2.14) RNA 0.82 – >1.73j, l <42%j – 110%l [93] Megasphaera elsdenii - 0.20 (C) 39°C (0.85) RNA 0.46 44% [94] Bacillus cereus 6 0.61 (C) 34°C (1.28) RNA 1.22 51% [95] Selenomonas ruminantium - 0.30 – 0.43l (C) 39°C (0.85) RNA 0.35 – 0.88l 50% – 78%l [96, 97] Salmonella enterica 7 1.66 (B) 37°C rRNA 1.63 102% [1] Enterobacter aerogenes (7) 0.94 (C) 35°C (1.18) RNA 0.80 123% – 154%l [98] Lactococcus lactis 6 1.9 (B) 30°C (1.80) RNA 0.51 391% [99] a Number of rrn operons per genome were obtained from the ribosomal RNA operon copy number database [100]. Where rrn copy number is not available for a species, values shown in parentheses are typical for the genus or family, if such estimates are possible. b Highest growth rates (temperature corrected for 37°C, see text) at which macromolecular data were available, shown as specific growth rate = ln(2)/(generation time). c Culture type indicated as batch (B) or chemostat (C). d Temperature at which strains were grown for macromolecular measurements. e Correction factor applied to actual growth rate to obtain temperature corrected growth rate shown in the third column, based on data from reference [56] f Similar investment in the translational apparatus between the comparison organism and E. coli assessed as follows: RC, similar ribosome concentration by cell volume; RNA, similar protein:RNA ratio; rRNA, similar protein:rRNA ratio. For all comparisons, E. coli data were taken from reference [3] with interpolation between discrete data points as necessary. g Growth rate at which E. coli makes a similar investment in the translational apparatus (by the criteria in column 5) as the comparison organism at the growth rate shown in column 3. h Translational power of the comparison organism expressed as a percentage of the translational power of E. coli. i For consistency, the comparison of ribosome concentration between this organism and E. coli made in the original reference is not used. Instead, ribosome concentration as a function of growth rate in E. coli was calculated from the data of reference [3] assuming a cell volume of 1.1 fl at a growth rate of 1.03 hr-1 and a constant ratio of cell volume to dry mass across growth rates. The comparison of translational power for this organism assumes that its protein concentration (protein mass per cell volume) is similar to E. coli. j Comparison organism makes a larger investment in the translational apparatus than E. coli growing at the fastest rate at which data are availabe. The comparison is made conservatively to data from the fastest E. coli growth rate. k Related to Desulfovibrio vulgaris by 16S rRNA gene sequence analysis. l Range of values shown corresponds to the maximum growth rates obtained for this organism in different culture conditions Table 2 Codon data Codon AA Codon frequency tRNA abund.c (%) Empirical rel. trans. ratesd Predicted rel. translation rates E. colia (× 10-3) Low biasb (× 10-3) Sc. 2e Sc. 3e Sc. 4e Sc. 5f UUU Phe 8.0 9.6 1.5 8.5 (7.6) (6.3) (3.2) 3.7 UUC Phe 23.4 21.8 1.5 12.0 (12.0) (10.8) (10.8) 6.4 UUA Leu 2.8 6.6 2.7 4.3 (5.1) (5.2) (1.9) 7.1 UUG Leu 4.3 17.1 3.8 8.7 (6.0) (6.5) (6.5) 8.9 UCU Ser 16.5 10.7 3.4 11.6 (10.3) (9.5) (9.5) 7.1 UCC Ser 11.8 7.2 1.2 14.7 (9.0) (8.0) (7.3) 6.0 UCA Ser 2.0 5.4 2.1 7.0 (4.6) (3.3) (4.6) 2.5 UCG Ser 2.5 7.1 2.6 9.0 (5.0) (3.7) (0.4) 2.8 UAU Tyr 6.8 11.3 2.7 4.3 (7.2) (6.1) (2.8) 7.3 UAC Tyr 16.6 12.1 2.7 8.4 (10.4) (9.5) (9.5) 11.5 UGU Cys 2.8 3.1 2.1 4.0 (5.2) (5.4) (1.9) 7.6 UGC Cys 3.8 3.6 2.1 7.0 (5.8) (6.3) (6.3) 8.9 UGG Trp 7.1 7.1 1.5 5.0 (7.3) (7.3) (7.3) 6.4 CUU Leu 3.9 15.2 2.6 8.4 (5.8) (3.9) (9.8) 6.3 CUC Leu 4.1 16.0 1.7 11.0 (5.9) (4.1) (12.0) 6.5 CUA Leu 0.8 4.4 0.9 0.6 (3.6) (0.6) (0.6) 2.9 CUG Leu 61.2 17.9 7.3 14.4 (18.6) (15.7) (15.7) 24.9 CCU Pro 4.4 9.0 1.8 8.4 (6.1) (4.5) (7.3) 2.7 CCC Pro 1.1 6.2 1.1 9.6 (3.9) (2.3) (0.5) 1.3 CCA Pro 5.2 12.1 0.8 1.6 (6.5) (4.9) (9.0) 2.9 CCG Pro 29.0 12.4 1.5 2.5 (13.2) (11.6) (11.6) 6.8 CAU His 6.8 9.0 1.2 4.0 (7.2) (6.3) (2.7) 3.5 CAC His 14.3 12.1 1.2 8.0 (9.7) (9.1) (9.1) 5.1 CAA Gln 7.1 10.7 1.2 5.6 (7.3) (5.8) (3.4) 3.3 CAG Gln 27.5 23.9 2.3 10.0 (12.9) (11.4) (11.4) 6.4 CGU Arg 44.2 21.4 7.5 14.0 (16.0) (13.7) (13.7) 31.3 CGC Arg 20.8 19.1 7.5 11.5 (11.4) (9.4) (10.5) 21.4 CGA Arg 0.7 11.3 7.5 3.0 (3.5) (1.7) (0.6) 3.9 CGG Arg 0.6 5.1 0.6 0.8 (3.4) (1.6) (0.6) 2.6 AUU Ile 15.9 21.5 6.8 10.2 8.2 4.1 17.1 AUC Ile 44.2 28.0 6.8 16.0 13.7 13.7 28.6 AUA Ile 0.5 11.2 0.3 3.3 1.5 0.6 1.4 AUG Met 21.8 21.8 1.4 11.7 10.5 10.5 5.7 ACU Thr 20.8 15.0 3.8 11.4 9.9 8.7 7.6 ACC Thr 26.9 18.7 1.8 12.8 11.3 11.3 8.7 ACA Thr 2.6 10.0 2.0 5.1 3.5 0.5 2.7 ACG Thr 4.2 10.8 2.9 6.0 4.5 7.1 3.4 AAU Asn 5.7 16.7 2.1 6.7 5.1 3.5 3.9 AAC Asn 29.4 18.4 2.1 13.3 11.7 11.7 8.9 AAA Lys 55.4 39.6 3.1 17.8 15.0 15.0 12.8 AAG Lys 17.4 33.2 1.2 10.6 8.4 4.5 2.9 AGU Ser 2.2 6.8 1.7 4.8 3.8 2.3 3.4 AGC Ser 9.4 7.2 1.7 8.2 7.9 7.9 7.0 AGA Arg 0.6 5.4 1.1 3.4 4.9 0.6 3.7 AGG Arg 0.0 4.5 1.7 2.6 1.1 0.6 0.8 GUU Val 43.5 38.8 7.9 15.9 13.6 13.6 23.3 GUC Val 7.7 13.2 2.0 7.5 5.7 0.6 9.8 GUA Val 22.5 20.1 6.0 11.8 9.8 10.5 16.8 GUG Val 15.1 16.8 6.0 9.9 8.0 8.5 13.7 GCU Ala 39.8 38.6 7.2 15.3 13.2 13.2 19.4 GCC Ala 11.9 15.7 1.1 9.0 7.2 0.5 10.6 GCA Ala 25.1 30.6 6.1 12.4 10.4 10.1 15.4 GCG Ala 24.3 16.2 6.1 12.2 10.3 8.2 15.1 GAU Asp 19.4 22.3 4.4 11.1 9.3 3.7 14.0 GAC Asp 34.0 31.1 4.4 14.2 12.4 12.4 18.5 GAA Glu 58.3 39.8 8.5 18.2 15.3 15.3 35.7 GAG Glu 17.1 35.6 3.4 10.5 8.3 4.5 7.7 GGU Gly 45.9 23.7 9.3 16.3 13.9 13.9 21.0 GGC Gly 34.4 23.0 7.3 14.3 12.1 10.7 18.2 GGA Gly 1.3 17.8 1.9 4.1 2.3 0.6 3.5 GGG Gly 2.4 19.5 3.2 4.9 3.2 8.7 4.8 a Proteome codon frequencies from reference [70] for E. coli growing at a specific growth rate of 1.73 hr-1, modified slightly as described in Methods. b Low bias codon frequencies representing the degree of codon bias present in the genome of T. pallidum, generated as described in Methods. c Summed abundance of all tRNA species cognate to the listed codon, expressed as a percentage of total tRNA, based on tRNA abundance data of references [70] and [69] and cognate specificity of reference [85], modified slightly as described in Methods. 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==== Front BMC GastroenterolBMC GastroenterolBMC Gastroenterology1471-230XBioMed Central 1471-230X-5-21563894710.1186/1471-230X-5-2Research ArticleImpact of hiatal hernia on histological pattern of non-erosive reflux disease Gatopoulou Anthie [email protected] Konstantinos [email protected] Alexandra [email protected] Vassilios [email protected] Alexandros [email protected] Nikolaos [email protected] Efthimios [email protected] Georgios [email protected] Endoscopy Unit, Democritus University of Thrace, Dragana, GR-68100 Alexandroupolis, Greece2 First Department of Internal Medicine, Democritus University of Thrace, Dragana, GR-68100 Alexandroupolis, Greece3 Department of Pathology, Democritus University of Thrace, Dragana, GR-68100 Alexandroupolis, Greece4 Second Department of Surgery, Democritus University of Thrace, Dragana, GR-68100 Alexandroupolis, Greece5 First Department of Surgery, Democritus University of Thrace, Dragana, GR-68100 Alexandroupolis, Greece2005 9 1 2005 5 2 2 27 6 2004 9 1 2005 Copyright ©2005 Gatopoulou et al; licensee BioMed Central Ltd.2005Gatopoulou et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background Hiatus hernia (HH) has major pathophysiological effects favoring gastroesophageal reflux and hence contributing to esophageal mucosa injury, especially in patients with severe gastroesophageal disease. However, prospective studies investigating the impact of HH on the esophageal mucosa in non-erosive reflux disease (NERD) are lacking. This study evaluated the association between the presence of (HH) and the histological findings in symptomatic patients with NERD. Methods Fifty consecutive patients with gastroesophageal reflux disease (GERD) were enrolled. After conventional endoscopy, Lugol solution was applied and biopsy specimens were obtained. Histological parameters including basal zone hyperplasia, papillary length and cellular infiltration were evaluated. The chi-square test with Yates' correlation was used for comparing discrete parameters between groups. However, Fisher's exact probability test was used where the expected frequencies were lower than 5. Wilcoxon's test for unpaired samples was preferred in cases of semi-quantitative parameters. Results The presence of HH along with more severe findings (0.01 <P < 0.05) was confirmed in 18 patients. NERD was observed in 29 (58%) patients. Basal zone hyperplasia and loss of glycogen accompanied HH in all cases, and the correlation was significant in NERD (P < 0.001). The remaining histological patterns were similar between erosive reflux disease and NERD in the presence of HH. Conclusion The presence of HH is correlated with more severe endoscopy findings, and predisposes for severe histological abnormality in cases of NERD. ==== Body Background Gastroesophageal reflux disease (GERD) is a common condition that affects 25–30% of the population [1]. It clearly involves multifactorial pathophysiology, yet the factors underlying why only some patients develop reflux esophagitis are unclear [2]. Symptoms and demographic data do not allow differentiation between the endoscopy-negative (non-erosive reflux disease; NERD) and endoscopy-positive (erosive reflux disease; ERD) forms of the disease. In fact most patients with typical symptoms of GERD have normal esophageal mucosa on upper endoscopy. Indeed, more than two-thirds of all patients with reflux symptoms never develop esophageal erosions, ulcers or strictures [3]. This group of NERD patients constitutes a significant clinical problem since they appear to be relatively resistant to proton-pump inhibitors (PPIs) [4,5]. Hiatal hernia (HH) has been considered to be one of the pathophysiological mechanisms that contributes to the development of GERD, promoting refluxate access and impaired acid clearance; however, the impact of this mechanism in NERD is unclear [2,6,7]. The aim of the present study was to clarify the possible association of HH with histological findings on a group of prospectively studied symptomatic patients with NERD. Methods Fifty patients (29 men, 21 women; aged 49.9 ± 6.6 years, mean ± SD) were evaluated prospectively in our endoscopy unit for symptoms compatible with GERD, namely heartburn, acid regurgitation and belching. A standardized questionnaire was completed for each patient during an interview with an experienced gastroenterologist. Demographic details of the GERD patients were recorded, including age, sex, smoking habits, tea, coffee and alcohol consumption, and concurrent medical conditions including hypertension and diabetes mellitus. None of the patients included in this study had a current or past history of peptic ulcer disease, previous gastric surgery, antihelicobacter therapy, or use of PPIs, non-steroidal anti-inflammatory drugs, steroids or tetracycline during the previous 4 weeks. Ethics approval was obtained from the ethics committee of the University Hospital of Alexandroupolis, and patients provided signed, informed consent for their biopsy specimens to be taken. Routine endoscopy was performed in all patients by the same endoscopist using a flexible endoscope (GIF-Q145, Olympus). The distance between the esophagogastric junction and the incisor teeth was recorded. Reflux esophagitis was graded in accordance with the Los Angeles classification [8]. HH was considered present if gastric folds were assessed as extending ≥2 cm above the diaphragmatic hiatus during quiet respiration [2]. At least four biopsy specimens were taken at 3 cm above the lower esophageal sphincter with biopsy forceps (Olympus) in a criss-cross manner. In order to improve endoscopic visualization and provide biopsy orientation, 20 ml of 2% potassium iodine solution (Lugol) was applied through a "spray" catheter [9-11]. To obtain sufficient material and to ensure an almost vertical pinch biopsy specimen, the opened forceps were withdrawn towards the tip of the endoscope, which was bent forwards maximally, and hence the forceps were pressed vertically against the esophageal wall. Specimens were fixed in 40 mg/L formaldehyde [12]. After all the sections had been obtained, they were assessed histologically in a blinded manner (i.e. without endoscopic or clinical information). Standardized reports completed by the histopathologist comprised an evaluation of the following histological parameters: basal zone hyperplasia, papillary length, dilatation of intraepithelial blood vessels, and semi-quantitative cellular infiltration by T-lymphocytes, neutrophils and eosinophils. Alterations in glycogen content, erosion, ulceration and chronic inflammation were also assessed as described previously [12-17]. The chi-square test with Yates' correlation was used to compare discrete parameters between groups. However, Fisher's exact probability test was used where expected frequencies were lower than 5. Wilcoxon's test for unpaired samples was preferred in cases of semi-quantitative parameters due to its greater power. Mean values and their 95% confidence limits were calculated. Statistical significance was set at P ≤ 0.05. All analyses were performed using the statistical software package "Statistica (version 6)". Results Endoscopy findings Endoscopy revealed esophageal mucosa with a normal appearance in 29 patients. The remaining 21 patients had esophagitis of variable severity (Table 1). Table 1 Endoscopy findings in patients with reflux disease. Endoscopy findings in patients with reflux disease, for HH+ and HH-. NERD ERD grade A ERD grade B ERD grade C ERD grade D Total HH+ 7 5 4 2 0 18 HH- 22 8 2 0 0 32 Total 29 13 6 2 0 50 HH was observed in 18 patients. Its presence (HH+) was correlated not only with the presence of erosions (P = 0.0196) (Figure 1), but also with the severity of the endoscopy findings (Wilcoxon's T1 score for unpaired samples: 576 for N1 = 18 and N2 = 32, 0.01 <P < 0.05) (Figure 2). Figure 1 Prevalence of HH among ERD and NERD patients. Prevalence of HH among ERD and NERD patients. P = 0.0196 when HH+ and HH- are compared. Figure 2 Relationship between HH and endoscopy findings (0.01 <P < 0.05). Relationship between HH and endoscopy findings. 0.01 <P < 0.05 when HH+ and HH- are compared. Histological findings Histological examinations of the biopsy specimens revealed esophagitis in 46 out of 48 patients, despite the normal appearance of the esophageal mucosa in most of them. Two specimens – one from a patient with ERD with HH and one from a patient with NERD with HH – were quantitatively inadequate and thus omitted. Although the remaining histological patterns were similar between ERD and NERD in HH+ (Figure 3), basal zone hyperplasia and loss of glycogen accompanied HH in all cases, with the correlation being highly significant in NERD (P = 2.61 × 10-6) (Figure 4). Figure 3 Histological findings among ERD and NERD patients in the presence of HH. Histological findings among ERD and NERD patients in the presence of HH. Basal zone hyperplasia and loss of glycogen is a ubiquitous histological feature in both ERD and NERD with HH. No statistically significant difference was observed between ERD and NERD with HH in any of the histological findings. Figure 4 Histological findings among NERD patients with and without hernia. Histological findings among NERD patients with and without hernia. P = 2.61 × 10-6 for basal zone hyperplasia and papillary elongation. Discussion The clinical spectrum of GERD is diverse. The disease follows a rather benign course in most patients. Indeed, it is estimated that NERD accounts for up to 70% of patients with GERD [1]. The pathophysiological mechanisms that contribute to the development of GERD include delayed gastric emptying, frequent and transient relaxation of the lower esophageal sphincter, impaired esophageal clearance of regurgitated gastric acid, and HH+ [2]. HH has recently re-emerged as an important factor in GERD [6,7,18]. It may diminish lower esophageal sphincter pressure, promote acid reflux and compromise emptying of the refluxate from the distal esophagus, prolonging acid contact with the esophageal mucosa [19-21], a mechanism that could explain the association of HH with more severe reflux [22,23]. Thus, although HH has been established as the strongest predictor of the presence and severity of esophagitis in GERD patients with esophagitis, there are no published data on the role of HH in symptomatic patients without endoscopic esophagitis. Our prospective study suggests that HH+, even in patients with an esophageal mucosa that appears normal endoscopically (NERD), indicates the existence of histological effects. Our population was characterized by similar clinical presentation, and HH was correlated not only with the presence of erosions (Figure 1) but also with the severity of the endoscopy findings (Figure 2). These results further support HH as a dominant predictive factor for erosive esophagitis, which has already been confirmed in previous studies [2,24-27]. In order to further investigate the role of HH in NERD patients, we studied the role of HH+ on the histological parameters of esophagitis. In our material, basal zone hyperplasia and loss of glycogen content was detected in all HH+ ERD patients and HH+ NERD patients (Figure 3). In contrast, no NERD patient without HH (HH-) exhibited similar histological abnormalities (Figure 4). These findings probably indicate that the development of NERD in HH+ patients is more closely related to the pathophysiology of ERD, and perhaps different from the mechanisms responsible for NERD in HH- patients. Little is known about the relationship between HH and the histological variables in non-erosive esophagitis. Our finding that basal zone hyperplasia and loss of glycogen content are more frequently prevalent in HH+ than in HH- among NERD patients as well as the fact that basal zone hyperplasia, loss of glycogen content and infiltration with T-lymphocytes are more frequent in ERD than in NERD suggests the that HH contributes directly to the development of both GERD and NERD, perhaps through decreased acid clearance. Conclusions HH+ not only appears to be a risk factor for NERD, but is also suggestive of the histological presence of microscopic GERD in symptomatic NERD patients. This finding could play an important role in the therapeutic management of NERD patients with PPIs in the future, since ERD patients respond better than NERD patients to antireflux therapy. Future studies should establish whether there is a cause-and-effect relationship between HH and response to PPIs in NERD patients. List of abbreviations HH: Hiatal hernia NERD: Non-erosive reflux disease GERD: Gastroesophageal reflux disease ERD: Erosive reflux disease HH+: Presence of hiatal hernia HH-: Absence of hiatal hernia PPI: Proton-pump inhibitors Competing interests The author(s) declare that they have no competing interests. Authors' contributions A.G. participated in the endoscopy studies and in the preparation of the manuscript. K.M. participated in the endoscopy studies and in the preparation of the manuscript. A.G. participated in the histological studies. V.P. contributed to the design of the study, performed the statistical analysis and produced the graphical presentations of the results. E.S. participated in the histopathological studies. A.P. contributed to the design of the study and critically reviewed the manuscript. N.L. contributed to the design of the study and critically reviewed the manuscript. G.M. coordinated the study. All the authors read and approved the final version of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-230X/5/2/prepub ==== Refs Spechler SJ Epidemiology and natural history of gastroesophageal reflux disease Digestion 1992 51 24 29 1397743 Avidam B Sonnenberg A Schnell TG Sontag SJ Risk factors for erosive reflux esophagitis: a case control study Am J Gastroenterol 2001 96 41 46 10.1016/S0002-9270(00)02242-5 11197285 Sonnenberg A Everhart JE Esophageal disease Digestive disease in the United States: Epidemiology and impact 1994 Washington, DC: U.S. Government printing office, NIH publication 94-1447 299 356 Kahrilas PJ Diagnosis of symptomatic gastro-oesophageal reflux disease Am J Gastroenterol 2003 98 15 23 10.1016/S0002-9270(03)00011-X Smout AJ Endoscopy-negative acid reflux disease Aliment Pharmacol Ther 1997 11 81 85 10.1046/j.1365-2036.1997.116287000.x Dent J The hiatal hernia slides back into prominence Gut 1999 44 449 450 10075944 Murray JA Camilleri M The fall and the rise of hiatal hernia Gastroenterology 2000 119 1779 1794 11113100 Dent J Brun J Fendrick A Fennerty MB Janssens J Kahrilas PJ Lauritsen K Reynolds JC Shaw M Talley NJ on behalf of the Genval Workshop Group An evidence based appraisal of reflux disease management – The Genval Workshop Report Gut 1999 44 1 16 9862814 Rajan E Burgart JL Gostout JC Endoscopic and histologic diagnosis of Barrett esophagus Mayo Clin Proc 2001 76 217 225 11213314 Tincani AJ Brandalise N Altemani A Scanavini RC Valerio JB Lage HT Molina G Martins AS Diagnosis of superficial esophageal cancer and dysplasia using endoscopic screening with 2% Lugol dye solution in patients with head and neck cancer Head Neck 2000 22 170 174 10.1002/(SICI)1097-0347(200003)22:2<170::AID-HED9>3.0.CO;2-7 10679905 Canto MI Vital staining and Barrett's esophagus Gastrointest Endosc 1999 49 12 16 Schindlbeck NE Wiebecke B Klauser AG Voderholzer WA Muller-Lissner SA Diagnostic value of histology in non-erosive gastroesophageal reflux disease Gut 1996 39 151 154 8977332 Riddell RH What mucosal biopsies have to offer Aliment Pharmacol Ther 1997 11 Supp 12 19 25 Riddell RH The biopsy diagnosis of gastroesophageal reflux disease, "carditis", Barrett oesophagus and sequelae of therapy Am J Surg Pathol 1996 20 31 50 Richter JE Castell DO Gastrosophageal reflux: pathogenesis, diagnosis and therapy Ann Intern Med 1982 97 93 103 6124198 Frierson HF Histology in the diagnosis of reflux esophagitis Gastroenterol Clin North Am 1990 19 631 644 1699893 Haggitt RC Histopathology of reflux – induced esophageal and supraesophageal injuries Am J Med 2000 6 109 111 10.1016/S0002-9343(99)00346-0 Quigley EM New developments in the pathophysiology of gastro-oesophageal reflux disease (GERD): Implications for patient management Aliment Pharmacol Ther 2003 17 43 51 10.1046/j.1365-2036.17.s2.14.x 12786612 Van Herawaarden MA Samson M Smout AJ Excess gastroesophageal reflux in patients with hiatus hernia is caused by mechanisms other than transient LES relaxations Gastroenerology 2000 119 1439 1446 Sloan S Redemaker AW Kahrilas PJ Determinants of gastroesophageal junction incompetence: hiatal hernia lower esophageal sphincter or both? Ann Intern Med 1992 117 977 982 1443984 Mittal RK Lange R McCallum RW Identification and mechanism of delayed esophageal acid clearance in subjects with hiatal hernia Gastroenterology 1987 92 130 135 3781181 Sloan S Kahrilas PJ Impairment of esophageal emptying with hiatal hernia Gastroenterology 1991 100 596 605 1993483 Zhu H Pace F Trape E Sangaletti O Bianchi Porro G Prevalence of hiatal hernia and its influence on gastroesophageal reflux Eur J Gastroenterol Hepatol 1994 6 393 397 Jones MP Sloan SS Rabine JC Ebert CC Huang CF Kahrilas PJ Hiatal hernia size is the dominant determinant of esophagitis presence and severity in gastroesophageal reflux disease Am J Gastroenterol 2001 96 1711 1717 10.1016/S0002-9270(01)02489-3 11419819 Yeom JS Park HJ Cho JS Lee SI Park IS Reflux esophagitis and its relation to hiatal hernia J Korean Med Sci 1999 14 253 256 10402166 Sontag SJ Schnell T Miller TQ Nemchausky B Serlovsky R O'Connell S Chejfec G Seidel UJ Brand L The importance of hiatal hernia in reflux esophagitis compared with lower esophageal sphincter pressure or smoking J Clin Gastroenterol 1991 13 628 643 1761836 Petersen H The clinical significance of hiatal hernia Scand J Gastroenterol 1995 211 Suppl 19 20	15638947	PMC546187	CC BY	2021-01-04 22:25:48	no	BMC Gastroenterol. 2005 Jan 9; 5:2	utf-8	BMC Gastroenterol	2,005	10.1186/1471-230X-5-2	oa_comm
==== Front BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-21563435310.1186/1471-2458-5-2Research ArticlePatients' request for and emergency physicians' prescription of antimicrobial prophylaxis for anthrax during the 2001 bioterrorism-related outbreak M'ikanatha Nkuchia M [email protected] Kathleen G [email protected] Allen R [email protected] Robert C [email protected] James T [email protected] Ebbing [email protected] Division of Infectious Disease Epidemiology, Pennsylvania Department of Health, Harrisburg, PA, USA2 Division of Infectious Diseases, Penn State Milton S. Hershey Medical Center, Hershey, PA, USA3 Department of Health Evaluation Sciences, Penn State Milton S. Hershey Medical Center, Hershey, PA, USA4 Division of Infectious Diseases, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA5 Center for Clinical Epidemiology and Biostatistics and Center for Education and Research on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA2005 5 1 2005 5 2 2 28 6 2004 5 1 2005 Copyright © 2005 M'ikanatha et al; licensee BioMed Central Ltd.2005M'ikanatha et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Inappropriate use of antibiotics by individuals worried about biological agent exposures during bioterrorism events is an important public health concern. However, little is documented about the extent to which individuals with self-identified risk of anthrax exposure approached physicians for antimicrobial prophylaxis during the 2001 bioterrorism attacks in the United States. Methods We conducted a telephone survey of randomly selected members of the Pennsylvania Chapter of the American College of Emergency Physicians to assess patients' request for and emergency physicians' prescription of antimicrobial agents during the 2001 anthrax attacks. Results Ninety-seven physicians completed the survey. Sixty-four (66%) respondents had received requests from patients for anthrax prophylaxis; 16 (25%) of these physicians prescribed antibiotics to a total of 23 patients. Ten physicians prescribed ciprofloxacin while 8 physicians prescribed doxycycline. Conclusion During the 2001 bioterrorist attacks, the majority of the emergency physicians we surveyed encountered patients who requested anthrax prophylaxis. Public fears may lead to a high demand for antibiotic prophylaxis during bioterrorism events. Elucidation of the relationship between public health response to outbreaks and outcomes would yield insights to ease burden on frontline clinicians and guide strategies to control inappropriate antibiotic allocation during bioterrorist events. ==== Body Background The September 11, 2001 terrorism events and the ensuing anthrax attacks in the United States were associated with widespread psychological trauma [1]. In response to the outbreak, public health officials disseminated recommendations for Bacillus anthracis postexposure prophylaxis and clinical management of patients with anthrax disease [2,3]. For asymptomatic persons, antimicrobial prophylaxis was indicated only for confirmed or suspected aerosol anthrax exposure documented by public health or law enforcement [4-6]. Public health officials assured the public that emergency drug supplies would be delivered to clinical settings as needed from the national antimicrobial stockpile [7]. Despite such assurances and caution against personal stockpiling and self-medication, media reports of increased demand for ciprofloxacin indicated a potential public health problem [8]. However, it was not clear to what extent individuals with self-identified risk of anthrax exposure approached physicians for antimicrobial prophylaxis. Additionally, the response of frontline clinicians to these requests had not been described. To address these questions, we used data from a large public health survey of Pennsylvania emergency physicians following the 2001 terrorist attacks [9]. Methods A total of 250 potential study subjects were randomly selected from the 2001 membership database (n = 1,060) of the Pennsylvania Chapter of the American College of Emergency Physicians (ACEP). Information in the database, such as contact telephone numbers and location of practice, is provided during enrollment or during membership renewals, and no active verification is done. Because the accuracy of the database was unknown, we used an 80% estimate based on a previous study of emergency physicians [10]. During November 2001-January 2002, we conducted a telephone survey with items designed to assess requests for and prescriptions of antibiotic prophylaxis for anthrax. For the types of antibiotics prescribed, the survey choices were ciprofloxacin, doxycycline, amoxicillin, penicillin VK, other, or none. Emergency departments with post-graduate training programs were categorized as "academic" while all others were categorized as "non-academic." Details concerning survey instrument design and data collection are described elsewhere [9]. Population and administrative data maintained by the Pennsylvania Department of Health's Bureau of Health Statistics were used to stratify respondents by geographic location. Counties with a population density of ≥ 450 persons per square mile were defined as "urban" while all others were categorized as "rural." "Eastern" Pennsylvania was defined as the Southeast and Northeast Districts of the Pennsylvania Department of Health. All other locations were considered "western." We used one-way frequency analyses to describe distributions of responses for all categorical items. Associations were quantified using odds ratios (ORs) with associated 95% confidence intervals (CIs). Statistical analyses were performed using SAS software (SAS Institute Inc., Cary, NC). Results Forty-three of the 250 physicians in the sample were excluded from the study because of insufficient contact information or because the physician was no longer practicing medicine in the state. Of the remaining 207 physicians (24% of the estimated population of 848 subjects with accurate information), 97 were interviewed (47% response rate). Sixty-four (66%) of the 97 respondents had received patient requests for antimicrobial prophylaxis against anthrax; of the physicians who received requests, 16 (25%) prescribed antibiotics. Physician setting (urban vs. rural; eastern vs. western) was not associated with either patient request for, or physician prescription of, antimicrobial prophylaxis (Table 1). Of the 52 respondents in urban areas, 38 (73%) had received requests for antibiotics while 26 (59%) respondents in rural counties had received such requests (odds ratio [OR], 1.9; 95% confidence interval [CI], 0.8 – 4.4). Similarly, the type of institution (academic versus non-academic) was not associated with requests for or prescriptions of antibiotics. Table 1 Physicians' response to patients' requests for anthrax prophylaxis. * Characteristic Setting no (%)† State location no (%)‡ Emergency department type no (%)§ Variable Urban Rural OR (95% CI) Eastern Western OR (95% CI) Academic Non academic OR (95% CI) Received requests for antibiotics 38 (73) 26 (59) 1.9 (0.8–4.4) 33 (70) 31 (63) 1.4 (0.6–3.2) 21 (68) 41 (66) 1.1 (0.4–2.7) Prescribed antibiotics for anthrax 10 (20) 6 (14) 1.5 (0.5–4.7) 6 (13) 10 (21) 0.6 (0.2–1.8) 7 (23) 9 (15) 1.8 (0.6–5.4) Requested testing for anthrax 18 (35) 11 (24) 1.6 (0.7–4.0) 15 (32) 14 (28) 1.2 (0.5–2.9) 8 (26) 21 (33) 0.7 (0.3–1.8) Abbreviations: OR: Odds ratio; CI: Confidence interval. * The ninety-seven survey respondents were used in the analysis. †Fifty-two physicians responded from practices located in one of the eleven "urban" counties that had a population density of ≥450 persons per sq mile in 2001. ‡Forty-seven respondents were from eastern Pennsylvania, defined as the Northeast and Southeast Districts. §Thirty-one physicians responded from practices that were considered "academic" based on presence of training for residents or fellows. Fifteen physicians prescribed antibiotic prophylaxis to 23 patients; nine physicians prescribed to one patient, four prescribed to two patients, and two physicians prescribed to three patients. One physician did not answer the question regarding number of patients prescribed prophylactic antibiotics. Ten physicians (63%) prescribed ciprofloxacin while 8 physicians (50%) prescribed doxycycline (Figure 1). Fourteen (88%) of the respondents that prescribed antibiotics to patients also referred the patients for diagnostic tests for anthrax exposure. Figure 1 Types of antibiotics prescribed by emergency physicians. A total of 16 physicians prescribed various antibiotics shown above. Fifteen physicians prescribed these types of drugs to 23 patients. One physician did not respond to the question on the number of patients the physician had given anthrax prophylaxis. Discussion Following the September 11 terrorist attacks and the ensuing anthrax outbreak, the majority of the emergency physicians we surveyed in Pennsylvania had received patient requests for anthrax prophylaxis; a quarter of these physicians prescribed antibiotics for these patients. Physicians that reported patients' requests for antimicrobial prophylaxis were distributed across the state, suggesting that patients' search for protection against anthrax was widespread. Our results are consistent with data on specimens submitted to Pennsylvania public health laboratory officials for B. anthracis analysis. During October-December 2001, the Pennsylvania Bureau of Laboratories received approximately 1400 specimens including white powder, environmental swabs, and letters from counties in eastern and western Pennsylvania districts. Of these, 27 (or about 18 requests per 100,000 population) came from Cambria County in the western district (PA DOH: unpublished data). There were 11,063 anthrax-related telephone inquiries received from October 8 to November 11, 2001 by the Centers for Disease Control and Prevention's (CDC) emergency operations centers; queries originated from all states and one US territory. Most of these calls were from members of the public concerning anthrax vaccines (≈58%), suggesting that search for protective measures against anthrax was widespread across the United States [11]. Other studies have documented an increased use of antimicrobial agents that was temporally related to the anthrax outbreak; this use could not be ascribed to that recommended by the CDC. For example, a recent national study reported that approximately 160,000 more ciprofloxacin and 96,000 more doxycycline prescriptions were written in 2001 compared to 2000 [12]. When other investigators compared ciprofloxacin utilization in 2001 with 2000, they found that it declined for all months except October 2001 when ciprofloxacin utilization increased 9.8%. They also found that the increase was not limited to areas where anthrax cases had occurred, suggesting that many Americans sought antibiotic prophylaxis [13]. Of the physicians reporting that they had prescribed antibiotics for anthrax prophylaxis, the majority used ciprofloxacin. This finding is consistent with other studies [12,13] and is likely because the initial CDC guidelines recommended ciprofloxacin prophylaxis for B. anthracis exposure until susceptibility results were known [4,5]. When tests showed that B. anthracis isolates recovered from patients involved in the anthrax attacks were susceptible to other antibiotics, public health officials indicated that doxycycline might be preferable over ciprofloxacin [2]. While both drugs are approved for postexposure prophylaxis [14], the rationale for favoring doxycycline was to prevent ciprofloxacin resistance in more common bacteria. Unfortunately use of antibiotics has inherent risks and costs and optimizing benefits is especially difficult in the midst of bioterrorist events. Consequences of antibiotic treatment of unexposed individuals include adverse drug reactions, increased risk of antimicrobial resistance, depletion of antibiotics, and monetary costs [15]. Furthermore, use of emergency departments for sporadic distribution of prophylactic antibiotics to persons presenting with self-identified risk appears inefficient. It is unclear whether these persons can be adequately managed in emergency departments without the support of public health and law enforcement officials. Public health response likely influences demand for and outcomes associated with antibiotics requests during bioterrorism attacks. When we asked physicians to suggest on what health departments could do to reduce the influx of patients to the emergency departments, they cited official communications to make the public "less worried." In Illinois, a surge in environmental samples received by public health officials for anthrax tests was associated with both media reports of anthrax cases in other states and a specific announcement on October 29, 2001 by the US attorney general and the FBI director. The announcement asked US citizens and law enforcement agencies to be on the "highest alert" based on "credible information" [16]. Lessons learned from the 2001 anthrax attacks in New Jersey suggest that communities in which the public health sector and clinicians have a strong working relationship are better prepared to meet mass prophylaxis needs [17]. Similarly, lessons learned in New York City during the same outbreak demonstrated the benefits of advance logistical planning for mass postexposure prophylaxis including an antibiotics distribution site and clear eligibility criteria [18]. We acknowledge some limitations to our results. First, as in any survey, these data are subject to non-response bias. But the 47% response rate is comparable with other telephone surveys conducted among physicians in general [19]. In addition, responders and non-responders had similar baseline characteristics, suggesting that these groups were comparable [9]. Second, the study is limited to types of antibiotics prescribed and cannot be used to estimate dosage, number of pills allotted to these patients, costs, or compliance to treatment for perceived or real anthrax exposure. Third, the indications for prophylaxis were not studied. While it is not certain, it is likely that at least the vast majority of the antibiotic perscriptions found in this study were outside indications described in public health guidelines. It is plausible that some patients sought prescriptions for storage; it is also likely that they at least initiated the antibiotic course. Conclusions Taken together with other recent studies of antibiotic utilization [12,13], these data suggest a need for public health response to increased demand for prophylaxis against perceived bioterrorism exposures. Experiences in areas where mass prophylaxis was delivered and suggestions made by the respondents in this study suggest that improved public health communications might reduce the influx of patients to emergency departments. In addition, as demonstrated by New York City's exemplary response to the West Nile virus outbreak in 1999 and the 2001 anthrax attacks, prior logistical plans and strong working relationships among public health officials and clinicians are essential [18,20]. Elucidation of the relationship between public health response to outbreaks and outcomes may offer lessons to reduce inappropriate demand for and prescriptions of antibiotics during bioterrorist events. List of abbreviations ACEP: American College of Emergency Physicians CDC: Centers for Disease Control and Prevention PA DOH: Pennsylvania Department of Health Competing interests The author(s) declare that they have no competing interests. Authors' contributions NMM conceived of the study and its design, and coordinated revision of the manuscript drafts with all authors. KGJ participated in data collection, study design, and contributed to the study's critical review. ARK participated in the design of the study and performed statistical analyses. RCA secured funding and participated in the study's critical review. JTR participated in the study design and coordination. EL contributed to the design of the study and participated in its critical review. All authors read, commented on, and approved of the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements We acknowledge with gratitude the time and interest of the emergency physicians that participated in the survey. We thank Joel Hersh, Director of the Bureau of Epidemiology for consultation on survey design. This study was supported in part by the Centers for Education and Research on Therapeutics (CERTs) grant (U18-HS10399) from the Agency for Healthcare Research and Quality. ==== Refs Silver RC Holman EA McIntosh DN Poulin M Gil-Rivas V Nationwide longitudinal study of psychological responses to September 11 JAMA 2002 288 1235 1244 12215130 10.1001/jama.288.10.1235 Centers for Disease Control and Prevention Update: investigations of bioterrorism-related anthrax and interim guidelines for clinical evaluation of persons with possible anthrax MMWR Morb Mortal Wkly Rep 2001 50 941 948 11708591 Centers for Disease Control and Prevention Anthrax: MMWR References Inglesby TV Henderson DA Bartlett JG Anthrax as a biological weapon: medical and public health management JAMA 1999 281 1735 1745 10328075 10.1001/jama.281.18.1735 Centers for Disease Control and Prevention Update: investigation of bioterrorism-related anthrax and interim guidelines for exposure management and antimicrobial therapy, October 2001 MMWR Morb Mortal Wkly Rep 2001 50 909 919 11699843 Swartz MN Recognition and management of anthrax–an update N Engl J Med 2001 345 1621 1626 11704686 10.1056/NEJMra012892 Centers for Disease Control and Prevention. Press Release Update: anthrax investigations, Florida and New York USA Today Demand for cipro rising M'ikanatha NM Lautenbach E Kunselman A Sources of bioterrorism information among emergency physicians during the 2001 anthrax outbreak Biosecurity & Bioterrorism: Biodefense Strategy, Practice and Science 2003 1 259 265 10.1089/153871303771861469 Reinhart MA Munger BS Rund DA American Board of Emergency Medicine Longitudinal Study of Emergency Physicians Ann Emerg Med 1999 33 22 32 9867883 Mott JA Treadwell TA Hennessy TW Rosenberg PA Wolfe MI Brown CM Butler JC Call-tracking data and the public health response to bioterrorism-related anthrax Emerg Infect Dis 2002 8 1088 1092 12396921 Shaffer D Armstrong G Higgins K Increased US prescription trends associated with the CDC Bacillus anthracis antimicrobial postexposure prophylaxis campaign Pharmacoepidemiol Drug Saf 2003 12 177 182 12733470 10.1002/pds.828 Brinker A Pamer C Beitz J Changes in ciprofloxacin utilization as shown in a large pharmacy claims database: effects of proximity to criminal anthrax exposure in October 2001 J Am Pharm Assoc 2003 43 375 378 10.1331/154434503321831085 Centers for Disease Control and Prevention Questions and answers about anthrax Navas E Problems associated with potential massive use of antimicrobial agents as prophylaxis or therapy of a bioterrorist attack Clin Microbiol Infec 2002 8 534 539 12197876 10.1046/j.1469-0691.2002.00495.x Dworkin MS Ma X Golash RG Fear of bioterrorism and implications for public health preparedness Emerg Infect Dis 2003 9 503 505 12702237 Bresnitz EA DiFerdinando GT Lessons from anthrax attacks of 2001: the New Jersey experience Clin Occup Environ Med 2002 2 227 252 Blank S Moskin LC Zucker JR An ounce of prevention is a ton of work: mass antibiotic prophylaxis for anthrax, New York City, 2001 Emerg Infect Dis 2003 9 615 622 12780998 Blendon RJ Donelan K Leitman R Physicians' perspectives on caring for patients in the United States, Canada, and West Germany N Engl J Med 1993 328 1011 1016 8450854 10.1056/NEJM199304083281407 Fine A Layton M Lessons from the West Nile viral encephalitis outbreak in New York City, 1999: implications for bioterrorism preparedness Clin Infect Dis 2001 32 277 282 11170918 10.1086/318469	15634353	PMC546188	CC BY	2021-01-04 16:28:55	no	BMC Public Health. 2005 Jan 5; 5:2	utf-8	BMC Public Health	2,005	10.1186/1471-2458-5-2	oa_comm
==== Front Cardiovasc DiabetolCardiovascular Diabetology1475-2840BioMed Central London 1475-2840-4-21564212210.1186/1475-2840-4-2Original InvestigationSaturated free fatty acids and apoptosis in microvascular mesangial cells: palmitate activates pro-apoptotic signaling involving caspase 9 and mitochondrial release of endonuclease G Mishra Rangnath [email protected] Michael S [email protected] Division of Nephrology, Department of Medicine, School of Medicine, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio 44106, USA2 Division of Nephrology, Department of Medicine, School of Medicine, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio 44106, USA2005 10 1 2005 4 2 2 4 11 2004 10 1 2005 Copyright © 2005 Mishra and Simonson; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background In type 2 diabetes, free fatty acids (FFA) accumulate in microvascular cells, but the phenotypic consequences of FFA accumulation in the microvasculature are incompletely understood. Here we investigated whether saturated FFA induce apoptosis in human microvascular mesangial cells and analyzed the signaling pathways involved. Methods Saturated and unsaturated FFA-albumin complexes were added to cultured human mesangial cells, after which the number of apoptotic cells were quantified and the signal transduction pathways involved were delineated. Results The saturated FFA palmitate and stearate were apoptotic unlike equivalent concentrations of the unsaturated FFA oleate and linoleate. Palmitate-induced apoptosis was potentiated by etomoxir, an inhibitor of mitochondrial β-oxidation, but was prevented by an activator of AMP-kinase, which increases fatty acid β-oxidation. Palmitate stimulated an intrinsic pathway of pro-apoptotic signaling as evidenced by increased mitochondrial release of cytochrome-c and activation of caspase 9. A caspase 9-selective inhibitor blocked caspase 3 activation but incompletely blocked apoptosis in response to palmitate, suggesting an additional caspase 9-independent pathway. Palmitate stimulated mitochondrial release of endonuclease G by a caspase 9-independent mechanism, thereby implicating endonuclease G in caspase 9-indpendent regulation of apoptosis by saturated FFA. We also observed that the unsaturated FFA oleate and linoleate prevented palmitate-induced mitochondrial release of both cytochrome-c and endonuclease G, which resulted in complete protection from palmitate-induced apoptosis. Conclusions Taken together, these results demonstrate that palmitate stimulates apoptosis by evoking an intrinsic pathway of proapoptotic signaling and identify mitochondrial release of endonuclease G as a key step in proapoptotic signaling by saturated FFA and in the anti-apoptotic actions of unsaturated FFA. free fatty acidsmicrovascularapoptosiscaspase 9lipotoxicitymesangial cells ==== Body Background Recent evidence suggests that intracellular accumulation of saturated free fatty acids (FFA) in vascular cells contributes to lipid-mediated cellular damage (see [1-4] for review). The cellular dysfunction associated with FFA overload, known as lipotoxicity, contributes to cell injury in settings of high FFA or triglycerides, such as obesity or type 2 diabetes [4]. Diverse mechanisms have been proposed to explain lipotoxicity including dysregulation of cell signaling, induction of a proinflammatory and prothrombotic state, or in some cases programmed cell death [1,4]. Indeed, saturated FFA have previously been shown to induce apoptotic cell death that is prevented in most cell types by unsaturated FFA [5-14]. In the microvasculature, the pro-apoptotic signaling pathways induced by saturated FFA and the anti-apoptotic pathways regulated by unsaturated FFA remain incompletely understood. Several mechanisms have been implicated in apoptotic cell death induced by saturated FFA. Some studies suggest that increased β-oxidation of FFA does not contribute to apoptotic cell death and suggest that unmetabolized FFA might be involved [1,13]. However, other studies contradict this observation and suggest a direct role for mitochondrial β-oxidation in the apoptotic response to palmitate.[10]. The finding that long-chain saturated but not unsaturated FFA cause apoptosis implicates a product made specifically from the saturated species. For instance, saturated but not unsaturated FFA are precursors for the pro-apoptotic lipid ceramide. Although palmitate does increase de novo ceramide synthesis in cultured cells, studies of the functional role of ceramide in palmitate-induced apoptosis have yielded conflicting results that might depend on the cell type in question [8,13,14]. Because saturated FFA are poor substrates for cardiolipin biosynthesis, decrements in cardiolipin and increased release of mitochondrial cytochrome-c have recently been implicated in apoptosis in breast cancer cells and cardiomyoctyes exposed to palmitate [12,13]. Another recent study demonstrated mitochondrial release of cytochrome-c in palmitate-treated pancreatic β-cells [14], which suggests that an intrinsic mitochondrial pathway of pro-apoptotic signaling might mediate the effects of saturated FFA on cell death. In the present study, we investigated the hypothesis that the saturated FFA palmitate induces apoptosis in microvascular mesangial cells and delineated the proapoptotic signals involved. We chose to study mesangial cells because lipids accumulate in mesangial cells in vivo in experimental models of type 2 diabetes, obesity, or hyperlipidemia [15-20], but the functional consequences of FFA accumulation are unclear. We report here that palmitate induces an intrinsic proapoptotic signaling pathway in mesangial cells that proceeds by a caspase 9-dependent pathway and by a caspase 9 -independent mechanism involving mitochondrial release of endonuclease G. In addition, we demonstrate that unsaturated FFA block both the caspase 9-dependent and -independent pathways of palmitate-stimulated apoptosis. Methods Reagents Antibodies used in these studies were as follows: human-specific active fragment of caspase 9 and human cytochrome-c (Cell Signaling, Beverley MA,), human active fragment caspase-8 and caspase-2 (BD Biosciences), endonuclease G (Chemicon, Temecula, CA), and β-Actin (Sigma, #A5316). Cell-permeable inhibitors of caspase 9 (Z-LEHD-FMK) and caspase-8 (Z-IETD-FMK) were from R&D systems (Minneapolis, MN). Etomoxir and 5-aminoimidazole-4-carboxamide-1-β-D-ribonucleoside (AICAR) were from Sigma and Toronto Research Chemicals (Ontario, Canada), respectively. Preparation of FFA-albumin complexes Fatty acid-albumin solutions were prepared by the protocol of Spector [21]. Briefly, sodium salts of FFA (Nu-Chek Prep, Elysian, MN) were added to PBS and gently warmed to facilitate solubility without damaging the fatty acid [21]. The warm, clear fatty acid salt solution was complexed to 5% fatty acid-free BSA in PBS at a 6:1 fatty acid to BSA molar ratio. The sterile filtered, complexed fatty acid solution was added to the serum-containing cell culture medium to obtain the indicated final FFA concentration. The final FFA concentration in the medium was confirmed with an enzymatic colorimetric assay (NEFA C, Wako). We also confirmed that addition of the complex to culture medium did not significantly alter the pH. Apoptotic cell death in cultured human mesangial cells Human mesangial cells (HMC), purchased from Cambrex Bioscience Inc. (Walkersville, MD), were maintained in Dulbecco's modified essential medium (Gibco-BRL) supplemented with 17% fetal bovine serum (FBS), 100 U/ml penicillin, 100 μg/ml streptomycin, 5 ng/ml selenite, and 5 μg/ml each of insulin and transferrin. Characterization was performed by phase contrast microscopy and by immunostaining for intermediate filaments and surface antigens as described previously [22]. Briefly, cells were positive for desmin, vimentin, and myosin, but did not stain for factor VIII, keratin, or common leukocyte antigen. To measure endogenous levels of cleaved caspase-3, cells were lysed in a buffer containing 20 mM Tris, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1 % Triton X-100, 2.5 mM Na pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4, and 1 μg/ml luepeptin. After adjusting for cell protein (DC Assay, BioRad, Hercules, CA), the amount of cleaved human caspase-3 (Asp 175) was measured by ELISA (Cell Signaling). For Western blotting of cleaved caspase proteins, the cells were washed with ice cold PBS and scraped in CHAPS extraction buffer(50 mM Pipes/HCI, pH 6.5, 2 mM EDTA, 0.1% Chaps, 20 μg/ml leupeptin, 10 μg/ml pepstatin A, 10 μg/ml aprotinin, 5 mM DTT, 2 mM Na pyrophosphate, 1 mM Na3VO4, and 1 mM NaF) and centrifuged at 2,000 × g for 10 min at 4°C. Protein content in the supernatant was assayed with the DC protein assay. An aliquot of the lysate (25 μg protein) was boiled in SDS sample buffer, resolved on a 4–12% SDS-PAGE gradient gel, and transferred to a 0.2 μm nitrocellulose membrane. After blocking in 5% non-fat dried milk in TBS-T (20 mM Tris-Cl, pH 7.5, 150 mM NaCl, 0.05% Tween 20) for 1 h, the membrane was washed 3 times with TBS-T for 5 min each and incubated overnight at 4°C with primary antibody in 3% BSA in TBS-T. After incubating with suitable HRP-labeled secondary Ab (1:2,000) and extensive washing, the proteins were detected by chemiluminescence with an average exposure ranging from 10–30 sec. As previously described [23], the Western blots were analyzed by densitometry in NIH Image by normalizing values for the relevant caspase fragment to the highest value within each experiments (maximum level = 1). To quantify the number of pyknotic nuclei, HMC on coverslips were washed once with PBS and fixed for 20 min with freshly-prepared 3.7% formaldehyde/20% sucrose in PBS. After washing twice with PBS the HMC were stained with 5 μg/ml Hoechst 33342 (Molecular Probes, Eugene OR) and mounted in Slow Fade Light (Molecular Probes). Using a Nikon Diaphot microscope, the number of pyknotic nuclei were counted and expressed as a percentage of the total number of nuclei counted (n = >300 nuclei per condition). DNA fragmentation was assessed by measuring release of nucleosomal fragments into the cytosol (Cell Death Detection ELISA Plus, Roche). Briefly, HMC in 24-well plates were centrifuged in situ for 10 min at 200 × g and the supernatant gently removed. The monolayer was incubated in lysis buffer for 30 min at room temperature and centrifuged again at 200 × g. The supernatant (i.e., cytosolic fraction) was assayed immediately for nucleosomal fragments. Enzymatic assay of caspase 9 in HMC HMC treated with FFA were lysed (20 mM Tris, pH 7.5, 150 mM NaCl, 1.0% Triton X-100) and frozen at -40°C. Equivalent amounts of total HMC protein were added to buffer containing the LEHD caspase 9 peptide substrate linked to a cleavable luciferase substrate, aminoluciferin (Promega). The amount of light produced in a coupled reaction with luciferase was measured once every hour for 3 hours in a Berthold Luminometer. Experiments with increasing amounts of cell protein confirmed that the assay was in the linear range under the conditions described. Measurements of cytochrome -c and endonuclease G redistribution To analyze cytochrome-c redistribution in HMC treated with FFA, cells were fractionated into cytosol and membrane fractions using 0.05% digitonin in an isotonic sucrose buffer exactly as described by Dong and coworkers [24]. Because cytochrome-c release occurs mostly from mitochondria, Western blot analysis of cytosol and membrane fractions is expected to reflect cytochrome-c translocation from mitochondria to the cytoplasm. In separate experiments, the same protocol was used to assess release into the cytoplasm of endonuclease G. Results Saturated but not unsaturated FFA cause apoptosis in cultured HMC Intracellular accumulation of long-chain saturated FFA (i.e., palmitate C16:0) has been shown to induce apoptosis in several cell types including cardiac myocytes and pancreatic β-cells [5,6,8,11,13]. To determine whether FFAs induce apoptosis in HMC, cells were incubated in medium supplemented with palmitate, stearate, oleate, or linoleate. The saturated FFA palmitate and stearate increased apoptosis in HMC as evidenced by cleavage of caspase-3 and DNA fragmentation (Fig. 1A,B). In contrast, the unsaturated FFA oleate and linoleate did not increase caspase-3 cleavage or DNA fragmentation compared to cells incubated with albumin alone (Control, Fig. 1A,B). The number of pyknotic nuclei, another prototypical feature of apoptotic cells, were significantly higher in palmitate-treated cells whereas the number of pyknotic nuclei in cell incubated with oleate were similar to control (Fig. 2A). To determine whether mitochondrial β-oxidation was necessary for palmitate-induced apoptosis, the number of pyknotic nuclei were measured in cells treated with etomoxir, an inhibitor of carnitine palmitoyltransferase I. Etomoxir significantly amplified palmitate-induced formation of pyknotic nuclei at 48 h (Fig. 2B). The number of pyknotic nuclei was unaffected by etomoxir alone compared to control. In contrast, stimulating fatty acid oxidation with AICAR, an activator of AMP-kinase, abolished the increase in pyknotic nuclei with palmitate (Fig. 2B). The doses of etomoxir and AICAR used here have been previously demonstrated to inhibit and stimulate, respectively, fatty acid β-oxidation [13]. Thus, using three different criteria for identifying apoptotic cell death, these data demonstrate that the saturated but not unsaturated FFA induces apoptosis in HMC, similar to the proapoptotic effects of saturated FFA in other cell types [5,6,8,11,13]. In addition, enhanced β-oxidation of palmitate is not involved in this process; indeed, increased disposal of palmitate via oxidation apparently protects mesangial cells. Palmitate activates an intrinsic proapoptotic signaling pathway in HMC We next investigated the signaling pathways by which palmitate induces apoptosis in HMC. To begin answering this question, we examined activation of initiator caspases associated with different pathways of apoptotic signaling. Cleavage of procaspase zymogens is required to form active heterotetrameric caspase complexes, so we analyzed the time course of caspase cleavage by Western blotting in 3 independent experiments. The p35 cleaved fragment of caspase 9 was elevated in cells treated with palmitate (Fig. 3A,B). After 48 h of palmitate, the amount of cleaved caspase 9 was similar to that in cells treated for 8 h with the robust apoptotic stimulus staurosporine (Fig. 3A, Pos lane). In contrast, palmitate did not increase cleavage of caspase-8 (Fig. 3A), but treatment with a strong caspase-8 activator, etoposide, confirmed that HMC expressed caspase-8 and that the Western blot correctly measured caspase-8 cleavage (Fig. 3A, Pos lane). Caspase-2 has recently been implicated as an initiator caspase in signals involving stress in the endoplasmic reticulum or nucleus [25]. However, palmitate did not induce caspase-2 cleavage in HMC whereas the known caspase-2 stimulus camptothecin activated robust cleavage of the p15 fragment (Fig. 3A). Reprobing of all blots for β-actin confirmed equal protein loading (Fig. 3A). Collectively, these results suggest that palmitate activates caspase 9, an important initiator caspase in the intrinsic pathway of proapoptotic signal transduction. To confirm that palmitate activated caspase 9, we used a luminometric caspase 9 substrate to directly measure caspase 9 enzyme activity in cytosolic extracts of HMC. Palmitate at 0.4 mM increased caspase 9 activity 2.2- and 4.8-fold at 24 and 48 h, respectively (Fig. 4). A lower dose of palmitate (0.2 mM) that also stimulated apoptosis (Fig. 2) increased caspase 9 activity. Importantly, oleate (0.4 mM) did not stimulate caspase 9 activity above control levels at any time point tested (Fig. 4). These results confirm that the saturated FFA palmitate increases caspase 9 enzyme activity. Because activation of caspase 9 by palmitate points to an intrinsic pathway of proapoptotic signaling, we asked whether palmitate could stimulate release of cytochrome-c from mitochondria. Redistribution of cytochrome-c to the cytoplasm is an important step in apoptosome formation and greatly enhances the enzymatic activity of caspase 9 [25,26]. Western blotting of cytoplasmic and membrane-enriched fractions that contain mitochondria was used to assess cytochrome-c distribution. In control cells (5% FBS plus albumin alone), cytochome-c resided exclusively in the membrane fraction (Fig. 5A,B). In cells treated with palmitate, a portion of total cytochrome-c was redistributed to the cytoplasmic fraction. Consistent with the inability of oleate to activate caspase 9, oleate did not appreciably redistribute cytochrome-c to the cytosol (Fig. 5). These results provide additional evidence that palmitate activates an intrinsic pathway of proapoptotic signaling in HMC. Inhibition of caspase 9 attenuates palmitate-induced apoptosis in HMC We next tested the functional role of caspase 9 activation in the palmitate-induced signal transduction cascade. Co-incubation with a cell-permeable selective inhibitor of caspase 9 blocked activation of caspase 9 enzyme activity by palmitate (Fig. 6A). The caspase 9 inhibitor alone had a minor effect (<0.2-fold inhibition) on basal caspase 9 activity, and a caspase-8 inhibitor did not block caspase 9 activity stimulated by palmitate (Fig. 6A). Under theses conditions, inhibition of caspase 9 abolished caspase-3 cleavage in cells treated with palmitate for 24 and 48 h (Fig. 6B). Formation of pyknotic nuclei by 0.2 mM palmitate was blocked in cells treated with the caspase 9 inhibitor at both 24 and 48 h. However, the number of pyknotic nuclei was only partially reduced in cells treated with 0.4 mM palmitate for 48 h (Fig 6C), even though under these conditions caspase 9 enzyme activity was completely inhibited (Fig. 6A). Similarly, DNA fragmentation by palmitate was incompletely blocked by the caspase 9 inhibitor at 48 h (Fig. 6D). These results show that inhibition of palmitate-stimulated caspase 9 blocks some but not all apoptotic death in HMC. Because the caspase 9 inhibitor completely blocked caspase-3 cleavage, these results also suggest that proapoptotic signaling by palmitate proceeds by both caspase 9/3-dependent and independent mechanisms. Mitochondrial release of endonuclease G in palmitate-treated HMC To elucidate the caspase 9/3-independent mechanisms of proapoptotic signaling by palmitate, we investigated the possibility that palmitate stimulates the release of other mitochondrial proapoptotic proteins that can contribute to nuclear changes independent of casapse-3. Endonuclease G is one such effector of apoptosis, a mitochondrial DNase released by a Bcl-2-dependent but caspase-independent mechanism [27]. To examine the extent to which palmitate can induce endonuclease G release, we treated HMC with palmitate and measured the presence of endonuclease G in the cytoplasm of fractionated cells. Palmitate increased the amount of endonuclease G released into the cytoplasm (Fig. 7A and 7B). The release of endonuclease G in palmitate-treated cells was not altered by caspase 9 inhibition (Fig. 7A and 7B). These results are consistent with the incomplete inhibition of DNA fragmentation observed when caspase 9 activation by palmitate was blocked in HMC (Fig. 6D). Collectively, these results suggest that palmitate-induced apoptosis proceeds by caspase 9-dependent mechanisms and by a caspase 9 -independent mechanism involving endonuclease G. Caspase 9-dependent and -independent pathways of palmitate-induced apoptosis are abolished by unsaturated FFA Unsaturated FFA have been reported to block apoptosis by saturated FFA [8,11,13], so we next asked whether unsaturated FFA block palmitate-induced apoptosis in HMC and whether they act by inhibiting the caspase 9-dependent or -independent pathway. The unsaturated FFA oleate and linoleate blocked the increase in caspase-3 cleavage and DNA fragmentation in cells treated with palmitate (Fig. 8A,B). Apoptosis induced by stearate was also effectively blocked in cells co-incubated with oleate or linoleate (Fig. 8A,B). Oleate also prevented the increase in caspase 9 activity in cells treated with palmitate (Fig. 9). The specificity of this assay for caspase 9 was confirmed by showing that the cell-permeable caspase 9 inhibitor prevented palmitate-stimulated enzyme activity whereas a caspase 8-selective inhibitor had no effect (Fig. 9). Thus, similar to other cell types, unsaturated FFA inhibit the apoptotic response to saturated FFA in HMC. Moreover, oleate and linoleate completely prevent palmitate-induced apoptosis, which contrasts with the partial blockade observed with inhibition of caspase 9 (Fig. 6). Because the unsaturated FFA more effectively blocked palmitate-induced apoptosis, we investigated whether this was because they blocked the caspase-independent release of endonuclease G in cells treated with palmitate. As expected, palmitate increased mitochondrial release of cytochrome-c and endonuclease G in HMC (Fig. 10A,B). Oleate alone had no effect on release of cytochrome-c or endonuclease G. Co-incubation of oleate with palmitate prevented the release of cytochrome-c and endonuclease G induced by palmitate (Fig. 10A,B). These results are consistent with the notion that oleate blocks both the caspase 9-dependent and -independent mechanisms of proapoptotic signaling by palmitate. In particular, the ability of oleate to block release of endonuclease G in palmitate-treated cells is one explanation for the superior antiapoptotic effect of oleate compared to caspase 9 antagonism. Discussion Our results show that the saturated FFA palmitate induces an intrinsic pathway of proapoptotic signaling in HMC, a vascular target cell of lipid-mediated injury in the kidney. In contrast, the monounsaturated FFA oleate did not induce proapoptotic signaling and instead protected HMC from palmitate-induced apoptosis. Evidence that palmitate induced an intrinsic pathway of proapoptotic signaling is that it increased cytochrome-c release, caspase 9 cleavage, and caspase 9 enzyme activity. We also showed that the palmitate-stimulated intrinsic pathway proceeded by a caspase 9-dependent mechanism and by a caspase 9 -independent mechanism involving endonuclease G. Both the caspase 9-dependent and -independent pathways were effectively blocked by oleate. In this study we report that palmitate causes apoptosis in HMC, a microvascular cell that accumulates lipids in vivo in settings of high FFA such as obesity and type 2 diabetes [15-20]. We chose palmitate because it is the most abundant saturated FFA complexed to human serum albumin [28]. We used complexes where the molar ratio of FFA to albumin was 6:1. Although the normal physiologic ratio of FFA to albumin is approximately 2:1, serum FFA levels are greatly elevated in patients with obesity, type 2 diabetes, and proteinuric renal diseases, yielding ratios of 6:1 or higher [29,30]. In addition, normal circulating FFA levels are approximately 0.5 mM [3], so the concentrations of individual FFA species (i.e., 0.2 – 0.4 mM) used in this study seem reasonable. Therefore, our experiments were designed to evaluate mechanisms of FFA-induced apoptosis relevant to type 2 diabetes. Previous studies have demonstrated that saturated but not monounsaturated FFA cause apoptosis in other non-renal cell types [5,6,8,11,13], but the ability of FFA to induce apoptosis appears to vary with the specific cell type in question [1]. In addition, the signal transduction pathways by which saturated FFA induce apoptosis are incompletely defined. Similar to a previous report in breast cancer cells [13], palmitate-induced apoptosis in HMC was enhanced by inhibition of fat oxidation and reversed by increasing fat oxidation. Although we did not directly measure the effects of these compounds on fatty acid oxidation, these results suggest that palmitate must be metabolized to promote apoptosis and that mitochondrial β-oxidation of the saturated FFA does not participate in the proapoptotic response to palmitate. Several observations from our study suggest that palmitate induces an intrinsic pathway of apoptotic signaling in HMC. First, palmitate stimulated accumulation of cytochrome-c in the cytosol, which is an important step in the intrinsic pathway to promote apoptosome formation and activation of caspase 9. Palmitate-induced release of cytochrome-c has been previously reported in β-cells and breast cancer cells [13,14]. Palmitate has also been shown to stimulate release of uncharacterized proapoptotic proteins when added directly to isolated mitochondria [31]. Second, palmitate stimulated caspase 9 cleavage and activity in HMC. To our knowledge activation of caspase 9 by palmitate has not been previously shown. Caspase 9 is an initiator caspase in many but not all intrinsic pathways of proapoptotic signaling [25,26]. In a stimulus- and cell type-specific manner, caspase 2 can function upstream of caspase 9 [32-35], but in our experiments palmitate did not stimulate cleavage of caspase 2, an indicator of caspase 2 activation. Also, we did not observe cleavage of caspase 8, an initiator caspase in the death receptor pathway, in response to palmitate. Thus an important result of our study is that the intrinsic pathway in palmitate-induced apoptosis appears to involve caspase 9. A functional role for caspase 9 in palmitate-induced apoptosis was suggested by experiments in which a cell-permeable caspase 9 inhibitor, Z-LEHD-FMK, blocked apoptosis in response to palmitate. Z-LEHD-FMK completely inhibited the activation of caspase 9 at 24 and 48 h of 0.4 mM palmitate. Under these conditions, Z-LEHD-FMK reversed caspase-3 cleavage induced by palmitate, suggesting that caspase 9 is required for activation of caspase-3 by palmitate. While Z-LEHD-FMK effectively inhibited the nuclear changes characteristic of apoptosis at 24 h, the caspase 9 inhibitor did not completely reverse pyknotic nuclei or DNA fragmentation at 48 h. For example, partial inhibition of DNA fragmentation was observed in HMC treated with 0.2 or 0.4 mM palmitate for 48 h, even though the palmitate-induced increment in caspase 9 was blocked. A possible explanation of these results is that when HMC are exposed to palmitate for 48 h, the saturated FFA recruits additional caspase 9-independent mechanisms of apoptotic or non-apoptotic cell death that are not induced at 24 h. A possible caspase 9-independent mechanism for palmitate-induced apoptosis would be the mitochondrial release of endonuclease G, which we demonstrated in HMC. Endonuclease G is a DNase normally located in the intermembrane space of mitochondria. Some apoptotic stimuli cause caspase-independent release of endonuclease G after which it translocates to the nucleus and cleaves DNA [27]. Release of endonuclease G in palmitate-treated HMC was not blocked by Z-LEHD-FMK, which could explain the partial inhibition of DNA fragmentation by Z-LEHD-FMK under conditions where caspase 9 activation by palmitate was completely blocked. It is also possible that a small amount of caspase 3 activity remains even in the presence of the caspase 9 inhibitor, and it is possible that this low level of caspase-3 activity also contributes to endonuclease G release. In striking contrast to the partial inhibition of palmitate-induced apoptosis by the caspase 9 antagonist, we found that the unsaturated FFA oleate and linoleate completely prevented caspase 3 cleavage and DNA fragmentation in cells treated with either palmitate or stearate. Oleate prevented mitochondrial release of cytochrome-c and the increase in caspase 9 in cells treated with palmitate. In addition, oleate blocked mitochondrial release of endonuclease G in palmitate-treated cells. Taken together, these results support the notion that oleate completely prevents palmitate-induced apoptosis because, unlike inhibition of caspase 9 alone, oleate blocks both the caspase 9-dependent and -independent pathways. Conclusions These results show that palmitate stimulates apoptosis by evoking an intrinsic pathway of proapoptotic signaling. In addition, we have identified mitochondrial release of endonuclease G as a key step in proapoptotic signaling by saturated FFA and in the anti-apoptotic actions of unsaturated FFA. We believe that these results might be relevant to the pathogenesis of microvascular injury in type 2 diabetes because FFA accumulate in microvascular cells, including mesangial cells, in vivo in experimental models of type 2 diabetes, obesity, or hyperlipidemia [15-20]. Thus lipid-driven apoptosis might contribute to the microvascular remodeling that leads to numerous complications in type 2 diabetes. List of Abbreviations FFA, free fatty acid; HMC, human mesangial cells; Competing Interests The author(s) declare that they have no competing interests. Authors' contributions RM carried out most of the technical studies and helped draft the manuscript. MSS conceived of the study, participated in its design and execution, and helped to draft the manuscript. Acknowledgements This work was supported by a grant from the Rosenberg Foundation of the Centers for Dialysis Care of Cleveland. Figures and Tables Figure 1 Saturated but not unsaturated FFA induce apoptosis in cultured HMC. HMC were plated for 24 h in media containing 17% FBS, then incubated starting at time 0 with the indicated FFA at 0.2 mM complexed to albumin or with albumin alone (Control), all in media containing 5% FBS. After 24 and 48 h, the level of (A) the cleaved fragment of caspase-3 and (B) cytoplasmic nucleosomal fragments were determined by ELISA and expressed relative to the 24 h control value. Data are mean ± SEM for n = 3 independent experiments in triplicate. , P < 0.01 by ANOVA versus control, oleate, or linoleate alone. Figure 2 Palmitate-induced formation of pyknotic nuclei and the role of mitochondrial β-oxidation. (A) HMC were treated with palmitate (Palm) or oleate as described in Fig. 1, and the number of pyknotic nuclei were counted after 24 and 48 h in the presence of FFA. (B) HMC incubated with 0.2 mM palmitate in the presence and absence of 0.2 mM etomoxir (Etom) or 0.5 mM AICAR. Cells were also incubated with the same doses of etomoxir or AICAR alone. Data are mean ± SEM for n = 3 independent experiments in duplicate. , P < 0.01 versus control (Con) or oleate alone (A) by Chi-square test. Figure 3 Palmitate stimulates cleavage of caspase 9 in HMC. (A) HMC treated with palmitate were analyzed for caspase cleavage by Western blotting of total HMC lysates with antibodies that recognize the cleavage products of human caspase 9 (p35), caspase-8 (p40 and p23), and caspase-2 (p15). For a positive control in each experiment, cells were treated for 8 h with an agent known to be a strong stimulus for the caspase in question: caspase 9, staurosporine 1 μM; caspase 8, etoposide 25 μM; and caspase 2, camptothecin 6 μM. The blots were reprobed with β-actin to ensure equal protein loading. (B) Densitometric analysis (mean ± SEM) of the p35 caspase 9 fragment from 3 independent experiments. , P < 0.01 by ANOVA versus control. Figure 4 Palmitate but not oleate increases caspase 9 enzyme activity. HMC were treated with palmitate (0.2 and 0.4 mM) or oleate (0.4 mM) for 24 and 48 h. Caspase 9 activity was then measured in HMC extracts adjusted for total protein using a luminometric assay with an LEHD peptide substrate as described in Materials and Methods. Data are mean ± SEM for n = 3 experiments in duplicate. , P < 0.01, , P < 0.05 versus control or oleate alone by ANOVA. Figure 5 Palmitate induces translocation of cytochrome-c in cultured HMC. (A) Cells treated with control (Con, i.e., albumin alone), palmitate (Palm, 0.4 mM), oleate (Ole, 0.4 mM), or staurosporine (St, 1 μM) for the times indicated. HMC were then rapidly separated into soluble cytosolic and insoluble membrane fractions. The amount of cytochrome-c (Cyto-C) was determined by Western blotting. Equivalent amounts of total protein were present across all lanes of the cytoplasmic and membrane fractions, but the membrane fractions contained approximately 3 times more protein than the corresponding lane in the cytoplasmic fraction. (B) Densitometric analysis of cytoplasmic p14 kDa cytochrome-c from 3 independent experiments. Data are mean ± SEM. , P < 0.01 by ANOVA versus control and oleate alone. Figure 6 Effect of caspase 9 inhibition on apoptosis of HMC induced by palmitate. (A) Cells were treated with control (Con) or palmitate (Palm) in the presence and absence of the cell permeable caspase 9 inhibitor (C9I, Z-LEHD-FMK) or the caspase 8 inhibitor (C8I, Z-IETD-FMK), both at 40 μM. Addition of the caspase inhibitors was concurrent with addition of the FFA. After 24 and 48 h, caspase 9 enzyme activity was measured and expressed as fold-change over control. (B) Palmitate and Z-LEHD-FMK were added to HMC as above, and cleavage of caspase-3 was measured by ELISA as described in Experimental Procedures. (C) The number of pyknotic nuclei was assessed in cells treated with palmitate with and without Z-LEHD-FMK. (D) DNA fragmentation, assessed by ELISA as the number of nucleosomal fragments in the cytosol, was measured in cells treated with palmitate and by Z-LEHD-FMK. For A-D, data are mean ± SEM for 3 independent experiments. , P < 0.01, , P < 0.05 versus control by ANOVA in (A, B and D) or Chi-square test in (C). Figure 7 Palmitate stimulates translocation of the proapoptotic endonuclease G protein by a caspase 9-independent mechanism. (A) HMC were incubated with palmitate (Palm) and palmitate plus the Z-LEHD-FMK caspase 9 inhibitor (C9I, 40 μM) for the times indicated. The cells were fractionated and the level of p33 endonuclease G protein was measured by Western blotting in the cytoplasmic fraction. Equivalent amounts of protein were added to each lane, as confirmed by reprobing with β-actin. (B) Densitometry of cytoplasmic p33 kDa endonuclease G protein from 3 independent experiments. *, P < 0.01, , P < 0.05 by ANOVA versus 0 time. Figure 8 Unsaturated FFA block apoptosis induced by saturated FFA. HMC were incubated for 24 or 48 h with 0.2 mM palmitate (Palm) or stearate (Ste) alone or in co-incubations with 0.2 mM of the unsaturated FFA oleate (Ole) or linoleate (Lin). Control cells were incubated with albumin alone. After 24 and 48 h, the level of (A) the cleaved fragment of caspase-3 and (B) cytoplasmic nucleosomal fragments were determined by ELISA and expressed relative to the 24 h control value. Data are mean ± SEM for n = 3 independent experiments in triplicate. , P < 0.01 by ANOVA versus control. Figure 9 Oleate inhibits caspase 9 activation by palmitate. HMC were treated with palmitate (0.2 and 0.4 mM) or palmitate plus oleate (0.2 mM) for 24 and 48 h. Caspase 9 activity was then measured in HMC extracts adjusted for total protein. In some experiments the cells were treated with palmitate plus selective inhibitors of caspase 9 (C9I) or caspase 8 (C8I) at 40 μM. Data are mean ± SEM for n = 3 experiments in duplicate. , P < 0.01, , P < 0.05 versus control or palmitate plus oleate by ANOVA. Figure 10 Oleate prevents mitochondrial release of cytochrome-c and endonuclease G in cell treated with palmitate. (A) HMC were incubated with 0.2 mM palmitate (Palm), 0.2 mM oleate (Ole), or palmitate plus oleate for the times indicated. The cells were fractionated and the levels of cytoplasmic p14 kDa cytochrome-c (Cyto-c) or p33 kDa endonuclease G (Endo-G) protein were measured by Western blotting. Equivalent amounts of protein were added to each lane, as confirmed by reprobing with β-actin. 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==== Front BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-6-21565690710.1186/1471-2474-6-2Research ArticleRandomized clinical trial of surgery versus conservative therapy for carpal tunnel syndrome [ISRCTN84286481] Martin Brook I [email protected] Linda M [email protected] William [email protected] Michel [email protected] Patrick J [email protected] Judith A [email protected] Jeffrey G [email protected] Department of Medicine, Division of General Internal Medicine, Multidisciplinary Clinical Research Center, Box 359736, 325 Ninth Ave. Seattle, Washington, 98104, USA2 Department Psychiatry and Behavioral Sciences, University of Washington, Box 356560, 1959 NE Pacific Street, Seattle, Washington, 98195 USA3 Department of Radiology, University of Washington, Box 357115, 1959 NE Pacific Street, Seattle, Washington, 98195, USA4 Department Neurological Surgery, Harborview Medical Center, Box 359766, 325 Ninth Ave., Seattle, WA 98104, USA5 Department of Biostatistics, University of Washington, Box 357232, 1959 NE Pacific Street, Seattle, Washington, 98195, USA2005 18 1 2005 6 2 2 9 11 2004 18 1 2005 Copyright © 2005 Martin et al; licensee BioMed Central Ltd.2005Martin et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Conservative treatment remains the standard of care for treating mild to moderate carpal tunnel syndrome despite a small number of well-controlled studies and limited objective evidence to support current treatment options. There is an increasing interest in the usefulness of wrist magnetic resonance imaging could play in predicting who will benefit for various treatments. Method and design Two hundred patients with mild to moderate symptoms will be recruited over 3 1/2 years from neurological surgery, primary care, electrodiagnostic clinics. We will exclude patients with clinical or electrodiagnostic evidence of denervation or thenar muscle atrophy. We will randomly assign patients to either a well-defined conservative care protocol or surgery. The conservative care treatment will include visits with a hand therapist, exercises, a self-care booklet, work modification/ activity restriction, B6 therapy, ultrasound and possible steroid injections. The surgical care would be left up to the surgeon (endoscopic vs. open) with usual and customary follow-up. All patients will receive a wrist MRI at baseline. Patients will be contacted at 3, 6, 9 and 12 months after randomization to complete the Carpal Tunnel Syndrome Assessment Questionnaire (CTSAQ). In addition, we will compare disability (activity and work days lost) and general well being as measured by the SF-36 version II. We will control for demographics and use psychological measures (SCL-90 somatization and depression scales) as well as EDS and MRI predictors of outcomes. Discussion We have designed a randomized controlled trial which will assess the effectiveness of surgery for patients with mild to moderate carpal tunnel syndrome. An important secondary goal is to study the ability of MRI to predict patient outcomes. ==== Body Background Carpal tunnel syndrome (CTS) is the most common peripheral nerve entrapment syndrome, with an annual incidence of 50–150 cases/100,000. It is an important cause of workplace morbidity [1] with approximately 30,000 cases of CTS resulting in days lost from work in 1996 (BLS US Department of Labor 1998; [2]). Typical symptoms include paresathesia, pain, and weakness in a median motor nerve distribution, which are often worse at night [3,4]. Although frequently idiopathic, CTS may be associated with diabetes, thyroid disorders, pregnancy, renal dialysis, and other conditions [5]. A Cochrane literature review [6] for randomized trials that compared surgical to non-surgical treatment of CTS included only one article [7]. This study demonstrated significant clinical improvement in electromyography and symptoms reported at 1 year for surgical release over splinting with a cohort of 22 women. More recently, Gerritsen et al, published a second randomized study of surgical release versus splinting in 176 patients with moderate carpal tunnel syndrome, defined by clinical and electrophysiological testing [8,9]. Surgical patients had greater improvement in the number of nights waking up due to symptoms, and severity of symptoms, as well as on a general improvement scale. However, the evidence is less clear for patients with a shorter duration of symptoms or the use of conservative therapies other than splinting, such as physical therapy and ultrasound [9,10]. Although there is generally a lack of rigorous scientific support for non-surgical treatments for CTS [2], there is limited evidence of benefit for certain interventions. Common conservative treatments for CTS include wrist splints, hand therapy, non-steroidal anti-inflammatory drugs (NSAIDs), and corticosteroid injection into the carpal tunnel [11-26]. Garfinkel [15] reported that yoga hand exercises resulted in improved Phalen sign compared with splints. Rozmaryn [14] reported that nerve and tendon gliding exercises coupled with traditional conservative care may reduce the need for surgery. Little evidence exists to support the use of NSAIDs for treating CTS. Celiker et al [16] found that corticosteroid injection into the carpal tunnel was superior to NSAID therapy and Davis and colleagues [12,13] reported that ibuprofen combined with nocturnal splints did not improve outcomes more than chiropractic manipulation [15,17-19]. Local injection of corticosteroid into the carpal tunnel improves short-term clinical outcomes, as compared with oral corticosteroids, intramuscular corticosteroid injections, NSAIDS, or splints alone. [16,20-23]. Herskovitz and colleagues [24] demonstrated a short term improvement in global symptom scores for CTS with oral corticosteroid compared to placebo, but Chang could not find a dose response [25,26]. While these finding suggest some potential therapeutic benefits, none of these therapies either alone or in combination have been rigorously compared to surgery. Although not commonly used, Ebenbichler [27] found that focused ultrasound significantly improved symptom and electrophysiological outcomes compared with sham ultrasound. Vitamin B-6 has also been suggested as a treatment for CTS, [28-32] but two studies [33] failed to demonstrate improvement in outcome. Diagnostic criteria for grading CTS severity as "mild" and "moderate" are not well established. Electrodiagnostic studies (EDS) do not correlate well with clinical severity and have not been shown to accurately predict outcomes for patients with mild to moderate CTS [34-42]. We designed a randomized clinical trial to compare surgical release to non-surgical treatment for patients with mild to moderate CTS. We will examine the association between outcome, as measured by symptoms and functional status, and baseline variables such as symptoms, function, occupational risk factors, EDS measures, demographics, signs and symptoms, and prior treatments. Our primary endpoint is at 12 months. In addition, our study will evaluate the ability of high resolution magnetic resonance imaging (MRI) of the wrist to predict who will benefit from surgical release. MRI has the potential to offer new insight into the diagnosis and management of patients with hand and wrist neurological symptoms. Unlike electrodiagnostic studies, MRI directly visualizes the median nerve and can detect abnormalities of both configuration (nerve compression) as well as signal (indicating intraneural edema and demyelination) [43-46]. Either or both of these findings have the potential to be better predictors of patient outcomes than electrodiagnostic studies. Finally, we will gather utilization data for each arm of the study to test the hypothesis that the incremental cost-effectiveness ratio of surgery falls below conventional cost-effectiveness thresholds. Methods/design We have designed a multi-center, randomized trial comparing surgical release to a multi-component, non-surgical therapy. The study protocol was approved by the University of Washington Human Subjects Division and all participants provide written informed consent. The two major goals of this study are to determine: 1) if surgery compared with conservative therapy benefits patients with mild or moderate carpal tunnel syndrome, and, 2) if high resolution magnetic resonance imaging (MRI) of the median nerve can identify patients for whom early surgery might be more efficacious than conservative therapy. Patients with mild to moderate CTS are recruited from six participating sites in the Puget Sound region of Washington State: the University of Washington (UW) affiliated practice sites (University of Washington Medical Center, UW Physicians Network, Harborview Medical Center, Puget Sound VA Health Care System), Virginia Mason Medical Center (Seattle), the Seattle Hand Surgery (affiliated with Swedish Hospital in Seattle), Proliance Surgeons (affiliated with Overlake and Evergreen Hospitals), and Management Services Organization of Washington in Tacoma. We recruit patients within the primary care clinics as well as the specialty referral clinics that treat patients with carpal tunnel syndrome. These clinics include neurological surgery, neurology, orthopedic surgery, and physical medicine and rehabilitation. Additionally, we identify potential subjects in the electrophysiology laboratories at each of our participating sites. Prior to study participation, patients are required to have a physician confirmation of suspected CTS and to obtain an EDS with or without an electromyeogram (EMG). We define mild to moderate carpal tunnel syndrome based on electrodiagnostic studies (EDS) and clinical findings. Specifically, patients are eligible for the study if EDS demonstrates any one of the following: (1) wrist median motor nerve conduction latency greater than or equal to > = 4.4 milliseconds (ms), (2) a 10 cm (thumb to wrist) median to radial sensory nerve ratio difference of > 0.5 ms, (3) an 8 cm mid-palm median to ulnar sensory nerve difference > 0.3 ms, (4) a 14 cm (digit four to wrist) median to ulnar difference of > 0.4 ms, or (5) a combined sensory index [47,48] ≥ 1.0 ms. Patients with normal EDS findings could still qualify for the study if they reported hand symptoms at night that awakened them, a positive flick test, and a "classic", "probable" or "possible" evaluation of a hand diagram. [49,50] Other study inclusion criteria are: age 18 years or older, no previous hand or wrist surgery on the study hand, no previous carpal tunnel release on the contralateral hand in the previous 6 months, symptoms in at least two digits in a median motor nerve pattern, able and willing to answer research questionnaires in English, and classification using the hand diagrams as at least "possible CTS"[49,50]. Exclusion criteria are prior CTS release on the involved hand; diffuse peripheral neuropathy, any known mass, tumor or deformity; any history of severe trauma to the wrist (such as fracture); a deformity of the study hand; and pregnant or lactating (table 1). Table 1 Eligibility Criteria Inclusion Exclusion Symptoms in at least two digits on one hand (to include thumb, index, middle, or ring finger.) Wrist or hand surgery within last 6 months Hand/ wrist symptoms >1 week Previous CTS release on study hand Expect to stay in area for 1 year. Moderate to severe arthritis involving hand or wrist Willing and able to complete phone interviews Known tumor, mass, or deformity in the hand or wrist Over age 18 History of severe trauma to the wrist. Able to complete questionnaires in English Pregnant or lactating Any one of these EDS findings Motor: Median motor latency (wrist) > = 4.4 ms Median Motor amplitude <= 3.8 mV Sensory: median-radial (10 cm thumb to wrist) difference >0.5 ms EMG (if done) evidence of denervation Sensory: Midpalm median-ulnar (8 cm) difference >0.3 ms Evidence of diffuse peripheral neuropathy. Sensory: Median-ulnar (14 cm digit IV to wrist) difference >0.4 ms Thenar atrophy Sensory: Combined Sensory index > = 1.0 ms (With normal EDS) Night pain that wakes patient AND Positive flick test Classic, probable, or possible hand pain diagram. Two consecutive weeks of standard (non-surgical) treatment for CTS, including a trial of wrist splints. Lack of improvement with conservative treatment documented by at least one of the following: Improvement less than 0.75 in the CTSAQ functional? Unable to achieve "satisfactory" level of work Patient defined symptoms as being "same" or "worse" over the last two weeks. Willing to schedule surgery within one week of randomization. Patients with evidence of severe CTS on EDS, EMG, or clinical findings are excluded from the study. Severe CTS is defined as a median motor amplitude of ≤ 3.8 mV, EMG evidence of denervation, or thenar atrophy. Study participants are required to have not improved after a minimum of two weeks of standard non-surgical treatment (typically, wrist splints and NSAIDs) and be willing to schedule surgery within one week if randomized to the surgical arm of the trial. Lack of improvement with conservative care is defined by any of the following: (1) < 0.75 point improvement [51] on the Carpal Tunnel Syndrome Assessment Questionnaire (see primary outcome measures), (2) self-reported inability to achieve a "satisfactory" level of work due to hand or wrist problems, or (3) self-report of symptoms as "same" or "worse" since they started conservative therapy. For patients with bilateral CTS, we designate a "study hand" based on the following priorities: 1) most severe according to patient reporting, 2) most severe based on electrodiagnostic reports, and 3) the dominant hand. We use a 50/50 computer-generated block randomization, stratified by enrollment site. The block size is randomly varied between 4 and 12 to reduce the potential for clinicians or research staff to predict treatment allocation. The treatment assignment is centrally administered and concealed in consecutively numbered opaque envelopes. Patients are randomized to receive either a surgical release of the median nerve or a package of multiple, non-surgical treatments tailored for individual patients. For those randomized to surgery, we attempt to schedule the surgery within two weeks of allocation or as soon as possible. Surgery is performed by a board-certified neurological surgeon or orthopedic surgeon. Either open or endoscopic surgery can be performed, depending on the surgeon's preference. Surgical patients receive clinical follow-up just as they would if not in the study. Typically, this includes a follow-up visit within one week for wound and suture management and several post-operative follow-up visits with a hand therapist to perform median nerve and tendon gliding exercises. We created expert focus groups and reviewed the relevant body of literature to develop the non-surgical treatment arm of study. Our focus group included experts in orthopedics, neurosurgery, physical medicine and rehabilitation, hand therapy, biostatistics, health services, behavioral science, and health economics. The non-surgical treatment is directed by a hand, occupational, or physical therapist and includes a minimum of three visits, separated by 6 weeks each. At the first visit, each patient receives an educational booklet, a prescription for NSAIDs (if they have not previously tried them), and specific exercises for their hand. The educational booklet details the hand exercises, describes the causes of carpal tunnel syndrome, and lists resources for obtaining additional information. Work and activity modifications are prescribed at the discretion of the hand therapist and additional hand therapy visits are prescribed as needed. Patients allocated to the non-surgical arm who have already undergone extensive physical therapy can opt to get ultrasound treatment immediately (see below). Patients in the non-surgical arm return six weeks after the randomization for a study visit. If a patient reports improvement, no changes to their therapy are made. For patients who do not improve, we offer oral vitamin B-6 supplements (100 mg per day) and ultrasound in addition to the existing therapy regimen. The ultrasound regimen used in this study consists of up to 12 sessions per week (for 6 weeks) of focused ultrasound at 1 Mhz, 1.0 W/sqr cm2, in pulsed mode 1:4, and 15-minutes each. Three months after randomization, patients return for the final non-surgical evaluation. If improved, they are instructed in a self-care maintenance program and advised to return to their provider if their symptoms worsen. If symptoms are not improved, patients are referred to a study surgeon for evaluation for crossover to receive surgery or corticosteroid injection. Outcomes measures are collected for patients in person at baseline and via telephone interviews at 3, 6, 9, and 12 months after randomization. Although it is impossible to blind providers and patients to the treatment the assignment, the telephone interviewer is blinded to study participants' treatment assignment. The functional status scale of the Carpal Tunnel Syndrome Assessment Questionnaire (CTSAQ) is the primary outcome measure. The CTSAQ is a self-report CTS functional status and symptom severity questionnaire with established validity, reliability and responsiveness [52,53]. The functional status scale assesses ability to perform eight common tasks involving the hands. The symptom severity scale consists of eleven items assessing symptoms of pain, numbness, and weakness at night and during the day. Each question is answered on a scale of 1 to 5 and the scales are scored by taking the mean of the responses, with a higher score indicating greater severity. The SF-36 Health Survey version 2 (QualityMetics Inc., Ware) has been used to assess general health status in samples of patients with a variety of diseases, including CTS [54]. It consists of eight domains (general health, physical functioning, role limitations due to physical problems, role limitations due to emotional problems, bodily pain, social function, mental health, and vitality) scored on a scale of 0 (worst health) to 100 (ideal health). We will compare the two groups on each scale as well as the physical and mental summary scores. The generic nature of the instrument allows comparison across health conditions. Study participants also complete the Symptom Check List SCL-90 12-item Somatization and 13-item Depression scales [55-57]. Participants respond to each question using a 5-point scale ranging from "not at all" to "extremely". Higher scores indicate greater somatization/depressive symptom severity. The 13-item Pain Catastrophizing Scale is used as both a predictor and a secondary outcome. A substantial volume of research had consistently found substantial associations between pain-related catastrophizing and pain-related disability [58-62]. We are interested in learning whether pain-related catastrophizing is a risk factor for poor outcomes in patients with CTS. In addition to the outcome measures, we obtain information on several other variables at baseline. This includes information on demographics, occupational risk factors, medical co-morbidity, hand pain history, work status, and litigation/compensation issues. The first three items of the Alcohol Use Disorders Identification Test (AUDIT) are administered to assess the frequency of alcohol use, amount of alcohol consumption, and binge drinking [63-65]. We are also collecting information on the costs of care following enrollment. We aim to estimate the cost to health purchasers and society based on billing and medical records, and a detailed resource use questionnaire (RUQ) at 3, 6, 9, and 12 months post-enrollment. We attempted to either measure or abstract from the medical records information that is generally included in the hand physical examination at the time of enrollment. The hand physical included measurements of patient height, weight, dominant hand, 2-point discrimination (measured at digits #2 and #5), wrist width and thickness (for MRI correlation), 2-point pinch strength (average of three efforts), Semmes-Weinstein monofilament test (from 1.65 to 6.65), Tinel sign, Phalen sign (held greater than 1 minute), and flick test. Not every site routinely completed all sensibility and/or strength tests. Mackinnon-Dellon disk-criminators™ are used to test the static two-point discrimination at the second and fifth digits as a measure of sensibility to correlate to the MRI findings. We tested each digit a minimum of three times and until the tester was confident that a clear endpoint was reached. The test was performed on both hands even in patients with unilateral disease. The disk prongs are held perpendicular to the long axis of the finger. The prongs are placed upon the skin only with sufficient pressure for a patient to determine that he is being stimulated. The three-point pinch strength is tested bilaterally, with the non-study hand being first. We recorded the mean of three serial efforts of the key pinch measurement (thumb pad to lateral aspect of middle phalanx of the index finger) using a B&L Engineered (B&L Engineering 3002 Dow Ave, Suite 416, Tuscin CA 92780) 30 lbs. pinch gauge calibrated to +/- 1% accuracy. Tinel sign is positive if tapping over the median nerve at the distal wrist crease for approximately 10 seconds reproduces the pain, numbness and or tingling in the patients hand or wrist. Phalen sign is positive if while holding both wrists flexed at 90 degrees with the dorsum of the hands in opposition to each other for a minimum of 1 minute produces dysesthesias and pain. A flick test is positive if in their response to being asked "What do you actually do with your hand(s) when your symptoms are at their worst?" a patients gestures by flicking the wrist(s) [66-74]. All subjects who enter the study undergo wrist MRI except for subjects who have MRI contraindications (e.g., metallic hardware within their body), are claustrophobic, who exceed the weight limit of the MRI table, who have scheduling difficulties, or refuse the MRI. We use phased-array surface coils to obtain high resolution images of the carpal tunnel and median nerve. The imaging protocol consists of a fast T1-weighted gradient echo coronal localizer, an axial T1-weighted series and a short-tau inversion recovery (STIR) T2-weighted series. This protocol has established reliability and, because of the short imaging times, patients usually are in the MR scanner for only 15–20 minutes. Imaging parameters for the axial series are listed in table 2. Table 2 Imaging Parameters T1- Weighted Axial T2-Weighted Axial Description Spin Echo STIR flip angle 90 90 Echo train length 6 TE Minimum/full 54 TR 450 3850 TI 160 Receiver bandwidth 16 12.8 Field of view 11 11 slice/skip 4/1 4/1 Saturation pulses Superior/inferior Superior/inferior freq/phase matrix 256 × 256 256 × 224 freq direction Right/left Right/left Number of excitations 2 2 Time 5:33 5:16 # images 20 11 The MRI is interpreted by a neuroradiologist without knowledge of the demographic data, clinical findings, or electrodiagnostic study findings. The key imaging variables are the degree and length of signal abnormality within the median nerve, flattening or swelling of the median nerve, although other qualitative and quantitative measurements will be made as well (table 3). Table 3 Key MRI Imaging Variables Median nerve Signal (degree and length of signal abnormality) Configuration (flattening or swelling) Fascicular pattern Flexor retinaculum bowing Flexor tendon sheath interspaces Patients receive a follow-up interview via telephone at 3, 6, 9 and 12 months. Hand specific symptom and functional disability scores, along with general health and psychological instruments are collected. For each interview, we attempt to contact participants a minimum of three times, varying the day and time of the call. In instances where we are unable to reach a person by phone, the survey instruments are mailed along with a postage paid return envelope. Some patients also consent to allow us to contact them for follow-up using e-mail. All data are collected onto hardcopies and entered into an MS Access database using a web-based data entry system that requires double entry of data to reduce errors in transcribing. Error reporting to identify out of range answers, inconsistent replies and compliance monitoring is routinely performed. We developed stopping rules using CTSAQ symptom scores, disability reporting, adverse event rates and rates of "red flag" answers to a question asking about thoughts of suicide on the SCL-90. A safety officer monitors these variables for group differences, and also monitors response variables, missing data, and protocol compliance. A formal evaluation of efficacy will be conducted after 100 patients have been randomized to test for group differences in the CTSAQ symptom severity score. We adopted O'Brien-Fleming boundaries for discontinuation, and therefore maintain an overall type I error rate of 5%. Additional stopping criteria include a suicidal ideation rate difference of 10%, a difference in the change from baseline rate of functional disability (as measured by the CTSAQ function scale) of 20%, or a difference in the number of days lost from work of 20%. The nature and severity of the adverse events will also be considered individually by the safety officer. Finally, individual item response rates on all answered questionnaires with less than 20% being incomplete, failure to collect greater than 75% at three-month follow-up, or enrollment rates below the expected rate by more than 25% provided grounds for the safety officer to recommend changes or stop the study. In the primary analysis the CTSAQ functional score at 12 months will be compared between the surgical and non-surgical treatment arms, using conventional t-tests and ANCOVA techniques to adjust for baseline values. The analysis will be conducted on an intention-to-treat basis. Secondary analyses will include a comparison of secondary outcome measures (CTSAQ symptom severity, the SF-36 scales, time lost from work) for the two treatment arms. Adjusted analyses of CTSAQ functional status and secondary outcomes at 1 year will be conducted using linear regression methods. To characterize the time evolution in the primary and secondary outcomes we will use linear mixed models (or Generalized Estimating Equations in the case of categorical variables) to analyze the repeated measures obtained at all follow-up visits. Finally, we will use exploratory regression methods to determine if baseline disability, psychological factors, MRI variables, and electrodiagnostic measures correlate with clinical outcomes. We will use aggregate results of patient outcomes on the CTSAQ and SF-36 scores to perform a descriptive analysis, uncontrolled for other baseline factors. We will explore factors that predict improvement in CTSAQ (symptom score and functional status) and general health scores as measured by the SF-36. Specific analysis and tests used will depend on the distribution of the tested values. We will also test the independent associations of the various mental health domains, the work-related risk factors, and physical findings to changes in the CTSAQ. Finally, we will use linear regression to test relevant associations while controlling for baseline demographics, co-morbidity and other important variables to identify factors that may predict improved outcomes at 1 year. As important sub-analyses we will also determine the reliability of quantitative MR median nerve measurements, determine the correlation of symptoms and function in patients with these quantitative MR measurements as well as with electrodiagnostic studies (EDS), and construct a cost-effectiveness model to test the hypothesis that MRI is an efficient method for selecting patients with mild or moderate CTS who are likely to benefit from surgery. The prospective cohort study by Katz et al. provided data on outcome differences between surgical and non-surgical patients [75]. They also used the CTSAQ as an outcome measure and found a 23–45% difference between surgical and non-surgical groups. Using pilot data we have calculated the mean reduction in CTSAQ function scores for 74 non-surgical patients as m0 = 0.264 (standard deviation = 0.670) and the mean reduction for 30 surgical patients as m1 = 0.818 (standard deviation = 1.033). The observed effect of surgery is m1 - m0 = 0.818-0.264 = 0.554 point greater reduction. We used the pilot data estimates of variance to calculate the sample size required to obtain sufficient power (80% or 90%) for various differences in means (m1-m0). If we consider the difference in means observed for the pilot data (m1-m0 = 0.554) then 48+48 = 96 subjects are adequate for 80% power and a total of 64+64 = 128 are required for 90% power (refer to following table). However, since we are recruiting patients with less severe disease, we would expect smaller improvements in the scores from baseline to follow-up and hence, smaller effect sizes. Thus, it is reasonable to power the study to detect a difference in means of 0.4 rather than 0.5. Since our primary analysis will use the more efficient ANCOVA, these sample size estimates based on observed change scores (post-pre) are slightly conservative. This should allow for a small percentage of subjects lost to follow-up. Similar power calculations were conducted to assess the power to detect an impact on CTSAQ symptom scores. The pilot data suggest a 0.67 point greater reduction in symptoms for the surgical patients, and variance estimates lead to sample sizes of 30+30 = 60 total patients, and 40+40 = 80 total patients to detect 0.67 point difference (table 4). Our study will aim to enroll 100 patients into each arm of the study and this will be sufficient to detect the smallest clinically relevant differences. Table 4 Power calculation In order to have 80% or 90% power for the analysis of reduction in function scores (change) we would need (per study arm) m1-m0 0.20 0.30 0.40 0.50 80% power 298 133 75 48 90% power 399 177 100 64 Discussion An RCT offers the best chance of answering, in an unbiased fashion, the relative efficacies of surgery compared with conservative therapy for patients with mild to moderate CTS. Our study is designed to test two main hypotheses: 1) that patients with mild or moderate CTS benefit from surgery more than conservative therapy, and; 2) that high-resolution wrist MRI accurately identifies which patients with mild to moderate symptoms are more likely to benefit from surgery. We expect our findings to be more generalizable to the primary care clinicians and include milder cases of CTS than Gerritsens' study. No previous study has sufficiently considered conservative treatment options of vitamin B6, ultrasound, anti-inflammatory drugs, and hand exercises in combination versus surgical treatment. Furthermore, our study offers the unique use of wrist MRI data to establish the diagnostic and predictive understanding of carpal tunnel syndrome. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements The study is funded by the National Institute of Arthritis and Musculoskeletal Skin Disease (NIAMS) (P60 AR48093) and is conducted by the University of Washington's Multidisciplinary Clinical Research Center, located in Seattle, Washington, U.S.A. ==== Refs Cheadle A Franklin G Wolfhagen C Savarino J Liu PY Salley C Weaver M Factors influencing the duration of work-related disability: a population-based study of Washington State workers' compensation Am J Public Health 1994 84 190 196 8296938 Feuerstein M Burrell LM Miller VI Lincoln A Huang GD Berger R Clinical management of carpal tunnel syndrome: a 12-year review of outcomes Am J Ind Med 1999 35 232 245 9987556 10.1002/(SICI)1097-0274(199903)35:3<232::AID-AJIM3>3.0.CO;2-G Katz JN Simmons BP Clinical practice. 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==== Front Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-4-21564932810.1186/1476-072X-4-2ResearchResearch protocol: EB-GIS4HEALTH UK – foundation evidence base and ontology-based framework of modular, reusable models for UK/NHS health and healthcare GIS applications Boulos Maged N Kamel [email protected] School for Health, University of Bath, Claverton Down, Bath BA2 7AY, UK2005 13 1 2005 4 2 2 19 10 2004 13 1 2005 Copyright © 2005 Boulos; licensee BioMed Central Ltd.2005Boulos; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. EB-GIS4HEALTH UK aims at building a UK-oriented foundation evidence base and modular conceptual models for GIS applications and programmes in health and healthcare to improve the currently poor GIS state of affairs within the NHS; help the NHS understand and harness the importance of spatial information in the health sector in order to better respond to national health plans, priorities, and requirements; and also foster the much-needed NHS-academia GIS collaboration. The project will focus on diabetes and dental care, which together account for about 11% of the annual NHS budget, and are thus important topics where GIS can help optimising resource utilisation and outcomes. Virtual e-focus groups will ensure all UK/NHS health GIS stakeholders are represented. The models will be built using Protégé ontology editor based on the best evidence pooled in the project's evidence base (from critical literature reviews and e-focus groups). We will disseminate our evidence base, GIS models, and documentation through the project's Web server. The models will be human-readable in different ways to inform NHS GIS implementers, and it will be possible to also use them to generate the necessary template databases (and even to develop "intelligent" health GIS solutions using software agents) for running the modelled applications. Our products and experience in this project will be transferable to address other national health topics based on the same principles. Our ultimate goal is to provide the NHS with practical, vendor-neutral, modular workflow models, and ready-to-use, evidence-based frameworks for developing successful GIS business plans and implementing GIS to address various health issues. NHS organisations adopting such frameworks will achieve a common understanding of spatial data and processes, which will enable them to efficiently and effectively share, compare, and integrate their data silos and results for more informed planning and better outcomes. ==== Body "Geocoding (street address matching or assignment of latitude and longitude) will be the basis for data linkage and analysis in the 21st century. The versatility of GIS supports the exploration of spatial relationships, patterns, and trends that otherwise would go unnoticed." – US Healthy People 2010 Objectives (item 23-3 – ) Introduction Geography matters – the need for an evidence-based, spatio-temporal approach to public health Geography plays a major role in understanding the dynamics of health, and the causes and spread of disease [1]. The classic public health triad composed of man, agent/vehicle and environment emphasises the importance of geographic location (environment or space where we live) in health and disease. Interactions within this triad can also change with time. Today's health planners aim at developing health policy and services that address geographic and social inequalities in health, and therefore should benefit from evidence-based approaches that can be used to investigate spatial aspects of health policy and practice, and evaluate geographic equity (or inequity) in health service provision [2]. On geo-information and the need for applications to support the decision maker According to the US Federal Geographic Data Committee (FGDC), geographic location is a key feature of 80–90% of all government data [3]. The same can be also said about government data in other countries, including data generated by the health sector in the UK. This locational or spatial reference is a "main key" in the transformation of data into information, and for linking and integrating many health and other datasets from disparate sources covering same and contiguous locations [4]. Unlike other resources like employees or funds, spatial data do not suffer any wear and tear from repeated use. On the contrary, reusing data increases the possibilities for improving the content quality of data collections and gaining new insights by linking and exploring the relationships between the different datasets that makeup the big picture [4]. This implies the need to develop applications and not just focus on data. An overemphasis on data acquisition, without health sector-linked applications, will not provide any momentum for further development. Visualisation, modelling and analysing activities will be the focus of value-added services in the coming years [4,5]. Methods must be identified and/or developed to process our spatial data assets to produce meaningful, bottom-line conclusions that can support the decision maker rather than mere bunches of facts. According to Openshaw [5], the ideal methods should be safe and usable by people with no higher degrees in statistical or spatial sciences. The methods should also respond to user needs on the ground, be highly automated, explicitly handle spatial data imprecision, and produce self-evident results that can be mapped and communicated to non-experts. On GIS and their health and healthcare applications In 2003, the US National Library of Medicine added the term "Geographic Information Systems" (GIS) to its controlled vocabulary thesaurus known as MeSH (Medical Subject Headings – see ), a step reflecting the importance and growing use of GIS in health and healthcare research and practices. The US FGDC defines GIS as "computer systems for the input, storage, maintenance, management, retrieval, analysis, synthesis, and output of geographic or location-based information. In common usage by organisations, GIS include hardware, software, and data. GIS also imply the people and procedures involved in GIS operation" (cited in [6]). The inclusion of "procedures" as part of the above definition is essential for GIS applications in a public health context, given the need to link the science and methods of geographic information science, spatio-temporal statistics, medical geography, epidemiology and public health to GIS output to avoid producing invalid or misleading results [6]. GIS offer a very rich toolbox of methods and technologies that goes far beyond the mere production of simple maps (or digital cartography). From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving. When properly used, GIS can be used to: inform and educate (professionals and the public); empower decision-making at all levels; help in planning and tweaking clinically and cost-effective actions, in predicting outcomes before making any financial commitments and ascribing priorities in a climate of finite resources; change practices; and continually monitor and analyse performance and changes, as well as sentinel events. Traditionally, two broad types of GIS applications can be distinguished which also reflect the two traditions in health geography (geography of disease and geography of healthcare systems), namely health outcomes and epidemiology applications, and healthcare delivery applications. There are also studies at the interface (overlap) between epidemiological and healthcare delivery applications, for example in relation to healthcare commissioning and needs assessment [1,7,8]. On current issues limiting a wide-scale, optimum adoption of GIS in the UK NHS and many other healthcare systems around the world The use of GIS in the UK National Health Service (NHS) can still be considered as an emerging technology (in this respect the NHS is not anyway better or more advanced than many other healthcare systems in less developed countries). Despite all their potentials, GIS remain very much under-utilised in the UK NHS in mostly low-level, non-strategic tasks, and in a largely fragmented and uncoordinated way. Spatial data and GIS are still not mentioned in any main UK health information strategy or policy document [7,8]. In striking contrast to this, the US National Health Information Infrastructure Strategy document (also known as "Information for Health") refers explicitly to GIS and real-time health and disease monitoring and states that "public health will need to include in its toolkit integrated data systems; high-quality community-level data; tools to identify significant health trends in real-time data streams; and geographic information systems" (see ). GIS are also explicitly included in the National Electronic Disease Surveillance System (NEDSS) specifications and systems architecture of the US Centres for Disease Control and Prevention (CDC – ). Although multiple novel spatial statistical and GIS methods are potentially available, we still need to unambiguously determine which method(s) and data specifically should be used by practitioners for each specific health condition of interest, and whether the proposed methods are cost-effective and scalable in the context of UK/NHS settings [8]. Moreover, several researchers have highlighted a gap between academic health-related applications of GIS and their everyday use within the NHS. Research undertaken in academia has certainly highlighted the benefits of spatial statistics and GIS approaches in mapping disease and in healthcare planning, but still needs to respond to NHS needs on the ground [7,8]. On the other hand, it is not uncommon for GIS research to include very practical and useful gems, but these often remain confined to the closed circles of researchers and hidden from the larger communities of GIS professionals and users. The best, current evidence derived from GIS research still needs to be embedded (and regularly updated) in everyday practice and all professional training programmes [8]. In a recent major review paper by this author, the reasons behind the under-utilisation of spatial information and GIS in the NHS, as well as the causes of the gap between academic GIS research and the current everyday use of GIS in the NHS were investigated, and remedial recommendations were made [8]. The reader is referred to this paper for further details. Overview and significance of the proposed research Research question How could GIS be beneficial in optimal ways in the context of UK/NHS settings and needs? (Or in other words, how to harness the importance of spatial information in the health sector in order to better respond to national health plans, priorities, and requirements, and to optimise NHS resource utilisation and improve health outcomes.) Any answer(s) or proposed solutions must be based on the best current evidence in the health GIS field. The proposed research This author proposes to build, consolidate and disseminate a comprehensive evidence base for GIS applications in health and healthcare in the context of UK/NHS settings, and an associated set of evidence-based GIS programme and application models (EB-GIS4HEALTH UK). We will learn from national projects running in the US (a leading country in health GIS) and elsewhere, while ensuring that EB-GIS4HEALTH UK's output properly fits the UK health and healthcare agenda. A systematic and critical review and consolidation are needed of the evidence for GIS for specific preventable, mitigable and treatable health conditions. A good model is the CDC "Guide to Community Preventive Services" [9]. Topics identified in this guide include alcohol abuse, cancer, diabetes, mental health, motor vehicle occupant injury, oral health, physical activity, sexual behaviour, social environment, tobacco product use, vaccine preventable diseases, and violence. The guide has started building an excellent evidence base, but this does not cover GIS methods and applications (Figure 1). Figure 1 Key to "Strength of Evidence". Key to "Strength of Evidence" as displayed within the CDC Community Guide . We propose to address a subset of the topics in the CDC Community Guide (diabetes and dental care – see below) during the three-year period of our project by conducting a focused review of GIS literature on the chosen topics, and then categorising the "nature of the scientific evidence" documenting whether GIS add any value to our understanding and management of the reviewed topics and/or the evidence that it would be feasible and cost-effective for the respective UK NHS and public health programmes tackling the reviewed topics to adopt GIS. Areas requiring further research will be also highlighted. A good example that comes to mind in this context is the 73-page "GIS for cancer" handbook titled "Using Geographic Information Systems Technology in the Collection, Analysis, and Presentation of Cancer Registry Data: A Handbook of Basic Practices" that was published by the North American Association of Central Cancer Registries . However, it should be noted that this particular handbook was conceived with the US healthcare system in mind and is thus more suited to US settings, though still useful as a model to follow. In reviewing GIS literature for the above mentioned purposes, this author appreciates the fact that the set of definitions and criteria for reviewing evidence as used in the CDC Community Guide is not directly usable for reviewing currently available GIS literature due to the nature of the latter; a modified set of definitions and criteria will first need to be developed. Furthermore, we will organise virtual e-focus groups that bring together UK programme administrators, practitioners and the public to complement the expected gaps and deficiencies in current GIS literature. The desired GIS information outputs and ways of using them within the NHS will be determined for both diabetes and dental care. Datasets (inputs) and the appropriate processing methods required to reach the desired outputs will be identified driven by the best available evidence (from the literature and e-focus groups), and vendor-neutral GIS programme and application models or ontologies will be created accordingly. Any limitations of these applications and any associated possible data/analysis problems or errors will be also highlighted, along with techniques for recognising and reducing their negative impact on result interpretation and any drawn conclusions. We will also launch EB-GIS4HEALTH UK Web server to disseminate our results and reach out to the wide NHS audience and the public. The project's value and potentials In a recent study by Higgs et al, a substantial proportion of respondents from health authorities (90%) and trusts (74%) stated that a dedicated Web site giving advice on GIS matters for NHS organisations would be helpful in promoting and disseminating good practice examples of GIS use in healthcare [10]. EB-GIS4HEALTH UK Web server will provide this kind of information and much more (for the topics covered during the duration of this project). In this way, EB-GIS4HEALTH UK will be also (partially) addressing the problems of "insufficient guidance" and "lack of awareness of the value of GIS to the NHS" that have been identified in different studies and reviews among the main factors hindering the wider use of GIS within the NHS [7,8,10]. EB-GIS4HEALTH UK will establish links between real-world NHS practice and the growing body of health and healthcare GIS research produced by the academia and research communities to ensure quality GIS practice and innovative applications are developed and implemented in the UK health service. By putting strong emphasis on real-world, practical GIS scenarios in a UK context, and by being based on the current best evidence, EB-GIS4HEALTH UK will also provide a much needed contribution to future national GIS training and raising awareness campaigns. Such evidence-based training and raising awareness activities have been strongly recommended by different researchers to improve the current poor GIS state of affairs within the NHS [8]. Raising awareness activities are also vital given the need to build business cases for the development of GIS within NHS organisations and to show the capabilities and "business benefits" of GIS to directors [10] (Figure 2). EB-GIS4HEALTH UK will act as a vehicle to reach out to policy and strategy makers in the UK health sector and will provide part of the evidence and "proof of concept and benefits" required to gain their support and long-term funding for realising the wider and ultimate vision of a national spatial data and information infrastructure and associated multivariate, GIS-enabled health surveillance services to run alongside and become coupled to the nation-wide integrated electronic health and social care records [8]. Figure 2 Why build an evidence base? Why an evidence base is needed (Modified from ). EB-GIS4HEALTH UK will provide semantically and procedurally consistent health and healthcare GIS application frameworks. This will help individual NHS organisations adopting such frameworks achieve a common understanding of data and processes (shared semantics), which in turn will enable them to efficiently and effectively share, compare, and integrate their data silos and results at local, regional and national levels for more informed planning, national comparisons, joined-up working, and better outcomes. The foundation for a wider vision EB-GIS4HEALTH UK also forms the basis of our vision of a next-generation intelligent tools specifically designed for the UK public health and NHS, and seamlessly weaved into everyday workflows and decision-making processes to enable users to focus and spend the larger part of their work time on what they want to achieve rather than on learning and overcoming the limitations of tools they are supposed to use to achieve their goals. These tools are part of our wider vision of a "national spatial data and information infrastructure and associated multivariate, GIS-enabled health surveillance services processing real-time or near-real-time data streams rather than just retrospective data" mentioned above and in [8]. Such tools must be able to convey reasonable conclusions (rather than just bunches of facts on thematic, coloured maps). The ideal tools also need to be fault-tolerant and capable of analysing and presenting assembled data in ways that facilitate only appropriate interpretations of integrated data. This can be achieved by using some form of user friendly, "intelligent", goal-oriented health GIS wizards based on robust statistical and epidemiological methods, so that only valid results and maps are produced, even when users attempt to select inappropriate settings for a particular analysis. The tools are also best designed and built to work in modular and nested fashions, so that they may be reused, linked and combined in different ways as needed to serve different scenarios and compound situations with little or no modifications (of the tools) [8]. This vision definitely calls for a sound evidence base and comprehensive conceptual blueprints (the proposed EB-GIS4HEALTH UK evidence-based models) to drive the envisaged tools and wizards. Why diabetes and dental care An incremental approach has been widely recommended in the literature for programmes with a national vision like EB-GIS4HEALTH UK. Rather than addressing all the spectrum of health and healthcare topics that are amenable to GIS processing in one project, we selected only two health conditions to start with. Diabetes and dental care were chosen because of their importance and the huge burden they place on the NHS, and the facts that they affect various age and socio-economic groups and involve a wide spectrum of preventive and curative interventions, which make it possible to apply the GIS models we are proposing to develop for these two topics to many other health and healthcare topics with little modification. Diabetes has a significant impact on the UK health and social services. Around five per cent of total NHS resources and up to ten per cent of hospital in-patient resources are used for the care of people with diabetes [11]. It has been estimated that diabetes accounts for some nine per cent (approximately £5.2 billion in 2000) of the annual NHS budget [12] (similar figures could be expected in many other countries). Moreover, in the UK, significant inequalities exist in the risk of developing diabetes, in access to health services and the quality of those services, and in health outcomes, particularly with regard to Type 2 diabetics. Reducing health inequalities is a core strand of The NHS Plan and, as the diabetes NSF (National Service Framework) is implemented, particular regard will need to be given to identifying the need for services, including unmet need; planning and delivering services on the basis of need, including reaching those who may not currently be accessing services or are accessing them late; ensuring the active involvement of users in service development; ensuring services are appropriate to individuals' needs, such as ethnicity, language, culture, religion, gender, disability, age and location; performance monitoring; and measuring and monitoring the health inequalities gap to ensure it is narrowing [11]. In all these areas, GIS can assist in analysing the socio-demographic makeup of service areas, and in planning and monitoring interventions and programmes to address these inequalities in the most efficient and effective ways, making sure NHS funds are properly allocated to areas most in need [13,14]. Regarding dental care, the cost of General Dental Services to the government in the year to March 2002 was £1.12 billion (about two per cent of the annual NHS budget of £65 billion in 2002) [15]. Under the recent Health and Social Care (Community Health and Standards) Act 2003, from April 2005 commissioning and contracting for NHS dentistry will devolve from the Department of Health to PCTs (Primary Care Trusts) [16]. Moreover, in September 2003, it was announced that £65.2 million will be made available to improve dental care for NHS patients (see ). Of this latter sum, £35 million will be allocated to enable PCTs to improve access, choice and quality for patients, and £30 million will be directed to information technology to integrate dentistry within the national information technology programme. (The latter has yet to recognise the many potentials of spatial information and GIS for the NHS.) Profiling service areas and needs assessment, dental healthcare commissioning, and improving/ensuring equitable access to dental services are all areas where GIS can make a positive difference. GIS have significant potential in examining spatial patterns in dental health, and in analysing patterns of registration and utilisation of dental services for different sectors of the community. GIS can reveal gaps in dental health provision, and thus help target resources and programmes to particular areas of needs. GIS can be also used to analyse the composition and spatial distribution of the dental workforce, and to inform the development and monitor the execution of programmes for attracting dental care professionals to work in under-served areas [2,17,18]. The project's beneficiaries These include: (1) the NHS and public health services in England (and the UK) – EB-GIS4HEALTH UK aims at adding the missing spatial information dimension to NHS organisations, and informing the development of successful GIS business plans for the health conditions under consideration; (2) the recently established NHSU, the corporate university for the NHS with Special Health Authority status – , could also benefit from EB-GIS4HEALTH UK as a foundation resource to provide evidence-based training programmes to NHS staff in "GIS for health and healthcare" and foster the much-needed NHS-academia collaboration. NHSU currently does not have programmes covering this important area. As an aside, one might add that when the Director of the Medical Informatics programme that the US CDC runs for its new staff conducted a poll of 40 students on the area they most wanted more information about, top of their list was GIS (Gerard Rushton, Department of Geography, University of Iowa, personal communication – December 2003); and (3) the UK citizenry and communities who will be empowered to become more active partners in their healthcare, and will also benefit on the long run from improved health services and outcomes as a result of the introduction of well-founded spatial information management within the NHS through EB-GIS4HEALTH UK and other synergistic/follow-on projects in the future. Aim, objectives and methods Aim EB-GIS4HEALTH UK aims at building a foundation evidence base and conceptual models for GIS applications in health and healthcare in the context of UK/NHS settings to (i) improve the currently poor GIS state of affairs within the NHS; (ii) help the NHS understand and harness the importance of spatial information in the health sector in order to better respond to national health plans, priorities, requirements, and NSFs (National Service Frameworks); and also (iii) foster the much-needed NHS-academia GIS collaboration. Objectives 1. Organise e-focus groups of representatives of all stakeholders of UK/NHS health GIS to inform the development of all EB-GIS4HEALTH UK products. 2. Review the literature and current UK Public Health/NHS data flows and practices of relevance, and build an evidence base of GIS applications in diabetes and dental care (the two topics chosen for this project) in the context of UK/NHS settings. 3. Build modular GIS programme and application models for diabetes and dental care tailored to UK/NHS settings (driven by the evidence identified through objectives 1 and 2). 4. Run a Web server to disseminate the project's evidence base and resultant GIS programme and application models for diabetes and dental care to the wide NHS audience and the public. 5. Conduct a small-scale formative evaluation of EB-GIS4HEALTH UK server use and potential impact on NHS resource utilisation and improving health outcomes. Methods I. Process: gathering the evidence; product: building EB-GIS4HEALTH UK evidence base for diabetes and dental care Gaining insight about UK/NHS settings and formulating UK oriented questions We will gain insight about current Public Health/NHS (in England) data assets, flows and processes of relevance to EB-GIS4HEALTH UK through our NHS collaborators, and by identifying key contacts and organising virtual e-focus groups (see below). This insight will help us come up with a set of "localised" (UK oriented) questions that we will have to answer through our literature review and further e-focus group rounds. Developing a search strategy for locating the evidence and criteria for reviewing it We will formulate a search strategy to locate potential resources for our evidence base. This will also cover the so-called grey literature. Our search strategy will span MEDLINE/PubMed and other journal resources not listed in PubMed, e.g., Cartography and Geographic Information Science , Transactions in GIS , International Journal of Geographical Information Science , CDC Public Health GIS News and Information , ESRI's HealthyGIS and ArcUser Online ( and ), etc. We will also hand search a range of textbooks on GIS applications in health and healthcare. We will also review the extensive online documentation of flagship UK and foreign projects and initiatives of relevance to our proposed research. These include, among others, the UK Department of Health NSFs ; the US CDC National Public Health Performance Standards Programme (NPHPSP – ); the US Primary Care Service Area Project (PCSA – ) and the related Dartmouth Atlas project ; the "Mapping A Shared Vision of Hope: The American Indian/Alaska Native (AI/AN) GIS Diabetes Atlas", which was developed to assist in the analysis, design, and evaluation of AI/AN diabetes intervention and prevention programmes (Shirley Baros, Earth Data Analysis Centre, University of New Mexico, personal e-mail communication – January 2004); Georgia Medical Care Foundation Diabetes Quality Indicators project ; and CDC's data systems for oral health . We will review studies that used GIS or spatial methods in diabetes and dental care (e.g., [2,13,14,17-19]), and studies describing GIS or spatial methods that can be used in diabetes and dental care. The soundness and robustness of all reviewed literature and methods will be assessed and quality of the evidence determined. The conventional set of definitions and criteria for reviewing the evidence in medical literature by distinguishing between case-control, prospective cohort studies, and so forth is not directly usable for reviewing health GIS literature. Most GIS literature is in the form of application/methodology reports and expert opinions/reviews; a modified set of criteria needs to be agreed upon that recognises the different nature of GIS literature and its technical aspects. One thing to look at is whether a given method/approach is successfully replicated in more than one study. This sort of "consensus of opinions" could be an indicator of good evidence quality. Another issue to cover in our reviews is whether a given piece of evidence is suitable to UK/NHS settings and datasets. The virtual e-focus groups We will organise virtual e-focus groups of UK public health and GIS/informatics programme administrators (including our NHS collaborators), practitioners and representatives of the public to (1) inform the project about NHS settings; (2) define and refine the key questions that decision makers would want to be able to answer with GIS for diabetes and dental care, and think explicitly about what data and methods should be used to answer those questions; (3) discuss the reviewed literature and guide us to any relevant literature we may have missed for our evidence base; (4) complement our review of the evidence by the group members' own experiences and consensus of opinions, especially in areas where the literature is lacking; and (5) provide iterative feedback on EB-GIS4HEALTH UK models, as these are developed and refined. The virtual e-focus groups will ensure that representatives of all stakeholders of UK/NHS health GIS are involved in formulating EB-GIS4HEALTH UK products, a very important ingredient of success [8,20]. The e-focus groups will take place online using threaded discussion technology on EB-GIS4HEALTH UK Web server. Simple audio-conferencing over the Internet, e-mail/e-mail list, and/or telephone may be also used if necessary. The project's team will moderate/facilitate and guide/coordinate the discussions by adopting a kind of Delphi process and/or "cluster analysis" approach (where group members list their ideas, opinions and own experiences, then these are sorted and compiled into "themes" by the moderators and presented back to the group, and further rounds of knowledge distillation and aggregation are carried out as appropriate). The evidence base/metadatabase A metadatabase of reviewed resources classified according to topics and applications will be created and populated. This will form EB-GIS4HEALTH UK searchable online evidence base (see below). The design of resource records in this metadatabase will be guided by the spirit of the matrix method for conducting and organising literature reviews [21]. A classification system will be developed to easily sort and categorise the reviewed evidence. This could be an adapted version of Hu et al's set of categories they have recently used in a small-scale GIS literature review, with the following top classes/sub-classes among others: data collection method(s) (e.g., field survey, disease surveillance system input, etc.); spatial data analysis method(s) (e.g., visualisation, exploratory, spatio-temporal modelling, etc.); study scale (e.g., country/state, city/town, suburb/district, etc.); resolution/geographic unit of analysis (areas, points, others); study purpose (e.g., identify disease risk factors, disease prediction, resource allocation, etc.); GIS software used; bias of study design/limitations; etc. (Wenbiao Hu and colleagues, Queensland University of Technology, Australia, unpublished report – February 2004). The metadatabase will include fields to record the strength of evidence and bottom-line summaries of the reviewed articles. EB-GIS4HEALTH UK models and their documentation will have cross-links to the underpinning evidence in the online metadatabase. Suitable input from the virtual e-focus groups will be also included in the evidence base. Resources and findings of insufficient evidence quality will still be documented in the evidence base (with a poor evidence quality rating attached to them), but will not be used in developing EB-GIS4HEALTH UK models. II. Process: translate evidence of acceptable strength into recommendations/modular GIS models; product: modular, conceptual GIS programme and application workflow models for diabetes and dental care Developing a general conceptual framework We will develop a general conceptual framework for EB-GIS4HEALTH UK models as outlined below, guided by Briggs' indicators methodology for environmental health hazard mapping [22,23], the US National Association of County and City Health Officials (NACCHO) core and extended health indicators, which form part of their Community Health Status Assessment (CHSA) Toolbox [24], and the Health Data Model (HDM), a project at the University of California at Santa Barbara (UCSB) to develop a US-oriented data model for health using proprietary ESRI software . EB-GIS4HEALTH UK will take the concepts and methodologies of these projects one step further by developing evidence-based, vendor-neutral modular GIS programme and application models rather than mere data models or vendor-specific solutions. The project's two types of models EB-GIS4HEALTH UK will feature two interrelated types of models: application models and programme models (Figure 3). Figure 3 The project's two types of models. EB-GIS4HEALTH UK features two interrelated types of models: (1) application models; and (2) programme models, each comprising at least two linked application models. Programme models can also have links with/feed into each other (not shown in this figure). An application model comprises (1) data and/or input from other models to be processed; (2) processing methods and tools (methods of geographic information science, spatio-temporal biostatistics, medical geography, epidemiology, health services and public health practice); (3) desired information outputs and output visualisation methods; as well as (4) all valid interpretations/inferences that can be made from the model's output and ways of using them within the NHS. The best current evidence (from the literature and virtual e-focus groups) will be used to formulate all EB-GIS4HEALTH UK models, and will form an integral part of their documentation. Application types For any single broad health/healthcare topic covered in EB-GIS4HEALTH UK (related to diabetes and dental care), modelled applications can include all or some of the following (among other possibilities): (1) monitoring and measuring NHS performance (many aspects could be measured and analysed such as improvements in the health of the general population, accessibility and utilisation of services, and outcomes of NHS care); (2) surveillance/monitoring/early detection of health and disease patterns and trends in populations; (3) profiling target populations and health service catchment areas; (4) selection of appropriate target groups for public health interventions, optimising these interventions to match target group profiles, and measuring intervention success; and (5) needs assessment and optimising healthcare system planning and responses, including new healthcare facility siting and improving hospital bed availability. EB-GIS4HEALTH UK models will take into account the influence of non-medical determinants (e.g., income, occupation, and environment) on population health status, qualitatively relate these determinants to health outcomes, and consider their implications on healthcare service planning and delivery [8,20]. For each application model, an ontology (conceptual data and process/workflow model) will be built to capture the properties of, and relationships between the different datasets involved in the application in question, as well as the various data elements (and their relationships) within individual datasets. The ontology will also capture the interactions of application data with all involved processing methods. This will help make explicit all application data requirements and processes, and facilitate data and application integration. A programme model is also an ontology that addresses a single broad health/healthcare topic, and comprises at least two application models linked together and interacting with each other in predetermined and purposeful ways towards a broader goal than that covered by a single application model. Application models may have uses in more than one programme (either unchanged or with slight modifications to suit different programmes, e.g., different input datasets). The project's ontology editor EB-GIS4HEALTH UK ontologies (the models) will be built using Protégé , a free ontology modelling tool from Stanford Medical Informatics with which this author has long experience [25,26]. Protégé features an extensive library of free plugins and applications that can extend the tool's basic functionality in many useful ways , including a Java-based Web application for sharing Protégé ontologies over the Web . Protégé is not alien to geographic information science; last year (2003) for example, a geographic information metadata (ISO 19115) ontology was developed at Drexel University, US, using Protégé ( – Figure 4). Figure 4 Protégé screenshot of the geographic information metadata (ISO 19115) ontology. Protégé screenshot of the geographic information metadata (ISO 19115) ontology developed at Drexel University, US, and available for downloading from . The ontology is distributed in OWL Web Ontology Language format, which is supported by Protégé. OWL is now also an official World Wide Web Consortium (W3C) Recommendation (see ). Anatomy of a model Metadata A model's ontology/documentation will specify the model's version/date and release history, the programme(s) (for application models)/topic(s)/sub-topic(s) to which the model relates, the model's rationale and role, any alternative or related models/model sets (programmes), links to the underpinning evidence (from the literature/the project's evidence base), and a listing of all agencies/NHS bodies involved in the model's process(es) (and their exact roles). Inputs Data involved in the modelled applications will be identified and assessed regarding availability, quality, characteristics, and constraints in terms of the model in question. Whenever this is possible, EB-GIS4HEALTH UK models will strive to refer to and model readily available data already collected for other purposes, since implementing new data collection processes can have prohibitive costs, and healthcare workers have repeatedly demonstrated poor compliance with additional data collection and administrative tasks [27]. For data that are not available but are "required" we will investigate and suggest the most efficient and effective way(s) for collecting and maintaining such datasets. During the course of the project, we will be identifying and communicating with the custodians of various datasets in the UK health sector and related sectors, as appropriate, to check the availability of required datasets to the UK NHS, note any constraints related to their release and use (e.g., privacy issues), and flag any need to review policies related to the release of such data. The results of all these data-related investigations will be included in the documentation of EB-GIS4HEALTH UK models. Processing/methods The spatial and related methods involved in processing the model's input to produce the desired outputs will be described in detail in the model. A reusable toolbox of "generic" methods will be identified to make this process easier for all the models we develop, e.g., "pattern spotters and testers" and "relationship seekers and provers" [5]. All methods used in EB-GIS4HEALTH UK models will be based on documented evidence of acceptable strength, and will be used in ways that allow only valid analyses and visualisations of data. Outputs and their interpretation The model's information output(s), any units of measurements used in presenting outputs, and any specified output visualisation methods will be also described in detail. The area across which the model can be used (scale of application or aggregation level) will be determined. Finally, the ways in which the model's output may be interpreted (in relation to the topic(s)/sub-topic(s) it covers) and linked to other EB-GIS4HEALTH UK models/programmes will be described. This includes determining what inferences can be made from apparent trends or patterns in the model's output and how such inferences can be used within the NHS, and any constraints on the interpretation of this output, due for example to data limitations or complexities in the relationships implied by the model. Data and methodological problems and limitations are not uncommon and a wide range of them has been well documented in the literature and must be anticipated and cared for [8]. Techniques for recognising and reducing their negative impact on conclusions drawn from spatial analysis will be investigated and also incorporated into the models. For a programme model, the component application models will be listed and their relationships and interactions within the programme also documented (Figure 3). What's next EB-GIS4HEALTH UK knowledge-based models (ontologies) in Protégé will not only be human-readable and visualisable in a variety of ways (to inform NHS GIS service developers and implementers), but it will be also possible to use the models to generate the necessary template databases for running the modelled applications. Furthermore, the resulting ontologies could form the basis for the development of goal-oriented, user friendly and fault tolerant health GIS wizards and solutions in the future using software agents technology [28,29], since we are also representing workflows and methods in our ontologies and not just data. The goal for EB-GIS4HEALTH UK application and programme models is to provide practical, vendor-neutral modular workflow models and ready-to-use, evidence-based frameworks for implementing GIS to address various health and healthcare topics within the NHS. NHS organisations adopting such frameworks will be able achieve a common understanding of data and processes (shared semantics), which in turn will enable them to efficiently and effectively share, compare, and integrate their data silos and results at all levels. III. Process: disseminate the project's evidence base and resultant GIS models for diabetes and dental care to the wide NHS audience and the public; product: EB-GIS4HEALTH UK Web server We will launch EB-GIS4HEALTH UK public Web server (with it's own domain name) to disseminate our evidence base, GIS models and associated documentation/metadata, as well as other project documentation and recommendations. The server will also host the virtual e-focus groups. The server will run one of the many free content management and discussion board platforms available today to manage and make searchable all of the project's online components. IV. Process: formative evaluation of EB-GIS4HEALTH UK online service; product: evaluation report We will advertise EB-GIS4HEALTH UK online service among target groups (NHS and academia) and the general public, and conduct a small-scale formative evaluation of its use and user-perceived utility of the project using an online user questionnaire, in addition to analysis of EB-GIS4HEALTH UK server transaction logs. This formative evaluation study will be carried during the third year of the project (after the online publication of the project's completed evidence base and associated GIS models for diabetes and dental care) with the goal to inform and guide any further development of EB-GIS4HEALTH UK or similar projects. The study will evaluate the potential impacts of EB-GIS4HEALTH UK on NHS resource utilisation and improving health outcomes, among other things, by surveying participants opinions. Evaluation results will be also available from EB-GIS4HEALTH UK public Web server. Methodological and ethical issues Research into ontology-driven geographic information systems (ODGIS) is very rapidly growing due to the many potential and unique advantages that ODGIS promise [30-36]. We anticipate that the ontological representation of health-related GIS/spatial methods will be one of the most challenging, but also most rewarding parts of our project (the description of data and metadata within EB-GIS4HEALTH UK models will be much easier and more straightforward by comparison). A useful discussion of how GIS methods could be described is presented in [36]. This author also advises the use of Tomlinson's methodology to plan for the successful deployment of GIS within the NHS, but only at a later stage [8,37,38]. Tomlinson's methodology seems a bit "lacking" when it comes to the health sector and that's where EB-GIS4HEALTH UK can come to help by providing a sound foundation upon which the methodology can be successfully applied at a later stage. For example, the "functions" (to use Tomlinson's terminology) and software used for health GIS analyses are a superset of those used in other sectors (e.g., functions provided by tools like and ). We conduct many complex analyses in health and healthcare to answer much more complicated questions (compared to other sectors), and there are many problematic issues surrounding our analyses like, for example, data confidentiality and Jacquez's famous "gee whiz" effect [8]. But more importantly, NHS users are currently not fully aware of all useful spatio-temporal analysis possibilities available to them (the academia/research-real-world practice/NHS split mentioned earlier), and as such will need a foundation project like EB-GIS4HEALTH UK to help them identify these many possibilities and uses (or "information products" to again use Tomlinson's terminology) that go far beyond the mere production of simple shaded maps to further empower their decision making processes. EB-GIS4HEALTH UK does not raise any ethical issues (in some of the applications to be modelled, the requirement for personal identifiable information will be referred to and modelled, but no actual real data will be used in the models). Public engagement in science Besides being one of the main project beneficiaries, the UK citizenry (and indeed anyone connected to the World Wide Web) will have full access to EB-GIS4HEALTH UK public reports and fully documented products on the project's public Web server. Representatives of the public will be also involved in the project's e-focus groups (see above). Lay audience short summaries of the project's progress and expected benefits in a jargon-free language will be also published on EB-GIS4HEALTH UK public Web server (see Table 1 for an example). Another valid possibility for EB-GIS4HEALTH UK would be to offer a public lecture/day on "the importance of location in health and healthcare, the project's nature and its value". The lecture could perhaps follow the successful model of GIS days . Table 1 EB-GIS4HEALTH UK example English, jargon-free summary for the lay (educated) audience EB-GIS4HEALTH UK example English, jargon-free summary for the lay audience Factors affecting health vary with location and over time. Geographic Information Systems (GIS) can help us better understand the geography and interactions of health-related events, exposures, and public health/healthcare resources, and also use this understanding to develop optimised prevention and intervention strategies and programmes. A wide range of GIS applications in health and healthcare have been described in the literature that could benefit public health decision makers, practitioners and the public. These GIS applications range from assisting in the early detection of a bioterrorist attack, to understanding and acting on the complex relationships between the environment, socio-economic factors and health, to healthcare needs assessment and the optimum siting of an appropriate new healthcare facility in a given community, and even route optimisation for ambulance vehicles and healthcare professionals doing home visits. However, despite all their potentials, GIS remain very much under-utilised in the NHS in mostly non-strategic tasks, and in a largely fragmented and uncoordinated way. Geographic data and GIS are still not mentioned in any main UK health information strategy or policy document, in striking contrast to the corresponding US strategy documents and specifications, which explicitly mention GIS. EB-GIS4HEALTH UK aims at helping the NHS understand and harness the importance of spatial information in the health sector in order to better respond to national health plans, priorities, and requirements. Virtual e-focus groups that include representatives of the public will inform the development of all EB-GIS4HEALTH UK products. These include a sound evidence base of GIS methods and applications that are relevant to UK practices and settings, and an associated set of evidence-based conceptual models or blueprints for developing successful GIS business plans and implementing GIS to address various health issues within the NHS. The project will focus on diabetes and dental care, which together account for about 11% of the annual NHS budget, and are thus important topics where GIS can help optimising resource utilisation and outcomes. However, products and experience gained in this project will be transferable to address other national health topics based on the same principles. EB-GIS4HEALTH UK ultimate beneficiaries are the UK citizenry and communities who will be empowered to become more active partners in their healthcare, and will also benefit on the long run from improved health services and outcomes, and reduced health inequalities as a result of the introduction of well-founded geographic information management within the NHS through this and other synergistic/follow-on projects. Commercial exploitation Most major GIS vendors and solution providers have been targeting the UK health sector for many years now (e.g., and ), but this has never resulted in any coherent national strategic adoption of GIS within the NHS. This is due to the fact that a successful national strategic implementation of GIS in the health sector is dependent upon many other very important ingredients besides the acquisition of hardware and software systems, and core digital geo-datasets [8]. This author believes that these GIS vendors and solution providers will be definitely interested in the output of EB-GIS4HEALTH UK, which should provide them (and the NHS) with a convincing application-oriented framework for tailoring their services to suit the requirements of a wide-scale strategic adoption of GIS by the UK health sector. Concluding remarks Healthcare systems work differently around the world and this has knock-on effect on health GIS. When it comes to UK health GIS, we still need to go back to the design board, and to link/integrate what research has to offer into workflow models and "recipes" that are both usable and useful in everyday practice in this country (England). All national health GIS stakeholders must be involved in this process, including representatives of the general public (the ultimate beneficiaries). EB-GIS4HEALTH UK is just the start towards this goal. An incremental approach has been widely recommended in the literature for programmes with a national vision like EB-GIS4HEALTH UK. The experience gained at the end of this project will hopefully be transferable to further develop the covered topics (diabetes and dental care – in subsequent follow-on projects), and for addressing other national health and healthcare topics not initially covered in this project, based on the same principles. It must be emphasised that the evidence base and conceptual GIS workflow models we are proposing to build in this project are not per se our ultimate goals. The actual purpose of this design/modelling exercise and its ultimate goals are optimising NHS resource utilisation and improving health outcomes by paving the way to the incorporation of the missing spatial information dimension in NHS organisations. This paper presented an overview of a project we are proposing to carry out here in England, where the author is based. However, the same concepts, principles and approaches described in this paper can be also applied in other countries, especially where financial constraints are not a major issue. The task might not be as easy as replacing "UK" in this proposal with another country name, but the author believes that the paper has provided enough details and set an example to enable "quick starting" similar health GIS foundation projects in other countries. Our proposal also fits very well into the spirit of Mark Musen's philosophical paper on medical informatics as an academic discipline, in which he argued that "informatics involves the construction of ontologies that define the concepts relevant to different aspects of human experience and the elucidation of problem-solving methods that can solve specific computational tasks" [39]. Notes A detailed breakdown of human and other resources required to complete this project with a timeline of the main EB-GIS4HEALTH UK execution tasks distributed over a suggested three-year duration of the project are not provided in this manuscript, but are directly available from the author. A slightly abridged version of this proposal has been submitted to, and very favourably reviewed by the UK Medical Research Council (MRC) during 2004, and received a final ALPHA-C banding (i.e., "work which is nationally competitive and will make valuable contributions to addressing important scientific and/or policy questions" – ). As one of the reviewers rightly noted, "the key aspect to this proposal is that it deals with an area that cannot be neatly fitted into a single category. It crosses over into a number of specialisms – health informatics, geographic information science (GIS), health, geography, public health, policy and practice, epidemiology, etc. It is therefore important to bear in mind that if the proposal is judged by criteria generally applicable to only one or two of these specialist areas, it might be found deficient and it would be quite inappropriate to do this". ==== Refs Kamel Boulos MN Roudsari AV Carson ER Health Geomatics: An Enabling Suite of Technologies in Health and Healthcare (Methodolical Review) J Biomed Inform 2001 34 195 219 11723701 Higgs G Richards W The use of geographical information systems in examining variations in sociodemographic profiles of dental practice catchments: a case study of a Swansea practice Prim Dent Care 2002 9 63 9 12024904 US Federal Geographic Data Committee Homeland Security and Geographic Information Systems – How GIS and mapping technology can save lives and protect property in post-September 11th America Public Health GIS News and Information 2003 52 20 23 The International Federation of Surveyors (FIG) in cooperation with the United Nations The Nairobi Statement on Spatial Information for Sustainable Development (FIG Publication no 30 – Recommendations of the International Conference on Spatial Information for Sustainable Development, Nairobi, Kenya, 2–5 October 2001) 2002 Frederiksberg, Denmark: The International Federation of Surveyors (FIG) Openshaw S Trends and perspectives in spatial analysis and GIS Presented at the GIS Data Conference: 22 May 1997; Athens, Greece Richards TB Croner CM Rushton G Brown CK Fowler L Geographic information systems and public health: mapping the future Public Health Rep 1999 114 359 73 10501137 Higgs G Gould M Is there a role for GIS in the 'new NHS'? 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==== Front BMC BiotechnolBMC Biotechnology1472-6750BioMed Central London 1472-6750-5-21564932110.1186/1472-6750-5-2Methodology ArticleRapid, single-tube method for quantitative preparation and analysis of RNA and DNA in samples as small as one cell Hartshorn Cristina [email protected] Aleksandra [email protected] Lawrence J [email protected] Department of Biology, Brandeis University, Waltham MA 02454-9110, USA2 Current address: Division of Cardiology, Beth Israel Medical Center, Harvard Institutes of Medicine, Boston, MA 02115, USA2005 13 1 2005 5 2 2 28 9 2004 13 1 2005 Copyright © 2005 Hartshorn et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Current methods for accurate quantification of nucleic acids typically begin with a template preparation step in which DNA and/or RNA are freed of bound proteins and are then purified. Isolation of RNA is particularly challenging because this molecule is sensitive to elevated temperatures and is degraded by RNases, which therefore have to be immediately inactivated upon cell lysis. Many protocols for nucleic acids purification, reverse transcription of RNA and/or amplification of DNA require repeated transfers from tube to tube and other manipulations during which materials may be lost. Results This paper introduces a novel and highly reliable single-tube method for rapid cell lysis, followed by quantitative preparation and analysis of both RNA and/or DNA molecules in small samples. In contrast to previous approaches, this procedure allows all steps to be carried out by sequential dilution in a single tube, without chemical extraction or binding to a matrix. We demonstrate the utility of this method by quantification of four genes, Xist, Sry and the two heat-inducible hsp70i (hsp70.1 and hsp70.3), as well as their RNA transcripts in single mouse embryos and in isolated blastomeres. Conclusion This method virtually eliminates losses of nucleic acids and is sensitive and accurate down to single molecules. ==== Body Background Real-time polymerase chain reaction (PCR) in combination with reverse transcription (RT) provides a powerful tool for accurate quantification of DNA and RNA copy numbers and has opened the way to the study of subtle modulations of gene expression in small numbers of cells, as well as small-scale genetic analyses aimed at establishing chromosome numbers, the presence of mutations, or allele dropout. The reliability of these measurements, however, depends on the accuracy of each step, including preparation and recovery of RNA and/or DNA, reverse transcription of RNA into cDNA, and quantifiable and specific amplification of all desired sequences. The importance of optimizing each of these steps is well recognized [1], as is the need to minimize the number of tube-to-tube transfers in order to avoid the loss of templates and decrease the risk of contamination. This risk is posed by environmental RNases, material carried over from sample to sample, as well as previously generated amplicons present on laboratory equipment. Sequential performance of several steps in a single-tube is therefore highly desirable, especially when starting with small numbers of target molecules, such as chromosomes of individual cells or a few virus particles [2-5]. Our laboratory has already demonstrated that bound proteins prevent reliable PCR amplification of genomic DNA and that a thorough proteolytic digestion followed by heat inactivation solves this problem [6]. For accurate gene expression studies, RNA molecules also need to be released intact and free of proteins from all subcellular compartments, but proteases cannot be used both because they are not fast enough to inhibit the RNases (particularly endogenous RNases, released in the sample upon cell disruption) and because RNA is sensitive to the high temperatures required for protease inactivation [7]. Commercial kits for RNA purification therefore commonly employ either chaotropic agents or lysis buffers containing strong detergents, or a combination of the two, in order to achieve rapid denaturation of proteins. Nucleic acids are then extracted to remove these chemicals, because their presence interferes with subsequent enzymatic reactions. Alternatively, some RT-PCR kits bypass nucleic acid purification in favor of a simple dilution step, but in this case only a small aliquot of the lysed sample can be added to the RT mixture, due to volume restrictions. This approach introduces imprecision of its own and makes single cell analysis impossible. On the other hand, gentler lysis conditions that are compatible with single-tube analysis of a whole small sample do not remove proteins completely, resulting in substandard template preparations. For instance, protocols involving simple freeze-thaw cycles to produce cell lysis do not generate protein-free RNA or DNA. Similarly, mild detergents that do not lyse the nuclear membrane preclude quantification of DNA or RNA located in the nucleus, and are unlikely to completely remove proteins bound to cytoplasmic RNA. The chaotropic agent guanidine isothiocyanate (GITC) has long been the chemical of choice for nucleic acid preparation. It is particularly useful for RNA studies [8,9], because it rapidly denatures all cellular proteins, as well as serum proteins, including RNases, added to culture media. GITC has also proven superior to all other tested methods for the recovery of either DNA or RNA extracted from mummified tissue [10]. Due to its strong chemical action, GITC at high concentrations offers the further advantage of allowing safe storage of the samples until they are processed for quantification. For the same reason, however, all traditional protocols require removal of GITC prior to RT and PCR to avoid inactivation of the enzymes involved. Typically GITC is removed by extraction with phenol-chloroform and purification of the nucleic acids through alcohol precipitation cycles [9], or by absorption of the freed RNA to a matrix such as glass fiber filters, silica-gel membranes, magnetic beads or proprietary compositions, usually followed by elution in a relatively large volume. Both these approaches are time-consuming and involve a number of steps that can lead to incomplete RNA recovery. In view of these limitations we devised an alternative strategy in which the sample is collected and denatured in a minimal volume of a GITC solution, briefly heated to allow dry storage, and, when needed, is directly analyzed in the same tube by performing quantitative RT-PCR in a volume large enough to lower GITC concentration to negligible levels. This new procedure, hereafter referred to as PurAmp (patent pending) and described here in full for the first time, is the only available method that allows a whole sample, such as a single embryo or cell, to be processed from lysis to RT-PCR in the same tube, under conditions that permit precise quantification of both RNA and genomic DNA copy numbers. This fully optimized method is sensitive enough to detect specific sequences within a single chromosome in one cell, yet robust enough to measure the presence of thousands of RNA molecules released from hundreds of cells. PurAmp has made it possible for us to conveniently investigate expression levels of two gene types essential for early mouse embryo development: Xist, responsible for X-inactivation and dosage compensation in female cells [11-13], and the heat-shock inducible hsp70.1 and hsp70.3, jointly called hsp70i [14,15]. Xist RNA is a noncoding transcript that exerts its particular function of gene-silencer by coating the inactive X-chromosome. Unlike hsp70i RNA and most other mRNAs, it is therefore localized in the cell nucleus and particularly challenging to extract. Besides their biological relevance, both Xist and the hsp70i genes offer the advantage of naturally-occurring unambiguous controls for the specificity of transcripts amplified with RT-PCR. Xist RNA is, in fact, virtually absent from male cleavage stage embryos [16-19], while hsp70i RNA is predominantly synthesized in response to stress although minimal levels of hsp70i transcripts are normally present in embryonic cells. A careful quantitative analysis of hsp70i heat and culture stress-response in preimplantation embryos at different developmental stages, and its implications for development, will be presented elsewhere (C. Hartshorn, A. Anshelevich and L. J. Wangh, in preparation). In addition, we have been able to detect and quantify the genomic sequences of Xist, the hsp70i and the male sex-determining gene Sry [20]. Because the number of these sequences is known and very low in samples comprised by an identifiable number of cells, such as early embryos that have undergone few cleavages, their precise quantification provided an optimal internal control to demonstrate the strength of this novel technique. The quick and reliable detection of DNA (or RNA) in very low copy number is, however, not limited to the role of internal control, but holds much wider utility for a variety of applications such as genetic studies and detection of viral sequences in a sample. Further, this method can be successfully employed for the study of individual cells, as shown by the present report, and is easily adaptable to analysis of subcellular fractions or aliquots from bodily fluids; it also minimizes the use of toxic chemicals and the possibility of contamination, while allowing dry storage of the collected samples. All these features concur to render PurAmp ideally suitable for fast but highly sensitive gene and gene expression screening of multiple samples, including small whole specimens or fractions of larger ones. Results Single-tube Xist RNA, Xist DNA and Sry DNA quantification in individual male and female blastocysts The PurAmp method presented in this study is performed in a single tube from cell lysis to cDNA or genomic DNA amplification, thus eliminating possible loss of template molecules due to procedures such as phase separation and recovery, repeated washing and re-suspension of nucleic acid pellets, elution from binding matrices and vessel-to-vessel transfer. This strategy offers an immediate improvement in the precision of gene expression analyses, at the same time shortening considerably the experimental protocol compared to traditional methods. In order to validate our method, we initially measured the Xist RNA content of a group of female mouse embryos at the blastocysts stage and quantified Xist and Sry DNA copy numbers in their male counterparts. Our previous analysis of these parameters in single female and male mouse embryos at different developmental stages provided us with an ample pool of data obtained with commercially available nucleic acids preparation methods [17-19], which we used as a reference for comparison with our new results. Figure 1 shows the real-time PCR plots obtained from six PurAmp-treated single embryos. Following RT, the accumulation of multiplexed Xist/Sry amplicons was detected using two molecular beacons conjugated to different fluorescent dyes. Both amplicons span intronless sequences of the genes [18], thus allowing in either case the simultaneous measurement of cDNA (when present) and genomic DNA copies. Each color in the plots of Fig. 1 identifies a specific embryo and is used for both its Sry and Xist signal. Three embryos were identified as male based on the presence of the Sry amplicon (Fig. 1, upper panel, lines in blue hues) and on the fact that in each case the Xist fluorescent signal (Fig. 1, lower panel, lines in blue hues) arose at the same "threshold cycle" (CT) as the Sry signal (see Methods for a definition of CT and details on signal quantification). This indicates the presence of the same number of copies of Sry and Xist templates, as expected for male blastomeres that contain one copy of the Sry gene on the Y-chromosome and one copy of the Xist gene on the X-chromosome. Neither gene is expressed in male blastocysts [16,17], and therefore these three samples contained only genomic Xist and Sry DNA. The three remaining embryos did not generate any Sry signal (Fig. 1, upper panel, lines in red hues), while their Xist signals arose significantly earlier than the others (Fig. 1, lower panel, compare lines in red and blue hues). Based on these data they could be identified as female embryos which contained thousands of copies of Xist RNA in addition to two copies of genomic Xist DNA per cell. These results are fully in agreement with those of our earlier analyses using traditional methods of nucleic acid preparation. Quantification of the real-time PCR data obtained from the three male embryos in Fig. 1 indicated that, on average, each embryo contained 125 ± 83 (mean ± s.d.) copies of Xist genomic DNA, consistent with the previous estimate of 165 ± 101 genomes per male blastocyst [17]. Both values are higher than the expected cell number per embryo at this stage (60–100, depending on culture conditions), due to endoreduplication in trophoblast cells [21], a phenomenon also responsible for some variability between samples. In contrast, a total of five female blastocysts analyzed via the PurAmp method yielded an average of 12,600 ± 5079 copies of Xist cDNA + genomic DNA per embryo. Since each female blastocyst contains about 250 copies of the Xist genomic sequence (twice the number of a male embryo), the accumulation of Xist RNA per female blastocyst averages above 12,000 copies, considerably higher than our previous measurement of 6797 ± 2894 copies obtained using a multistep nucleic acids isolation procedure [17]. The data in Figure 2 demonstrate that the amplification efficiency of both the Xist and Sry sequences is neither decreased nor increased by the presence of diluted denaturing solution (0.4 mM GITC, as in the PurAmp protocol) during real-time PCR. In fact, additional experiments revealed that Xist/Sry real-time PCR was unaffected by a GITC concentration as high as 20 mM (not shown). Taken together these results suggest that the higher levels of Xist cDNA measured using the PurAmp method are due to an improvement in RNA recovery at the initial step of cell lysis. Quantification of low-to-high Xist RNA and DNA copy numbers in single embryos and blastomeres As shown above, blastocysts are comprised by many cells and contain hundreds of copies of the Xist and Sry genes and thousands of copies of Xist transcripts, with rather wide sample-to-sample fluctuations. In order to more carefully determine the quantitative capability of the new assay, we next analyzed embryos at earlier developmental stages containing lower and, in some cases, precisely known numbers of template copies. Figure 3 illustrates the real-time PCR plots of the Xist amplicons generated in the course of two separate experiments by, right-to-left, i) a 3-cell male embryo (yellow); ii) a 4-cell male embryo (green); iii) a single blastomere isolated from a 4-cell female embryo (light purple); iiii) a 4-cell female embryo (red); iiiii) a female blastocyst (blue). The gender of each embryo was confirmed by the detection of an Sry-specific fluorescent signal in male samples (Fig. 3, inset). The quantitative analysis of these results confirmed that the 3-cell male embryo contained 3 copies of the Xist gene, while the 4-cell male embryo contained 6 copies of the Xist gene, indicating that DNA duplication had occurred in two of the blastomeres. It has long been known that two of the blastomeres of a 4-cell embryo divide ahead of the other two [22], an observation in agreement with our finding. The numbers of Xist templates measured in these male embryos also confirmed the expectation that these samples did not contain Xist RNA because Xist is not expressed in male cells [13,17]. Conversely, the Xist signal of the female 4-cell embryo arose about five cycles earlier than the Xist signal of the 4-cell male embryo (compare red and green curves), denoting the presence of Xist transcripts (157 copies of Xist RNA assuming diploidy of all cells and calculated from a total of 165 cDNA + genomic DNA templates), albeit at considerably lower levels than those measured in the female blastocyst (9750 copies of Xist RNA assuming the aforementioned average number of genomes per blastocyst of 125, and based on 10,000 copies of total Xist cDNA + genomic DNA templates). These measurements are consistent with other studies demonstrating that Xist transcripts are accumulated in the developing embryo beginning at the late 2-cell stage [23] and with our previously published Xist developmental profile [17]. The quantitative accuracy of the PurAmp method was further confirmed by the fact that the Xist signal generated by a single blastomere isolated from a 4-cell female embryo arose 2.2 cycles later than the Xist signal of the whole 4-cell female embryo (compare light purple and red curves, Fig. 3). A left-to-right shift of 2 cycles is exactly what is expected for a fourfold decrease in template numbers quantified by real-time PCR amplification. Figure 4 illustrates this point by showing Xist RNA + genomic DNA levels in two individual blastomeres isolated from a 4-cell female embryo, as compared to Xist template levels measured in intact 4-cell embryos of different sex and then calculated on a per cell basis. Even at this early developmental stage, the presence of Xist RNA is clearly detectable in the female samples, absent from the male, and not affected by the blastomere isolation procedure [18]. Hsp70i RNA and DNA measurements in heat shocked and non-heat shocked single embryos and blastomeres In order to more extensively test the validity of the PurAmp approach to template quantification in single cells, we measured transcript levels of the heat shock-inducible genes hsp70.1 and hsp70.3 in blastomeres isolated from embryos at the pre-compaction 8-cell stage, when cells can be easily counted and separated. The sequences of these two genes are almost entirely identical, they are located on the same chromosome and they encode the same protein [14,15]. For this reason, there has been some confusion in their identification and nomenclature in past studies. Heat-inducible hsp70 transcription, previously indicated as hsp70.1 expression, is now more precisely designated as the sum of hsp70.1 and hsp70.3 (hsp70i) RNAs. A preliminary set of experiments was carried out on embryos at the blastocyst stage, when heat shock response is fully established [14], with the goal of evaluating the effect of hyperthermia on hsp70i expression. Like Sry, the hsp70i are naturally intronless genes and therefore once again our pair of PCR primers simultaneously amplified both genomic DNA and cDNA sequences. The data in Table 1 clearly indicated that heat shock (see Methods) produced a sharp rise in hsp70i template numbers due to the presence of thousands of copies of hsp70i RNA, although these numbers were considerably lower when samples were prepared with a multistep/multitube phenol-chloroform extraction [17,18] rather than with the PurAmp method. During these initial experiments, embryos were allowed to recover for 30–40 minutes after heat shock. Under these conditions, however, only five out of seven blastocysts exposed to hyperthermia showed an increase in hsp70i RNA levels. Based on these results, the duration of the recovery period was increased to at least two hours in all following experiments, eliminating the finding of "non responsive" embryos. Copy numbers of hsp70i RNA were then quantified in whole 8-cell embryos that had or had not been exposed to a temperature increase, as summarized in Table 2. The number of hsp70i genomic DNA copies measured in the absence of RT was consistent with the presence of four copies of the genes per cell, one hsp70.1 and one hsp70.3 on each chromosome 17, and with the fact that some of the cells analyzed had already duplicated their DNA. Only a minimal amount of hsp70i RNA, calculated as the difference of template copy numbers obtained with and without reverse transcription (hsp70i cDNA + genomic DNA less hsp70i genomic DNA), was present in non-heated embryos, indicating that the embryos were not stressed by culture conditions [24]. Expression of hsp70i RNA increased sharply after a 30-minute heat treatment, followed by a recovery period of either 2 or 3 hours necessary for transcripts synthesis and accumulation. Some of the heat-shocked embryos were harvested intact, while others were dissociated in single blastomeres. The average numbers of hsp70i template copies per blastomere were calculated from the isolated cells (not all cells of dissected 8-cell embryos could be recovered) and compared to the average per blastomere values obtained from whole embryos. The results show that the amount of hsp70i RNA + genomic DNA per cell calculated by these two approaches is very similar. A post-heating recovery period of 3 hours rather than 2 hours increased the hsp70i RNA levels only slightly, indicating that the onset of transcription and the major build-up in RNA occur quickly. Single cells derived from non-heated embryos contained only trace amounts of hsp70i RNA, consistent with whole embryo measurements, and PCR efficiency was, again, unaffected by the PurAmp components (not shown). Efficiency of DNase treatment within the PurAmp protocol The PurAmp method described above automatically results in the quantitative recovery of genomic DNA, which can then be used as a quality control and a convenient internal standard for the simultaneous recovery and measurement of mRNA [[17-19], see Discussion]. Applications such as microarray analysis, however, are based on RNA-only amplification. For this reason, we introduced a DNase digestion step preceding RT in the Xist/Sry PurAmp protocol and analyzed the efficacy with which the genomic DNA was degraded. Genome numbers in embryos at the morula stage were calculated by counting Xist and Sry copies in five male samples (as detailed for blastocysts, see above) and averaged at 21.3 ± 8.9, consistent with the fact that embryos at this stage are normally comprised of 16-to-32 cells. After treatment with DNase I, only 0.8 ± 1.5 genomes per embryo were still present in a group of 15 male samples, demonstrating that the enzyme had successfully degraded 96.3% of the DNA. Figure 5 illustrates the effects of DNase digestion on Xist (upper panel) and Sry (lower panel) DNA in a group of eight male and eight female single embryos. As expected, control male samples (blue lines) contained equal numbers of Xist and Sry copies, as shown by the equal CT values, corresponding to the genomic DNA copy number. Both amplicons were absent from DNase-treated embryos that could be identified as male because they were devoid of Xist RNA (yellow lines). In contrast, control female samples (red lines) contained Xist RNA and DNA but lacked Sry. DNase treatment caused a delay in the Xist signals arising from female embryos (green lines), consistent with the elimination of all 42 copies of genomic Xist DNA in each embryo plus some decrease in the number of Xist transcripts. The amount of RNA recovered in these samples averaged at 75% of control levels. The fact that some RNA is lost during the DNase step is not surprising as it is well known that some RNA hydrolysis is unavoidable (see Discussion), and is not linked to the single-tube procedure. We anticipate that further optimization of the available DNase protocols and reagents will minimize this problem. Thus, the data in Fig. 5 demonstrate overall that a DNase digestion step can be successfully inserted within the PurAmp procedure, without disruption of the DNase enzymatic activity. Discussion It is increasingly clear that individual cells in a population do not exhibit identical patterns of gene expression and, hence, that expression profiling is more informative if it is quantitative and carried out at the single cell level [25-27]. This consideration is particularly relevant to current efforts aimed at understanding early mammalian embryogenesis in which totipotent cells generated during the first few cell divisions gradually become committed to particular lines of development. The mechanisms of this process are under intensive scrutiny and appear to be rooted in differential gene expression resulting from epigenetic modifications. It is in this context that we have been measuring RNA levels in single cells of cleaving embryos [18,19] and it is to increase the reliability of these measurements that we have now developed the PurAmp method. This completely single-tube approach is easy to use, eliminates loss of material, and improves the quantitative accuracy of gene and gene expression studies. First, cell lysis and protein denaturation occur very rapidly upon delivery of the sample to crystalline GITC, thus ensuring both protein removal from DNA and RNA and inactivation of cellular nuclease that would otherwise quickly degrade RNA [8]. Transcripts localized in the nucleus, such as Xist RNA, are freed and made available to reverse transcription as well as cytoplasmic mRNA molecules, a result unattainable by mild detergent treatment that leaves nuclei intact [25]. Second, the brief heating period after cell lysis enhances complete denaturation of proteins and also reduces the volume of the sample, thereby further increasing the guanidine concentration. The semi-dry sample can then be safely stored without risk of nuclease activity. Third, carrying out cell lysis in nanoliter volumes allows a manifold dilution of the chaotropic agent after addition of the RT cocktail, so that RT can be performed on the whole sample and in the same vessel in which it was collected without inhibition of the enzymatic activity. Finally, RT and PCR can be carried out immediately after cell lysis, rather than after cumbersome and lengthy nucleic acid preparation procedures, thereby further reducing the time required to process many samples, as well as the risk of contamination. Our quantitative measurements of Xist RNA levels in developing mouse embryos highlight one of several merits of the PurAmp method over the traditional, multistep approach to nucleic acids purification [17,18]. In fact, while genome numbers obtained with the two methods are similar as expected, Xist RNA levels are higher with the single-tube protocol. The same culture conditions and procedures were used in the two groups of experiments, making it unlikely that differences in embryo quality were the cause of the increase in Xist RNA. We, therefore, conclude that the higher levels of Xist RNA observed using the new procedure reflect improved template preparation with efficient inactivation of RNases and reduced loss of RNA molecules. Xist RNA is known to trigger X-chromosome silencing through interactions with numerous proteins and possibly with the nuclear matrix scaffold [28,29]. The results presented in this study clearly show that the very high initial concentration of GITC thoroughly breaks up protein-RNA interactions, but the denaturant does not inhibit subsequent RT once is diluted. Similarly, our quantification of hsp70i templates in heat-shocked blastocysts supports the view that larger pools of RNA are detected in PurAmp-treated samples than when using phase separation-based nucleic acid extraction. The later method, in fact, presents several steps that require extreme care to avoid loss of material, including complete recovery of the upper phase, thorough precipitation of all nucleic acids molecules, and repeated re-suspension and washing of barely visible pellets. All these manipulations render the results obtained with this technique particularly operator-dependent, while, in contrast, PurAmp simply requires sequential addition of reagents into the same tube. Once the sample is delivered to the LysoDot in the reaction vessel (see Figure 6 and Methods section), therefore, this technique is much less dependent on the operator's specific skill. Individual blastomeres of pre-compaction mouse embryos are easily harvested due to their size, and laser zona drilling efficiently preserves RNA pools allowing dependable single-cell analysis [18]. We thus used measurements of Xist and hsp70i RNA levels in single blastomeres to further validate the quantitative accuracy and reliability of the PurAmp method, as shown by the fact that transcript levels in individual cells are comparable to average RNA levels per cell calculated from whole embryos. Based on these results we anticipate that PurAmp will prove useful for quantification of RNA levels in small pieces of tissue from many sources, as well as single cells and even fractions of cells such as neuronal dendrites and axons [30]. The small volume in which denaturation is carried out is also amenable to analysis of biological material isolated by laser capture microdissection or laser pressure catapulting [31]. Genomic DNA has recently been proposed as the optimal standard for gene expression studies [32] and it is the required internal standard when cDNA is quantified with the strategy of amplification competition [33]. In this case, DNA and RNA are purified together, as in our experiments, and one set of primers is designed to co-amplify a genomic sequence that spans an intron as well as the corresponding intronless cDNA. The alternative strategy that we developed makes use of primer sets that do not span introns and therefore amplify genomic DNA sequences that have the same length and composition as their corresponding cDNA's, eliminating any possible difference in PCR efficiency for the two types of templates [[17-19]; C. Hartshorn, A. Anshelevich and L. J. Wangh, in preparation]. We have found it very informative to measure genomic DNA copy numbers in addition to RNA levels of the genes under study, because this strategy provides a reliable internal control for primer specificity and for nucleic acids recovery, particularly when performing single-cell analyses. In the case of early mouse embryos, detection of the Sry gene, which is not expressed at those stages, has also allowed us to identify the sex of each embryo. The recovery and quantification of genomic DNA together with RNA has previously enabled us to establish genome number averages for developing embryos [17,19]. While these numbers are very similar to the number of cells in early embryos, measurements of DNA copies are more accurate because they indicate whether the cells have completed S phase. Genome quantification becomes even more critical after the late 8-cell stage, because the embryos compact making it very difficult to count individual cells. Moreover, endoreduplication takes place in trophoblasts at the blastocyst stage, greatly increasing the number of genome copies present in those cells [21]. All these factors render the counting of DNA copy numbers important if gene expression data are to be calculated on a per-genome basis, independently from a cell's ploidy. A further reason to preserve DNA molecules in preparations for RT-PCR is that all DNase digestion protocols currently available lead to partial hydrolysis of RNA when the enzyme is heat-inactivated in the presence of divalent cations at the end of the reaction [34]. We have consistently found a decrease in amplified cDNA in DNase-treated samples, particularly when performing RNA isolation with traditional methods (unpublished results), even when a chelating agent was added prior to the heating step. Incomplete RNA recovery after DNase inactivation in the presence of EDTA was not evident in past reports, due to the use of non-quantitative methods of nucleic acids analysis [35]. Our real-time PCR results, however, agree with numerous more recent findings [see ref. [36] for an overview of DNase-related problems]. Efforts have been made, therefore, to devise alternative ways to eliminate the DNase once digestion has occurred. These methods, however, depend on removal of the enzyme which, in turn, implies manipulations such as phenol extraction that may still generate nucleic acids loss. For all of the above reasons as well as the fact that we are working with very small amounts of material, we prefer single-tube preparation-to-amplification of both RNA and DNA templates, an approach made possible for the first time by the procedure described in this paper. Previously reported single-tube template preparation protocols, in fact, have been aimed at measuring only specific RNAs and employ lysis buffers containing low concentrations of the mild detergent NP-40 [25,37], or they bypass the lysis step altogether and are limited to neuron studies [38]. While these methods are valuable for detection of protein-free RNA molecules, they utilize non-denaturing conditions, as clearly demonstrated by the addition of proteic RNase inhibitors to the extraction buffers, and therefore preclude quantitative analysis of DNA [6] as well as of protein-bound RNA pools. Conclusions Due to its ability to thoroughly remove proteins from both RNA and DNA molecules in a rapid and simple way, PurAmp is suitable to a wide variety of applications, including gene expression quantification, studies on genetic mutations, and viral detection. Because the presence of DNA is undesirable for certain applications, such as microarray-based expression profiling, we have also shown that a DNase digestion step can be easily included in the single-tube format. We anticipate that treatment with other enzymes, such as cellulase in the case of plant cells, can similarly be inserted into the PurAmp protocol to digest other "undesired" components of particular cells prior to amplification. Thus, PurAmp is a very flexible technique that affords the investigator a variety of ways of processing the contents of a lysed sample with a heightened level of precision (Fig. 6). Methods Embryo culture and single blastomere isolation For most experiments, frozen late 2-cell stage embryos (B6C3F1 females bred with B6D2F1 males) were obtained from Embryotech Laboratories, Inc. (Wilmington, MA), and were cultured as previously described [17] until the desired stage of development. For the DNase experiment, frozen 8-cell embryos obtained from the same source were grown to the morula stage. Blastocyst stage embryos used for hsp70i measurements were also grown from frozen 8-cell embryos. Single blastomeres were isolated from either 4-cell embryos (for Xist measurements) or pre-compaction 8-cell embryos (for hsp70i measurements) after drilling the zona using a ZILOS-tk™ zona infrared laser optical system (beam = 1480 nm) (Hamilton Thorne Biosciences, Inc., Beverly, MA), according to a procedure developed in our laboratory and described elsewhere [18,19]. PurAmp multiplex measurements of Xist/Sry RNA + DNA in individual embryos or blastomeres All experimental procedures were carried out using rigorous precautions aimed at avoiding or destroying environmental RNases contamination [17-19]. Dried droplets of denaturing solution, hereafter called "LysoDots", were prepared prior to embryo collection by delivering 20-nl aliquots of the denaturing solution (see below) to the inside surface of the lids of PCR-grade reaction tubes (Applied Biosystems, Foster City, CA). Precise measurement of the droplets size was obtained following the method previously described by Wangh [39]. The denaturing solution composition was: 0.25% sarcosyl, 2 M GITC, 100 mM β-mercapto-ethanol, 0.01 M sodium citrate, pH 7.0 (all reagents from Stratagene, La Jolla, CA), 1% (vol/vol) dimethylsulfoxide (Sigma Chemical Company, St. Louis, MO). LysoDots were prepared in advance, allowed to dry under sterile conditions, and then stored at room temperature in closed PCR tubes. Immediately before harvesting, individual embryos were placed in 3 ml of Dulbecco's PBS devoid of calcium and magnesium chloride [17]. Dulbecco's PBS containing 0.4% polyvinyl pyrrolidone (both products from Sigma) was used when isolating single blastomeres [18]. After one wash in the same buffer, each embryo or cell was aspirated into a glass capillary having an internal diameter of 0.2 mm [39] and tapered at the end so that the inner volume of the tapered tip would contain about 20 nl. Tapering was obtained by pulling the glass capillaries in a Micro-Pipette Puller (Industrial Science Associates, Inc., Ridgewood, NY). The embryo (or cell) was expelled directly onto the LysoDot in a volume of PBS as close as possible to 20 nl. Microscope observation revealed that the GITC crystals dissolved instantly upon addition of the sample-containing PBS and, thus, that cell lysis occurred immediately. Tubes were closed upside down and heated at 75–77°C for 5 minutes, after which their content was once again dry or semi-dry. The samples were then stored at -20°C until the next step. In order to perform reverse transcription, each sample was carefully re-solubilized in the lid by addition of 6 μl of Random Hexamers mixture (4.2 ng/μl) in DEPC-treated water (all RT reagents were from a ThermoScript™ RT-PCR System kit, Invitrogen, Life Technologies, Carlsbad, CA). Tubes were closed, inverted, briefly centrifuged and incubated for 5 minutes at 65°C in order to allow primer/RNA hybridization. The remaining reagents needed for RT were then added to the tube in a volume of 4 μl, and the reaction was carried out according to the protocol suggested by the manufacturer. As previously described [17-19], all RT reagents were used at the suggested concentrations except for the absence of DTT, but volumes were halved so that each assay was performed in just 10 μl, which increased to 10.5 μl after RNase H digestion. The full volume of each sample was then mixed with 89.5 μl of complete PCR amplification cocktail containing sequence-specific molecular beacons as detection probes [40]. Multiplex real-time PCR of Xist and Sry genomic DNA + cDNA templates was thus performed in a final volume of 100 μl, as detailed elsewhere, in the presence of 4 units of Taq DNA polymerase (Promega, Madison, WI) [18,19]. Real-time PCR was carried out in an ABI Prism® 7700 Sequence Detector (Applied Biosystems, Foster City, CA) and fluorescence readings were taken at the annealing temperature. PurAmp assay for hsp70i RNA and DNA measurements in individual embryos or blastomeres Embryos were heat-shocked at 43°C for 30 minutes, followed by a recovery period of 30–40 minutes (blastocysts), or 2–3 hours (8-cell embryos, as indicated) at 37°C. The hsp70i assay was carried out similarly to the one for the Xist/Sry multiplex, by sequential dilutions of denaturant, RT and PCR reagents. The procedure for collection and lysis of the samples was the same. Dry samples were re-solubilized with 6 μl of random decamer primers (8.3 μM) in nuclease-free water (all RT reagents were from a Cells-to-cDNA™ II kit, Ambion, Inc., Austin, TX). After a 3 minute incubation at 75°C to optimize primer binding to RNA, all other reagents needed for RT were added to the sample and the reaction was carried out according to the manufacturer's instructions. As for the Xist/Sry assay, all RT mixture components were used at the suggested concentrations, but volumes were halved so that RT was performed in a final volume of 10 μl. An RNase H digestion step was included at the end of RT, as in the case of the Xist/Sry assay. Real-time PCR was carried out in a final volume of 100 μl, by adding the PCR reagents to the sample after completion of RNase H digestion. The chosen hsp70i primers were localized at positions 1245/1305 of the hsp70.1 GenBank sequence with accession number M35021 (5' CCGCCTACTTCAACGAC 3', upstream primer; 5' ATCCGCAGCACGTTTA 3', downstream primer) and were identical to sequences within the hsp70.3 gene, previously known as hsp70A1 (GenBank sequence with accession number M76613) [14]. Because the hsp70i was the only amplicon generated in this assay, it was not necessary to design a sequence-specific detection probe. In this case, SYBR® Green, a fluorescent dye that binds to double-stranded DNA, was used as fluorescent probe for real-time PCR. The specificity and purity of the amplicon was confirmed by both gel electrophoresis and analysis of the melt profile, as previously described [17]. The composition of the cocktail for hsp70i PCR was the following: 50 mM Tris, pH 8.3, 3 mM MgCl2, 0.3 μM each primer, 0.25 mM each dNTP, 1:62,500 SYBR Green (from a "10,000X concentrate in DMSO" purchased from FMC BioProducts, Rockland, ME), and 4 units of Taq DNA polymerase (Promega, Madison, WI). The polymerase was incubated at a 1:1 (v/v) ratio with Platinum® Taq antibody (Invitrogen) for 5 minutes before addition to the reaction mixture (hotstart PCR). The cycling profile was: 95°C for 5 minutes; 10 cycles consisting of the following four steps: 95°C (20 sec), 64°C (30 sec), 72°C (30 sec), 84°C (15 sec); 35 cycles with the following four steps: 95°C (20 sec), 59°C (30 sec), 72°C (30 sec), 84°C (15 sec). Fluorescence readings were acquired at 84°C, in order to exclude fluorescent signals due to the possible formation of primer dimers late in the reaction. A number of embryos were also processed as "No RT" controls, with the same protocol used for the other samples but without inclusion of reverse transcriptase in the RT mixture. These controls were used to quantify hsp70i genomic DNA copy numbers in the absence of cDNA [17]. Multistep/multitube nucleic acid extraction For the preliminary studies on hsp70i expression in blastocysts, some of the embryos were processed using a commercially available multistep/multitube kit, as previously described [18]. Briefly, nucleic acids (DNA and RNA) from each sample were purified using phenol:chloroform:isoamyl alcohol phase separation (Micro RNA Isolation Kit, Stratagene, La Jolla, CA) with a ratio of 100 μl of phenol and 45 μl of chloroform/isoamyl alcohol solution per assay. Transfer RNA (10 μg/assay; Sigma Chemical Company, St. Louis, MO) was added as a co-precipitant. Pellets were washed twice, once in isopropanol followed by overnight precipitation at -20°C and once in 75% ethanol, and then nucleic acids were reverse transcribed and analyzed by real-time PCR exactly as detailed above for the PurAmp-treated samples. Quantification of Xist, Sry and hsp70i amplicons Calculation of template copy numbers was based on the "threshold cycle" (CT) at which each fluorescent signal was first detected above background. CT values were compared to standard scales obtained from analysis of male mouse genomes at known copy numbers, as detailed previously [17-19] and further exemplified in the Result section. Briefly, a two-fold difference in the number of templates amplified results in a shift of one cycle between two CT determinations. A lower CT value indicates an earlier detection of the fluorescent signal and therefore more templates present at the start of the reaction [reviewed in ref. [41]]. One male mouse genome contains one copy of Sry (Y-chromosome), one copy of Xist (X-chromosome) and four copies of hsp70i (two of hsp70.1 and two of hsp70.3, both on chromosome 17). Insertion of a DNase I digestion step within the Xist/Sry PurAmp protocol Embryos were grown to the morula stage and were individually harvested and lysed as detailed above. Controls were processed for multiplex detection of Xist/Sry with the described PurAmp protocol. RNA-only samples were prepared by inserting a DNase digestion step prior to RT, as follows. Lysed, dried samples were re-suspended with 4 μl of DNase mixture containing: 20 mM Tris-HCl, pH 8.4; 2 mM MgCl2, 50 mM KCl (Invitrogen's DNase I Reaction Buffer) and 1 unit of DNase I (Ambion) in nuclease-free water. After an incubation of 20 minutes at room temperature, the reaction was terminated by adding 1 μl of a 10 mM EDTA solution, pH 8.0 (Invitrogen). The nuclease was inactivated by heating the samples at 65°C for 10 minutes, according to the protocol recommended by Invitrogen. One μl of a 25 ng/μl RT primer solution was then added to the sample, so that the final primer concentration was now the same as in the assay without DNase (see above). RT and PCR were then carried out as detailed for the "No DNase" assay. Authors' contributions CH devised the finalized form of the PurAmp method, carried out the Xist/Sry measurements and drafted the manuscript. AA established heat shock conditions and performed the hsp70i quantification experiments. LJW coordinated the study and contributed to all aspects of its design. Acknowledgements This work was funded by Brandeis University. Figures and Tables Figure 1 Multiplex Xist/Sry template detection in individual blastocysts processed with the single-tube PurAmp method. Sry (upper panel) and Xist (lower panel) real-time PCR plots generated by six single embryos at the blastocyst stage processed via PurAmp. All steps, starting with cell lysis, were performed by progressive dilution in the same optical-grade tube. Xist and Sry amplicons (genomic DNA plus cDNA when RNA was present) were detected simultaneously by using sequence-specific molecular beacons. Each color identifies a single embryo and is used in both panels. Female embryos (lines in red hues) were easily distinguished from male embryos (lines in blue hues) based on the presence of Xist RNA, which causes the female Xist signals to be much earlier than the male Xist signals, and on the absence of the Y-chromosome-specific Sry gene. Quantification of Xist and Sry copy numbers (given in the text) was obtained based on the CT of the real-time PCR plots (see Methods), using a genomic DNA standard curve as detailed in Fig. 2. The horizontal line in each chart indicates the threshold used to determine the CT values. The dashed vertical lines facilitate the comparison of Sry and Xist CT values in male embryos. Figure 2 Xist/Sry real-time PCR efficiency in the presence or absence of diluted denaturant. Standard curve used for the quantification of multiplexed Xist and Sry amplicons, obtained by serial dilution of male mouse genomic DNA at known copy number (abscissa). The Xist (light purple) and Sry (green) signals had very similar CT values (ordinate) at every DNA concentration tested, because the two PCR reactions had been optimized to be equally efficient. The addition of denaturing solution at the concentration used in the PurAmp assays did not inhibit PCR (red, Xist; blue, Sry), as also seen in the case of the hsp70i amplicon. Figure 3 Accuracy and efficiency of the PurAmp protocol for wide-range copy number quantification. Xist real-time PCR plots generated from individual embryos or cells processed with the PurAmp method. In the samples analyzed, the number of Xist genomic DNA + cDNA (RNA) templates increased right-to-left from 3 (yellow) to 10,000 (blue), as detailed in the Results section. Yellow, 3-cell male embryo; green, 4-cell male embryo; light purple, single blastomere from a 4-cell female embryo; red, 4-cell female embryo; blue, female blastocyst. Male samples also produced an Sry signal (inset), confirming sex assignment based on the presence or absence of Xist expression. Figure 4 Quantification of Xist RNA levels in isolated blastomeres and whole embryos at the 4-cell stage. Comparison of Xist genomic DNA + cDNA (RNA) levels measured in single blastomeres isolated from a 4-cell female embryo and in whole 4-cell embryos of either sex. Whole embryo data are presented on a per cell basis. Light purple bars, two individual blastomeres harvested from the same female embryo. Red bar, whole female embryo (per blastomere average). Two Xist template copies in each female cell are accounted for by the presence of Xist genomic DNA, while the remaining copies signal the presence of Xist RNA. Green bar, whole male embryo (per blastomere average): as expected, the number of Xist copies in this sample corresponds to the presence of one copy of the Xist gene per cell and the absence of Xist transcripts. Sexing was confirmed in all samples by the presence or absence of the Sry gene. Figure 5 Introduction of a DNase step within the single-tube PurAmp protocol for Xist/Sry quantification. Effect of DNase digestion on Xist (upper panel) and Sry (lower panel) template copy numbers measured in male and female single embryos at the morula stage. Male embryos: blue, non-treated; yellow, DNase-treated. Female embryos: red, non-treated; green, DNase-treated. The DNase enzyme displayed full activity under the conditions tested, as shown by the absence of genomic DNA in treated male samples and detailed in the text. Figure 6 Easiness and versatility of the PurAmp method. In a streamlined version of the procedure, LysoDots (20 nl or less, see Methods for a definition) are dispensed to the lids of PCR tubes resting on a heating block. The LysoDots are quickly dried before adding the samples in a minimal volume of PBS. A second brief heating reduces the lysed samples to the semi-dry state, ready for storage or immediate processing, which may include a variable number and type of enzymatic reactions prior to the final PCR step. This will be a real-time PCR assay when quantification of the templates is desired. Table 1 Comparison of hsp70i copy numbers measured in single blastocysts: multistep/multitube versus PurAmp method hsp70i DNA + RNA copies Multistep/multitube extraction PurAmp No Heat shock, + RT 512 ± 69 (3)a 967 ± 548 (4) Heat shock, + RT 2240 ± 1184 (3) 18,800 (2) a The number of single embryos averaged in each case is shown in parentheses. Table 2 Correlation between hsp70i copy numbers in single blastomeres and whole 8-cell embryos, ± heat shock hsp70i DNA copies No-heat shock, No RT Whole embryos (4)a Per blastomere average ± s.d. 5 ± 2 hsp70i DNA + RNA copies No-heat shock, + RT Whole embryos (4) Per blastomere average ± s.d. 19 ± 7 Heat shock, + RT Embryo 1b Embryo 2c Blastomere 1 459 750 Blastomere 2 311 705 Blastomere 3 438 281 Blastomere 4 667 730 Blastomere 5 563 491 Blastomere 6 1251 Per blastomere average ± s.d. 488 ± 134 701 ± 325 Whole embryosb (6) Per blastomere average ± s.d 500 ± 172 Whole embryosc (2) Per blastomere average 650 a The number of single embryos analyzed in each case is shown in parentheses. b Heat shock followed by 2-hour recovery prior to analysis. c Heat shock followed by 3-hour recovery prior to analysis. ==== Refs Bustin SA Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems J Mol Endocrinol 2002 29 23 39 12200227 10.1677/jme.0.0290023 Fiorenza MT Mangia F Quantitative RT-PCR amplification of RNA in single mouse oocytes and preimplantation embryos Biotechniques 1998 24 618 623 9564535 Olmos A Cambra M Esteban O Gorris MT Terrada E New device and method for capture, reverse transcription and nested PCR in a single closed-tube Nucleic Acids Res 1999 27 1564 1565 10037824 10.1093/nar/27.6.1564 Yan L Kaczorowski G Kohler M One-tube protocol for single-cell reverse transcriptase-polymerase chain reaction Anal Biochem 2002 304 267 270 12009706 10.1006/abio.2002.5623 Callahan JD Brown F Osorio FA Sur JH Kramer E Long GW Lubroth J Ellis SJ Shoulars KS Gaffney KL Rock DL Nelson WM Use of a portable real-time reverse transcriptase-polymerase chain reaction assay for rapid detection of foot-and-mouth disease virus J Am Vet Med Assoc 2002 220 1636 1642 12051502 Pierce KE Rice JE Sanchez JA Wangh LJ QuantiLyse™: reliable DNA amplification from single cells Biotechniques 2002 32 1106 1111 12019784 Eigner J Boedtker H Michaels G The thermal degradation of nucleic acids Biochim Biophys Acta 1961 51 165 168 13726119 10.1016/0006-3002(61)91028-9 Chirgwin JM Przybyla AE MacDonald RJ Rutter WJ Isolation of biologically active ribonucleic acid from sources enriched in ribonuclease Biochemistry 1979 18 5294 5299 518835 Chomczynski P Sacchi N Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction Anal Biochem 1987 162 156 159 2440339 10.1016/0003-2697(87)90021-2 Konomi N Lebwohl E Zhang D Comparison of DNA and RNA extraction methods for mummified tissues Mol Cell Probes 2002 16 445 451 12490146 10.1006/mcpr.2002.0441 Borsani G Tonlorenzi R Simmler MC Dandolo L Arnaud D Capra V Grompe M Pizzuti A Muzny D Lawrence C Willard HF Avner P Ballabio A Characterization of a murine gene expressed from the inactive X chromosome Nature 1991 351 325 329 2034278 10.1038/351325a0 Brockdorff N Ashworth A Kay GF Cooper P Smith S McCabe VM Norris DP Penny GD Patel D Rastan S Conservation of position and exclusive expression of mouse Xist from the inactive X chromosome Nature 1991 351 329 331 2034279 10.1038/351329a0 Kay GF Penny GD Patel D Ashworth A Brockdorff N Rastan S Expression of Xist during mouse development suggests a role in the initiation of X chromosome inactivation Cell 1993 72 171 182 8425217 10.1016/0092-8674(93)90658-D Christians E Michel E Renard JP Developmental control of heat shock and chaperone gene expression. 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==== Front BMC Clin PatholBMC Clinical Pathology1472-6890BioMed Central London 1472-6890-5-21564211310.1186/1472-6890-5-2Research ArticleStainable hepatic iron in 341 African American adults at coroner/medical examiner autopsy Barton James C [email protected] Ronald T [email protected] Asia K [email protected] Robert M [email protected] Southern Iron Disorders Center, Birmingham, Alabama, USA2 Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA3 Immunogenetics Program, Department of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama, USA4 Jefferson County Coroner/Medical Examiner Office, Birmingham, Alabama, USA5 Division of Forensic Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, USA2005 10 1 2005 5 2 2 18 8 2004 10 1 2005 Copyright © 2005 Barton et al; licensee BioMed Central Ltd.2005Barton et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Results of previous autopsy studies indicate that increased hepatic iron stores or hepatic iron overload is common in African Americans dying in hospitals, but there are no reports of hepatic iron content in other cohorts of African Americans. Methods We investigated the prevalence of heavy liver iron deposition in African American adults. Using established histochemical criteria, we graded Perls' acid ferrocyanide-reactive iron in the hepatocytes and Kupffer cells of 341 consecutive African American adults who were autopsied in the coroner/medical examiner office. Heavy staining was defined as grade 3 or 4 hepatocyte iron or grade 3 Kupffer cell iron. Results There were 254 men and 85 women (mean age ± 1 SD: 44 ± 13 y vs. 48 ± 14 y, respectively; p = 0.0255); gender was unstated or unknown in two subjects. Approximately one-third of subjects died of natural causes. Heavy staining was observed in 10.2% of men and 4.7% of women. 23 subjects had heavy hepatocyte staining only, six had heavy Kupffer cell staining only, and one had a mixed pattern of heavy staining. 15 subjects had histories of chronic alcoholism; three had heavy staining confined to hepatocytes. We analyzed the relationships of three continuous variables (age at death in years, hepatocyte iron grade, Kupffer cell iron grade) and two categorical variables (sex, cause of death (natural and non-natural causes)) in all 341 subjects using a correlation matrix with Bonferroni correction. This revealed two positive correlations: hepatocyte with Kupffer cell iron grades (p < 0.01), and male sex with hepatocyte iron grade (p < 0.05). We also analyzed the relationship of steatosis, inflammation, and fibrosis/cirrhosis in 30 subjects with heavy iron staining using a correlation matrix with Bonferroni correction. There were significant positive correlations of steatosis with inflammation (r = 0.5641; p < 0.01), and of inflammation with fibrosis/cirrhosis (r = 0.6124; p < 0.01). Conclusions The present results confirm and extend previous observations that heavy liver iron staining is relatively common in African Americans. The pertinence of these observations to genetic and acquired causes of iron overload in African Americans is discussed. ==== Body Background Hepatic iron overload was detected by Perls' acid ferrocyanide staining and atomic absorption spectrometry at autopsy in more than one percent of African American adults who died in hospitals [1,2]. In one study [1], liver specimens from 326 unselected adult African Americans subjects were stained for iron; liver iron was quantified using atomic absorption spectrometry in subjects in whom increased stainable iron was observed. Four subjects (1.2%), two men and two women aged 50 to 63 years, had hepatic iron indices adjusted for previous erythrocyte transfusion that were ≥ 1.9 (range 1.9 – 5.6) [1]. In a second study [2], hepatic iron concentrations of liver tissue from autopsies in 99 African Americans were quantified. Thirty-one (31.3%) had an elevated hepatic iron concentration, including nine (9.1%) who had an hepatic iron concentration greater than twice the upper limit of normal and no evident cause of secondary iron overload [2]. These results suggest that iron overload not attributable to erythrocyte transfusion is relatively common in African Americans. In contrast, screening programs that included cohorts of African Americans presumably representative of the general African American population identified a much lower proportion of subjects with possible iron overload [3-5] than is suggested by the results of hospital autopsy series [1,2]. These studies used an elevated transferrin saturation phenotype criterion generally regarded as the best for screening whites for HFE-associated hemochromatosis [3-5]. In these studies, ≤ 0.9% of African Americans adults had a positive screening result(s), and ≤ 0.09% were subsequently demonstrated to have hemochromatosis or iron overload [3-5]. Taken together, these observations suggest that previous reports of increased hepatic iron content in African Americans dying in hospital may have overestimated the prevalence of non-transfusion iron overload in African American adults in the general population, or that the ideal phenotype for screening African Americans for primary iron overload differs from that which is optimal for screening whites for HFE-associated hemochromatosis. Thus, we graded Perls' acid ferrocyanide-reactive iron in the livers of 341 consecutive African American adults who underwent autopsy in the coroner/medical examiner office. We selected this population for study because such subjects are more representative of the general African American population than persons who died in hospital. We then compared these observations with autopsy results reported previously in African Americans who died in hospitals [1,2]. The pertinence of these observations to genetic and acquired causes of iron overload in African Americans is discussed. Methods Selection of study subjects The performance of this study was approved by the Institutional Review Board of the University of Alabama at Birmingham and by the Coroner/Medical Examiner's Office of Jefferson County, Alabama. We evaluated formalin-fixed tissue obtained in 361 consecutive, unselected coroner/medical examiner autopsy cases of African-American adults (age >18 years) in Jefferson County, Alabama; deaths in all of the present cases occurred during the interval 1998 – 2002. Each subject was identified as African American by his/her previous medical histories or legal records, or by the coroner/medical examiner staff. Most autopsies were performed on the same day that the respective bodies were available for evaluation at the Coroner/Medical Examiner's Office; other autopsies were completed within ~ 18 hours. All tissues were placed directly in fixative at the time of collection at autopsy. Liver was not available or was not interpretable due to autolysis in 20 cases. Available records in each case were reviewed; age at death, sex, summary of known illnesses, and cause of death were tabulated for all cases for which liver tissue was available. Histologic technique, iron grading, and liver morphology Tissues obtained at autopsy were routinely fixed in 10% neutral buffered formalin. Triplicate sections of paraffin-embedded liver were prepared. One section was stained with hematoxylin and eosin, another with Perls' acid ferrocyanide technique to demonstrate non-heme ferric iron [6], and a third with Masson trichrome technique. Appropriate positive and negative control specimens were prepared with each staining batch and reviewed. All slides were simultaneously reviewed by three investigators, and the iron grades assigned in each case represent their consensus opinions. Hepatocellular iron was graded according to these criteria: grade 0 – no visible iron; grade 1 – iron visible in very few hepatocytes; grade 2 – iron visible in 5 – 10% of hepatocytes; grade 3 – iron visible in ≥ 40% of hepatocytes; and grade 4 – abundant iron visible in most hepatocytes [7]. Kupffer cell iron was graded according to these criteria: grade 0 – no visible iron in Kupffer cells; grade 1 – iron visible in ≤ one-third of Kupffer cells; grade 2 – iron visible in one third to ≤ two-thirds of Kupffer cells; and grade 3 – abundant iron visible in more than two-thirds of Kupffer cells [7]. Hepatocyte or Kupffer cell iron of grades 0 or 1 was defined as normal. Increased stainable iron was defined as hepatocyte and/or Kupffer cell iron grade ≥ 2 [7]. Heavy iron staining was defined as hepatocyte iron grade of 3 or 4, or Kupffer cell iron grade of 3. Steatosis, inflammation, and fibrosis/cirrhosis were assessed as described in detail elsewhere [8]; these abnormalities were graded as absent (0) or present (+). We designated a gradient of stainable iron in hepatocytes from the periportal area decreasing towards the hepatic venule as present or absent; visualization of a gradient required hepatocyte iron staining of grade ≥ 2 [9,10]. The presence or absence of hepatic cirrhosis was determined using Masson trichrome-stained specimens as described previously [8]. Statistical considerations The present data set consisted of observations in 341 subjects and their respective livers. Analyses were performed with a computer spreadsheet (Excel 2000®, Microsoft Corp., Redmond, WA), and a statistical program (GB-Stat® v. 10.0, 2003, Dynamic Microsystems, Inc., Silver Spring, MD). Descriptive data are displayed as enumerations, percentages, mean ± 1 S.D., medians, and ranges. Frequency values were compared using chi-square analysis or Fisher exact test, as appropriate. Mean values were compared using student t-test. Some data were analyzed using a correlation matrix with Bonferroni correction. Values of p < 0.05 were defined as significant. Results General characteristics of study subjects There were 254 men (mean age 44 ± 13 y; median age 42 y; range 26 – 99 y), and 85 women (mean age 48 ± 14 y; median age 45 y; range 26 – 89 y). The mean age of men was lower than that of women (p = 0.0255). There were two subjects for whom gender was unstated or unknown. Approximately one-third of subjects died of natural causes (Table 1). Table 1 Causes of death of 341 African Americans autopsied in the coroner/medical examiner office.1 Causes of Death Subjects, n Percentage Homicide 130 38.1 Natural Cause 107 31.4 Accident 90 26.4 Unknown 7 2.1 Suicide 5 1.5 Not Stated 2 0.6 1Chronic alcoholism was listed for 15 subjects (the respective cause of death in each subject was ruled to be "natural cause"). Two subjects were reported to have diabetes mellitus; one had grade 3 hepatocyte and grade 1 Kupffer cell iron. No subject was reported to have iron overload, hemochromatosis, heritable or acquired forms of anemia, or treatment with erythrocyte transfusion. Liver iron grades In the 341 evaluable subjects, there was a significant positive correlation of grades of stainable iron in hepatocytes and Kupffer cells across the 341 evaluable subjects (Pearson r coefficient = 0.2370; p < 0.0001). However, the mean hepatocyte iron grade was 0.83 ± 0.96; the mean Kupffer cell iron grade was 0.32 ± 0.32. Increased hepatocyte iron grades were observed in 64 men (25.2%) and 8 women (9.4%) (p = 0.0021). Increased Kupffer cell iron grades were observed in 20 men (7.9%) and one woman (1.2%) (p = 0.0266) (Tables 2, 3). Table 2 Iron grades of 341 Perls' acid ferrocyanide-stained liver sections.1 Grade No. of subjects with hepatocyte staining (%) No. of subjects with Kupffer cell staining (%) 0 164 (48.1) 270 (79.2) 1 104 (30.5) 50 (14.7) 2 49 (14.4) 14 (4.1) 3 21 (6.2) 7 (2.1) 4 3 (0.9) not available1 1The present grading system does not include Kupffer cell iron staining greater than grade 3. In all subjects, the mean hepatocyte grade (± 1 SD) was 0.83 ± 0.96; the mean Kupffer cell grade was 0.32 ± 0.31. A gradient of stainable iron in hepatocytes from the periportal area decreasing towards the hepatic venule was observed in 56 subjects. Table 3 Histologic findings in 30 African American subjects with heavy liver iron staining1 Age, years Sex Hepatocyte iron grade Kupffer cell iron grade Steatosis Inflammation Fibrosis/cirrhosis 26 M 3 1 0 0 0 27 M 1 3 0 0 0 28 M 3 0 0 0 0 29 M 3 0 0 0 0 30 M 3 1 0 0 0 34 M 3 0 0 0 0 34 M 0 3 0 0 0 37 M 3 2 + + 0 37 M 3 1 + + 0 39 M 3 1 0 0 0 39 M 3 1 0 + + 40 M 2 3 + + 0 42 M 3 0 0 0 0 43 M 3 0 0 0 0 44 M 4 1 0 + 0 44 M 4 0 0 + +2 46 M 3 0 0 + 0 49 M 3 0 0 0 0 50 M 1 3 0 + 0 52 M 3 0 + + + 55 M 3 0 0 0 0 59 M 3 0 + + 0 59 M 0 3 0 + + 63 M 3 1 + + + 67 M 2 3 0 + + 91 M 3 0 + + +2 33 F 3 3 0 0 0 50 F 4 0 0 + +2 51 F 3 0 + + + 54 F 3 0 0 0 0 1 Heavy iron staining was defined as hepatocyte iron grade of 3 or 4, or Kupffer cell iron grade of 3. Steatosis, inflammation, and fibrosis/cirrhosis were assessed as described in detail elsewhere [8]; these abnormalities were graded as absent (0) or present (+). 2 These subjects had hepatic cirrhosis [8]. Heavy iron staining was observed in 30 subjects (8.8%), including 10.2% of men and 4.7% of women (p = 0.1202). The mean age of men and women with heavy iron staining was similar: 45 ± 15 y and 47 ± 9 y (p = 0.7389). 24 subjects (7.1%) had grade 3 or 4 hepatocyte iron; of these, one also had grade 3 Kupffer cell iron. Seven subjects (2.1%) had grade 3 Kupffer cell iron; of these, one also had grade 3 hepatocyte iron. Altogether, 23 subjects had heavy iron staining in hepatocytes only (Fig. 1), six subjects had heavy iron staining in Kupffer cells only (Fig. 2), and one subject had a mixed pattern of heavy hepatocyte and Kupffer cell iron staining (Fig. 3). The causes of death of the 30 subjects who had heavy iron staining were similar to those of subjects with lower iron grades (data not shown). Figure 1 Photomicrograph of non-cirrhotic liver stained with Perls' technique. Liver of a 44 year-old African American man who died of pneumonia. There is a predominance of iron staining (grade 4) in hepatocytes. Original magnification 40×. Figure 2 Photomicrograph of non-cirrhotic liver stained with Perls' technique. Liver of a 34 year-old African American man who died of homicide. There is a predominance of iron staining (grade 3) in Kupffer cells; there is faint diffuse staining of hepatocytes (grade 1). Original magnification 40×. Figure 3 Photomicrograph of non-cirrhotic liver stained with Perls' technique. Liver of a 33 year-old African American woman who died of accidental trauma. There is heavy iron staining in hepatocytes (grade 3) and Kupffer cells (grade 3). Original magnification 40×. We analyzed the relationships of three continuous variables (age at death in years, hepatocyte iron grade, and Kupffer cell iron grade) and two categorical variables (sex, cause of death (natural and non-natural causes)) using a correlation matrix with Bonferroni correction. This revealed two significant positive correlations: hepatocyte iron grade with Kupffer cell iron grade (p < 0.01), and male sex with hepatocyte iron grade (p < 0.05). Histologic findings in 30 subjects with heavy iron staining These subjects were comprised of 26 men and 4 women (Table 3). Hepatic steatosis was observed in eight subjects (26.7%), hepatic inflammation was observed in 16 subjects (53.3%), and hepatic fibrosis or cirrhosis was observed in nine subjects (30.0%). However, 14 subjects (46.7%) did not have steatosis or inflammation; none of these 14 subjects had fibrosis/cirrhosis. We analyzed the relationship of hepatocyte and Kupffer cell iron grades, steatosis, inflammation, and fibrosis/cirrhosis in these 30 subjects (Table 3) using a correlation matrix with Bonferroni correction. There was a significant negative correlation of hepatocyte and Kupffer cell iron grades (r = -0.7368; p < 0.01). There was a significant positive correlation of steatosis with inflammation (r = 0.5641; p < 0.01). There was a significant positive correlation of inflammation with fibrosis/cirrhosis (r = 0.6124; p < 0.01). Subjects with chronic alcoholism Fifteen subjects had histories of chronic alcoholism; three of these had heavy liver iron staining. In each case, iron staining was confined to hepatocytes. The percentage of subjects with heavy iron staining was similar in 15 subjects reported to have chronic alcoholism and in 326 subjects not reported to have chronic alcoholism (20.0% vs. 8.3%; p = 0.1356). Three persons who had histories of chronic alcoholism also had hepatic cirrhosis. A 36 year-old man had grade 0 hepatocyte and grade 0 Kupffer cell iron. A 46 year-old man had grade 2 hepatocyte iron and grade 0 Kupffer cell iron. A 50 year-old woman had grade 4 hepatocyte iron and grade 0 Kupffer cell iron (Fig. 4). Figure 4 Photomicrograph of cirrhotic liver stained with Perls' technique. Liver of a 50 year-old African American woman with a history of chronic alcoholism. There is a predominance of iron staining (grade 4) in hepatocytes, and prominent staining of bile ductule cells. Micronodular cirrhosis and moderate-severe steatosis were also present. Original magnification 100×. Subjects with cirrhosis Five subjects had hepatic cirrhosis (1.5%). Three had histories of chronic alcoholism (described above). One of these three subjects, a 50 year-old woman, had grade 4 hepatocyte iron and grade 0 Kupffer cell iron (Fig. 4). Two other subjects, neither of whom had a history of alcoholism, also had hepatic cirrhosis. One was a 44 year-old man with grade 4 hepatocyte iron who died of pneumonia (Fig. 1; Table 3). The other was a 91 year-old man with grade 3 hepatocyte iron who died of intracranial hemorrhage (Table 3). Discussion Heavy hepatocyte or Kupffer cell iron staining was observed in 8.8% of the present subjects. This is consistent with prevalence estimates of hepatic iron overload reported in hospital autopsy series of African Americans from other geographic areas [1,2]. The present subjects were relatively young, on the average, and approximately two-thirds died of non-natural causes. In contrast, the subjects in previous hospital autopsy series were much older, on the average, and most died of natural causes [1,2]. In one hospital autopsy series, all subjects but one were men [2]. Although there was a predominance of men in the present study, our series included 85 women. We observed that the percentages of men with increased hepatocyte or Kupffer cell iron grades were greater than those of women. This is in agreement with the greater mean iron stores of African American men than women detected in assessments of iron nutrition, and with the predominance of men in clinical and autopsy series of African Americans with primary iron overload [1,7,11,12]. Altogether, the present subjects may be more representative of African American adults in the general population than those in hospital autopsy series [1,2], although there may be fewer available observations regarding medical history in the present cases than in African Americans who died in hospital [1,2]. Microscopic estimation of liver iron content correlates well with atomic absorption spectrometry measurements in subjects in whom the histologic distribution of hepatic iron and clinical circumstances suggest hemochromatosis, i.e., predominance of hepatocyte iron and no apparent explanation for iron overload [9,13-15]. These histologic criteria were pertinent to 77% of the present subjects. On the other hand, the relationship of iron grades and quantitative liver iron measurements is not well documented in subjects in whom hepatic iron deposition occurs predominantly in macrophages, like 20% of the present subjects. Hepatic iron concentrations and indices have been used as conservative, surrogate diagnostic criteria for primary iron overload in African Americans [1,2,7,16], although there has been no validation of their use in such cases. Further, some African Americans who had iron overload demonstrated by therapeutic phlebotomy had normal hepatic iron concentrations and indices [7,17]. Elevated hepatic iron indices have also been reported to occur in a variety of other conditions [18-20]. Primary iron overload in African Americans is often associated with preferential deposition of iron in macrophages in multiple organs [1,7,21]. In some cases, this is associated with the inheritance of the Q248H missense mutation of the ferroportin gene FPN1 [17,21,22]. Two of thirteen (15.4%) African American iron overload index patients and two of 39 (5.1%) African American control subjects who reside in central Alabama were heterozygous for FPN1 Q248H [21]. Nine of the present 341 (6.0%) subjects had heavy iron staining confined to Kupffer cells. Thus, FPN1 Q248H could account for heavy iron staining in some of the present subjects. Other African Americans with primary iron overload have a predominance of hepatocyte iron deposition. This is consistent with hemochromatosis phenotypes associated with HFE genotypes typical of hemochromatosis in whites (HFE C282Y/C282Y or C282Y/H63D) [16,21], or with common types of hemoglobinopathy or thalassemia [7,21]. Other African Americans with primary iron overload and a predominance of hepatocyte iron staining have missense mutations of the hemojuvelin gene HJV on Ch1q [23] or the erythroid-specific 5-aminolevulinate synthase gene ALAS2 on ChX [24,25]. Other putative African iron overload alleles may account for iron overload in some cases [26]. However, performing DNA analyses to detect mutations of iron-associated genes was beyond the scope of the present work. Acquired disorders account for increased hepatic iron deposition in some African Americans. In the present study, we did not observe any subject who had heavy liver iron staining and fibrosis or cirrhosis who did not also have hepatic steatosis or inflammation. Chronic viral hepatitis C occurs in approximately 1.8% of the overall U.S. population [27], and the prevalence of chronic hepatitis C is significantly greater in African Americans than whites in the U.S. [27,28]. A greater proportion of African Americans than persons of other races respond to chronic hepatitis C infection with an increase in iron stores, after adjustment for age, alcohol intake, gender, menopausal status, education, body mass index, and poverty index [29]. More than half of the present subjects who had heavy iron staining had hepatic inflammation. It is plausible that some of these had viral hepatitis C, although this is unproven. More than one-quarter of the present subjects who had heavy iron staining also had hepatic steatosis. The prevalence of non-alcoholic steatosis and steatohepatitis is lower in African Americans than in whites [30,31], although some risk factors for non-alcoholic hepatic steatosis and steatohepatitis (obesity, insulin resistance, and diabetes mellitus) are significantly greater in African Americans than in whites in the U.S. [30-32]. Taken together, these findings suggest the development of hepatic fibrosis or cirrhosis in African Americans who have heavy hepatic iron deposition may require the synergistic effects of hepatic steatosis or inflammation. Iron overload sometimes develops spontaneously or after repeated erythrocyte transfusion in African Americans with heritable or acquired anemia, or with myelodysplasia or acute leukemia [7,15,33-41]. Although there were no reports of heritable or acquired anemia or of multiple erythrocyte transfusions in the present subjects, such circumstances were frequent in hospital autopsy series of African Americans [1,2]. Three of the five present subjects with micronodular cirrhosis had a history of chronic alcoholism. African Americans are at greater risk than whites for developing several alcohol-related conditions, including hepatic cirrhosis [42,43]. In the present study, however, the prevalence of heavy liver iron staining was similar in subjects with and without histories of chronic alcoholism. Conclusions We conclude that heavy liver iron staining is common in African American adults who were autopsied in the coroner/medical examiner office. The different histologic patterns of heavy liver iron staining we observed in the present subjects are consistent with the phenotypic and genotypic heterogeneity of primary iron overload in African Americans [21] and with the phenotypic heterogeneity of iron overload of other causes [7,29,33-41]. However, the present results do not demonstrate specific genetic or acquired causes for heavy liver iron staining in individual subjects. Further, the present results do not prove that the present subjects with heavy liver iron staining had systemic iron overload. Competing interests The author(s) declare that they have no competing interests. Authors' contributions JCB conceived and designed the study, reviewed and graded the liver specimens, contributed to the statistical analyses of data, and contributed to writing the manuscript. RTA reviewed and graded the liver specimens, contributed to the statistical analyses of data, and contributed to writing the manuscript. AKR reviewed and graded the liver specimens and contributed to writing the manuscript. RMB provided information on the autopsy cases, provided the liver specimens, reviewed the histology of selected liver specimens, and contributed to writing the manuscript. All authors approved of the manuscript in its final form. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgments This work was supported in part by Southern Iron Disorders Center, the Immunogenetics Program, and NCI training grant R25 CA 76023-04 UAB Cancer Research Experiences for Student (CaRES). ==== Refs Wurapa RK Gordeuk VR Brittenham GM Khiyami A Schechter GP Edwards CQ Primary iron overload in African Americans Am J Med 1996 101 9 18 8686721 10.1016/S0002-9343(96)00053-8 Brown KE Khan CM Zimmerman MB Brunt EM Hepatic iron overload in blacks and whites: a comparative autopsy study Am J Gastroenterol 2003 98 1594 1598 12873584 10.1016/S0002-9270(03)00367-8 Baer DM Simons JL Staples RL Rumore GJ Morton CJ Hemochromatosis screening in asymptomatic ambulatory men 30 years of age and older Am J Med 1995 98 464 468 7733125 10.1016/S0002-9343(99)80346-5 Phatak PD Sham RL Raubertas RF Dunnigan K O'Leary MT Braggins C Cappuccio JD Prevalence of hereditary hemochromatosis in 16031 primary care 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==== Front BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-5-41565198510.1186/1472-6963-5-4Research ArticlePatient involvement in medical decision-making and pain among elders: physician or patient-driven? Borders Tyrone F [email protected] Ke Tom [email protected] James [email protected] Gina [email protected] Department of Health Management and Policy, University of North Texas School of Public Health, 3500 Camp Bowie Blvd., Fort Worth, TX 76107, USA2 Division of Health Services Research, Texas Tech University School of Medicine, Lubbock, Texas, USA3 Department of Anesthesiology, Texas Tech University School of Medicine, Lubbock, Texas, USA4 Baylor Medical School, Houston, Texas, USA2005 14 1 2005 5 4 4 30 6 2004 14 1 2005 Copyright © 2005 Borders et al; licensee BioMed Central Ltd.2005Borders et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Pain is highly prevalent among older adults, but little is known about how patient involvement in medical decision-making may play a role in limiting its occurrence or severity. The purpose of this study was to evaluate whether physician-driven and patient-driven participation in decision-making were associated with the odds of frequent and severe pain. Methods A cross-sectional population-based survey of 3,135 persons age 65 and older was conducted in the 108-county region comprising West Texas. The survey included self-reports of frequent pain and, among those with frequent pain, the severity of pain. Results Findings from multivariate logistic regression analyses showed that higher patient-driven participation in decision-making was associated with lower odds (OR, 0.82; 95% CI, 0.75–0.89) of frequent pain, but was not significantly associated with severe pain. Physician-driven participation was not significantly associated with frequent or severe pain. Conclusions The findings suggest that patients may need to initiate involvement in medical decision-making to reduce their chances of experiencing frequent pain. Changes to other modifiable health care characteristics, including access to a personal doctor and health insurance coverage, may be more conducive to limiting the risk of severe pain. ==== Body Background Persons age 65 years and older commonly endure a multitude of chronic and debilitating conditions which contribute to persistent pain [1]. Estimates of the prevalence of pain among the community-dwelling elderly range between 25% and 50% [2,3]. Pain has been found to have a substantial effect on health-related quality of life [2], the use of over the counter and prescription drugs [1,4], and the utilization of medical care [5]. As the number of elderly persons in the United States rises, more research is needed to determine how the delivery of medical care could be altered to limit the onset of pain and its subsequent burden on health status and the health care system. Increasing patients' involvement in the medical decision-making process is one potentially fruitful means of improving pain management. Several studies suggest that patients, especially those with chronic conditions, who have opportunities to participate in care have more positive health outcomes than those who do not [6,7]. While other studies have pointed out that the positive correlation between patient participation and health outcomes is more suggestive than conclusive, Guadagnoli and Ward have stated that physicians should nevertheless strive to engage their patients in decision-making for humanitarian reasons [8]. Although patients' participation may improve their health outcomes, the effect can be diminished among elderly patients. Elderly patients, as compared to younger patients, have been shown to be less participatory in medical-decision making [9-11]. Using a longitudinal cohort, the Medical Outcomes Study found that patients older than 75 years were less participatory [12]. Other studies have also shown that older people tend to exhibit more conversational behaviors [13], give more socially desirable responses [14], and defer to physicians' authorities [15]. The primary objective of the present study was to examine how participation in decision-making was associated with the occurrence of pain among a cohort of community-dwelling elders. In contrast to previous studies, we differentiated two types of participation in decision-making. The first type is physician-driven in which the physician takes the initiative to ask questions and offer choices to patients. The second type is patient-driven, in which the patient takes the initiative to ask questions and express preferences. We hypothesized that stronger physician and patient-driven participation in decision-making would be associated with lower odds of frequent pain and, among those with frequent pain, lower odds of severe pain. We also tested for the effects of other health care factors which might be conducive to pain management, such as tenure with a personal doctor. The findings have implications for how older patients interact with their physicians as well as how physicians and clinic managers organize health services. Methods Sample and setting Data were obtained through a longitudinal, population-based study of community-dwelling elders, the Texas Tech 5000 Survey. The Texas Tech 5000 Survey was conducted in a 108-county region of West Texas, a geographically and ethnically diverse area encompassing approximately half of the state's land mass. The survey has been described in detail elsewhere [16-21]. Briefly, approximately 65,000 households were randomly selected from residential telephone listings and screened to identify a cohort of 5,000 individuals age 65 years and older. Age-qualified individuals were subsequently tested for cognitive impairment using a telephone version of the Mini Mental State Evaluation [22]. Ninety-three percent of individuals did not have impairment and thus were eligible for participation in the study. Excluding telephone numbers that were never reached, those who refused the cognitive screener, and individuals who failed the cognitive screener, the eligible sample size was 6,942. Two follow-up surveys have since been conducted among the original cohort. Selected questions measuring satisfaction with care and health-related quality of life were included in each wave. To limit respondent burden, most questions were asked only during one wave (the patient and physician-driven participation in decision-making and perceived pain questions were only asked during Wave 3). Of the 6,942 households that were eligible for participation in Wave 1 of the survey, 5,006 persons participated, yielding a baseline response rate of 72%. The data presented here are from 3,135 subjects who participated in all three waves of the survey, yielding an overall response rate of approximately 45%. While some subjects were obviously lost to follow-up, the demographic composition of the study samples remained similar over the study period. The Texas Tech Health Sciences Center Institutional Review Board for the Protection of Human Subjects approved the study. Measures Frequent and severe pain The occurrence and severity of pain were measured in Wave 3 using items developed for and included in two nationally representative surveys of older persons, the Health and Retirement Study (HRS) and Assets and Health Dynamics among the Oldest Old (AHEAD) [23]. First, the frequency of pain was measured by asking respondents if they were "often troubled with pain?" Responses were categorized to distinguish those persons who were often troubled (referred to hereafter as frequent pain) and those who were not often troubled by pain, as has been done in previous studies [2]. The severity of pain was assessed among those persons who reported frequent pain through a single item asking, "how bad is the pain most of the time?" Responses were categorized to differentiate those persons with mild or moderate pain versus severe pain. Sociodemographic factors A number of sociodemographic, health care, and health status measures were included. Sociodemographic factors were gender, age (continuous), marital status, educational status (high school graduate vs. less than high school education), and place of residence (urban, rural, and frontier). An urban area is a metropolitan county, or a county with a total population of at least 50,000, whereas a rural area is a county with fewer than 50,000 persons. Rural counties were further classified according to whether they were frontier areas, or counties with fewer than 7 persons per square mile [24]. Health care factors Health care variables were health insurance coverage, the number of physician visits in the last 6 months, tenure with a personal doctor, an index measuring physician-driven participation in decision-making, and an index measuring patient-driven participation in decision-making. Health insurance coverage was coded as Medicaid, Medicare, Medicare plus other private or government coverage, other private or government coverage, and no insurance. Tenure with a personal doctor was measured using a single question asking if the individual had a personal doctor and, if so, the duration of tenure with the physician (less than 1 year, 1 to 2 years, 3 to 4 years, and 5 or more years). An index of physician-driven participation in decision-making was created using three items taken from the Medical Outcomes Study [12]. The physician-driven participation in decision-making questions included: 1) How often does your doctor ask you to help make the decision when there is a choice between treatments?, 2) How often does your doctor give you some control over your treatment?, and 3) How often does your doctor ask you to take some of the responsibility? Response options for each item ranged from 0 (never) to 4 (very often). The aggregation of the three items divided by the total number of items produces a score between 0 and 4 with a higher index score indicating greater involvement. For the present data set, the physician-driven participation index had reasonable internal consistency (Cronbach's alpha = 0.69), which was similar to that found in the Medical Care Outcomes Study (Cronbach's alpha = 0.74) [12]. Three questions adopted from a study of older patients' communication during medical visits [25] were used to measure patient-driven participation in decision-making. These questions included: 1) How often do you write out a list of symptoms, complaints, and medications before visiting a doctor?, 2) How often do you express preferences for tests, medications, and treatments?, and 3) How often do you call to clarify information or report symptoms or side effects after a visit? As was the case for the physician-driven index, the patient-driven participation index ranges between 0 and 4 with a higher score indicating greater involvement. The Cronbach's alpha for the patient-driven participation index was 0.58. Health status Overall health status (categorized as excellent, very good, good, and fair or poor) was measured using a general health item from a brief health-related quality of life instrument (the SF-12) [26]. Mental health status was assessed with the mental component score (MCS) of the SF-12. Additional health status variables included whether the individual had ever been diagnosed with arthritis and the number of additional comorbid conditions (categorized as none, one, two, and three or more). Statistical analysis Chi-square tests were first conducted to determine whether there was an association between each categorical sociodemographic, health care, and health status factor and frequent and severe pain. T-tests were conducted to determine if there was a difference in each continuous sociodemographic, health care, and health status factor between individuals with and without frequent pain and individuals with and without severe pain. Next, multivariate logistic regression analyses were conducted to determine if physician or patient-driven participation in decision-making were associated with the odds of frequent pain and, among those with frequent pain, the odds of severe pain. The potential for multicollinearity between the covariates was assessed by calculating their variance inflation factors; no problems with multicollinearity were found. Results Description statistics for individuals with frequent pain A total of 1,333 (42.5%) of the 3,135 survey participants had frequent pain. Table 1 presents percentages for frequent pain by categorical sociodemographic, health care, and health status variables. Several categorical sociodemographic variables were significantly associated with frequent pain. Frequent pain was less common among males (compared to females) and among married persons (compared to single persons). Frequent pain was more common among persons with less than a high school degree as compared to those with at least a high school degree. Health insurance was insignificant, but tenure with a personal doctor was associated with frequent pain. Specifically, pain was least common among individuals with no personal doctor, compared to those who had a personal doctor. Frequent pain was more common among persons with more comorbid diseases or conditions (compared to those with none), those with arthritis (compared to those without arthritis), and those with poorer self-rated general health. As shown in Table 2, persons with frequent pain had a higher number of physician visits in the previous six months but lower physician and patient-driven participation than those without frequent pain. Moreover, those with frequent pain had worse (lower) mental component scores (MCS) than those without frequent pain. Table 1 Prevalence of frequent and severe pain by categorical independent variables Sociodemographics N = 3,135 Frequent Pain % Severe Pain % Gender Male 952 35.71 6.33 Female 2,183 45.5 10.4 Race/Ethnicity White 2,678 42.5 8.41 Hispanic 327 42.5 11.6 Other 130 43.9 19.2 Education < high sch. grad. 785 45.93 13.31 High school grad. 2,350 41.4 7.8 Marital Status Married 1,661 40.83 8.1 Not married 1,474 44.5 10.3 Rural / urban residency Urban 1,720 43.9 9.6 Rural, non-frontier 1,072 40.8 8.7 Frontier 343 41.1 8.5 Income <$10,000 491 47.91 14.32 $10–20,000 658 47.7 10.3 $21–30,000 505 43.0 7.7 $31,000 and higher 892 36.0 5.7 Health care Insurance Uninsured 93 46.2 21.51 Medicare 917 41.8 9.2 Medicaid 341 43.7 12.6 Medicare+other ins. 1,498 43.4 7.9 Other private/gov. ins 286 37.8 7.7 Tenure with doctor No personal doctor 426 37.81 10.6 Less than 1 yr 227 44.9 9.2 1–2 yrs 414 45.7 10.1 3–4 yrs 454 42.3 8.4 5 or more yrs 1,614 42.7 8.7 Health status No. of comorbidities 0 1,690 37.23 6.32 1 874 47.1 10.5 2 384 48.7 14.1 3 or more 187 56.7 18.7 Ever diagnosed with arthritis Yes 1,988 55.01 12.82 No 1,147 20.8 2.9 General health status Excellent 316 15.23 2.23 Very good 720 31.1 5.6 Good 1,040 41.0 3.1 Fair 687 55.8 13.8 Poor 360 69.2 29.2 1 p < 0.0001, 2 p < 0.01, 3 p < 0.05 note: Analyses of severe pain were limited to those with frequent pain. Table 2 Means and standard deviations for continuous independent variablesby frequent and severe pain Frequent pain Severe pain Yes No Yes No Sociodemographics Mean age (SD) 75.4(6.3) 75.3(6.3) 75.7(6.7) 75.3(6.3) Health care Mean no. physician visits (SD) 6.3(8.5)1 3.9(6.1) 8.0(9.5)1 5.8(8.1) Mean physician-driven participation index (SD) 1.8(1.2)3 1.9(1.2) 1.9(1.2)3 1.7(1.2) Mean patient-driven participation index (SD) 2.4(1.0)1 2.7(1.0) 2.4(1.0) 2.4(1.1) Health status Mean SF-12 mental component score (SD) 52.5(9.9)1 54.8(7.4) 48.7(12.1)1 53.6(9.0) 1 p < 0.0001, 2 p < 0.01, 3 p < 0.05 note: Analyses of severe pain were limited to those with frequent pain. Description statistics for individuals with severe pain Among the 1,333 individuals with frequent pain, a total of 287 (21.5%) had severe pain. As shown in Table 1, severe pain was less common among males (compared to females) but more common among other races/ethnicities (compared to non-Hispanic whites), persons with less than a high school degree (compared to at least a high school degree), and persons with lower household income. Severe pain was most common among individuals without health insurance. It was more common among persons with more comorbid diseases or conditions (compared to those with none), those with arthritis (compared to those without arthritis), and those with poorer self rated general health status. As shown in Table 2, those with severe pain had more physician visits and higher physician participation in decision-making. Those with severe pain had lower or worse mental component scores and a higher number of physician visits in the previous six months (compared to those without severe pain). Multivariate analyses of the odds of frequent pain Findings from multivariate logistics analyses are shown in Table 3. Males had lower odds (OR, 0.81; 95% CI 0.67, 0.98) of frequent pain than females. Race/ethnicity was not significantly associated with frequent pain. Compared to urban residents, those residing in a rural area had lower odds (OR, 0.77; 95% CI 0.64, 0.92) of frequent pain than urban residents. Income, marital status, and frontier residence were not significantly associated with the odds of frequent pain. Individuals who had more physician visits in the previous six months had a higher odds of frequent pain (OR, 1.02; 95% CI 1.01, 1.04). Table 3 Multivariate logistic regression of sociodemographic, health care, and health status factors on frequent and severe pain Variable (reference group) Frequent pain OR (95% CI) Severe pain OR (95% CI) Sociodemographics Age 0.99 (0.97, 1.00) 1.00 (0.98, 1.02) Male (vs. female) 0.81 (0.67, 0.98)3 0.78 (0.54, 1.14) Race/ethnicity Hispanic (vs. non-Hispanic white) 0.74 (0.54, 1.01) 0.74 (0.44, 1.26) Other (vs. non-Hispanic white) 0.84 (0.56, 1.26) 2.28 (1.24, 4.21)2 Less than high school grad. (vs. grad.) 0.97 (0.78, 1.20) 1.08 (0.76, 1.55) Married (vs. single) 0.92 (0.77, 1.10) 1.00 (0.72, 1.39) Residence Rural (vs. urban) 0.77 (0.64, 0.92)3 0.89 (0.65, 1.23) Frontier (vs. urban) 0.86 (0.66, 1.12) 0.78 (0.48, 1.28) Income $10–20,000 (vs. < $10,000) 1.06 (0.81, 1.39) 0.81 (0.52, 1.26) $21–30,000 (vs. < $10,000) 1.02 (0.75, 1.38) 0.96 (0.57, 1.62) $31,000 and higher (vs. < $10,000) 0.90 (0.67, 1.20) 0.87 (0.52, 1.46) Missing (vs. < $10,000) 0.95 (0.72, 1.25) 0.99 (0.63, 1.56) Health care Insurance Medicare (vs. uninsured) 0.69 (0.41, 1.16) 0.41 (0.19, 0.86)3 Medicaid (vs. uninsured) 0.72 (0.44, 1.18) 0.35 (0.17, 0.71)2 Medicare plus other. (vs. uninsured) 0.85 (0.52, 1.39) 0.31 (0.15, 0.64)2 Other private/gov. ins. (vs. uninsured) 0.64 (0.37, 1.09) 0.34 (0.15, 0.76)2 No. of physician visits past 6 months 1.02 (1.01, 1.04)2 1.02 (1.00, 1.03) Physician-driven participation index 0.99 (0.91, 1.06) 1.14 (0.99, 1.30) Patient-driven participation index 0.82 (0.75, 0.89)3 0.93 (0.80, 1.09) Tenure with doctor Less than 1 year (vs. no personal doctor) 0.92 (0.64, 1.33) 0.51 (0.27, 0.98)3 1–2 years (vs. no personal doctor) 0.95 (0.70, 1.30) 0.74 (0.43, 1.25) 3–4 years (vs. no personal doctor) 0.87 (0.64, 1.18) 0.56 (0.33, 0.96)3 5 or more years (vs. no personal doctor) 0.92 (0.71, 1.19) 0.65 (0.42, 0.99)3 Health status No. of comorbidities 1 (vs. none) 1.06 (0.87, 1.27) 1.18 (0.84, 1.66) 2 (vs. none) 0.96 (0.74, 1.25) 1.50 (0.99, 2.28) 3 or more (vs. none) 1.01 (0.71, 1.43) 1.51 (0.90, 2.50) Ever diagnosed with arthritis (vs. never) 3.62 (3.03, 4.33)1 1.54 (1.01, 2.35)3 SF-12 mental component score 0.99 (0.98, 1.00) 0.98 (0.97, 1.00) General health status Excellent (vs. poor) 0.14 (0.09, 0.22)1 0.49 (0.20, 1.19) Very Good (vs. poor) 0.30 (0.21, 0.41)1 0.24 (0.14, 0.42)1 Good (vs. poor) 0.41 (0.31, 0.54)1 0.33 (0.21, 0.50)1 Fair (vs. poor) 0.69 (0.52, 0.92)2 0.56 (0.39, 0.81)3 1 p < 0.0001, 2 p < 0.01, 3 p < 0.05 note: Analyses of severe pain were limited to those with frequent pain. Physician-driven participation was not significantly associated with the odds of frequent pain. However, elders with higher patient-driven participation had lower odds (OR, 0.82; 95% CI 0.75, 0.89) of frequent pain, confirming our hypothesis that persons who take a more active role in their medical treatment are less likely to experience pain. Individuals who had been diagnosed with arthritis at some point in their lives had a higher odds of frequent pain (OR, 3.62; 95% CI 3.03, 4.33) than those without arthritis. Those who rated their general health as excellent (OR, 0.14; 95% CI 0.09, 0.22), very good (OR, 0.30; 95% CI 0.21, 0.41), good (OR, 0.41; 95% CI 0.31, 0.54), and fair (OR, 0.69, CI 0.52, 0.92) had lower odds of frequent pain than those who rated their health as poor. Multivariate analyses of the odds of severe pain Among those with frequent pain, there were no gender difference in the odds of severe pain. Persons of other race/ethnicity (primarily Black/African Americans) had a higher odds (OR, 2.28; 95% CI 1.24, 4.21) of severe pain than non-Hispanic whites. Income, marital status, rural residence, and frontier residence were not significantly associated with the odds of severe pain. The number of physician visits was not significantly associated with severe pain. However, having insurance had a significant impact on the odds of severe pain. Compared to those without health insurance coverage, those with Medicare (OR, 0.41; 95% CI 0.19, 0.86), Medicaid (OR 0.35; 95% CI 0.17, 0.71), Medicare plus other private or government coverage (OR 0.31; 95% CI 0.15, 0.64), and those with other private or government coverage (OR 0.34; 95% CI 0.15, 0.76) had a significantly lower odds of severe pain. Although physician and patient-driven participation were not significantly related to the odds of severe pain, tenure with one's personal doctor was a significant factor. Elders who had been seeing their doctor for less than 1 year (OR, 0.51; 95% CI 0.27, 0.98), 3–4 years (OR, 0.56; 95% CI 0.33, 0.96), or 5 or more years (OR, 0.65; 95% CI 0.42, 0.99) had lower odds of severe pain than elders who had no personal doctor. As expected, several health status measures were also significant. Those who had arthritis had higher odds of severe pain (OR, 1.54; 95% CI 1.01, 2.35) than those without arthritis. Finally, individuals who rated their general health as very good (OR, 0.24; 95% CI 0.14, 0.42), good (OR, 0.33; 95% CI 0.21, 0.50), and fair (OR, 0.56; 95% CI 0.39, 0.81) had lower odds of severe pain than those who rated their health as poor. Discussion We found that patient-driven participation in decision-making was associated with lower odds of frequent pain, which is supported by previous research indicating that adult patients who are more actively engaged in their treatment have greater reductions in symptoms and improvement in health status [27], better psychological outcomes [28], and higher satisfaction with health care [29]. Thus, to delay or prevent the development of frequent pain, elderly patients may need to initiate discussions about symptoms with their physicians when they first experience them. However, because many elderly patients often defer to the doctor to initiate involvement in medical decisions [9-15], this may prove to be a difficult task. Little research has investigated how to promote active participation in medical care decision-making, but one prior study which involved sharing of a patient's medical record and the delivery of brief education about his/her disease prior to a physician visit demonstrated that patient involvement in decision making increased [30]. While neither physician nor patient-driven participation in decision making were significantly associated with pain severity, another factor related to the strength of the doctor-patient relationship was significant. In the present study, having a personal doctor, no matter how long the tenure of the relationship was, reduced the odds of severe pain. The finding of a beneficial effect of having a personal doctor, at least in terms of the severity of pain, is consistent with prior studies which have shown that having a usual source of care is positively correlated with an individual's access to the health care system [31-33], satisfaction with medical care [34], and promotion of proper medication use [35]. A usual, personal doctor undoubtedly has a more thorough knowledge of a patient's medical history and problems, which could enable him/her to more effectively manage pain treatment and coordinate care with specialists, if necessary. If this is the case, managers and leaders of physician clinics that have a high mix of elderly patients should ensure that patients can visit a regular doctor to promote better pain management. In addition to having a personal doctor, access to any type of health insurance coverage was associated with the odds of severe pain. Approximately 12 percent of persons in the study sample reported that they had no health insurance coverage at all, including Medicare, and 21.5% of those without insurance had severe pain. The percentage of patients in this group with severe pain was nearly 2 times higher than the percentage of patients in the other insurance categories. Many older persons may not be eligible for public health insurance because they have not contributed to the social security system for a minimal amount of time. This may be particularly common in the southwestern United States where there are larger numbers of Hispanic immigrants. Expansion of health insurance coverage to this group could improve their ability to visit physicians and other health providers when they experience pain and, ultimately, lead to better pain management. Further research is warranted to more clearly elucidate how characteristics of different health insurance plans, such as gatekeeping and cost sharing, affect access to physician services for pain treatment. Several demographic indicators were also significantly associated with frequent and severe pain. The gender differences beckon the question of whether medical care providers treat older women's pain less effectively or appropriately than men's. No differences were found between Hispanics and non-Hispanic whites, but other races (the majority of whom were Black/African American) had higher odds of severe pain than non-Hispanic whites. However, because of the heterogeneity of the other racial category, it is difficult to discern which particular racial groups experience severe pain. Research which includes greater numbers of other racial categories is thus warranted. In regard to health status, the results support that individuals with three or more comorbid diseases have a relatively higher odds of frequent pain and severe pain than those with no comorbid diseases. One disease, arthritis, was of particular interest and therefore was treated as a separate variable. Almost two-thirds of the subjects had arthritis, which is not unexpected for persons age 65 and older. Persons with arthritis had a much higher odds (over 3 times the odds) odds of frequent pain than individuals without arthritis. Moreover, those with arthritis had approximately 1.5 times the odds of severe pain. The magnitudes of these associations imply that efforts to more effectively treat arthritis could lead to improvements in pain management. While the present study has contributed to our understanding of the relationship between doctor-patient interactions and persistent pain, it is not without several limitations. Because the study was cross-sectional in design, it is impossible to infer any causal relationships. Although the pain measures were adapted from a nationally representative cohort study of older persons [23], they may not adequately reflect the frequency, duration, and severity of pain. The generalizability of the findings may be limited to regions of the southwestern United States that are similar in geographic and ethnic makeup, such as Texas, New Mexico, Colorado, Arizona, and California. However, we suspect that the associations found in the present study would hold true among elders residing in other parts of the United States. In summary, future studies should employ longitudinal designs, include more detailed measures of pain and be conducted among other populations. Conclusions Despite these potential limitations, the present study suggests that several strategies could be implemented to limit the incidence and severity of pain among community-dwelling elders. Health policy makers and insurance companies might implement new reimbursement schemes to encourage visits to a personal physicians in order to improve pain and other health outcomes. Managers of physician clinics should consider organizing practices to ensure that older patients are able to make timely appointments with a personal provider. Finally, patients themselves could help reduce their chances of having frequent pain by becoming more involved in their care. These are just a few examples of how changes to the organization and delivery of care might affect pain-related health outcomes. Future research should evaluate how a range of physician characteristics (e.g. specialty and age), physician clinic characteristics (e.g. solo or group practice), insurance characteristics (e.g. HMO, PPO, or FFS coverage), and patient characteristics (e.g. trust in physician) influence pain and pain treatment. Competing interests The author(s) declare that they have no competing interests. Authors' contributions TFB conceived the overall study, performed the statistical analyses, and led the drafting of the manuscript. KTX assisted with the study design, interpretation of statistical findings, and drafting of the manuscript. JH assisted with interpretation of the findings and drafting of the clinical implications. GK contributed to data management, statistical analyses, and drafting of the methods section. All authors read and approved the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements This study was support, in part, by grant No. 90-AM-2378 from the Administration on Aging, Department of Health and Human Services. Points of view or opinions expressed in this study do not necessarily represent official Administration on Aging policy. We would like to thank P. Prithvi Raj, M.D., and James E. 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==== Front Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-4-41565199810.1186/1476-4598-4-4ResearchGene expression profiling of tumours derived from rasV12/E1A-transformed mouse embryonic fibroblasts to identify genes required for tumour development Vasseur Sophie [email protected] Cédric [email protected] Ezequiel L [email protected] Jean Charles [email protected] Juan L [email protected] INSERM U.624, Stress Cellulaire, 163 Avenue de Luminy, Case 915, Parc Scientifique et Technologique de Luminy, 13288 Marseille Cedex 9, France2005 16 1 2005 4 4 4 27 10 2004 16 1 2005 Copyright © 2005 Vasseur et al; licensee BioMed Central Ltd.2005Vasseur et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background In cancer, cellular transformation is followed by tumour development. Knowledge on the mechanisms of transformation, involving activation of proto-oncogenes and inactivation of tumour-suppressor genes has considerably improved whereas tumour development remains poorly understood. An interesting way of gaining information on tumour progression mechanisms would be to identify genes whose expression is altered during tumour formation. We used the Affymetrix-based DNA microarray technology to analyze gene expression profiles of tumours derived from rasV12/E1A-transformed mouse embryo fibroblasts in order to identify the genes that could be involved in tumour development. Results Among the 12,000 genes analyzed in this study, only 489 showed altered expression during tumour development, 213 being up-regulated and 276 down-regulated. The genes differentially expressed are involved in a variety of cellular functions, including control of transcription, regulation of mRNA maturation and processing, regulation of protein translation, activation of interferon-induced genes, intracellular signalling, apoptosis, cell growth, angiogenesis, cytoskeleton, cell-to-cell interaction, extracellular matrix formation, metabolism and production of secretory factors. Conclusions Some of the genes identified in this work, whose expression is altered upon rasV12/E1A transformation of MEFs, could be new cancer therapeutic targets. rasE1AMEFmicroarraygene expressiontumour development. ==== Body Background Cellular transformation is a complex process which involves activation of proto-oncogenes and inactivation of tumour-suppressor genes [1]. After transformation, the cells can generate malignant tumours, by mechanisms only partly understood yet. It is supposed that some modifications in the pattern of gene expression will promote survival of transformed cells in situ, other modifications will favour eventual formation of metastases [2], the capacity to adapt a new microenvironment being of major importance for successful tumour development and progression [reviewed in [3]]. Therefore, identification of genes whose expression is altered during tumour formation should provide important information on the underlying molecular mechanisms. In the present work, we used the Affymetrix-based DNA microarray technology to analyze gene expression profiles of tumour-derived from rasV12/E1A-transformed primary mouse embryonic fibroblasts (MEFs), in order to identify genes associated with tumour development. Results and Discussion The ras oncogene can transform most immortalized rodent cells to generate tumour cells, whereas transformation of primary cells requires either a cooperating oncogene or the inactivation of a tumour suppressor gene. The adenovirus E1A oncogene cooperates with ras to transform primary rodent fibroblasts [4] and injection of athymic mice with such transformed fibroblasts induces tumour development. The rasV12/E1A model of tumour formation was used in this work to analyze genes necessary for tumour progression. This model was chosen because transformation is induced in a simple and controlled way, avoiding the difficulties of analyzing the multiple and complex transformation mechanisms observed in cellular models derived from human tumours. Mouse embryo fibroblasts (MEFs) were chosen for transformation by rasV12/E1A to keep the model homogeneous, the host being the athymic mouse. Because non-transformed MEFs are unable to induce tumour when injected into athymic mice, we previously analyzed the change in gene expression profile induced in MEFs by rasV12/E1A-transformation [5], the idea being that such genetic changes are, directly or indirectly, responsible for the capacity of transformed MEFs to form tumours upon injection. As a follow-up, we used in this work microarray analysis to compare expression profiles of about 12,000 genes in rasV12/E1A-transformed MEFs and in the tumours formed after their injection into nu/nu mice. With Affymetrix microarray technology, differential expression values greater than 1.7 are likely to be significant, based on internal quality control data. We present data that use a more stringent ratio, restricting our analysis to genes overexpressed or under-expressed at least 2.0 fold in tumours, compared to the parent rasV12/E1A-transformed fibroblasts. Most striking findings are summarized below while complete data are presented in Tables 1 and 2 (see additional files 1 and 2), values being the average of three separate experiments. Among the 12,000 genes analyzed in this study, only 489 (4%) showed altered expression upon tumour development. Whereas 213 were up-regulated, 276 were down-regulated. Sixty seven genes encode ESTs. For 10 genes, expression data from microarrays were confirmed (Figure 1) by semiquantitative RT-PCR (see Material and Methods). Figure 1 Confirmation of microarray results by sequantitative RT-PCR analysis. Total RNA was isolated from primary embryo fibroblasts (MEFs), rasV12/E1A MEFs and rasV12/E1A MEF-induced tumours. In these three preparations, mRNA encoding arginase 1, bone morphogenetic protein 10, cathepsin S, insulin-like growth factor binding protein 4, interferon stimulated gene 12, serum deprivation response, thrombomodulin, adenylate kinase 1, connective tissue growth factor and S100 calcium binding protein A11 were amplified by RT-PCR as described in Material and Methods section. It is noteworthy that, to form tumours, transformed cells require the vicinity of blood vessels and components of the stroma, fibroblasts and inflammatory cells. Consequently, mRNA quantified in our system will come from transformed cells growing within the tumour and from stromal cells provided by the host. For example, mRNAs for haemoglobin, selectin or immunoglobulin heavy chain (V10 family) will very probably originate from erythrocytes, endothelium and leucocytes respectively. Gene transcription, mRNA processing and translation-associated genes Down-regulation was observed for some DNA-binding proteins and transcriptional factors such as zinc finger protein 36, C3H type-like 2, forkhead box M1, T-box 14, SET and MYND domain containing 2, sine oculis-related homeobox 1 homolog (Drosophila), liver-specific bHLH-Zip transcription factor, deformed epidermal autoregulatory factor 1 homolog (Drosophila), HMG box Bromodomain (5 domains) Zinc finger C2H2 type, BTB (POZ) domain containing 14A, Jun oncogene, general transcription factor III A, MYB binding protein (P160) 1a, general transcription factor II H polypeptide 1, transcription factor Dp 1, myeloblastosis oncogene-like 2, general transcription factor IIF polypeptide 1, v-ets erythroblastosis virus E26 oncogene homolog 1 (avian), transcriptional regulator SIN3B homolog (yeast), RNA polymerase I transcription factor RRN3, Kruppel-like factor 5, general transcription factor III C 1, nucleosome assembly protein 1-like 1, general transcription factor IIB and transformation related protein 53 (p53) whereas CCR4-NOT transcription complex subunit 7, transcription factor 15, dimerization cofactor of hepatocyte nuclear factor 1 alpha (TCF1), chromobox homolog 3 (Drosophila HP1 gamma), ets variant gene 1, basic helix-loop-helix domain containing class B2, pre-B-cell colony-enhancing factor 1, homeo box C6, BTB (POZ) domain containing 1, zinc finger protein 37, enhancer of zeste homolog 1 (Drosophila) and AT rich interactive domain 3B (Bright like) were up-regulated. A number of genes involved in RNA maturation, protein translation, processing and secretion were down-regulated in tumours, such as eukaryotic translation initiation factor 4 gamma 1, ER degradation enhancer mannosidase alpha-like 1, paraspeckle protein 1, CUG triplet repeat, RNA binding protein 1, copper chaperone for superoxide dismutase, splicing factor arginine/serine-rich 2 interacting protein, ribosomal protein S6 kinase polypeptide 4, sorting nexin 14, sorting nexin 17, elongation protein 3 homolog (S. cerevisiae), eukaryotic translation initiation factor 5B, peptidylprolyl isomerase F (cyclophilin F), ubiquitin-conjugating enzyme E2S, DnaJ homolog subfamily C member 3, SEC14-like 1 (S. cerevisiae), mitochondrial ribosomal protein L18, mitochondrial isoleucine tRNA synthetase, brix domain-containing protein, proteasome 26S subunit non-ATPase 1, COP9 subunit 5, small nuclear ribonucleoprotein polypeptide A, heat shock protein 105, heat shock protein 1, ribosome-binding protein p34, splicing factor 3b subunit 1, proteasome 26S subunit non-ATPase 7, mitochondrial processing peptidase beta, translocating chain-associating membrane protein 1, mitochondrial ribosomal protein L44, mitochondrial ribosomal protein S25, mitochondrial ribosomal protein S10, eukaryotic translation initiation factor 3 subunit 1 alpha, eukaryotic translation initiation factor 1A, COP9 subunit 2, RNA-binding region (RNP1, RRM) containing 1, DNAJ domain-containing and Der1-like family protein, whereas only 7 genes from this group were up-regulated including ubiquitin specific protease 18, proteosome subunit beta type 8, heterogeneous nuclear ribonucleoprotein U, ERO1-like (S. cerevisiae), proteasome subunit beta type 10, hnRNP-associated with lethal yellow and peptidylprolyl isomerase (cyclophilin)-like 2. Altogether these results show that transcriptional factors and DNA-binding proteins involved in transcription, as well as proteins involved in RNA maturation, protein translation, processing and secretion are preferentially down regulated during tumour development, suggesting that protein synthesis is less active in tumours than in transformed cells in vitro. Interferon-induced genes It is interesting to note that 11 genes activated by interferon were found up-regulated in tumours from 3.2 to 44.6 folds. They comprise the interferon stimulated gene 12 and genes encoding the interferon-induced protein with tetratricopeptide repeats 1, the interferon-induced protein with tetratricopeptide repeats 3, the interferon regulatory factor 7, the interferon alpha-inducible protein, the interferon consensus sequence binding protein 1, the interferon-inducible GTPase, the interferon induced transmembrane protein 2, the interferon gamma induced GTPase, the interferon-g induced GTPase and the interferon gamma-inducible protein 16 Genes encoding signalling factors Expression of several genes involved in signalling was modified in tumours. Among up-regulated were genes encoding the TYRO protein tyrosine kinase binding protein, ADP-ribosylation factor-like 4, ARF-GAP RHO-GAP ankyrin repeat and pleckstrin homology domains-containing protein 3, guanosine diphosphate dissociation inhibitor 2, SET domain bifurcated 1, acid phosphatase 5, guanylate nucleotide binding protein 3, SH3-domain GRB2-like 2, rap2 interacting protein x, TGFB inducible early growth response 1, pleckstrin homology domain containing family A (phosphoinositide binding specific) member 1, A kinase (PRKA) anchor protein 8, PDZ and LIM domain 4, protein tyrosine phosphatase non-receptor type 13, calcium and integrin binding family member 2, diaphanous homolog 2 (Drosophila), SH3-domain GRB2-like 3, Janus kinase 3, dual specificity phosphatase 9, Nik related kinase, synaptojanin 2, zinc finger RAN-binding domain containing 1, inositol hexaphosphate kinase 1, spleen tyrosine kinase, ribitol kinase putative, B lymphoid kinase, DEAD box polypeptide 27, mitogen activated protein kinase kinase kinase kinase 1, tumor-associated calcium signal transducer 1 and ral guanine nucleotide dissociation stimulator. Among down-regulated during tumour development where genes encoding adenylate kinase 1, serum deprivation response, ATP-binding cassette sub-family F member 2, protein kinase C alpha, RIO kinase 1 homolog (yeast), protein phosphatase 1 regulatory (inhibitor) subunit 2, phosphotidylinositol transfer protein beta, RAN GTPase activating protein 1, v-crk sarcoma virus CT10 oncogene homolog (avian)-like, serum/glucocorticoid regulated kinase, DEAD box polypeptide 48, nucleolar GTPase, calcium/calmodulin-dependent protein kinase II delta, cellular retinoic acid binding protein I, AFG3(ATPase family gene 3)-like 1 homolog (yeast), transforming growth factor beta regulated gene 4, enabled homolog (Drosophila), Rho GDP dissociation inhibitor gamma, S100 calcium binding protein A11 (calizzarin), cornichon homolog (Drosophila), butyrate-induced transcript 1, phosphatidylinositol 3-kinase regulatory subunit polypeptide 1 (p85 alpha), mitogen activated protein kinase 1, IQ motif containing GTPase activating protein 1, protein kinase C epsilon, transducin (beta)-like 3, serine/threonine kinase 16, thymoma viral proto-oncogene 1, ATP-binding cassette sub-family B (MDR/TAP) member 7, protein phosphatase 1 regulatory (inhibitor) subunit 7, dual specificity phosphatase 1, Rho-associated coiled-coil forming kinase 2, acid phosphatase 1 soluble, PAK1 interacting protein 1, RAN binding protein 1, dual specificity phosphatase 16, RAB23 member RAS oncogene family, WD repeat domain 26, PDZ and LIM domain 1 (elfin) and mitogen activated protein kinase kinase 4. Apoptosis-related genes The rasV12/E1A-transformed MEFs are very sensitive to apoptosis in vitro [6] whereas, on the contrary, these cells do not show signs of apoptosis in tumours as judged by microscopic analysis. A possible explanation is provided by the observation that many proapoptotic genes are down-regulated in tumours, such as growth arrest and DNA-damage-inducible 45 beta, wild-type p53-induced gene 1, Bcl2-associated X protein, programmed cell death 2, TP53 apoptosis effector, programmed cell death 6 interacting protein, apoptotic chromatin condensation inducer 1, cell division cycle and apoptosis regulator 1, large tumour suppressor 2 and caspase 7 and several antiapoptotic genes are up-regulated, including genes encoding the spermatogenesis apoptosis-related protein and BCL2/adenovirus E1B 19kDa-interacting protein 1 (NIP3). Cell growth-involved genes Another interesting point to be underscored is that expression of many cell growth-related genes was found decreased in tumours, whereas none of them was up-regulated. Among up-regulated genes were those coding for he bone morphogenetic protein 10, cytokine receptor-like factor 1, insulin-like growth factor binding protein 4, ephrin A2, schlafen 4, early growth response 2, retinoblastoma-associated factor 600, receptor tyrosine kinase-like orphan receptor 2, cyclin-dependent kinase 7 (homolog of Xenopus MO15 cdk-activating kinase), inhibitor of growth family member 4 and neoplastic progression 1 were up-regulated and connective tissue growth factor, ephrin B2, neural proliferation differentiation and control gene 1, nerve growth factor beta, Eph receptor A2, neoplastic progression 3, cyclin G1, bone morphogenetic protein receptor type 1A, cell division cycle 34 homolog (S. cerevisiae), cyclin-dependent kinase inhibitor 1A (p21), SGT1 suppressor of G2 allele of SKP1 homolog (S. cerevisiae), nuclear casein kinase and cyclin-dependent kinase substrate, G two S phase expressed protein 1, prohibitin, nucleostemin, CDK2-associated protein 1 and block of proliferation 1 This is not a surprise since transformed cells grow more rapidly in vitro than during tumour development. Angiogenesis-involved genes Some proangiogenic genes such as angiopoietin-like 4, selectin, endothelin 1, angiopoietin 2 and endothelial PAS domain protein 1 were up-regulated during tumour growth whereas the antiangiogenic factor thrombospondin 1 was found to be down regulated. Surprisingly, the proangiogenic endothelial cell growth factor 1 (platelet-derived) and the angiomotin like 2 were found down-regulated. Cytoskeleton and cell-to-cell contact genes Expression of some genes involved in cytoskeleton and cell-to-cell contact was modified in tumours. mRNAs encoding junctophilin 3, fibrillin 2, plexin A3, ARP1 actin-related protein 1 homolog A, MYOSIN-IXA homolog, myosin IB, tubulin alpha 1, cadherin 3, integrin beta 5, dynamin and stathmin-like 4 genes were up-regulated whereas mRNAs encoding MAP/microtubule affinity-regulating kinase 2, nucleoporin 54 kDa, smooth muscle cell associated protein-1, plakophilin 2, annexin A3, myosin X, ARP1 actin-related protein 1 homolog B (yeast), follistatin, golgi associated gamma adaptin ear containing ARF binding protein 2, filamin beta, vinculin, cytoskeleton-associated protein 1, annexin A11, lamin A, cortactin, pericentrin 2, gap junction membrane channel protein alpha 1, importin 4, nucleolar protein 5, exportin 7, vinculin, actinin alpha 1, nucleoporin 88, fibulin 2 and CD44 antigen were down regulated. Extracellular matrix compounds In cancer, transformed and mesenchymal cells synthesize and secrete several compounds which participate to tumour organization. In tumour cells, genes encoding the procollagen type XVIII alpha 1, Nice-4 protein homolog isoform 1, glycophorin A, collagen type V alpha 1 and procollagen type VI alpha 1 were up-regulated whereas, to our surprise, procollagen type V alpha 2 was found down regulated. Metabolic enzymes and secretory factors Finally, an interesting finding is that the majority of genes encoding enzymes involved in metabolism were down regulated in tumours whereas the majority of secretory factors or their receptors were up-regulated (see additional files 1 and 2) suggesting some reduction of intracellular metabolic activity and increased signal exchanges during tumour formation. Conclusion The microenvironment of the metastatic cancer cell and the interaction between these cells and the stroma play critical roles in tumour development and progression. However, the molecular mechanisms and genes involved in tumour development remain largely unidentified. In the experimental model of tumour formation used in this study, we identified 489 genes whose expression is modified during tumour formation. Among them, 213 were up-regulated and 276 were down-regulated. These genes are involved in a variety of cellular functions, including control of transcription, mRNA processing, regulation of translation, activation of some interferon-induced genes, intracellular signalling, apoptosis, cell growth, angiogenesis, cytoskeleton, cell adhesion, extracellular matrix formation, metabolism and production of secretory factors. These results can be interpreted in two ways i/ many cellular functions need to be adapted to allow successful tumour development or ii/ successful tumour formation has induced changes in gene expression. In fact, both interpretations are probably correct but the important point is that among them are found the genes involved in adaptation of the cancer cell to a new environment, which are potential targets for cancer therapy. This study therefore suggests that, after screening ~12,000 genes, the most interesting candidates for clinical applications are among the 213 genes up-regulated in the tumour. Material and Methods Transformation of MEFs by retroviral infection Primary embryo fibroblasts were isolated from 14.5 day-old SV129J mouse embryos and grown in Dulbecco's modified Eagle's medium supplemented with 10% foetal calf serum as previously described [7]. We transduced MEFs with the pBabe-rasV12/E1A retroviral vector which expresses both the rasV12 mutated protein and the E1A oncogene to obtain transformed fibroblasts. pBabe-rasV12/E1A plasmids were obtained from S. Lowe. Bosc 23 ecotropic packaging (106) cells were plated in a 6-well plate, incubated for 24 hr, and then transfected with PEI with 5 μg of retroviral plasmid. After 48 hr, the medium containing the virus was filtered (0.45 μm filter, Millipore) to obtain the retroviral supernatant. MEFs were plated at 2 × 105 cells per 35 mm dish and incubated overnight. For infection, the culture medium was replaced by an appropriate mix of the retroviral supernatant and culture medium (V/V), supplemented with 4 μg/ml polybrene (Sigma), and cells were incubated at 37°C. Transformation of MEFs by the pBabe-rasV12/E1A retroviral vector was evaluated by examining changes in their morphological aspect, by quantifying expression of the RAS protein by western blot, by monitoring cell proliferation, colony formation in soft-agar and tumours in nude mice as previously described [7]. Tumour induction in athymic mice Suspensions of the pBabe-rasV12/E1A transformed MEFs (106/200 μl PBS) were injected subcutaneously into the flank of male 8 week-old nu/nu mice, and tumours were allowed to develop for 20 days. Tumours were removed and stored at -80°C. Microscopical analysis reveals that tumours contain about 15% of vascular and stromal cells. Microarray Total RNA from rasV12/E1A-transformed cells and tumours from three independent experiments was isolated by Trizol (Gibco-BRL). Twenty μg of total RNA was converted to cDNA with SuperScript reverse transcriptase (Gibco-BRL), using T7-oligo-d(T)24 as a primer. Second-strand synthesis was performed using T4 DNA polymerase and E. coli DNA ligase followed by blunt ending by T4 polynucleotide kinase. cDNA was isolated by phenol-chloroform extraction using phase lock gels (Brinkmann). cDNA was transcribed in vitro using the T7 BioArray High Yield RNA Transcript Labeling Kit (Enzo Biochem, New York, N.Y.) to produce biotinylated cRNA. Labelled cRNA was isolated using an RNeasy Mini Kit column (Qiagen). Purified cRNA was fragmented to 200–300 mer cRNA using a fragmentation buffer (100 mM potassium acetate-30 mM magnesium acetate-40 mM Tris-acetate, pH 8.1), for 35 min at 94°C. The quality of total RNA, cDNA synthesis, cRNA amplification and cRNA fragmentation was monitored by micro-capillary electrophoresis (Bioanalizer 2100, Agilent Technologies). The cRNA probes were hybridized to an MG u74Av2 Genechip (Affymetrix, Santa Clara, CA). Fifteen micrograms of fragmented cRNA was hybridized for 16 h at 45°C with constant rotation (60 rpm). Microarrays were processed in an Affymetrix GeneChip Fluidic Station 400. Staining was made with streptavidin-conjugated phycoerythrin (SAPE) followed by amplification with a biotinylated anti-streptavidin antibody and a second round of SAPE, and then scanned using an Agilent GeneArray Scanner (Agilent Technologies). The signal intensities for the β-actin and GAPDH genes were used as internal quality controls. The ratio of fluorescent intensities for the 5' and 3' ends of these housekeeping genes was <2. Scanned images were analyzed with the Microarray Suite 5.0 software (Affymetrix). Validation of gene expression profiles One microgram of total RNA from primary embryo fibroblasts, rasV12/E1A transformed MEF and its derived tumour was subjected to PCR with reverse transcription using the One Step RT-PCR kit (Promega) according to the manufacturer's protocol. Selected RNA species were amplified using the following primers: arginase 1, sense, 5'-gaaaaggccgattcacctgag-3' and antisense, 5'-atgtggcgcattcacagtcac-3'; bone morphogenetic protein 10, sense, 5'-ggatctggacctggactcaga-3' and antisense, 5'-gaagctttctgggaattcttg-3'; cathepsin S, sense, 5'-gaagggctgcgtcactgaggt-3' and antisense, 5'-acaccgcttttgtagaagaag-3'; insulin-like growth factor binding protein 4, sense, 5'-gaaggtgtagagtagaggctc-3' and antisense, 5-ggaccagaatggggccattcc-3'; interferon stimulated gene 12, sense, 5'-ctcaacatgttgggaacactg-3' and antisense, 5-catctcctgcgtagtctgtac-3'; serum deprivation response, sense, 5'-gtctagtattataacctaacc-3' and antisense, 5-aagagtagagagttcgagccc-3'; thrombomodulin, sense, 5'-cagaaatttcaggtaaccaaa-3' and antisense, 5-tcagctcggcacgaagcacac-3'; adenylate kinase 1, sense, 5'-cactgggtgccaaggagctgt-3' and antisense, 5-ggcttcctgtgtaatgagacc-3'; connective tissue growth factor, sense, 5'-ggagtcagagccttgtctgtt-3' and antisense, 5-agtcataatcaaagaagcagc-3'; and S100 calcium binding protein A11, sense, 5'-gctgttttccaaaagtacagc-3' and antisense, 5-cgcttctgggaagtttggatg-3'. Reverse transcription was carried out 45 min at 48°C followed by 25–32 cycles of PCR, each cycle consisting in a denaturing step for 1 min at 94°C, an annealing step for 2 min at 56°C, and a polymerization step for 2 min at 72°C. PCR products were separated on a 1.0% agarose gel containing ethidium bromide and photographed under ultraviolet light. Authors' contributions SV prepared cells and retroviruses, CM carried out RNA purification and RT-PCR analysis, ELC was in charge of microarray hybridization, JCD participated in the design of the study, JLI participated in the analysis of data and wrote the manuscript. Supplementary Material Additional File 1 Genes up-regulated during tumour development. Genes found up-regulated by microarray analysis are listed, with their GenBank accession number, the over-expression factors (relative to rasV12/E1A transformed MEFs) observed in three separate experiments. Click here for file Additional File 2 Genes down-regulated during tumour development. Genes found down-regulated by microarray analysis are listed, with their GenBank accession number, the down-regulation factors (relative to rasV12/E1A transformed MEFs) observed in three separate experiments. Click here for file Acknowledgments This work was supported by grants from La Ligue and Canceropôle PACA. ==== Refs Vogelstein B Kinzler KW Cancer genes and the pathways they control Nat Med 2004 10 789 799 15286780 10.1038/nm1087 Liotta LA Kohn EC The microenvironment of the tumour-host interface Nature 2001 411 375 379 11357145 10.1038/35077241 Kataoka H Tanaka H Nagaike K Uchiyama S Itoh H Role of cancer cell-stroma interaction in invasive growth of cancer cells Hum Cell 2003 16 1 14 12971620 Ruley HE Transforming collaborations between ras and nuclear oncogenes Cancer Cells 1990 2 258 268 2223387 Vasseur S Malicet C Calvo EL Labrie C Berthezene P Dagorn JC Iovanna JL Gene expression profiling by DNA microarray analysis in mouse embryonic fibroblasts transformed by rasV12 mutated protein and the E1A oncogene Mol Cancer 2003 2 19 12685932 10.1186/1476-4598-2-19 Putzer BM Stiewe T Parssanedjad K Rega S Esche H E1A is sufficient by itself to induce apoptosis independent of p53 and other adenoviral gene products Cell Death Differ 2000 7 177 188 10713732 10.1038/sj.cdd.4400618 Vasseur S Hoffmeister A Garcia S Bagnis C Dagorn JC Iovanna JL p8 is critical for tumour development induced by rasV12 mutated protein and E1A oncogene EMBO Rep 2002 3 165 170 11818333 10.1093/embo-reports/kvf023	15651998	PMC546195	CC BY	2021-01-04 16:36:36	no	Mol Cancer. 2005 Jan 16; 4:4	utf-8	Mol Cancer	2,005	10.1186/1476-4598-4-4	oa_comm
==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-91565507110.1186/1471-2105-6-9SoftwareGATA: a graphic alignment tool for comparative sequence analysis Nix David A [email protected] Michael B [email protected] Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA2 Department of Genome Science, Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA2005 17 1 2005 6 9 9 30 7 2004 17 1 2005 Copyright © 2005 Nix and Eisen; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Several problems exist with current methods used to align DNA sequences for comparative sequence analysis. Most dynamic programming algorithms assume that conserved sequence elements are collinear. This assumption appears valid when comparing orthologous protein coding sequences. Functional constraints on proteins provide strong selective pressure against sequence inversions, and minimize sequence duplications and feature shuffling. For non-coding sequences this collinearity assumption is often invalid. For example, enhancers contain clusters of transcription factor binding sites that change in number, orientation, and spacing during evolution yet the enhancer retains its activity. Dot plot analysis is often used to estimate non-coding sequence relatedness. Yet dot plots do not actually align sequences and thus cannot account well for base insertions or deletions. Moreover, they lack an adequate statistical framework for comparing sequence relatedness and are limited to pairwise comparisons. Lastly, dot plots and dynamic programming text outputs fail to provide an intuitive means for visualizing DNA alignments. Results To address some of these issues, we created a stand alone, platform independent, graphic alignment tool for comparative sequence analysis (GATA ). GATA uses the NCBI-BLASTN program and extensive post-processing to identify all small sub-alignments above a low cut-off score. These are graphed as two shaded boxes, one for each sequence, connected by a line using the coordinate system of their parent sequence. Shading and colour are used to indicate score and orientation. A variety of options exist for querying, modifying and retrieving conserved sequence elements. Extensive gene annotation can be added to both sequences using a standardized General Feature Format (GFF) file. Conclusions GATA uses the NCBI-BLASTN program in conjunction with post-processing to exhaustively align two DNA sequences. It provides researchers with a fine-grained alignment and visualization tool aptly suited for non-coding, 0–200 kb, pairwise, sequence analysis. It functions independent of sequence feature ordering or orientation, and readily visualizes both large and small sequence inversions, duplications, and segment shuffling. Since the alignment is visual and does not contain gaps, gene annotation can be added to both sequences to create a thoroughly descriptive picture of DNA conservation that is well suited for comparative sequence analysis. ==== Body Background The most widely used methods for aligning DNA sequences rely on dynamic programming algorithms initially developed by Smith-Waterman and Needleman-Wunsch [1,2]. These algorithms create the mathematically best possible alignment of two sequences by inserting gaps in either sequence to maximize the score of base pair matches and minimize penalties for base pair mismatches and sequence gaps. Although these methods have proven invaluable in understanding sequence conservation and gene relatedness, they make several assumptions. One of their assumptions in generating the "best" alignment is that sequence features are collinear. For example, segments X, Y, Z in sequence one are also ordered as X, Y, and Z in sequence two. Another assumption is that short segments, like Y, have not become inverted or duplicated (e.g. X, Y, Y', Z). These rearrangement events are prone to be gapped out in dynamic programming and thus described as unrelated. Local alignment algorithms can be used to identify these rearrangements provided an exhaustive search is performed, but typically, only the highest scoring local alignments are considered valid and other, lower scoring local alignments are assumed to be spurious matches between unrelated sequences. When aligning protein coding sequences, dynamic programming works quite well. Evolution exerts significant functional constraint on protein coding sequences. When an inversion, duplication or segment-shuffling event occurs, the protein is often compromised by truncation due to the introduction of frame shifts and stop codons. These deleterious mutations are typically lost and not observed in the surviving population. When aligning this type of constrained sequence element, dynamic programming works quite well. Functional non-coding sequences do not appear to be as constrained in the ordering of elements as protein coding sequences [3-6]. Compact cis-regulatory modules, for example, enhance or suppress eukaryotic gene expression in response to external stimuli and play key roles in development and differentiation. One of the best characterized eukaryotic enhancers is the even-skipped stripe 2 element in Drosophila that controls transcription of the second transverse stripe of even-skipped mRNA during embryogenesis. Functional and comparative sequence analysis of stripe 2 clearly demonstrate that the enhancer maintains its specific activity across species yet displays significant small-scale insertions, deletions, and rearrangements of transcription factor binding sites within the module [7,8]. Tracing the evolutionary path of such non-coding elements is proving difficult with current alignment tools and may be assisted by a visual alignment program like GATA. Implementation GATA employs a two tiered architecture in aligning DNA sequences. GATAligner executes and processed BLASTN output. GATAPlotter displays the processed alignments and annotation from GATAligner. GATAligner The GATAligner application (figure 1) uses the NCBI bl2seq and BLASTN programs [9,10] to generate all possible local alignments between two input DNA sequences that score above a very low cut off (see Table 1). To avoid problems associated with visualizing both large and small local alignments, see Results/ Discussion, a sliding window is advanced at one base intervals across each local alignment. Windowed sequences scoring above a defined score are saved. To reduce the number of windowed sequences, each is compared to its neighbours and joined if they are of the same score and orientation. The score is not changed. These "sub-alignment" objects contain several features: a score, an orientation, a reference to the parental local alignment, the aligned sequences, and start and stop coordinates for each sequence. The sub-alignments are then saved to disk. This alignment and post-processing takes less than a minute for two 50 kb sequences using a window size of 24 and a score cut off of 25 bits on an 800 MHz PowerPC laptop computer. Our initial goal was to create a high resolution sequence alignment and visualization tool to use in identifying small sequence rearrangements, like those associated with evolving non-coding regulatory DNA. We initially divided the first sequence into overlapping windows offset by one base pair. A Smith-Waterman dynamic programming algorithm was then used to align each window against the entire second sequence. Windows were scored, merged, and saved as above. Although this method is more rigorous than using BLASTN, it took 20–50 times as long, and did not produce significantly different results (data not shown). It should be noted that BLASTN requires seven consecutive identical bases to align two sequences. Thus in rare cases, some windows will be missed, for example, GGGGGGcTTTTTTaCCCCCCgAAAAAA versus GGGGGGaTTTTTTgCCCCCCtAAAAAA. GATAPlotter The GATAPlotter application (figure 2) takes sub-alignment objects created by GATAligner and displays them graphically. Two boxes connected by a line are used to represent each sub-alignment. The boxes are plotted against horizontal representations of the input sequences with the reference sequence on top. The size of each box is determined by the start and stop positions in the sub-alignment. The shading of the boxes and connector line are scaled according to the sub-alignment score where solid black represents the highest score obtained, light grey the lowest. Lastly the colour of the connecting line is used to indicate the sub-alignment orientation, black for +/+, red for +/-. Where windows overlap, those with the highest score are displayed on top. Single clicking on overlapping windows retrieves all of the underlying windowed sequence alignment information. Double clicking fetches all of the associated local alignment information as parsed from BLAST. GATAPlotter also has the capability to display extensive gene annotation for one or both input sequences. The principle component of gene annotation rendering by GATA is the "GeneGroup" (figure 3). Each GeneGroup is drawn independent of other GeneGroups and is allowed to float within the panel to avoid overlap. A typical GeneGroup contains one DNA sequence from which one or more "TransGroups" are derived. Each TransGroup contains exons, an RNA transcript and possibly a protein translation. Each of these features are described using the Berkeley Drosophila Genome Project GFF format [11]. Coding and non-coding DNA sub features are only created in the presence of translation features and represent the most conservative estimation of what is protein coding sequence. If any translation predicts a larger coding region than the others, this is adopted for the entire GeneGroup. A point of confusion by many is that exons encode protein peptides. This is not necessarily true (i.e. 5' and 3' UTRs) and has lead to a variety of annotation rendering errors. When parsing a GFF file, GATAPlotter looks for the following GFF features: exon, translation, transcript, gene, rna, transpos, misc, where an * represents one or more wild cards. These wild card "genes" are interpreted as the closing feature on the GeneGroup from which all the proceeding TransGroups are derived. Annotation for both strands is drawn together; arrows are used to indicate orientation. Features not recognized by the parser are interpreted as novel user defined elements and rendered in their own tracks. GFF annotation examples, templates, and extensive descriptions are provided under the GATAPlotter "Documentation" menu. See table 2 and 3 for a complete listing of program options. Results and discussion To illustrate the types of rearrangements GATA can distinguish, examine figures 4 and 5. Both contain alignments between Drosophila melanogaster and D. pseudoobscura. Figure 4 contains three highly similar genes. Figure 5, inversions of putative enhancers. Annotation for each was obtained from whole_genome_annotation_dmel_RELEASE3-1.gff [11]. Orthologous sequences were isolated using the FlyCatcher program [12]. In cases where alignment windows overlap, the lowest scoring windows are drawn first and higher scoring windows placed on top. Both, connecting lines and their associated boxes are shaded according to score. Several related alignment and visualization tools have proven useful in comparative sequence analysis. Dot plot analysis can be used to identify duplications and inversions. Programs such as Dotter, JDotter, Dotlet, and Family Relations [13-16] generate graphical representations of sequence conservation by scoring identity between two perpendicular sequence representations. Although, mapping annotation to dot plots containing duplications and inversions is rather difficult and counter intuitive. Programs such as Artemis/ACT, LALNVIEW and to some extent, PLALIGN [17-20], utilize alignment information generated from dynamic programming algorithms to create box-line-box representations of each local alignment. These are similar to GATA but do not divide local alignments into window scored sub-alignments. This is unfortunate since window scoring enables a more detailed view of the actual sequence similarity within a large local alignment. Moreover, meaningful visualization using these browsers requires setting a high cut off score for the visualized local alignments. This effectively eliminates smaller, lower scoring local alignments that may provide alternative or even better inverted local alignments. GATA's windowed post-processing overcomes these associated problems. One program that is proving quite useful in avoiding the collinearity problem while still using a dynamic programming algorithm is Shuffle LAGAN [21]. Alignments generated by Shuffle LAGAN are combine with alignment annotation viewers such as VISTA [22,23] to align entire genomes. K-BROWSER/ MAVID and Mauve are two additional genome browser/ aligners that look equally promising [24-26]. Although, it should be noted, these programs are designed to provide genome wide alignments and identify large-scale rearrangements, GATA is best suited at interrogating non-coding DNA sequences between 0–200 kb in size for both large and small rearrangements. One of the major challenges facing bioinformaticians is the development of alignment and visualization tools for multi-species comparative sequence analysis. Within the fly community alone, 12 divergent species of diptera and hymenoptera will be sequenced within 3 years. A variety of higher eukaryotes including human, mouse, rat, dog, chimp, cow, chicken, opossum, and platypus have or are in the process of being completely sequenced. How can one visualize the alignment and species-specific annotation for 12 orthologs of a particular gene or a genomic segment? The GATA alignment paradigm is well suited to this challenge and will play a prominent role in GATA's development. Conclusions As comparative sequence analysis accelerates, scientists need more sophisticated alignment and visualization tools to define the evolutionary relationships and functional significance between particular orthologous sequences. This is especially true for regulatory, non-coding DNA that can show significant small-scale rearrangements. These new tools must incorporate detailed annotation alongside views of sequence conservation while providing easy access to the underlying sequence information. GATA provides one such solution. Availability and requirements Project name: GATA, graphic alignment tool for comparative sequence analysis. Project home pages: and Operating system(s): Platform independent Programming language: Java Other requirements: Java 1.4 or higher License: GNU GPL Any restrictions to use by non-academics: None Authors' contributions DAN designed and constructed the GATA programs with advice and supervision from MBE. Acknowledgements DAN received postdoctoral support from the American Cancer Society and would like to thank Lisa Simerinko for her assistance with Java and GUIs. Figures and Tables Figure 1 Screen capture of the GATAligner program. Figure 2 Screen capture of the GATAPlotter program. An alignment between D. melanogaster and D. pseudobscura surrounding gene CG1877. Figure 3 Rendered gene annotation in GATA. A typical protein coding gene is visualized as a GeneGroup comprised of multiple TransGroups containing Exons, Introns, and a Protein transcript. Arrows designate orientation. The DNA glyph is rendered as both Non-Coding and Coding elements. Figure 4 Example: gene triplication. An example of a gene triplication in D. melanogaster and D. pseudobscura surrounding gene CG14745 Figure 5 Example: sequence inversion. An example of a sequence inversion event between D. melanogaster and D. pseudobscura surrounding gene CG8930. Table 1 GATAligner parameters and features NCBI-BLASTN Parameters Nucleotide Match Score added to the total for each match. Nucleotide Mismatch Score subtracted from total for each mismatch. Gap Creation Score subtracted from total for each new gap. Gap Extension Score subtracted from total for each additional base in a gap after its creation. Low Complexity Mask Use of DUST to mask and thus not align regions of low complexity. GATAligner Parameters Window Size Size of window used to score sub-alignments in each local alignment. Sub-alignments smaller than the window size will be saved provided they are at or above the score cut off. When aligning longer sequences, increase the size of the window as well as the cut off score to minimize non-related alignments. Score Cut Off Score (raw or bits) at which windowed sub-alignments are saved or discarded. The higher this score is set the faster GATAligner will run. Set between 20–25 bits for a window size of 24 when aligning sequences less than 10 KB. For larger sequences, increase the cut off and window size (e.g. 30 bits and 30 bp). Start Positions for Reference and Comparative Sequences Use this to maintain register with gene annotation. Multithreaded GATAligner is multithreaded. Queue up multiple alignments. Table 2 GATAPlotter menus File-Menu Open or Close Alignments Open a new GATA plot or close the present GATA plot. Quit Quit the entire application. Save GATAPlot Image Use to save a high resolution PNG file of the GATAPlot. Save GATAPlotter Settings Select this menu option to save the current settings. These will be used upon opening new GATA plots. Generic Track settings are not saved. To restore the defaults, select the Redraw Using Defaults from the Windows menu and then the Save GATAPlotter settings. Alternatively, delete the GATAPlotterPreferences file in the GATA folder. Alignment-Menu Sizing parameters A variety of parameters to change the height, width, thickness and relative location of the Alignment panel shapes. Set Nucleotides Per Pixel Allows for specifying the number of nucleotides that are rendered per pixel enabling size synchronization between different GATAPlots. Fetch Conserved Sequences Use this option to reformat both the reference and conserved sequence using the visible alignment boxes in the GATA plot. Upon selection, a dialog box will appear asking how you would like to reformat the non-boxed sequences. These non-conserved sequences can be replaced with any single character (e.g. N or X) or converted to lower or upper case. Use the sliders in the Tools Panel to adjust what is visible. GATAligner Parameters Select to retrieve all the GATAligner settings used in making the GATA alignments and GATA plot. (e.g. score cut off, window size, match, mismatch, etc). Annotation-Menu (These menu items are only available if gene annotation has been added to the GATA plot.) Gene Groups Use to hide or show all of the gene groups (Protein, RNA, DNA) or labels. Protein, RNA, DNA Select whether to hide, show or change their colour. Scale Ruler Select to hide or show, change the colour, or move the scale bar. Tracks This option contains global effectors for generic tracks. RefTrks/ CompTrks If generic features are found within the GFF file, each is assigned its own track. Their thickness, colour, visibility, and label visibility can be modified using the appropriate options. Pix Btw A variety of adjustments can be made to the number of pixels that are placed between features. Negative numbers are valid if you want to overlap features. Line Thickness Line thickness can be set to control the size of Protein, RNA, and DNA features. Colour The background panel and label colour can be set using these options. Scale Track Colours By Score If generic tracks have been generated and are associated with a score, they can be shaded using the scaling feature. Select the method GATA should us to convert the reported scores to linear numbers. (e.g. Often hits to a position weight matrix are scored in log units. Select the appropriate base log 10, log 2, or natural log.) After converting the scores for a particular track, a range is estimated and used to adjust the opacity of each feature from 30% for the lowest scoring feature to 100% for the highest scoring feature. This allows visual comparison of features within a track. Comparisons between tracks are only valid if they have the same range. Windows-Menu Show All Retrieves and displays all hidden windows. Hide All Hides all windows except the main GATAPlotter alignment window. ReDraw Using Defaults Redraws all panels using GATAPlotter default values. Documentation-Menu Extensive documentation for GATA including examples. Table 3 GATAPlotter windows and features Tools Window Score Sliders Sliders can be used to control the minimum score and maximum score used in deciding which sub-alignment box-line-boxes are displayed. The units are a normalized range where zero is set to the value assigned in GATAligner to the Lower Score Cut Off. 100 is set to the value obtained by multiplying the Window Size by the Nucleotide Match. The actual window bit and Expect values set by the sliders are shown in the adjacent boxes. Since the shading is relative to these minimum and maximum values, be careful in making comparisons in shading between two GATA plots. Such comparisons are only valid if the same window size, cut off score, and scoring scheme were used. Check the actual score by clicking on the shaded box or connecting line to see the real bit score. (Bit scores are scoring system independent and can be used to directly compare alignments. Raw scores are relative to the settings for match, mismatch, gap, etc.) Zoom Buttons The zoom buttons allow for zooming in and out. Ref or Cmp These numbers report the position of the mouse, in base pairs, when the mouse passes over one of the sequence bars. Mouse Clicks Single clicking a gene annotation feature retrieves and displays all information associated with that feature in the Text Console Window. Likewise single clicking an alignment box or line displays the sub-alignment information. Double clicking fetches the sub-alignment and its parental local alignment. The sub-alignment is indicated by the asterisks in the larger local alignment. All visible alignments beneath a mouse click are retrieved. Use the Score Sliders to determine which boxes are visible. If you are interested in a sub section of the alignment, drag the mouse over the region and a reformat box will appear. If you drag the mouse over one sequence and it contains box-line-boxes, these will to used to fetch the corresponding sequence from the other sequence. If you drag the mouse over both sequences, sub sequence sections will be retrieve regardless of the location of box-line-boxes. Text Console A resizable scrolling container for text messages generated by mouse clicking. ==== Refs Smith TF Waterman MS Identification of common molecular subsequences J Mol Biol 1981 147 195 197 7265238 10.1016/0022-2836(81)90087-5 Needleman SB Wunsch CD A general method applicable to the search for similarities in the amino acid sequences of two proteins J Mol Biol 1970 48 443 453 5420325 LudWig MZ Functional evolution of noncoding DNA Curr Opin Genet Dev 2002 12 634 639 12433575 10.1016/S0959-437X(02)00355-6 Markstein M Levine M Decoding cis-regulatory DNAs in the Drosophila genome Curr Opin Genet Dev 2002 12 601 606 12200166 10.1016/S0959-437X(02)00345-3 Wray GA Hahn MW Abouheif E Balhoff JP Pizer M Rockman MV Romano LA The evolution of transcriptional regulation in eukaryotes Mol Biol Evol 2003 20 1377 1419 12777501 10.1093/molbev/msg140 McGregor AP Shaw PJ Hancock JM Bopp D Hediger M Wratten NS Dover GA Rapid restructuring of bicoid-dependent hunchback promoters within and between Dipteran species: implications for molecular coevolution Evol Dev 2001 3 397 407 11806635 10.1046/j.1525-142X.2001.01043.x Ludwig MZ Patel NH Kreitman M Functional analysis of eve stripe 2 enhancer evolution in Drosophila: rules governing conservation and change Development 1998 125 949 958 9449677 Ludwig MZ Bergman C Patel NH Kreitman M Evidence for stabilizing selection in a eukaryotic enhancer element Nature 2000 403 564 567 10676967 10.1038/35000615 Tatusova TA Madden TL BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences FEMS Microbiol Lett 1999 174 247 250 10339815 10.1016/S0378-1097(99)00149-4 Altschul SF Gish W Miller W Myers EW Lipman DJ Basic local alignment search tool J Mol Biol 1990 215 403 410 2231712 10.1006/jmbi.1990.9999 Misra S Crosby MA Mungall CJ Matthews BB Campbell KS Hradecky P Huang Y Kaminker JS Millburn GH Prochnik SE Smith CD Tupy JL Whitfied EJ Bayraktaroglu L Berman BP Bettencourt BR Celniker SE de Grey AD Drysdale RA Harris NL Richter J Russo S Schroeder AJ Shu SQ Stapleton M Yamada C Ashburner M Gelbart WM Rubin GM Lewis SE Annotation of the Drosophila melanogaster euchromatic genome: a systematic review Genome Biol 2002 3 research0083.1 0083.22 12537572 10.1186/gb-2002-3-12-research0083 Nix DA FlyCatcher Sonnhammer EL Durbin R A dot-matrix program with dynamic threshold control suited for genomic DNA and protein sequence analysis Comput Appl Biosci 1996 12 507 510 9021269 Brodie R Roper RL Upton C JDotter: a Java interface to multiple dot plots generated by dotter Bioinformatics 2004 20 279 281 14734323 10.1093/bioinformatics/btg406 Pagni M Junier T Dotlet Brown CT Rust AG Clarke PJ Pan Z Schilstra MJ De Buysscher T Griffin G Wold BJ Cameron RA Davidson EH Bolouri H New computational approaches for analysis of cis-regulatory networks Dev Biol 2002 246 86 102 12027436 10.1006/dbio.2002.0619 Rutherford K Parkhill J Crook J Horsnell T Rice P Rajandream M-A Barrell B Artemis: sequence visualisation and annotation Bioinformatics 2000 16 944 945 11120685 10.1093/bioinformatics/16.10.944 ACT: Artemis Comparison Tool Duret L Gasteiger E Perriere G LALNVIEW: a graphical viewer for pairwise sequence alignments Comput Appl Biosci 1996 12 507 510 9021269 Pearson WR PLALIGN Brudno M Malde S Poliakov A Do CB Couronne O Dubchak I Batzoglou S Glocal alignment: finding rearrangements during alignment Bioinformatics 2003 19 i54 62 12855437 10.1093/bioinformatics/btg1005 Brudno M Poliakov A Salamov A Cooper GM Sidow A Rubin EM Solovyev V Batzoglou S Dubchak I Automated whole-genome multiple alignment of rat, mouse, and human Genome Res 2004 14 685 92 15060011 10.1101/gr.2067704 Shah N Couronne O Pennacchio LA Brudno M Batzoglou S Bethel EW Rubin EM Hamann B Dubchak I Phylo-VISTA: interactive visualization of multiple DNA sequence alignments Bioinformatics 2004 20 636 643 15033870 10.1093/bioinformatics/btg459 Chakrabarti K Pachter L Visualization of multiple genome annotations and alignments with the K-BROWSER Genome Res 2004 14 716 720 15060015 10.1101/gr.1957004 Bray N Pachter L MAVID: Constrained Ancestral Alignment of Multiple Sequences Genome Res 2004 14 693 699 15060012 10.1101/gr.1960404 Darling AC Mau B Blattner FR Perna NT Mauve: multiple alignment of conserved genomic sequence with rearrangements Genome Res 2004 14 1394 1403 15231754 10.1101/gr.2289704	15655071	PMC546196	CC BY	2021-01-04 16:02:51	no	BMC Bioinformatics. 2005 Jan 17; 6:9	utf-8	BMC Bioinformatics	2,005	10.1186/1471-2105-6-9	oa_comm
==== Front Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-3-11565507510.1186/1476-7120-3-1Case ReportClinical and echocardiographic features of aorto-atrial fistulas Ananthasubramaniam Karthik [email protected] Henry Ford Heart and Vascular Institute, Detroit MI 48202, USA2005 17 1 2005 3 1 1 19 12 2004 17 1 2005 Copyright © 2005 Ananthasubramaniam; licensee BioMed Central Ltd.2005Ananthasubramaniam; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Aorto-atrial fistulas (AAF) are rare but important pathophysiologic conditions of the aorta and have varied presentations such as acute pulmonary edema, chronic heart failure and incidental detection of the fistula. A variety of mechanisms such as aortic dissection, endocarditis with pseudoaneurysm formation, post surgical scenarios or trauma may precipitate the fistula formation. With increasing survival of patients, particularly following complex aortic reconstructive surgeries and redo valve surgeries, recognition of this complication, its clinical features and echocardiographic diagnosis is important. Since physical exam in this condition may be misleading, echocardiography serves as the cornerstone for diagnosis. The case below illustrates aorto-left atrial fistula formation following redo aortic valve surgery with slowly progressive symptoms of heart failure. A brief review of the existing literature of this entity is presented including emphasis on echocardiographic diagnosis and treatment. ==== Body Case A 66 year old male with history of rheumatic heart disease and aortic valve replacement (AVR) (twice for severe native and subsequent prosthetic valve regurgitation) presented with progressive worsening fatigue, exertional dyspnea and paroxysmal nocturnal dyspnea. His other medical problems included poorly controlled hypertension and hyperlipidemia. Following his second AVR 4 years prior to this presentation, a routine follow-up 2-dimensional echocardiogram (TTE) had shown preserved left ventricular and prosthetic valve function and a aorto-atrial fistula with color flow between the aorta-and the left atrium. Notably, only a soft and short ejection murmur across the mechanical prosthesis was appreciated and no continuous murmurs were heard. This was felt to be a possible postoperative complication currently not of clinical significance given his asymptomatic status and he was treated medically and did well for the last 3–4 years. Physical examination during this visit revealed an afebrile patient with a blood pressure of 138/84 mm Hg, regular pulse of 84/minute. An ejection systolic murmur (2/6 in intensity) was heard all over the precordium likely from the flow across his prosthesis. No continuous murmurs were heard. No evidence for clinical heart failure, anemia, jaundice or infection was noted. Laboratory tests revealed no leukocytosis and blood cultures were negative. Given prior echo documentation of fistula and new symptomatology suggestive of heart failure, a transesophageal echocardiography (TEE) was requested for more detailed assessment of prosthesis and AAF. TEE revealed normal left ventricular function, normal aortic prosthesis function with trivial aortic regurgitation. An echolucent area above the mechanical prosthesis, close to the left atrium near the orifice of the left coronary artery was noted. There appeared to be expansion of a portion of this lucency into the left atrium during systole suggesting communication with the aorta (Fig 1) with turbulent color flow from the aorta into the left atrium (suggestive of AAF) throughout the cardiac cycle but mainly in systole as shown by color and continuous wave doppler (Figs 2 and 3). Compared to the prior 2-D echo there appeared to be mild left atrial dilation, mild left ventricular hypertrophy and significantly more prominent fistula flow suggesting either progressive shunting and enlargement of the fistula over time or underestimation by the prior 2-D echo. Although the echocardiographic findings mimicked changes which could also be related to endocarditis (abscess around prosthesis with pseudoaneurysm formation), the absence of any obvious vegetations or prosthetic malfunction combined with lack of clinical and laboratory evidence of endocarditis favoured a more slowly progressive postoperative complication rather than an infectious process. Based on his heart failure symptoms and progressive increase in AAF size and flow, surgical correction was recommended. He underwent uncomplicated surgical repair of the AAF which was found during surgery to be inferior to the left coronary ostium. No evidence of abscess or infection was found and the prosthesis appeared intact and healthy. The echo lucent area represented a postoperative weakening of the aortic wall adjacent to the left atrium, predisposing to the fistula formation and was also repaired. Intra-operative TEE showed no residual fistula by color flow at the site of repair. Figure 1 Off axis 2-D short axis TEE view demonstrates the left atrium (LA) the prosthetic aortic valve (AV). An echolucent area (EL) around the aortic valve protruding into LA is seen with focal outpouching into the LA. This represents weakening of the wall of the aorta near the posterior aspect of the LA Figure 2 Off axis 2-D short axis TEE view with the probe advanced further into mid-esophagus: demonstrates turbulent color flow entering the LA from the EL region of the aortic valve illustrated in Fig 1 Figure 3 Continuous wave Doppler signal across turbulent jet showing high velocity throughout cardiac cycle but predominantly in systole consistent with aorto-left atrial fistula Discussion In a large collection of about 4000 cases of thoracic aortic aneurysms, Boyd first reported AAF as an incidental finding on autopsy back in 1924 [1]. Most of common etiologies of AAF are related to its occurrence as a result of bacterial endocarditis, paravalvular abscess, ruptured sinus of Valsalva, aortic dissection and possibly of congenital etiology [2-6]. Clinical presentation of AAF could vary from an acute presentation with acute chest pain syndrome due to rupture in the setting of dissection [5] or a refractory heart failure picture in the setting of endocarditis [7] and aortic dissection [8]. In a previous report we have highlighted the fulminant course of prosthetic valve endocarditis due to Proteus mirabilis leading to aorto-right atrial fistula from rupture of a pseudoaneurysm secondary to prosthetic valve endocarditis [9]. Isolated case reports of AAF as an immediate postoperative complication has also been reported [10]. Role of Echocardiography TTE and TEE form an integral part of assessment of patients presenting with chest pain and heart failure symptoms particularly if audible murmurs or valvular pathologies are suspected. TTE is the intial test of choice in routine prosthetic aortic valve assessment and gradients estimation. Nevertheless TEE is superior to TTE in real time assessment of prosthetic valve function and morphology and for better delineation of intracardiac pathology such as complications of endocarditis namely root abscess and fistulas [7,11,12]. TEE has better signal to noise ratio and proximity of transducer to the heart leading to higher quality images with lesser attenuation. Furthermore since aorto-left atrial fistulas usually occur from the posterior aspect of the aorta, this area is better delineated with TEE than TTE. Turbulent flow of AAF can be mistaken on TTE particularly if near the prosthetic valve for prosthetic malfunction in the setting of endocarditis or heart failure. Inter-chamber communications and the fistulous tracts that are particularly small are best tracked by multiplane TEE. The exact origin, chamber communications and even the size of the fistulous opening can be well assessed by TEE. Furthermore, coexistent complications with AAF in the setting of aortic endocarditis such as presence of annular abscess, extension to the upper interventricular septum or the subaortic area and pseudoaneurysm formation are best seen by TEE. Nevertheless cases of underestimation of cardiac involvement also been reported with TEE [9]. This reveals the limitations of viewing a three dimensional structure such as the heart in a two dimensional fashion, a void which may be filled by 3-dimensional (3-D) echocardiography. This case highlights the importance of intraoperative TEE in guiding valvular surgery identifying potential intraoperative cardiac complications, which can be corrected in the same setting. Since repeat sternotomy and cardiac surgery by itself carries a higher risk of perioperative complications, intraoperative pre and post pump TEE play an integral role in guiding the surgeons as to any new complications which may have risen during surgery. This is more so in valve surgeries or aortic reconstruction surgery where a real time 2-D TEE with color assessment pre and post pump provides important information regarding success of surgical intervention and new complications. Since clinical diagnosis of AAF is difficult, definitive diagnosis is by a thorough echocardiographic evaluation (TTE and TEE). Apart from antibiotic treatment for endocarditis, definitive treatment revolves around surgical correction. Since many of these patients may have had some form of surgical intervention in the past [5], reoperation is challenging. Mortality is high in patients who are continued on medical therapy particularly in the setting of aortic dissection [5,7]. Surgical intervention consists of repairing the affected aortic segment, replacing prosthesis if the valve is destroyed, annular debridement in the setting of abscess and suture of the fistula. Conclusion Aorto atrial fistulas are rare but important complications of many disease processes of the aorta and aortic valve. Classical clinical signs of continuous murmurs may not be present and echocardiography forms the cornerstone of diagnosis. AAF should be suspected in patients with poorly controlled heart failure and prior aortic surgery. Prompt surgical repair is usually helpful in relieving symptoms and decreasing mortality. Competing interests The author declares that he has no competing interests in preparation of this mansucript and has fully contributed to preparation of this manuscript. ==== Refs Boyd LJ A study of four thousand cases of aneurysm of the thoracic aorta Am J Med Sci 1924 168 654 68 Oliveira JSM Bestetti RB Marin-Neto JA Costa RS Carneiro JJ Ruptured aortic dissection into the left atrium: A rare case of congestive heart failure Am Heart J 1991 121 936 8 2000768 10.1016/0002-8703(91)90218-7 Arnett EN Roberts WC Valve ring abscess in active infective endocarditis; frequency, location and clues to clinical diagnosis from study of 95 necropsy patients Circulation 1976 54 140 5 1277418 Sakakibara S Konno S Congenital aneurysms of sinus of Valsalva. A clinical study Am Heart J 1962 63 708 19 14496168 10.1016/0002-8703(62)90018-2 Lindsay J Aortocameral fistula: a rare complication of aortic dissection Am Heart J 1993 126 441 3 8338017 10.1016/0002-8703(93)91064-L Topocuoglu MS Salih OK San M Kayhan C Ulus T Aorto-Left Atrial Fistula with Bicuspid Aortic Valve and Coronary Artery Origin Anomaly Ann Thorac Surg 1997 63 854 6 9066423 10.1016/S0003-4975(96)01219-2 Archer TP Mabee SW Baker PB Orsinelli DA Leier CV Aorto-Left Atrial fistula: A Reversible Cause of Acute Refractory Heart Failure CHEST 1997 111 828 31 9118732 Caruso A Iarussi D Materazzi C Dialetto G Covino F Bossone E Cotrufo M Aortic Dissection with Fistula to Left Atrium: Diagnosis by Transesophageal Echocardiography with Successful Repair J Am Soc Echocardiogr 2000 13 69 72 10625836 10.1067/mje.2000.102208 Ananthasubramaniam K Karthikeyan V Aortic ring abscess with Aorto-atrial fistula complicating Fulminant Prosthetic Valve Endocarditis due to Proteus mirabilis J Ultrasound Med 2000 19 63 66 10625192 Patsouras D Argyri O Siminilakis S Michalis L Sideris D Aortic Dissection with Aorto-Left Atrial Fistula Formation Soon After Aortic Valve Replacement: A Lethal Complication Diagnosed By Transthoracic and Transesophageal Echcoardiography J Am Soc Echocardiogr 2002 15 1409 11 12415238 Shanewise JS Martin RP Assessment of endocarditis and associated complications with transesophageal echocardiography Crit Care Clin 1996 12 411 27 8860847 Tamms MA Guyssenhoven EJ Bos E de Jaegere P Roelandt JR Sutherland GR Bom N Enhanced morphological diagnosis in endocarditis by transesophageal echocardiography Br Heart J 1990 63 109 2317403	15655075	PMC546197	CC BY	2021-01-04 16:38:31	no	Cardiovasc Ultrasound. 2005 Jan 17; 3:1	utf-8	Cardiovasc Ultrasound	2,005	10.1186/1476-7120-3-1	oa_comm
==== Front Int Semin Surg OncolInternational seminars in surgical oncology : ISSO1477-7800BioMed Central London 1477-7800-2-21564711710.1186/1477-7800-2-2Case ReportLessons learnt from the painful shoulder; a case series of malignant shoulder girdle tumours misdiagnosed as frozen shoulder Quan Gerald MY [email protected] Derek [email protected] Steven [email protected] Gerard [email protected] Peter FM [email protected] Department of Orthopaedics, St. Vincent's Hospital Melbourne, Australia2 Department of Medical Imaging, St. Vincent's Hospital Melbourne, Australia3 Division of Surgical Oncology, Peter MacCallum Cancer Institute, Australia2005 12 1 2005 2 2 2 1 11 2004 12 1 2005 Copyright © 2005 Quan et al; licensee BioMed Central Ltd.2005Quan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Adhesive capsulitis or frozen shoulder is a common condition characterized by shoulder pain and stiffness. In patients in whom conservative measures have failed, more invasive interventions such as arthrographic or arthroscopic distension can be very effective in relieving symptoms and improving range of movement. However, absolute contraindications to these procedures include the presence of neoplasia around the shoulder girdle. We present five cases referred to our institution where the diagnosis of shoulder joint malignancy was delayed, following prolonged, ineffective treatment for frozen shoulder. These cases highlight the importance of careful review of the radiology and the need for reconsideration of the diagnosis in refractory "frozen shoulder". Frozen shoulderadhesive capsulitishydrodilatationdistensiontumour ==== Body Introduction Frozen shoulder was first described by Codman in 1934, as an idiopathic painful restriction in the range of shoulder joint movement, in the presence of normal plain radiographs [1]. It is also known as "adhesive capsulitis", based on the presence of chronic synovitis and a contracted, thickened joint capsule seen during open surgery of the shoulder joint [2]. It is usually a self-limiting condition, with a mean duration of one to three years [3]. The natural clinical course involves an initial painful phase, followed by progressive stiffness, with a gradual return of functional range of motion [4]. However, between 15 – 50% of patients have persisting severe refractory pain that is unresponsive to conservative management involving physiotherapy, non-steroidal anti-inflammatories and subacromial corticosteroid injections [5,6]. More aggressive treatment options for these patients include manipulation under anaesthesia, arthrographic capsular distension (hydrodilatation), and arthroscopic or open capsular release [7,8]. Hydrodilatation is commonly performed as treatment for frozen shoulder as it is minimally invasive, inexpensive, does not require an anaesthetic, and is effective [9-12]. The procedure involves insertion of a needle into the glenohumeral joint under radiologic guidance, followed by gradual distension of the capsule with a combination of local anaesthetic, corticosteroid and normal saline, until lysis of adhesions and capsular rupture are achieved [13]. Arthrography performed at the beginning of the procedure by injecting radio-opaque contrast material into the shoulder joint is the definitive diagnostic investigation for frozen shoulder, and is associated with decreased joint volume and obliteration of the axillary fold and subscapular bursa. Tumours around the shoulder girdle are uncommon causes of shoulder pain and stiffness, but often present with symptoms and a clinical history identical to that of a frozen shoulder. A strict contraindication to arthrographic or arthroscopic distension of the shoulder is the presence of a local oncological process. Such procedures may change the surgical management from being a limb-preserving resection to a forequarter amputation. In the past month, five patients have been referred to us with malignant tumours around the shoulder joint, all previously diagnosed as having a frozen shoulder. All patients had undergone prolonged conservative management and hydrodilatation, with persistence of symptoms. Two of the patients had also undergone arthroscopic surgery. The following cases illustrate the importance of reconsidering the diagnosis in refractory frozen shoulder and the value of a detailed clinical history and examination and careful consideration of radiologic imaging in assessing recalcitrant "frozen shoulder". Case Reports Case 1 A 60 year old woman presented to her local medical officer with an eighteen month history of worsening right shoulder pain and stiffness. She was initially treated with oral analgesia followed by a cortisone injection without improvement. Two months later she had a hydrodilatation of the shoulder but her symptoms persisted. MRI was then performed, which demonstrated a large permeative tumour arising from the scapula (Figure 1). She was subsequently referred to us, and underwent staging studies and needle biopsy. Histologic sections were consistent with Ewing's sarcoma. Figure 1 A Plain radiograph of the right shoulder, showing an irregular, mixed lytic and sclerotic lesion in the glenoid (arrow), that was not appreciated by the reporting radiologist. B Arthrogram performed prior to hydrodilatation. C Coronal TSE post-contrast MR image, showing a diffusely enhansive scapular lesion extending into the inferior aspect of the gleno-humeral joint. D Axial TSE post-contrast MR image showing diffuse enhancement of the tumour extending on either side of the scapular blade with bony destruction. E Bone scan showing increased uptake in the area of the lesion on the delayed static image. F Thallium functional scanning showing retained thallium activity in the glenoid region at 4 hrs. Subsequent biopsy was consistent with Ewing's sarcoma. Case 2 A 42 year old man was referred to an orthopaedic specialist with a history of sudden onset left shoulder pain following a work related activity. He was initially diagnosed with rotator cuff tendinopathy and subacromial impingement, and had a course of intensive physiotherapy followed by arthroscopic shoulder surgery, without improvement in symptoms. Two months later a minor incident involving his left shoulder led to an increase in pain and swelling and reduction in movement. Hydrodilatation was then performed but pain and function of the shoulder continued to worsen. On retrospective review of plain x-rays of the shoulder, a destructive lesion at the metaphysis with a cortical breach medially in the region of the surgical neck of the humerus was realized (Figure 2). Further anatomic imaging showed an aggressive tumour mass in the proximal diaphysis of the humerus and humeral head extending into the adjacent soft tissues. He was referred to us, and subsequent biopsy of the lesion was consistent with a high-grade pleomorphic sarcoma. After a course of chemotherapy, the patient underwent en bloc resection of the tumour, via an extra-articular approach. Definitive histopathologic diagnosis was malignant fibrous hystiocytoma. Figure 2 A Plain radiograph of the left shoulder showing a lytic lesion affecting the proximal humerus, with cortical irregularity medially (arrow), that was not initially recognized. B At the time of arthrographic distension, the lesion (arrow) was more apparent, but remained unnoticed. C Sagittal TSE post-contrast MR image showing an enhancing lesion within the proximal humerus extending outside the bone. D Axial TSE post-contrast MR image showing the tumour destroying the humeral head and extending into the gleno-humeral articulation. E Bone scan showing increased uptake in the area of the lesion on the delayed static image. F Thallium functional scanning showing retained thallium activity in the proximal humerus at 4 hrs. Histological sections from the biopsy and surgical resection specimen were consistent with a malignant fibrous histiocytoma. Case 3 A 50 year old women was referred to an orthopaedic specialist with a 6 month history of episodic pain in the right shoulder, with associated decreased range of movement. Initial plain x-rays were unremarkable. She then underwent a variety of procedures, which included repeated subacromial corticosteroid injections, arthrographic distension, manipulation under anaesthetic, and arthroscopic debridement and acromioplasty. On arthroscopy, a marked synovitis was observed, to which her ongoing symptoms and the development of a palpable mass on the anterior aspect of the shoulder was initially attributed. Repeat plain radiographs two years after the onset of her symptoms demonstrated a large lesion extending from the glenoid cartilage into the base of the coracoid process (Figure 3). She was then referred to us, where staging radiologic imaging studies and CT-guided biopsy was consistent with a low-grade chondrosarcoma. The patient subsequently underwent en bloc resection of the tumour. Figure 3 A Initial plain radiographs of the shoulder were unremarkable. B Repeat radiographs after two years of failed treatment, showing an irregular mixed lytic and sclerotic lesion destroying the coracoid process of the scapula (arrow), which was not appreciated. C Arthrogram performed prior to hydrodilatation similarly showing the destructive process, which remained unnoticed. D Axial T1-weighted post-contrast MR image showing a heterogenous contrast-enhancing lesion destroying the glenoid and extending into the gleno-humeral joint. The lesion is lobulated and loculated with central areas of lower signal intensity, suggestive of a chondroid lesion. E Bone scan showing increased uptake in the area of the lesion on the delayed static image. F Thallium functional scanning showing no retention of thallium by the lesion at 4 hrs. Biopsy confirmed low-grade chondrosarcoma. Case 4 A 68 year old man had previously had a squamous cell carcinoma of the upper back excised, after which he had a three year history of shoulder pain and stiffness. He was treated for a frozen shoulder and received intensive physiotherapy and multiple subacromial corticosteroid injections, followed by hydrodilatation. Initially this seemed to settle his symptoms, although a month later pain and stiffness recurred, with marked reduction in shoulder function, and he was referred to us. An MRI was performed, which showed lesions in the supraspinatus and trapezius muscles, which were consistent with metastatic deposits (Figure 4). The patient underwent a course of palliative radiation therapy. Figure 4 Patient had persistent pain and stiffness following hydrodilatation. A Plain shoulder radiographs were normal. B STIR MR image showing multiple high signal intensity lesions in the supraspinatus muscle. A presumptive diagnosis of metastatic squamous cell carcinoma was made. Case 5 A 55 year old female with a past history of malignant fibrous histiocytoma of the left thigh resected five years previously, presented to her local medical officer with right shoulder pain. Plain films were performed at the time which appeared normal (Figure 5). Subsequent treatment included multiple intra-articular injections of corticosteroid and local anaesthetic, however her pain and associated restricted movement worsened. She was referred for an orthopaedic surgical opinion and shoulder ultrasound. An arthroscope was performed which demonstrated rotator cuff pathology but failed to reveal the actual cause of the patient's symptoms. One year after the onset of her original symptoms, repeat plain films showed destruction of the glenoid and coracoid process. A bone scan demonstrated increased osteoblastic activity involving the coracoid process and right humeral head and relative photopaenia of the glenoid. Functional thallium scintigraphy showed increased metabolic activity around the right shoulder joint with CT and MRI scanning confirming destruction of the glenoid with an associated soft tissue mass and involvement of the humeral head (Figure 5). CT-guided percutaneous biopsy was performed and diagnosis of malignant fibrous histiocytoma made. Figure 5 A Initial plain radiographs of the right shoulder appeared normal. B CT scan was performed after a year of progressive shoulder pain and stiffness, showing a destructive lesion involving the glenoid (arrow). C Axial T1-weighted and post-contrast (D) MR images showing destruction of the glenoid with an associated soft tissue mass and involvement of the humeral head. E Bone scan showing increased osteoblastic activity involving the coracoid process and right humeral head and relative photopaenia of the glenoid. F CT-guided percutaneous biopsy was able to obtain a histological diagnosis of malignant fibrous histiocytoma. Discussion Adhesive capsulitis or frozen shoulder is a common condition that may affect up to 5% of the general population in their lifetime. Although the aetiology of frozen shoulder is unknown, it has been associated with diabetes mellitus, thyroid disease, ischaemic heart disease and various autoimmune conditions [14]. Other causes of shoulder pain and stiffness that need to be excluded include rotator cuff pathology, arthritis, fractures, infection and local tumours [15,16]. Arthrographic or arthroscopic distension with shoulder capsular rupture are effective treatment modalities in well-selected patients with refractory frozen shoulder symptoms despite intensive conservative management [5-7]. In a recent randomised, double blinded study, Buchbinder et al. [12] demonstrated a significant improvement in both pain and range of motion in patients treated with hydrodilation compared with arthrogram alone. However, absolute contraindications to surgical intervention for frozen shoulder include neurological abnormalities originating from the cervical spine, presence of infection, and an ongoing oncological process. Tumours of the shoulder girdle are uncommon causes of shoulder pain and restricted movement. In most cases, they are diagnosed based on the presence of a soft tissue mass on clinical examination, as well as characteristic radiographic changes. Robinson et al. [17] suggested that younger patients with bony tenderness elicited by gentle tapping are more likely to have a shoulder neoplasm. However, in up to 10% of shoulder neoplasms, plain x-rays are normal, and these patients may present with painful limitation of shoulder motion that can be difficult to distinguish from primary frozen shoulder. Indeed, in one series of 140 patients with frozen shoulder referred for manipulation, 2% had a primary chest wall tumour [18]. Misdiagnosis, inappropriate surgery and delayed therapy for shoulder symptoms due to malignancy may potentially have grave consequences. Our five patients had locally invasive malignant tumours, and received prolonged conservative and interventional treatment for "frozen shoulder" before the definitive diagnosis of tumour was made. In all cases of recalcitrant frozen shoulder resistant to conventional treatment, less common causes for shoulder pain and stiffness such as an ongoing oncological process must be considered. A detailed clinical history and examination is critical in the assessment of a painful, stiff shoulder. Plain antero-posterior and axillary lateral radiographs of the shoulder should be performed as a routine, and these films then require careful review by an experienced radiologist prior to undertaking any invasive procedures. More sensitive radiological investigations such as radionucleotide scanning and CT scanning or MRI should be considered when shoulder symptoms are atypical or progress despite invasive management, if there is suspicion of malignancy, or if there are any bony abnormalities evident on plain radiographs. Abbreviations MRI: magnetic resonance imaging, CT: computed tomography, TSE: turbo spin echo, STIR: short tau inversion recovery. ==== Refs Codman EA The Shoulder 1934 Boston: Thomas Todd Neviaser JS Adhesive capsulitis of the shoulder. A study of pathologic findings in periarthritis of the shoulder J Bone Joint Surg 1945 27 211 222 Reeves B The natural history of the frozen shoulder syndrome Scand J Rheumatol 1975 4 193 196 1198072 Hannafin JA Chiaia TA Adhesive Capsulitis: a Treatment Approach Clin Orthop 2000 372 95 109 10738419 Hazleman BL The painful stiff shoulder Rheumatol Phys Med 1972 11 413 421 4646489 Shaffer B Tibone JE Kerlan RK Frozen shoulder. A long-term follow-up J Bone Joint Surg Am 1992 74 738 746 1624489 Ogilview-Harris DJ Biggs DJ Fitsialos DP MacKay M The resistant frozen shoulder. Manipulation versus arthroscopic release Clin Orthop 1995 319 238 248 7554636 Warner JJP Allen A Marks PH Wong P Arthroscopic release for chronic, refractory adhesive capsulitis of the shoulder J Bone Joint Surg Am 1996 78 1808 1816 8986657 Mulcahy KA Baxter AD Oni OO Finlay D The value of shoulder distension arthrography with intraarticular injection of steroid and local anaesthetic: a follow-up study Br J Radiol 1994 67 263 266 8130999 Wybier M Parlier-Cuau C Baque MC Champsaur P Haddad A Laredo JD Distension arthrography in frozen shoulder syndrome Semin Musculoskelet Radiol 1997 1 251 256 11387073 Bell S Coghlan J Richardson M Hydrodilatation in the management of shoulder capsulitis Australas Radiol 2003 47 247 251 12890243 10.1046/j.1440-1673.2003.01171.x Buchbinder R Green S Forbes A Hall S Lawler G Arthrographic joint distension with saline and steroid improves function and reduces pain in patients with painful stiff shoulder: results of a randomised, double blind, placebo controlled trial Ann Rheum Dis 2004 63 302 310 14962967 10.1136/ard.2002.004655 Andren L Lundberg B Treatment of rigid shoulders by joint distension during arthrography Acta Orthop Scand 1965 36 45 53 14308098 Hannafin JA Strickland SM Frozen Shoulder Curr Opin Orthop 2000 11 271 275 10.1097/00001433-200008000-00008 Rizk TE Pinals RS Frozen shoulder Semin Arthritis Rheum 1982 11 440 452 7048533 10.1016/0049-0172(82)90030-0 Leffert RD The Frozen Shoulder Instr Course Lect 1985 34 199 203 3833940 Robinson D Halperin N Agar G Alk D Rami K Shoulder girdle neoplasms mimicking frozen shoulder syndrome J Shoulder Elbow Surg 2003 12 451 455 14564266 10.1016/S1058-2746(03)00092-2 Demaziere A Wiley AM Primary chest wall tumour appearing as frozen shoulder. Review and case presentations J Rheumatol 1991 18 911 914 1895276	15647117	PMC546198	CC BY	2021-01-04 16:38:37	no	Int Semin Surg Oncol. 2005 Jan 12; 2:2	utf-8	Int Semin Surg Oncol	2,005	10.1186/1477-7800-2-2	oa_comm
==== Front BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-5-51565199510.1186/1471-2148-5-5Research ArticleValidating viral quasispecies with digital organisms: a re-examination of the critical mutation rate Comas Iñaki [email protected] Andrés [email protected]ález-Candelas Fernando [email protected] Institut Cavanilles de Biodiversitat i Biologia Evolutiva. Universitat de València. Spain2005 15 1 2005 5 5 5 12 8 2004 15 1 2005 Copyright © 2005 Comas et al; licensee BioMed Central Ltd.2005Comas et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background In this report we re-examine some recent experiments with digital organisms to test some predictions of quasispecies theory. These experiments revealed that under high mutation rates populations of less fit organisms previously adapted to such high mutation rates were able to outcompete organisms with higher average fitness but adapted to low mutation rates. Results We have verified that these results do hold in the original conditions and, by extending the set of initial parameters, we have also detected that the critical mutation rate was independent of population size, a result that we have found to be dependent on a different, contingent factor, the initial fitness vector. Furthermore, in all but one case, the critical mutation rate is higher than the error threshold, a key parameter in quasispecies theory, which prevents its extrapolation to natural viral populations. Conclusion From these results we conclude that digital organisms are useful tools for investigating evolutionary patterns and processes including some predictions from the quasispecies theory. ==== Body Background RNA viruses are among the most infective pathogens affecting plants, animals and humans. Several of their features such as their reduced genomes, high genetic heterogeneity, large population sizes, short generation times and fast evolutionary rates place them among the best models for evolutionary and population genetic studies [1,2]. These same features explain why they are so difficult to eradicate. Many of them are able to establish chronic infections because their high mutation rates allow them to escape from the immune system pressure. As a consequence, selection, that translates in competition with the host and among viral variants, usually results in the persistence of the most infective, pathogenic or more persistent variants. The molecular bases for this genetic variability are three mechanisms differentially used by each kind of virus: mutation, homologous and non-homologous recombination and genome rearrangement [3]. Attempts to model the evolutionary dynamics of RNA viruses incorporate their most relevant features, such as large population sizes (due to their short replication times RNA viruses can reach population sizes of around 1010individuals in short times), high mutation rates (in the order of 0,1–1 mutations per genome and replication round (m/g/r) derived from lack of proof-read correction in the polymerase), and small genome sizes (ranging from 3 to 30 kilobases). For years RNA virus population dynamics has been studied under the classical population genetics framework [1], thus allowing the development of models that explained their evolution in terms of selection, mutation, genetic drift and, less importantly, migration within and among hosts. Under this framework theoretical predictions such as the Red Queen hypothesis [4], frequency-dependent selection [5,6] or clonal interference [7] have been demonstrated with experimental populations of viruses. Despite these achievements in the late 70's some results suggested that the evolution of RNA viruses might be better explained by a quasispecies model. The quasispecies concept was formulated by Eigen [8] in his studies on the evolution of the first replicons. The concept arises as an alternative to the neutral theory [9] which requires small population sizes and large genomes. A population of replicons with these characteristics cannot explore the whole neutral space of an adaptive landscape and, consequently, the stochastic differentiation of the molecules is possible. But in the case of molecules with small genomes and large population sizes (such as early replicons) the whole neutral space can be explored thus avoiding the effect of the genetic drift. This property along with high mutation rates allows quasispecies formation in viral populations. A quasispecies has been defined as a cloud of mutants organized around one or a few high fitness variants and with very low Hamming distances among them. The high mutation rates are the connective agent between the members of the quasispecies, with their frequencies depending on their replication fidelity and that of the rest of neighbor mutants. This mutational coupling implies that the object of natural selection is the quasispecies as a whole and not each individual variant. The quasispecies structure has three important implications [10]: - Selection acts upon the quasispecies as a whole and not upon individual variants. The result is that under appropriate conditions lower fitness variants can outcompete higher fitness ones (survival of the flattest vs. survival of the fittest). - Genetic drift has no relevant effects: their tiny genomes, large population sizes and high mutation rates allow the exploration of all the neutral space around the master sequence. - The average consensus sequence remains stable along quasispecies evolution. This model contrasts sharply with conventional population genetic models in which the existence of a large number of neutral mutations would lead to genetic drift of the population and the individual is the unit of selection rather than a cloud of related variants [11]. This last difference is most relevant when quasispecies theory is applied to real entities, such as RNA viruses, in two contexts. First, RNA viruses represent the vast majority of emerging pathogens and there is a growing interest in the application of evolutionary principles for the control, prevention and treatment of diseases caused by them [2]. Second, RNA viruses represent the best example of measurably evolving populations [12] and as such are widely used to experimentally test many postulates of evolutionary theory [2,13]. Hence, differences on the nature of the unit of evolution in RNA viruses may have important consequences in practical applications and experimental verification of evolutionary theory. The difficulty in experimentally testing some predictions of quasispecies theory has led to the search of alternative systems. In this work we have used digital organisms as an approximation to the population dynamics of RNA viruses. Digital organisms are self-replicating entities and compete for access to resources, in this case CPU cycles, as implemented in the AVIDA platform [14,15]. Avidians are programs (genomes) composed by arrays of logical instructions (genes) that allow them obtaining CPU cycles. There are 28 possible instructions. The number of instructions in a digital organism is equivalent to the genome size in a biological organism [16,17]. Many studies have been done using the AVIDA platform. The possibility that genome sizes change during the course of evolution and the rewards that they can obtain by the combination of functions have allowed investigations about the evolution of genomic complexity [18]. Furthermore the possibility of studying their evolution throughout long periods of adaptation and competition has allowed the reproduction of studies originally performed with other asexual organisms such as viruses and bacteria [19]. Other studies have attempted to determine the effect of each possible mutation on the fitness of a genome and the nature of their interactions [20,21]. In this work we have focused in a recent study by Wilke et al. [22] with digital organisms in which they concluded the validity of one of the principal tenets of quasispecies theory. The prediction is that less fit organisms can outcompete fitter organisms when mutation rates are high. The dynamics of their experiments consists of generating, from a common ancestor, pairs of organisms adapted to high (lower fitness organisms) and low (higher fitness organisms) mutation rates. Then, competition experiments between high and low fitness organisms are performed at different mutation rates. As the mutation rate increases, these experiments result in the winner being always the lower fitness variant. This indicates that previous adaptation to high mutation rates generates less fit but very robust variants. Therefore, under high mutation rates these variants generate a better adapted cloud of mutants. On the other hand, high fitness variants are in higher but steeper adaptation peaks and in consequence are more sensitive to mutation. Therefore at high mutation rates there is survival of the flattest and not survival of the fittest. Here we have extended the original experimental conditions in order to study the effect and interaction between three of the key factors in the quasispecies model: population and genome sizes and mutation rates. Our results indicate that chance events in the form of historical contingency play an important role in the evolution of these populations. Moreover we have established a new, corrected mutation rate necessary for quasispecies formation with a higher value than the original one. The implications of this correction are discussed. Results We considered three factors affecting the critical mutation rate in our digital organisms: genome size, population size and the influence of the initial fitness vector. This is a vector of randomly assigned priorities for the first time evaluation of each organism fitness, incorporated to prevent the system from collapsing if all organisms simultaneously try to enter the CPU, and can be interpreted as a historical, contingent factor in evolution. The range of genome sizes studied varied from 54 to 272 instructions (Table 1). Our exploratory experiments indicated that the initial fitness vector might have an important effect on the results of competition between pairs of organisms adapted to low and high mutation rates. This was most apparent when comparing results using the original, fixed initial vector used by Wilke et al. [22] for all the competitions involving the same pair of organisms and those obtained when the initial fitness vector was a random one, with different values for each experiment. Hence, in the original study for 3600 individuals the critical mutation rates varied between 0.88 and 3.66 with a mean value, normalized according to our criterion for estimating the critical mutation rate, of 1.386 (standard deviation, SD = 0.777). In our experiments for this same population size and random initial vectors, critical genomic mutation rates ranged between 0.5 and 3 but with a higher average value, 2.045 (SD = 0.757). Consequently, we decided to proceed with two series of experiments, one using the same initial fitness vector for all the competition experiments for each pair of organisms and the other with initial fitness randomly assigned in each competition. Table 1 The twelve digital organisms used in the experiments. Size reflects the number of instructions in the corresponding genomes (genome size). Organism Size C185 54 C212 62 C148 70 C119 86 C280 90 C238 92 C216 96 C149 108 C202 134 C295 207 C274 241 C222 272 Fixed initial fitness vector Table 2 shows the critical rates for organism and population size in the experiments with the same, fixed initial fitness vector for each pair of organisms. Only population sizes equal or lower than N = 3600 individuals were assayed, since the original experiments involved only 3600 individuals. Hence, it was impossible to assign the same initial fitness vector used by Wilke et al. [22] to larger population sizes. In three of the twelve competitions (Table 2) we encountered some differences with respect to the critical rate calculated by Wilke et al. [22]. In organisms C202 and C149 this rate was smaller (1.75 and 0.5 instead of 2.25 and 0.88, respectively) and larger for organism C238 (1.25 instead of 0.88). The remaining rates are equal to those obtained by Wilke et al. [22] and the differences are due to the better approximation obtained in the original paper through some extra experiments. In our case these additional experiments were not performed because we were more interested in comparing the rates between the two parts of our study. Table 2 Critical mutation rates using one fixed vector per organism. The corresponding values obtained by Wilke et al. [22] for 3600 individuals are shown in the last column ("Original"). Population size Organism 250 500 1250 2500 3600 Original C185 1.25 1.25 1.25 1.25 1.25 1.13 C212 1.25 1.25 1.25 1.25 1.25 1.13 C148 0.75 0.75 0.75 0.75 0.75 0.88 C119 1.75 1.75 1.75 1.75 1.75 1.75 C280 1.25 1.25 1.25 1.25 1.25 1.13 C238 1.25 1.25 1.25 1.25 1.25 0.88 C216 1.25 1.25 1.25 1.25 1.25 1.25 C149 0.5 0.5 0.5 0.5 0.5 0.88 C202 1.75 3 1.75 1.75 1.75 2.25 C295 1.75 1.75 1.75 1.75 1.75 1.88 C274 3 3 3 3 3 3.6 C222 3 3 3 3 3 3.6 Random initial fitness vector As expected from our preliminary results, the use of an initial random vector for each experiment and not for each organism resulted in clear differences with the results encountered by Wilke et al. [22]. These differences are shown in Table 3, which presents a summary of the critical mutation rates obtained for each organism and population size using one random vector in each experiment and those originally with one fixed initial vector and N = 3600. In eight of the 11 cases studied the critical rate was higher than the original value. Only in two cases this value was equal to the one originally reported by Wilke et al. and in one case, for organism C222, it was lower. Table 3 summarizes the critical mutation rate encountered for each organism and population size (see also Fig. 1). The correlation between the critical mutation rate and population size for each organism allows the separation of the twelve organisms in three main groups (Table 4): (i) those with a significant, positive correlation rate (C212, C148, C119, and C202); (ii) organisms with no significant correlation (C185, C222, C280, C149, C216 and C295), and (iii) organism C238, which is the only one with a significant, negative correlation rate. Organism C274 was excluded from this analysis because it did not show a clear pattern of fixation. Table 3 Critical mutation rates using one random vector in each experiment. The last column ("Original") presents the results obtained by Wilke et al. [22] for 3600 individuals and one fixed vector in all the experiments with each organism. Population size Organism 250 500 1250 2500 3600 6400 10000 Original C185 2.25 2.25 2.25 2.25 2.25 2.25 2.25 1.13 C212 0.5 0.5 1.25 1.25 1.25 1.25 1.25 1.13 C148 0.5 0.5 1.25 1.25 1.75 1.75 1.75 0.88 C119 0.5 0.5 1.75 1.75 1.75 1.75 1.75 1.75 C280 1.75 2.25 1.75 2.25 2.25 2 2.25 1.13 C238 2 2 1.75 1.75 1.75 1.75 1.75 0.88 C216 3 3 3 3 3 3 3 1.25 C149 1.5 2 2.25 1.75 2.25 1.75 2 0.88 C202 0.5 0.5 0.5 0.5 2.75 2.75 2.75 2.25 C295 2.25 2.25 3 2.25 3 2.75 2.75 1.88 C274 No pattern 3.6 C222 0.5 0.5 0.5 0.5 0.5 0.5 0.5 3.5 Figure 1 Critical mutation rates using one fixed initial vector per organism. Critical mutation rate (Uc) versus population size (N) for each organism used in the experiments with one fixed initial fitness vector per experiment. In order to obtain exact replicates of the original simulation [22] we did not included population sizes larger than N = 3600. Table 4 Correlation between population size and critical mutation rate in digital organisms. Correlation coefficients (r) were calculated from the experiments with one random vector in each case (Table 3). An asterisk indicates a significant difference from r = 0 for α = 0.05. Two asterisks indicate a significant difference after Bonferroni's correction (α' = 0.0045). Organism r P-value C185 Constant C212 0.791 0.034 * C148 0.932 0.002 ** C119 0.791 0.034 * C280 0.487 0.268 C238 -0.791 0.034 * C216 Constant C149 0.277 0.547 C202 0.866 0.012* C295 0.552 0.199 C274 Not applicable C222 Constant Nevertheless, despite these differences between organisms we cannot conclude that there is a globally significant effect of population size on critical mutation rate. Using Bonferroni's correction for multiple comparisons we obtained a new significance level α' = 0.0045. Therefore only organism C148 has a significant, positive correlation (r = 0.932, P = 0.002). But the differences in the use of a random or fixed initial fitness vector are clear and can be observed by comparing Figures 1 and 2 where values of the critical mutation rate for each organism according to population size are represented. It seems that the use of one fixed initial vector per organism reduces variability in the results. Figure 2 Critical mutation rates using random initial vectors per organism. Critical mutation rate (Uc) versus population size (N) for each organism used in the experiments of one random initial fitness vector per organism. Table 5 shows the critical mutation values found for a population size of 10000 individuals. It can be observed that there is no correlation between critical mutation rate and genome size (r = -0.269, P = 0.424). For comparison, we also compiled similar data for RNA viruses (Table 6), including retroviruses, and we did not encounter a significant correlation (r = 0.636, P = 0.125) between genome size and mutation rate (Fig. 3). Table 5 Population size and critical mutation rate in digital organisms. Correlation (r = -0.267, P = 0.428) between critical mutation rates (UC) calculated for a population size of N = 10000 individuals and genomic size of digital organisms. Organism Size UC C185 54 2.25 C212 62 1.25 C148 70 1.75 C119 86 1.75 C280 90 2.25 C238 92 1.75 C216 96 3 C149 108 2 C202 134 2.75 C295 207 2.75 C284 241 N.A. C222 272 0.5 Table 6 Population size and critical mutation rate in viruses. Correlation (r = 0.636, P = 0.125) between the experimentally calculated genomic mutation rate (μg) and genomic size of some RNA viruses (adapted from [35]). Virus Size (kb) μg Lytic RNA viruses VSV [27] 11.2 1.07 Poliovirus [36] 7.4 0.81 Influenza A virus [36] 13.6 0.99 Retroviruses [37] Spleen necrosis virus 7.8 0.16 Molony murine leukemia virus 8.4 0.029 Rous sarcoma virus 9.3 0.43 HIV-1 [38] 9.2 0.22 Figure 3 Genomic mutation rates necessary for quasispecies formation for each of the eleven digital organisms with a population size N = 10000. Discussion The quasispecies model requires a series of conditions to be fulfilled. These requirements are related to four key factors: population size, mutation rate, genome size and neutrality [11,23]. In our study with digital organisms we have analyzed three of these factors and we have related them to known results in virus evolution. The three main conclusions derived from this study are: 1) The use of different initial fitness vectors for otherwise identical experiments results in unpredictable effects on critical mutation rates for different population sizes. These effects were not detected in the original experiments by Wilke et al. [22] and can alter their conclusions, as contingency, or historical factors, are introduced in the system through different initial conditions leading to different final outcomes. 2) There is no significant correlation between genome size and critical mutation rate. 3) The originally calculated critical mutation rates underestimate their real values. Despite the lack of a general correlation between critical mutation rate and population size, the comparison of Figures 1 and 2 reveals a clear difference with the initial study. By using one fixed initial fitness vector per organism, Wilke et al. [22] eliminated variability in the outcome of competition (Fig. 2). Our study results in different individual responses to changes in population size. In fact, only three of the organisms analyzed maintained a constant response to these changes when different random vectors were used in each of the experiments. Therefore it will be interesting to analyze why certain organisms are more strongly influenced by population size than others. Under the quasispecies model it is expected that increasing population size will favor the establishment of a quasispecies [11]. A large population size allows the exploration of the neutral space that surrounds the master sequence hence avoiding the effects of genetic drift. If a correlation between the two factors is to be expected, then it should be negative, as with larger population sizes a lower mutation rate is needed to maintain the equilibrium quasispecies structure. Nevertheless, theoretical and simulations results by Wilke et al. [22] indicate that this is not a true correlation but a phase transition as at low population sizes the critical mutation rate becomes more difficult to ascertain. Our results do not allow to discriminate between both alternatives but they show substantially more variability among organisms (Fig. 1) than the ones reported by Wilke et al. [22], hence pointing at a more complex scenario than that depicted in a simple phase transition. Another key factor in the quasispecies model is genome size. The establishment of the quasispecies is easier in populations with small genomes, as in these the number of neutral sites is reduced and therefore the neutral space is also smaller. However the relationship between critical mutation rate and genomic size to be expected is somewhat contradictory. On the one hand, larger genomes need higher mutation rates because the neutral and adaptive landscapes are larger. On the other hand, it is well known that large genomes require a stability not supplied by high mutation rates, hence the existence of an error threshold that will be discussed later. In fact Eigen [24] proposed that there should be a negative correlation between these two factors. However in our analyses we have found no such correlation neither in digital organisms nor in experimental data with RNA viruses (Tables 5 and 6), in agreement with [25]. However the absence of a significant correlation does not necessarily mean that there is no relationship between the two factors. Genomic mutation rates impose a limit on the maximum genome size but this does not imply that the best adaptive strategy is to reach the maximum variability attainable for the corresponding genome size [26]. Our correction to the critical mutation rates estimated in the original paper relates directly to the limits imposed by the mutation rate. In most cases Wilke et al. [22] obtained critical mutation rates larger than 1 (between 1.13 and 3.5). However, in our experiments we have found these rates to be even larger. This correction in the mutation rate needed for the establishment of a quasispecies is important because estimates of genomic mutation rates of RNA viruses are usually about or below 1 [27,28] (Table 6). This limit is known as the error threshold and is another key concept for quasispecies theory. It represents the mutation rate beyond which the information in the molecules would be lost due to degeneracy. The critical mutation rates obtained in the vast majority of cases here reported are larger than 2. If these values were similar in "real" virus populations then they would be beyond the error threshold and therefore the viral quasispecies would not be possible. Therefore, it is important to determine up to which point the comparison of mutation rates between viruses and digital organisms is valid. There are two extreme possibilities: either it is not valid, and therefore digital organisms cannot be invoked as a proof of the evolution of RNA viruses as quasispecies, or if the analogy is possible this means that, at least in the case of RNA viruses, the quasispecies is a theoretical possibility but the practical conditions needed are not met. The presence of an error threshold in viruses is a consequence of a trade-off between the maximization of variability (genomic mutation rate) and the maintenance of molecule integrity (genomic size). It is this trade-off, translated into an error threshold, which might prevent virus quasispecies formation. In this way, the error threshold would not be proof of their existence [29] but rather of their impossibility in RNA viruses. In conclusion, although some predictions from quasispecies theory are not fulfilled in our experiments, we do have observed the principal prediction that lower fitness competitors can win the competition to high fitness ones, but only under very high mutation rates. Recently, several papers [11,23,30] have pointed out the possibility that RNA viruses do not meet all the requirements for quasispecies persistence. The results from this study also suggest that the necessary mutation rates are not attainable either. One possible explanation is that viruses are necessarily more constrained in their evolution than digital organisms. Some experiments demonstrate that the variability found in natural isolates of RNA viruses is not correlated to their mutation rate because some form very conserved RNA secondary structures [31]. Similarly, it has been demonstrated the frequent selection of the same mutations in the HIV gag region in isolates from different patients, an indication of the limited adaptive solutions able to produce escape mutants to the immune response of cytotoxic T lymphocytes [32]. Further restrictions could be related with the mechanisms and routes of virus infection [33]. Analogies are very useful in science, but they have to be used cautiously. Similar features and dynamics between digital organisms and RNA viruses are tempting and usually lead to conclude that both kinds of entities are governed by the same laws. This is not necessarily the case, as practitioners of the comparative method know. In any case, digital organisms are an extraordinary system to experiment with controlled, repeatable evolution conditions and further work with them is necessary to ascertain which evolution features are of their own and which are of common application to other evolving entities. Methods Experimental design The project was started with the twelve pairs of organisms generated in a previous experiment [22] that had been adapted to two different mutational regimes. The 12 ancestral organisms originating each of the twelve pairs were adapted to low mutation rates (0.5 mutations/genome/replication round – m/g/r) and to high mutation rates (2 m/g/r) for 1000 generations. In all the cases the organisms adapted to a low mutation rate, denoted A, had a significantly larger fitness than their corresponding pair, adapted to a high mutation rate and denoted B. With these twelve pairs we followed the same experimental procedure designed by Wilke et al. [22]. Basically, we placed in competition equal numbers of A and B organisms during 50 generations. Unlike the original experiment, we did not restrict to a single population size (N = 3600) but we added four smaller (N = 250, 500, 1250, 2500) and, when possible, two larger (N = 6400 and 10000) sizes. The mutation rates under which the competitions were performed were 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 m/g/r. The A organisms carried a label such that we could follow their proportion in the population. Initial fitness vector In AVIDA organisms occupy the limiting environmental resource, the computer CPU, depending on their "fitness". In order to prevent the collapse of the system when all the competing organisms simultaneously try to use the CPU, there exists one feature designed to prevent the simultaneous replication of all organisms at the start of the competition, when all the organisms might be equally fit since they have not been tested yet in the environment. This is achieved by asynchronously introducing organisms in the competition system by assigning an initial fitness to each organism that introduces a small time lag in the accession to the CPU. For this, Wilke et al. [22] generated an initial fitness vector in the population for each pair of competing organisms. This vector was generated at random and assigned a different initial fitness for each of the 3600 individuals in the original competition. All the experiments for each pair of organisms were carried out with the same initial fitness vector. During exploratory experiments we noticed that this vector could play a decisive influence in the result of the competition. In consequence, we divided the study into two parts. In the first one we kept the vector assigned by Wilke et al. [22] to each pair of competing organisms, and we adapted it for other population sizes whenever possible (N = 250, 500, 1250, 2500 individuals). On the other hand, for all the population sizes (including N = 6400 and 10000 individuals) we generated a different random initial vector for each experimental replicate. Therefore, for this second part we generated 252 distinct vectors for pair of organisms in contrast to the five (one per population size) generated in the first part of our study or the single one generated by Wilke et al. [22] for N = 3600. Critical mutation rate determination The critical mutation rate is "the midpoint between the highest rate at where A prevailed and the lowest rate where B prevailed" [22]. It represents the rate at which the quasispecies effect is important. We measured this critical parameter as the average of the two rates at which a shift in the winner was observed. It is necessary to clarify the conceptual difference between the critical mutation rate and the error threshold. The first one is the rate at which the prediction of quasispecies theory that organisms with lower fitness can win the competition is fulfilled. However the error threshold is the genomic mutation rate beyond which the information in the molecules that compose the quasispecies loses sense due to mutational degeneracy [29]. In practical terms, this means that this is the maximum rate that the virus can support. The relationship between the two rates is clear: the critical mutation rate must be necessarily lower or equal than the error threshold because otherwise the quasispecies effects cannot be measured. AVIDA configuration We used versions 1.4 and 1.6 of the AVIDA program. Basically, digital organisms are chains of instructions that act over the CPU with the objective of reproducing as fast as possible. In this manner the CPU time becomes the limiting resource in their evolution. During replication their genomes can mutate and, as a result, a system with variation and therefore with selection and evolution is obtained. Genome sizes of the twelve pairs of digital organisms varied between 54 and 272 instructions (Table 1). AVIDA works with some input files that determine the characteristics of the world during the population's evolution. In this case we used the "COPY_MUT_PROB" in the "GENESIS" file, which is the mutation rate that results from dividing the genomic mutation rate by the genome size. In the "EVENT_LIST" file we specified the order of introduction of the individuals and marked each with a hereditary label (A = 1, B = 0). Generally 50 generations were enough for the fixation of the A or B organism in the population. Configuration files used in these experiments are available from the authors web site [39]. Statistical analysis Each mutation rate-population size combination was replicated six times. For the verification of the relation between the population size and the critical mutation rate we used Pearson's correlation coefficient and a significance level of 5% using Bonferroni's correction [34]. The same analysis was used for the correlation between genomic sizes and critical mutation rate. Both analyses were carried out with SPSS 11.0 (SPSS Inc.). Authors' contributions IC performed all the simulations, made the statistical analyses and wrote the first draft of the manuscript. AM contributed to the design of the experiments and the discussion of the results. FGC designed and supervised the experiments, discussed the results and their analyses and wrote the final version of the manuscript. All the authors read and approved the final manuscript. Acknowledgements This research was sponsored by Conselleria de Sanitat (Generalitat Valenciana), project Grupos03/204 from Agencia Valenciana de Ciencia y Tecnología-Programa AGGV and project BMC2001-3096 from Ministerio de Ciencia y Tecnología (Spain). IC was recipient of an undergraduate student training fellowship from the Spanish Ministerio de Educación y Cultura. ==== Refs Moya A Elena SF Bracho MA Miralles R Barrio E The evolution of RNA viruses: A population genetics view Proc Nat Acad Sci USA 2000 97 6967 6973 10860958 10.1073/pnas.97.13.6967 Moya A Holmes EC González-Candelas F The population genetics and evolutiony epidemiology of RNA viruses Nature Reviews Microbiology 2004 2 279 288 15031727 10.1038/nrmicro863 Domingo E Holland JJ RNA virus mutations and fitness for survival Annu Rev Microbiol 1997 51 151 178 9343347 10.1146/annurev.micro.51.1.151 Clarke DK Duarte EA Elena SF Moya A Domingo E Holland J The red queen reigns in the kingdom of RNA viruses Proc Natl Acad Sci USA 1994 91 4821 4824 8197141 Elena SF Miralles R Moya A Frequency-dependent selection in a mammalian RNA virus Evolution 1997 51 984 987 Yuste E Moya A Lopez-Galindez C Frequency-dependent selection in human immunodeficiency virus type 1 Journal of General Virology 2002 83 103 106 11752706 Miralles R Gerrish PJ Moya A Elena SF Clonal interference and the evolution of RNA viruses Science 1999 285 1745 1747 10481012 10.1126/science.285.5434.1745 Eigen M Self-organisation of matter and the evolution of biological macromolecules Naturwissenschaften 1971 58 465 465 4942363 10.1007/BF00623322 Kimura M The neutral theory of Molecular Evolution 1983 Cambridge University Press Eigen M McCaskill JS Schuster P Molecular quasi-species Journal of Physical Chemistry 1988 92 6881 6891 Jenkins GM Worobey M Woelk CH Holmes EC Evidence for the Nonquasispecies Evolution of RNA Viruses Mol Biol Evol 2001 18 987 994 11371587 Drummond AJ Pybus OG Rambaut A Forsberg R Rodrigo AG Measurably evolving populations Trends in Ecology & Evolution 2003 18 481 488 10.1016/S0169-5347(03)00216-7 Elena SF Lenski RE Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation Nat Rev Genet 2003 4 457 469 12776215 10.1038/nrg1088 Adami C Brown CT Evolutionary learning in the 2D artificial life system "Avida" Artifical Life IV 1994 MIT Press 377 381 Wilke CO Adami C The biology of digital organisms Trends Ecol Evol 2002 17 528 532 10.1016/S0169-5347(02)02612-5 Ofria C Adami C Collier TC Evolution of differentiated expression patterns in digital organisms Lecture Notes in Artificial Intelligence 1999 1674 129 138 Ofria C Adami C Landweber LF and Winfree E Evolution of genetic organization in digital organisms Proceedings DIMACS Workshop on Evolution as Computation 1999 New York, Springer-Verlag 167 184 Adami C Ofria C Collier TC Evolution of biological complexity Proc Natl Acad Sci USA 2000 97 4463 4468 10781045 10.1073/pnas.97.9.4463 Wagenaar DA Adami C Influence of chance, history, and adaptation on digital evolution Artif Life 2004 10 181 190 15107230 10.1162/106454604773563603 Lenski RE Ofria C Collier TC Adami C Genome complexity, robustness and genetic interactions in digital organisms Nature 1999 400 661 664 10458160 10.1038/23245 Wilke CO Adami C Interaction between directional epistasis and average mutational effects Proc R Soc Lond B Biol Sci 2001 268 1469 1474 11454290 10.1098/rspb.2001.1690 Wilke CO Wang JL Ofria C Lenski RE Adami C Evolution of digital organisms at high mutation rates leads to survival of the flattest Nature 2001 412 331 333 11460163 10.1038/35085569 Holmes EC Moya A Is the quasispecies concept relevant to RNA viruses? 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==== Front Microb Cell FactMicrobial Cell Factories1475-2859BioMed Central London 1475-2859-4-41565507310.1186/1475-2859-4-4ResearchCharacterization and performance of a toluene-degrading biofilm developed on pumice stones Di Lorenzo Alessandra [email protected] Mario [email protected] Palma [email protected] Rodolfo [email protected] Adriano [email protected] Pasquale [email protected] Raffaello [email protected] Alteriis Elisabetta [email protected] Dip.to Fisiologia Generale ed Ambientale, Sez. Igiene e Microbiologia, Università degli Studi "Federico II", Naples, Italy2 Dip.to Ingegneria Chimica e Alimentare, Università di Salerno, Fisciano, Salerno, Italy3 EniTecnologie S.p.A, Monterotondo, Rome, Italy2005 17 1 2005 4 4 4 10 11 2004 17 1 2005 Copyright © 2005 Di Lorenzo et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Hydrocarbon-degrading biofilms in the treatment of contaminated groundwaters have received increasing attention due to the role played in the so-called "biobarriers". These are bioremediation systems in which a microbial consortium adherent to a solid support is placed across the flow of a contaminated plume, thus promoting biodegradation of the pollutant. Results A microbial consortium adherent to pumice granules (biofilm) developed from a toluene-enriched microflora in a mini-scale system, following continuous supply of a mineral medium containing toluene, over a 12-month period. Observation by scanning electron microscopy, together with quantification of the biomass attached to pumice, evidenced the presence of abundant exopolymeric material surrounding the cells in the biofilm. Toluene removal monitored during 12-month operation, reached 99%. Identification of the species, based on comparative 16S ribosomal DNA (rDNA) sequence analysis, revealed that Rhodococcus erythropolis and Pseudomonas marginalis were the predominant bacterial species in the microbial consortium. Conclusion A structurally complex toluene-degrading biofilm, mainly formed by Rhodococcus erythropolis and Pseudomonas marginalis, developed on pumice granules, in a mini-scale apparatus continuously fed with toluene. ==== Body Background Surface-attached microbial communities, known as biofilms, are traditionally employed in fixed-film reactors for wastewater treatment. More recently, biofilms originating from the indigenous microflora of a contaminated groundwater have received increasing attention due to the possibility to develop in situ bioremediation systems, directly placed across the flow of a contaminated plume, the so-called "biobarriers" [1]. These are particularly attractive in the case of hydrocarbon contaminated groundwater, since the target contaminants can be destroyed by the attached biomass, leaving potentially non-toxic chemicals as biodegradation products. Indeed, the reactive system of a biobarrier is represented by the biofilm developed on the solid support starting from the autochtonous microbial population of the groundwater, where potentially degrading species are present. In this communication, we report the characterization of a toluene-degrading biofilm developed on pumice granules in two packing mini-columns, employed as a laboratory-scale biobarrier, over a 12-month period. That time was long enough for a mature consortium to establish on a solid support [2]. The main objective of the work was to analyse the structure and composition of the biofilm established on pumice stones in a mini-scale apparatus and to ascertain its degradative capability towards toluene, as a starting point for potential applications of a pumice-bound microbial consortium in bioremediation. Pumice, a rock of volcanic origin very abundant in Southern Italy, was chosen as a support material for its high-surface-area-for-unit-volume and relatively low price. Results Pumice granules packed mini-columns of two different heights were colonized by a microbial consortium, isolated from a gasoline contaminated groundwater sample and enriched in the presence of toluene as the sole carbon and energy source. Afterwards, the mini-columns (henceforth named columns 1 and 2, see Methods for details) were continuously supplied with a mineral medium containing toluene as the target contaminant, and then sacrificed after 6- and 12-month operation, respectively. Biofilms were characterized with three complementary approaches: observation by scanning electron microscopy, quantification of the attached biomass, and identification of the bacterial species, based on comparative 16S ribosomal DNA (rDNA) sequence analysis. Further, the biodegradative capability of the biofilm developed in column 2 was determined by measuring the efficiency of toluene removal, over a 12-month period. Scanning electron microscopy observation of the biofilm Scanning electron micrographs of Fig. 1 show the surface of the pumice support and the development and microscopic structure of the biofilm. Fig. 1A shows the macroporous structure of pumice stone before colonisation, Fig. 1B shows the surface of the pumice granules after 6 months of operation (column 1): rod shaped cells are evident. After 12 months operation (column 2), a thick layer of extracellular material covered the support, interspersed with a few single bacteria, both at the bottom (Fig. 1C) and the top (Fig. 1D) of the column. Quantification of the biomass adhering to pumice Table 1 reports data on the determination of biomass adhering to pumice granules after 6- and 12-month operation (columns 1 and 2). In the case of column 1, the determination of biomass, carried out by burning and total protein assay (see Methods), showed that a biofilm of about 3 mg dry weight of cells per gram of pumice was established after 6-month run of the column with both methods. In the case of column 2, data obtained with the burning procedure clearly showed a significant increase in organic material (calculated as volatile solids, VS), both at the bottom and the top of the column. On the contrary, data based on total protein assay evidenced that the biomass increased only in the lower part of the column. The discrepancy between data obtained with the two procedures could be attributed to the presence of a massive exopolymeric material in the 12 month-old biofilm, presumably of polysaccharidic nature and unavoidably weighed when the burning method is employed. The electron microscopy analysis suggests that this material is present both in the upper and lower part of the column; its production is linked to the biofilm maturation occurring in the second 6-month period. In fact, an abundant extracellular material covering the cells was evident by scanning electron microscopy of pumice granules sampled from both the upper and lower part of the column (Fig. 1). These considerations suggest that the protein assay could be a more reliable method to evaluate the actual amount of cells in the biofilm and to measure differences in biomass along the reactor. The different extent of colonization of the upper and lower part of column 2 could be presumably due to a higher nutrient supply at the bottom of the apparatus. Similarly, other authors [3,4] reported larger microbial populations at the inlet of the apparatus compared to the outlet, reflecting in this case a declining gradient of contaminant concentration along the reactor. Identification of the bacterial species Identification of the species based on 16S rDNA comparative analysis revealed that in both biofilms (column 1 and 2) the majority (85%) of the attached cells was represented by Rhodococcus erythropolis (99% of identity), whereas Pseudomonas marginalis (98% of identity) represented only 10% of the entire consortium. On the contrary, in the enriched culture (the microbial consortium employed to start the development of the biofilm), Pseudomonas marginalis was the predominant species (86%) and Rhodococcus erythropolis was only 10% of the consortium. Apparently, adhesion to the pumice support promoted the growth of Rhodococcus erythropolis, modifying the initial ratio between the two species. Less represented species in the three consortia examined (biofilms from column 1 and 2, as well as enriched culture) were identified as Agrobacterium tumefaciens (98% of identity), Hydrogenophaga palleronii (98% of identity), Chryseobacterium scophtalum (98% of identity), Leucobacter komagatae (98% of identity), Brachybacterium articum (96% of identity), Beta proteobacterium (98% of identity), Microbacterium liquefaciens (99% of identities) and Acinetobacter sp. (98% of identity). The biodegradative capability of the biofilm The biodegradative capability of the biofilm developed on the pumice granules over 12 months was evaluated in terms of toluene removal efficiency (RE), calculated as the slope of the line obtained plotting the average toluene elimination rate (ER) vs. the loading rate (LR) applied to column 2 (Fig. 2). A linear relationship between the elimination rate and the applied loading rate was obtained over the whole experiment, implying that the column operated below its maximal degradation capacity. An overall toluene removal efficiency of 99 % was calculated from the slope of the line. Toluene oxidation in the presence of nitrate as electron acceptor [5] may be postulated as the possible mechanism of toluene removal by the attached cells, since nitrate is present in the feeding medium and microaerophilic conditions presumably established along the column. Moreover, analysis of effluent revealed a nitrate concentration reduction (data not shown) consistent with the contribute of denitrifying bacteria in the biodegradation reaction. Conclusions The results obtained in this work demonstrate that a structurally complex toluene-degrading biofilm developed on pumice granules, following their inoculation with a microbial consortium obtained by enrichment of toluene-contaminated water. In the biofilm, Rhodococcus erythropolis and Pseudomonas marginalis were the predominant species. Apparently, the two species, once attached to pumice stone, gave rise to a specialised community by the production of exopolymers functioning as biofilm stabilizers [6]. Pseudomonas sp. is the most studied single-species, biofilm-forming bacterium [6,7]; differently, the molecular details of biofilm formation by the Gram positive Rhodococcus sp. have not been investigated so far, though the genus Rhodococcus is present in biofilm reactors [8] for its broad metabolic diversity and ability to degrade hydrophobic pollutants [9]. Although the microbiological complexity of the consortium established on pumice deserves further investigation in order to clarify the specific role of each species and its contribution to toluene degradation, the results obtained in this work may be relevant for a final design of a biobarrier in which a cheap support as pumice is used for biofilm formation. In particular, the molecular analysis performed revealed the strong impact of immobilization on the structure of the toluene-degrading community, demonstrating the importance of a molecular approach to characterize microbial biofilms. Methods A microbial consortium was obtained following enrichment of a gasoline contaminated groundwater sample and subsequently used to inoculate the packing material in the columns. The enrichment was carried out in 500 ml-flasks, adding 100 ml of groundwater to 100 ml of minimal salt medium (MSM), containing high sulphate and nitrate concentrations [10] supplemented with 20 mg l-1 toluene (final concentration) as the sole carbon and energy source, sparged with air and incubated at 25°C for two weeks. After this acclimation period, 25% (v/v) of the culture was transferred into fresh MSM medium containing the same toluene concentration and incubated in the same conditions. To allow culture enrichment, the transfer was repeated 10 times, until a biomass (dry weight determination after centrifugation at 3000 g and washing of the collected cells) of 1.2 mg d. w. ml-1 was achieved. To study the biofilm development, two glass columns (2.5 cm in diameter and 1 or 3 cm in height), were packed with pumice granules (0.4–0.6 mm in size, average particle density 0.48 g ml-1), respectively previously washed with distilled water, autoclaved (120°C, 20 min) and then soaked in MSM medium containing 20 mg l-1 toluene. The ratio between column and particle diameter was ~50 to reduce wall effects and preferred channelling; the column void volume was 60%. The columns were provided with teflon supports, tubes and connections to prevent abiotic removal of toluene. For each column, the attachment of microbial cells to the support material was carried out by recirculating the enriched culture at a low flow rate (0.008 l h-1) for one week. After this period, the two columns, henceforth referred as column 1 (2.5 × 1 cm) and column 2 (2.5 × 3 cm), were continuously fed upwards at a flow rate (Q) of 0.026 ± 0.003 l h-1, with MSM plus toluene for 6 and 12 months, respectively, and then sacrificed. Operations were carried out at 25°C. MSM was continuously sparged with air and mixed with the solution containing toluene just before entering the columns. The set-up for the continuous operation of the two minicolumns is schematically presented in Fig. 3. The inlet concentration of toluene (Si) was changed step-wise every 4 weeks, from an initial concentration of 0.77 ± 0.03 up to 4.42 ± 1.21 mg l-1, allowing two weeks to achieve culture acclimation before conducting the biodegradation assays in the following two weeks. A total of six values of toluene concentration in the inlet were established, being the highest inlet concentration achieved during the sixth month of operation; then, column 1 was sacrificed, and column 2 was fed with 4.42 ± 1.21 mg l-1 toluene during the remaining 6 months, in order to permit biofilm maturation under constant feeding conditions. In the case of column 2, values of toluene concentration in the inlet (Si) and in the outlet (Se) were employed to calculate toluene loading rate (LR) and elimination rate (ER) according to the following equations: ER = Q (Si-Se) / V and LR = Si·Q/V, where, Q is the inlet feed flow rate (0.026 l h-1) and V is the total volume (0.015 l) of the column, respectively. Toluene determination Toluene concentration in the gas and liquid phase was determined by the headspace method on a gas chromatographic system (HP 6890 Series) equipped with flame ionization detector (FID), calculating the area of the chromatographic peaks. Toluene concentration values in the outlet of column 2 were the average of at least 10 determinations, carried out either after culture acclimation during the first 6-month operation, or every week during the following 6-month period, when column 2 was maintained at the highest LR. Neither evidence of abnormal biomass growth nor loss of mechanical properties of the supports were recorded during the entire time course of the experiment. Scanning electron microscopy analysis To perform scanning electron microscopy analysis, pumice granules collected from both columns 1 and 2, together with not colonised granules were fixed with 2.5 % glutaraldehyde in 15 g l-1 NaCl for two hours at 4°C, processed according to [2] and analysed with a scanning electron microscope (Cambridge 250 Mark3). Biomass determination The biomass attached to the support was estimated as volatile solids (VS), after drying at 105°C and then burning at 600°C of the colonised pumice granules, collected from both columns. The difference in mass between the dried and the burned samples was the weight of VS (being ash equal to 8 ± 2% of the dry biomass). Biomass was also indirectly determined by total protein assay (Bio-Rad, Richmond, CA, USA) after detachment of the cells from the support by bead-beating in a homogenizer (Fast prep, BIO 101 Thermo Savant) and their lysis (60°C for 90 min with 0.6% w/v SDS), assuming that the average protein content in bacteria is about 50% of cell dry weight [11]. Since colonization of the top and the bottom parts of column 2 was considerably different to the naked eye, granules were sampled from both ends of the column and separately processed for all the determinations. Identification of the bacterial species To identify the microbial species in the biofilm of columns 1 and 2, cells removed from the pumice granules by bead-beating (see above) were plated on TSA (Tryptone Soy Agar). An average of 100 colonies were isolated after serial dilutions from each consortium, and cultured individually in Tryptone Soy Broth (TSB). In parallel, to identify the microbial species present in the enriched culture employed to inoculate the packing material, an average of 100 colonies from the enriched culture were isolated on TSA and cultured individually in TSB. Chromosomal DNA was extracted from cells of the isolated strains according to standard methods for bacteria [12] and used as template for PCR amplification performed with 30 cycles, each consisting of 30 s at 94°C for DNA denaturation; 40 s at 52°C for primer annealing, and 90 s at 72°C for elongation. Primers used in PCR reaction were P1 (5'-GCGGCGTGCCTAATACATGC) and P2 (5'-CACCTTCCGATACGGCTACC), annealing to nucleotides 40 to 59 and 1532 to 1513, respectively, of B. subtilis rrnE. These primers are normally utilized as universal primers for eubacteria. After PCR amplification, 1.5 Kbp 16S ribosomal DNA fragments were purified from agarose gels and sequenced. Ribosomal DNA gene sequences of the isolates where compared by BLAST program with those present in the DNA GenBank [13]. Authors' contribution DLA:carried out biofilm characterization. VM:carried out the identification of species and helped to draft the manuscript. PP: partecipated in the design of the study. VR: partecipated in the design of the study and setting up of laboratory apparatus. BA: performed toluene analysis. SP: partecipated in the setting up of laboratory apparatus. SR: partecipated in the design of the study and helped to draft the manuscript. DAE: conceived of the study, and partecipated in its design and coordination, drafted the manuscript. All authors read and approved the final manuscript. Acknowledgements The work has been carried out under the financial support of MIUR (Italian Ministry of Education, University and Research) within the Eureka Project: E! 2113 Water Biotreatment Using Inert Supports WABIS. Figures and Tables Figure 1 Scanning electron micrograph survey of pumice granules and biofilm development. Before colonisation (A) pumice granules are blank. After 6 month of operation (B), rod shaped cells cover the pumice surface. In the 12 month biofilm, an abundant exopolymeric matrix is visible on pumice granules both at the bottom (C) and top (D) of the column. Figure 2 Toluene elimination rate of column 2 plotted as a function of toluene loading rate. Loading rate was increased over a 12-month period as described in the text. Figure 3 Schematic diagram of the mini-scale apparatus during continuous operation. a, air filter; b, feed tank; c, air diffuser; d, toluene tank; e, peristaltic pump; f, column 1; g, column 2; h, i effluent tanks. Table 1 Biomass adhering to pumice granules after 6- and 12- month operation Operation time (months) Total biomass (mg cells d.w. g-1 pumice) Burning Protein assay 6 2.86 3.40 12 (top) 9.52 3.25 12 (bottom) 12.58 6.44 ==== Refs Starr RC Cherry JA In situ remediation of contaminated groundwater: the funnel and gate system Groundwater 1996 32 465 476 von Canstein H Leonhauser YLJ Haase E Felske A Deckwer W Wagner-Dobler I Spatially oscillating activity and microbial succession of mercury-reducing biofilms in a technical-scale bioremediation system Appl Environ Microbiol 2002 68 1938 1946 11916716 10.1128/AEM.68.4.1938-1946.2002 Kuhn EP Colberg PJ Schnoor JL Wanner O Zehnder AJB Schwarzenbach RP Microbial transformation of substituted benzenes during infiltration of river water to groundwater: laboratory column studies Environ Sci Technol 1985 19 961 968 Mallakin A Ward OP Degradation of BTEX compounds in liquid media and in peat biofilters J Ind Microbiol 1996 16 309 318 Yerushalmi L Manuel MF Guiot SR Biodegradation of gasoline and BTEX in a microaerophilic biobarrier Biodegradation 1999 10 341 352 10870550 10.1023/A:1008327815105 Stoodley P Sauer K Davies DG Costerton JW Biofilms as complex differentiated communities Annu Rev Microbiol 2002 56 187 209 12142477 10.1146/annurev.micro.56.012302.160705 O'Toole G Kaplan HB Kolter R Biofilm formation as microbial development Annu Rev Microbiol 2000 54 49 79 11018124 10.1146/annurev.micro.54.1.49 Vinage I von Rohr PR Biological waste gas treatment with a modified rotating biological contactor. 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Control of biofilm growth and long-term performance Bioprocess Biosyst Eng 2003 26 69 74 14564499 10.1007/s00449-003-0334-0 Hidalgo A Juareguibeitia A Prieto MB Rodriguez-Fernandez C Serra JL Llama MJ Biological treatment of phenolic industrial wastewaters by Rhodococcus erythropolis UPV-1 Enzyme Microb Technol 2002 31 221 226 10.1016/S0141-0229(02)00078-9 Harms H Zehnder AJB Influence of substrate diffusion on degradation of dibenzofuran and 3-chlorodibenzofuran by attached and suspended bacteria Appl Environ Microbiol 1994 60 2736 2745 8085817 Atkinson B Mavituna F Biochemical engineering and biotechnology handbook 1991 New York Stockton Press Sambrook J Fritsch EF Maniatis T Molecular cloning: a laboratory manual 1989 Cold Spring Harbour Laboratory, Cold Spring Harbor Press Altschul SF Gish W Miller W Myers EW Lipman DJ Basic local alignment search tool J Mol Biol 1990 215 403 410 2231712 10.1006/jmbi.1990.9999	15655073	PMC546200	CC BY	2021-01-04 16:05:47	no	Microb Cell Fact. 2005 Jan 17; 4:4	utf-8	Microb Cell Fact	2,005	10.1186/1475-2859-4-4	oa_comm
==== Front BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-5-21564413210.1186/1471-2261-5-2Research ArticleThe association of spatial T wave axis deviation with incident coronary events. The ARIC cohort Vaidean Georgeta D [email protected] Pentti M [email protected] Ronald J [email protected] Eric A [email protected] Lloyd E [email protected] Aaron R [email protected] Wayne D [email protected] Zhu-Ming [email protected] Richard S [email protected] Gerardo [email protected] Department of Epidemiology, University of North Carolina at Chapel Hill, USA2 Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA3 Department of Epidemiology and Department of Medicine, University of North Carolina at Chapel Hill, USA4 Department of Biostatistics, University of North Carolina at Chapel Hill, USA5 Division of Epidemiology, University of Minnesota, Minneapolis, Minnesota, USA2005 11 1 2005 5 2 2 22 6 2004 11 1 2005 Copyright © 2005 Vaidean et al; licensee BioMed Central Ltd.2005Vaidean et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Although current evidence suggests that the spatial T wave axis captures important information about ventricular repolarization abnormalities, there are only a few and discordant epidemiologic studies addressing the ability of the spatial T wave axis to predict coronary heart disease (CHD) occurrence. Methods This prospective study analyzed data from 12,256 middle-aged African American and white men and women, from the Atherosclerosis Risk in Communities Study (ARIC). Following a standardized protocol, resting standard 12-lead, 10-second electrocardiograms were digitized and analyzed with the Marquette GE program. The median follow-up time was 12.1 years; incident coronary heart disease comprised fatal and non-fatal CHD events. Results The incidence rate of CHD was 4.26, 4.18, 4.28 and 5.62 per 1000 person-years respectively, across the spatial T wave axis quartiles. Among women for every 10 degrees increase in the spatial T wave axis deviation, there was an estimated increase in the risk of CHD of 1.16 (95% CI 1.04–1.28). After adjustment for age, height, weight, smoking, hypertension, diabetes, QRS axis and minor T wave abnormalities, this hazard rate ratio for women fell to 1.03 (0.92–1.14). The corresponding crude and adjusted hazard ratios for men were 1.05 (95% CI 0.96–1.15) and 0.95 (0.86–1.04) respectively. Conclusions In conclusion, this prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow up. It is doubtful that spatial T wave axis deviation would add benefit in the prediction of CHD events above and beyond the current traditional risk factors. ==== Body Background Ventricular repolarization abnormalities play an important role in the determination of arrhythmia and sudden cardiac death [1,2] and may reflect subclinical myocardial ischemia changes. The process of repolarization at rest is routinely quantified from standard 12-lead electrocardiogram (ECG), either as time-domain indexes such as the QT interval and its derivations, or as abnormalities of the ST segment or of the T wave. While these indexes have all been reported to be associated to some degree with incident coronary heart disease events, they have some limitations. The widely used QT interval reflects only the temporal aspect of the repolarization, and as currently defined and measured from the 12-lead ECG, QT dispersion has major conceptual and technical limitations [3,4]. Theoretical and experimental studies suggest that ventricular repolarization occurs in a nonlinear and inhomogeneous fashion [5-10]. As a consequence, spatial measures of repolarization that take into account T-wave complexity using the T-wave vector (axis) should be more accurate and useful surface ECG markers of repolarization abnormalities than simple scalar intervals from the ECG, such as the QT interval or QT dispersion [11-16]. Clinical studies have shown that the T wave axis reflects changes associated with autonomic adaptive or maladaptive influences [17], systemic hypertension [18,19], coronary occlusion [20], and microalbuminuria in individuals without diabetes mellitus [21]. Moreover, it has recently been shown that spatial T wave axis deviation has good measurement properties and is repeatable [22], a finding that supports its use in clinical and epidemiological research. Two prospective population-based reports on T wave axis deviation measured from standard 12-lead ECGs in older populations (the Rotterdam study [23], and Cardiovascular Health Study (CHS) [24] suggest that it is an indicator of increased risk of coronary heart disease and total mortality, independent of other cardiovascular risk factors. In the cohort of high-risk, middle-aged men from the Multiple Risk Factor Intervention Trial (MRFIT), baseline spatial T wave axis deviation was not significantly associated with incident coronary events, although the change over time in the spatial T wave axis deviation was reported to be associated with incident events on long-term follow-up [25]. These studies suggest that spatial T wave axis deviations capture changes in the ventricular repolarization process that are of potential clinical and epidemiologic importance. The aim of this study was to assess whether a single base-line measurement of spatial T wave axis deviation is associated with prospectively ascertained coronary events in a population-based, bi-ethnic cohort of middle-aged men and women, to determine if such an association is independent of other risk factors, and to compare its association with that of other indexes of altered repolarization, such as a prolonged QT interval and minor T wave abnormalities. Methods Between 1987 and 1989, the Atherosclerosis Risk in Communities (ARIC) study examined population-based samples of residents aged 45 to 64 years from 4 communities in North Carolina, Mississippi, Minnesota, and Maryland. From the initial sample of ARIC baseline participants (n = 15,792) we excluded those not having an ECG (n = 205), and because of small numbers, self-identified ethnicity other than white or African American (n = 48). Further, we excluded participants likely to present secondary repolarization abnormalities such as myocardial infarction (self-reported or ECG-diagnosed, n = 790), use of digitalis or anti-arrhytmic drugs (n = 115), or pathologies likely to alter the measurement or interpretation of the spatial T wave axis as identified by the Minnesota code (MC), such as major Q or QS waves (MC 1.1, 1.2), ST depression or elevation (MC 4.1 to 4.4 and MC 9.2), negative T waves (MC 5.1 or 5.2), WPW pattern (MC 6.6), ventricular conduction defects (MC 7.1, 7.2, 7.4, and atrial fibrillation or flutter (MC 8.3), (n = 2093). Participants who had ECG evidence or history of myocardial infarction, coronary bypass, or angioplasty) (n = 162) at baseline were excluded from analysis. Participants whose angina status was positive or unknown by the Rose questionnaire were not excluded because the questionnaire's validity, especially in women, has been questioned [26]. The final study sample was 12,256 participants. Baseline measurements ECG measurements, processing, and definition of the spatial T wave axis During a fifteen-minute supine rest, trained and certified technicians positioned disposable Ag/AgCl electrodes and recorded a supine, resting 12-lead, 10-second electrocardiogram using a MAC Personal Cardiographer™ (Marquette Electronics, Inc., Jupiter, FL). Detailed procedures used for electrode placement and skin preparations are described in the Operation Manual [27]. ECGs were digitized at 250 Hz and sent daily via modem to the Epidemiological Cardiology Research (EPICARE) Center, North Carolina. The EPICARE Center, blinded to participant identity processed the ECGs using the most recent version of the Marquette GE program, version 12SL. Quality assurance procedures were designed specifically for ECG acquisition and processing [27]. These programs included: standardized electrode positions were assured by marking of the skin using a standardized flexible ruler according to a detailed procedure manual. Electrical noise, overall and beat-to-beat electrocardiogram drift were monitored and quality-scored using a five level grading system to assist technicians in identifying unacceptable electrocardiograms and maintaining quality. Technical performance criteria were established and monitored by the EPICARE Center. All technicians were specifically trained and certified. All ECGs were read according to the Minnesota code [28] without knowledge of clinical or demographic data. All resting 12 lead ECGs coded as having key abnormalities by Minnesota codes including any 1-code, any 1-2 code or 2-2, 5-1 or 2 or any 9-2, 6-4, 7-1-1 or 7-2-1 codes and a 10% random sample of all other ECGs read by computer also were visually coded at the Minnesota ECG Reading Center. Adjudication of discrepancies was performed at the Minnesota Center. The computer-assigned codes are used as study data except where adjudication resulted in a code different from the original computer codes. Spatial T-wave axis was calculated as previously described [24] from integrated T-wave amplitudes of the XYZ leads. Briefly, the inverse transformation by Dower et al. [29] was used to derive Frank's XYZ leads, with the polarity of the Z lead inverted in order to generate QRS and T wave patterns with a more familiar waveform in that lead. Thus, the positive direction of the Z axis is in the anterior direction. Spatial T-wave axis was calculated from the scalar product between the T vector and a unit vector in a normal reference direction (x = 1/√3, y = 1/√3, and z = -1/√3, where x, y and z are the unit vector components in the X, Y and Z directions). T axis expresses spatial T vector deviation from the approximate normal direction of the T vector 45° anteriorly in the XZ plane and at 45° elevation from the Y axis. The repeatability of the spatial T wave axis deviation was determined using identical study procedures in a group of apparently healthy volunteers with demographic characteristics similar to the ARIC participants. The repeatability of the spatial T wave axis deviation was high with an intra-class correlation coefficient of 0.87 [22]. The Cornell voltage index was determined as the sum of R wave amplitude in leads aVL and the S wave amplitude in lead V3 [30]. The QT interval from the digital 12-lead ECG was determined by the NOVACODE program [31]. An overall QT interval was calculated from the common QRS onset and T offset for all 12 leads together. To attempt correction for the heart rate dependence, we used several approaches: Bazett's and Fridericia's formulae as well as the QT Index. For comparability with other studies we report the QT interval "corrected "by the Bazett's formula [32], in spite of its well-known limitations. Covariate measurements The baseline examination was carried out in 1987–1989 and consisted of a home interview of all potential cohort members including items on cardiovascular risk factors, socioeconomic factors and family medical history. After obtaining informed consent, the clinic examination consisted of medical history interview, blood pressure and anthropometric measurements, venipuncture for blood samples and a 12-lead standard electrocardiogram. Anthropometrics were measured with participants wearing scrub suits and no shoes. Height, measured to the nearest centimeter and weight, measured to the nearest pound, were used to calculate body mass index (BMI). Waist-to-hip ratio was defined as the waist girth at the umbilicus (centimeters) divided by the maximum girth of the hips (centimeters). Smoking status was defined as "current smoker" if the person answered "yes" to both of the following questions: "Have you ever smoked cigarettes? and " Do you now smoke cigarettes?". Cholesterol and triglycerides were measured enzymatically. LDL-cholesterol was calculated. Hypertension was defined as having systolic blood pressure values equal or higher than 140 mmHg, or diastolic blood pressure values equal or higher than 90 mmHg or use of blood pressure lowering medication use in the past two weeks [27]. Ascertainment and classification of incident CHD cases Deaths and hospitalization events were ascertained by annual follow-up calls to the cohort members, review of vital records, and community surveillance of hospitalized and fatal events. CHD death was defined as lacking a probable non-CHD cause, and occurring in the context of a recent myocardial infarction, chest pain within 72 hours of death, or a history of CHD. Events were classified independently by two members of the Mortality and Morbidity Classification Committee and discrepancies were adjudicated by a third member. Descriptions of event ascertainment and classification have been published [33,34]. For the present study, we included CHD events occurring between the ARIC baseline examination and December 31, 2000. The median follow-up time was 12.1 years (maximum of 14.1). We defined CHD incidence as (1) a definite or probable MI, (2) a silent MI between examinations ascertained by ECG, or (3) a definite CHD death. Data analysis We treated spatial T wave axis deviation as a continuous variable. For the purpose of comparison, however we categorized the spatial T wave axis deviation using thresholds employed in previously published reports: normal (<30 degrees), borderline (30 to 45 degrees), and abnormal deviation (>=45 degrees). Cox proportional hazards regression [35] was used to model time-to-event in the presence of censoring. Time-at-risk (time-to-event or time-to-censoring) was calculated from the date of the baseline examination to the earliest of the following: date of coronary event, date of death, date of last follow-up contact, or December 31, 2000. Hazard ratios with 95%confidence intervals were derived for each variable. When treating spatial T wave axis as a categorical variable, dummy variables were created. Interactions between spatial T wave axis and gender, QRS axis, minor T wave abnormalities and QT interval, were tested as cross-product terms in the multivariable model. Significance of interaction was assessed based on the likelihood ratio test (p < 0.10). Statistical analyses were conducted for each gender to allow for well-known differences in ventricular repolarization by gender, and because of a statistically significant interaction between spatial T axis and gender on CHD events. SAS version 8.1 (SAS Institute, Cary, North Carolina) was used in all analyses. Results Sample characteristics The study population consisted of 12,256 participants, of which 7,143 (58.3%) were women and 3,050 (24.9%) were African American. The mean (SD) age of the study population at baseline was 53.8 (5.7) years. The cohort was followed up for a maximum of 14.1 years [mean (SD) 11.6 (2.3) years]. During this time 653 incident coronary heart disease events occurred. Of the total number of events, 250 coronary heart disease events occurred among women (cumulative incidence 3.5%) and 403 (cumulative incidence 7.9%) among men. The average annual incidence rate was 4.6 per 1000 person-years (95% CI, 4.2–4.9 per 1000 person-years). Correlates of spatial T wave axis deviation The distribution of the spatial T wave axis deviation at baseline was similar for men and women, with a mean (SD) of 23.9 (10.7) degrees in men and 23.2 (11.6) degrees in women. Several traditional cardiovascular risk factors showed graded associations across the spatial T wave axis deviation quartiles, with similar patterns among both men and women (Table 1). Participants of African American ethnicity, patients with hypertension and diabetes, and those with higher Cornell voltage presented larger values of spatial T wave axis deviation; lower values for the spatial T wave axis were observed among participants with larger values of the QRS axis, T wave axis in the frontal plane and among smokers. We considered the frontal plane T wave axis for ensuring comparability with other published studies. Other covariates analyzed were anthropometric measures (weight, BMI, waist-to-hip ratio), systolic and diastolic blood pressure, glucose levels, total-, and LDL-cholesterol, QT interval, the prevalence of minor T wave abnormalities. All showed statistically significant, positive associations with spatial T wave axis quartile. Age was not associated with spatial T wave axis deviation. Table 1 Characteristics of the study population at baseline examination by spatial T wave axis extreme quartiles. The ARIC Study. Mean (SD) and percentages Variable (units) Spatial T wave axis deviation quartile Women, n = 7143 Men, n = 5133 T axis quartile 1: 0.33–14.75° T axis quartile 4: 30.20–60.76° T axis quartile 1: 0.19–16.19° T axis quartile 4: 30.41–60.88° Age (years) 53.5 (5.7) 53.7 (5.6) 54.4 (5.7) 54.4 (5.7) African-American % 18.4 41.7 13.0 32.1 Height (cm) 162.3 (5.9) 161.9 (6.0) 176.5 (6.2) 175.5 (6.7) Weight (Lb) 151.7 (32.5) 168.6 (37.8) 182.3 (30.8) 190.6 (31.8) BMI (kg/mm) 26.2 (5.4) 29.2 (6.3) 26.6 (4.1) 28.1 (4.3) WHR 0.88 (0.08) 0.90 (0.08) 0.96 (0.06) 0.96 (0.05) Smokers % 26 21 32 29 SBP (mmHg) 116.8 (18.2) 122.9 (19.4) 118.8 (15.5) 126.1 (18.5) DBP (mmHg) 70.3 (10.3) 74.2 (11.1) 73.3 (9.8) 78.2 (11.6) HTN % 25 43 22 44 Glucose (mmol/L) 5.8 (2.3) 6.1 (2.5) 5.8 (1.4) 6.2 (2.3) Diabetes % 8 13 7 14 HDL-C (mmol/L) 1.5 (0.4) 1.5 (0.4) 1.2 (0.4) 1.2 (0.4) LDL-C (mmol/L) 3.5 (1.0) 3.6 (1.0) 3.6 (1.0) 3.6 (1.0) Total Cholesterol (mmol/L) 5.6 (1.1) 5.7 (1.1) 5.4 (1.0) 5.5 (1.0) Tri-glyc. (mmol/L) 1.3 (0.8) 1.4 (1.0) 1.6 (1.2) 1.7 (1.1) QRS axis (degrees) 52.5 (30.0) 32.8 (32.0) 52.1 (34.3) 24.0 (35.9) Cornell voltage (uV) 926.1 (400.6) 1188.9 (437.4) 1165.6 (44.7) 1489.8 (490.6) QT interval (ms) 394.2 (27.4) 403.9 (28.3) 395.0 (29.2) 400.1 (29.8) Heart rate (bpm) 70.2 (9.8) 67.7 (9.9) 62.3 (10.3) 66.7 (10.1) QTc (ms) 399.2 (18.5) 403.9 (19.1) 415.0 (16.4) 418.5 (18.7) T axis frontal plane (degrees) 61.5 (12.3) 38.3 (25.4) 62.2 (12.5) 31.4 (26.8) Minor T wave abn.% 5.3 16.4 3.8 16.8 BMI = Body Mass Index, WHR = waist-to-hip-ratio, QTc = QT interval corrected for heart rate. bpm = beats per minute. SBP, DBP systolic and diastolic blood pressure, HTN, hypertension Of particular relevance is the association of the spatial T wave axis with hypertension and with the heart rate. The relationship between the spatial T wave axis and hypertension status (according to the JNC-VII classification) is presented in Table 2 by gender and adjusted for age, height and weight. The more pronounced the hypertension status the larger the mean values for the spatial T wave axis. Among the 3860 individuals defined as hypertensive, the mean (SD) values for the spatial T wave axis adjusted for age, height and weight were higher among those with uncontrolled HTN compared to those having the blood pressure levels below the treatment goal: 26.73 (0.27) degrees vs. 25.90 (0.27) (p = 0.03). Table 2 Mean spatial T wave axis deviation adjusted for age, height and weight, by JNC VII classification of blood pressure and gender. The ARIC study JNC VII BP stage Women Men N (%) Mean T axis (SE) N (%) Mean T axis (SE) Normal 3765 (52.73) 9.86 (0.10) 2373 (46.44) 11.23 (0.11) Pre-HTN 2286 (32.02) 18.29 (0.10) 1911 (37.40) 19.80 (0.11) Stage 1 HTN 841 (11.78) 25.81 (0.10) 648 (12.68) 26.63 (0.11) Stage 2 HTN 248 (3.47) 39.04 (0.10) 178 (3.48) 38.13 (0.1) We found a small but significant inverse association between spatial T wave axis deviation and heart rate. From the first to the fourth T wave axis quartile the mean (SE) values for the heart rate were: 70.24 (0.30), 69.05 (0.23), 69.20 (0.23), 67.70 (0.23) beats per minute for women and 67.26 (0.28), 65.74 (0.28), 65.56 (0.28), 66.67 (0.28) beats per minute for men. Differences between CHD cases and non-cases Compared to those who remained free of disease, men and women who subsequently developed CHD tended to have higher mean age, blood pressure, glucose and atherogenic lipids at baseline. They also were more likely to be smokers, have hypertension, diabetes, a higher Cornell voltage, minor T wave abnormalities and a lower mean values for the QRS axis in frontal plane. The mean QT interval and the mean T wave axis in frontal plane were not significantly different between cases and non-cases. In contrast to men, women who developed CHD had higher mean values for body mass index, waist to hip ratio, and heart rate at baseline. Spatial T wave axis deviation was only slightly increased among cases compared to non-cases (25.2 degrees versus 23.2 degrees among women, and 24.4 degrees versus 23.9 degrees among men). Mean values for the QT interval, heart rate, or T wave axis in frontal plane were not statistically different. Incident CHD events and relative risks of CHD Overall, the cumulative incidence of CHD was almost uniform across the distribution of the spatial T wave axis deviation, with an increase in the last quartile. The incidence rate was 4.26, 4.18, 4.28 and 5.62 per 1000 person-years across quartiles of spatial T wave axis deviation respectively. As Table 3 illustrates, the incidence rate ratio was 1.62 (95%CI of 1.15 to 2.27) for women in the upper quartile of the spatial T wave axis deviation compared with women in the bottom quartile. Adjustment for age, height and weight attenuated this ratio to 1.44 (95%CI of 1.02 to 2.04). Among men, the incidence rate ratio comparing the highest with the lower quartile was 1.09 (0.83–1.42), which was unchanged (1.08, 0.83–1.42) after adjustment for age, height and weight. Table 3 Cumulative incidence, incidence density rates and rate ratios for coronary heart disease at 12-years follow-up by spatial T wave axis quartile by gender. The ARIC study Risk estimate Spatial T wave axis deviation quartile among women T axis quartile 1: 0.33–14.75° T axis quartile 2: 14.75–22.38° T axis quartile 3: 22.39–30.32° T axis quartile 4: 30.33–60.88° Number of event-free participants at baseline 1787 1788 1784 1784 Number of CHD Events 54 57 53 86 Cumulative Incidence [%] 3.02 3.19 2.97 4.82 Person-years 21168 21176 21108 20838 Unadjusted Incidence Rate per 1000 person-years (95% CI) 2.56 (1.87–3.23) 2.70 (1.99–3.39) 2.51 (1.83–3.19) 4.13 (3.25–4.99) Unadjusted Incidence Rate Ratio (95% CI) 1 (referent) 1.05 (0.72–1.53) 0.98 (0.67–1.44) 1.62 (1.15–2.27) Adjusted Incidence Rate Ratio 1 (referent) 1.03 (0.71–1.50) 0.93 (0.64–1.36) 1.44 (1.02–2.04) Risk estimate Spatial T wave axis deviation quartile among men T axis quartile 1 0.19–16.19° T axis quartile 2 16.19–21.77° T axis quartile 3 21.77–30.40° T axis quartile 4 30.19–60.76° Number of event-free participants at baseline 1281 1276 1279 1277 Number of CHD Events 104 92 96 111 Cumulative Incidence [%] 8.12 7.21 7.51 8.69 Person-years 14699 14605 14603 14410 Unadjusted Incidence Rate per 1000 person-years (95% CI) 7.08 (5.72–8.44) 6.30 (5.01–7.59) 6.57 (5.26–7.89) 7.70 (6.27–9.13) Unadjusted Incidence Rate Ratio (95% CI) 1 (referent) 0.89 (0.67–1.18) 0.93 (0.70–1.23) 1.09 (0.83–1.42) Adjusted* Incidence Rate Ratio 1 (referent) 0.90 (0.68–1.20) 0.94 (0.72–1.25) 1.08 (0.83–1.42) * adjusted for age, height and weight Cox regression-modeling of the unadjusted association between CHD and spatial T wave axis deviation among women showed that for every 10 degrees increase in the spatial T wave axis deviation, there was a 1.16 (1.04 and 1.28)-fold increase in the risk of CHD (Table 4). When treating spatial T wave axis deviation categorically, borderline and abnormal deviation was associated with a 1.59 (1.20–2.10) and 1.69 (1.04–2.75)-fold higher risk of developing CHD compared to the normal category. When adjusting for age, height, weight, smoking, hypertension, diabetes mellitus, QRS axis and T wave minor abnormalities, this association for women was no longer statistically significant (hazard rate ratio of 1.03 (0.92–1.14). Neither heart rate or the QT interval (regardless of the formula used for heart rate correction), did qualify as confounders, as they were not associated with incident events in this study population. Table 4 Hazard rate ratios (95%CI) of coronary heart disease at 12 years of follow-up for ten degrees increase in the spatial T wave axis deviation and for the comparison of the borderline and/or the abnormal categories with the normal spatial T wave axis category. Data stratified by gender. The ARIC study Model Women N = 7143 Men N = 5113 Continuous T wave axis deviation* Categorical T wave axis deviation** Continuos T wave axis deviation* Categorical T wave axis deviation** Crude 1.16 (1.05–1.28) 1.59 (1.20–2.10) 1.69 (1.04–2.75) 1.05 (0.96–1.15) 1.11 (0.89–1.40) 1.27 (0.79–2.04) Adjusted for age (55 yr) 1.15 (1.05–1.27) 1.58 (1.19–2.09) 1.64 (1.01–2.67) 1.05 (0.96–1.15) 1.10 (0.88–1.39) 1.23 (0.76–1.98) Adjusted for age, height and weight 1.11 (1.00–1.23) 1.46 (1.10–1.94) 1.49 (0.92–2.44) 1.05 (0.96–1.15) 1.10 (0.87–1.39) 1.22 (0.75–1.97) Adjusted for age, height, weight and QRS axis 1.10 (1.00–1.22) 1.44 (1.09–1.92) 1.47 (0.90–2.40) 1.03 (0.94–1.13) 1.07(0.84–1.35) 1.17(0.72–1.89) Adjusted for age, height, weight, QRS axis and smoking 1.10 (1.00–1.22) 1.49 (1.12–1.99) 1.42 (0.87–2.32) 1.02 (0.93–1.12) 1.04(0.82–1.32) 1.09 (0.67–1.76) Adjusted for age, height, weight, QRS axis, smoking, HTN, and DM 1.04 (0.94–1.15) 1.32 (0.99–1.76) 1.06 (0.64–1.77) 0.95 (0.86–1.04) 0.93(0.73–1.18) 0.85(0.52–1.39) * Spatial T wave axis deviation was treated as a continuous variable and risk estimates are expressed for ten degrees (approximately 1 standard deviation in the distribution) increase in the spatial T wave axis deviation. Spatial T wave axis deviation was treated as a categorical variable and risk estimates are expressed for the comparison of the borderline category (30–45 degrees) to the normal category in the first row of the cell and for the comparison of the abnormal category (≥45 degrees) to the normal category (≤30 degrees) in the second row of the cell. * HTN and DM indicate hypertension and diabetes mellitus respectively. Hypertension status was defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or current use of antihypertensive medication. Diabetes status was defined as fasting glucose ≥126 mg/dL, nonfasting glucose ≥200 mg/dL, or a physician diagnosis or pharmacological treatment for diabetes. Among men, there was no significant association between spatial T wave axis deviation and CHD events. This lack of association persisted after adjusting for covariates. T wave axis in the frontal plane was not associated with coronary events either; the crude associations were nil and persisted as such in multivariate analysis in both women and men. When using the same T wave axis deviation categories as in the CHS and Rotterdam studies, borderline and abnormal deviation was associated with a 1.11 (0.89–1.40) and 1.27 (0.79–2.04)-fold higher risk for developing CHD compared to the normal category. After adjusting for age, height, and weight and as well for smoking, hypertension, diabetes, QRS axis and T wave minor abnormalities, the association remained statistically non-significant. The prevalence of an abnormal spatial T wave axis deviation (larger than 45 degrees) in the ARIC population was extremely low (4.5%) so these risk estimates need to be interpreted with caution. Minor T wave axis abnormalities were only slightly associated with the risk of developing a coronary heart disease event, in both men and women. The hazard rate ratios for CHD in relation to minor T wave abnormalities were 1.96 (1.37–2.81) for women and 1.31 (0.92–1.88) for men. After adjustment for age, height, weight, smoking, hypertension, diabetes and QRS axis, these estimates became 1.25 (0.86–1.80) for women and 1.02 (0.71–1.47) for men. The measured (uncorrected for heart rate) QT interval was not associated with coronary events, in either gender group. Incidence rates for coronary events in the first and the last gender-specific QT interval quartiles were 8.04 and 8.00 per 1000 person-years among men and 3.8 and 4.5 per 1000 person-years among women. Adjustment for age and heart rate (either by using the heart rate corrected QT interval by Bazett's formula, QT Index or by including heart rate in the Cox proportional models) did not change this lack of association. Discussion This prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow-up. The QT interval was not associated, and minor T wave abnormalities were only weakly associated, with the incidence of coronary events after adjustment for various covariates. Given the lack of association between spatial T wave axis and coronary events in this study, several issues must be considered. Potentially, incomplete ascertainment or misclassification of CHD events could bias the results and obscure an underlying association. The excellent quality control strategies employed by the ARIC study, the highly standardized protocol for data collection, independent case ascertainment through the cohort follow-up, and the epidemiologic surveillance system in place at each of the four ARIC study communities make the possibility of misclassification of coronary heart disease very unlikely. Inadequate statistical power to detect a meaningful association is not likely, due to the large sample size, long follow-up and relatively large number of events. The minimum effect size that the present study could have missed was computed by dichotomizing spatial T wave axis deviation, then contrasting "borderline" and "abnormal " deviation with the "normal" category (nQuery Advisor, version 4.0 [36]). Given the observed distribution of T wave axis deviation in the present study, a log-rank test of the survival curves at 12 years of follow up would have approximately 79 to 99% power to detect differences between the survival curves corresponding to hazard ratios between 1.11 and 1.22, at a 0.05 two-sided significance level. Our results do not confirm the findings of the Rotterdam study [23] and the CHS [24] with regard to the spatial T wave axis. The risk of CHD associated with abnormal deviation (more than 45 degrees) versus normal T wave axis deviation (less than 30 degrees) was 2.9 (95% CI 2.0–4.3)) in the Rotterdam study and 1.58 (95%CI 1.25–1.99) in the CHS study. In contrast, findings from the present study are similar to those from the Multiple Risk Factor Intervention Trial (MRFIT) [25], which did not find a significant association between spatial T wave axis deviation at baseline and incident CHD events, although there was a significant relation between change in spatial T axis and long-term mortality from CHD. The discrepancy between our results and those reported in previous articles has several potential explanations, involving true variation between study populations (differences between study populations in terms of exposure distribution, demographic characteristics, absolute cardiovascular risk level or effect modification by genetic or environmental factors) and different patterns of confounding by other cardiovascular risk factors. Compared to participants in the present study, those in studies that found a significant association between T wave axis deviation and CHD were older (Rotterdam study and CHS), and more likely to have a history of myocardial infarction, angina pectoris (Rotterdam study) or silent (ECG-diagnosed) myocardial infarction and hypertension at baseline (CHS study). These study populations also had more ECG evidence of ischemia and infarction as reflected by the presence of Q-waves and major ST-T abnormalities (Rotterdam study) or T wave inversion (CHS study) but this was not accounted for these confounders in the analysis. By contrast, the MRFIT study and the present ARIC study excluded these participants, preserving only those with minor T wave abnormalities, and found only weak or non-significant associations with CHD events. Studies reporting stronger associations also had shorter follow-up time. To explore the potential impact of these dissimilarities, we performed several post hoc analyses under different scenarios. i) without excluding any participants In comparing individuals without CHD at baseline with prevalent CHD cases at baseline, the spatial T wave axis deviation was significantly different: 26.66 (16.81) and 44.88 (27.29) degrees respectively (p value < .0001). The entire distribution of the spatial T wave axis deviation among those with CHD at baseline was shifted to the right of the distribution of participants without CHD at baseline. This difference persisted statistically significant even after adjustment for age and several standard cardiovascular risk factors (anthropometry, smoking, hypertension, diabetes, heart rate, QRS axis and QT interval) with adjusted mean values of 26.79 and 41.47 degrees respectively. However, among those without CHD at baseline in our study, there were various conditions or markers of clinical interest, some of established predictive value, such as ST segment depression, negative T waves or atrial fibrillation. In the clinical setting, the need for improved prediction and discriminatory ability of a new potentially useful marker is particularly useful in the absence of such markers. After excluding Minnesota codes representing secondary repolarization abnormalities, this difference was markedly attenuated, with mean spatial T wave axis deviation of 23.84 (12.36) for those free of CHD and 24.08 (15.34) degrees for those with CHD. This suggests that spatial T wave axis has no ability to discriminate between CHD cases and CHD-free individuals beyond and above secondary repolarization abnormalities. This might be one of possible explanations for the discrepant results between different cohort studies exploring T wave axis, as different studies have used different exclusion criteria. As an alternative to the exclusion of Minnesota codes reflecting secondary repolarization abnormalities, we performed a separate analysis this time including QRS duration as a covariate. The relationship of the spatial T wave axis deviation with incident CHD events followed the same pattern as in the restricted dataset, i.e. a slight increase in the incidence rates in the fourth quartile of the T wave axis, for each gender. The crude hazard rate ratios (95%CI) for incident CHD events for a 10 degree increase in the spatial T wave axis were 1.18 (1.13–1.23) for women and 1.14 (1.09–1.19) for men. After adjustment for the same covariates as in the restricted data set and for the QRS duration, the hazard rate ratios (95%CI) were 1.05 (0.99–1.16) and 1.04 (0.98–1.1). ii) restricted to fatal events only Ventricular repolarization abnormalities are closely related to the propensity for arrhythmia. Although none of the published population-based studies on T wave axis deviation attempted to ascertain arrhythmic events, it is plausible that the arrhythmic component of the CHD events was the driving force of the observed associations in some published studies. This suggestion is supported by the fact that in both, Rotterdam and CHS study, the association between T wave axis and coronary events was stronger for fatal than non-fatal events. Similarly, the spatial QRS-T angle was recently reported to be associated with fatal but not with non-fatal cardiac events [37]. To explore this issue we restricted our analysis to fatal coronary events. During an average follow-up time of 11.6 years, 143 fatal events were recorded in our study population, 62 among women and 81 among men, with an overall cumulative incidence of 1.17%. The relationship of fatal events with the spatial T wave axis deviation followed the same pattern as the combined outcome, i.e. a slight increase in the incidence rates for CHD only in the fourth quartile of the spatial T wave axis deviation, for each gender. As expected, in the multivariate models (Table 5), the crude hazard rate ratios for fatal events were slightly larger than for the combined outcome. After adjustment for a limited number of risk factors (due to the small number of events), these modest associations were no longer statistically significant. However, these patterns must be interpreted with caution. While in the Rotterdam and CHS studies, the incidence rate for fatal events was 4.2 (95% CI 3.3–5.1) and 5.8 (95% CI 5.0–6.7) per 1000 person year respectively, this rate was much lower in our study: 1.01 per 1000 person-years (95% CI, 0.84–1.17). Table 5 Hazard rate ratios (95%CI) for fatal and combined, fatal and nonfatal coronary heart disease events at 12 years of follow-up for ten degrees increase in the spatial T wave axis deviation. Data stratified by gender. The ARIC study. Model Fatal + non-fatal events Fatal events Women Men Women Men Crude 1.16 (1.04–1.29 1.05 (0.96–1.15) 1.27(1.04–1.55) 1.24 (1.02–1.50) Adjusted* 1.06 (0.95–1.17) 0.96 (0.88–1.06) 1.11 (0.91–1.35) 1.09 (0.90–1.33) *Adjusted for weight smoking HTN and DM iii) similarly, we repeated the analysis without excluding those with negative T waves corresponding to MC 5.2, restricted to the age group greater than 55 years, restricted to participants with both hypertension and diabetes at baseline, restricted to CHD events occurring after a shorter follow-up (4–6 years). While the incidence rates were larger in absolute value in each T wave axis quartile, in relative terms no association with CHD events was detected. Experimental models to link the spatial T wave axis deviation with coronary evens are still sparse, but it can be speculated that an abnormal spatial T wave axis may possibly reflect disturbances in the repolarization process caused by subclinical myocardial disease, with or without an increased propensity for arrhythmic events, and thus an increased risk for fatal cardiac events. This is supported by the fact that subclinical disease is likely more prevalent in the older population of the CHS and Rotterdam study (which found an association between T wave axis and incident coronary events) than in the younger populations of the ARIC and MRFIT studies (both reporting a lack of association). Some indicators of subclinical disease such as the left ventricular hypertrophy (LVH) are clearly more prevalent in the Rotterdam than in the ARIC study. The presence of detected or undetected subclinical disease (LVH, patchy myocardial fibrosis) may lead to ventricular electrical instability and a higher proportion of cardiac death related to primary arrhythmic events in a population. Thus, measured or unmeasured, subclinical disease may play a confounding role, and suggests one more explanation for the discrepancy of results between different studies. The ARIC, CHS and MRFIT study protocols all used the same definition of the spatial T wave axis deviation, while the Rotterdam study used the frontal plane T wave axis. There are no data available to explain the discrepancy of results solely on the basis of different measurement of the T wave axis. The comparison of spatial T wave axis deviation with its temporal counterpart, the QT interval raises an intriguing question. Incident coronary events were strongly associated with T wave axis in the cohort of older adults of the Rotterdam study, which examined a population of older adults in whom a prolonged QT interval was previously reported to be predictive. In the current work, neither spatial T wave axis deviation nor the QT interval (as measured or heart-rate corrected by different formulae) was associated with incident coronary events. In our study, a statistical interaction between QT interval and spatial T wave axis deviation was not detected within the range of the QT interval in this population (data not shown). It is important to mention that in the Rotterdam study the mean QT interval (measured with identical methodology as in our study) was considerably larger than in the present study. From electrophysiological point of view, an abnormal ventricular repolarization can alter the QT interval, the T wave axis or both. It is therefore possible that a given magnitude of an altered spatial T wave axis in the presence of a QT interval within "the normal" ranges, may not be sufficient to elicit any causal association with incident coronary events. Our study found an inverse association between spatial T wave axis deviation and heart rate. Heart rate influences have been reported on the frontal plane T wave angle [18] and on the spatial ST-T vector [38]. The physiologic explanation of the dependency of an angular measure on heart rate is not clear. It is plausible that this phenomenon is similar to some degree with that observed for another index of altered repolarization, namely T wave alternans, characterized by the change in amplitude, morphology and axis of the spatial T wave and associated with an increased risk of sudden cardiac death. Even if the mechanism of heart rate dependency of the spatial T wave axis remains elusive, it raises questions about the need for "rate correction" of this measurement. Our study confirms the results of other studies [18,19] in finding a strong association between spatial T wave axis and hypertension status. The explanation is likely related to the presence of an increased left ventricular wall in the context of the left ventricular hypertrophy. It is also possible that increased spatial Twave axis deviation is related to other processes present in the hypertensive myocardium, such as microfocal areas of fibrous tissue and/or increased alteration of ionic channels. These findings suggest that spatial T wave axis deviation may serve as an auxiliary early marker of repolarization abnormalities in hypertensive individuals. Due to the low prevalence of left ventricular hypertrophy in our study population, we were not able to further explore this issue. Strengths and limitations The present study has several strengths. The highly standardized data collection procedure and ECG protocol increase the internal validity of the findings. The ECG recording was performed by trained technicians using a standardized protocol for lead placement. The events ascertainment in the ARIC study is ensured by a highly standardized quality assurance protocol. The inclusion of apparently healthy participants in a population-based study, of African American and white men and women, the large sample size and the geographic distribution of the study population enhance generalizability. This study also has some limitations. The narrow age range precluded an extensive investigation of the role of age. The study also was limited by the lack of accurate measurements for characterizing the elliptical shape of the thoracic cavity. Constitutional and anthropometric variables such as the chest transverse diameter or chest shape have been associated with a shift of the T wave axis [39] or changes in average T wave potential amplitudes. A slender body with a limited spatial sampling of the thorax directly overlaying the heart can cause lower amplitudes in women than in men [40]. While our study attempted to account for this fact by adjusting for height and weight, a more accurate measurement of thoracic cavity, shape or body surface area would probably result in a better ascertainment of spatial T axis. Nonetheless, previous studies reporting positive findings did not account for the shape of thoracic cavity either, and it is unlikely that such measurement would have influenced the findings of the present study. Ventricular replarization is a complex process, which cannot be fully captured by only a limited number of indexes such as QT interval and spatial T wave axis, exploring either the temporal aspects or the general direction of this process. Other indexes or more complex descriptors of the T wave loop have been described to be accurate markers of an altered repolarization [41]. The spatial QRS-T angle has recently regained attention and several studies have attested its usefulness in post infarction [12] and hypertensive patients [19]. The spatial QRS-T angle was also the strongest predictor of fatal cardiac events, even after controlling for the frontal plane T axis, in the large population-based cohort of men and women aged 55 years or older of the Rotterdam study [37]. Unfortunately, this index is not currently available in our database. Conclusions In conclusion, this prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow up. It is doubtful that T wave axis deviation would add benefit in the prediction of CHD events above and beyond the current traditional risk factors. The QT interval was not associated with incident coronary events, and minor T wave abnormalities were only weakly associated among women. Competing interests The author(s) declare that they have no competing interests. Authors' contributions GV performed the overall study design, statistical analysis and manuscript preparation. PR designed the data collection protocol, participated in the interpretation of the ECG data, provided comments on the manuscript. RP participated in the quality control of the ECG data, participated in the interpretation of the findings and with comments on the manuscript. EAW participated in the interpretation of the findings and with comments on the manuscript, provided assistance in the manuscript preparation. LEC provided oversight of the statistical analysis, verified the appropriateness of statistical models, provided assistance in the manuscript preparation. ARF participated in the interpretation of the findings and with comments on the manuscript, provided assistance in the manuscript preparation. WDR collaborated in the study design, participated with comments on the manuscript. ZMZ managed the automated coding of ECGs and the quality assurance. RC participated with comments on the manuscript. GH collaborated in the design of the study, provided comments on the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgment The authors thank the staff and participants in the ARIC study for their important contributions. ==== Refs Zareba W Maison-Blanche P Locati E Noninvasive Electrocardiology in Clinical Practice 2001 Armonk, NY, Futura Publishing Company Surawicz B Schlant RC The pathogenesis and clinical significance of primary T-wave abnormalities Advances in Electrocardiography 1972 Grune & Stratton, New York Rautaharju PM QT and dispersion of ventricular repolarization: the greatest fallacy in electrocardiography in the 1990s Circulation 1999 99 2477 8 10318742 Rautaharju PM Why did QT dispersion die? 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==== Front Clin Mol AllergyClinical and molecular allergy : CMA1476-7961BioMed Central London 1476-7961-3-11564414210.1186/1476-7961-3-1ResearchAllergens of the entomopathogenic fungus Beauveria bassiana Westwood Greg S [email protected] Shih-Wen [email protected] Nemat O [email protected] Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA2 Department of Pediatrics, University of Florida, College of Medicine, 32610, USA2005 11 1 2005 3 1 1 16 11 2004 11 1 2005 Copyright © 2005 Westwood et al; licensee BioMed Central Ltd.2005Westwood et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Beauveria bassiana is an important entomopathogenic fungus currently under development as a bio-control agent for a variety of insect pests. Although reported to be non-toxic to vertebrates, the potential allergenicity of Beauveria species has not been widely studied. Methods IgE-reactivity studies were performed using sera from patients displaying mould hypersensitivity by immunoblot and immunoblot inhibition. Skin reactivity to B. bassiana extracts was measured using intradermal skin testing. Results Immunoblots of fungal extracts with pooled as well as individual sera showed a distribution of IgE reactive proteins present in B. bassiana crude extracts. Proteinase K digestion of extracts resulted in loss of IgE reactive epitopes, whereas EndoH and PNGaseF (glycosidase) treatments resulted in minor changes in IgE reactive banding patterns as determined by Western blots. Immunoblot inhibitions experiments showed complete loss of IgE-binding using self protein, and partial inhibition using extracts from common allergenic fungi including; Alternaria alternata, Aspergillus fumigatus, Cladosporium herbarum, Candida albicans, Epicoccum purpurascens, and Penicillium notatum. Several proteins including a strongly reactive band with an approximate molecular mass of 35 kDa was uninhibited by any of the tested extracts, and may represent B. bassiana specific allergens. Intradermal skin testing confirmed the in vitro results, demonstrating allergenic reactions in a number of individuals, including those who have had occupational exposure to B. bassiana. Conclusions Beauveria bassiana possesses numerous IgE reactive proteins, some of which are cross-reactive among allergens from other fungi. A strongly reactive potential B. bassiana specific allergen (35 kDa) was identified. Intradermal skin testing confirmed the allergenic potential of B. bassiana. ==== Body Background Microorganisms are currently under intensive study for use as biopesticides [1-3]. Several fungal species including Metarhizium anisopliae, Verticillium lecanii, and Beauveria bassiana are being used as biocontrol agents for a number of crop, livestock, and human nuisance pests [4-7]. Strains of B. bassiana have been licensed for commercial use against whiteflies, aphids, thrips, and numerous other insect and arthropod pests. B. bassiana fungal formulations are being spread onto a range of vegetables, melons, tree fruits and nuts, as well as organic crops. As alternatives to chemical pesticides these agents are natural occurring and are considered to be non-pathogenic to humans, although a few cases of B. bassiana mediated tissue infections have been reported [8,9]. Airborne mold spores are widespread, and many have been identified as inhalant allergens eliciting type I hypersensitive reactions in atopic individuals [10-14]. Common allergenic moulds include the anamorphs of ascomycetes and constitute many species within the Alternaria, Aspergillus, and Cladosporium genera [15-19]. The genes encoding for numerous fungal allergens have been isolated, and their protein products expressed and characterized. Purified fungal allergens have been shown to be bound by human IgEs and to elicit allergic reactions in atopic individuals using skin prick tests. Patients with mould allergies often display IgE-mediated responses to multiple fungi, a phenomenon typically thought to result from the presence of common cross-reactive allergen(s) [15,20-22], although parallel independent sensitization to multiple fungal allergens can also occur. In this regards, identification of genus and/or species specific allergens would provide useful tools in differentiating allergic reactions due to primary sensitization and those mediated by cross-reactive epitopes. In the present study, we demonstrate Beauveria bassiana crude extracts contain numerous allergens capable of being recognized by human serum IgEs. The allergens were proteinaceous in nature, and immunoblot inhibition experiments revealed the presence of shared epitopes between Beauveria and several other common fungal moulds. Potential Beauveria-specific allergens were also identified, including a strongly reactive ~35-kDa protein band. Intradermal skin testing using B. bassiana extracts resulted in allergenic reactions in several individuals, including some who have had occupational exposure to the fungus. Methods Strains and cultures Beauveria bassiana (ATCC 90517) was grown on Sabouraud dextrose + 0.5–1% yeast extract or Potato dextrose (PD) media on either agar plates or in liquid broth. Plates were incubated at 26°C for 10–12 days and conidia were harvested by flooding the plate with sterile dH2O containing 0.01% Tween-20. Liquid cultures were inoculated with conidia harvested from plates at 0.5–1 × 105 conidia/ml. Extract preparation Alternaria alternata, Aspergillus fumigatus, Candida albicans, Cladosporium herbarum, Epicoccum purpurascens, and Penicillium notatum were acquired from Greer Laboratories inc., (Lenoir, NC). Extracts were resuspended in TE (40 mM Tris-HCl, pH 8.0, 1 mM EDTA) to a final concentration of 2 mg/ml. Beauveria bassiana was grown in Sabouraud's broth containing 1% yeast extract with aeration at 25°C for 3–5 d. Cellular mass was harvested by centrifugation (10,000 × g, 10 min) and freeze-dried. Cells were resuspended in TE containing 0.1% phenylmethylsulfonyl fluoride (PMSF) and homogenized using a bead-beater apparatus. Precipitations Crude extracts of B. bassiana were subjected to three successive precipitations before use in Western blots. Acetone precipitation Homogenized B. bassiana extracts (50 ml) were mixed with 8 × volume (400 ml) of acetone (kept at -20°C), with rapid stirring, and incubated overnight at -20°C. The precipitate was collected by centrifugation (30 min, 4000 × g), and the pellet was air dried (10 min) before being resuspended in TE containing 0.1% PMSF. Streptomycin precipitation (removal of DNA) Streptomycin sulfate (5 ml of 10% solution) was added dropwise to resuspended acetone precipitated extracts (40 ml) at 4°C with rapid stirring. Samples were incubated for an additional 30 min on ice before being centrifuged (15 min, 10,000 × g) in order to remove the precipitate. Proteins in the resultant supernatant were precipitated using ammonium sulfate. Ammonium sulfate The proteins present in the streptomycin sulfate treated supernatant were precipitated using ammonium sulfate (75%, final concentration). Saturated ammonium sulfate (120 ml) was added dropwise to the Beauveria extract (40 ml) at 4°C with rapid stirring. The solution was allowed to stir overnight at 4°C and precipitated proteins were harvested by centrifugation (30 min, 100,000 × g). The protein pellet was resuspended in TE containing 0.1% PMSF (40 ml) and extensively dialyzed against the same buffer before use. SDS-Polyacrylamide gel electrophoresis (PAGE) Protein samples (30–40 μg) were analyzed by sodium-dodecyl-sulphate-polyacrylaminde gel electrophoresis (SDS-PAGE, 10% Bis-tris gel, Invitrogen, Carlsbad, CA) using standard protocols. Gels were stained with Gelcode blue stain reagent (Pierce, Rockford, IL) and subsequently de-stained with dH20. Western blotting Protein samples were separated under reducing conditions using 10% Bis-tris polyacrylamide gels (Invitrogen Mops system) and transferred to polyvinylidene-fluoride (PVDF) membranes (Invitrogen) as described. Immunoblot experiments were performed using individual and pooled human sera as the primary antibody solution as indicated. Typically, sera were diluted 1:5 with Tris-HCl buffered saline (TBS) containing 5% dry milk + 0.1% Tween-20. IgE-specific reactivity was visualized using a horseradish peroxidase (HRP) conjugated goat anti-human IgE (polyclonal) secondary antibody (BioSource International, Los Angeles, CA). Membranes were washed with TBS containing 0.1% Tween-20 and bands were visualized using the Immuno-Star HRP detection system (Biorad, Hercules, CA). Enzyme Treatments The ammonium sulfate fraction of B. bassiana crude extracts was treated with Proteinase K (ICN-Biomed, Aurora, Oh) following standard protocols. Typically, samples (36 μl) were incubated with 4 μl Proteinase K solution (10 mg/ml in 50 mM Tris-HCl, pH 7.5) for 2 hr at 37°C before analysis. Samples were also treated with endoglycosidase-H (EndoH, New England Biolabs, Beverly, MA) and peptide: N-Glycosidase F (PNGaseF, New England Biolabs) according to the manufacturer's recommendations. For EndoH and PNGaseF treatments, samples (36 μl) were denatured in 4 μl 10 × denaturing buffer (0.5% SDS, 1% β-mercaptoethanol) at 100°C for 10 min prior to the addition of the EndoH (5 μl of 10 × G5 Reaction Buffer, 50 mM sodium citrate, pH 5.5) and PNGaseF reaction buffers (50 mM sodium phosphate pH 7.5) and enzymes (5 μl), respectively. Reactions were incubated at 37°C for 2 h before being analyzed by SDS-PAGE and Western blotting. Immunoblot inhibition IgE binding to B. bassiana proteins were competed with proteins of other fungal extracts. SDS-PAGE resolved B. bassiana proteins were electroblotted to PVDF membranes as described above. Membranes were blocked with TBS containing 5% dry milk + 0.1% Tween-20 and strips were incubated with pooled human sera (1:5 v/v in same buffer) containing 100–500 μg of the indicated fungal crude protein extract. Skin sensitivity profiles to fungal extracts Patients were tested with 9 common fungal extracts for allergy diagnosis using a skin prick assay. The following extracts were obtained from ALA-Abello (Round Rock, TX); Alternaria tenius, Aspergillus fumigatus, Cephalosporium (Acremonium strictum), Curvularia spp. Bipolaris, Epicoccum nigram, Fusarium spp., Helminthosporium sativum, Hormodendrum horde, Penicillium (mixed, P. chrysogenum and P. notatum). Extracts were tested using a 1:10 dilution of the 20,000 PNU/ml stock solution, and skin sensitivity was recorded on a relative scale from 0–4 reflecting the size of induration or weal (4 representing the highest reactivity) and using histamine (0.1 mg/ml) reaction scored as a 3 if no interference was present. Intradermal skin testing B. bassiana crude extracts were prepared as described above but were extensively dialyzed against 0.15 N NaCl and filtered through a 0.22 μm filter before use. Subjects were given intradermal injections of 0.1 ml crude extract ranging in concentration from 0.01–1 mg/ml. Control injections included saline and histamine (0.1 mg/ml). Allergenic reactions were allowed to develop for 15–30 min before the height and width of the reactions were recorded. Results Identification of IgE reactive bands An ammonium sulfate fraction of B. bassiana proteins was resolved on SDS-PAGE (Fig. 1, lane B) and transferred to PVDF membranes as described in the Materials and Methods. Membranes were probed with sera from individual patients who were reactive to various moulds (Table 1), which was pooled and used to demonstrate IgE reactivity against antigens present in B. bassiana extracts (Fig. 1). Serum mix-I represents pooled sera derived from patients E, J, K, L, and M, as well as three additional patients that were only tested (skin prick) against Aspergillus and Penicillium, displaying test scores of 3–4 for each. These data demonstrate human IgE binding of allergens present in B. bassiana extracts. Initial blots showed 12–15 distinct reactive protein bands, ranging in molecular mass from 12 kDa to >95 kDa (under denaturing conditions); with the most prominent bands located around 64, 45, and 35 kDa. Control experiments omitting either the primary or secondary antibody incubation steps resulted in complete loss of signal. Proteinase K digestion of samples also resulted in loss of all signal (Fig. 1, lane 4), indicating the proteinaceous nature of the IgE reactive bands. Since the carbohydrate moieties of several protein allergens are known to play a role in their allergenicity and even cross-reactivity [20-22], samples were treated with the deglyocosylating enzymes EndoH and PNGaseF. Control experiments incubating samples in the EndoH denaturing buffer without any enzyme altered the IgE-reactive signals (Fig. 1, lane 5), however, samples treated with EndoH did not appear any different than control reactions (Fig. 1, lane 6). Similar results were obtained in PNGaseF digests (data not shown). These data appear to indicate that the B. bassiana IgE-antigen profiles observed on Western blots are proteins with minimal contributions due to glycosylation. Figure 1 SDS-PAGE and immunoblot analysis of Beauveria bassiana crude extracts. SDS-PAGE, Gelcode blue stained, lanes A) 5 μg protein standards, and B) 40 μg B. bassiana crude extract. Immunoblots probed with pooled serum mix-I (patients displaying mould allergies), lanes 1), 5 μg protein standards, 2) 20 μg B. bassiana crude extract, 3) 40 μg crude extract, 4) 40 μg crude extract, Proteinase K treated, 5) 40 μg crude extract, denaturing buffer control (no EndoH), 6) 40 μg crude extract, EndoH treated Table 1 Allergic profile of patients A–G, obtained by skin prick testing. Patient ID Individual Skin Reactivity to Fungal Extracts* Alt† Asp Cep Cur Epi Fusa Helmin Hormo Pen A 3 2 0 3 2 2 0 0 3 B 3 2 0 2 3 2 2 2 0 C 4 0 0 3 0 2 0 0 0 D 0 0 2 2 0 2 0 2 2 E 3 2 0 3 2 3 3 0 0 F 4 1 1 2 4 0 2 0 0 G 3 0 0 4 3 0 2 0 0 *Skin test scores were registered using a relative scale from 0–4 with 4 representing the highest reactivity as described in the Materials and Methods. †Abbreviations used; Alt, Alternaria tenius; Asp, Aspergillus fumigatus; Cep, Cephalosporium (Acremonium strictum); Cur, Curvularia spp. Bipolaris; Epi, Epicoccum nigram; Fusa, Fusarium spp.; Helmin, Helminthosporium sativum; Hormo, Hormodendrum horde; Pen, Penicillium (mixed, P. chrysogenum and P. notatum). Immunoprint Analysis of B. bassiana: Reactivity with Individual Sera In order to determine the variation and distribution of serum IgEs reactive to B. bassiana extracts, individual sera from patients displaying mould allergies (Fig. 2, lanes A–G) as well as random sera from the general population (Fig. 2, lanes H–M) were used as probes for Western blots (Fig. 2). The reactivity of pooled sera from patients A–G (termed serum mix-II) is also shown (Fig. 2, lane 2). Skin prick test results for patients A–G are shown for comparative purposes (Table 1) and represent the clinically determined reactivity of each patient to extracts of the tested fungal species. Patients (A–G) were selected based skin prick reactivity to at least 4 different fungi with scores of 2 or greater. Identical concentrations of B. bassiana extract (40 μg) were resolved by SDS-PAGE, blotted to PVDF membranes, and the lanes were cut into separate strips. Each strip was treated with a 1:5 dilution of each respective serum as described in the Materials and Methods (Fig. 3, lane 2 is the sera pool). A total of 16 individual sera were tested, with the sera from three patients displaying no IgEs reactive to proteins present in the B. bassiana extracts. The results for the remaining 13 sera are shown in Fig. 2. The data show a large individual variation in serum IgEs capable of binding epitopes present in B. bassiana extracts, both in terms of banding distribution and the intensity of the reaction. No correlation was observed between measurements of total IgE and the observed binding to B. bassiana allergens. Some patients displayed strong reactions to multiple bands, whereas others to a more limited set of epitopes. Distinct strongly reactive bands ranging from 40 kDa to approximately 200 kDa could be seen in sera A, E, and to a lesser extent L. A strongly reactive 35 kDa band was visible in sera C, G, E, and L. Several sera displayed IgEs that bound to only a limited set of 2–3 allergens (C, F, G, weak bands in B, I, J, K, and M). Blots probed with one serum (H) resulted in a large smear ranging from ~30 kDa to 55 kDa. The reason for the observed smear is unknown and efforts to distinguish separate bands by manipulating the concentrations of either sera or extract were unsuccessful. A number of bands (based upon molecular mass) appeared to be common to several sera including proteins of approximately 35, 42–48, and 60 kDa. A number of allergens of high molecular weight (~100–200 kDa) were also visible; however the resolution in this range on the Western blots is poor. Figure 2 Immunoprint analysis of B. bassiana extracts (40 μg crude extract/strip) probed with individual sera. Lane 1) 5 μg protein standards, 2) pooled serum mix-II (patients displaying mould allergies). Lanes A)–G) membrane strips treated with individual sera, Lanes H)–M) membrane strips probed with individuals having had occupational exposure to B. bassiana and other fungi (see intra-dermal skin test results for individuals J–M, Table 2). Figure 3 IgE immunoblot inhibition with fungi. B. bassiana protein strips (40 μg crude extract) were blocked and incubated with mix containing (500 μl) pooled sera (mix-II) and 2) no additions, 3) 40 μg Alternaria alternata crude extract, 4) 400 μg Alternaria alternata, 5) 40 μg Aspergillus fumigatus, 6) 400 μg Aspergillus fumigatus, 7) 400 μg Cladosporium herbarum, 8) 400 μg Candida albicans, 9) 400 μg Epicoccum purpurascens, and 10) 400 μg Penicillium notatum protein. Intradermal Skin Testing A total of ten individuals were tested for allergenic reactivity to B. bassiana crude extracts using an intradermal delivery procedure. Data using 1 mg/ml B. bassiana crude extract and histamine controls are presented in Table 2. Seven out of the ten individuals (ID #s, J–O, and Q) displayed skin reactivity reactions to the B. bassiana extracts (Table 2, also see corresponding Western blot results for individuals J, K, L, and M; Fig. 2). It is interesting to note that 4 (J–M) of 5 individuals (plus S) that have had occupational exposure to B. bassiana displayed skin reactivity as well as bands on Western blots. A preliminary correlation was observed between the B. bassiana/histamine ration and the in vitro reactivity of individual sera in Western blots. Individuals J, K, and M, displayed B. bassiana/histamine control ratios <1, also showed weak bands in Western blots (Fig. 2), whereas individual L who had a B. bassiana/histamine ratio = 1.65, reacted against numerous epitopes in the extract and with a higher intensity. Table 2 Intradermal skin test results using B. bassiana extract Patient ID Histamine control1 (0.1 mg/ml) B. bassiana Extract (1 mg/ml) B. bassiana/Histamine Induration2 Erythema2 Induration2 Erythema2 Induration ratio3 J4,5 7 × 6 12 × 16 8 × 8 12 × 13 0.65 K4,5 20 × 15 55 × 50 13 × 12 14 × 13 0.52 L4,5 11 × 10 16 × 33 13 × 14 26 × 28 1.65 M4,5 15 × 16 36 × 44 10 × 12 10 × 12 0.30 N 16 × 14 38 × 58 10 × 11 21 × 17 0.49 O 21 × 16 39 × 59 9 × 8 18 × 21 0.21 P 15 × 17 44 × 45 5 × 4 5 × 4 0.08 Q 15 × 14 36 × 38 9 × 12 10 × 13 0.51 R 15 × 15 55 × 38 4 × 4 11 × 13 0.07 S4 20 × 19 38 × 43 4 × 4 4 × 4 0.04 1In all instances saline control injections produced indurations of 3–4 mm × 3–4 mm. 2Values recorded in mm × mm. 3Induration ratio expressed as B. bassiana reaction area (mm2)/histamine reaction area (mm2). 4Individual with occupational exposure to Beauveria bassiana. 5See Western Blot results for individual, Fig. 2. Cross-reactivity among different fungi In order to determine the extent of cross-reactivity of B. bassiana allergens with other fungi, immunoblot inhibition experiments were performed. Identical concentrations of B. bassiana crude extract (40 μg) were resolved by SDS-PAGE, blotted to PVDF membranes, and lanes were cut into separate strips. Each strip was treated with a 1:5 dilution pooled sera (serum mix-II) as the primary antibody supplemented with concentrations of fungal crude extracts as described in the Materials and Methods. Fig. 3 shows Western blots in which the binding of human IgEs to allergens present in B. bassiana extracts were competed with: excess crude extracts from Alternaria alternata (Fig 3, lanes 3, 4), Aspergillus fumigatus (lanes 5, 6), Cladosporium herbarum (lanes 7), Epicoccum purpurascens (Lane 8), Penicillium notatum (lane 9), and Candida albicans (lane 10). There was complete loss of all signals using 2-fold excess B. bassiana extract as the competitor (data not shown). These data indicate that while Beauveria possess many epitopes in common with several other fungi, notably Alternaria and Penicillium, a 35-kDa major reactive band was not inhibited by any extract tested. Discussion Although it is well known that fungi are important triggers of respiratory allergies, the potential allergenicity of entomopathogenic fungi used in biocontrol has largely been untested. Aerobiological surveys of conidial fungi and skin sensitivity tests to fungal extracts performed in the 1980s in the Netherlands revealed that although Beauveria could barely be detected in airborne samples, and represented less than 0.1% of the airborne fungal "flora", the incidence of allergic skin test reaction to Beauveria was the highest of all fungal species tested [10,23,24]. In rural areas, the use of fungi in agricultural pest management practices can greatly increase the potential for human exposure to these agents. Likewise, in urban settings, the commercialization of fungal products for household use may potentiate a much wider problem since indoor air concentrations of the moulds can greatly increase. For these reasons, an examination of the allergenic potential of Beauveria bassiana is imperative. The present study demonstrated the allergenic potential of B. bassiana directly by intradermal skin testing of individuals and in vitro by revealing the presence of serum IgEs capable of binding allergens present in fungal crude extracts. Over 20 different IgE binding proteins were observed using Western blots probed with sera from patients displaying mould allergies. Results using individual sera revealed a wide variation in IgE-binding proteins between sera, although several common bands, including a protein with an apparent molecular mass of 35 kDa were visible among the sera of several patients. Our in vitro observations were confirmed by intradermal skin testing on individuals using B. bassiana extracts. While the testing sample population was small, these results indicated that our extracts were able to elicit allergic reactions in individuals, including some that have had occupational exposure to the fungus. Concentrations of ~1 mg/ml of B. bassiana extracts were required to elicit indurations equivalent to 0.1 mg/ml histamine in most individuals, indicating the possibility of potent allergens in the Beauveria extract. Interestingly, not all individuals specifically exposed to B. bassiana displayed allergic reactions and individuals J, K, and M (who did display mild allergic reactions, Table 2) did not react to the 35 KDa protein based upon Western blotting results (Fig. 2). We do not, however, have any quantifiable index of exposure for the individuals in our sample and any interpretations should be made with some caution. Numerous studies have revealed the presence of cross-reactive proteins among fungal species between genera [15,20-22,25-27]. In our experiments, (excess) crude extract from a test organism was added during the primary antibody (human sera) incubation. Common or shared epitopes between B. bassiana and the test fungus would result in a loss of signal due to competition for reactive IgEs. However, IgEs reactive to Beauveria-specific allergens would not be affected, resulting in no change in the corresponding reactive bands on a Western blot. Loss of a signal would indicate that a homolog or shared epitope (IgE-reactive) exists between the two fungal species, implying that primary sensitization by one organism can result in an allergic reaction when exposed to the homologous allergen of another organism. Competitive immunoblot inhibition experiments revealed significant epitope homology between B. bassiana and several clinically important fungi responsible for IgE-mediated allergic reactions in atopic individuals. Thus, an allergic reaction to Beauveria exposure may arise in patients sensitized to other fungi. Extracts from A. alternata and E. purpurascens almost completely competed with allergens present in the B. bassiana extract with the notable exception of the ~35 kDa allergen. Competition experiments using A. fumigatus, C. herbarum, C. albicans, and P. notatum extracts also indicated the presence of many shared epitopes, although distinct (non-competed) IgE-binding B. bassiana proteins of 35 kDa, 64 kDa, and >200 kDa molecular mass were detectable. These proteins, particularly the 35 kDa allergens may represent B. bassiana specific allergens. Experiments are underway to characterize the 35 kDa allergen, which may lead to a diagnostic assay for B. bassiana sensitization. Finally, our analysis of potential B. bassiana allergens was limited to cell extracts grown under specific conditions and did not include the culture filtrate. Extracellular proteases, an important class of fungal proteins that can elicit allergenic reactions, have been characterized from a number of fungal species [28-31], and are likely to be present in B. bassiana. A careful examination of culture growth conditions is also warranted in order to provide a standardized reagent for testing purposes. Conclusions Although Beauveria holds promise as an arthropod biological control agent, there have been few reports on the allergenic potential of these organisms. Identification of B. bassiana specific allergens can lead diagnostic methods for determining sensitization to this organism and may provide a rational basis for allergen attenuation in order to yield safer biocontrol products. The observed cross-reactivity among proteins of B. bassiana and the fungi tested, highlight the importance of considering the possibility that multiple fungal sensitivity can occur due to exposure to a single fungus. Further testing should be performed to determine the scope, severity, and range of allergenic reactions to B. bassiana. Competing Interests The author(s) declare that they have no competing interests. Authors' contributions GSW carried out the immunoassays and other in vitro experiments. SWH performed the clinical experiments and participated in the design of the study. NOK conceived of the study, and participated in its design and coordination, and drafted the manuscript. Acknowledgements We would like to thank Ruby Teng and Moya Chin for technical assistance. This paper is Florida Agricultural Experimental Station Journal series number R-10187. ==== Refs McCoy CW Baker RR and Dunn PE Entomogenous fungi as microbial pestidides New Directions in Biological Control 1990 New York, NY, A.R. 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==== Front BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-5-31564933110.1186/1472-6963-5-3Research ArticleA primary care, multi-disciplinary disease management program for opioid-treated patients with chronic non-cancer pain and a high burden of psychiatric comorbidity Chelminski Paul R [email protected] Timothy J [email protected] Katherine M [email protected] Steven D [email protected] Thomas M [email protected] J Stephen [email protected] Robert M [email protected] Mary E [email protected] Darren A [email protected] Michael P [email protected] Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA2 Division of Pharmacotherapy, University of North Carolina at Chapel Hill School of Pharmacy, Chapel Hill, North Carolina, USA3 Robert Wood Johnson Clinical Scholars Program, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA2005 13 1 2005 5 3 3 21 9 2004 13 1 2005 Copyright © 2005 Chelminski et al; licensee BioMed Central Ltd.2005Chelminski et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Chronic non-cancer pain is a common problem that is often accompanied by psychiatric comorbidity and disability. The effectiveness of a multi-disciplinary pain management program was tested in a 3 month before and after trial. Methods Providers in an academic general medicine clinic referred patients with chronic non-cancer pain for participation in a program that combined the skills of internists, clinical pharmacists, and a psychiatrist. Patients were either receiving opioids or being considered for opioid therapy. The intervention consisted of structured clinical assessments, monthly follow-up, pain contracts, medication titration, and psychiatric consultation. Pain, mood, and function were assessed at baseline and 3 months using the Brief Pain Inventory (BPI), the Center for Epidemiological Studies-Depression Scale scale (CESD) and the Pain Disability Index (PDI). Patients were monitored for substance misuse. Results Eighty-five patients were enrolled. Mean age was 51 years, 60% were male, 78% were Caucasian, and 93% were receiving opioids. Baseline average pain was 6.5 on an 11 point scale. The average CESD score was 24.0, and the mean PDI score was 47.0. Sixty-three patients (73%) completed 3 month follow-up. Fifteen withdrew from the program after identification of substance misuse. Among those completing 3 month follow-up, the average pain score improved to 5.5 (p = 0.003). The mean PDI score improved to 39.3 (p < 0.001). Mean CESD score was reduced to 18.0 (p < 0.001), and the proportion of depressed patients fell from 79% to 54% (p = 0.003). Substance misuse was identified in 27 patients (32%). Conclusions A primary care disease management program improved pain, depression, and disability scores over three months in a cohort of opioid-treated patients with chronic non-cancer pain. Substance misuse and depression were common, and many patients who had substance misuse identified left the program when they were no longer prescribed opioids. Effective care of patients with chronic pain should include rigorous assessment and treatment of these comorbid disorders and intensive efforts to insure follow up. ==== Body Background Chronic, non-cancer pain, defined as pain of greater than 3 months duration, is a common, important health issue. The prevalence of chronic pain ranges from 20% to 60% [1]. The prevalance of low back pain is greater than 30%[2], and the prevalance of migraine is approximately 15%[3,4]. Pain disorders, including headache, back pain, arthritis and other musculoskeletal pain, are estimated to cost the United States economy $61 billion per year in lost productive time [5]. It is frequently asserted that chronic non-cancer pain is undertreated [6-8]. The optimal approach to treating chronic pain is controversial [9-13]. The realization that acute pain and cancer pain were often undertreated led to a liberalization of opioid use in these populations in the late 1980's and throughout the 1990's [6,14-16]. Subsequently, many advocates and practitioners called for an increase in the use of opioids for patients with chronic non-cancer pain as well [14,17,18]. Other experts assert that use of opioids for chronic pain should be limited [10,19]). used with caution [20], or eschewed altogether due to potential for substance misuse and lack of proven efficacy [13]. In large part, uncertainty about how to best manage patients with chronic pain stems from a lack of research in this area [21]. The limited available research comes from studies performed in specialty practice settings [11,22-25]; few studies have examined the care of chronic pain in primary care [26,27]. Unlike other common chronic diseases such as congestive heart failure or diabetes, there are few clinical trials to inform practice guidelines. Chronic pain management is complex. Like other chronic diseases, chronic pain is multi-faceted, and it is attended by its own set of comorbidities. It is complicated by substantial psychological and functional impairment in the forms of depression, disability, and loss of livelihood [28-30]. It is also costly. Patients with depression and back pain or migraine incur 3 to 4 times higher medical costs than patients with these pain conditions alone [31]. Generalists, even if well-versed in the biopsychosocial model of disease, often feel unprepared to manage chronic pain. They may lack the training in using certain pharmacological regimens, such as combining chronic opioids with psychiatric and pain modulating agents [27,32]. They may fear regulatory scrutiny and sanction when prescribing opioids, and they may be wary of fostering opioid dependence, misuse or addiction [33,34]. These barriers result in variations and inconsistencies in care that leave both patients and providers frustrated. We developed a disease management program to improve the management of chronic pain in opioid-treated patients attending an academic general medicine practice. Traditional models of office-based care focus on diagnonsis and acute management of medical problems in the context of a single provider-patient relationship. In contrast, disease management emphasizes: (1.) The use of multi-disciplinary teams providing integrated care; (2.) Evidence-based algorithms; (3.) Interval visits to monitor response to therapy; and (4.) Information systems that permit tracking of outcomes and adjustment of therapy [35-39]. We applied these principles in developing and implementing an intensive, structured, and coordinated program to improve the management of patient with chronic pain. We focused our program on patients treated with opioid medications and attempted to provide an environment that would allow the safe and effective use of these drugs. We sought to determine if this program could improve pain, functional, and psychiatric outcomes in a 3 month uncontrolled trial. Methods Development of the program In designing the program, we reviewed existing research, contacted experts, and conducted informal assessments of the main barriers to effective pain management in our practice [40]. Barriers existed at the provider and patient levels. These included part-time providers, frequent provider turnaround, physicians in training, and a socio-economically-disadvantaged, geographically dispersed patient population with multiple comorbidities. We applied lessons and systems from existing disease state management programs in diabetes, anticoagulation, and heart failure in our practice [41]. Patient recruitment Patients were eligible if they had pain of greater than 3 months duration and were either taking or considering opioid therapy. Attending and resident physicians were encouraged to refer patients if they were having difficulty managing their pain or if they suspected misuse of opioid medications. We publicized the program through educational conferences with residents and attendings and through informal communication within our practice. Baseline assessment After obtaining informed consent, a research assistant administered a comprehensive baseline assessment to gather socio-demographic data and review the medical history with an emphasis on the medical management of pain. Validated measures of pain, disability and psychological status were included. Using an 11 point scale, the Brief Pain Inventory (BPI) asked patients to rate their pain at the time of the interview and at its worst, least, and average over the past month [42,43]. The 7 item Pain Disability Index (PDI), a measure of pain related disability, asked patients to rate the degree of disability on a 10 point scale [44-47]. Higher scores indicate higher disability and the scale discriminates between high and low levels of disability. To assess depression, we used the Center for Epidemiological Studies-Depression Scale (CESD) [48]. This twenty item tool rates affective symptoms on a scale of 0 to 3. Intervention Patients were managed by a multi-disciplinary team consisting of the patient's primary care physician, a clinical pharmacist, a program assistant with training in health behavior, and a psychiatrist with sub-specialization in pain management who saw patients with the team one half-day per week. One nurse was dedicated to checking-in study patients and obtaining urine specimens. At entry, all patients signed a Medication Contract (Appendix A) [49] and provided a sample of urine for toxicological testing (UTS) [50,51]. The Medication Contract specified the conditions under which opioids would or would not be prescribed. The clinical pharmacist or psychiatrist modified or titrated a patient's pain medications in consultation with the primary care physician. During titration of medications, patients returned at one month intervals. Medical management adhered to published guidelines and expert opinion on the management of chronic, non-cancer pain [17,52-54]. The principles of management were: • Longer-acting opioids (long-acting morphine, fentanyl patches, methadone, sustained-release oxycodone) were initiated in patients who had been receiving short-acting agents that were only partially effective. • Short-acting, potent opioids (usually oxycodone preparations) were prescribed for breakthrough pain. • Longer-acting opioids were titrated at interval visits. • Less costly, generic medications (e.g. methadone and long-acting morphine) were preferred over proprietary products of equal or lesser efficacy [55]. • Tricyclic anti-depressants, gabapentin, and other agents were used adjunctively, especially for neuropathic pain. To address psychiatric comorbidity, patients with depression and other complex psychiatric conditions (e.g. psychotic depression and bipolar disorder with substance misuse) received psychiatric evaluation. Depression was diagnosed based on clinical interview and CESD score. In addition, primary care physicians could request psychiatric consultation on patients who had unaddressed psychiatric problems. The clinical pharmacist, psychiatrist and primary care physicians used CESD scores to guide treatment of patients scoring in the depressed range. The program, however, did not employ a strict protocol for depression treatment. As defined in our medication agreement with participants, we prospectively monitored substance misuse through clinical history, review of medications, communication with pharmacies and providers, and urine toxicological screening (UTS). Medications were documented in the electronic medical record and our program database. Discrepencies and inconsistencies were discussed with the patient's primary provider. We contacted a patient's pharmacy to verify procurement of medications, and, if substance misuse was suspected, we contacted additional pharmacies to ascertain whether or not a patient was receiving opioids from multiple sources. A UTS was obtained at each visit and was correlated with the patient's reported history of medication use. In collaboration with our institution's toxicologist, results of the UTS were verified using the appropriate confirmatory assays. For example, the presence or absence of "opiates" on the UTS was confirmed using gas chromatography. In addition, all positive results for amphetamines were confirmed with gas chromatography due to the possibility of assay interference from other medications [50,51]. We defined serious substance misuse prospectively as any of the following: 1. Cocaine or amphetamines detected on UTS; 2. Procurement of opioids from more than one provider on a regular basis ("doctor collecting"); 3. Diversion of opioids; 4. UTS negative on at least two occasions for prescribed opioids in the context of a reported history that the patient was taking the medication as prescribed (We considered repeatedly "negative" urines as an indicator of possible diversion.); 5. UTS positive on at least two occasions for opioids not prescribed by our practice (an inappropriate or inconsistent urine). A positive cannabinoid finding on UTS was not defined as serious substance misuse for the purposes of our study, but we counseled patients to refrain from marijuana use. Patients were advised at entry into our program (and in the Medication Contract) that serious violations of the contract would result in discontinuation of opioids. Past instances of serious misuse were not subject to sanction. We constituted a formal practice-wide committee to evaluate and respond to suspected misuse. It consisted of the practice director, two attending physicians, a clinical pharmacist, two resident physicians, and a nurse. The committee deliberated through secure email and considered the violations defined above. Patients committing serious substance misuse were offered referral to substance abuse experts at our institution. In most cases, opioid therapy would be reconsidered in 6 months if the patient participated in substance abuse counseling (The practice policy addressing substance misuse is included as Appendix B.). Reassessment At 3 month follow-up, a research assistant reassessed each patient's clinical status. Pain, disability, and mood scores were re-measured using the instruments previously described. The research assistant was not blinded to study participation status. Analysis Descriptive statistics are reported as means and percents. Paired t-tests were used to compare changes in pain, disability and depression scores from baseline to 3 month follow-up. McNemar's test was used to measure differences in proportions of patients receiving treatment for depression at 0 and 3 months. We also compared changes in pain scores based on changes in opioid dose. All analyses were performed using Stata 7.0 (College Station, TX). The research protocol was approved by the University of North Carolina School of Medicine Committee on the Protection of the Rights of Human Subjects. The funding sources had no role in the collection or interpretation of the results. Results Between December 2002 and May 2003, 85 patients agreed to participate in the study. Table 1 presents the baseline demographic characteristics of the study participants. All patients completed baseline assessment and 63 (73%) completed the 3 month assessment. Of the 22 patients who did not complete 3 month assessment, 15 did not return after a serious violation of the medication contract led to the discontinuation of opioids, 4 patients were lost to follow up, and 3 changed their venue of primary care. There were no important differences in baseline demographic, pain, depression, or disability scores between completers (n = 63) and all non-completers (n == 22), although some differences emerged among non-completers who committed substance misuse (n = 15) (Table 2.). Table 1 Univariate Analysis N = 85 Mean age, y (± SD) 51 (9.6) Range 27–76 Male, % 60 White Race, % 78 Marital Status, % Married 49 Stable relationship 7 Unmarried 44 Disabled, % 65 Education, % Not high school graduate 38 HS graduate 28 Some college 34 Income <$20,000/yr, % 83 Medicare or Medicaid, % 58 Uninsured,% 29 History of Smoking, % 87 H/O Alcohol Use, % 75 H/O Substance Use, % 44 H/O Depression, % 51 Table 2 Characteristics of Study Completers and Non-Completers Characteristic Completers (N = 63) Non-Completers (N = 22) P-Value Non-Completers with Substance Misuse (N = 15) P-Value Age, y 51 49 0.422 48 0.215 % Male 62 55 0.544 67 0.732 % White 81 68 0.216 60 0.083 % High School Graduate 62 62 0.975 64 0.890 % Disabled 66 63 0.815 54 0.405 % Uninsured 29 32 0.774 33 0.716 % Substance Use 40 55 0.226 67 0.059 CESD 24 27 0.416 31 0.050 PDI 47 41 0.141 46 0.810 Pain Scores Worst in last month 9.2 9.3 0.727 9.2 0.946 Least in last month 4.6 4.4 0.768 4.7 0.884 Average in last month 6.5 6.5 0.925 6.6 0.861 Current pain 6.8 7.3 0.361 7.5 0.279 Patient characteristics The average age of patients was 51 years, 60% were male, and 78% were white, most (83%) had an income less than $20,000 per year and 65% were disabled. Forty-four percent had a history of illicit substance use (e.g. marijuana, cocaine, amphetamines). All patients had pain of at least 3 months duration and 90% had pain for greater than 1 year. At baseline 93% were receiving opioids. At 3 month follow up, 97% were receiving opioids. Table 3 presents the principal pain types. The lumbar spine was the most frequently involved primary site. Overall, axial spine pain accounted for 49% of the primary pain reported by patients. Myofascial pain and polyarticular arthritis were also frequently represented. Patients in the "Other" category commonly had mixed etiologies of pain, often attributable to previous trauma or surgery. One chronic headache patient is included in this category. Table 3 Primary Pain Type (N = 85) Number (%) Spine 42 (49) Lumbar 30 (35) Cervical 7 (8) Thoracic 5 (6) Diffuse (Fibromyalgia, Chronic fatigue syndrome) 13 (15) Polyarthritis 8 (9) Knee 5 (6) Abdomen 4 (5) Diffuse neuropathic 4 (5) Elbow & Hip 2 (2) Other 7 (8) Effect of the intervention Table 4 presents the effect of the intervention on pain, functional status and depression. Baseline results reveal high pain scores. The worst pain was 9.2, the least pain was 4.6, the average pain was 6.5, and current pain was 6.8. The average PDI score, 47.0, suggested substantial disability. There was a high prevalence of depression. The mean CESD score, 24.0, falls in the "severely depressed" category of the scale. Table 4 Pre and Post Intervention (N = 63) Pre Post Improvement, % P-Value* Pain at worst in the last month& 9.2 8.1 12 <0.001 Pain at least during the last month 4.6 3.9 15 0.038 Pain on average during the last month 6.5 5.5 15 0.003 Pain right now 6.8 5.8 15 0.014 Pain Disability Index 47.0 39.3 16 <0.001 CESD 24.0 18.0 25 <0.001 % CESD in depression range: Conventional cutoffs£ 79.4 54.0 32 0.003 Chronic pain cuttoffs¶ 38.1 23.8 37 0.049 % Depression medication 44.4 52.4 15 0.059 * Paired t-test except where indicated McNemar's test &Score 1–3 is mild pain; 4–6, moderate pain; 7–10, severe pain £ Score of ≥ 15 ¶Score of ≥ 27 At 3 month follow up, BPI pain scores improved by 12% to 15%, and all reductions were statistically significant. The mean depression score improved from 24.0 to 18.0 (p < 0.001), and the proportion of patients scoring in the depression range decreased from 79% to 54% (p = 0.003). We did not correct for multiple comparisons because of the exploratory nature of our analyses. Some authors have demonstrated that the conventional cutoffs for the CESD (depression ≥ 15; severe depression ≥ 22) may lack specificity in patients with chronic pain and have proposed a CESD threshold of 27 for diagnosing depression in this population [56]. Using this threshold, the proportion of patients scoring in the depressed range decreased from 38% to 24% (p = 0.049). Pharmacologically, depression was undertreated at baseline. The proportion of depressed patients receiving anti-depressants increased from 44% at baseline to 52% at 3 months (p= 0.059). Relationship between pain and opioid dosing The mean daily opioid dose in milligram equivalents of morphine increased from 72 mg per day to 91 mg per day (A milligram morphine equivalent approximates a milligram of oxycodone.). Forty-eight percent of patients had their opioid dose increased over 3 months. In these patients, the mean opioid equivalent increased from 53 mg to 105 mg per day. No clear relationship emerged between opioid dosing and improvements in pain, disability, and depression scores, after adjusting for baseline pain, disability and depression (Table 5.). Table 5 Effect of Opioid Increase on Pain (N = 63) Opioids Increased P-Value Yes (n = 30) No (n = 33) Δ Pain at worst in the last month 1.40 0.99 0.37* Δ Pain at least during the last month 0.80 0.52 0.66* Δ Pain on average during the last month 0.96 0.94 0.93* Δ Pain right now 1.14 0.87 0.70* Δ Pain Disability Index 8.34 7.03 0.63¶ Δ CESD 5.21 6.71 0.74 * Adjusted for baseline pain ¶Adjusted for baseline PDI Adjusted for baseline CESD Substance misuse Twenty-seven patients (32%) committed some form of serious substance misuse (Table 6.). Although we confirmed only one instance of diversion, we suspect that patients with repeatedly negative UTS's or inconsistent UTS's may have been diverting their medications. Substance misusers accounted for the preponderance of subjects who dropped out of the study. Table 2 compares selected baseline characteristics between substance misusers who did not complete three months and subjects who completed the trial. Although the numbers are small, there is a trend toward greater representation of non-white race, history of illicit substance use, worse depression, and higher pain scores at baseline assessment among substance misusers who did not complete the trial. Table 6 Substance Misuse (N = 27) Misuse Number (%) Stimulants on UTS 13 (15) Cocaine 11 (14) Amphetamines 2 (2) Diversion 1 (1) Doctor collecting 3(3) Inappropriate/Inconsistent UTS 2 (2) Negative ("Clean") Urines 7 (8) Prescription adulteration 1 (1) Discussion We found that a multi-disciplinary, primary care-based, disease management program can improve pain, depression and disability scores in opioid-treated patients with chronic pain in a 3 month uncontrolled trial. The improvements across all outcomes support an improved quality of life resulting from the intervention. These improvements were obtained using an approach that balanced the potential benefits and adverse effects of opioids. We hyothesize that these improvements resulted from the combined effects of systematizing pain management and treating depression. Improvements appear independent of opioid dosing. The clinical significance of the 12% to 15% improvement in pain scores is unclear. Uncontrolled trials in specialty pain clinics have reported a 20% to 25% reduction in pain scores [57]. Some research suggests that a 30% decrease in pain scores (i.e. about 2 points) represents clinically significant relief of pain [58], but the issue of how to interpret pain scales clinically is not resolved. The improvement in depression scores was clinically important and may reflect combined effects of intensification of pharmacological management for depression and pain and the systematization of care. Although the reciprocal relationship between pain and depression has been established in previous studies, the effect that treating one condition has on the other has not been well-assessed [29]. One recent study demonstrated that the presence of severe pain predicted a poor response to antidepressant therapy, and thus it is plausible that intensifying pain management would have a beneficial effect on depression [59]. Clearly, the improvements in depression scores seen in this study cannot be attributed to increasing anti-depression pharmacological therapy alone because the proportion of patients treated with anti-depressants increased from 44% to 52% only. We have since added a structured treatment algorithm to increase the use of anti-depressants. It is important to note that there was a statistically signficant trend toward greater depression among substance misusers who did not complete the trial; thus, it is possible that the trial overestimates the effect of multi-disciplinary management on depression outcomes. To our knowledge, this is the first study to prospectively examine the effects of multi-discplinary pain management on the outcomes of pain, disability, depression, and substance misuse in an academic primary care practice caring for a wide range of patients. Previous studies conducted in a military clinic[60] and a health maintenance organization [26] demonstrated improved pain and functional scores with systematic, multidisciplinary intervention. The military trial was uncontrolled and enrolled referred patients into a specialty clinic. The HMO trial was conducted in a primary care setting. It was controlled, and did evaluate pain, function and mood outcomes. Neither study systematically monitored substance misuse. We documented a high prevalence of substance misuse (32%). We did not assess addiction per se. The prevalence of substance misuse and addiction in patients receiving chronic opioids is unclear and depends on the populaton under study. Some authors have asserted that addiction and substance misuse are uncommon consequences of opioid use for pain. One widely cited reference estimated the prevalence of addiction at approximately 4 in 10,000 treated patients [61]. Others have reported prevalences of addiction ranging from 3% to 17% [62,63]. A recent retrospective study in a primary care setting documented a high prevalence of opioid misuse: 24% in a resident physician clinic and 31% in a Veterans Administration outpatient clinic[64], but the criteria used to determine the prevalence of opioid misuse were limited by chart review. Some behaviors defined as indicators of opioid misuse (e.g. lost or stolen medications, requests for early refills) could be construed as indicators of inadequately treated pain (i.e. "pseudoaddiction"[65]) and not substance misuse or addiction. Opioid misuse not only complicates the management of pain in the individual patient, but has negative societal consequences as well, especially when opioids are diverted from their intended use [66-68]. Several states have documented increases in unintentional deaths from opioids, especially diverted methadone [69-71]. National surveys demonstrate dramatic increases in the non-medical use of OxyContin® and other prescription drugs among teens and young adults [72,73]. The trauma literature has documented recent increases in opioid use among patients admitted to trauma centers [74]. In response, there have been state and national initiatives to reduce prescription drug misuse [75,76]. Though high rates of substance misuse are a source of concern, our program may serve as an example for how care can be organized to reduce misuse without eschewing the benefits of opioid medications. Although the prevalence of substance misuse in our study population is higher than reported in clinical trials of opioids, these trials have occurred in specialty settings with selected populations. They excluded patients with a history of substance misuse, or have not systematically monitored patients for substance misuse [11,12,23,66,77]. In addition, they commonly excluded patients with psychiatric illness (including depression) which is a strong predictor of substance misuse [23,66,78-80]. Patients in our program had a high baseline prevalence of depression, previous substance and alcohol abuse, and other psychiatric disorders (Table 1.). The strong relationship between mental illness and substance abuse disorders is well known and thus the high prevalence of substance misuse is not entirely unexpected [81]. Previous studies of mental illness have documented a high coexisting prevalence of substance and alcohol misuse: 32% with unipolar depression; 61% with bipolar depression; 47% with schizophrenia; 84% with personality disorders: and 24% with anxiety disorders [78,82]. We specifically sought out patients whose pain management was difficult for providers or in whom substance misuse was suspected. Many had established or suspected psychiatric diagnoses. How to identify chronic pain patients at risk for drug misuse and to treat their pain remains a challenge [83-89]. The pattern of substance misuse in our population often suggested polysubstance abuse; this places patients at especially high risk of morbidity and mortality [90]. Although patients committing substance misuse were offered substance abuse treatment referral, only two followed through and most did not return to our program. The option of pain management without the use of opioid analgesics was offered to all patients who committed substance misuse. The difficulty in obtaining mental health and substance abuse treatment services is a pressing public health issue and a topic of national debate in the United States [81]. In our sample there was a clear trend toward increased comorbid depression among substance misusers who did not complete three month follow up (Table 2.). Despite the availability of on-site psychiatric consultation, our program was not successful in retaining and managing a challenging subset of patients with substance misuse and depression. We are aware that some of our substance misusing patients migrated to other practices in order to obtain opioids and other controlled substances. Our program implemented policies to prevent migration of patients within our practice (Appendix B.) and the University of North Carolina Health Care System. The cornerstone of these policies was meticulous documentation in an electronic medical record that is accessible to all physicians at our medical center and to hospitals and physicians affiliated with our system in the surrounding communities. In general, though, we have no direct control over migration that occurs outside of our practice and our health care system. In order to curtail migration and "doctor shopping," some states have implemented centralized monitoring systems for opioids and other controlled substances. North Carolina is currently exploring the feasibility of such a system. A description of the operational state monitoring programs is available online through the United States Drug Enforcement Agency Diversion Control Program website [91]. We believe that our results may be generalizable to other academic primary care practices that serve diverse patient populations with a high burden of medical and psychiatric comorbidity. The etiologies and sites of pain were similar to those reported in population-based epidemiological surveys and clinical trials in primary care, except that headache was under-represented in our population [1,12,92]. The results replicate epidemiological research that demonstrates a strong interaction between pain and the psychiatric comorbidities of depression and substance misuse [87,88]. Our study may be more applicable to the general medical setting than previous trials examining the effects of opioids on chronic pain because we did not exclude patients with serious psychiatric comorbidity or those suspected of substance abuse. To be effective, pain management should encompass more than pharmacological management directed at pain scores; it should address a variety of behavioral and psychosocial factors that contribute to suffering [86,93]. Our study has several limitations. It was uncontrolled and of relatively short duration. The research assistants were not blinded to pre- and post-treatment assessments. The improvements could reflect secular trends, although the chronic nature of our patients' symptoms and disability makes this less likely, and improvements were noted across all of the pre-specified outcomes. Not all patients receiving chronic opioids in our practice were referred. Thus, we may have over-estimated the prevalence of substance misuse because providers were more likely to refer "problem" patients in whom they suspected opioid misuse. Another limitation of our study is its individualized nature. We did not adhere to strict algorithms for diagnosis and treatment and did not test a single intervention. The evidence-base for managing chronic pain in the general medicine setting is limited and the multi-modal nature of our intervention was by necessity empirical and exploratory. As such, we decided to allow more latitude and individualization in treatment choice. We have used our experience and the data collected to develop more robust algorithms to guide the management of pain and depression and to make psychiatric referral when appropriate. As a corollary to our multi-modal approach, it is difficult to ascertain if the improvements derived from pain medications, intensification of depression therapy, or simply participation in an organized program. Improvements and changes in behavior that occur as a result of becoming a target of special interest in a program are often referred to as a Hawthorne effect [94,95]. Conclusions In a 3 month trial conducted in an academic primary care setting, a systematic, multi-disciplinary approach to chronic pain management that included the use of opioids and tools to prevent misuse was effective in improving pain, depression, and function scores. Future efforts will be directed at examining their durability and promoting their sustainability. A randomized control trial would determine whether these are real effects or represent a secular trend. Comorbid depression and substance misuse were common. Efforts will also be made to further characterize the interaction of these and other comorbid psychiatric conditions with chronic pain. Chronic pain patients with substance abuse are a challenging subset of patients who could benefit from new research and initiatives to mitigate the risk of abuse while ameliorating pain control. Competing interests The author(s) declare that they have no competing interests. Authors' contributions PC developed the study designand intervention, performed the statistical analyses, and drafted the manuscript.TI developed the study designand intervention, administered surveys and directed pharmacologic management, assisted in editing and revising themanuscript.KF participated in developing the study design, administering surveys, data and projectmanagement, and editing and revising the manuscript. SP participated in study design, developing pharmacologic protocols, performing psychiatric evaluations, and editing and revising the manuscript.TM participated in developing the study design and editing and revising the manuscript.JP assisted in administering surveys, data management and collection, and editing and revising the manuscript.RM participated in the development of the study design, data management, editing and revising the manuscript.MB participated in the development of the study design, editing and revising the manuscript. DD provided statistical analytical support and assisted in the drafting, editing, and revising of the document.MP developed the study design, supervised the overall conduction of the study, participated in data analysis, and assisted in the drafting, editing and revising of the manuscript. Appendix A: Medication Contract Patient Name ____________________________ Diagnosis ______________________________ Physician Name __________________________ Telephone Number _______________________ I agree to abide by the following guidelines for managing my prescription for opiate pain medications: 1. I will only request and receive opiate (narcotic) pain medications from Dr. _________ or from his/her designee in the Internal Medicine Clinic Pain Service. I agree to inform any other physicians participating in my care of this agreement. If another physician wishes to suggest changes in pain management, they can contact Dr. ___________ during regular business hours, but no changes will be made without such contact. 2. Dr. ____________ and I have agreed that I will receive the following: medicine ______________, directions __________ quantity _____, per ___ days, medicine ______________, directions __________ quantity _____, per ___ days, medicine ______________, directions __________ quantity _____, per ___ days. I will not request refills prior to this date. I understand that if my medicines are lost or stolen, they will not be refilled prior to the next refill date. If I use up my supply of medication before the date of the next refill, I understand that my doctor will not provide extra medication. I further understand that I may suffer symptoms of withdrawal. I will inform my doctor in a timely manner if I miss taking a dose of my medication, have an increased need for the pain medication, or have difficulty taking the medication as prescribed. If I find that the current dose of pain medication is no longer adequate, I will discuss this situation with my doctor at a scheduled visit. 3. I agree to use _________________________________________________Pharmacy, located at _____________________________________________________________, telephone ________________________, for filling prescriptions for all of my pain medicine. 4. I will bring all unused pain medicine to every office visit, including all current prescription vials. 5. While this contract is in effect I will not abuse alcohol or other illicit drugs. As a part of this program, urine drug screening will occur at enrollment and may be required at future visits. 6. I will not sell or share opiate medications. 7. If I violate the terms of this contract, I understand that my doctor and other doctors in the Internal Medicine Clinic will no longer prescribe opiate medications for me. If this occurs, I understand that I may receive care elsewhere or continue with my current doctor and not receive opiate medicines. If I change doctors, I agree to allow my current physician to contact my new physician to transfer medical information including information about chronic pain treatment. 8. I understand that my doctor may verify whether or not I have a history of criminal drug convictions. Patient Signature ________________________________________________ (print name) ________________________________________________ Physician signature ________________________________________________ Date ________________________________________________ Appendix B UNC General Internal Medicine Practice Pain Review Committee: Policy on Serious Opioid or Controlled Substance Misuse & Misconduct Definition The Pain Review Committee defines serious misuse or misconduct with regard to opioid medications and other controlled substances as any of the following: 1. Forgery or alteration of prescriptions. 2. Use of cocaine or other stimulant medications (e.g. amphetamines) concurrently with prescribed opioids and their detection on urine toxicological testing. Stimulant abuse is strongly indicative of polysubstance abuse. 3. Diversion of opioids or controlled substances. 4. Doctor collecting or shopping: Procuring controlled substances from more than one provider and misrepresenting the fact. This is a felony in North Carolina. 5. Negative or "clean" urines: The absence of prescribed opioids from urine toxicological testing on at least two occasions in the context of a history that the patient is taking the medication as directed. This finding suggests diversion and/or substituting a separate urine sample. 6. Inappropriate or inconsistent urines: The presence on urine toxicological testing of opioids or other controlled substances (excluding cannabinoids) not prescribed by our clinic or pain program on at least two occasions without a reasonable explanation. This finding suggests polysubstance abuse, doctor collecting, or drug bartering (a form of diversion). Procedure Serious misuse or misconduct is a special category of misuse. It results in the immediate discontinuation of opioids in the Internal Medicine Clinic. The Pain Review Committee will address instances of serious misuse on an expedited basis. Individual cases will not require the review of the entire committee. The following procedure applies: • The specific violation will be documented in the electronic medical record. • Instances of serious misuse discovered by the pain management team will be discussed with the patient's primary care provider (PCP). • The provider who discovers the violation will report it to the Clinic Director, Dr. Thomas Miller, or designee on the Pain Review Committee. (The designee will be either Dr. Paul Chelminski or Dr. Timothy Ives.) The designee will inform Dr. Miller of the violation and make a recommendation to Dr. Thomas Miller and the entire Pain Review Committee for the immediate discontinuation of opioids. • Dr. Miller will make the final disposition on the recommendation. • Committee members will receive communication about recommendations and disposition through email. Committee members may recommend alternative sanctions. • The PCP will be informed of the disposition. • The patient will receive verbal and written notice of disposition. Sanctions A. Serious misuse or misconduct will lead to one of two possible sanctions: 1. Forgery or alteration of prescriptions and diversion will result in immediate and permanent discontinuation of opioids and/or other controlled substances. The clinic director will decide whether instances of forgery or diversion also merit dismissal from the clinic. 2. Stimulant use, doctor collecting/shopping, negative urines, or inappropriate urines will result in immediate discontinuation of opioids and/or other controlled substances with possible re-evaluation in six months for a first violation. The Committee will stipulate substance abuse counseling as a condition for re-evaluation. B. Two serious violations of the medication contract will result in permanent discontinuation of controlled substances. Provider Issues 1. The Pain Review Committee cannot compel attending physicians to cease prescribing opioids or other controlled substances; however, providers who continue to prescribe these medications must understand that this practice may jeopardize their DEA license and/or expose them to regulatory and even criminal investigation. If the attending continues to prescribe opioids after a recommendation of discontinuation by the Committee, the patient is not eligible for re-enrolment in the General Internal Medicine Pain Program after six months. 2. The responsibility for stewardship and teaching of resident physicians requires special oversight of residents' patients who receive controlled substances. The residency program has an obligation to promote appropriate clinical practice and protect residents from practices that may jeopardize their professional status. In addition, residents prescribe scheduled substances under the authority of the hospital's DEA number, and the inappropriate prescription of opioids may expose the hospital to sanction. If the committee recommends discontinuation of opioids for the patient of a resident, the committee will instruct the resident that he or she can no longer prescribe scheduled medications for this patient. Likewise, other residents in the practice are not authorized to prescribe opioids to patients of residents or attendings who have had opioids discontinued. Communication of Decisions 1. The patient should be informed of discontinuation verbally. Usually, this responsibility will fall to the PCP, but in certain instances it may fall to the person discovering the violation (e.g. the pharmacist who sees the patient in clinic for follow up in the pain program). 2. Dr. Miller will send the patient a registered letter. 3. The PCP will be copied on the letter. 4. A copy of the letter will be entered into the permanent electronic medical record as a Phone Message. 5. The patient's problem list on the electronic medical record will contain the entry "VIOLATION OF MEDICATION CONTRACT" and will be annotated "PATIENT VIOLATED THE MEDICATION CONTRACT SIGNED WITH GENERAL MEDICINE, AND WAS DISMISSED FROM NARCOTICS – SEE [date] CIS NOTE." Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements Findings previously presented at Innovations in Practice Management Oral Session at annual meeting of the Society of General Internal Medicine, Chicago, Illinois, 2004. 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==== Front J Circadian RhythmsJournal of Circadian Rhythms1740-3391BioMed Central London 1740-3391-3-11564932610.1186/1740-3391-3-1ResearchProlactin daily rhythm in suckling male rabbits Alvarez Pilar [email protected] Daniel [email protected] Pilar [email protected] Pilar [email protected] Ana [email protected] Departamento de Biología Celular, Facultad de Medicina, Universidad Complutense de Madrid, 28040 Madrid, Spain2 Departamento de Fisiología, Facultad de Medicina, Universidad de Buenos Aires, 1121 Buenos Aires, Argentina3 Departamento de Bioquímica y Biología Molecular III, Facultad de Medicina, Universidad Complutense de Madrid, 28040 Madrid, Spain4 Departamento de Producción Animal, E.T.S.I. Agrónomos, Universidad Politécnica de Madrid, Spain2005 13 1 2005 3 1 1 18 11 2004 13 1 2005 Copyright © 2005 Alvarez et al; licensee BioMed Central Ltd.2005Alvarez et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background This study describes the 24-h changes in plasma prolactin levels, and dopamine (DA), serotonin (5HT), gamma-aminobutyric acid (GABA) and taurine concentration in median eminence and adenohypophysis of newborn male rabbits. Methods Animals were kept under controlled light-dark cycles (LD 16:8, lights on at 08:00 h), housed in individual metal cages, and fed ad libitum with free access to tap water. On day 1 after parturition, litter size was standardized to 8–9 to assure similar lactation conditions during the experiment. Groups of 6–7 suckling male rabbits were killed by decapitation on day 11 of life at six different time points during a 24-h period. Results Plasma prolactin levels changed significantly throughout the day, showing a peak at the beginning of the active phase (at 01:00 h) and a second maximum during the first part of the resting phase (at 13:00 h). Median eminence DA concentration also changed significantly during the day, peaking at the same time intervals as plasma prolactin. A single maximum (at 13:00 h) was found for adenohypophysial DA concentration. Individual adenohypophysial DA concentrations correlated significantly with their respective plasma prolactin levels. A maximum in median eminence 5HT concentration occurred at 21:00 h whereas adenohypophysial 5HT peaked at 13:00 h. Median eminence 5HT concentration and circulating prolactin correlated inversely. In the median eminence, GABA concentration attained maximal values at 21:00 h, whereas it reached a maximum at 13:00 h in the pituitary gland. Median eminence GABA concentration correlated inversely with circulating prolactin. In the median eminence, taurine values varied in a bimodal way showing two maxima, at the second half of the rest span and of the activity phase, respectively. In the adenohypophysis, minimal taurine levels coincided with the major plasma prolactin peak (at 01:00 h). Circulating prolactin and adenohypophysial taurine levels correlated inversely. Conclusion The correlations among the changes in the neurotransmitters analyzed and circulating prolactin levels explain the circadian secretory pattern of the hormone in newborn male rabbits. ==== Body Background The mechanisms that regulate prolactin secretion are complex [1]. Two major regulatory inhibitory inputs for prolactin secretion are dopamine [2] and gamma-aminobutyric acid (GABA) [3-6]. In addition, many other neuromodulators have been implicated in the control of prolactin secretion, among them, vasoactive intestinal peptide, thyrotropin releasing hormone and serotonin (5HT) [1]. More recently, taurine has also been implicated in the regulation of prolactin secretion [1]. It is well known that basal secretion of prolactin varies throughout the day, describing a characteristic pattern with maximal values close to the light-dark transition [7,8]. Such a circadian pattern has been described not only in rodents (rat and mouse) but also in many other species [1]. In the rat, we previously demonstrated changes of the secretory pattern of prolactin along the year [7-11], as well as a function of aging [12,13]. The rat is very immature at birth, so that newborn and suckling rats are very sensitive to manipulations that can affect adulthood [14-18]. Circadian rhythms of developing mammals seem to be entrained by the rhythmicity of their mother [19,20], and several studies have indicated that maternal melatonin is necessary to entrain the circadian rhythms in the newborn [21,22]. The rabbit is probably the best-studied laboratory animal in the wild, due to its abundance, size and importance as an agricultural pest [23,24]. Wild and laboratory rabbits are essentially nocturnal and display a clear daily pattern of activity [25]. The rabbit possesses a number of behavioral specializations that make it uniquely suited for circadian studies. Female rabbits visit their altricial young only for a few minutes once every 24 h to nurse, and survival of the young depends on the tight circadian-controlled synchronization in behavior and physiology with the mother. This unusual pattern of maternal care and the demands it places on the litter provide an excellent opportunity to analyze circadian rhythms during early development [25]. In contrast to the large amount of information available on circadian rhythms in adult mammals, studies on circadian phenomena in neonates are few [26,27]. For example, in 21 day-old male rats the daily circadian pattern of prolactin secretion seen in adults is absent [18]. Considering that no information on circadian rhythmicity of prolactin secretion in neonatal male rabbits is available, we undertook the present study to analyze whether neonatal male rabbits show defined 24-h changes in plasma prolactin levels and whether neonatal male rabbits show circadian changes in DA, 5HT, GABA and taurine concentration in median eminence and the adenohypophysis, all of which are well known modulators of prolactin secretion. Methods Animals This study was performed using 24 multiparous, lactating Californian × New Zealand White crossbreed doe rabbits. Animals were housed in research facilities of the Animal Production Department. They were maintained under controlled light-dark cycles (LD 16:8, light on at 08:00 h), housed in individual metal cages, fed at libitum using a commercial pellet diet (Lab Rabbit Chow, Purina Mills, Torrejón de Ardoz, Madrid, Spain) with free access to tap water. On day 1 after parturition, litter size was standardized to 8–9 by adding or removing kits to assure similar lactation conditions during the experiment. This study was performed according to the CEE Council Directive (86/609, 1986) for the care of experimental animals. Groups of 6–7 suckling male rabbits were killed by decapitation on day 11 of life at six different time points throughout a 24-hour cycle. The brains were quickly removed, and the median eminence and the anterior pituitary were taken out. Anterior pituitaries were weighed and homogenized in chilled (0–1°C) 2 M acetic acid. After centrifugation (at 15000 × g for 30 min, at 5°C), the samples were either analyzed for DA and 5HT or boiled for 10 min and further centrifuged at 14000 rpm for 20 min to measure GABA and taurine. Hormone assay Plasma prolactin levels were measured by a specific homologous RIA method [28] using AFP-991086 antibody supplied by the National Institutes of Health (NIH, Bethesda, MD, USA) and Dr. A. F. Parlow (Harbour-UCLA Medical Center, CA, USA). The titer of antibody used was 1:62,500. The PRL standard used was RbPRL-RP-1. Hormone was labeled with 125I by the chloroamine-T method [29]. The volume of plasma for PRL determinations was 10 μl. Staphylococcus aureus (prepared by the Department of Plant Physiology, U.A.M., Madrid, Spain) was used to precipitate the bound fraction [28]. All samples were measured in the same assay run to avoid inter-assay variations. The sensitivity of the assay for PRL was 0.125 ng/ml and the intra-assay coefficient of variation was < 5%. The intra-assay coefficient of variation was calculated using a pool of plasma measured ten times in the same assay; mean (± S.E.M.) concentration was 106.9 ± 4.1 ng/ml. Catecholamine and indoleamine analysis DA and 5HT concentration was measured by high pressure liquid chromatography (HPLC) using electrochemical detection (Coulochem, 5100A, ESA; USA), as described elsewhere [12]. A C-18 reverse phase column eluted with a mobile phase (pH 4. 0.1 M sodium acetate, 0.1 M citric acid, 0.7 mM sodium octylsulphate and 0.57 mM EDTA containing 10% methanol, v/v) was employed. Flow rate was 1 ml/min, at a pressure of 2200 psi. Fixed potentials against H2/H+ reference electrode were: conditioning electrode: -0.4 V; preoxidation electrode: +0.10; working electrode: +0.35 V. Indoleamine and catecholamine concentration was calculated from the chromatographic peak heights by using external standards and was expressed as pg/μg protein. The linearity of the detector response for DA and 5HT was tested within the concentration ranges found in median eminence and adenohypophysial supernatants. Amino acid analysis Amino acids were isolated and analyzed by HPLC with fluorescence detection after precolumn derivatization with O-phthalaldehyde (OPA) as described elsewhere [30]. An aliquot of the tissue supernatant containing homoserine as an internal standard was neutralized with 4 M NaOH and was then incubated at room temperature with OPA reagent (4 mM OPA, 10% methanol, 2.56 mM 2-mercaptoethanol, in 1.6 M potassium borate buffer, pH 9.5) for 1 min. The reaction was stopped by adding acetic acid (0.5 % v/v). Samples were immediately loaded through a Rheodyne (Model 7125) injector system (50 μl loop) to reach a C-18 reverse-phase column (4.6 mm ID × 150 mm, Nucleosil 5, 100A). Elution was achieved by means of a mobile phase consisting of 0.1 M sodium acetate buffer (pH 6.5) containing 35 % methanol, at a flow rate of 1 mL/min and a pressure of 140 Bars. The column was subsequently washed with the same buffer containing 70 % methanol and re-equilibrated with the elution buffer before re-use. The filter fluorometer was set at the following wavelengths: excitation: 340 nm, emission: 455 nm. The procedure allowed a distinct separation and resolution of the amino acids measured. Amino acid content was calculated from the chromatographic peak heights by using standard curves and the internal standard. The linearity of the detector response was tested within the concentration ranges found in median eminence and adenohypophysial extracts. Statistics Statistical analysis of results was performed by a one-way analysis of variance (ANOVA) followed by post-hoc Tukey-Kramer's multiple comparisons tests. Curve estimation in regression analysis was made by using SPSS software, version 10.1 (SPSS Inc., Chicago, ILL). P values lower than 0.05 were considered evidence for statistical significance. Results Figure 1 shows the levels of prolactin throughout the day in suckling male pups. Plasma prolactin levels changed significantly throughout the day (F = 21.1; p < 0.0001), showing two maxima, a major one at the beginning of the active phase (at 01:00 h) and a second one during the first part of the resting phase (at 13:00 h). Figure 1 24-h changes in plasma prolactin levels of 11 days old male rabbit pups. Groups of 6–7 pups were killed by decapitation at 6 different time intervals throughout a 24 h cycle. Bar indicates scotophase duration. Results are the means ± SEM. a p < 0.01 vs. all time points. b p < 0.01 vs. 01:00 h, 05:00 h and 13:00 h, Tukey-Kramer's multiple comparisons test. For further statistical analysis, see text. Figures 2,3,4,5 depict the changes in median eminence and adenohypophysial concentration of DA, 5-HT, GABA and taurine. Mean plasma prolactin concentration is plotted as a reference in every case. Figure 2 24-h changes in median eminence and adenohypophysial DA concentration in 11 days old male rabbit pups. Groups of 6–7 pups were killed by decapitation at 6 different time intervals throughout a 24 h cycle. Bar indicates scotophase duration. Results are the means ± SEM. Circulating prolactin levels are shown in shaded line. Letters indicate the existence of significant differences between time points within each tissue after a Tukey-Kramer's multiple comparisons test, as follows: a p < 0.01 vs. all time points. b p < 0.01 vs. 05:00 h, 09:00 h and 21:00 h. c p < 0.01 vs. 05:00 and 21:00 h. For further statistical analysis, see text. Figure 3 24-h changes in median eminence and adenohypophysial 5HT concentration in 11 days old male rabbit pups. Groups of 6–7 pups were killed by decapitation at 6 different time intervals throughout a 24 h cycle. Bar indicates scotophase duration. Results are the means ± SEM. Circulating prolactin levels are shown in shaded line. Letters indicate the existence of significant differences between time points within each tissue after a Tukey-Kramer's multiple comparisons test, as follows: a p < 0.01 vs. all time points. b p < 0.01 vs. 01:00 h, 09:00 h, 17:00 and 21:00 h. For further statistical analysis, see text. Figure 4 24-h changes in median eminence and adenohypophysial GABA concentration in 11 days old male rabbit pups. Groups of 6–7 pups were killed by decapitation at 6 different time intervals throughout a 24 h cycle. Bar indicates scotophase duration. Results are the means ± SEM. Circulating prolactin levels are shown in shaded line. Letters indicate the existence of significant differences between time points within each tissue after a Tukey-Kramer's multiple comparisons test, as follows: a p < 0.01 vs. all time points. b p < 0.01 vs. 01:00 h, 05:00 h and 09:00 h, p < 0.05 vs. 17:00 h. For further statistical analysis, see text. Figure 5 24-h changes in median eminence and adenohypophysial taurine concentration in 11 days old male rabbit pups. Groups of 6–7 pups were killed by decapitation at 6 different time intervals throughout a 24 h cycle. Bar indicates scotophase duration. Results are the means ± SEM. Circulating prolactin levels are shown in shaded line. Letters indicate the existence of significant differences between time points within each tissue after a Tukey-Kramer's multiple comparisons test, as follows: a p < 0.01 vs. all time points. b p < 0.01 vs. 01:00 h, 09:00 h and 13:00 h. For further statistical analysis, see text. Median eminence DA concentration changed in a bimodal way as a function of time of day, showing two maxima, coinciding with those of plasma prolactin at the active and resting phase of the diurnal cycle (F = 14.1; p < 0.0001, Figure 2). In the case of adenohypophysial DA concentration, a single maximum occurred during the first half of the rest phase (at 13:00 h) (F = 29.9; p < 0.0001). Only in the adenohypophysis, plasma prolactin and DA concentration correlated in a direct way. This correlation was best described by a log model with r2 = 0.16, b0 = -123.7 and b1= 18.1 (F = 4.69, p= 0.04). As shown in Figure 3, a maximum in median eminence 5HT concentration occurred at the second half of the rest span (F = 64.1; p < 0.0001) whereas a maximum in adenohypophysial 5HT levels was found at the first half of rest span. Circulating prolactin and median eminence 5HT concentration correlated inversely in a linear way (r2= 0.18, b0 = 677.6 and b1 = -4.9, F = 5.3, p < 0.03). Figure 4 shows the changes in median eminence and adenohypophysial GABA concentration. In the median eminence, GABA concentration attained maximal values at the rest phase, with a peak at late evening (i.e. at 21:00 h, F = 11.1, p < 0.0001). In the anterior pituitary, GABA concentration reached a maximum at 13:00 h (F = 21.6, p < 0.0001). Circulating prolactin and median eminence GABA concentration correlated inversely in a linear way (r2= 0.21, b0 = 25.7 and b1 = -0.22, F = 6.6, p < 0.01). Figure 5 depicts the 24-h changes in taurine concentration. In the median eminence, taurine values varied in a bimodal way showing a peak at the second half of the rest period, a nadir at the early activity span (coinciding with the prolactin peak) and a second maximum late in the activity phase (at 05:00 h, F = 32.9, p < 0.0001). Likewise, in the adenohypophysis, taurine levels exhibited minimal values at the time of the prolactin peak (i.e., at 13:00 h, F = 21.6, p < 0.0001). Circulating prolactin and adenohypophysial taurine levels correlated inversely in a linear way (r2= 0.42, b0 = 11.6 and b1 = -0.11, F = 17.4, p < 0.0001). Discussion The present study, performed in neonatal male rabbit pups sacrificed at 6 different time intervals during a 24-h cycle, describes for the first time significant changes in plasma prolactin levels throughout the day. In concomitant measurements of median eminence and adenohypophysial concentration of DA, 5HT, GABA and taurine, a clear daily pattern was found in almost every case. Contrasting with neonatal rats that did not display any circadian pattern of plasma prolactin [18], a daily rhythm of plasma prolactin occurred in neonatal male rabbits, with a maximal value attained 1 h after lights-off (at 01:00 h) and a secondary peak found during the first part of the resting phase (at 13:00 h). In adult rabbits, daily patterns of prolactin secretion depend on light/dark phases [25]. The present results indicate that, already on day 11 of life, male rabbit pups display daily changes in plasma prolactin levels, remarkably similar to those described in adult male rats (e.g., the maximum displayed 1 h after the dark onset) [7-10]. The activity of several nuclei of rabbit hypothalamus increases with age and with experience of anticipatory arousal [27]. However, no study has been published on the regulatory mechanism of prolactin in rabbits. Considering that DA is the major inhibitory input for prolactin secretion [1,32], the present study indicating that DA concentration in median eminence of rabbit pups is high during the rest phase of the day (when plasma prolactin levels are low), and decreases at day-night transition (coinciding with the increase in circulating prolactin), may support a cause-effect relationship. The afternoon decrease in median eminence DA concentration could be a prerequisite for prolactin release in neonatal male rabbits [2]. However, median eminence DA concentration of male rabbit pups also presents a peak during the activity phase (01:00 h) associated with the highest prolactin levels. Therefore, the data suggest that the inhibitory regulatory influence of DA on prolactin secretion is exerted mainly during the light phase of the photoperiod, whereas during the dark phase other hypothalamic neuromodulators could be operative, as it was previously described in rats [13]. These hypotheses must be tested rigorously (e.g., by using pharmacological blocking agents) before a definitive conclusion can be made. Among other possible neuromodulators of prolactin secretion, the arcuate nucleus receives a dense serotonergic innervation consisting of a population of brainstem neurons arising mainly from the midbrain raphe nuclei [33] and from fibers originated in 5HT cell bodies located within the hypothalamus. There is a close proximity of 5HT fibers to dopaminergic cell bodies in the arcuate nucleus [34]. Therefore, an indirect effect of 5HT on prolactin release could be linked to the modulation of the inhibitory dopaminergic inputs to the pituitary. Our foregoing results agree with this hypothesis since 5HT concentration in median eminence changes diurnally in an opposite way to that of plasma prolactin levels, albeit without a significant correlation between them. Indeed, previous experiments in rats indicated that 5HT could probably modulate directly the secretion of prolactin [13]. Taurine has also been implicated in the regulation of prolactin release [5,13,35,36]. The foregoing results indicate that in median eminence and anterior pituitary of male rabbit pups taurine concentration varies inversely to plasma prolactin levels, displaying a mirror pattern. In the adenohypophysis a negative correlation between plasma prolactin and taurine levels was found, similarly to previous data obtained in rats [13]. Therefore, taurine may play a role in prolactin regulation in newborn rabbits. A relatively dense innervation of GABA terminals exists in the external layer of the median eminence [37], and the ability of median eminence neurons to release GABA in portal blood has been demonstrated [38]. We previously demonstrated a possibly inhibitory control of GABA on prolactin secretion during the activity phase in male rats [3-6]. Results obtained in the present study in suckling male rabbits support such an inhibitory effect of GABA on plasma prolactin levels exerted mainly during the dark phase of daily photoperiod. The data indicate that GABA concentration in median eminence decreased during the day-night transition, while plasma prolactin levels were increasing. Actually, in median eminence a negative correlation between GABA concentration and plasma prolactin was found, thus suggesting an inhibitory effect of GABA on prolactin secretion. GABA acting on specific receptors in the anterior pituitary has been reported to suppress prolactin secretion [39,40], although whether this effect was physiological has been questioned [40]. Data from literature suggest that the role of GABA on prolactin release is quite complex [41]. In some conditions, such as aging [13] or hyperprolactinemia [6], the inhibitory role of GABA becomes more pronounced whereas the inhibitory control exerted by DA diminishes. Our results in male rabbit pups indicated that, although no correlation between plasma prolactin and pituitary GABA concentration was found, the pattern may confirm the main role of this amino acid in the control of prolactin secretion during the dark phase of the photoperiod that was developed later. Again, all these hypotheses must be tested. e.g. pharmacologically, before a definitive conclusion on this matter can be drawn. Conclusions In suckling male rabbits plasma prolactin and median eminence and anterior pituitary concentration of several neuromodulators change on a daily basis. The existence of significant correlations among several of the neurotransmitters analyzed and plasma prolactin levels may explain the circadian secretory pattern of prolactin at this age in suckling rabbits. Collectively, the present results differ from the reported absence of circadian rhythmicity of prolactin and median eminence and adenohypophysial neuromodulators in rats at a comparable age. Competing Interests The author(s) declare that they have no competing interests. Authors' Contributions MPA and PC carried out the experiment and the immunoassays and the analysis of catecholamines, indoleamines and amino acids. DPC and AIE designed the experiments. Also, DPC performed the statistical analysis. PR took care of the experimental animals. AIE supervised its technical implementation and drafted the manuscript. All authors read and approved the final manuscript. 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==== Front BMC ImmunolBMC Immunology1471-2172BioMed Central London 1471-2172-6-21565507910.1186/1471-2172-6-2Research ArticleExpression profile of immune response genes in patients with Severe Acute Respiratory Syndrome Reghunathan Renji [email protected] Manikandan [email protected] Li-Yang [email protected] Hiok-Hee [email protected] Dessmon [email protected] Bernard P [email protected] Alirio J [email protected] Department of Physiology, National University of Singapore, Singapore2 Department of Infectious Disease, Tan Tock Seng Hospital, Singapore3 Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore4 Department of General Medicine, Tan Tock Seng Hospital, Singapore2005 18 1 2005 6 2 2 10 9 2004 18 1 2005 Copyright © 2005 Reghunathan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Severe acute respiratory syndrome (SARS) emerged in later February 2003, as a new epidemic form of life-threatening infection caused by a novel coronavirus. However, the immune-pathogenesis of SARS is poorly understood. To understand the host response to this pathogen, we investigated the gene expression profiles of peripheral blood mononuclear cells (PBMCs) derived from SARS patients, and compared with healthy controls. Results The number of differentially expressed genes was found to be 186 under stringent filtering criteria of microarray data analysis. Several genes were highly up-regulated in patients with SARS, such as, the genes coding for Lactoferrin, S100A9 and Lipocalin 2. The real-time PCR method verified the results of the gene array analysis and showed that those genes that were up-regulated as determined by microarray analysis were also found to be comparatively up-regulated by real-time PCR analysis. Conclusions This differential gene expression profiling of PBMCs from patients with SARS strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection, as we observed a complete lack of cytokine genes usually triggered during a viral infection. Our study shows for the first time how the immune system responds to the SARS infection, and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease. ==== Body Background Severe acute respiratory syndrome (SARS) emerged in 2003, as a new epidemic form of life-threatening infection [1]. As of September 2003, there were 8098 cases of SARS from 29 countries with 774 deaths (WHO). SARS is characterized by high fever, malaise, rigors, headache, dry cough, and progression to interstitial infiltration in lungs with eventual mortality of greater than 10% in many countries [2]. SARS has been shown to be caused by a novel coronavirus; SARS-CoV, with genome sequences recently published [3-7]. However, the pathogenesis of SARS is poorly understood. Major hematological features of this disease are lymphopenia, transient thrombocytopenia, and normal neutrophil and monocyte counts [8]. It has been shown that SARS coronavirus infects and replicates in a wide variety of host cells, including PBMCs, in susceptible animals and human beings [9,10]. Hence, to understand the host response to this pathogen, we profiled the gene expression patterns of peripheral blood mononuclear cells (PBMC) from SARS patients, compared to healthy controls using oligo nucleotide microarrays. We found that in the PBMC from SARS patients a number of genes were differentially expressed, as compared to healthy controls, including immune-related genes and these genes are not the typical ones expected in a viral infection. During a viral infection, most cell types in the body respond by secreting high levels of type 1 interferons (IFN-α and IFN-β) [11]. IFN-α/β can directly induce antiviral activities in neighboring cells, preventing viral spread by increasing the resistance of uninfected cells toward the virus. Moreover, these IFNs can activate Natural Killer (NK) cells mediated cytotoxity toward virus-infected cells [11,12], and there is accumulating evidence that IFN-α/β contribute to driving the adaptive-immune response in the T helper cell type 1 (Th1) direction, via stimulation of IFN-γ expression [12]. NK cells can produce IFN-γ [13], which activates leukocytes, such as monocytes/macrophages, that, in turn, participate in the antiviral responses by producing free radicals and proinflammatory cytokines such as TNF-α [13]. During the response to viral infections, a key role is played by the expansion and activation of CD4+ and CD8+ T cells, which are central to the antiviral immunity, including their capability to inhibit replication and clear the infection. CD8+ cells have a direct effector role through cytotoxic T lymphocyte mediated lysis, and cytokine and chemokine production [14]. The role of CD4+ T cells in antiviral immunity is highly dependent on production of cytokines, notably IFN-γ [15], and the cytolytic activity exerted by a subset of CD4+ T cells [16]. Activation, coordination, and regulation of the above-described antiviral responses are mediated by complex mechanisms, where cytokines play important roles. However, to our surprise, we found that the patients' response of SARS appears to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes, neither for major cytokines including IFNs, nor for genes involved in complement mediated cytolysis, suggesting that the immune response against SARS-CoV may be different from other viral infections. Results and discussion To study the differential immune-gene expression patterns induced by SARS coronavirus, PBMCs from patients with SARS and normal subjects were examined using microarray technology. We shooed to use PBMCs as these cells are more easily obtained from patients compared to other infected tissues, and the SARS-CoV has recently been shown to infect PBMCs [9,10]. The method of global gene expression analysis using oligonucleotide microarrays has proven to be a sensitive method to develop and refine the molecular determinants of several human disorders, including cancer and autoimmune diseases, and has provided us with signatures of the immune response [17]. Using this technology, complemented with powerful analytical methods, we compared the gene expression profiles of PBMC from a series of SARS patients with those of healthy donors. To ensure the reliability and reproducibility of the microarray analysis, Pearson Correlation factors using the signal from all the normal samples were calculated. All four control arrays have Pearson correlation coefficients (r) of >0.95, which suggests an excellent reproducibility among individual arrays in the same experiment and between normal control experiments. We analyzed the expression pattern of over ~8,700 genes from the PBMCs of 10 SARS patients, and compared the expression patterns with healthy control samples. Student's unpaired T-test (P < 0.01) was performed and significant genes were selected. Cluster analysis was performed to find distinct groups of genes that were significantly changed. The criteria used for cluster analysis is different from filter criteria used for differentially expressed genes. 248 genes were selected for Hierarchical clustering (Figure 1) which includes all the 186 differentially expressed genes. The severity of the disease does not seem to change the genetic profile of the PBMCs: 5 of our patients were in intensive care, whereas the other 5 patients did not require intensive care, at the time the blood was taken. However, no substantial gene-expression differences were observed that could correlate to disease severity. To our surprise, the expression of genes coding for cytokines was completely absent, and the immune-related genes which were overexpressed are usually associated with innate-immune response against bacterial infection and not against a viral infection (Table 1). Several genes were highly up-regulated in patients with SARS, such as, genes coding for Lactoferrin, S100A9 and Lipocalin 2 among others (Table 1). Lactoferrin is a 78- to 80-kDa glycoprotein synthesized by glandular epithelial cells and mature neutrophils, frequently used as a marker for neutrophil degranulation at sites of inflammation, having well recognized bacteristatic and bactericidal properties [18]. It is now clear that the biological actions of lactoferrin are not restricted to its bacteristatic and bactericidal properties. Indeed, a wide array of actions has been reported for lactoferrin, including enhancing NK cell activity, as well as stimulating neutrophil aggregation and adhesion [18]. The myeloid-related proteins, S100A8, S100A9, and S100A12 have been shown to regulate lymphocyte, monocyte, and neutrophil migration and are found in the extracellular milieu during infections and inflammatory episodes [19,20]. The high levels of serum S100A8/A9 in chronic inflammatory pathologies such as rheumatoid arthritis and inflammatory bowel disease, as well as during bronchitis and tuberculosis [20], suggest that they might play a role in inflammatory reactions. Lipocalins are a family of small, secreted proteins, which have little amino acid sequence homology (20–30%) but share a common three-dimensional structure [21,22]. Tissue distribution studies have revealed that a member of this family 24p3 lipocalin (24p3) is mainly expressed in the liver during an acute phase response [21]. It has also been detected in spleen, lung and the uterus. In the latter location, its expression has been found to be coincident with parturition, a time of major tissue remodeling and inflammation [22]. 24p3 has also been detected in the conditioned media of LPS stimulated murine PU5.1.8 macrophages and therefore it has been suggested to function in defense against infectious agents [22]. However, recent evidence proposed that the lipocalins may trigger apoptosis in immune cells via an unknown cell surface receptor [23]. SARS patients with respiratory distress fulfill criteria for Acute Respiratory Distress Syndrome (ARDS) and diffuse alveolar damage is seen in the lungs on histological examination of postmortem [24]. We speculate that upregulation of lipocalins are part of the host response in limiting unwanted tissue damage and in reducing inflammation and lung fibrosis. In addition, enhanced expression of lipocalins may play a role in the potential cause of lymphopenia observed in the majority of patients. There is also up-regulation of expression of genes, such us, Bactericidal Permeability Increasing Protein (BPI) and Carcino Embryonic Antigen related Cell adhesion Molecule 8 (CEACAM 8 or CD66b). BPI is released from activated neutrophils and is an endogenous antibiotic, which rapidly kills Gram negative bacteria by high affinity binding to the LPS component of the cell wall [25]. CEACAM 8 is expressed by activated monocytes and granulocytes, and significant up-regulation of this gene indicates the involvement of innate immune cells in SARS. Other genes which encode proteins like Leukotrien-B4 receptor (LTB4R), Leukotrien-A4 hydrolase, IL-8 receptor (IL-8RA), anaphylatoxin C3a receptor-1 (C3aR1), Neutrophil Cytosolic Factor 1 (NCF 1), S100 calcium binding protein A9, Defensin, (DEFA 1/4), LPS binding protein CAP18 (CAMP), and Peptidoglycan Recognition Protein (PGLYRP) are involved in chemotaxis, inflammatory reaction and superoxide metabolism [26]. Similarly, Formyl Peptidase Receptor (FPR) genes are expressed by activated neutrophils. Regulation of expression of FPR on neutrophils plays a key role in neutrophil polarization and chemotaxis. Chemotaxis is effected by a neutrophil membrane mesh, via remodeling of the actin component of the membrane [27]. Up-regulation of CD24 and FcγR3A indicates some degree of neutrophil, B cell and NK cell activity. FcγR3a, a low affinity receptor for IgG, expressed in activated macrophages and NK cells, facilitates Antibody Dependent Cell mediated Cytotoxicity (ADCC) by NK cells [13]. There is moderate up-regulation of the kappa light chain of the Nuclear Factor (NFκB 1A) and the B-cell lymphoma 3-encoded protein (BCL3) (Table 1). The major form of NFκB is a heterodimer of p65/p50 subunits, which regulates expression of many proteins in activated immune cells by binding to DNA. It interacts with NFκB through its ankyrin repeats and it favors p50 dimerization by recruiting p50 monomers from the cytoplasmic pool of p105/p50 dimers, and thereby enhancing nuclear translocation and DNA binding of p50 dimers [28]. BLC3 is expressed by activated B cells and T cells on mitotic stimuli, playing an important role in transcription activation [29]. In SARS patients there is down-regulation of genes (Table 2) which regulate proliferation and differentiation of T-cells, such as the Lymphocyte-specific protein tyrosine kinase (LCK), which is required for phosphorylation of CD3. There is down-regulation of genes coding for the epsilon polypeptide of the TCR (CD3E), IL-2 induced T-cell kinase (ITK), the Zeta chain of the TCR (CD3Z), the Alpha 4 subunit of VLA-4 receptor (ITGA4), the Chemokine (C-C motif) Receptor 7 (CCR7), and the Interleukin 10 receptor alpha (IL10 RA). All these point to a potential state of unresponsiveness towards the SARS-CoV antigens. It has been described that T cell function in patients with peritonitis had decreased Th1 function, without increased Th2 response, and furthermore T cell proliferation and cytokine secretion correlated with mortality [30]. In our study, there is up-regulation of genes involved in homeostasis and cell growth (Table 3); for example, genes involved in DNA synthesis, nucleosome assembly and protein synthesis, such as, genes encoding Translation Initiation Factor 1A (EIF1 AY), and histone proteins. There is down-regulation of genes coding for negative regulators of the cell cycle, such as, Retinoblastoma-like 2 protein (RBL2), Cyclin-Dependent Kinase Inhibitor 1B (CDKN1B) and anti-apoptoic protein TNF induced protein GG2-1 etc (data not shown). These findings indicate a high degree of proliferation of immune cells; however, there is no significant level of increase in the expression of cytokines like IL-2 or IL-3 and their receptors, which are required for the proliferation and the differentiation of T cells and effector functions of B cells and NK cells. So the increased cell proliferation may be of a granulocyte lineage rather than a monocytic lineage. We confirmed our microarrays findings by Real time PCR on selected upregulated genes such as Lactoferrin Lipocalin, S100P, FCGR3A, and TLR2 (Figure 2). The average expression of all the five genes by real-time RT-PCR was compared to the average expression levels found by microarray analysis (Table 4). The real-time PCR method verified the results of the gene array analysis and showed that those genes that were up-regulated as determined by microarray analysis were found to be up-regulated by real-time PCR analysis. Moreover, since we did not find any upregulation of cytokine-genes usually associated with a viral infection, we selected a few cytokines and compared the SARS samples, with samples collected from 5 influenza virus-infected patients. The observed results, in figure 2, showed that in the influenza patients the mRNA for type I interferons (IFNα and IFNβ), TNFα and IL-12-p40 were upregulated, whereas genes upregulated in the SARS patients (LTF, Lipocalin, S100P, FGR3A and TLR2), were not upregulated in the influenza patients. Therefore, firstly, this confirms that cytokines usually triggered during a viral infection were not detected in the SARS samples (but were triggered in the influenza patients); and secondly, genes triggered in the SARS patients were not triggered in influenza patients. In this study we have performed extensive analysis of gene expression of PBMCs of SARS patients using a microarray platform that includes more than 8000 gene sequences. However, one potential drawback in our current experimental design is that the gene expression levels were compared between patient samples and normal controls using PBMCs, rather than purified individual cell types. While it would be of interest to determine the exact proportions of monocytes, lymphocytes and contaminating granulocytes in our PBMC preparations, unfortunately, given the highly infective nature of patient samples and lack of suitable flowcytometry facility with appropriate bio-safety control, FACS analysis was, however, not possible. Our results suggest that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes or major cytokines including IFNs (α,β, and γ), or genes involved in complement mediated cytolysis, suggesting that the immune response against the SARS-CoV may be different from other viral infections, or that the virus may be using a unusual strategy to evade the host immune system and cause the pathogenesis and mortality. Conclusions This differential gene expression profiling of PBMCs from patients with SARS, strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes or major cytokines, including IFNs, or complement mediated cytolysis, suggesting that the immune response against the SARS-CoV may be different from other viral infections or that the virus may be using a different strategy to evade the host immune system and cause the pathogenesis and mortality. The severity of the disease does not seem to change the genetic profile of the PBMCs: 5 of our patients were in intensive care, whereas the other 5 patients did not require intensive care, at the time the blood was taken. However, no substantial gene-expression differences were observed that could correlate to disease severity. Moreover, we confirmed (by real-time-PCR) that cytokines usually associated with viral infections (such as interferons and other cytokines) were not detected in the SARS samples, but were triggered in another viral infection (influenza patients). We also showed that genes triggered in the SARS patients were not triggered in the influenza patients. Our study shows, for the first time, how the immune system responds to the SARS infection and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease. Methods Patients and peripheral blood RNA extraction Our study included ten adult patients (S1-S10) who were diagnosed with SARS according to the World Health Organization (WHO) SARS criteria and admitted to the Tan Tock Seng Hospital, Singapore, for treatment and four additional normal human subjects (C1-C4). Our protocol was approved by the Tan Tock Seng Hospital Research Ethics committee. Peripheral blood was collected by vein-puncture of the brachial vein; mononuclear cells (PBMC) were prepared using histopaque (Sigma- Aldrich, St. Louis, MO) according to manufacturer's recommendations. Sample preparation and processing procedures were carried out as described in the Affymetrix Gene Chip Expression Analysis Manual (Affymetrix Inc., Santa Clara, CA). Briefly, total RNA was extracted from PBMCs, using Trizole method (Invitrogen, Carlsbad, CA) and further purified using RNeasy columns according to manufacturer's instructions (Qiagen, Valencia, CA). Integrity of total RNA was confirmed by formamide gel electrophoresis and quantification was carried out by measuring the A260 nm. Generation of cDNA and labeled cRNA 5 μg of total RNA was used to synthesize double stranded cDNA using T7-(dT24) oligonucleotide primer and Superscript reverse transcriptase (Invitrogen). The resultant cDNA was purified by phenol:chloroform extraction and ethanol precipitation in presence of 7.5 M ammonium acetate. 1 μg of purified cDNA was subsequently used to synthesize biotin labeled cRNA by in vitro transcription (IVT) using T7 RNA polymerase at 37°C for 5-6 hr as per manufacturers' instructions (ENZO labeling kit, Ambion, USA). Labeled cRNA obtained after IVT was purified using RNeasy columns (Qiagen). Purified cRNA was fragmented using fragmentation buffer (40 mmol/L Tris acetate, pH 8.1, 100 mmol/L Potassium acetate, 30 mmol/L Magnesium acetate) at 94°C for 35 min. Microarray hybridization and scanning Fragmented cRNA (10 to11μg/ probe array) was used to hybridize to human focus array (HG-Focus Array) at 45°C for 16 hr with constant rotation of 60 rpm in a Gene chip hybridization oven 640 (Affymetrix). The chips were washed and stained using Gene chip fluidics Station 400 (Affymetrix). Staining was performed using streptavidine phycoerythrin conjugate (SAPE, Molecular Probes, Eugene, OR), followed by the addition of biotinylated antibody to streptavidine (Vector Laboratories, CA), and finally with streptavidine phycoerythrin conjugate. Probe arrays were scanned using Agilent Gene Array Scanner Series US 74900593 (Agilent technologies, USA). Data filtering and analysis An absolute expression analysis was performed using Microarray Suite Software 5.0 (Affymetrix) and relative mRNA expression levels were expressed as plus or minus fold changes compared to normal controls. Each chip was scaled to an overall intensity of 500 to correct for minor differences in overall chip hybridization intensity, and to allow comparison between chips. The data from ~8700 genes was imported in to MicroDB 3.0 and Data Mining Tool 3.0 (Affymetrix) for further analysis. Pearson Correlation coefficient (r) was used to ensure the reproducibility of the data using signal from normal samples. T-test was performed to identify genes that were differentially expressed in SARS patients over normal samples. The statistical significance of the differential expression of any gene was assessed by computing P value for each gene. Any gene for which this P value was < 0.01 was considered to be differentially expressed. We selected 186 genes for further analysis that met the following criteria: (i) Changes in expression of at least 2 fold higher or lower comparing the normal (ii) Signal >500 (iii) Detection P < 0.01 and (iv) Genes which met the above criteria in at least 30% of samples. Finally, data representing 10 SARS patients fold changes over control samples were averaged. Genes were annotated according to biological process using the Gene ontology: tool for the unification of biology from the Gene Ontology Consortium [31]. The complete set of raw data was deposited into the NCBIs' Gene Expression Omnibus (GEO) and it can be accessed through the GEO accession 'GSE1739' . Hierarchical clustering Unweighted average linkage Hierarchical clustering was applied for samples using the 'Genesis' software [32]. Genes with 'Present' call (P < 0.01) and significantly changes in expression of at least 2 fold higher or lower comparing the normal were selected. Finally 248 genes which are passing this filter criteria in at least 15% of samples and above were selected for clustering. Real-time quantitative PCR Real-time PCR was performed for ten genes, namely: Lactoferrin (Primers: 5' tcg tcc tgc tgt tcc tcg ggg 3' and 5' tcc agc ggt cct gcg aag gcc 3'); Lipocalin (Primers: 5' aag ccc ctg ctc ctg gcc atc agc 3' and 5' cga cct gat gct gta tgc cac gtg 3'); S100P (Primers: 5' cat gat cat aga cgt ctt ttc 3' and 5' aca cga tga act cac tga agt 3'); TLR2 (Primers: 5' gta tct gca agg gca gct cag gat 3' and 5' ttc ctc aag gaa ggt aag tcc agc 3'); FCGR3A (Primers: 5' ctc cgg ata tct ttg gtg act 3' and 5' tgc aga gca gtg ttc ttc cag 3'); IFNA (Primers: 5'atg gcc ttg acc ttt gct tt 3' and 5'tgg aag att tcc tca tag c 3'); IFNB (Primers: 5'atg acc aac aag tgt ctc ctc caa a 3' and 5' ttc ttc cag gac tgt ctt ca 3'); IL-12 p40 (Primers: 5'atg tgt cac cag cag ttg gtc atc 3' and 5'ctg aat gtc aaa tca gta ct 3'); TNFA (Primers: 5'gag tga caa gcc tgt agc cca tgt tgt agc 3' and 5'gca atg atc cc a aag tag acc tgc cca gac 3'); GAPDH (Primers: 5'acc aca gtc cat gcc atc ac 3' and 5'tcc acc acc ctg ttg ctg ta 3'). For amplicon detection, the Light Cycler RNA Master SYBR Green Kit (Roche) was used as described by the manufacturer. PCRs were performed in a LightCycler® instrument (Roche) as follows: reverse transcription at 61°C for 20 min, initial denaturation at 95°C for 2 min; amplification for 45–65 cycles of denaturation (95°C, 5s, ramp rate 2°C /s), annealing (optimal temperature, 5s, ramp rate 2°C /s) and extension (72°C, product length [bp]/25 s, ramp rate 2°C /s). A single online fluorescence reading for each sample was taken at the end of extension step. Quantitative results were expressed by identification of the second derivative maximum points, which marked the cycles where the second derivatives of the fluorescence signal curves are at maximum. These points were expressed as fractional cycle numbers. Then, these cycle numbers were plotted against the logarithm of the concentrations of serially 2-fold diluted standard samples to obtain a standard curve. The concentrations of unknown samples were calculated by extrapolation from this standard curve. Positive sample specificity was confirmed by determining the melting curve (95°C, 5s, ramp rate 20°C /s; 68°C, 15s, ramp rate 20°C /s; 95 °C, 0s, ramp rate 0.1°C /s, continuous measurement). Authors' contributions RR carried out the sample preparation, hybridization and drafted the manuscript. MJ carried out statistical analysis, data filtration, and data deposition and helped with manuscript preparation. LYH, HHC and DT provided the blood samples and the clinical comments for the study. BPL advised on study design and development. AJM devised and coordinated the study, revised and finalized the manuscript. All authors read and approved the final manuscript. Acknowledgements This work was supported by Biomedical Research Council-Young Investigator Award grant R-185-000-044-305. We thank A-K Fraser-Andrews for proofreading the manuscript. Figures and Tables Figure 1 Clustering and distribution of differentially expressed genes. A. Hierarchical clustering of gene expression data from PBMCs from 10 SARS patients showing different classes of gene expression profiles. Each row represents a separate gene and each column a separate SARS patient. 248 genes have been selected for this analysis which is described in methods. The expression index for each gene (rows) in each sample (column) is indicated by a color code. The color scale ranges from saturated green for log ratios -3.0 and above to saturated red for log ratios 3.0 and above. Red indicates increased gene expression levels, whereas green indicates decreased levels compared with normal samples. B. Pie chart showing the percentage distribution of the differentially expressed genes from the PBMCs of 10 SARS patients. Figure 2 Real-Time PCR of selected genes. PBMCs were isolated from 4 healthy volunteers (Control), from 10 SARS infected patients (SARS), and from 5 influenza virus infected patients (Influenza). Total RNA was extracted from all the samples and Light-Cycler Real-Time PCR was performed. The concentrations of these genes mRNA were calculated using respective standard curves. Lactoferrin expression (LTF); Lipocalin expression (Lipocalin); S100P expression (S100P); FCGR3A expression (FCGR3A); TLR2 expression (TLR2); Interferon Alpha expression (IFNa); Interferon Beta expression (IFNb); Interleukin 12-p40 expression (IL-12); Tumor Necrosis Factor Alpha expression (TNFa); and GAPDH expression (GAPDH). Results are expressed as average ± SD of gene expression for each group (control n = 4; SARS n = 10; and influenza n = 5). Table 1 Immune-response related genes which were found to be significantly up-regulated in PBMCs of SARS patients. Level of expression is expressed in Fold change (average of fold changes of ten patients, S1-S10) as compared to that of control samples from normal human subjects (C1-C4). Gen Bank ID Description Gene name Fold change (S1-S10) NM_002343.1 Lactotransferrin LTF 149.63 M33326.1 Carcinoembryonic antigen-related cell adhesion molecule 8 CEACAM8 97.29 NM_005564.1 Lipocalin 2 LCN2 80.40 NM_004660.2 S100 calcium binding protein A9 S100A9 65.39 NM_001725.1 Bactericidal permeability-increasing protein BPI 49.94 NM_005091.1 Peptidoglycan recognition protein PGLYRP 47.86 NM_001925.1 Defensin alpha 4 DEFA4 46.88 U19970.1 Antimicrobial LPS-binding protein CAP18 CAMP 43.36 NM_005980.1 S100 calcium-binding protein P S100P 39.52 NM_005143.1 Haptoglobin HP 34.78 NM_004084.2 Defensin alpha 1 DEFA1 29.51 NM_006865.1 Leukocyte immunoglobulin-like receptor, subfamily A, member 3 LILRA3 19.43 NM_002870.1 RAS oncogene family (RAB13) RAB13 14.85 NM_003064.1 Secretory leukocyte protease inhibitor SLPI 11.35 NM_000265.1 Neutrophil cytosolic factor 1 NCF1 11.30 NM_000634.1 Interleukin 8 receptor alpha IL8RA 10.85 AL138717 Rag D protein RAGD 7.84 BG327863 CD24 antigen CD24 7.69 NM_000419.2 integrin, alpha 2b (antigen CD41B) ITGA2B 5.50 NM_001828.3 Charot-Leyden crystal protein CLC 5.47 AI078167 Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha NFKBIA 5.21 NM_005621.1 S100 calcium-binding protein A12 S100A12 5.05 NM_003255.2 Tissue inhibitor of metalloproteinase 2 TIMP2 4.93 NM_002029.1 Formyl peptide receptor 1 FPR1 4.59 NM_005384.1 Nuclear factor, interleukin 3 regulated NFIL3 4.50 M25915.1 Complement cytolysis inhibitor (CLI) CLU 4.07 NM_002965.2 S100 calcium-binding protein A9 S100A9 4.05 NM_014214.1 Inositol (myo)-1(or 4)-monophosphatase 2 IMPA2 3.95 NM_003189.1 T-cell acute lymphocytic leukemia 1 TAL1 3.94 U62027.1 C3a receptor C3AR1 3.83 NM_001150 Alanyl (membrane) aminopeptidase ANPEP 3.83 BE789881 RAS oncogene family RAB31 3.75 NM_001769.1 CD9 antigen (p24) CD9 3.73 NM_014358.1 C-type lectin, superfamily member 9 CLECSF9 3.53 NM_006834.1 RAS oncogene family member (RAB32) RAB32 3.40 J04162.1 Leukocyte IgG receptor (Fc-gamma-R) FCGR3A 3.32 NM_006866.1 Lleukocyte immunoglobulin-like receptor, subfamily A2 LILRA2 3.30 U41070.1 Leukotriene b4 receptor LTB4R 3.23 NM_000632.2 Integrin, alpha M ITGAM 3.20 BC003393.1 Phosphoinositide-3-kinase, catalytic, beta polypeptide PIK3CB 3.13 NM_002432.1 Myeloid cell nuclear differentiation antigen MNDA 3.08 NM_003264.1 Toll-like receptor 2 TLR2 3.05 NM_004475.1 Flotillin 2 FLOT2 3.04 NM_003461.1 Zyxin ZYX 2.96 NM_003897.1 Immediate early response 3 IER3 2.91 AF119873.1 Serine (or cysteine) proteinase inhibitor alpha-1 SERPINA1 2.87 NM_016205.1 Platelet derived growth factor C PDGFC 2.74 NM_005479.1 Frequently rearranged in advanced T-cell lymphomas FRAT1 2.72 AV756141 Colony stimulating factor 2 receptor beta CSF2RB 2.71 NM_006019.1 T-cell, immune regulator 1 TCIRG1 2.59 J02959.1 Leukotriene A-4 hydrolase LTA4H 2.59 BC004188.1 Tubulin, beta, 2 TUBB2 2.57 NM_001780.1 CD63 antigen CD63 2.53 NM_003254.1 Tissue inhibitor of metalloproteinase 1 TIMP1 2.37 AF070673.1 Stannin SNN 2.35 BC003570.1 Similar to vesicle-associated membrane protein 3 VAMP3 2.31 Table 2 Immune-response related genes which were found to be significantly down-regulated in PBMCs of SARS patients. Level of expression is expressed in Fold change (average of fold changes of ten patients, S1-S10) as compared to that of control samples from normal human subjects (C1-C4). Genbank ID Description Gene name Fold change (S1-S10) D13720.1 IL2-inducible T-cell kinase ITK 3.64 AF288571.1 Lymphoid enhancer factor-1 LEF1 3.63 NM_005356.1 Lymphocyte-specific protein tyrosine kinase LCK 3.20 NM_003151.1 Signal transducer and activator of transcription 4 STAT4 3.14 BG435404 ADP-ribosylation factor-like 7 ARL7 2.99 J04132.1 T cell receptor zeta-chain CD3Z 2.96 BG532690 Alpha 4 subunit of VLA-4 receptor ITGA4 2.91 NM_001838.1 Chemokine (C-C motif) receptor 7 CCR7 2.83 AI743792 Sialyltransferase 1 SIAT1 2.71 NM_001558.1 Interleukin 10 receptor, alpha IL10RA 2.71 BF669455 Sialomucin CD164 2.58 NM_000733.1 Epsilon polypeptide of CD3 CD3E 2.58 AI073984 Interferon consensus sequence binding protein 1 ICSBP1 2.48 NM_005475.1 Lymphocyte adaptor protein LNK 2.38 AB014560. Ras-GTPase activating protein SH3 domain-binding protein 2 G3BP2 2.31 NM_004757.1 Small inducible cytokine subfamily E, member 1 SCYE1 2.19 Table 3 Genes involved in homeostasis and cell growth, which were found to be significantly up-regulated in PBMCs of SARS patients. Level of expression is expressed in Fold change (average of fold changes of ten patients, S1-S10) as compared to that of control samples from normal human subjects (C1-C4). Gen Bank ID Description Gene name Fold change (S1-S10) BC005248.1 Eukaryotic translation initiation factor 1A EIF1AY 44.79 NM_001008.1 Ribosomal protein S4, Y-linked RPS4Y 20.75 NM_001062.1 transcobalamin I (vitamin B12 binding protein, R binder family) TCN1 16.88 NM_002357.1 MAX dimerization protein MAD 11.88 NM_012387.1 Peptidyl arginine deiminase, type V PADI4 9.12 NM_000184.1 Hemoglobin, gamma G HBG2 8.7 NM_000559.1 Hemoglobin, gamma A HBG1 8.1 NM_004653.1 SMC (mouse) homolog, Y chromosome SMCY 7.07 NM_002934.1 Ribonuclease, RNase A family, 2 RNASE2 5.99 M60721.1 Homeo box 1 HLX1 5.85 NM_005178.1 B-cell CLLlymphoma 3 BCL3 5.11 NM_001964.1 Early growth response 1 EGR1 4.9 NM_006931.1 Solute carrier family 2, member 3 SLC2A3 4.89 NM_002863.1 Phosphorylase, glycogen PYGL 4.89 NM_021122.2 Fatty-acid-Coenzyme A ligase, long-chain 2 FACL2 4.87 L13974.1 Nuclear factor (erythroid-derived) 2 NFE2 4.77 AI313324 H2A histone family, member O HIST2H2AA 4.48 BC002649.1 H1 histone family, member 2 HIST1H1C 4.44 AF073310.1 insulin receptor substrate-2 IRS2 4.43 NM_005330.2 hemoglobin, epsilon 1 HBE1 3.97 AF073890.1 Cathepsin X precursor CTSZ 3.69 AB002559.1 syntaxin binding protein 2 STXBP2 3.69 NM_017445.1 H2B histone family, member S HIST1H2BK 3.64 NM_003118.1 Secreted protein, acidic, cysteine-rich (osteonectin) SPARC 3.62 AL353759 Two genes for novel histone 1 HIST1H2AC 3.54 NM_003681.1 Pyridoxal (pyridoxine, vitamin B6) kinase PDXK 3.54 BC001886.1 Ribonucleotide reductase M2 polypeptide RRM2 3.53 AI082078 Actinin, alpha1 ACTN1 3.42 AF087942.1 Glycogenin-1L GYG 3.39 AL538601 Sjogren syndrome antigen A2 SSA2 3.36 NM_001805.1 CCAATenhancer binding protein CEBPE 3.26 NM_003528.1 H2B histone family, member Q HIST2H2BE 3.13 BC001906.1 Similar to metaxin 1 MTX1 3.11 NM_006755.1 Transaldolase 1 TALDO1 3.07 BC000893.1 Histone family, member A HIST1H2BK 3.06 AL161952.1 Glutamate-ammonia ligase GLUL 2.99 BC002842.1 H2B histone family, member B HIST1H2BD 2.78 NM_004219.2 Pituitary tumor-transforming 1 PTTG1 2.75 NM_012228.1 Pilin-like transcription factor PILB 2.73 L12711.1 Transketolase TKT 2.68 BE561479 Glyceraldehyde-3-phosphate dehydrogenase GAPD 2.61 NM_012331.2 Methionine sulfoxide reductase A MSRA 2.6 M33197 Glyceraldehyde-3-phosphate dehydrogenase GAPD 2.59 NM_005195.1 CCAAT enhancer binding protein CEBPD 2.57 NM_018840.1 Putative Rab5-interacting protein C20orf24 2.45 AA314406 Thyroid Hormone Receptor Associated protein TRAP95 2.44 NM_004832.1 Glutathione-S-transferase like; glutathione transferase omega GSTTLp28 2.42 NM_013272.2 Solute carrier family 21 (organic anion transporter) SLC21A11 2.42 Table 4 Expression of microarray analysis compared with Real-time RT-PCR analysis Gene name Microarray Realtime RT-PCR Mean expression* Fold increase mRNA conc.(ng)* Fold increase LTF 12403.95/83.78 148.05 1204/13 92.6 Lipocalin 9788/135.94 72 1455/25 58.2 FCGR3A 6202.81/1885.35 3.29 650/205 3.17 S100P 7496.57/292.49 25.63 475/45 10.5 TLR2 3459.33/1141.69 3.03 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29 10802651 10.1038/75556	15655079	PMC546205	CC BY	2021-01-04 16:03:32	no	BMC Immunol. 2005 Jan 18; 6:2	utf-8	BMC Immunol	2,005	10.1186/1471-2172-6-2	oa_comm
==== Front BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-61565199010.1186/1471-2458-5-6Study ProtocolEffectiveness and cost-effectiveness of a multidisciplinary intervention programme to prevent new falls and functional decline among elderly persons at risk: design of a replicated randomised controlled trial [ISRCTN64716113] Hendriks Marike RC [email protected] Haastregt Jolanda CM [email protected] Joseph PM [email protected] Silvia MAA [email protected] Harry FJM [email protected] Eijk Jacques ThM [email protected] Department of Health Care Studies, section Medical Sociology, Faculty of Health Sciences, Maastricht University, The Netherlands2 Department of Health Organisation Policy and Economics, Maastricht University, The Netherlands3 Department of General Practice, Maastricht University, The Netherlands2005 14 1 2005 5 6 6 10 12 2004 14 1 2005 Copyright © 2005 Hendriks et al; licensee BioMed Central Ltd.2005Hendriks et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Falls are common among community-dwelling elderly people and can have a considerable impact on quality of life and healthcare costs. People who have sustained a fall are at greater risk of falling again. We replicated a British randomised controlled trial which demonstrated the effectiveness of a multidisciplinary intervention programme to prevent falls. The objective is to describe the design of a replication study evaluating a multidisciplinary intervention programme on recurrent falls and functional decline among elderly persons at risk. The study consists of an effect evaluation, an economic evaluation and a process evaluation. Methods/design The programme is aimed at community-dwelling elderly people aged 65 years or over who have visited an accident and emergency department (A&E department) or a general practitioners' cooperative (GP cooperative) because of a fall. The design involves a two-group randomised controlled trial. Participants are followed for twelve months after baseline. The intervention programme consists of a detailed medical and occupational therapy assessment with referral to relevant services if indicated. People in the control group receive usual care. The main outcome measures of the effect evaluation are number of falls and daily functioning. The economic evaluation will be performed from a societal perspective. A process evaluation will be carried out to evaluate the feasibility of the intervention programme. ==== Body Background Publishing the design of a study This article describes the design of a replication of a randomised controlled trial (RCT) evaluating the effectiveness and cost-effectiveness of a multidisciplinary intervention programme to prevent further falls among elderly people at risk. Publishing the design and protocol of a study before results are available is important for several reasons. A published protocol allows easier comparison between what was originally intended and hypothesised and what was actually done [1], and it gives readers greater insight into the methodological quality of a study. Furthermore, it has often been recognised that negative or adverse outcomes are less likely to be published [1-3]. Publishing the design of a study before its start announces that a study will be undertaken, which encourages publication of the results and in any case informs researchers where they can find the data for inclusion in systematic reviews [1,2]. Thus, publishing a design article can prevent publication bias. Prevention of falls About one-third of people over the age of 65 fall at least once a year [4]. People who have fallen show an increase in morbidity, mortality and healthcare utilisation [5], which implies increased healthcare costs. In addition, people who have sustained a fall are at greater risk of falling again [5]. Since preventing falls has been a matter of interest for years, many programmes aimed at preventing falls have been developed and evaluated. Unfortunately, many of these have turned out to be ineffective [4]. However, there is now considerable evidence of the effectiveness of multifaceted interventions. Programmes likely to be effective in preventing falls among elderly people are multidisciplinary, multifactorial programmes screening for health and environmental risk factors and offering interventions, both for the general population of community-dwelling elderly people and for elderly people with a history of falling selected because of known risk factors [4]. An example of such an intervention programme is the successful programme developed by Close et al. [5]. This programme is aimed at people aged 65 years or older who live in the community and have visited an accident and emergency department because of a fall. The intervention programme consists of a detailed medical and occupational therapy assessment with referral to relevant services if indicated. The intervention has been evaluated in a randomised controlled trial, which demonstrated that this multidisciplinary intervention implemented among people at risk was highly effective in reducing the number of recurrent falls and associated injuries in London (United Kingdom) [5]. Because details of the status of the participants, the context of the intervention and the content and presentation appear to be critical, it has been recommended to re-evaluate effective intervention programmes in different healthcare systems [4]. We therefore decided to evaluate the effectiveness of the intervention developed by Close [5] in Dutch healthcare, by replicating this study in the Netherlands. Objective and research questions The main objective of our current study is to evaluate the effects of a multidisciplinary intervention programme on recurrent falls and functional decline among elderly persons who have visit a general practitioners' cooperative (GP cooperative) and/or an accident and emergency department (A&E department) because of a fall. This objective has resulted in the following research questions. • Is a multidisciplinary intervention programme more effective than usual care in preventing new falls and functional decline among community-dwelling elderly people who visit a GP cooperative and/or A&E department at a hospital because of a fall? • Is the multidisciplinary intervention programme cost-effective compared to usual care when assessed from a societal perspective? Besides the effect evaluation and economic evaluation, a process evaluation is being carried out to assess the feasibility and applicability of the intervention programme for those receiving and implementing the intervention. Design and methods Design Figure 1 shows the design of the study presented, which is a two-group randomised controlled trial. At this stage, the randomisation process has already been completed. Randomisation was achieved by means of computerised alternative allocation and performed by an external agency. Randomisation took place after completion of a self-administered baseline questionnaire. People allocated to the control group received usual healthcare, while people in the intervention group underwent a medical and occupational therapy assessment. The intervention period is scheduled to last for a maximum of 3.5 months after the baseline measurement. After baseline measurement, all subjects are followed for a twelve-month period. During this follow-up period, falls and healthcare utilisation are measured continuously. Subjects are contacted monthly by telephone for an interview about their falls and healthcare utilisation. In addition, self-administered questionnaires are sent to the subjects after four and twelve months. Figure 1 Study Design We have taken various measures to ensure blinding in the data collection process. Questionnaires are collected anonymously and sorted by number. Follow-up measurements by phone are contracted out to an independent call centre, whose operators are unaware whether the subjects have been allocated to the intervention or the control group. The study design and protocols were approved by the Medical Ethics Committee of the University Hospital and University of Maastricht. Target population Various studies have been conducted to assess the effectiveness of programmes to prevent falls. Although most studies were aimed at the general population of elderly people, details of the status of the participants appear to be critical [4,6]. Several authors have suggested that interventions are likely to have greater effect when targeting people at risk [7,8]. People who attend an A&E department with an injurious fall form a high-risk group, and are expected to be more receptive to an intervention programme aimed at reducing falls than the general population of community-dwelling elderly people. In a study by Close et al., about half of the patients who attended an A&E department with a fall had experienced an earlier fall in the previous year, compared to about one third of the elderly people in the general population [5]. Like Close et al. [5], we chose community-dwelling elderly people aged 65 years or over who had sustained an injurious fall as the target population of our intervention programme. The following definition of a fall was used: 'A fall is an event which results in a person coming to rest inadvertently on the ground or other lower level'. This definition is derived from that used by the Kellogg International Work Group [9]. Recruitment of the study population Recruitment of subjects took place at the local GP cooperative and the A&E department of the University Hospital in Maastricht. The Maastricht GP cooperative is a group of GPs from practices in the town of Maastricht and the surrounding area who have founded a non-profit organisation to provide care for their own patients after hours [10]. The Maastricht GP cooperative has been set up at the hospital's A&E department and covers the out-of-hours service for all local GPs [11]. The following inclusion criteria were used: age 65 years or older, community-dwelling, having visited the A&E department or GP cooperative at the University Hospital Maastricht for the consequences of a fall, and living in Maastricht or its surrounding area. People were only allowed to enter the programme after completing and returning an informed consent form. Exclusion criteria were: not able to speak or understand Dutch, not able to complete questionnaires or interviews by telephone, cognitive impairment (a score of less than 4 on the Abbreviated Mental Test 4 (AMT 4) [12,13], long-term admission to a hospital or other institution (more than four weeks from the date of inclusion), permanently bedridden, or fully dependent on a wheelchair. Sample size calculation Sample size calculations were based on the expected effects of the intervention on the main outcome measure, the percentage of people sustaining a fall during one year of follow-up. The study by Close et al. [5] found that the percentage of persons who sustained a recurrent fall was 52% in the usual care group and 32% in the intervention group. If we want to detect the same reduction in the percentage of persons sustaining a recurrent fall in our study, with a power (1-beta) of 90% and alpha of 0.05, we need 123 patients in each group (a total of 246). Based on the experiences of Close et al. and our own experiences in trials among elderly people in the Netherlands [14], we expect a dropout rate of about 25% during the one-year follow-up period. This means that about 164 persons per group (a total of 328) have to be included in the study. The inclusion period was 14 months. Intervention programme To adapt the programme developed by Close et al. [5] to the Dutch situation, and to make improvements based on recent insights, we performed a review of the literature, convened a consensus meeting and tested the adapted version in a pilot study (n = 36). Based on this process, we made some improvements to Close et al.'s programme. The final programme includes a medical and occupational therapy assessment resulting in recommendations. The medical assessment consists of an examination performed by a geriatrician, a geriatric nurse and a rehabilitation physician to identify and address risk factors for falling. The examination includes a comprehensive general examination, but in addition focussed on a more detailed assessment of visual acuity, stereoscopic vision, mobility, balance, cognition, affect, use of medication and examination of feet and footwear. Recommendations or indications for referral resulting from this examination are sent to the patient's GP. After the medical assessments, an occupational therapist visits the patients to identify possible risk factors for falling in the home environment. The therapist makes recommendations regarding adaptations to the home environment, assistive devices, home care and behavioural change. Recommendations by the occupational therapist are sent directly to the subjects themselves and to their GPs. As stated before, the intervention period is scheduled to last for a maximum of 3.5 months after the baseline measurement. An important addition to Close et al.'s protocol [5] is the collaboration with a rehabilitation physician (physiatrist) in the medical part of the intervention. In addition to the screening by a geriatrician, our programme also involves screening by a rehabilitation physician who examines the subjects' feet and the shoes which the subjects wore at the time of the fall. Details of the process of adaptation and the contents of the intervention programme will be published elsewhere. Usual care in the Netherlands People in the control group receive usual care. At present, no guidelines exist for the systematic assessment of the underlying causes of an injurious fall presented at an A&E department or GP cooperative in the Netherlands. In usual care, medical risks and other risk factors such as environmental hazards in the home and patients' risk behaviour are not systematically registered and addressed by hospital physicians, specialists or general practitioners. Moreover, no systematic attention is currently being paid to the specific consequences of an injurious fall for the daily functioning of individual patients in their unique situation. We placed no restrictions on co-interventions. Effect evaluation The primary outcome measures of the effect evaluation are number of falls and daily functioning. Number of falls is subdivided into three separate measures: the percentage of elderly people sustaining a fall during the one-year follow-up period, recurrent falls during follow-up (i.e., the percentage of elderly people sustaining two or more falls), and injurious falls during follow-up (the percentage of elderly people receiving medical care after a fall). Falls are recorded continuously by means of a fall calendar during the twelve-month follow-up period. Subjects are called monthly to report their falls as recorded on the fall calendar relating to the previous month. We decided to measure daily functioning by means of the Frenchai Activity Index (FAI) [15], in contrast to Close et al. [5], who used the Barthel Index. Our reason for choosing this instrument was that the FAI has proved to be suitable for the general population of elderly people [14] and has at least two advantages over the Barthel Index. One is that the Barthel Index shows a ceiling effect when applied to elderly people who have sustained a fall [5]. The other is that most activities of daily living (ADL) scales, like the Barthel Index, do not refer to complex activities like housekeeping, recreation, hobbies and social interaction. These so-called instrumental abilities (IADL) may affect the quality of life considerably, and the FAI focuses primarily on these IADL abilities [15]. The FAI is measured by means of self-administered questionnaires at baseline and after four and twelve months. Our secondary outcome measures are: recuperation from the fall, health complaints, perceived health measured by means of the first two items of the RAND-36 [16], ADL and IADL disability measured by means of the GARS (Groningen Activity Restriction Scale) [17], mental health measured by means of the HADS (Hospital Anxiety and Depression Scale) [18,19] and quality of life measured by means of the European Quality of Life instrument (EuroQol) [20]. The secondary outcome measures are assessed by means of self-administered questionnaires at four and twelve months. Besides the primary and secondary outcome measures, we assess some background variables which are considered to be predictors, confounders or effect modifiers. The following personal characteristics are assessed: age, sex, marital status, living alone and socio-economic status. In addition, we assess the circumstances and causes of the falls reported at the GP cooperative and/or A&E department, the consequences of the falls (using the Falls Handicap Inventory [21]), the type of injury, falls in the previous year (retrospective), the patient's height, weight, use of medication and social contacts (using an adjusted version of items 4 and 5 of the Rand Social Health Battery)[22,23], and the occurrence of life events. All background variables are measured at baseline. Economic evaluation The economic evaluation in our study is being performed from a societal perspective, which implies that all costs and outcomes are taken into account if possible. The economic evaluation will be a combination of a cost-effectiveness and a cost-utility analysis. The primary outcome measure for the cost-effectiveness analysis will be the percentage of people sustaining a fall during one year of follow-up. As mentioned above, falls are recorded by means of a calendar. Within the cost-utility analysis, the effects are measured in terms of generic health-related quality of life descriptions, measured according to the standard Dutch version of the EuroQol (EQ 5-D) [20] in self-administered questionnaires at baseline and after four and twelve months. A direct value for every state of health is generated using the social tariff [24], which involves an algorithm for interpolating EuroQol results to population utilities. We will assess programme costs, healthcare costs and patient and family costs. All costs are measured by means of a cost diary [25], in which patients continuously record volumes of health care utilisation during the twelve-month follow-up period. Subjects are asked to report their cost diary relating to the previous month during the same monthly telephone interview in which they report falls from the calendar. The volume of each category we measure will be multiplied by the cost price of each category. Cost prices are presented in Euros. Health care costs are estimated according to the Dutch guideline for cost analysis in health care research [26]. Where such guidelines are not available for a specific category, real costs or tariffs are used to estimate costs. Process evaluation The process evaluation involves assessing the extent to which the intervention programme is performed according to protocol, the nature of the recommendations made to the participants, participants' compliance with these recommendations and the opinions of participants, physicians and therapists about the intervention programme and recommendations. Data on these topics are collected using the following methods: structured registration forms for the medical and occupational parts of the intervention programme; self-administered evaluation forms filled in by the participants after the medical intervention; interviews by telephone with the participants six weeks or longer after the recommendations are sent and interviews with all participating physicians and therapists at the end of the intervention period. Analysis Data will be primarily analysed according to the intention-to-treat principle, i.e., including all participants with valid data, regardless of whether they received or did not receive the intervention. Subsequently, the results of the intention-to-treat analysis will be compared with the results of an on-treatment analysis, to assess whether protocol deviations have caused bias. Participants with documented deviations from the study protocol (i.e., participants in the intervention group who did not receive the entire intervention or participants in either the intervention or the control group with incomplete follow-up data) will be excluded from this on-treatment analysis. Comparability between the intervention and control groups will be assessed at baseline to check for differences between the two groups. Outcome at four and twelve months will be compared between the intervention and control groups by both univariate and multivariate techniques. We will use multivariate analysis to adjust for possible differences in baseline scores and background variables between the intervention and control groups. Dropouts and losses-to-follow up will be described. The economic evaluation will involve calculating cost-effectiveness and cost-utility ratios. The additional costs and additional benefits of the intervention programme compared with usual care will be examined by calculating incremental cost-effectiveness and cost-utility ratios. These incremental ratios represent the difference in mean costs between the intervention and usual care groups in the numerator and the difference in mean effects in the denominator[27]. Since the recruitment period is only 14 months, and the follow-up period is also relatively short (12 months), it is unlikely that there will be substantial differences between costs made by and for patients who started in the first part of the recruitment period and those who started in the last part. Therefore, discounting of costs is not required. Finally, a sensitivity analysis will be done to assess the generalisability of the assumptions made in the costing process. This sensitivity analysis, which involves calculating the upper and lower limits of the confidence interval of cost and effect variables, will allow us to explore and quantify the uncertainty not related to sampling variations. The process evaluation will mainly be analysed by means of descriptive techniques. Progress of the study Recruitment of eligible subjects commenced in December 2002 and ended in February 2004, resulting in a total of 333 eligible subjects being included in the trial. Randomisation resulted in the allocation of 166 participants to the intervention group and 167 to the control group. Of the 333 persons recruited, 105 (32 %) are male and 228 (68 %) are female. The follow-up period is to end in May 2005. Results on the effects of the programme will be available in 2006 and will be published in the relevant journals. Discussion Although the intervention has been subject of earlier research, this study will provide new information about the effectiveness in the Dutch situation. Furthermore, the results of the economic evaluation can provide information about the cost-effectiveness of the intervention and the effects on quality of life. In case of shown effectiveness and cost-effectiveness, we aim to implement this intervention into usual healthcare. Competing interests The author(s) declare that they have no competing interests. Authors' contributions All author's read and approved the final version of the manuscript. MH is the investigator and performed most of the writing of this manuscript. JH supervises the planning of the project and wrote the study design. JD supervises the planning and methodological aspects of the project. SA supervises the economic evaluation. HC supervises the clinical part of the project. JE is the principal investigator of the study. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements This study was funded by the Netherlands Organisation for Health Research and Development (ZonMw), grant number 945-02-053. ==== Refs Godlee F Publishing study protocols: making them visible will improve registration, reporting and recruitment BMC News and Views 2001 2 4 Thornton A Lee P Publication bias in meta-analysis: its causes and consequences J Clin Epidemiol 2000 53 207 216 10729693 10.1016/S0895-4356(99)00161-4 Steenstra IA Anema JR Bongers PM de Vet HC van Mechelen W Cost effectiveness of a multi-stage return to work program for workers on sick leave due to low back pain, design of a population based controlled trial [ISRCTN60233560] BMC Musculoskelet Disord 2003 4 26 14629775 10.1186/1471-2474-4-26 Gillespie LD Gillespie WJ Robertson MC Lamb SE Cumming RG Rowe BH Interventions for preventing falls in elderly people Cochrane Database Syst Rev 2003 CD000340 14583918 Close J Ellis M Hooper R Prevention of falls in the elderly trial (PROFET): a randomised controlled trial Lancet 1999 353 93 97 10023893 10.1016/S0140-6736(98)06119-4 van Haastregt JC Diederiks JP van Rossum E de Witte LP Crebolder HF Effects of preventive home visits to elderly people living in the community: systematic review BMJ 2000 320 754 758 10720360 10.1136/bmj.320.7237.754 Tinetti ME Baker DI McAvay G Claus EB Garrett P Gottschalk M Koch ML Trainor K Horwitz RI A multifactorial intervention to reduce the risk of falling among elderly people living in the community N Engl J Med 1994 331 821 827 8078528 10.1056/NEJM199409293311301 Cumming RG Thomas M Szonyi G Salkeld G O'Neill E Westbury C Frampton G Home visits by an occupational therapist for assessment and modification of environmental hazards: a randomized trial of falls prevention J Am Geriatr Soc 1999 47 1397 1402 10591231 The prevention of falls in later life. 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==== Front Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-4-11563894810.1186/1475-2875-4-1ResearchSevere falciparum malaria in Gabonese children: clinical and laboratory features Dzeing-Ella Arnaud [email protected] Obiang Pascal C [email protected] Rose [email protected] Timothy [email protected] Béatrice [email protected] Monique [email protected] Ulrich [email protected] Joseph [email protected] Eric [email protected] Edouard [email protected] Peter G [email protected] Sanjeev [email protected] Maryvonne [email protected] Department of Parasitology, Mycology and Tropical Medicine, Faculty of Medicine, University of Health Sciences (USS), Libreville, Gabon2 Department of Paediatrics, Centre Hospitalier de Libreville (CHL), Gabon3 Malaria Clinical Research Unit, CHL, Gabon4 Department of Infectious Diseases, St. George's Hospital Medical School, Cranmer Terrace, London, UK5 Department of Intensive Care-Emergency, CHL, Gabon6 Department of Parasitology, Eberhard Karls Universität, Tübingen, Germany7 Department of Biochemistry, Faculty of Medicine, USS, Libreville, Gabon2005 9 1 2005 4 1 1 20 6 2004 9 1 2005 Copyright © 2005 Ella et al; licensee BioMed Central Ltd.2005Ella et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Malaria continues to claim one to two million lives a year, mainly those of children in sub-Saharan Africa. Reduction in mortality depends, in part, on improving the quality of hospital care, the training of healthcare workers and improvements in public health. This study examined the prognostic indicators of severe falciparum malaria in Gabonese children. Methods An observational study examining the clinical presentations and laboratory features of severe malaria was conducted at the Centre Hospitalier de Libreville, Gabon over two years. Febrile children aged from 0 to 10 years with Plasmodium falciparum infection and one or more features of severe malaria were enrolled. Results Most children presenting with severe falciparum malaria were less than 5 years (92.3% of 583 cases). Anaemia was the most frequent feature of severe malaria (67.8% of cases), followed by respiratory distress (31%), cerebral malaria (24%) hyperlactataemia (16%) and then hypoglycaemia (10%). Anaemia was more common in children under 18 months old, while cerebral malaria usually occurred in those over 18 months. The overall case fatality rate was 9%. The prognostic indicators with the highest case fatality rates were coma/seizures, hyperlactataemia and hypoglycaemia, and the highest case fatality rate was in children with all three of these features. Conclusions Prompt and appropriate, classification and treatment of malaria helps identify the most severely ill children and aids early and appropriate management of the severely ill child. ==== Body Introduction Each year 500 million infections and up to 2.7 millions deaths are attributable to malaria [1], about 90% of these deaths occur in children in sub-Saharan Africa [2]. Eighty percent of the deaths occur during the first 24 hours following admission [3-5]. Despite a better understanding of pathophysiology and management of malaria, childhood mortality remains unacceptably high [6]. The acquisition of malaria immunity is closely linked to the level of transmission and severe Plasmodium falciparum infection is very rare after the age of 5 years in highly endemic areas. Presentations of severe malaria are different at different ages and in areas with different levels of transmission. In Gabon, a country of about 1.2 million people, malaria is the main cause of neurological, haematological and infectious emergencies at the Centre Hospitalier de Libreville (CHL), the country's tertiary referral centre [7]. Malaria transmission is hyperendemic and perennial with an entomological inoculation rate of 50 per person-year [8]. Previous studies in Africa have shown three frequent presentations of severe falciparum malaria: cerebral malaria, metabolic malaria (hyperlactaemia, acidosis or respiratory distress) and severe anaemia [3,5,9]. The case fatality rate of severe anaemia, however, is low and in some studies it is not an independent predictor of death [9,10]. In Africa, malaria mortality remains high for a number of reasons including limited access to healthcare and increased drug resistance [6]. Better classification of severe malaria could aid clinicians caring for children with severe malaria to avoid diagnostic delays, identify the children most likely to die and thus improve management by targeting resources to the sickest children. This prospective observational study was designed to determine the clinical and laboratory features that identify those children most severely ill with malaria. Methods This study was carried out in the CHL between 1st August 2000 and 31st July 2002. The CHL is the largest public hospital in the country, situated in Libreville, the capital city (pop. 500,000). Services at the CHL include an Emergency Department (20 beds), a Paediatrics Unit (80 beds) and an Intensive Care Unit (12 beds). Ethical permission for the study was granted by the Gabonese Ministry of Health. Febrile children were referred the study team and seen on admission by a Malaria Clinical Research Unit (MCRU) clinician, summary data were recorded on a pro-forma sheet. A blood sample (2 ml) was drawn (anti-coagulated with EDTA) for quantitative examination of a blood film for malarial parasites using the Lambaréné method [11], measurement of haemoglobin concentration, white cell count, platelet count (STKS, Coulter Corporation) and blood lactate and glucose concentrations. Within 15 minutes of blood sampling, blood lactate and glucose concentrations were measured using Accusport™ hand held analyser (Bohringer, Manheim, Inc., Germany) and One-touch Analyzer (LifeScan, Inc., USA) [11]. Blood films were defined as negative if there were no asexual forms of P. falciparum in 100 high power fields of a thick film. The schematic process of the inclusion is shown in Figure 1. Figure 1 Schematic process of the screening of febrile children in the Centre Hospitalier de Libreville, Gabon. Febrile children (or those with history of fever in the last 48 hours) were considered for inclusion in the study if they were: aged 0 to 10 years of age (inclusive), had malaria (> 2 asexual forms of P. falciparum seen on blood film) and had one or more of the following features of severity: [9,12,13]: Blantyre coma score (BCS) ≤ 2 defining cerebral malaria, repeated observed seizures (3 or more observed in 24 hours), lactate concentration in whole blood or capillary blood ≥ 5 mmol/L, glucose concentration in whole blood or capillary blood ≤ 2.2 mmol/L, severe anaemia (haemoglobin concentration of < 5 g/dL) and/or haematocrit concentration < 15%). Children were excluded from the study if informed consent was not obtained from a relative or if an alternative diagnosis was made clinically or by investigation (such as cerebrospinal fluid examination, chest radiography or blood culture). Respiratory distress was defined as the presence of one or more of these features [14]: abnormalities in respiratory rate (according to the age), rhythm (Kussmaul's or Cheyne-Stokes's breathing) and signs of distress such as nasal flaring, intercostal or subcostal recession. Management All children enrolled were hospitalised and treated with parenteral quinine (12.5 mg salt/kg/day, intravenous; Quinimax*, Sanofi-Synthelabo, France) without a loading dose, followed by oral quinine when tolerated. Pyrexial children received paracetamol suppositories (60 mg/kg/day, rectal; Efferalgan™, Bristol- UPSA, France). Seizures were controlled with diazepam (0.3 mg/kg, iv or 0.5 mg/kg rectal; Valium™, Roche, France). Severe anaemia was corrected by transfusion of packed red cells (15 ml/kg over 4 hours) screened for blood borne infections. Hypoglycaemia was treated with a slow intravenous injection of hypertonic glucose 40 %(Braun, Germany) at a dose of 1 ml/kg. Nasal oxygen at 6 1/minute was given to children with respiratory distress. Follow up An MCRU clinician performed a full physical examination daily until discharge for each child. Laboratory assessments including parasitaemia, blood glucose and lactate were performed during hospitalization as necessary. The outcome (survived, death) was recorded. Statistical analysis Statistical analysis was carried out with Epi info 6.04 (ENSP-Epiconcept- InVS, Corp.) and Stata Statistical Software (version 7.0, Stata Corporation, College Station, Texas, USA). Normality of data distribution was checked using either Shapiro-Wilks or Kolmogrov -Smirnov test. Normally distributed data were analysed by two-tailed Student's T test and non-normally distributed data with the Mann-Whitney U statistic. Proportions were compared with χ2 tests with Yates' correction or Fisher's exact test. ANOVA test were used for multiple comparisons of variances, with Tukey's post hoc test. Assessments of prognostic factors were conducted with logistic regression model. A p value < 0.05 was considered as significant. Specific prevalence for each subgroup has been defined as the ratio of the number of cases observed in this sub-group over the population of this same sub-group. Hyperlactataemia is usually defined as a blood lactate concentration of ≥ 5 mmol/L. As the Accusport has been found to have poor agreement with the gold standard YSI 2300 [11] or YSI 1500 sport [15], a definition of hyperlactataemia as blood lactate concentration ≥ 10 mmol/L was used to increase the specificity in the analysis. Results Demographic and clinical data During the study period (1st August 2000-31st July 2002), 8,036 febrile children were screened for malaria at the hospital. The data for 7,980 of these children were analysed: 4,368 male (54.5%) and 3,612 female (45.5%). Seventy-five percent these were less than 5 years of age. 3,156 (39.3%) of the 8,036 febrile children screened in the MCRU had a positive blood film for P. falciparum. A lower prevalence of malaria was seen in children aged< 6 months (3.7%, n = 118, p < 0.001). Specific prevalence of malaria rises after the 6 first months of life until it reaches a maximum at 47 months (47.5%), after which it declines again. Severe anaemia was most frequent in children less than 24 months old with 68 % of the cases of severe malarial anaemia occurring before this age. In contrast, the highest specific prevalence of cerebral malaria was found in children aged > 12 months. Sixty five percent of cases of cerebral malaria occurred in children aged between 12–48 months. Characteristics of severe malaria The admission clinical, laboratory and parasitological characteristics of the 583 children with severe malaria are shown in the Figure 2. Two hundred and ninety nine of the severe malaria cases were male (51.3%) and 536 (92%) of the children with severe malaria were less than 5 years old. A history of vomiting, seizures and anti-malarial treatment before admission were reported in 322 (55.2%), 267 (45.8%) and 315 (54%) of the children with severe malaria respectively. Despite significant fluctuations in rainfall, the number of malaria cases per month was sustained during the study period, confirming the perennial transmission of malaria in this region. Figure 2 Admission characteristics of the study population (All results are mean ± SD except those specified. Severe anaemia was the commonest feature of severe malaria present in 395 (67.8%,) of the children. Neurological presentations (either coma or repeated convulsions) were present in 228 (39.2%) of the children. Respiratory distress occurred in 181 (31%) of the children ; hyperlactataemia in 73 (15.7%) and hypoglycaemia in 33 (6.2%) of the children with severe malaria. Hyperparasitaemia (> = 20% of circulating infected red blood cells) was relatively rare, occurring in only 24 (4.1%) of the children with severe malaria. Renal failure, acute pulmonary oedema and spontaneous bleeding are uncommon complications of childhood malaria [5,9,10] and were not seen in this study. Circulatory collapse was not found alone in this study and was not considered in the statistical analysis. One hundred and forty six (25%) children with severe malaria were afebrile on admission to hospital. Case rate fatality Figure 3 shows a Venn diagram summarizing clinical and laboratory features of severe malaria and case fatality rates. Figure 3 Venn diagram of features of 463 cases with a complete dataset of clinical measures. Case fatality rates shown in brackets. Of 583 patients, 52 died (40% male), giving an overall case fatality rate of 8.9%. Seven children were lost to follow-up. Forty seven deaths (90%) occurred within the first 24 hours after admission. Neurological sequelae were present in 27 (5 %) of the 531 survivors. The case fatality rate was significantly higher in females than males (10.9% vs.6.9%, p = 0.006). Children who died were older than those who survived (mean (SD) = 35.4 (41.9) vs. 25.0 (18.2) months, p = 0.0009). Of the 52 deaths in the course of the study, 32 (61.5%) presented with cerebral malaria, 32 (61.5%) presented with respiratory distress, and 20 (50% of the 40 with measurements) had hyperlactataemia. Thirty (60%) of the children who died presented with convulsions, and 13 (25%) with hypoglycaemia. Twenty four (46.2%) of those who died had severe anaemia. Mortalities in each sub-group demonstrated that hyperparasitaemia (1 death in 24, 4%) and severe anaemia (24 deaths in 395, i.e. 6%) had a better prognosis than cerebral malaria (32 deaths in 142, i.e. 22.5%) and hypoglycaemia (15 deaths in 60, i.e. 25%). Thirty-two (17.7%) of the 181 patients with RD died, as did 30 (11%) of the 264 patients with convulsions and 20 (27.4 %) of the 73 patients with hyperlactataemia. A multiple logistic regression model identified coma (OR = 3.6, 95% CI = 1.8–7.1, p < 0.001), hyperlactataemia (OR = 6.98, 95% CI = 3.5–13.8, p = 0.0001), respiratory distress (OR = 2.0, 95% CI = 1.0–3.9, p = 0.033) and hypoglycaemia (OR = 4.0, 95% CI = 1.7–9.4, p = 0.001) as independent predictors of a fatal outcome. Severe anaemia, hyperparasitaemia and thrombocytopaenia were not shown to be predictors of death (Figure 4). Figure 4 Prognostic indicators at the time of admission. OR – odds ratio, SD – standard deviation. Discussion Malaria remains a serious health problem in sub-Sahara Africa. It was the most common reason for neurological emergencies during 2001 at the CHL [7]. This study was designed to describe the epidemiology, clinical and laboratory presentations of severe falciparum malaria in childhood presenting to CHL, in order to improve the diagnosis, classification and appropriate management of malaria. It is not possible to exclude absolutely all children with alternative diagnoses on clinical examination and simple investigation alone, which is a problem shared by all other similar studies on severe malaria [3-5,9,10,14,16-18]. The small numbers with alternative diagnoses should not affect the conclusions of this or other studies. Most cases (92%) of severe malaria were in children less than 5 years old. Similar observations have been made in another group of children hospitalized for malaria in Gabon [19]. Elsewhere, severe malaria tends to occur in older children [20]. Differences in the age of presentation of severe malaria may be the result of lower background immunity or other undefined variables [21]. The study confirms the stable and perennial transmission of malaria in Gabon, which contrasts with reports from other countries in West Africa where malaria transmission is predominantly at the end of the long rainy season [22,23]. Fever is a characteristic feature of P. falciparum infection, but a sizeable proportion of these children (25%) with severe malaria were afebrile on admission as observed elsewhere [23]. Self-medication with antipyretic or antimalarial agents was common (about 50% of the children) and may contribute to this finding. There are obvious implications for the diagnosis of malaria, which may be underestimated using clinical criteria alone. Severe anaemia was the most frequent feature of severity in this study, but was associated with decreased mortality. A similar observation in a recent Ghanaian study showed a better outcome in children with severe anaemia [17]. These findings confirm that severe malaria anaemia has a lower case fatality rate than other complications of severe malaria, consistent with several other studies where severe anaemia was not an independent predictor of in-hospital mortality [9,10,18]. The case fatality rate of severe anaemia without other markers of severe malaria is 1 to 2%, where blood transfusion is available [3,9,10,14,18] raising questions about the value of severe anaemia as a defining feature of the syndrome of severe malaria. Despite the increasing toll of HIV infection, and the continuing burden of diarrhoeal disease, malnutrition and respiratory tract infections, malaria remains a major cause of childhood death in endemic regions [1]. The overall case fatality rate of severe malaria in the study was 8.9% (52 deaths/583 cases), which is in keeping with studies from other geographic areas, where case fatality rates range between 8 and 40% [4,5,9,14,16,20,22,24,25]. Most of these deaths (90% in this study) occurred in the first twenty-four hours of hospital admission, a finding also in keeping with other studies [5]. The independent prognostic indicators in this study were cerebral malaria, respiratory distress, hypoglycaemia and hyperlactataemia. These observations are entirely consistent with a large number of studies where the independent predictors of a fatal outcome in malaria are impaired consciousness and metabolic dysfunction (as measured by hyperlactataemia, hypoglycaemia, acidosis or respiratory distress) [3,5,9,10,14,16-18]. The metabolic complications of malaria are complex and a number of interrelated measures have been used in different studies. Severe malaria is associated with a metabolic acidosis [16] and hyperlactataemia [5]. Respiratory distress has been associated with acidosis and hyperlactataemia in some studies [26]. These features of metabolic malaria probably all result from increased anaerobic metabolism due to tissue hypoxia [27]. Estimates of the prevalence of hypoglycaemia have been reported in Africa, ranging from 8% to 34% [28,29]. In severe childhood malaria hypoglycaemia results from impaired gluconeogenesis and increased tissue demand for glucose [27,28] and quinine induced hyperinsulinaemia. Blood glucose concentrations should be monitored in all children hospitalised for malaria especially those who receive quinine. The definition of hyperlactataemia used in this study was a blood lactate concentration higher than the conventional cut-off (≥ 5 mmol/L). This was necessary because of the limitations of the analyser used, but probably means that the frequency of true hyperlactataemia was underestimated. The Accusport™ analyser used has been shown to have poor agreement with "gold standard" machines [11,15,30]. Hyperlactataemia is a frequent and serious complication of severe malaria in childhood [5,9,10], which may be due microcirculatory sequestration of parasitized erythrocytes resulting in increased production of lactate by anaerobic glycolysis [31]. A recent study showed a correlation between hyperlactataemia and high plasma glutamine levels in severe malaria. This correlation may reflect impaired gluconeogenesis [31]. Lactate disposal is proportional to blood lactate concentration and can be increased by dichloroacetate [27,32]. Lactic acidosis, as confirmed in this study, is an established strong predictor of a fatal outcome in falciparum malaria in African children [5,9,10] and may prove a target for further interventional studies to improve survival. Respiratory distress was present in 31% of these children. This is higher than the frequency reported in other studies of severe malaria: 4.9% in Burkina [20], 6.4% in Togo [22] and 13.7% in Kenya [14]. These differences may partly be explained by low inter-observer agreement for this variable, geographical variations in disease pattern as well as differing definitions of severe malaria. Results from many studies consistently show that respiratory distress is a life-threatening syndrome in childhood malaria [14,33]. Respiratory distress was significantly associated with both hyperlactataemia and cerebral malaria in this study. The Blantyre coma score has long been established in children as a good indicator of cerebral dysfunction in malaria [3] and has enabled better standardization of studies on cerebral malaria in African children. The case-fatality rate associated with cerebral malaria (22%) is similar to that in Gambian children (27%) [4] but is higher than that observed for Kenyan (17%) [14] and Malawian children (15%) [3]. It has been postulated that with the higher the level of malaria transmission, immunity is acquired earlier, perhaps altering the presentation of severe malaria from predominantly a cerebral syndrome to that of severe anaemia [34,35]. The clinical and laboratory presentations of severe malaria are described in a hospitalized population of children in Gabon. The severe cases are likely to be only the "tip of the iceberg", many children living far from health care units may die whilst travelling to the nearest hospital. Most deaths from malaria occurred in the first 24 hours of admission, which highlights the need for early recognition of the most severely ill children. Early diagnosis and classification of severe malaria would allow appropriate management, including basic adjunctive therapy such as to prevent hypoglycaemia, and better use of scarce healthcare resources. Together these improvements could contribute to a reduction in the intractably high mortality due to the disease. Authors' contributions AD is a MCRU clinician. He participated to the study and wrote the article. PN conducted the study for his MD thesis. RT and MB are paediatricians who participated in the study. TP is a UK collaborator who participated in the study and helped write the article. MM was involved with the intensive care of the children. UMR participated in the study as a MCRU clinician. JJ helped to write the article. EK did all the statistical analysis. ENM, PK, SK and MK coordinated the realization of the study and edited the final version approved by all authors. Acknowledgements We would like to thank the medical, nursing, and laboratory staff of the Centre Hospitalier de Libreville especially Miss Flore Moussavou, Dr. Josseaume, Dr. Egonhan, Prof. Ngaka, Prof. R. Tchoua for advice and aid in conducting this study. We would also like to thank Dr I. Oye Ondo, Dr. B. Okissi, Dr. MC Memvie, M. 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==== Front BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-6-11564931810.1186/1471-2121-6-1Research ArticleThe tumor suppressor Scrib interacts with the zyxin-related protein LPP, which shuttles between cell adhesion sites and the nucleus Petit Marleen MR [email protected] Sandra MP [email protected] Philippe [email protected] Torik AY [email protected] Erik [email protected] de Ven Wim JM [email protected] Laboratory for Molecular Oncology, Department of Human Genetics, University of Leuven & Flanders Interuniversity Institute for Biotechnology (VIB), Herestraat 49, B-3000 Leuven, Belgium2005 13 1 2005 6 1 1 10 9 2004 13 1 2005 Copyright © 2005 Petit et al; licensee BioMed Central Ltd.2005Petit et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background At sites of cell adhesion, proteins exist that not only perform structural tasks but also have a signaling function. Previously, we found that the Lipoma Preferred Partner (LPP) protein is localized at sites of cell adhesion such as focal adhesions and cell-cell contacts, and shuttles to the nucleus where it has transcriptional activation capacity. LPP is a member of the zyxin family of proteins, which contains five members: ajuba, LIMD1, LPP, TRIP6 and zyxin. LPP has three LIM domains (zinc-finger protein interaction domains) at its carboxy-terminus, which are preceded by a proline-rich pre-LIM region containing a number of protein interaction domains. Results To catch the role of LPP at sites of cell adhesion, we made an effort to identify binding partners of LPP. We found the tumor suppressor protein Scrib, which is a component of cell-cell contacts, as interaction partner of LPP. Human Scrib, which is a functional homologue of Drosophila scribble, is a member of the leucine-rich repeat and PDZ (LAP) family of proteins that is involved in the regulation of cell adhesion, cell shape and polarity. In addition, Scrib displays tumor suppressor activity. The binding between Scrib and LPP is mediated by the PDZ domains of Scrib and the carboxy-terminus of LPP. Both proteins localize in cell-cell contacts. Whereas LPP is also localized in focal adhesions and in the nucleus, Scrib could not be detected at these locations in MDCKII and CV-1 cells. Furthermore, our investigations indicate that Scrib is dispensable for targeting LPP to focal adhesions and to cell-cell contacts, and that LPP is not necessary for localizing Scrib in cell-cell contacts. We show that all four PDZ domains of Scrib are dispensable for localizing this protein in cell-cell contacts. Conclusions Here, we identified an interaction between one of zyxin's family members, LPP, and the tumor suppressor protein Scrib. Both proteins localize in cell-cell contacts. This interaction links Scrib to a communication pathway between cell-cell contacts and the nucleus, and implicates LPP in Scrib-associated functions. ==== Body Background At the heart of structural and functional integrity of multicellular entities is the ability of each and every cell of it to successfully integrate signals arising from soluble factors, cell-substratum adhesion and cell-cell adhesion [1]. Correct processing of these signals allows appropriate cellular growth, differentiation, and tissue morphogenesis, but malfunctions often lie at the basis of pathologies such as tumor growth and metastasis. At sites of cell adhesion, more and more proteins are being identified that not only play a role in maintaining cell shape and motility but that, in addition to these structural functions, are also implicated in signaling events. Because of this dual function, these proteins have to interact, via multiple binding motifs, with components of both the actin cytoskeleton and signaling pathways that regulate e.g. gene expression. A protein that may play a role in these processes is the LPP (Lipoma Preferred Partner) protein [2]. LPP is a member of the zyxin family of LIM domain proteins, which consists of five members: zyxin [3], TRIP6 (Thyroid Receptor Interacting Protein 6) [4], LPP [2], ajuba [5] and LIMD1 (LIM Domain containing 1) [6]. LPP has three LIM domains (zinc-finger protein interaction domains) at its carboxy-terminus, which are preceded by a proline-rich pre-LIM region containing a number of protein interaction domains (Fig. 1A). LPP has been shown to localize at sites of cell adhesion, such as focal contacts, which are membrane attachment sites to the extracellular matrix, and cell-cell contacts. However, apart from its localization in cell adhesion sites, this protein has also been shown to localize transiently in the nucleus. Because of its structural features and its characteristic to shuttle between the nucleus and the cytoplasm, LPP has been proposed to be a scaffolding protein involved in signal transduction from sites of cell adhesion to the nucleus. Figure 1 Schematic representation of human LPP and Scrib proteins. (A) Schematic representation of the human LPP protein. Human LPP contains a proline-rich pre-LIM region followed by three tandem LIM domains. In its pre-LIM region, LPP harbors one nuclear export signal, two VASP binding sites and one α-actinin binding site. Furthermore, LPP has two regions in common with its family member TRIP6 and one region with its family members zyxin, TRIP6 and LIMD1. At its carboxy-terminus, LPP has a binding site for Scrib. (B) Schematic representation of the human Scrib protein human Scrib is a 1630 amino acid protein that contains 16 leucine-rich repeats (LRR) followed by 2 domains that are specific for the LAP family of proteins (LAP-specific domain (LAPSD) a and b), and 4 PDZ domains. The corresponding position of the mouse Scrib prey clone that was isolated in a yeast-two hybrid screen using LPP as bait is indicated. The amino acid sequence of the PDZ domains of mouse Scrib was compared to that of human Scrib, and percentage identity is indicated for each PDZ domain. Originally, we identified the LPP gene, as being the preferred translocation partner of the HMGA2 gene in a subgroup of lipomas, which are benign tumors of adipose tissue [2]. In these tumors, HMGA2/LPP fusion transcripts are expressed and identical fusion transcripts have also been found in a subgroup of pulmonary chondroid hamartomas [7], in a parosteal lipoma [8], and in a soft tissue chondroma [9]. In a case of acute monoblastic leukemia, the LPP gene acts as translocation partner of the MLL gene and the tumor expresses MLL/LPP fusion transcripts [10]. All tumor-specific fusion transcripts that are expressed in the above mentioned tumors encode similar LPP fusion proteins containing AT-hooks (DNA binding domains) of the HMGA2 or MLL proteins followed by LIM domains of LPP. These fusion proteins are mainly expressed in the nucleus [11]. At cell adhesions, the LPP protein interacts with α-actinin and VASP (vasodilator-stimulated phosphoprotein) via its pre-LIM region that contains an α-actinin binding site located near its N-terminus and two VASP-binding ("FP4")-motifs (Fig. 1A) [11,12]. In the nucleus, LPP has transcriptional activation capacity in reporter gene assays suggesting that it is involved in the regulation of gene expression [11]. The nucleocytoplasmic distribution of this protein involves a nuclear export signal (NES) that also resides in the pre-LIM region (Fig. 1A) [11]. Recently, we have shown that the LIM domains of LPP cooperate to target the protein to focal adhesions, and that the linker between LIM domains 1 and 2 plays a pivotal role in this targeting [13]. When overexpressed in the cytoplasm of cells, these LIM domains deplete endogenous LPP and vinculin from focal adhesions suggesting a role for LPP in focal adhesion assembly [13]. Recently, LPP was found to be highly expressed in smooth muscle [14,15], and a role for LPP in regulating cell motility was proposed [14]. In an effort to learn more about the molecular function of LPP, we performed a yeast two-hybrid screening experiment to identify potential LPP-interacting proteins. Here, we report that LPP interacts with Scrib, a member of the LAP (leucine-rich repeat and PDZ domain) family of proteins [16]. Scrib is a functional homologue [17] of Drosophila Scribble, a tumor suppressor that plays a role in the regulation of cellular adhesion, cell shape and polarity [18,19]. In follow-up of the results of the yeast two-hybrid screening, we have performed various experiments to find out whether the observed interaction also occurs in mammalian cells and have substantiated this interaction in vitro and in vivo. Furthermore, we have studied whether or not the Scrib protein plays a role in the subcellular targeting of LPP. Results Screening for LPP-interacting proteins by yeast two-hybrid In a previous study [13], we showed that the LIM domains of LPP are the major units for targeting LPP to focal adhesions. LIM domains are cysteine- and histidine-rich domains that form two zinc fingers capable of mediating protein-protein interactions [20,21]. However, the protein(s) that is/are responsible for the targeting of LPP to focal adhesions, i.e. protein(s) that bind(s) to the LIM domains of LPP, are not yet known. To identify protein binding partners of the LIM domains of LPP, we performed a yeast two-hybrid screening experiment. We made use of a yeast two-hybrid system that is based on transcriptional activation of two reporter genes HIS3 and LacZ whose expression is driven by upstream GAL4 DNA-binding sites. Because all three LIM domains of LPP cooperate to target LPP to focal adhesions [13], we initially focused on a screening using a bait that contained all three LIM domains. Unlike in mammalian cells, where we have shown that the three LIM domains of LPP have transcriptional activation capacity [11], this bait, although well expressed, did not activate the reporter genes in yeast cells (results not shown). This is similar to what has been found for zyxin's LIM domains [22], but in contrast to what has been found for the three LIM domains of TRIP6 that do activate reporter genes in yeast [22]. However, the bait containing all three LIM domains of LPP appeared to be very sticky since thousands of yeast colonies were obtained in which both reporter genes were activated. In an effort to reduce background activity, we deleted the first LIM domain, or the first and the second LIM domain, in the bait, leaving the two most carboxy-terminal, or the most carboxy-terminal LIM domain(s) intact, respectively. These deletions completely abolished all background activity making these baits the baits of choice to perform a library screening. Here, we report about the screening that was performed with the bait containing only the most carboxy-terminal LIM domain of LPP. As described before [13], we showed that the third LIM domain of LPP only has a very weak targeting capacity for focal adhesions. This makes it very unlikely that, by using this bait, we would pick up a protein that targets LPP to these structures, which was the initial goal of our studies. Indeed, our screening did not reveal any focal adhesion binding partners of LPP, however, in stead, we found another very interesting LPP-interacting protein as will be outlined in the following sections. A mouse embryonal cDNA library was screened using a bait (pGBT9-LPPWT) containing the third LIM domain and carboxy-terminus of human LPP (amino acids 531–612). Among ~1.0 × 106 yeast cotransformants (Leu+ and Trp+), 56 clones were His+ of which 23 were LacZ+ too. PCR analysis of these His+/LacZ+ clones, using prey-specific insert-flanking primers, revealed that 21 of the 23 obtained clones, contained a prey-construct having a 2 kb cDNA insert (results not shown). Subsequent fragmentation of the obtained 2 kb PCR products, representing the cDNA inserts of the prey-constructs, using the HaeIII restriction enzyme (frequent cutter), indicated that all 21 isolated prey-constructs, having a 2 kb insert, were identical. The 2 kb cDNA insert of one representative prey-construct was completely sequenced and the sequence was submitted to the NCBI database (Genbank accession no. AF271735). A BLAST (Basic Local Alignment Search Tool)-search revealed that this mouse prey-construct encoded an amino- and carboxy-terminally truncated protein comprising four PDZ domains that was almost identical to the human Scrib protein (Fig. 1B), indicating that the prey-construct represented mouse Scrib. The Scrib protein contains a set of 16 leucine-rich repeats (LRRs) near its amino-terminus and four PDZ (PSD-95, Discs large, ZO-1) domains distributed throughout the remainder of the protein (Fig. 1B). The partial mouse Scrib protein, expressed by the prey-construct, corresponded to amino acids 709 – 1242 of human Scrib (Fig. 1B). Further analysis indicated that the isolated prey-construct, which was named pACT2-mScrib, activated the HIS3 and LacZ reporter genes of the yeast only in the presence of pGBT9-LPPWT, identifying pACT2-mScrib as a true positive (Table 1, upper three rows). Table 1 Interaction of LPP with Scrib in the yeast two-hybrid system BAIT PREY HIS LACZ pGBT9 pACT2-mScrib - - pGBT9-LPPWT pACT2 - - pGBT9-LPPWT pACT2-mScrib + + pGBT9-LPPS609A pACT2 - - pGBT9-LPPS609A pACT2-mScrib + + pGBT9-LPPT610A pACT2 - - pGBT9-LPPT610A pACT2-mScrib - - pGBT9-LPPD611A pACT2 - - pGBT9-LPPD611A pACT2-mScrib + + pGBT9-LPPL612A pACT2 - - pGBT9-LPPL612A pACT2-mScrib - - Yeast cells (CG-1945), cotransformed with a bait and a prey as indicated, were selected on medium containing 5 mM 3-AT, lacking Trp, Leu and His. Yeast colonies were tested for the expression of β-galactosidase. + indicates strong positive interaction; - indicates no interaction. LPP binds to the PDZ domains of Scrib via its C-terminal tail Since the pACT2-mScrib prey-construct contained four PDZ domains, and since PDZ domains are one of the most commonly found protein-protein interaction domains in organisms from bacteria to humans [23], it was most likely that Scrib would bind to LPP via its PDZ domains. The LPP-bait that was used to screen the library was pGBT9-LPPWT containing the third LIM domain and carboxy-terminus of human LPP. Although PDZ domains have been shown to bind LIM domains [24], binding to carboxy-terminal peptides appears to be the typical mode of interaction [25]. The common structure of PDZ domains comprises six β strands (βA-βF) and two α helices (αA and αB), which fold in an overall six-stranded β sandwich [25]. The binding specificity of PDZ domains is critically determined by the interaction of the first residue of helix α B (position αB1) and the side chain of the -2 residue of the C-terminal ligand. This forms the basis for PDZ classification [25]. Since all four PDZ domains of Scrib contain a histidine at position αB1, they are classified as class I PDZ domains. Therefore, based on what has been demonstrated for this subclass of PDZ domains [25,26], the carboxy-terminal sequence of Scrib target proteins is predicted to require a hydrophobic amino acid (h) at the 0 (carboxy-terminus) position, and a serine (S) or threonine (T) at the -2 position. Theoretically, the carboxy-terminus of the LPP protein, being -STDL, thus completely fulfils the criteria for binding to the PDZ domains of Scrib. To evaluate these predictions experimentally and to demonstrate that the binding of LPP to Scrib is specific, we performed yeast two-hybrid experiments using pGBT9-LPPWT as well as pGBT9-LPPS609A, pGBT9-LPPT610A, pGBT9-LPPD611A and pGBT9-LPPL612A as bait. The last four baits are identical to pGBT9-LPPWT except for a point mutation to alanine, respectively introduced at serine609 (-3 position), threonine610 (-2 position), aspartate611 (-1 position) and leucine612 (position 0). As prey, we used pACT2-mScrib. As summarized in Table 1, this alanine-scan mutant analysis identified threonine610 (-2 position) and leucine612 (0 position) of LPP as being essential for binding to Scrib indicating a PDZ domain-mediated specific interaction between Scrib and the carboxy-terminus of LPP. Additional yeast two-hybrid analysis showed that LPP did not interact with Erbin, PICK1, PSD-95, Syntenin, CASK, or AF6 PDZ domains, as summarized in Table 2. Table 2 Interaction of LPP with PDZ domains of proteins different from Scrib BAIT PREY HIS -HIS pGBT9-LPPWT Scrib-PDZ + + Erbin-PDZ + - PICK1-PDZ + - PSD95-PDZ + - Syntenin-PDZ + - CASK-PDZ + - AF6-PDZ + - Yeast cells, cotransformed with pGBT9-LPPWT and a pACT2-prey as indicated, were selected on medium lacking Leu and Trp, and either containing His or no His with 5 mM 3-AT. + indicates growth of yeast transformants; - indicates no growth of yeast transformants. LPP interacts with Scrib PDZ domains in mammalian cells We verified the Scrib-LPP interaction, which was identified in yeast cells, in mammalian two-hybrid experiments. Doing the assay in mammalian cells rather than in yeast cells, provides a more physiological environment: proteins are more likely to be in their physiological configuration, i.e. appropriately folded and modified posttranslationally, etc. Interaction between bait- and prey-proteins in a mammalian two-hybrid assay takes place in the nucleus. For an accurate performance of this assay, this means that bait- and prey-proteins should be localized in the nucleus. In contrast to the yeast assays, where we used partial bait-proteins, we wanted to use full length bait-proteins in the mammalian assay. However, since LPP contains a nuclear export signal (NES) (amino acids 117–128) in its pre-LIM region [11], we used bait-proteins in which this NES had been deleted. To verify whether deletion of the NES in LPP induced nuclear accumulation of the bait-proteins that were used in the mammalian two-hybrid assay, we introduced wild-type and mutated LPP-bait-proteins in 293T cells. While pM-LPPWT, containing GAL4-fused full length wild-type human LPP with an intact NES, was excluded from nuclei, pM-LPPdNESWT, containing GAL4-fused full length human LPP with a deletion of the NES, was accumulating in the nuclei of the cells (results not shown). These results indicate that deletion of the NES in the LPP bait proteins used in this study indeed induce nuclear accumulation of these proteins. To perform the mammalian two-hybrid experiments, we used as baits: pM-LPPdNESWT, containing full length human LPP with a deletion of its NES, and pM-LPPdNEST610A and pM-LPPdNESL612A, which are identical to pM-LPPdNESWT except for a point mutation to alanine introduced at threonine610 (position -2) and leucine612 (position 0), respectively. As determined in the yeast two-hybrid assay, each of the threonine610 and leucine612 residues is critical for the interaction with Scrib. As prey-protein, we used pSNATCH-hScribPDZ containing a part of the human Scrib protein (amino acids 669–1233) encompassing all four PDZ-domains. As summarized in Fig. 2, the interaction between wild-type full length LPP and Scrib PDZ domains resulted in high levels of luciferase reporter activity. These high levels dropped to background levels when pM-LPPdNEST610A or pM-LPPdNESL612A were used as baits in combination with Scrib as prey. The "background" levels of luciferase that were detected when pM-LPP-baits were used in combination with pSNATCH (empty prey-vector) as prey, are due to the intrinsic transcriptional activation activity of the LPP protein [11]. Figure 2 Scrib interacts with LPP in mammalian cells. pM-bait- and pSNATCH-prey-constructs were cotransfected into 293 cells in the combination indicated, together with a GAL4-regulated luciferase reporter and a CMV-β-galactosidase internal control. Cell lysates were assayed for luciferase activity 18–24 hours after transfection. Relative luciferase activity is reported as the average of three independent duplo experiments (with standard error). These results indicate that LPP binds to Scrib PDZ domains and that this binding is abolished when amino acids at position 0 or -2 are mutated. Development and characterization of Scrib antibodies To analyze expression and intracellular distribution of Scrib in cultured cells, we prepared a Scrib-specific antibody (Scrib-472), as described in the Methods section. The Scrib-472 antibody recognized a protein of an apparent molecular mass of more than 200 kDa in a number of different cell extracts (Fig. 3A). Scrib was easily detected in the epithelial cell lines 293 and MDCKII, in the fibroblast cell line CV-1, and also in the T lymphocyte cell line Jurkat (Fig. 3A). These results indicate that our antibody recognizes Scrib-proteins of different species, being human (Jurkat and 293), monkey (CV-1) and dog (MDCKII). The Scrib-472 antibody also reacted with an Xpress-hScrib fusion protein produced in 293T cells transfected with the corresponding DNA (Fig. 3B). In Fig. 3B, lane 2, which depicts a Western analysis of untransfected 293T cell lysate with Scrib-antibodies, no band of endogenous Scrib is seen. Longer exposure, however, did show a band indicating that Scrib is expressed in these cells, however, 293T cells express much lower levels of endogenous Scrib as compared to 293 cells (our unpublished observations). The Scrib protein was migrating slower in SDS gels than would be expected from its theoretically calculated molecular mass (175 kDa). Possible explanations include anomalous migration per se, and posttranslational modifications. To investigate whether the Scrib-472 antibody not only recognizes denatured Scrib protein on Western blots but also is capable of detecting Scrib in fixed cells, MDCKII cells were grown to confluency on glass coverslips and stained with the Scrib-472 antibodies. From previous studies, it is known that Scrib is localized in cell-cell contacts [17]. As shown in Fig. 3C, the Scrib-472 antibody indeed is capable to detect native Scrib in cell-cell contacts in fixed cells. Figure 3 Characterization of anti-Scrib antibodies. (A) Total cell extracts were prepared from the following cell lines: human embryonic kidney epithelial cells (293) (lane 1), dog normal kidney epithelial cells (MDCK) (lane 2), human T lymphocytes (Jurkat) (lane 3), and African green monkey kidney fibroblast cells (CV-1) (lane 4). Approximately 30 μg of protein from each extract was analysed by SDS-PAGE and Western blotting with the Scrib-472 antibodies. The position of molecular markers are as shown. (B) Total cell extracts of 293T cells, either not transfected (lane 2), or transiently transfected with Xpress-hScrib that is composed of the full length human Scrib protein fused to an Xpress-epitope-tag at its amino-terminus (lanes 1 and 3) were analyzed by SDS-PAGE and Western blotting with an anti-Xpress antibody (lane 1) or with the Scrib-472 antibody (lanes 2 and 3). The position of molecular markers are as shown. (C) MDCKII cells, grown on glass coverslips, were fixed and stained with Scrib-472-antibodies. Immunofluorescence was visualized by epifluorescence microscopy. Scrib is not localized in focal adhesions in CV-1 and MDCKII cells, and is dispensable for targeting LPP to these structures We have shown before that LPP is localized in cell-cell contacts [11] and also for human Scrib, it was shown that it is localized in these structures [17] (also shown in Fig. 3C and 5, upper right panel). Since LPP is not only localized in cell-cell contacts but also in focal adhesions [11,13], we investigated whether also Scrib had the ability to localize at these structures. For this purpose, we used two different cell lines: the epithelial cell line MDCKII and the fibroblast cell line CV-1. However, in contrast to LPP, Scrib could not be detected in focal adhesions as shown by staining CV-1 cells with Scrib-472 antibodies (Fig. 4, upper left panel). Identical results were obtained in MDCKII cells (results not shown). Focal adhesions were indeed present, as these structures could be stained using vinculin antibodies used as a marker for focal adhesions (CV-1 cells: Fig. 4, upper right panel; MDCKII cells: results not shown). If Scrib had been present in focal adhesions, we would have detected it there, because, as shown in Fig. 3A, Scrib is highly expressed in CV-1 as well as in MDCKII cells, and as shown in Fig. 3C, Scrib-472 antibodies are able to detect Scrib in its native conformation in fixed cells. Moreover, a hScrib-GFP protein expressed in CV-1 or MDCKII cells was never detected in focal adhesions (results not shown) but was localized in cell-cell contacts (MDCKII cells: Fig. 5, lower left panel). The nature of the nuclear staining observed in CV1-cells stained with the Scrib-472 antibody (Fig. 4, upper left panel) is aspecific, as it is also obtained with the corresponding pre-immuneserum. In addition, nuclear staining was never obtained when an hScrib-GFP protein was transiently overexpressed in these cells (results not shown). Nuclear staining was also not detected in MDCKII cells as shown in Fig. 3C and 5, upper right panel. These results indicate that, in contrast to LPP, which is localized both in focal adhesions and in cell-cell contacts in CV-1 and MDCKII cells, Scrib is only localized in cell-cell contacts but not in focal adhesions in these cells. Figure 4 Scrib is not localized in focal adhesions in CV-1 cells, and is dispensable for targeting LPP to these structures. Upper panels: CV-1 cells, grown on glass coverslips, were double labelled with Scrib-472 antibodies (left panel) and anti-vinculin antibodies (right panel) used as a marker for focal adhesions. Lower panels: CV-1 cells were transiently transfected with wild-type human LPP (left panel), or LPP with a mutated carboxy-terminus (T610A) (right panel), as GFP-fusions. GFP-fluorescence was visualized by epifluorescence microscopy. Figure 5 Scrib and LPP are localized in cell-cell contacts but are dispensable for targeting each other to these structures. Upper panels: MDCKII cells, grown on glass coverslips, were double labelled with anti-LPP antibodies (left panel) and anti-Scrib antibodies (right panel). Lower panels: MDCKII stable cell lines, expressing GFP-fusion proteins containing wild-type human LPP (upper left panel), LPP with a mutated carboxy-terminus (T610A) (upper right panel), human wild-type Scrib (lower left panel), or Scrib with a deletion of all its PDZ domains (lower right panel), were grown on glass coverslips (Scrib) or on Transwell-Clear polyester membranes (LPP). GFP-fluorescence was visualized by epifluorescence microscopy (Scrib) or by confocal microscopy (LPP). As deduced from these results, we hypothesized that Scrib was not involved in targeting LPP to focal adhesions. Indeed, evidence for this hypothesis was obtained by transfecting CV-1 cells with a construct expressing GFP-LPPWT containing full length wild-type LPP, or GFP-LPPT610A, which is identical to GFP-LPPWT except for a point mutation to alanine introduced at threonine610, which abolishes binding to Scrib. No difference in focal adhesion localization could be detected between wild-type and mutated GFP-LPP fusion proteins (Fig. 4, lower panels). Scrib and LPP are dispensable to target each other to cell-cell contacts Since Scrib and LPP both localize in cell-cell contacts [11,17] (Fig. 5, upper panels), we investigated whether Scrib was responsible for targeting LPP to cell-cell contacts. For this, we made stable MDCKII cell lines expressing wild-type and mutated GFP-coupled forms of the LPP protein, of which the mutant form is not able to bind anymore to Scrib. However, as shown in Fig. 5, lower panels, LPP proteins that could not bind to Scrib anymore were still able to localize in cell-cell contacts in a similar way as their wild-type counterparts. These results indicate that Scrib is not responsible for targeting LPP to cell-cell contacts. We next investigated whether LPP was responsible for targeting Scrib to cell-cell contacts. To look into this aspect, we made stable MDCKII cell lines expressing either wild-type full length Scrib-GFP or a mutated Scrib-GFP protein lacking all four PDZ domains (deletion of amino acids 725–1227). However, both the full length Scrib-GFP protein as well as the mutated form lacking all four PDZ domains localized equally well in cell-cell contacts (Fig. 5, lower panels). These results indicate that the PDZ domains of Scrib are dispensable for targeting the protein to cell-cell contacts, and as a consequence LPP is not necessary to locate Scrib in cell-cell contacts. In summary, these results indicate that LPP and Scrib are dispensable to target each other to cell-cell contacts. There is a direct interaction between the carboxy-terminus of LPP and the PDZ domains of Scrib To further assess the binding between LPP and Scrib, we investigated whether there is a direct interaction between these two proteins. For this, we performed GST pull-down experiments. In vitro translated full length Scrib was tested for binding with glutathione beads, which were coupled with GST-LPP-LTWT, GST-LPP-LTL612A, or GST alone. GST-LPP-LTWT contains 40 amino acids of the pre-LIM region, the three LIM domains, and the wild-type carboxy-terminal tail of human LPP. GST-LPP-LTL612A is identical to GST-LPP-LTWT except for a point mutation to alanine introduced at leucine612 (position 0). All GST-fusion proteins as well as GST alone were expressed well in E. coli (Fig. 6A). As shown in Fig. 6B, Scrib interacted specifically with the wild-type LPP protein but not with its mutated form, GST-LPP-LTL612A or with GST alone. These results indicate that there is a specific and direct interaction between LPP and Scrib. Figure 6 Direct interaction between the carboxy-terminus of LPP and the PDZ domains of Scrib. (A) GST fused to either wild-type LPP (40 amino acids of the pre-LIM region, the three LIM domains and the tail), or a similar LPP molecule with a mutated carboxy-terminus (L612A) and GST alone were expressed in E. coli, purified and analyzed by SDS-PAGE and Coomassie Blue staining. All proteins were expressed well. Protein markers are as indicated. (B) In vitro synthesized [35S]-methionine-labelled full length Scrib was incubated with immobilized GST or with either one of the above-described GST fusion proteins and allowed to interact over night at 4°C. After extensive washing, bound proteins were eluted in sample buffer, separated by SDS-PAGE and visualized by autoradiography. The amount of synthesized protein loaded as a reference on the gel corresponds to 10% of the input used in each binding experiment. (C) All four PDZ domains of Scrib (amino acids 616 – 1490), either wild-type or mutated as indicated, were synthesized in vitro and [35S]-methionine-labelled. these labelled proteins were incubated with immobilized GST or with GST-LPP-LTWT and allowed to interact over night at 4°C. Bound proteins were eluted in sample buffer, separated by SDS-PAGE and visualized by autoradiography. The amount of synthesized protein loaded as a reference on the gel corresponds to 10% of the input used in each binding experiment. To further investigate the requirements in the Scrib protein for binding to LPP, we performed additional GST pull-down experiments. From our previously described experiments (yeast and mammalian two-hybrid), it was clear that the PDZ domains of Scrib bind to LPP. These findings were confirmed by using GST pull-down: as shown in Fig. 6C, upper panel, a portion of the Scrib protein encompassing all four PDZ domains was efficiently pulled down by GST-LPP-LTWT. To find out which of the four PDZ domains of Scrib was responsible for the observed interaction with LPP, we mutated the PDZ domains of Scrib, one at the time, by destroying their carboxylate binding loop (LG → AE), and tested how efficiently these mutated proteins were pulled down by GST-LPP-LTWT. From the results, which are presented in Fig. 6C, we can conclude that all four PDZ domains of Scrib more or less contribute to the binding to LPP, but that PDZ 3 is most important, since binding to GST-LPP-LTWT was almost completely abolished when the carboxylate binding loop of this PDZ domain was destroyed. Scrib can target LPP to an ectopic location in vivo through its PDZ domains Evidence for an in vivo interaction between Scrib and LPP was obtained by performing mitochondrial targeting experiments. We tested if Scrib was sufficient to recruit LPP to an ectopic location in living cells. The membrane anchor of the ActA sequence has been shown previously to be sufficient to target proteins expressed in mammalian cells to the surface of mitochondria [27,28]. This ectopic localization allows testing ligand recruitment in vivo. For this purpose, we generated a chimera named Xpress-hScrib-mito made up by an Xpress-epitope tag fused to the amino-terminus of human full length Scrib and linked in frame to the membrane anchor of the Listeria monocytogenes protein ActA (mito). Expression of this construct was confirmed by Western blotting with the use of an anti-Xpress antibody (results not shown). CV-1 cells were transiently transfected with Xpress-hScrib-mito and full length wild-type or carboxy-terminally mutated LPP green fluorescent protein fusions. Cells were stained with an anti-Xpress antibody and examined by fluorescence microscopy. In all transfected cells, the Xpress-hScrib-mito chimera localized to mitochondria, as shown in Fig. 7, left upper and middle panels. As shown in Fig. 7, upper right panels, wild-type LPP can be recruited to Xpress-hScrib-mito on mitochondria. This recruitment of LPP to Xpress-Scrib-mito-coated mitochondria was completely abolished when the carboxy-terminus of LPP was mutated (Fig. 7, middle panels). Figure 7 Scrib can recruit LPP to an ectopic location in vivo through its PDZ domains. CV-1 cells were transiently co-transfected with Xpress-hScrib-mito or Xpress-hScribdPDZ-mito, and GFP-fusions of wild-type full length human LPP, or LPP with a mutated carboxy-terminus (T610A). Xpress-hScrib-mito and Xpress-hScribdPDZ-mito are composed of the human full length Scrib protein with or without its PDZ domains, respectively, which is fused to an Xpress-epitope-tag at its amino-terminus, and to an ActA-derived mitochondrial membrane anchor at its carboxy-terminus, Cells were stained with an anti-Xpress antibody to detect Xpress-Scrib(dPDZ)-mito. Immunofluorescence and GFP were visualized by epifluorescence microscopy. The focal adhesion localization of the GFP-LPP proteins is not visible in these pictures because a focal plane corresponding to mitochondrial staining is shown. To investigate the importance of the PDZ domains of Scrib in this recruitment of LPP, we deleted all four PDZ domains (amino acids 724–1192) from Xpress-hScrib-mito (=Xpress-hScribdPDZ-mito) and tested whether this PDZ-less protein still was able to recruit LPP to mitochondria. As shown in Fig. 7, lower panels, Xpress-hScribdPDZ-mito lost its ability to recruit LPP to mitochondria. These results indicate that Scrib can recruit LPP to an ectopic location in vivo, and that the PDZ domains of Scrib are an absolute requirement for this activity. As mentioned above, the Xpress-hScrib-mito chimera localized to mitochondria in all cells that expressed this protein. However, LPP, which was co-expressed, was only recruited to mitochondria in a small fraction of these cells. This issue will be further addressed in the Discussion section. Discussion In the course of our studies of chromosomal aberrations in benign tumors, we have previously discovered the LPP gene as being rearranged in certain subtypes of these tumors [2], and identified the LPP protein as a member of the zyxin family of proteins [11]. In this study, we report that LPP specifically interacts with Scrib. We provide evidence that this interaction is mediated by the carboxy-terminus of LPP on the one hand, and the PDZ domains of Scrib on the other hand. Futhermore, we show that Scrib is not necessary for targeting LPP to focal adhesions, and that Scrib and LPP are dispensable to target each other to cell-cell contacts. Scrib is a member of the LAP (LRR (leucine-rich repeat) and PDZ (PSD-95/Discs-large/ZO-1)) family of membrane-associated proteins that play a role in the regulation of cell polarity [16]. LAP family members have been identified in mammals (Erbin, Densin-180, Lano, and Scrib) [29-32], in Caenorhabditis elegans (LET-413) [33], and in Drosophila melanogaster (Scribble) [18]. LAP proteins contain a set of leucine-rich repeats (LRRs) at their amino-terminus, and either four (Scrib and Scribble), one (Erbin, Densin-180 and LET-413) or no (Lano) copies of the PDZ domain. A specific characteristic of these proteins are the LAP-specific domains (LAPSa and b), which are located carboxy-terminally of the LRRs [34]. Most information regarding the function of Scrib comes from studies in Drosophila melanogaster. Drosophila Scribble was identified as being required for the apical confinement of polarity determinants in epithelia [18]. Mutations in Scribble cause aberrant cell shapes and loss of the monolayer organization in embryonic epithelia. Scribble is localized in septate junctions and loss of Scribble function results in the misdistribution of apical proteins and adherens junctions to the basolateral cell surface. Subsequent studies in Drosophila provided evidence that Scribble is a tumor suppressor and cooperates with two other tumor suppressors, Lethal giant larvae (Lgl) and Discs-large (Dlg) to regulate cell polarity and growth control [19]. Recently, these three tumor suppressors were shown to regulate cell size and mitotic spindle asymmetry in Drosophila neuroblasts [35]. The role of Scribble in tumorigenesis was further supported by the discovery that Scribble mutants cooperate with oncogenic Ras or Notch to cause neoplastic overgrowth of the eye disc [36], and that cooperation between oncogenic Ras and inactivation of Scribble leads to metastatic behavior [37]. Additional studies in Drosophila implicate Scribble in the regulation of synaptic plasticity and synaptic vesicle dynamics [38,39], and show that Scribble is essential for olfactory behavior in Drosophila [40]. As for mammalian Scrib, little information is available at the moment. Relating to the control of cell polarity and proliferation, human Scrib was found to be a functional homologue of the Drosophila scribble protein [17]. Polarity defects and tumorous overgrowth of Scribble-mutant flies are rescued by human Scrib predicting an important role for human Scrib in the suppression of mammalian tumorigenesis. Further support for this hypothesis, was obtained by the fact that human and mouse Scrib are targeted for degradation by high-risk papillomavirus E6 proteins [32,41]. Human papilloma viruses cause papillomas or warts on skin, genital tissues, and the upper respiratory tract, and high-grade lesions progress to carcinomas at a high frequency. The high-risk subgroup of human papilloma viruses detected in these lesions have been causally linked to the development of over 90% of uterine cervical carcinomas, the second leading cause of cancer-related deaths among women world-wide. High-risk papilloma virus E6 proteins direct Scrib for degradation by directly binding to the PDZ-domains of Scrib. In this regard, it is noteworthy that we observed a remarkable aspect regarding the expression levels of Scrib in a number of mammalian cell lines. As already mentioned before (Fig. 3), we noticed that 293T cells expressed much lower levels of Scrib as compared to 293 cells. 293T cells are derived from 293 but, in contrast, these cells stably express Simian Virus 40 largeT antigen. SV40 large T is a powerful oncoprotein capable of transforming a variety of cell types [42]. Its transforming activity is attributed to its binding and manipulation of the function of certain key tumor suppressors and cell regulatory proteins such as retinoblastoma and p53. However, certain factors that contribute to its full transformation potential are not yet completely understood. We hypothesize that large T induces the downregulation of Scrib expression, and that Scrib contributes to the transformation potential of SV40 large T. In addition to its role as a tumor suppressor, Scrib was also implicated in the regulation of planar cell polarity, a role that is not established for Drosophila Scribble [43], and it was shown that disruption of Scrib is the causal factor for the severe neural tube defects that occur in the circletail mouse [44]. Disruption of neural tube closure leads to a group of disorders termed neural tube defects, which are one of the commonest causes of congenital malformation and lethality in humans. The most severe form of neural tube defect is craniorachischisis, in which almost the entire brain and spinal cord remain open. Craniorachischisis comprises 10–20% of human neural tube defects, and is caused by a failure to initiate neural tube formation at the start of neurulation. Circletail is one of only two mouse mutants that exhibit craniorachischisis. The fact that Scrib was identified as the gene that was mutated in this mouse attributes an important role for Scrib in development [44]. We show here that Scrib is expressed equally well in very different cell types, such as Jurkat cells, which are human T lymphocytes, epithelial cells such as 293 and MDCKII cells, and in fibroblasts such as CV-1 cells. As described above, the function of Scrib and its Drosophila ancestor Scribble have been mainly addressed in epithelial cells. To our knowledge, nothing is known yet about the function of Scrib in other cell types such as lymphocytes and fibroblasts. We show here that LPP specifically binds to and partially co-localizes with Scrib in cell-cell contacts of epithelial and fibroblastic cell lines. Previous studies have shown that PDZ domain proteins play an important role in the targeting of proteins to specific membrane compartments and in the assembly of these proteins into supramolecular complexes [25]. Therefore, we investigated whether Scrib was essential to localize LPP in cell-cell contacts. However, as demonstrated by these experiments, Scrib is not necessary to target LPP to these structures. These findings are similar to what has been found for targeting of zyxin family members to focal adhesions. Recently, zyxin and TRIP6 were shown to interact with members of the p130Cas family of signal transducers, which are focal adhesion components [22]. This interaction is primarily mediated by the LIM domains of zyxin and TRIP6. One specific function associated with the LIM domains of zyxin family members is targeting to focal adhesions. Despite this feature of the zyxin family LIM domains, and despite their interaction with p130Cas, it was shown that p130Cas is not required for focal adhesion localization of zyxin and TRIP6 [22]. We also investigated whether LPP was responsible for targeting of Scrib to cell-cell contacts. However, as demonstrated by our experiments, also this appeared not to be the case. In fact, our results indicate that all of the PDZ domains of Scrib are dispensable for targeting the protein to cell-cell contacts. For epithelial cells, these results are in agreement to what has been published in the course of our investigations by Legouis and Jaulin-Bastard et al. [45], who have shown that a point mutation of a specific proline residue that is located at position 305 in LRR number 13 of human Scrib is enough to abolish membrane localization. Taken into account that PDZ domains vary in their range and stringency of specificity [25], it is not excluded that LPP might bind to other PDZ domains than the ones of Scrib. Concerning Scrib, to date, three other proteins have been described that bind to the PDZ domains of Scrib: as mentioned above, the high-risk human papillomavirus E6 protein [32] interacts with the PDZ domains of human Scrib, whereas the GUKH (guanylate kinase holder) protein was shown to bind to the PDZ domains of Scribble at Drosophila synapses [38], and very recently, mammalian Scrib was shown to form a tight complex with the βPIX exchange factor at neuronal presynaptic compartments [46]. These findings raise the possibility that different binding partners of the Scrib PDZ domains, including LPP, can compete with each other for binding to Scrib, and as such play a role in processes in which Scrib is involved. In this regard, it is worth mentioning that the binding of LPP to Scrib appears to be regulated. In our mitochondrial targeting experiments (Fig. 7), we noticed that the full length wild-type LPP-protein was not targeted to Scrib-coated mitochondria in all cells. In fact, in the majority of these cells, full length wild-type LPP was not recruited by Scrib. We hypothesize that the binding of LPP to Scrib is regulated by an intra- or intermolecular interaction of LPP, as a result of which the carboxy-terminal tail is hidden in such a way that it is not available anymore for binding to Scrib. One piece of information that supports this hypothesis is the observation that, in contrast to full length LPP, the carboxy-terminal region containing only the LIM-domains and the tail but lacking the pre-LIM region, was efficiently recruited to Scrib-coated mitochondria in nearly 100% of the cells examined while carboxy-terminally mutated versions were not recruited (our unpublished results). Our observations are similar to what has been reported for the binding of zyxin to the tumor suppressor warts/LATS1. In in vitro binding experiments, it was demonstrated that parts of the zyxin protein containing LIM domains 1 and 2 efficiently bind to warts/LATS1 while the full length protein does not bind [47]. Based on these findings, the authors speculated that the LIM1/2 domains are masked in full-length zyxin, and that intramolecular and/or intermolecular modifications may regulate the interaction between zyxin and warts/LATS1. Conclusions Taken together the fact that LPP shuttles between cell adhesion sites and the nucleus [11,48], and the evidence that we have provided here that Scrib interacts with LPP, establishes that Scrib is connected to the communication pathway between cell adhesion sites and the nucleus of which LPP is an important element, and suggests that LPP is implicated in Scrib-associated cellular events. Methods Plasmid constructs The GFP-LPP construct was described before [11]. A construct expressing Xpress-hScrib-mito was made by cloning the coding region of human Scrib with a mutated stop codon in the pcDNA3.1/His vector (Life Technologies) followed by inserting a DNA fragment encoding the membrane anchor of ActA (LILAMLAIGVFSLGAFIKIIQLRKNN; a kind gift of Evelyne Friederich, Centre de Recherche Public-Santé, Luxembourg) behind the mutated stop codon. All amino acid changes in Scrib and LPP were made, using the QuikChange™ Site-Directed Mutagenesis Kit (Stratagene) according to the supplier's protocols. All synthetic mutations, ligation sites and PCR-amplified regions were verified by sequencing. Protein expression was checked by Western blotting. Construction and sequencing of a full-length human Scrib cDNA The KIAA0147 partial cDNA clone was kindly provided by Takahiro Nagase (Kazusa DNA Research Institute, Japan). In order to obtain full-length 5'-cDNA sequences encoding human Scrib, RNA-linker mediated 5'-RACE (RLM-RACE) was performed according to published protocols [49] using RNA isolated from HEK293 cells. The RLM-anchor primer sequence is: 5'-GGGCATAGGCTGACCCTCGCTGAAA-3'. The gene-specific primers are 1) 5'-CACGTCCAGCTCCACCAGCTGCATG-3' and 2) 5'-GAAGTTGGCCACCTCGGGAGGCAAC-3' (nested). This allowed us to construct a composite cDNA of about 5.1 kb which was completely sequenced (Genbank accession no. AF240677). Yeast two-hybrid system The Matchmaker Two-Hybrid System 2 was used (Clontech). All experiments were performed in the yeast reporter strain CG-1945. Bait-constructs were made using the vector pGBT9 (Clontech). The prey-constructs pACT2-AF6, pACT2-Erbin, pACT2-PICK1, and pACT2-PSD95 were kindly provided by Jean-Paul Borg (INSERM, Marseille, France), and were described in Audebert and Navarro et al., (AF6, Erbin, PICK1) [46] and in Saito et al., (PSD-95) [31]. The prey-constructs pACT2-Syntenin and pACT2-CASK were kindly provided by Pascale Zimmermann (University of Leuven & VIB, Belgium). An oligo(dT)- and randomly primed prey-cDNA library constructed with mRNA from 12.5 day embryonic mice using pACT2 as vector [50] was kindly provided by Kristin Verschueren and Danny Huylebroeck (University of Leuven & VIB, Belgium). The prey-library was screened as follows: yeast strain CG-1945, containing a HIS3 and a lacZ reporter gene under the control of promoters containing GAL4-binding sites, was transformed with 66 μg of bait-DNA and 33 μg of prey-library-DNA using a LiAc high efficiency transformation protocol [51]. Transformants were grown for 10 days at 30°C on triple selective (lacking Trp, Leu and His) synthetic dropout (SD---) agar plates containing 5 mM 3-AT (Sigma). Transformed His+ yeast colonies were restreaked on new SD--- agar plates and grown for another 1 to 2 days. Colony-lift filter assays were performed for the qualitative measurement of β-galactosidase activity according to standard protocols. Cell culture, stable cell lines and transfections Cell lines used included CV-1 (ATCC CCL-70), HEK293 (ATCC CRL-1573), 293T (HEK 293 cells expressing the SV40 T-antigen), Jurkat (ATCC TIB-152), and MDCK strain II (Dog normal kidney epithelial cells). Jurkat cells were grown in RPMI 1640 (Life Technologies) supplemented with 10% fetal bovine serum. All other cell lines were grown in DMEM/F12 (1:1) (Life Technologies, Inc.) supplemented with 10% fetal bovine serum. Cells were cultured at 37°C in a humidified CO2 incubator. Transient transfections were performed using FuGene™ 6 Transfection Reagent (Boehringer Mannheim) according to the supplier's instructions. Cells were incubated at 37°C for 18–24 hours before analysis. Stable MDCK strain II cell lines were made expressing wild-type and carboxy-terminally mutated human GFP-LPP proteins, wild-type full length Scrib-GFP, or Scrib-GFP lacking all four PDZ-domains. Transfection of MDCK cells was performed using Lipofectamine 2000 Reagent (Life Technologies) according to the manufacturer's instructions. Transfected cells were selected in medium containing 250 μg/ml G418 (Life Technologies), and resistant colonies were isolated 10–14 days later. Individual clones were screened for expression of the respective GFP fusion proteins by Western blotting using a rabbit polyclonal anti-GFP antibody (Tebu Bio). Mammalian two-hybrid system Bait-constructs were made using pM-vectors [52], prey-constructs were made in the pSNATCH-vector [53]. 24 hours upon seeding, semi-confluent HEK293 cells on 24-well plates were transiently cotransfected with 100 ng DNA of a bait-construct, 100 ng DNA of a prey-construct, 250 ng DNA of a luciferase reporter construct and 50 ng of CMV-β-galactosidase DNA (internal control for transfection efficiency). The reporter construct contains the gene encoding the firefly luciferase enzyme, which is under the control of a minimal promoter containing five consecutive GAL4-binding sequences (kindly provided by W. Schaffner and D. Escher, Zürich, Switzerland). Cell lysates were prepared 18 to 24 hours after transfection and assayed for luciferase activity as described previously [11]. In vitro transcription/translation and GST pull-down assays All in vitro translation reactions were carried out using the TNT T7 Quick Coupled Transcription/Translation System (Promega) following the manufacturer's instructions. For GST pull-down assays, bacterial expression constructs were made using pGEX-5X vectors (Amersham-Pharmacia Biotech) directing the synthesis of glutathione S-transferase (GST) fusion proteins containing wild-type or mutated forms of human LPP. These fusion proteins were purified according to manufacturer's instructions and verified by SDS-PAGE. GST fusion proteins or GST alone, bound to glutathione-agarose beads, were incubated with in vitro synthesized [35S]-methionine-labelled full length human Scrib protein, or a portion of the human Scrib protein encompassing all four PDZ domains (amino acids 616–1490) (wild-type or mutated) in NENT100 buffer (100 mM NaCl, 20 mM Tris-HCl pH = 7.6, 1 mM EDTA, 0.1% NP-40, protease inhibitors). This mixture was tumbled overnight at 4°C. Subsequently the beads were washed 5 times in 500 μl NENT100 buffer, resuspended in 25 μl SDS-PAGE sample buffer and incubated at 95°C for 5 minutes. Proteins were separated by SDS-PAGE and interacting Scrib was detected by autoradiography. Scrib-specific antiserum and commercial antibodies The Scrib-specific polyclonal antiserum Scrib-472 was prepared by Eurogentec by immunization of rabbits with a keyhole limpet hemocyanin (KLH) coupled peptide 1612CSSRRPVRPGRRGLGPVPS1630 (19 C-terminal AA of human Scrib). For the detection of endogenous LPP in MDCKII cells, a LPP-specific monoclonal antibody was used (BD Biosciences, Transduction Laboratories). For the detection of GAL4-fusion proteins in immunocytochemistry, a rabbit polyclonal anti-GAL4 DNA-binding domain antibody (Tebu Bio) was used. Vinculin was detected in cells with a monoclonal anti-vinculin antibody (Sigma, clone hVIN-1), Xpress-tagged proteins with a monoclonal anti-Xpress antibody (Life Technologies). Fluorescently-tagged Alexa-antibodies (Molecular Probes) were used as secondary antibodies for immunofluorescence detection. SDS-PAGE and Western blotting Eukaryotic cell extracts were prepared by harvesting the cells in PBS (phosphate buffered saline), and subsequent lysis of the cell pellets in SDS-PAGE sample buffer (60 mM TRIS-HCl pH = 6.8, 12% glycerol, 4% SDS, 5% β-mercapto-ethanol). Protein concentrations in cell extracts were determined using BCA Protein Assay Reagent (Pierce) according to the manufacturer's instructions. 30 μg of proteins of each cell extract were loaded on 5% SDS-polyacrylamide gels. After size-separation, proteins were electrophoretically transferred to PROTEAN Nitrocellulose Membranes (Schleicher and Schuell). ECL Western blotting was performed using Western Lightning Chemiluminescence Reagent Plus (Perkin Elmer Life Sciences) according to the supplier's instructions. GFP-fluorescence and indirect immunocytochemistry CV-1 or 293T cells seeded on glass coverslips, and MDCKII cells seeded on glass coverslips or on Transwell-Clear polyester membranes (0.4 μm, Costar) were fixed in 4% formaldehyde for 20 minutes at room temperature followed by three washes in PBS containing 0.1 mM CaCl2 and 0.1 mM MgCl2 (PBS++). For GFP-fluorescence, slides were mounted in vectashield mounting medium (Vector Laboratories, Inc.) and analyzed on a Zeiss Axiophot fluorescence microscope equipped with an RT slider SPOT camera (Diagnostic Instruments, Inc.) using SPOT RT Software v3.4, or by confocal microscopy (MRC-1024 Laser Scanning Confocal Imaging System; Bio-Rad). For indirect immunocytochemistry, after fixation, quenching was performed by incubating the cells for 10 minutes at room temperature in PBS++ containing 50 mM NH4Cl. Cells were then permeabilized with 0.4% Triton-X-100 for 5–10 minutes at room temperature. Unspecific binding was blocked with 0.5% Blocking Reagent (Roche) in PBS++ for 30 minutes at room temperature. Subsequently, the slides were incubated with primary antibodies for 1 hour at room temperature. After washing the cells three times in PBS++, bound primary antibodies were detected with fluorescently labelled secondary antibodies (Molecular Probes) for 30 minutes at room temperature. Following three washes in PBS++, slides were mounted and analyzed as described for GFP-fluorescence. Authors' contributions MMRP designed the study, performed the yeast and mammalian two-hybrid experiments, as well as the mitochondrial targeting experiments, made the LPP-stable cell lines, performed the confocal microscopy with these cell lines, and prepared the manuscript. SMPM constructed most of the DNA constructs, made the Scrib-stable cell lines, performed the epifluorescence microscopy with these cell lines, and carried out the Western blotting experiments. PA performed the GST pull down experiments, and contributed to the characterization of the Scrib-472 antibodies. TAYA contributed to the establishment of a full length Scrib cDNA clone, and to the writing of the manuscript. EJ contributed to the development of the Scrib-472 antibodies, to the establishment of a full length Scrib cDNA clone, and to the funding of the project. WJMVDV contributed to the writing of the manuscript and to the funding of the project. Acknowledgements We thank Wim Keysers, Isabelle Orlans and Nancy Weyns for excellent technical assistance, and Jan Brants, Koen Crombez, and Gisèle De Geest for interesting discussions. This work was supported in part by GOA (Geconcerteerde Onderzoeksacties) grant 2002/10, and by grants from the Cancer Research Program of "Fortis Verzekeringen", the Fund for Scientific Research (F.W.O.-Vlaanderen Krediet Aan Navorsers, 1.5.098.03), the Belgian Federation against Cancer (project A5890), and the University VIS program (project 99/010). 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==== Front BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-5-11564933010.1186/1471-2180-5-1Methodology ArticleDoxycycline-regulated gene expression in the opportunistic fungal pathogen Aspergillus fumigatus Vogt Keith [email protected] Ruchi [email protected] Judith C [email protected] David S [email protected] Dept. of Pathology & Laboratory Medicine, University of Cincinnati College of Medicine, 231 Albert Sabin Way, Cincinnati, OH 45267-0529, USA2005 13 1 2005 5 1 1 30 9 2004 13 1 2005 Copyright © 2005 Vogt et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Although Aspergillus fumigatus is an important human fungal pathogen there are few expression systems available to study the contribution of specific genes to the growth and virulence of this opportunistic mould. Regulatable promoter systems based upon prokaryotic regulatory elements in the E. coli tetracycline-resistance operon have been successfully used to manipulate gene expression in several organisms, including mice, flies, plants, and yeast. However, the system has not yet been adapted for Aspergillus spp. Results Here we describe the construction of plasmid vectors that can be used to regulate gene expression in A. fumigatus using a simple co-transfection approach. Vectors were generated in which the tetracycline transactivator (tTA) or the reverse tetracycline transactivator (rtTA2s-M2) are controlled by the A. nidulans gpdA promoter. Dominant selectable cassettes were introduced into each plasmid, allowing for selection following gene transfer into A. fumigatus by incorporating phleomycin or hygromycin into the medium. To model an essential gene under tetracycline regulation, the E. coli hygromycin resistance gene, hph, was placed under the control of seven copies of the TetR binding site (tetO7) in a plasmid vector and co-transfected into A. fumigatus protoplasts together with one of the two transactivator plasmids. Since the hph gene is essential to A. fumigatus in the presence of hygromycin, resistance to hygromycin was used as a marker of hph reporter gene expression. Transformants were identified in which the expression of tTA conferred hygromycin resistance by activating expression of the tetO7-hph reporter gene, and the addition of doxycycline to the medium suppressed hygromycin resistance in a dose-dependent manner. Similarly, transformants were identified in which expression of rtTA2s-M2 conferred hygromycin resistance only in the presence of doxycycline. The levels of doxycycline required to regulate expression of the tetO7-hph reporter gene were within non-toxic ranges for this organism, and low-iron medium was shown to reduce the amount of doxycycline required to accomplish regulation. Conclusions The vectors described in this report provide a new set of options to experimentally manipulate the level of specific gene products in A. fumigatus ==== Body Background Aspergillus fumigatus is a saprophytic filamentous fungus that has become the leading mould pathogen in leukemia treatment centers and transplantation units in developed countries, second only to Candida spp. as a cause of systemic mycosis [1]. Despite some advances in therapy, currently available drugs for the treatment of aspergillosis continue to be hampered by problems with efficacy, toxicity, and the emergence of drug resistance. Moreover, a recent review of the Aspergillus case-fatality rate demonstrated that more than 50% of patients die with, or as a result of, aspergillosis, despite having received the reference standard of therapy [2]. The continued expansion of the immunosuppressed population emphasizes the need for increased understanding of both the basic biology and virulence of this mould so that more effective antifungal therapies can be developed. The completion of the annotated sequence of the A. fumigatus genome is expected to greatly facilitate efforts to determine the contribution of specific gene products to the virulence of this opportunistic pathogen. Unfortunately, the genetic tractability of A. fumigatus has lagged behind some other fungal systems, particularly in the area of conditional expression systems. Inducible promoter systems have proven to be instrumental for the elucidation of gene function in a number of species, most notably with essential genes. Experimental manipulation of gene expression in A. fumigatus is presently accomplished through the use of DNA cassettes that are introduced into the organism as transgenes [3-5], inserted into specific chromosomal loci [3,6] or expressed from a multi-copy nonintegrating vector [7]. An inducible expression system based upon the ethanol-inducible alcA promoter from A. nidulans has been successfully used in A. fumigatus [8]. However, the conditions required to regulate the alcA promoter can have significant effects on the metabolism of the organism and thus remain a concern for many applications, particularly for in vivo studies. The tetracycline operator system has been used to regulate gene expression in a number of species. The system is based upon the E. coli tetracycline-resistance operon, a regulatory unit that detects minute concentrations of tetracycline and mounts an appropriate resistance response. Expression of the operon is controlled by a repressor protein, TetR that binds to operator sequences (tetO) in the promoter/enhancer region of the operon and prevents transcription. In the presence of tetracycline TetR is unable to bind tetO, which releases the repression and allows the operon to be expressed. This system has been adapted for experimental gene regulation in eukaryotes by fusing TetR to the VP16 transcriptional activating domain of herpes simplex virus VP16, thereby creating a synthetic tetracycline-regulatable transcriptional activator protein (tTA) that can be used to regulate a gene that is under the control of a tetracycline-responsive promoter (reviewed in [9] and shown schematically in Fig. 1). A tetracycline-regulated promoter is constructed by introducing one or more copies of the tetO sequence upstream of a minimal promoter region and the gene of interest (Fig. 1). In the absence of tetracycline, tTA is free to bind to the tetO-promoter and drive the expression of the downstream gene. The addition of tetracycline to the medium prevents tTA from binding the tetO sequences and the promoter is inactive. A variation of this system uses a 'reverse' tetracycline transactivator, rtTA that only binds tetO in the presence of tetracycline. In this case, a gene under tetO control is expressed in the presence of tetracycline, but not in its absence [10]. The TetR/tetO system is biologically active in a number of eukaryotes [11-13], including yeasts [14-17], but has not yet been adapted to the filamentous fungi. In this report we demonstrate that the tetracycline-regulated promoter system can be used to manipulate gene expression in Aspergillus fumigatus using a simple co-transfection procedure. Results Vector construction Details on plasmid construction are provided in Methods. The plasmids are shown schematically in Fig. 2 and the individual components are summarized in Table-1. Effects of doxycycline on the growth of A. fumigatus Tetracyclines are small lipophilic antibiotics that readily diffuse into eukaryotic cells by passive diffusion. Doxycycline was selected for this study since it has the highest association equilibrium constant to TetR among the common tetracycline derivatives [18], and has been reported to be most effective in the regulation of tetracycline-regulated promoters in S. cerevisiae [19]. For the doxycycline system to be effective, the levels of doxycycline required to regulate a tetO promoter must not be within a toxic range for the organism. To determine the range of doxycycline concentrations that are tolerated by A. fumigatus, conidia were spotted onto the center of plates of Aspergillus minimal medium containing 0 – 500 μg/ml of doxycycline and colony diameter was measured with time. Concentrations up to 100 μg/ml had little effect on radial growth rates, all of which were within 5% of each other (Fig. 3). However, growth rate was reduced by 16% at 200 μg/ml and by 34% at 500 μg/ml of doxycycline. These results indicate that doxycycline can be used up to 100 μg/ml in minimal medium with no detectable effects on growth rate. Regulated expression of an essential gene by the 'tet-off' system Inducible promoter systems are particularly useful for creating strains that can be inducibly depleted of an essential gene product [20]. To model an essential gene under tetO control we used heterologous expression of the E. coli hygromycin resistance gene, hph. The hph gene encodes a phosphotransferase that is essential to A. fumigatus in the presence of toxic concentrations of the aminoglycoside antibiotic hygromycin. The hph gene was cloned into a plasmid downstream of a hybrid promoter comprised of seven copies of the TetR binding sequence (tetO7) linked to a 175 bp minimal gpdA promoter from A. nidulans (p482, Fig. 2). The linearized tetO7-hph reporter plasmid was co-transfected into A. fumigatus protoplasts together with a linearized plasmid expressing the tetracycline transactivator, tTA (p444, Table-1) and transformants were selected on the basis of their resistance to hygromycin. Although p444 carries the ble gene, phleomycin selection was not included in this experiment. Thirteen hygromycin-resistant colonies were obtained from protoplasts transformed with the tetO7-hph reporter construct alone. Since these are integrative plasmids, the observed background colonies are presumed to be a consequence of positional effects at the site of integration, resulting in basal levels of expression of the hph reporter construct. By contrast, 192 colonies were obtained following co-transfection with p444 and the tetO7-hph reporter plasmid, suggesting that expression of tTA was driving expression of the tetO7-hph transgene and thus conferring hygromycin resistance. Fifty of these hygromycin-resistant colonies were randomly isolated and plated onto secondary hygromycin selection plates in the presence or absence of 100 μg/ml doxycycline. A total of five transformants showed increased hygromycin sensitivity in the presence of doxycycline, two of which were selected for further analysis: one showing marked hygromycin sensitivity in doxycycline (tTA-2) and one showing moderate hygromycin sensitivity (tTA-1). Conidia from each of these transformants were spotted into the center of a plate of minimal medium containing both doxycycline and hygromycin and the radial growth of the colony was monitored with time. The pH of the medium in this experiment was adjusted to 8 in order to maximize the hygromycin toxicity. As shown in Fig. 4A, the tTA-2 transformant showed tight regulation of the phenotype of hygromycin sensitivity. Doxycycline concentrations as low as 30 μg/ml completely arrested growth, indicating that a concentration of doxycycline that is inert to the growth of A. fumigatus (Fig. 3) can be used to modulate expression of an essential gene under tetO control in this fungus. Importantly, concentrations of doxycycline below 30 μg/ml could be used to manipulate the degree of hygromycin resistance; at 5 μg/ml and 2 μg/ml of doxycycline, the radial growth rate of the organism was reduced by 68% and 55%, respectively (data not shown). Higher levels of doxycycline were required to suppress the growth of the tTA-1 transformant on hygromycin medium (Fig. 4A). Northern blot analysis showed that the tTA-1 strain expressed about 5-fold more hph RNA than tTA-2 (Fig. 4B), which was consistent with the fact that tTA-1 grew faster than tTA-2 in the presence of the same concentration of hygromycin (Fig. 4A, compare tTA-1 and tTA-2, no doxycycline). The doublet shown in Fig. 4B was occasionally seen on Northern blots hybridized to the hph probe and is presumed to represent alternative splicing of the primary hph transcript. The higher levels of hph RNA in the tTA-1 strain could be due to a combination of increased tTA expression (which would be expected to be susceptible to doxycycline regulation) and/or basal expression from one or more integrated copies of the tetO7-hph reporter gene (which would not be affected by doxycycline). Since there was a clear dose-response effect of doxycycline on hph expression and hygromycin resistant growth in this strain (Fig. 4A and 4B), it is likely that the two strains differ primarily in the amount of tTA that they express. Although Northern blot analysis showed barely detectable levels of tTA in either strain (data not shown), undetectable levels of tTA have been reported in other applications of the tetracycline regulatory system and are thought to be due to the toxic effects of overexpression [21]. Since very low levels of tTA protein are actually required to regulate a tetO promoter [21], even a small difference in tTA expression level that is beyond the limit of detection of a Northern blot could influence the amount of doxycycline required to suppress tTA activity in this transformant. Regulated expression of an essential gene by the 'tet-on' system A limitation of the tTA-regulated system is that it requires inhibition of transcription rather than activation. To address this, a 'reverse' tTA has been generated (rtTA) that requires interaction of the transactivator with tetracyclines before tetO binding can occur, a system that is referred to as 'tet-on' [10]. Unfortunately, the mutations that reverse the response to doxycycline also reduce binding affinity for doxycycline ten-fold, thus requiring higher levels of doxycycline for maximal induction. Since there may be adverse effects associated with high doxycycline concentration in A. fumigatus under some conditions [22], we chose a derivative of rtTA that contains additional mutations that restore binding affinity for doxycycline [23]. One particular variant, rtTA2S-M2, also contains a multimerized minimal VP16 activation domain to enhance transcriptional activity, and its sequence has been manipulated to optimize expression in eukaryotic cells [23]. Using the same co-transfection approach used for the tTA system, the tetO7-hph reporter (p500, Fig. 2) was co-transfected into A. fumigatus protoplasts together with a linearized plasmid that expresses rtTA2S-M2 (p474, Fig. 2) and the transformants were plated onto medium containing both hygromycin and doxycycline. In this experiment, a modified tetO7-hph reporter was used in which a 280 bp terminator sequence from the A. fumigatus cgrA gene [29] was inserted upstream of the tetO7 repeats to minimize read-through from flanking sequences (p500). Doxycycline was incorporated into the medium at 100 μg/ml to ensure that the tetO7-hph transgene would be expressed at sufficient levels to protect against hygromycin toxicity. Approximately 15% of 27 hygromycin resistant colonies showed reduced growth when shifted to hygromycin medium without doxycycline, one of which was selected for further analysis. As shown in Fig. 5, the inability of this transformant to grow in the presence of hygromycin was restored by the incorporation of as little as 5 μg/ml of doxycycline into the medium, indicating that low levels of doxycycline are biologically active as regulators of the tetO promoter in A. fumigatus. A further increase in hygromycin resistance was achieved at 15 μg/ml of doxycycline, but concentrations above 15 μg/ml had no additional effect. Northern blots analysis confirmed that the levels of hph RNA in the rtTA transformant were increased by the addition of doxycycline to the medium (Fig. 5). When hybridization intensity was normalized to SYBR-green II-stained rRNA bands by phosphorimager analysis, the levels of hph expression in the presence of both concentrations of doxycycline (Fig. 5) were thirty-fold greater than in the absence of added doxycycline. Doxycycline-regulation is enhanced by low- iron medium A recent report has shown that iron blocks the accumulation and activity of tetracyclines in bacteria [24]. Since iron is a standard component of Aspergillus minimal medium, its presence may limit the efficiency of doxycycline-mediated gene regulation, particularly if transcriptional modulators with lower affinity for doxycycline are used. Fig. 6 shows the effects of lowering the iron concentration on doxycycline-mediated suppression of the tetO7-hph transgene in the tTA-1 clone showed in Fig. 4. In comparison to standard minimal medium, where 200 μg/ml of doxycycline was required to reduce expression in this strain (Fig. 4A and 4B), only 5 μg/ml was required in medium containing one tenth the normal concentration of FePO4·4H20 (Fig. 6). This indicates that iron may also impair the accumulation of doxycycline in A. fumigatus and that the choice of medium could have significant effects on doxycycline-mediated gene regulation. Wild type A. fumigatus showed no reduction in radial growth rate on this low-iron minimal medium (data not shown). Discussion The tetracycline-inducible method of gene regulation has become one of the most popular tools to manipulate gene expression in eukaryotes [25]. The efficacy of the system is attributed to the use of prokaryotic regulatory elements that respond to low concentrations of tetracyclines without affecting eukaryotic physiology, allowing control of gene expression without the concern for pleiotropic effects mediated by the effector. Although widely used in higher eukaryotes, including the model yeast S. cerevisiae [19], the system has not yet been reported in filamentous fungi. Candida albicans and C. glabrata are the only pathogenic fungi in which the system has been successfully applied thus far, however neither of these studies used the tetR-VP16 fusions upon which the tTA and rtTA systems are based [15-17]. In this study we show that both the tet-off (tTA) and tet-on (rtTA) systems can be used to regulate the expression of a hygromycin resistance reporter gene in A. fumigatus. Since the hph gene is essential in the presence of toxic levels of hygromycin, the ability to control hygromycin resistance by modulating the levels of hph transcription validates the system as a tool for analysis of essential genes in A. fumigatus. In the tTA system we found that individual transformants varied in the amount of doxycycline that was necessary to regulate expression of the tetO7-hph reporter gene. Since doxycycline prevents the tTA protein from binding to the tetO sequence, this is most likely due to variability in the amount of tTA protein that is expressed in each transformant. A limitation of the tTA approach described here is that the majority of the hygromycin-resistant transformants from the tTA/tetO7-hph co-transfection were not susceptible to regulation by doxycycline. This may be due in part to leaky expression of the tetO7-hph reporter, caused by enhancers in the proximity of the integration site [21,25]. A second possibility is that the levels of tTA coming from the gpdA promoter used in this study were too high to be removed by non toxic concentrations of doxycycline. Since lower levels of tTA expression are more readily suppressed by doxycycline, it is conceivable that a weaker promoter used to drive tTA would increase the frequency with which doxycycline-regulatable transformants can be isolated. Lower levels of tTA expression could also be accomplished by using a shorter segment of the gpdA promoter used in this study. The ability to quantitatively control expression from the tetO7-hph reporter gene was also observed in a strain expressing the reverse transactivator, rtTA. Concentrations of doxycycline from 2 μg/ml to 15 μg/ml gave a graded response of hygromycin resistance, indicating that A. fumigatus is responsive to concentrations of doxycycline that are similarly effective in S. cerevisiae [19] and C. albicans [15]. Moreover, this level of sensitivity falls within the range of doxycycline concentrations that can be achieved in mouse tissues [15,16], raising the possibility of using this system to modulate the expression of virulence-related genes in pathogenesis studies on A. fumigatus. Only 15% of the hygromycin-resistant colonies from an rtTA/tetO7-hph co-transfection showed doxycycline-dependent hygromycin resistance however, suggesting that some of the hygromycin resistance was due to leaky expression of the tetO7-hph gene. Leakage of tetO7-regulated genes has been described in other systems, and is attributed to enhancers located in the proximity of the integration site that increase expression of the tetO-linked gene [21,25]. This type of problem will affect tetO7-controlled genes regardless of whether they are integrated randomly in the genome or targeted to specific loci. Conclusions This report establishes the utility of the tetracycline-regulated system as an approach to regulate gene expression in A. fumigatus. A limitation of the system was that only 10–15% of the transformants could be regulated by doxycycline, either when tTA or rtTA were used, emphasizing the need to screen for regulatable transformants. A recent approach to limit the problem of leakiness of a tetO-driven gene is the use of trans-silencer proteins comprised of fusions between tetR and a transcriptional silencing domain [26,27]. It is conceivable that the incorporation of a synthetic A. fumigatus-derived trans-silencer protein into the co-transfection approach described in this study would improve the efficiency of the system. Methods Vector construction All vectors are based on the pBluescript plasmid (Stratagene) and were linearized prior to transfection. PCR amplification of components were performed using standard amplification protocols using PfuTurbo DNA polymerase (Stratagene). Hph Reporter Constructs (p482 and p500) A segment containing seven copies of the tet operator sequence (tetO7) was PCR amplified from pUHD10-3 [12] with the forward primer 5'-aagcttgcgtatcacgaggccctttc and the reverse primer 5'-aagcttctcgacccgggtaccgag (added HindIII cloning sites are underlined) and cloned into the HindIII site of pBluescript. A 1.6 kb fragment containing a minimal gpdA promoter from A. nidulans (-175 relative to the ATG of the hph open reading frame), the hph gene encoding resistance to hygromycin, and the trpC terminator from A. nidulans, was then PCR amplified from pAN7-1 [28] with forward primer 5'-gagctccccatcttcagtatattcatc (added SstI cloning site underlined) and reverse primer 5'-tctagatcgcgtggagccaagagcgg (added XbaI cloning site underlined) and cloned downstream of tet07 into the SstI and XbaI sites of the plasmid, creating p482. To minimize read-through from flanking sequences into tet07, a 280 bp segment of the terminator region of A. fumigatus cgrA [29] was inserted upstream of tet07 PCR to create p500. The cgrA terminator was PCR amplified from genomic DNA of A. fumigatus isolate H237 using the forward primer 5'aagcttacagcagaagaatctctc (added HindIII cloning site underlined) and reverse primer 5'ctcgagatgattcatgacgtatattc (added XhoI cloning site underlined), cloned into pCR2.1-Topo (Invitrogen), excised with HindIII, and inserted upstream of tetO7 in p482 to create p500. tTA expression vectors (p473, p434, and p444) A segment of the A. nidulans gpdA promoter was amplified from pAN7-1 [28] (position -679 to -1, with +1 being the start of the hph open reading frame) using the forward primer 5'-aagcttcggagaatatggagctt (added HindIII cloning site underlined) and the reverse primer 5'-gaattcggtgatgtctgctcaag (added EcoRI cloning site underlined) and cloned into pBluescript at the same sites. The tTA gene was then PCR amplified from pUHD15-1 [12] with the forward primer 5'-gaattctggcaatgtctagattagataaaag (added EcoRI cloning site underlined) and reverse primer 5'-atcatgtctggatcctcgcg (internal BamHI site underlined) and cloned into the EcoRI and BamHI sites downstream of the gpdA (-679) promoter. A 280 bp segment of the terminator region of A. fumigatus cgrA [29] was then amplified from H237 genomic DNA using the forward primer 5'-actagtacagcagaagaatctctc (added SpeI site underlined) and reverse primer 5'-gcggccgcatgattcatgacgtatattc (added NotI site underlined) and inserted into the SpeI and NotI sites downstream of tTA. To introduce phleomycin selection into this construct, a phleomycin resistance cassette containing the A. nidulans gpdA promoter, the Streptoalloteichus hindustanus ble gene encoding resistance to phleomycin, and the S. cerevisiae CYC1 terminator was amplified from pBCphleo (Fungal Genetics Stock Center) using the forward primer 5'-cctcaggcggagaatatggagcttcatcg and the reverse primer 5'-cctcaggaattaaagccttcgagcgtccc. The PCR product was cloned into pCR-Blunt II-TOPO (Invitrogen), excised with KpnI and XhoI and inserted into the PgpdA-tTA construct to create p444. The phleomycin cassette was excised from p444 with HindIII and re-ligated to create p473. To introduce hygromycin selection into p444, the phleomycin cassette was excised with KpnI and HindIII and replaced with a hygromycin resistance cassette (containing the A. nidulans gpdA promoter, the hph gene encoding resistance to hygromycin, and the trpC terminator from A. nidulans) that was amplified from pAN7-1 [28] with forward primer 5'-ggtacccggagaatatggagcttc (added KpnI cloning site underlined) and reverse primer 5'-aagcttgcttgagagttcaaggaag (added HindIII cloning site underlined) to make p434. rtTA expression vectors The tTA gene was excised from p473 with EcoRI and BamHI and replaced with an EcoRI-BamHI fragment containing the rtTA2s-M2 variant of rTA from pUHrT62-1 (generous gift from C. Berens, Erlangen, FRG) to create p474. To introduce phleomycin resistance into p474, the phleomycin resistance cassette was excised from p444 with KpnI and HindIII and cloned into the same sites in p474 to create p480. To introduce hygromycin resistance into p474, the hygromycin resistance cassette described in p434 was excised from an unrelated plasmid as a HindIII fragment and cloned into the HindIII site of p474 to make p502. Strains and culture conditions The A. fumigatus strains used in this study are listed in Table-1. The wild-type strain, H237, is a clinical isolate. Conidia were harvested from strains grown on Aspergillus minimal medium plates [30]. This minimal medium contains 4.5 μM FePO4·4H20. For low-iron minimal medium, the FePO4·4H20 concentration was reduced to 0.45 μM. Plasmids were introduced into A. fumigatus protoplasts as previously described [3]. Following transformation, protoplasts were plated onto 20 ml of osmotically stabilized minimal medium containing 100 μg/ml doxycycline (for transformations involving rtTA-expressing plasmids) or no added doxycycline (for transformations involving tTA-expressing plasmids). After incubating at room temperature overnight, each plate was overlaid with 10 ml of minimal medium top agar containing 0.5% agar, 1M sorbitol, and 8 mg hygromycin B (Invivogen, San Diego, CA). Doxycycline was also incorporated into the top agar overlay (100 μg/ml) for experiments involving rtTA-expressing plasmids. Colonies arising on these primary plates were transferred onto secondary selection plates containing the same selective agents, and conidia from the secondary plates were replated onto selective medium at low density to isolate colonies derived from single conidia. All subsequent experiments were performed on monoconidial isolates. For co-transfection experiments, 5 μg of the linearized tetO7-hph reporter construct was co-transfected with 5 μg of the linearized tTA plasmid (p444), or 50 μg of the linearized rtTA plasmid (p474). For experiments addressing the effects of doxycycline on hygromycin sensitivity, ten thousand conidia were spotted onto the surface of Aspergillus minimal medium agar containing hygromycin and doxycycline at the concentrations specified in the Figure legends. The plates were then incubated at 37°C, and colony diameter was measured with time. Radial growth rates were calculated from the exponential part of the resulting growth curves. Northern blot analysis For analysis of hph gene expression, RNA was isolated from overnight cultures in minimal medium supplemented with the indicated concentrations of doxycycline by crushing in liquid nitrogen and extracting RNA from the crushed mycelium with phenol/chloroform. Twenty micrograms of total RNA were fractionated by formaldehyde gel electrophoresis as previously described [20], transferred to positively charged nylon membranes (MSI, Inc., Westborough, MA, USA) and hybridized to a 32P-labeled hph DNA probe under stringent conditions in 50% (v/v) formamide/5XSSC (1X SSC is 0.15 M NaCl/0.015 M Na3·citrate, pH 7.6)/2X Denhardt's solution/10% (w/v) dextran sulfate/1% (w/v) sodium dodecyl sulfate (SDS). The hph probe was an 800 bp EcoRI-BamHI fragment from pAN7-1 [28] containing a segment of the hph open reading frame. Hybridization intensity was quantified with a Phosphorimager (Molecular Dynamics) and normalized for differences in gel loading by quantitating the relative levels of SYBR-green II-stained rRNA (Molecular Probes, Inc., Eugene, OR, USA). List of abbreviations tTA tetracycline transactivator rtTA reverse tetracycline transactivator TetR tetracycline repressor tetO TetR binding sequence hph hygromycin resistance gene ble phleomycin resistance gene Authors' contributions KV participated in vector construction, gene transfer into A. fumigatus, screening of transformants and drafting the manuscript. RB participated in plasmid construction. JCR contributed to the planning of the study. DSA conceived of the project and directed its design and execution. All authors have read and approved the final manuscript. Acknowledgements We thank Jay card for assistance with the figures. This work was supported by National Institutes of Health Grant R03AI53184 to DSA. Figures and Tables Figure 1 Schematic overview of the tetracycline-regulated gene expression system used in this study. The hygromycin resistance gene, hph, is under the control of seven copies of the TetR binding sequence, tetO, linked to a minimal promoter (Pmin). In the tTA-dependent expression system (A), the tTA protein (green circles) promotes hygromycin resistance of A. fumigatus by binding to the tetO promoter and activating transcription of the hph gene. Incorporation of doxycycline into the medium prevents tTA from binding to tetO, resulting in hygromycin sensitivity due to the absence of hph expression. The reverse tTA system (B) takes advantage of a reverse tetracycline transactivator, rtTA (blue circles), that binds to the tetO promoter only in the presence of doxycycline. In this case, rtTA promotes hygromycin resistance of A. fumigatus only when grown in the presence of doxycycline. Figure 2 Schematic representation of plasmid constructions. The tTA gene is expressed from the A. nidulans promoter in three plasmids: p473 (no selection), p434 (hygromycin resistance encoded by hph) and p444 (phleomycin resistance encoded by ble). The rtTA2S-M2 gene is expressed from the A. nidulans gpdA promoter in p474 (no selection), p502 (hygromycin selection encoded by hph) and p480 (phleomycin resistance encoded by ble). Two versions of the tetO7-hph reporter construct are shown below. Plasmid p482 contains seven copies of tetO, a 175 bp minimal gpdA promoter (Pmin), and the hph gene encoding resistance to hygromycin. Plasmid p500 contains the same components, with the addition of a 280 bp fragment of the A. fumigatus cgrA terminator region upstream of the tetO7 promoter to reduce read-through from flanking sequences. All plasmids have been deposited in the Fungal Genetics Stock Center for distribution. Figure 3 Sensitivity of A. fumigatus to doxycycline. Ten thousand conidia were spotted onto plates containing Aspergillus minimal medium and the indicated concentrations of doxycycline (μg/ml) and colony diameter was measured for 8 days at 37°C. This experiment used standard Aspergillus minimal medium adjusted to pH 6.5, but similar results were obtained when the pH was adjusted to 8 (data not shown). Figure 4 Effects of doxycycline on the hygromycin sensitivity of two A. fumigatus strains that express tTA (p444) in conjunction with the tetO7-hph reporter gene (p482). (A): Ten thousand conidia from the tTA-1 or tTA-2 transformants were spotted onto the center of a plate containing 1 mg/ml hygromycin and the indicated concentrations of doxycycline (μg/ml) and colony diameter was measured with time. (B): Doxycycline regulation of hph RNA levels in the tTA-1 and tTA-2 strains by Northern blot analysis. Total RNA isolated from overnight cultures grown in the presence of 0–200 μg/ml doxycycline was fractionated by agarose gel electrophoresis, transferred to nylon membranes, and probed with a 32P-labeled hph probe. (C): Hybridization intensity in each lane of the Northern blot in (B) was normalized to levels of the SYBR-green II-stained rRNA by phosphorimager analysis and is shown as a percentage of the levels seen in the tTA-1 transformant in the absence of doxycycline. Figure 5 Effects of doxycycline on hygromycin sensitivity of a strain expressing rtTA in conjunction with the tetO7-hph reporter gene. Ten thousand conidia were spotted onto the center of a plate containing 750 μg/ml hygromycin and the indicated concentrations of doxycycline (μg/ml) and colony diameter was measured with time. Below: levels of hph RNA levels in the rtTA transformant by Northern blot analysis. Total RNA isolated from overnight cultures grown in the presence of 0, 5 or 50 μg/ml of doxycycline was fractionated by agarose gel electrophoresis, transferred to nylon membranes, and probed with a 32P-labeled hph probe. Figure 6 Doxycycline-regulation is enhanced in low-iron medium. Total RNA was isolated from an overnight culture of the tTA-1 transformant (Fig. 4) growing in Aspergillus minimal medium supplemented with one tenth the normal concentration of iron and the indicated concentrations of doxycycline (0–50 μg/ml). RNA was fractionated by agarose gel electrophoresis, transferred to nylon membranes, and probed with a 32P-labeled hph probe. Wild type A. fumigatus is included in the first lane as a negative hybridization control. Hybridization intensity was normalized to levels of SYBR-green II-stained rRNA by phosphorimager analysis and is presented in the graph as a percentage of the signal obtained in the tTA-1 strain in the absence of doxycycline (0). Table 1 Plasmids and strains Plasmids Selectable marker Transactivator Promoter Gene Terminator Promoter Gene Terminator p473 - - - gpdA(-679) tTA cgrA p434 gpdA(-679) hph trpC gpdA(-679) tTA cgrA p444 gpdA(-679) ble CYC1 gpdA(-679) tTA cgrA p474 - - - gpdA(-679) rtTA cgrA p502 gpdA(-679) hph trpC gpdA(-679) rtTA cgrA p480 gpdA(-679) ble CYC1 gpdA(-679) rtTA cgrA p482 tetO7-gpdA(-175) hph trpC - - - p500 TcgrA-tetO7-PgpdA(-175) hph trpC - - - Strains Genotype/construction Source wt H237 David Holden Af-tTA-1 H237 (p444, p482) – isolate 1 This study Af-tTA-2 H237 (p444, p482) – isolate 2 This study Af-rtTA H237 (p480, p500) This study ==== Refs Ho PL Yuen KY Aspergillosis in bone marrow transplant recipients Crit Rev Oncol Hematol 2000 34 55 69 10781748 Lin SJ Schranz J Teutsch SM Aspergillosis case-fatality rate: systematic review of the literature Clin Infect Dis 2001 32 358 366 11170942 10.1086/318483 Bhabhra R Miley MD Mylonakis E Boettner D Fortwendel JR Panepinto J Postow M Rhodes JC Askew DS Disruption of the Aspergillus fumigatus gene encoding nucleolar protein CgrA impairs thermotolerant growth and reduces virulence Infect Immun 2004 72 4731 4740 15271935 10.1128/IAI.72.8.4731-4740.2004 Fortwendel JR Panepinto JC Seitz AE Askew DS Rhodes JC Aspergillus fumigatus rasA and rasB regulate the timing and morphology of asexual development Fungal Genet Biol 2004 41 129 139 14732259 10.1016/j.fgb.2003.10.004 Wasylnka JA Moore MM Uptake of Aspergillus fumigatus Conidia by phagocytic and nonphagocytic cells in vitro: quantitation using strains expressing green fluorescent protein Infect Immun 2002 70 3156 3163 12011010 10.1128/IAI.70.6.3156-3163.2002 Langfelder K Philippe B Jahn B Latge JP Brakhage AA Differential expression of the Aspergillus fumigatus pksP gene detected in vitro and in vivo with green fluorescent protein Infect Immun 2001 69 6411 6418 11553585 10.1128/IAI.69.10.6411-6418.2001 Liu W May GS Lionakis MS Lewis RE Kontoyiannis DP Extra copies of the Aspergillus fumigatus squalene epoxidase gene confer resistance to terbinafine: genetic approach to studying gene dose-dependent resistance to antifungals in A. fumigatus Antimicrob Agents Chemother 2004 48 2490 2496 15215099 10.1128/AAC.48.7.2490-2496.2004 Romero B Turner G Olivas I Laborda F De Lucas JR The Aspergillus nidulans alcA promoter drives tightly regulated conditional gene expression in Aspergillus fumigatus permitting validation of essential genes in this human pathogen Fungal Genet Biol 2003 40 103 114 14516763 10.1016/S1087-1845(03)00090-2 Gossen M Bonin AL Bujard H Control of gene activity in higher eukaryotic cells by prokaryotic regulatory elements Trends Biochem Sci 1993 18 471 475 8108860 10.1016/0968-0004(93)90009-C Gossen M Freundlieb S Bender G Muller G Hillen W Bujard H Transcriptional activation by tetracyclines in mammalian cells Science 1995 268 1766 1769 7792603 Weinmann P Gossen M Hillen W Bujard H Gatz C A chimeric transactivator allows tetracycline-responsive gene expression in whole plants Plant J 1994 5 559 569 8012406 Gossen M Bujard H Tight control of gene expression in mammalian cells by tetracycline-responsive promoters Proc Natl Acad Sci U S A 1992 89 5547 5551 1319065 Stebbins MJ Yin JC Adaptable doxycycline-regulated gene expression systems for Drosophila Gene 2001 270 103 111 11404007 10.1016/S0378-1119(01)00447-4 Nagahashi S Nakayama H Hamada K Yang H Arisawa M Kitada K Regulation by tetracycline of gene expression in Saccharomyces cerevisiae Mol Gen Genet 1997 255 372 375 9267432 10.1007/s004380050508 Nakayama H Mio T Nagahashi S Kokado M Arisawa M Aoki Y Tetracycline-regulatable system to tightly control gene expression in the pathogenic fungus Candida albicans Infect Immun 2000 68 6712 6719 11083786 10.1128/IAI.68.12.6712-6719.2000 Nakayama H Izuta M Nagahashi S Sihta EY Sato Y Yamazaki T Arisawa M Kitada K A controllable gene-expression system for the pathogenic fungus Candida glabrata Microbiology 1998 144 ( Pt 9) 2407 2415 9782488 Stoyan T Gloeckner G Diekmann S Carbon J Multifunctional centromere binding factor 1 is essential for chromosome segregation in the human pathogenic yeast Candida glabrata Mol Cell Biol 2001 21 4875 4888 11438645 10.1128/MCB.21.15.4875-4888.2001 Degenkolb J Takahashi M Ellestad GA Hillen W Structural requirements of tetracycline-Tet repressor interaction: determination of equilibrium binding constants for tetracycline analogs with the Tet repressor Antimicrob Agents Chemother 1991 35 1591 1595 1929330 Gari E Piedrafita L Aldea M Herrero E A set of vectors with a tetracycline-regulatable promoter system for modulated gene expression in Saccharomyces cerevisiae Yeast 1997 13 837 848 9234672 10.1002/(SICI)1097-0061(199707)13:9<837::AID-YEA145>3.0.CO;2-T Moy TI Boettner D Rhodes JC Silver PA Askew DS Identification of a role for Saccharomyces cerevisiae Cgr1p in pre-rRNA processing and 60S ribosome subunit synthesis Microbiology 2002 148 1081 1090 11932453 Freundlieb S Baron U Bonin AL Gossen M Bujard H Use of tetracycline-controlled gene expression systems to study mammalian cell cycle Methods Enzymol 1997 283 159 173 9251018 Hughes CE Harris C Peterson LR Gerding DN Enhancement of the in vitro activity of amphotericin B against Aspergillus spp. by tetracycline analogs Antimicrob Agents Chemother 1984 26 837 840 6524900 Urlinger S Baron U Thellmann M Hasan MT Bujard H Hillen W Exploring the sequence space for tetracycline-dependent transcriptional activators: novel mutations yield expanded range and sensitivity Proc Natl Acad Sci U S A 2000 97 7963 7968 10859354 10.1073/pnas.130192197 Avery AM Goddard HJ Sumner ER Avery SV Iron blocks the accumulation and activity of tetracyclines in bacteria Antimicrob Agents Chemother 2004 48 1892 1894 15105154 10.1128/AAC.48.5.1892-1894.2004 Berens C Hillen W Gene regulation by tetracyclines. Constraints of resistance regulation in bacteria shape TetR for application in eukaryotes Eur J Biochem 2003 270 3109 3121 12869186 10.1046/j.1432-1033.2003.03694.x Deuschle U Meyer WK Thiesen HJ Tetracycline-reversible silencing of eukaryotic promoters Mol Cell Biol 1995 15 1907 1914 7891684 Belli G Gari E Piedrafita L Aldea M Herrero E An activator/repressor dual system allows tight tetracycline-regulated gene expression in budding yeast Nucleic Acids Res 1998 26 942 947 9461451 10.1093/nar/26.4.942 Punt PJ Oliver RP Dingemanse MA Pouwels PH van den Hondel CA Transformation of Aspergillus based on the hygromycin B resistance marker from Escherichia coli Gene 1987 56 117 124 2824287 10.1016/0378-1119(87)90164-8 Boettner Huebner N Rhodes JC Askew DS Molecular cloning of Aspergillus fumigatus CgrA, the ortholog of a conserved fungal nucleolar protein Med Mycol 2001 39 517 521 11798057 Cove DJ The induction and repression of nitrate reductase in the fungus Aspergillus nidulans Biochim Biophys Acta 1966 113 51 56 5940632	15649330	PMC546209	CC BY	2021-01-04 16:03:40	no	BMC Microbiol. 2005 Jan 13; 5:1	utf-8	BMC Microbiol	2,005	10.1186/1471-2180-5-1	oa_comm
==== Front BMC PhysiolBMC Physiology1472-6793BioMed Central London 1472-6793-5-11564711110.1186/1472-6793-5-1Research ArticleImmunolocalization of KATP channel subunits in mouse and rat cardiac myocytes and the coronary vasculature Morrissey Alison [email protected] Erika [email protected] Jennifer [email protected] Lavanya [email protected] Chowdhury Piyali [email protected] Sandra [email protected] Gwendolyn [email protected] XiaoYong [email protected] Hidetada [email protected] Tomoe Y [email protected] Michael [email protected] Jonathan P [email protected] Andrew [email protected] William A [email protected] Pediatric Cardiology, NYU School of Medicine, New York, USA2 Pharmacology, NYU School of Medicine, New York, USA3 Physiology and Neurosciences, NYU School of Medicine, New York, USA4 Department of Molecular Physiology, National Cardiovascular Center Research Institute, Osaka, Japan5 BHF Laboratories and Department of Medicine, University College London, UK2005 12 1 2005 5 1 1 9 8 2004 12 1 2005 Copyright © 2005 Morrissey et al; licensee BioMed Central Ltd.2005Morrissey et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Electrophysiological data suggest that cardiac KATP channels consist of Kir6.2 and SUR2A subunits, but the distribution of these (and other KATP channel subunits) is poorly defined. We examined the localization of each of the KATP channel subunits in the mouse and rat heart. Results Immunohistochemistry of cardiac cryosections demonstrate Kir6.1 protein to be expressed in ventricular myocytes, as well as in the smooth muscle and endothelial cells of coronary resistance vessels. Endothelial capillaries also stained positive for Kir6.1 protein. Kir6.2 protein expression was found predominantly in ventricular myocytes and also in endothelial cells, but not in smooth muscle cells. SUR1 subunits are strongly expressed at the sarcolemmal surface of ventricular myocytes (but not in the coronary vasculature), whereas SUR2 protein was found to be localized predominantly in cardiac myocytes and coronary vessels (mostly in smaller vessels). Immunocytochemistry of isolated ventricular myocytes shows co-localization of Kir6.2 and SUR2 proteins in a striated sarcomeric pattern, suggesting t-tubular expression of these proteins. Both Kir6.1 and SUR1 subunits were found to express strongly at the sarcolemma. The role(s) of these subunits in cardiomyocytes remain to be defined and may require a reassessment of the molecular nature of ventricular KATP channels. Conclusions Collectively, our data demonstrate unique cellular and subcellular KATP channel subunit expression patterns in the heart. These results suggest distinct roles for KATP channel subunits in diverse cardiac structures. ==== Body Background ATP-sensitive (KATP) channels are widely expressed in both excitable and non-excitable tissue types throughout the body. However, differences exist in the functional and pharmacological properties of various KATP channels in different tissues. This functional diversity of KATP channels is also reflected in the cardiovascular system. KATP channels are abundantly expressed in ventricular myocytes, where they are probably best characterized. These channels have a high unitary conductance, are inhibited by ATP in the micromolar range, are blocked by glibenclamide (but not tolbutamide) and opened by pinacidil (and not by diazoxide). KATP channels also exist in the coronary vasculature, where they function to maintain basal coronary blood flow [1]. KATP channels in the coronary smooth muscle have a low unitary conductance (~30 pS) and are blocked by glibenclamide and activated by KATP channel openers and adenosine [2]. KATP channels exist in the coronary endothelium [3], but their biophysical properties remain largely unidentified. In addition to this diverse distribution of plasmalemmal KATP channels in the heart, KATP channels with unique biophysical and pharmacological profiles are also believed to be expressed in the mitochondrial inner membrane [4]. KATP channels are increasingly well characterized at the molecular level. In order to express a functional channel that resembles native KATP channels in terms of their biophysical and pharmacological properties, a combination of two types of subunits is necessary. It is now understood that Kir6 subunits form a pore-forming structure through which K+ ions transverse the membrane whereas SUR subunits assemble with the latter to modulate the channel's function and to confer unique pharmacological properties to the channel complex [5,6]. Two genes each code for the two known Kir6 subfamily members (Kir6.1 and Kir6.2) and for the two known SUR members (SUR1 and SUR2). Alternative splicing of SUR2 gives rise to at least two functionally relevant isoforms (SUR2A and SUR2B) with distinct pharmacological profiles [5]. It is widely believed that ventricular KATP channels consist of the specific combination of Kir6.2 and SUR2A subunits and that KATP channels in vascular smooth muscle consist of Kir6.1 and SUR2B subunits. This view is consistent with results from gene targeting experiments, which demonstrate the absence of functional sarcolemmal KATP channels in ventricular myocytes from Kir6.2(-/-) mice and the coronary abnormalities that develop in Kir6.1 and SUR2 null mice [5]. Although they are powerful tools, gene knockout approaches can overemphasize certain important aspects of gene function and may overlook more subtle effects of protein function and interaction. At first sight, these models do not adequately explain the reports of SUR1 mRNA expression in the heart [7], or the observation that anti-SUR1 antisense oligonucleotides inhibit KATP channels of ventricular myocytes [8]. They also do not provide a functional basis for the known expression of Kir6.1 mRNA and protein in cardiac myocytes [9-12]. or explain the molecular composition of the endothelial KATP channel. The specific cellular and subcellular localization of proteins can be used to predict their function. We therefore used antibodies specific for each of the KATP channel subunits to determine their cellular and subcellular localization in the mouse and rat heart. Our results suggest distinct roles for each of the KATP channel subunits in diverse cardiac structures. Results Given the reports of expression of each of the KATP channel subunits in the heart (see earlier), we performed immunohistochemistry and immunocytochemistry to determine the cellular and subcellular localization of Kir6.1, Kir6.2, SUR1 and SUR2 subunits in mouse and rat ventricle. To this end, we stained frozen sections of cardiac ventricular tissue as well as cardiac myocytes enzymatically isolated from mouse and rat hearts. Where possible, we used different antibodies to the subunits to ensure that the staining pattern observed was specific. Characterization of the antibodies used in this study We performed Western blotting to determine the specificity of the antibodies used in this study. Three different anti-Kir6.1 antibodies (NAF-1, CAF-1 and C-16) all detect a band that migrates with an apparent molecular size of 44 kDa in Western blotting of rat heart membrane fractions (Fig 1). A 44 kDa band was also detected by the 78A antibody (not shown). These antibodies did not cross-react with Kir6.2, since they detected only Kir6.1 (and not Kir6.2) in parallel experiments on cells transfected with various KATP channel subunit combinations (data not shown, see also reference [13]). Figure 1 Characterization of the antibodies used in this study. Rat heart membrane fractions were separated on 8–12% denaturing PAGE and Western blotting was performed using Kir6.1 (NAF1, CAF-1 or C-16) or Kir6.2 (G-16 or 76A) antibodies. In other experiments, lysates of HEK293 cells stably transfected with Kir6.1/SUR2B, or lysates of COS7L cells transiently transfected with SUR1, SUR2A or SUR2B cDNAs, were separated with PAGE and respectively immunoblotted with anti-SUR2 (C-15) or anti-SUR1 (C-16) antibodies. Molecular size markers are indicted as appropriate. Both the 76A and G-16 anti-Kir6.2 antibodies have previously been characterized and we demonstrated that they specifically detect a ~38 kDa band in Western blotting of Kir6.2 transfected cells and do not detect heterologously expressed Kir6.1 protein [13,14]. Here we show that both of these antibodies also detect Kir6.2 subunits as a ~38 kDa band in Western blotting of heterologously expressed Kir6.2 protein or rat heart membrane fractions (Fig 1). The anti-SUR1 antibodies specifically detect SUR1 protein (170 kDa) in cell lysates of COS7L cells transiently transfected with SUR1/Kir6.2 cDNA as well as in membrane fractions obtained from mouse hearts [15]. In the cell lystates from SUR2B/Kir6.2 transfected cells, the SUR2 antibody recognizes a specific band at 150 kD in transfected cells only (Fig 1) and did not detect SUR1 (not shown). Thus, each of the antibodies used in this study detected proteins at the correct molecular size in Western blotting and did not cross-react non-specifically with other proteins. Kir6.1 localization in the murine heart We used an immunohistochemistry approach to identify the localization of Kir6.1 protein in cryostat sections of mouse ventricles. We used three separate antibodies that produced similar results (NAF-1, CAF-1 and C-16). We also used another anti-Kir6.1 antibody (78A) but this antibody did not perform well in these assays. A typical result is shown in Fig 2A where Kir6.1 protein was ubiquitously detected throughout the ventricle. Closer inspection shows that Kir6.1 protein is expressed in a sarcomeric striated pattern in ventricular myocytes. This effect is more pronounced in epicardial myocytes (left hand side of panel A and Fig 2B). In the midmyocardium, a punctate staining pattern is apparent (Fig 2C). This was observed in over 50 different cryosections that we examined (also observed with the CAF-1 antibody). Figure 2 Immunohistochemistry demonstrating the regional distribution of Kir6.1 subunits in cryostat sections of the mouse ventricle. A: The low magnification image demonstrates regional expression differences with more prominent myocyte staining apparent in the epicardium (left) than in the mid-myocardium (right). Image width is 750 μm. B and C: Higher magnification of the same slide shown in previous panel showing an epicardial (B) and midmyocardial section (C). The image widths are respectively 102 and 96 μm. D: Double stained section of a midmyocardial section using NAF-1 anti-Kir6.1 antibodies (D1) and ICAM-2 antibodies as a marker of endothelial cells. (D2). The image width is 119 μm. The secondary antibodies used were Cy-3 conjugated donkey anti-rabbit and Cy-5 conjugated donkey anti-rat F(ab')2 IgG fragments. The cylindrical shape of the punctate structures in Fig 2C is reminiscent of coronary blood vessels. We tested various antibodies to find suitable markers for smooth muscle- and endothelial cells in the coronary vasculature. We found antibodies against smooth muscle α-actin to be a good marker for coronary vessels (Fig 3A) typically having diameters upwards of about 10 μm. We also tested an antibody against the apical endothelial protein ICAM-2 and found the antibody to detect endothelial cells lining the inner layer of coronary arteries (Fig 3B and 3C). Additionally, the anti-ICAM-2 antibodies stained a large number of smaller vessel-like structures having diameters typically smaller than 15 μm (average around 6–8 μm). These smaller vessels apparently do not have an appreciable amount of vascular smooth muscle cells, as judged by the lack of smooth muscle α-actin staining, suggesting that they may be coronary capillaries (the possibility that some of them may be pre-capillary arterioles with low amounts of smooth muscle cannot be excluded). To examine the localization of Kir6.1 in coronary blood vessels, we co-stained mouse ventricle with NAF-1 anti-Kir6.1 antibodies and anti-ICAM-2 antibodies (a marker for the vascular endothelium). As shown in Figure 2D, there is clear correspondence between Kir6.1 staining and ICAM-2 localization, suggesting high expression levels of Kir6.1 in the coronary vasculature. Figure 3 Immunohistochemistry markers to distinguish between vascular smooth muscle and endothelial cells. A cryosection of mouse heart ventricle was simultaneously stained with FITC-conjugated smooth muscle α-actin antibody (A) and an antibody against the apical endothelial membrane protein, ICAM-2 (B). The secondary antibodies to detect ICAM-2 was Cy-5 conjugated donkey anti-rat (pseudo-colored red for clarity). Panel C is an overlay of the preceding two panels. The image width is 238 μm. Given the high sensitivity of the NAF-1 antibody, we were able to perform a sub-cellular localization study of Kir6.1 protein in the mouse heart (Fig 4; this approach was not possible with other antibodies, which produced less sensitive staining). We co-stained a cryostat section with antibodies against smooth muscle α-actin (A1), Kir6.1 (A2) and the endothelial ICAM-2 protein (A3). An overlay of these three images is shown in panel B. A higher magnification of the area roughly represented by the boxed area (panel C) demonstrates that Kir6.1 subunits are ubiquitously expressed and are present in ventricular myocytes, the coronary smooth muscle walls as well as in endothelial cells. Of these cells types, the highest expression levels appear to occur in the vasculature. Figure 4 Triple stain immunohistochemistry of mouse ventricle demonstrating the distribution of Kir6.1 subunits in the coronary vasculature. The cryostat section was stained with antibodies against smooth muscle α-actin (A1), Kir6.1 (A2) and endothelial-specific ICAM-2 (A3). B:Overlay of the three panels shown in panels A. C: Higher magnification of the boxed area highlighted in panel B. Kir6.2 localization in the murine heart We used several antibodies against Kir6.2 subunits to determine their cellular localization. Both the 76A and the G-16 antibodies (Santa Cruz) gave similar results. The W62 antibody developed by us [13] did not seem to stain more than background and we therefore assumed this antibody not to work in this assay. In terms of staining in the vasculature, we detected Kir6.2 subunit expression mainly in the endothelium (arrows in Fig 5A) and not in smooth muscle (as judged by a lack of co-localization with smooth muscle α-actin). Evidently, Kir6.2 subunits were expressed in cardiac myocytes as well, as demonstrated in Fig 5B. The staining occurs in a sarcomeric striated pattern in ventricular myocytes (distance interval of roughly 2.2 μm). Although not as apparent as with Kir6.1, there appears to be expression of Kir6.2 subunits in small coronary blood vessels (10 μm or less). These structures are also stained by Kir6.1 antibodies (arrows in Fig 5B and 5C), suggesting co-localization of Kir6.1 and Kir6.2 subunits in small coronary blood vessels. Figure 5 Immunohistochemistry demonstrating the cellular distribution of the Kir6.2, SUR1 and SUR2 subunits in cryostat sections of the mouse ventricle. A: Co-staining of mouse ventricular section with smooth-muscle α-actin (green) and anti-Kir6.2 antibodies (Santa Cruz). The latter antibody was detected with Cy-5 conjugated donkey anti-goat IgG (blue). B and C: Double-staining with anti-Kir6.2 antibodies (Santa Cruz) and CAF-1 anti-Kir6.1 antibodies (detected with Cy-3 conjugated donkey anti-chicken IgY (red). The image width of panel A is 109 μm and for panels B and C it is 89 μm. D: Co-staining of a midmyocardial section with anti-SUR1 antibodies (Cy3-conjugated donkey anti-goat secondary antibodies were used; shown in red) and FITC-conjugated smooth muscle anti-actin antibodies (shown in green). E and F: Cryosections stained with a pan anti-SUR2 antibody (secondary antibody was Cy-3 conjugated anti goat IgG. The image widths for panels D-F respectively are 238, 238 and 88 μm. Localization of SUR subunits in murine heart Using anti-SUR1 antibodies, we observed staining predominantly in cardiac ventricular myocytes (Fig 5D). The lack of staining of either large or small coronaries suggests that SUR1 subunits are not expressed in the coronary vasculature of the mouse heart (note the lack of co-localization of SUR1 with smooth muscle α-actin). In contrast, pan-SUR2 antibodies stain both ventricular myocytes in a regular striated sarcomeric pattern as well as small coronary blood vessels (Fig 5E and 5F). Note the lack of high expression levels in larger coronary arteries (round structure in Fig 5F), demonstrating that expression of SUR2 subunits occurs predominantly in small coronary vessels (less than 10 μm). In separate experiments, we did not detect a particularly strong co-localization with smooth muscle α-actin (not shown), ruling against the possibility that SUR2 is strongly expressed in the smooth muscle cells of larger coronary vessels. Subcellular localization of KATP channel subunits in enzymatically isolated ventricular myocytes Fixation and permeabilization procedures can affect the outcome of immunocytochemistry experiments. We therefore compared three different methods. Isolated cardiac myocytes were (a) fixed with paraformaldehyde and permeabilized with Triton X-100, (b) fixed and permeablized in a single step by ice-cold methanol or (c) subjected to a two-step protocol with paraformaldehyde fixation followed by methanol fixation/permeabilization. Typical results obtained with the anti-Kir6.2 (76A) antibody are shown in Fig 6. In general, we found methanol fixation to preserve membrane staining but to cause a loss of intracellular fluorescence (Fig 6A). Paraformaldehyde fixation, in contrast, better preserved staining of intracellular structures (Fig 6B). The two-step fixation protocol in general gave similar results to paraformaldehyde fixation, but the fluorescence intensity was generally much higher and more clearly defined (Fig 6C). This may be due to better preservation of protein antigenicity and/or improved permeabilization (and hence improved antibody accessibility). Our data are consistent with a recent report describing the two-step protocol to better preserve the in-vivo subcellular localization of proteins [16]. We consequently used the two-step protocol for all subsequent experiments to examine the subcellular localization of KATP channel subunits. Figure 6 Effect of different fixation protocols on immunocytochemistry of ventricular myocytes. Enzymatically isolated ventricular myocytes were subjected to fixation protocols with (A) methanol, (B) paraformaldehyde or (C) sequential double fixation with paraformaldehyde followed by methanol. The primary antibody in all cases was against Kir6.2 subunits (76A) and the secondary antibody was Cy-3 conjugated donkey anti-rabbit IgG. The image widths are respectively 137, 107 and 139 μm. The subcellular distribution of Kir6.1 and Kir6.2 subunits in isolated mouse ventricular myocytes is shown in Fig 7A. Kir6.2 subunits are expressed in a regular striated pattern throughout the myocyte (A2). Kir6.1 subunits showed a similar expression pattern (A1), with the exceptions that (a) Kir6.1 expression appears to be more punctate and (b) staining is more prominent at the myocardial surface. Since we used antibodies developed in different species (chicken anti-Kir6.1 antibody and rabbit anti-Kir6.2 antibody) we were able to detect both proteins in the same myocyte with little cross-reactivity of the secondary antibodies. Although there is some degree of overlap in the subcellular expression of Kir6.1 and Kir6.2 subunits (yellow in A3), it is apparent that there are areas within the cell that express Kir6.2 but not Kir6.1 subunits. Thus, the possibility exists that these subunits may have different functional roles within the ventricular myocyte. Similarly, SUR1 and SUR2 subunits also have distinct subcellular localizations. We consistently observed strong surface staining with SUR1 antibodies (Fig 7B) whereas SUR2 antibodies diffusely stain throughout the width of the cell in a sarcomeric repeating pattern (Fig 7C). To address the question whether there is co-localization of any of the KATP channel subunits with mitochondria, we used MitoTracker Red as a mitochondrial marker. We found MitoTracker staining to dissipate during immunocytochemistry protocols and co-labeling with KATP channel antibodies was therefore not an effective strategy. Nevertheless, the pattern of MitoTracker staining that we observed shortly after fixation (Fig 7D) was inconsistent with predominant subcellular distribution of any of the KATP channel subunits and we conclude therefore that KATP channel subunits are not abundantly expressed in cardiac mitochondria. Figure 7 Immunocytochemistry of isolated mouse ventricular myocytes demonstrating the subcellular localization of Kir6.1, Kir6.2, SUR1 and SUR2 subunits. A: Double staining of a ventricular myocyte with the CAF-1 anti-Kir6.1 antibody (A1) and 76A anti-Kir6.2 antibody (A2). Panel A3 is an overlay of panels A1 and A2. Secondary antibodies used were Cy-3 conjugated donkey anti-chicken IgY (red) and Cy-2 conjugated donkey anti-rabbit IgG (green). Yellow in panel C demonstrates areas of co-localization. The image width is 91 μm. B: Ventricular myocyte probed with anti-SUR1 antibodies and detected with Cy-3 conjugated donkey anti goat secondary antibodies. Image width is 148 μm. C: Staining with a pan-SUR2 antibody (detected with Cy-2 conjugated donkey anti-goat IgG). The image width is 229 μm. D: An isolated myocyte was stained with MitoTracker Red (500 nM) before being paraformaldehyde fixed and viewed with confocal microscopy Image width is 47 μm. Experiments were designed closer to examine the subcellular expression of SUR1 subunits relative to Kir6 pore-forming subunits (Fig 8). There was a remarkable degree of co-localization of SUR1 subunits with Kir6.1 subunits (Fig 8A) but not with Kir6.2 subunits (Fig 8B). Figure 8 Subcellular localization of Kir6.1, Kir6.2 and SUR1 subunits in ventricular myocytes. A: Co-staining of a mouse ventricular myocyte with antibodies to Kir6.1 (A1: NAF-1) or SUR1 (A2: C-16). The secondary antibodies were Cy2-conjugated donkey anti-rabbit and Cy3-conjugated donkey anti-goat. The degree of co-localization after background elimination is depicted in panel A3 by plotting pixel intensities of the two channels against each other. Pseudo-coloring of pixels indicates degree of co-localization (black is low and white high). B: Co-staining of a mouse cardiac myocyte with anti-Kir6.2 antibodies (76A) and anti SUR1 antibodies. Secondary antibodies used were Cy-2 conjugated donkey anti-rabbit to detect Kir6.2 (green) and Cy-3 conjugated donkey anti-goat IgG to detect SUR1 (red). A 3-D reconstruction was performed (58 stacked images with a voxel size of 0.23 μm3). The image width is 119 μm. The images shown on the bottom and right insets are cut-through projections of the cell width (the regions of cross-sections areas shown by the white lines; the middle of each image is also indicated by a white line for clarity). We finally examined the subcellular expression patterns of KATP channel subunits in another species. We chose rat ventricular myocytes for this purpose. We essentially obtained the same results as with mouse myocytes. Using different antibodies against Kir6.1 (NAF-1 and C16) we observed strong surface expression of this subunit with a smaller degree of intracellular labeling (Fig 9A and 9B). SUR1 antibodies also strongly labeled the cell surface (Fig 9C). In contrast, Kir6.2 and SUR2 antibodies both labeled repetitive patterns at sarcomeric distances (Fig 9D and 9E). Furthermore, there is strong co-localization of Kir6.2 and SUR2 subunits in ventricular myocytes (yellow in Fig 9F). Figure 9 Subcellular localization of KATP channel subunits in isolated rat ventricular myocytes. A: Staining with NAF-1 anti-Kir6.1 antibodies (detection with Cy-3 conjugated donkey anti rabbit). Image width is 128 μm. B: Staining with a different anti-Kir6.1 antibody (C16, Santa Cruz); secondary antibody used was Cy-3 conjugated donkey anti-goat IgG. Image width is 136 μm. C: Staining with an anti-SUR1 antibody. Image width is 128 μm. D: Co-localization of Kir6.2 and SUR2 subunits. The preparation was co-stained with an anti-Kir6.2 antibody (76A; detection with Cy-2 conjugated donkey anti-rabbit IgG; green) and SUR2 antibodies (detected with Cy-5 conjugated donkey anti-goat IgG; pseudo-colored red for clarity). Panel D3 is an overlay of panels D1 and D2; yellow is indicative of co-localization. Image width is 125 μm. Discussion Antibodies used in this study A significant strength of our study is that we extensively characterized the antibodies used. We performed Western blotting with membrane fractions obtained from the heart to ensure that a band of the expected size is detected. Further, we chose antibodies that showed little cross-reactivity with other proteins, as judged by the absence of non-specific bands. In as far as it was possible we used different antibodies to the same KATP channel subunits in immunostaining experiments to ensure that the same cellular and subcellular distribution staining patterns occurred. Although not shown, we always performed negative controls to ensure that no staining was observed when the primary antibodies were omitted (to eliminate non-specific staining by the secondary antibodies used) or that staining could be blocked by preincubation of antibody with the peptide to which the antibody has been raised. Further, we used the primary antibodies at the lowest concentrations possible to eliminate possible non-specific cross-reactivity with other proteins. Our study is a comprehensive description of the cellular and subcellular expression patterns of KATP channel subunits in the heart given our stringent criteria and the panel of antibodies available to us. Expression of Kir6.2 and SUR2 subunits in ventricular myocytes Sarcolemmal KATP channels in ventricular myocytes have been described more than two decades ago [17]. Cardiac sarcolemmal KATP channels have been described to consist of hetero-octameric complexes of Kir6.2 and SUR2A subunits [5,18,19]. This concept was based on the similarities in the biophysical and pharmacological characteristics when comparing heterologously expressed Kir6.2/SUR2A channels with native cardiac KATP channels [20] and also because of the known expression of Kir6.2 and SUR2A mRNA and protein in the heart. Our data demonstrate both Kir6.2 and SUR2A subunits to be expressed in ventricular myocytes. Furthermore, we find that these two subunits co-localize, which is consistent with the biochemical, functional and pharmacological data supporting the concept that they can combine to form a heteromeric channel complex [5]. Our data are also in agreement with the finding that knockout mice deficient of Kir6.2 or SUR2 subunits lack KATP channels in the ventricular myocyte [21,22]. It is interesting that Kir6.2/SUR2 subunits are expressed in a regular striated pattern in ventricular myocytes. Furthermore, close inspection of the images shows that both Kir6.2 and SUR2 expression is somewhat punctate. These observations are in complete agreement with previous studies describing the expression of SUR2 isoforms in the t-tubules and sarcolemma [23] and the subcellular localization of sarcolemmal KATP channels as determined by functional microscopy. Scanning ion conductance (patch clamp) microscopy data have demonstrated KATP channels to be organized in small clusters and to be anchored in the Z-grooves (t-tubular openings) of the sarcolemma [24]. Collectively, these data suggest that KATP channels are predominantly expressed in the t-tubular system. The implications of KATP channels present in the t-tubular system are not entirely clear. Since t-tubular ion channels may control action potential propagation into the cardiac myocyte, it may be possible for KATP channels to have a role in the spread of excitation and action potential duration, particularly during conditions of metabolic impairment when these channels are more prone to opening. A shorter action potential duration in the t-tubular system would imply less Ca2+ entry at the local control sites of SR Ca2+ release and hence reduced contractility, which may in part explain the negative inotropic effects observed with KATP channel openers [25]. However, the picture may be more complex since both Kir6.1 and SUR1 subunits are also expressed in ventricular myocytes. Expression of Kir6.1 and SUR1 subunits in ventricular myocytes We found clear expression of Kir6.1 and SUR1 subunits in cardiac ventricular myocytes. Interestingly, both of these two subunits are strongly expressed at the sarcolemmal surface, but their functions in the sarcolemma are currently not understood. Since ventricular KATP channels can be recorded in hearts from knockout mice lacking Kir6.1 subunits [9], it appears that Kir6.1 subunits are not an absolute requirement for the formation of functional ventricular KATP channels. It may be possible for Kir6.1 subunits to have a role in the pathophysiological setting, as demonstrated by the upregulated Kir6.1 expression levels during cardiac remodeling after ischemia or hypoxia [11,26]. To our knowledge, cardiac KATP channels have not been studied in SUR-/- mice. However, SUR1 antisense oligonucleotides inhibit KATP channels in rat ventricular myocytes [8], suggesting a functional role for these subunits in ventricular sarcolemmal KATP channel function. Our data, demonstrating that both Kir6.1 and SUR1 subunits exhibit strong sarcolemmal expression, may require a reassessment of the molecular composition of ventricular KATP channels during normal and pathophysiological conditions. Expression of Kir6 and SUR subunits in mitochondria The concept has evolved that KATP channels are expressed in the mitochondrial inner membrane and that these channels are involved in the protection of the heart afforded by ischemic preconditioning [27,28]. The molecular nature of mito-KATP channels remains to be identified. There are almost as many reports describing the presence of Kir6.0 subunits in cardiac mitochondria [23,29,30]. as there are denouncing their existence in these organelles [31,32]. We did not observe strong localization of KATP channel subunits in ventricular mitochondria. However, the technique of immunocytochemistry does not have sufficient resolution to rule out the existence of KATP channel subunits in mitochondria and our data therefore do not add significantly to this debate, other than demonstrating that KATP channel subunits are not abundantly expressed in mitochondria of ventricular myocytes. Expression of KATP channel subunits in the coronary smooth muscle A tight coupling exists between metabolic status in the heart and coronary blood flow. KATP channels have been identified in several different vascular tissues, including the coronary vasculature [33]. KATP channels in coronary resistance vessels have also been implicated in physiologically important stimuli such as regulation of basal vascular tone, autoregulation of blood flow, hypoxia-induced coronary vasodilation, reactive hyperemia (a clinical index of coronary reserve) and ischemia (reviewed in [33,34]). Molecularly, the identity of coronary smooth muscle KATP channels has been characterized less extensively than the KATP channels in cardiomyocytes. A recent study employing in situ hybridization histochemistry examined Kir6.1 and SUR2B mRNA expression in different vascular beds, including the coronary vasculature [35]. Strong mRNA expression of these two subunits was found in coronary resistance arteries. Interestingly, Kir6.1/SUR2B expression was not found in coronary veins or venules. We found expression of Kir6.1 and SUR2B protein in primary human coronary artery smooth muscle cells and cryosections of human ventricle [13]. The present study is the first systematic and comparative characterization of KATP channel subunit expression in the intact coronary vasculature. We find Kir6.1 expression in blood vessels of different sizes, including large vessels such as the aorta and large arteries, but also in small resistance arterioles (as defined by their diameter of larger than 12–15 μm and the presence of a well-defined smooth muscle layer). Without using specific markers, we were not able to distinguish objectively between venules and arterioles, but we did occasionally observe vessels with a thin smooth muscle layer (possibly veins or venules) that only expressed Kir6.1 faintly (if at all). Collectively, our data using various anti-Kir6.1 antibodies generally suggest that Kir6.1 subunits are expressed in coronary arterial smooth muscle, and possibly to a lesser extent in coronary veins. In contrast, we did not observe any staining of the coronary smooth muscle with anti-Kir6.2 antibodies. We found strong staining of smaller coronary resistance vessels with the anti-SUR2 antibodies. We did not have access to suitable SUR2 isoform-specific antibodies, but the staining most probably reflects SUR2B expression. Curiously, we failed to see strong SUR2 expression in larger coronary arteries. This result is in apparent contradiction to the description that SUR2B mRNA expression occurs in larger coronaries [35] and the lack of KATP channels in the aortic cells of the SUR2 knockout mouse [36]. Reasons for this discrepancy are unclear, but may relate to the lack of sensitivity of the anti-SUR2 antibodies used, thus underestimating SUR2 protein expression in other structures. We did not observe SUR1 protein expression in the coronary smooth muscle. Our data are therefore in full support of the notion that KATP channels in coronary artery smooth muscle (particularly the smaller resistance vessels) are comprised of Kir6.1/SUR2 subunits (most likely the SUR2B isoform). Expression of KATP channel subunits in the coronary endothelium The evidence that endothelial KATP channels play a role in regulation of coronary blood flow is compelling. Endothelial KATP channels, for example, contribute to shear stress-induced endothelial release of the vasodilator nitric oxide in rabbit aorta [37] and may also mediate vasodilation in response to hyperosmolarity or acidosis in the coronary microvasculature [38,39]. Furthermore, the powerful vasodilatory effect of adenosine may also be mediated (at least in part) by endothelial KATP channels by stimulating the release of nitric oxide from the endothelium [40]. In the present study, we used immunohistochemistry approaches and identified Kir6.1, Kir6.2 and SUR2 protein in the endothelium lining coronary vessels (Fig. 4) as well as in coronary capillary endothelium (as defined by their small size of less than 10 μm, the presence of the endothelial marker ICAM-2 and the absence of vascular smooth muscle). Our data are supported by previous studies. Using RT-PCR techniques, it has been established that guinea pig coronary endothelial cells express Kir6.1, Kir6.2 and SUR2B subunits [41]. The presence of Kir6.1 and SUR2B mRNA also detected in the coronary endothelium using in situ hybridization histochemistry techniques [35]. Recently, using a combination of RT-PCR and Western blotting techniques, we also identified Kir6.1, Kir6.2 and SUR2B mRNA and protein expression in primary human coronary artery endothelial cells [13]. Importantly, in the latter study we used co-immunoprecipitation approaches to demonstrate that native human coronary endothelial KATP channels are heteromeric Kir6.1/Kir6.2 complexes in combination with SUR2B subunits [13]. Thus, the biophysical nature, modes of regulation and functional consequences of these heteromeric KATP channels in the endothelium may differ fundamentally from homomeric KATP channels found in other tissues. The investigation of endothelial KATP channels is currently the subject of some of our ongoing studies. Reservations For this type of study, the specificity of antibodies used is always a concern. To overcome this problem, we used multiple different antibodies where possible and obtained similar staining patterns. However, we only had access to a single antibody to each of the SUR subunits and consequently we have not been able to verify the specificity of these antibodies by comparing different antibodies to each other (as we have done in the case of the Kir6 subunits). Furthermore, we did not have access to antibodies to the various splice variants of SUR1 or SUR2 and our data therefore do not address the possibility of regional expression differences. A definitive study will require the use of tissues obtained from knockout animals (i.e. the immunostaining should be unequivocally absent in tissues from knockout animals). Viable knockout animals for each of the proteins under consideration have been generated, but we have not been able to obtain these animals (or tissues from these animals) for this purpose. Therefore, although we have taken every step possible to minimize non-specificity issues, our results should be interpreted within this limitation. Conclusions Our study is a comprehensive analysis of the various KATP channel subunits in the heart. We found each of the KATP channel subunits to be expressed in ventricular myocytes, but with varying expression patterns. The roles of Kir6.1 and SUR1 subunits in ventricular myocytes remain to be elucidated and may require a reassessment of the molecular nature of the cardiac KATP channel. Coronary smooth muscle expresses predominantly Kir6.1 and SUR2 subunits, whereas the coronary endothelium expresses Kir6.1, Kir6.2 and SUR2 subunits. Thus, there is wide diversity of KATP channel subunit expression within the heart which determines the functional responses of various cell types to physiological and pathophysiological demands. Methods Immunohistochemistry Adult mice were sacrificed by pentobarbital overdose and the hearts were rapidly removed. All animal experiments were approved by the institutional Animal Care Review Board. Hearts were perfused at 37°C through the aorta (Langendorff mode) with Tyrode's solution (in mM: NaCl 137, KCl 5.4, HEPES 10, CaCl2 1.8, MgCl2 1, NaH2PO4 0.33; pH adjusted to 7.4 with NaOH) containing pinacidil (100 μM) to cause maximal vasodilatation as to clear the vasculature of blood. Hearts were fixed by switching the perfusate to paraformaldehyde (4% in phosphate-buffered saline (PBS), pH adjusted to 7.4) for 15 minutes at room temperature. The heart was incubated in 4% paraformaldehyde overnight at 4°C. Following fixation, the tissue was incubated overnight at 4°C in 30% sucrose in PBS. The tissue was then embedded in M1 embedding matrix (Thermo Shandon, Pittsburgh, PA) and placed on dry ice until frozen. The block containing the tissue was sectioned using a cooled (-20°C) cryostat (Microm Cryo-Star HM 560, Kalamazoo MI) at 15 μm thicknesses. The sections were transferred to Superfrost Plus slides (Fisher Scientific) for further processing. Tissue sections were allowed to warm to room temperature. The staining protocol was carried out in a moist chamber to avoid dehydration. Blocking was performed for 60 min with Tris-buffered saline (TBS; in mM 137 NaCl, 50 Tris, 2.7 KCl, pH 7.4) containing 4% goat or donkey serum (depending on the secondary antibody being used) and 0.2% Triton X-100 at room temperature. The slides were incubated overnight at 4°C with primary antibodies (see below) diluted in TBS containing 0.1% serum and 0.2% Triton X-100. Double or triple immunofluorescent studies were carried out by incubating the tissue sections with more than one primary antibody at the same time. Sections were washed three times for 15 min each in TBS, and incubated with fluorescently labeled secondary antibodies (see below) for 1 h at room temperature. The samples were again washed three times for 15 min each in TBS. Sections were drained by blotting with filter paper and a drop of mounting medium (containing an anti-fade reagent) was added to the slides before mounting with a standard coverslip. The mounting medium was allowed to dry before the slides were imaged using a Leica PS2 confocal microscope equipped with an Argon 488 nm gas laser and Helium Neon lasers (543 and 633 lines). Most images were obtained using an emission pin hole of 1.1–1.6 AE with either a 20× (0.7 NA) or a 63× (1.2 NA) oil objective. Isolation and immunocytochemistry of cardiac myocytes Single ventricular myocytes were isolated from adult rats or mice using previously described procedures. Briefly, adult animals were sacrificed and the heart was rapidly removed and perfused in Langendorff mode (at 37°C) for sequential 5 min periods with Tyrode's solution and nominally Ca2+-free Tyrode's solution (same composition, but without the addition of CaCl2). Myocytes were dispersed using collagenese (Sigma type I; Sigma-Aldrich Chemical Corp, St. Louis, MO, USA) and proteinase (Sigma type XXIV). The ventricles were removed and chopped into small pieces and digested using Ca2+-free Tyrode's solution containing the same enzymes. Single dissociated myocytes were plated onto laminin (10 ug/cm3)-coated glass coverslips and incubated at 37°C for 15 min to allow attachment to the coverslips before being fixed. In some experiments, myocytes were incubated with 500 nM MitoTracker Red 580 (Molecular Probes, Eugene, OR) during this period. Three different fixation protocols were employed. Some myocytes were fixed in paraformaldehyde (4%) for 15 min at room temperature, whereas with others fixation and permeabilization was performed in a single step by incubation with ice-cold 100% methanol for 5 minutes at -20°C. However, in the majority of the myocytes presented in this study a two-step fixation protocol was used [16], in which myocytes were first fixed with paraformaldehyde (as described above) followed by methanol fixation/permeabilization. Irrespective of the fixation method, myocytes were then washed with Ca2+ and Mg2+-free PBS (Invitrogen, Carlsbad, CA). Myocytes were then incubated with 0.1% Triton X-100 (in PBS) for 15 min at room temperature, which permeabilizes surface membranes as well as those of intracellular organelles (this step was omitted when fixing the cells only with methanol but was included in the two-step fixation protocol). Following washing (2 × 5 min) and blocking (5% goat serum in PBS; 2 × 10 min) the cells were incubated with primary antibodies (1 h at room temperature), washed (3 × 10 min in PBS-serum) and incubated with secondary antibodies (45 min at room temperature). Following 4 washes (with PBS; 10 min each) coverslips were mounted and viewed using confocal microscopy. As a negative control, the primary antibody was adsorbed with the peptide against which it was made (when available). Negative staining controls (not shown) also included a null control, in which the primary antibody was omitted, which tested for non-specific staining of the secondary antibody. To avoid background from secondary antibodies alone, we normally pre-blocked the tissue with 5% normal serum from the same host species as the labeled secondary antibody. We used labeled secondary antibodies that have been pre-adsorbed against mouse and human and we titrated the labeled secondary antibody to obtain a maximal signal-to-noise ratio. Transfection of cells The coding regions of Kir6.1 and Kir6.2 (a gift from Dr. S. Seino, Kobe University Graduate School of Medicine, Japan) were subcloned into pcDNA3. HEK-293 or COS-7L cells were cultured in D-MEM (Invitrogen) supplemented with 10% heat-inactivated fetal bovine serum and 20 μg/mL gentamycin. Cells were co-transfected according to the manufacturer's recommendations (Fugene 6, Roche Applied Science, Indianapolis, IN) with Kir6.1 or Kir6.2 cDNA (obtained from Dr S. Seino), SUR1 (Dr J. Bryan, Baylor College of Medicine, Texas) or SUR2A cDNAs (a gift from Dr Seino). Cells were lysed 48 h post-transfection. Preparation of mouse heart membrane fraction Adult Sprague Dawley rats were anesthetized and the hearts were rapidly removed. Membranes were prepared essentially as described before [42]. The protein content was determined and equal amounts of proteins were subjected to Western blotting. Western blotting Cells were solubilized in lysis buffer [25 mmol/L Tris, 150 mmol/L NaCl, 5 mmol/L EDTA, 1% (v/v) Triton X-100, 0.5% (w/v) deoxycholate, pH 7.5 supplemented with a cocktail of protease inhibitors (Sigma)]. After centrifugation (10 min at 16,000 g), an equal volume of 2 × Laemmli loading buffer was added to the lysate. Proteins were separated by 10% SDS-PAGE and transferred to Immun-blot PVDF membrane (Bio-Rad Laboratories, Hercules, CA). The membrane was blocked and incubated with primary antibody (see below). As secondary antibodies, we used HRP-conjugated anti-rabbit, anti-mouse IgG (Amersham Biosciences, Piscataway, NJ) or anti-goat IgG in TBS-Tween for 1 hour and detected using a chemiluminescent substrate (SuperSignal, Pierce Biotechnology, Rockford, IL). Antibodies Antibodies against Kir6.1 subunits Antibodies (NAF1) were raised against a peptide corresponding to residues 20–31 of the Kir6.1 N-terminus (ENLRKPRIRDRLP). There is a high degree of sequence similarity in Kir6.1 subunits between species in this region. We also raised a Kir6.1 antibody (CAF-1) to this peptide in chickens. In each case, a C-terminal cysteine was added for conjugation purposes. Peptides were synthesized and the antibodies were generated commercially (Quality Controlled Biochemicals, Hopkinton, MA). Each of these antibodies were peptide affinity purified. There is sequence similarity (81% identity) between this peptide and the recently-identified human beta-V spectrin. We tested for possible cross-reactivity of NAF-1 with beta-V spectrin using antibodies generously provided by Dr Jon Morrow (Yale University) and demonstrated that our antibodies had no cross-reactivity with beta-V spectrin (please see online Additional file 1). It should further be noted that the antibody epitope has no similarity with mouse beta-V spectrin (see UniGene Cluster Hs.198161). Other anti-Kir6.1 antibodies that we attempted to use with varying degrees of success included the 78A antibody generated in the laboratory of Dr Tinker, which was raised in rabbits against a peptide corresponding to amino acids 399–420 of rat Kir6.1 with a terminal cysteine added for coupling purposes (RRNNSSLMVPKVQFMTPEGNQC) and the goat anti-Kir6.1 R-14 or C-16 C-terminal antibodies (sc-11224 and sc-11225; Santa Cruz Biotechnology, Santa Cruz, CA). In our hands, the 78A and R-14 antibodies failed to detect Kir6.1 protein in Western blotting of cardiac membrane fractions and did not appear to stain above background in immunohistochemistry assays (not shown). Antibodies against Kir6.2 subunits We used a goat anti-Kir6.2 G-16 antibody (sc-11228; Santa Cruz Biotechnology, Santa Cruz, CA) and an antibody (76A) developed in Dr Tinker's laboratory against a peptide (DALTLASSRGPLRKRSC) corresponding to a peptide within the Kir6.2 C-terminus (amino acids 357–372). Antibodies against SUR subunits We used a goat anti-SUR1 C-16 antibody (sc-5789; Santa Cruz Biotechnology) developed to an epitope mapping at the C-terminus and a goat anti-SUR2 C-15 C-terminal antibody (sc-5793; Santa Cruz Biotechnology). This antibody was initially sold as a pan-SUR2 antibody and was able to detect the SUR2A protein in Western blots of cell lysates from Kir6.2/SUR2A transfected cells, although with less sensitivity compared to SUR2B-transfected cells (data not shown). Currently, the antibody with the same catalog number is sold as being specific to SUR2B. We have not tested recent batches of this antibody for isoform specificity. Other antibodies Other antibodies used for immunolocalization included a mouse monoclonal α-actin smooth muscle antibody preconjugated to FITC (Clone 1A4; Sigma-Aldrich Corp, St. Louis, MO) and a rat monoclonal antibody (clone 3C4; BD Biosciences Pharmingen, San Diego, CA) raised against ICAM-2 (CD102), which is a cell surface glycoprotein constitutively expressed on vascular endothelial cells. Secondary antibodies used included Cy3-conjugated donkey anti-rabbit IgG, Cy3-conjgated donkey anti-chicken IgY, Cy5-conjugated F(ab')2 fragment donkey anti-rat IgG and a Cy3- or Cy5-conjugated donkey anti-goat IgG (all from Jackson ImmunoResearch Laboratories Inc, West Grove, PA). List of abbreviations Kir = Inward rectifying K+ channel family SUR = Sulphonylurea receptor KATP channel = ATP-sensitive K+ channel Authors' contributions AM, ER and JL carried out the immunohistochemistry experiments. PDC, SH and GL carried out the immunocytochemistry experiments. TN, AM, LP, HY, XT, JPG and AT participated in characterization of the antibodies. WAC, MA and TN participated in the design of the study. WAC conceived the study, participated in study design and coordination, and manuscript preparation. All authors made substantive intellectual contributions to the study, read and approved the final manuscript. Supplementary Material Additional File 1 NAF-1 antibody does not cross-react with beta V spectrin. COS7L cells have been transfected with Kir6.1 cDNA and cell lystates were subjected to Western blotting with anti-beta V spectrin antibodies or NAF-1 anti-Kir6.1 antibodies. Click here for file Acknowledgements These studies were supported by the National Institutes of Health (R01-HL064838), the American Heart Association (Established Investigator Award to WAC) and in part by the New York Masonic Seventh District Association, Inc. ==== Refs Samaha FF Heineman FW Ince C Fleming J Balaban RS ATP-sensitive potassium channel is essential to maintain basal coronary vascular tone in vivo Am J Physiol 1992 262 C1220 C1227 1590361 Quayle JM Dart C Standen NB The properties and distribution of inward rectifier potassium currents in pig coronary arterial smooth muscle J Physiol (Lond) 1996 494 715 726 8865069 Nilius B Viana F Droogmans G Ion channels in vascular endothelium Annu Rev Physiol 1997 59 145 170 9074759 10.1146/annurev.physiol.59.1.145 Paucek P Mironova G Mahdi F Beavis AD Woldegiorgis G Garlid KD Reconstitution and partial purification of the glibenclamide-sensitive, ATP-dependent K+-channel from rat liver and beef heart mitochondria J Biol Chem 1992 267 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Relation to functional IKr channels J Biol Chem 2000 275 5997 6006 10681594 10.1074/jbc.275.8.5997	15647111	PMC546210	CC BY	2021-01-04 16:38:19	no	BMC Physiol. 2005 Jan 12; 5:1	utf-8	BMC Physiol	2,005	10.1186/1472-6793-5-1	oa_comm
==== Front BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-6-21563663610.1186/1471-2296-6-2Research ArticleAssessment of dizziness among older patients at a family practice clinic: a chart audit study Kwong Eugene CK [email protected] Nicholas JG [email protected] Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada2 Department of Family and Community Medicine, Women's College Campus, Sunnybrook and Women's College Health Sciences Center, 60 Grosvenor Street, Toronto, Ontario, M5S 1B6, Canada2005 6 1 2005 6 2 2 23 2 2004 6 1 2005 Copyright © 2005 Kwong and Pimlott; licensee BioMed Central Ltd.2005Kwong and Pimlott; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Dizziness is a common complaint among the elderly with a prevalence of over 30% in people over the age of 65. Although it is a common problem the assessment and management of dizziness in the elderly is challenging for family physicians. There is little published research which assesses the quality of dizziness assessment and management by family physicians. Methods We conducted a retrospective, chart audit study of patients with dizziness attending the Sunnybrook Family Practice Center of Sunnybrook and Women's College Health Sciences Center (SWCHSC) in Toronto. We audited a random sample of 50 charts of patients from 310 eligible charts. Quality indicators across all dizziness subtypes were assessed. These quality indicators included: onset and course of symptoms; symptoms in patients' own words; number of medications used; postural blood pressure changes; symptoms of depression or anxiety; falls; syncope; diagnosis; outcome; specialty referrals. Quality indicators specific to each dizziness subtype were also audited. Results 310 charts satisfied inclusion criteria with 20 charts excluded and 50 charts were randomly generated. Documentation of key quality indicators in the management of dizziness was sub-optimal. Charts documenting patients' dizziness symptoms in their own words were more likely to have a clinical diagnosis compared to charts without (P = 0.002). Conclusions Documentation of selected key quality indicators could be improved, especially that of patients' symptoms in their own words. ==== Body Background Dizziness is a common complaint among the elderly, with a prevalence of more than 30% in people over age 65 [1] and it accounts for 2% of consultations in the primary care setting [2]. Drachman and Hart [3] described four subtypes of dizziness: vertigo, lightheadedness, dysequilibrium, and others. Several recent community-based studies of dizziness shows that, among the 4 dizziness subtypes, the proportion of vertigo was more uniform, ranging from 28 to 32% [1,4-6]. Reported frequencies of specific diagnoses for dizziness varies widely however, depending on: 1) clinical setting (primary care setting, referral center or emergency department); 2) patient age or patient populations examined; and 3) investigator bias. These methodological problems limit the generalizability of the etiological studies [3]. Kroenke et al [7] found in their systematic review that dizziness was attributed to peripheral vestibulopathy in 44%, central vestibulopathy in 11%, psychiatric causes in 16%, other conditions in 26%, and an unknown cause in 13% of cases. Life-threatening illness is rare in patients with dizziness (with cerebrovascular disease accounting for 6%, cardiac arrythmia for 1.5% and brain tumor for <1%) [7]. However, many do have serious functional impairment, such as increased risks for falls and increased incidence of symptom-related fears, anxiety or depression [8-10]. Many patients with chronic dizziness, particularly the elderly, are under-referred for specialist consultation and thus are not receiving timely treatment [5]. When assessing dizziness, what concerns a family physician most are: 1) how to distinguish serious causes of dizziness from less urgent ones; 2) how to manage patients with chronic but yet debilitating dizziness; and 3) how to decide on the right timing and the appropriate specialty for referral. However, many family doctors describe dizziness as "confusing" and "discouraging" problem [8] and expensive investigations like electro-nystagmography and MRI are rarely helpful [4]. In fact, a diagnosis cannot be ascertained in many patients with dizziness and many patients may have more than one diagnosis [11], making management difficult. To date, there are no evidence-based guidelines in the management of dizziness among elderly patients in a primary care setting because most past studies on dizziness have been retrospective or in referral settings. Traditionally, the approach to dizziness is "disease-oriented", in which the clinician aims, at a minimum, to exclude potentially fatal causes and possibly to diagnose a specific cause for treatment. On the other hand, some authors like Tinetti et al [12] and Kao et al [13] regard dizziness in the elderly as a "geriatric syndrome", because it represents dysfunction in more than one body system and has multiple predisposing risk factors. This function-oriented approach focuses on impairment reduction to reduce morbidity associated with dizziness, regardless of etiology. Tinetti's epidemiological population-based study [12] found that seven characteristics were associated with dizziness in the elderly: anxiety, depression, using five or more medications, impaired balance, past myocardial infarction, postural hypotension, and impaired hearing. Despite differences in the above two approaches, both share in common certain key quality indicators as reflected in recent studies and reviews [4,7,11-15]. The purpose of this chart audit study was to assess the extent to which family physicians included these key quality indicators when assessing and managing the dizzy elderly patient. Methods A retrospective chart audit was conducted at the Family Practice Center (Sunnybrook Campus) of Sunnybrook and Women's College Hospital Health Sciences Center. Inclusion criteria for the chart audit were: 1) Patients with a International Classification of Disease (ICD-9) diagnostic billing code of "780" (dizziness); 2) Patients seen between Feb 1st 2001 and Jan 31st 2003; 3) Patients 65 years of age or older when seen. Exclusion criteria were: 1) Patients who are discharged from service or died; 2) Patients whose presenting symptoms were not dizziness or any of its subtypes. A chart audit intake form was designed [Additional File 1], which included quality indicators important in the diagnosis and management of dizziness, based on recommendations from several recent review articles and peer-reviewed studies [4,7,11-15]. A random sample from the eligible charts was then audited and the data analyzed for descriptive statistics using SPSS. The general outcome measures/quality indicators across all dizziness subtypes include the documentation of : 1) onset and course of symptoms; 2) symptoms in the patients' own words; 3) number of medications used; 4) postural blood pressure changes; 5) symptoms of depression or anxiety; 6) falls; 7) syncope; 8) diagnosis; 9) outcome of dizziness; 10) specialty referrals. The quality indicators specific to vertigo include the documentation of 1) episode duration; 2) relationship to head turning; 3) tinnitus and hearing loss; 4) ear examination; 5) neurological examination; 6) spontaneous nystagmus; 7) positional nystagmus (Hallpike manoevre) (16). These patients were also audited for whether audiometry was ordered and whether Epley's manoevre [17] was offered if BPV was diagnosed. The quality indicators specific to lightheadedness include the documentation of 1) relationship to postural change; 2) cardiac symptoms; 3) syncope; 4) orthostatic blood pressure changes. These patients were also audited for whether ECG or Holter monitoring were ordered. The quality indicators specfic to disequilibrium include the documentation of 1) falls; 2) neurological exam; 3) cerebellar signs; 4) Gait examination; 5) Romberg's sign; 6) visual acuity. The quality indicators specifically to other non-classifiable dizziness include the documentation of symptoms of depression and anxiety. Results 310 charts satisfied the inclusion criteria with 20 charts excluded. A random sample of 50 charts were generated for the audit. The demographics of the sample, including age, gender living situation, are described in Table 1. Of note is that 62% of the patients are 80 years of age or older and 28% of patients are living alone. Table 1 Demographics and living situation of patients (n = 50) Total % Male % Female Age > or = 65 100% (n = 50) 42% (n = 21) 58% (n = 29) Age < 80 38% (n = 19) 16% (n = 8) 22% (n = 11) Age > or = 80 62% (n = 31) 26% (n = 13) 36% (n = 18) Age range 65–91 66 to 89 65 to 91 Living Alone 28% (n = 14) 2% (n = 1) 26% (n = 13) Living with Spouse 18% (n = 9) 12% (n = 6) 6% (n = 3) Living with Family 8% (n = 4) 2% (n = 1) 6% (n = 3) Living situation Not Documented 46% (n = 23) 26% (n = 13) 20% (n = 10) The distribution of different subtypes of dizziness is described in Table 2, with more patients presenting with lightheadedness (40%) and dysequilibrium (38%) than vertigo (28%). There are more females than males among the patients with lightheadedness (30% vs. 10%) and vertigo (16% vs. 10%) whereas the ratio of females to males is roughly the same among those with dysequilibrium (20% vs. 18%). 30% (n = 15) of patients presented with more than one subtype of dizziness. Table 2 Symptomatology distribution of patients Dizziness Subtype Yes No Not Documented Total Female Male Total Total Vertigo 26% (n = 13) 16% 10% 26% (n = 13) 48% (n = 24) Lightheadedness 40% (n = 20) 30% 10% 10% (n = 5) 50% (n = 25) Disequilibrium 38% (n = 19) 20% 18% 6% (n = 3) 56% (n = 28) Others 26% (n = 13) 12% 14% 6% (n = 3) 68% (n = 34) The onset and diagnoses of dizziness are described in Table 3. 70% of patients have a precipitating factor, the commonest ones being postural change, movement and head turning. 46% of patients have no diagnosis while 10% of patients have more than one diagnoses. Among patients with an ascertained diagnosis, the most common ones are BPV (12%), labyrinthitis (10%) and TIA/Stroke (8%). Significantly, patients were more likely to be diagnosed if their symptoms were documented in their own words compared to those without such documentation (see Table 4). Table 3 Onset and diagnoses of dizziness Percentage of Patients Onset Spontaeous 4% (n = 2) Precipitating Factors present 70% (n = 35) Postural change 38% (n = 19) Head turning 12% (n= 6) Any movement 18% (n= 9) Walking 10% (n = 5) Anxiety 2% (n = 1) Other factors 26% (n = 13) Not Documented 26% (n = 13) Diagnosis No 46% (n = 23) Yes 54% (n = 27) BPV 12% (n = 6) Labyrinthitis 10% (n = 5) TIA/Stroke 8% (n = 4) Hypertension 6% (n = 3) Depression/Anxiety 6% (n = 3) Arrhythmia 4% (n = 2) Alcohol 4% (n = 2) Dehydration 4% (n = 2) Others 4% (n = 2) More than One Diagnoses 10% (n = 5) Table 4 Documentation of patients' symptoms in their own words Documentation With Diagnosis Without Diagnosis Yes (n = 22) 82%* (n = 18) 18% (n = 4) No (n = 28) 32% (n = 9) 68% (n = 19) *statistically significant (P = 0.002) The documentation of general and dizziness subtype-specific quality indicators in history and physical examination are described in Table 5. It also was observed that: 1) 60% of all patients were taking at least 5 medications; 2) three vertiginous patients with associated ear symptoms were not offered audiometry; 3) none of the four vertiginous patients with an abnormal Hallpike test were documented to be treated by Epley's manoevre; 4) in the lightheadedness subgroup, ECG was ordered in only 40% and Holter monitoring in only 30% of patients. Table 5 Documentation of general and dizziness subtype-specific quality indicators in history and physical examination for Patients with Vertigo, Lightheadedness, Disequilibrium and Others Yes No Not Documented General (n = 50) Postural BP changes 6% 44% 50% Associated Depression 6% 8% 86% Associated Anxiety 14% 0% 86% Falls 6% 30% 64% Syncope 2% 36% 62% Vertigo (n = 13) Episode Duration 38% N/A 62% Relationship to Head Turning 38% 8% 54% Tinnitus 23% 54% 23% Hearing Loss 0% 0% 100% Ear Examination 39% (Normal:31%) (Abnormal:8%) N/A 61% Neurological Exam 92% N/A 8% Spontaneous Nystagmus 46% (Normal 38%) (Abnormal 8%) N/A 54% Hallpike Manoevre 39% (Normal 8%) (Abnormal 31%) N/A 61% Lightheadedness (n = 20) Relationship to Postural change 55% 15% 30% Chest Pain 5% 60% 35% Palpitation 5% 55% 40% Syncope 5% 50% 45% Orthostatic drop in BP 10% 55% 35% Orthostatic rise in pulse 0% 15% 85% Disequilibrium (n = 19) Falls 11% 32% 57% Gait examination 58% (Normal: 26%) (Abnormal:32%) N/A 42% Neurological exam 68% N/A 32% Romberg's sign 42% (Normal: 37%) (Abnormal: 5%) N/A 58% Cerebellar signs 47% (Normal: 42%) (Abnormal: 5%) N/A 53% Visual acuity exam 10% (Normal: 5%) (Abnormal: 5%) N/A 90% Others (n = 12) Depression 0% 25% 75% Anxiety 25% 0% 75% N/A: Not Applicable As for the course of dizziness, only 2 patients have worsening symptoms (4%) and 60% of patients are referred to specialty services, the commonest ones of which are ENT (12%), neurology (8%) and cardiology (6%). Discussion A striking finding from this study was that 46% of the patients did not have any diagnosis and 10% of them had more than one diagnosis. This finding is in accordance with the data from the review by Sloan et al [11] and illustrates the difficulty of diagnosing dizziness in a primary care setting. This is also reflected by a 40% referral rate to specialty services, which is higher compared to the 16% referral rate shown in a recent study [5]. On the other hand, the dizziness symptom worsened with time in only 4% of patients in this study, which is consistent with previous work [7] showing the generally "benign" course of this condition. The distribution of etiological causes of dizziness in this sample is also consistent with those of previous studies [7] with peripheral vestibular disorders (BPV and labyrinthitis) being the most common and accounting for 22% of diagnoses. Effective history taking and communication between family physicians and patients is of crucial importance in the diagnosis of dizziness. The present chart audit study showed that family physicians were more likely to reach a diagnosis when patients' symptoms were documented in their own words, compared to those without such documentation. Overall, the documentation rate of key quality indicators important to all dizziness subtypes were low, such as falls, syncope, symptoms of depression and anxiety, and orthostatic blood pressure changes. A history of falls is associated with increased morbidity but this was documented in only 36% of patients. This is especially worrying given that 28% of patients in our sample were known to be living alone. The finding that 60% of patients with dizziness are using 5 or more medications is consistent with Tinetti's population-based cross-sectional study [12]. Among vertiginous patients, the documentation rates for episode duration, relationship to head turning, hearing loss, and Hallpike maneuver were far from satisfactory. Among lightheaded patients, the documentation rates for symptom relationship to postural change, chest pain, palpitation, syncope, and orthostatic blood pressure changes are better but there is still room for improvement. Among patients with disequilibrium, the documentation rates for falls, gait examination, cerebellar signs, Romberg's sign and visual acuity examination were again sub-optimal. Among patients with non-specific dizziness, symptoms of depression and anxiety were also sub-optimally documented. This study has several limitations. First, being a retrospective study, its strength is limited because there is no standardized strategy or protocol for data collection among different family physicians. Second, a chart audit is prone to documentation bias and incompleteness, depending on individual family physicians. In addition, the sample size of 50 is relatively small given the complexity of the clinical problem. Moreover, only one diagnostic billing code was used. Although this would catch the presenting symptom at its undifferentiated stage, the drawback is that we may underestimate the actual scope of the problem by ignoring patients who were coded more specifically by their dizziness subtypes. In addition, only one hospital site, namely Sunnybrook hospital, was selected for chart audit. Being an academic teaching center with predominantly older patients, the patient data from this hospital alone may not be generalizable to those of a community clinic setting. Future directions for study would include the conduction of more prospective cohort studies on primary care patients using a standardized protocol for data collection This would assure uniform and consistent evaluation with the least amount of selection bias. Ideally, the use of inception cohorts would allow for better definition of the causes and natural history of dizziness in persons having their first episode. More prospective studies on dizziness outcomes with extended follow-up periods, as well as studies on different management strategies and specialty referral patterns for dizziness in the primary care setting would be helpful to family physicians. The ultimate goal is to identify the clinical and demographic or situational characteristics in a primary care setting that could help predict management decisions, such as diagnosing the most likely attributable cause of dizziness, delivering the most effective treatment for the symptom, and making the most timely and appropriate referral. Conclusions Regardless of whether one regards dizziness as a "geriatric syndrome" or as a discrete "disease" for which a clear assessment algorithm could be established, there are certain quality indicators that are helpful for either approach but their documentation were found to be sub-optimal in this chart audit study. Allowing patients to describe their symptoms in their own words may help to improve diagnosis and management. Competing interests The author(s) declare that they have no competing interests. Authors' contributions EK contributed to the design and conception of the study, carried out the chart audit, analyzed the data and wrote the first draft of the manuscript. NP contributed to the design and conception of the study, supervised the conduct of the chart audit and data analysis and wrote and revised subsequent versions of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 1. Chart Audit Form MS Word document with the chart audit form used in the study Click here for file Acknowledgements We would like to thank Ms. Sandy Cummings for her help with statistical analysis, Mr Eric Crighton for his help with chart pull and generation of the random sample, and Ms Judith Manson for her logistics support at the family practice center (Sunnybrook site) of Sunnybrook and Women's College Health Sciences Center. ==== Refs Colledge NR Wilson JA Macintyre CC MacLennan WJ The prevalence and characteristics of dizziness in an elderly community Age Ageing 1994 23 117 120 8023718 Sloan PD Dizziness in primary care. Results from the National Ambulatory Care Survey Fam Pract 1989 29 33 38 Drachman DA Hart CW An approach to the dizzy patient Neurology 1972 22 323 34 4401538 Colledge NR Barr-Hamilton RM Lewis SJ Sellar RJ Wilson JA Evaluation of investigations to diagnose the cause of dizziness in elderly people: a community based controlled study BMJ 1996 313 788 792 8842072 Bird JC Beynon GJ Prevost AT Baguley DM An analysis of referral patterns for dizziness in the primary care setting Br J Gen Pract 1998 48 1828 1832 10198501 Yardley L Owen N Nazareth I Luxon L Prevalence and presentation of dizziness in a general practice community sample of working age people Br J Gen Pract 1998 48 1131 1135 9667086 Kroenke K Hoffman RM Einstadter D How Common are various causes of dizziness Southern Medical Journal 2000 93 160 167 10701780 Bailey KE Sloane PD Mitchell M Preisser J Which primary care patients with dizziness will develop persistent impairment? 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==== Front BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-41564711410.1186/1471-2164-6-4Research ArticleStructure and evolution of the mouse pregnancy-specific glycoprotein (Psg) gene locus McLellan Andrew S [email protected] Beate [email protected] Gabriela [email protected] Tomomi [email protected] Freda [email protected] Melanie [email protected] Katsuzumi [email protected] Tom [email protected] Wolfgang [email protected] Department of Biochemistry, Biosciences Institute, University College Cork, College Road, Cork, Ireland2 Tumor Immunology Group, LIFE Center, University Clinic Grosshadern, Ludwig-Maximilians-University Muenchen, Marchioninistrasse 23, D-81377 Muenchen, Germany3 Laboratory of Molecular and Cellular Biology, Department of Life Sciences, Faculty of Bioresources, Mie University, 1515 Kamihama, Tsu, Mie 514-8507, Japan4 Institute of Molecular Medicine and Cell Research, Albert-Ludwigs-University Freiburg, Stefan-Meier-Str. 17, D-97104 Freiburg, Germany5 Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA6 Division of Rheumatology and Clinical Immunology, Department of Medicine, University Hospital Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany2005 12 1 2005 6 4 4 27 9 2004 12 1 2005 Copyright © 2005 McLellan et al; licensee BioMed Central Ltd.2005McLellan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The pregnancy-specific glycoprotein (Psg) genes encode proteins of unknown function, and are members of the carcinoembryonic antigen (Cea) gene family, which is a member of the immunoglobulin gene (Ig) superfamily. In rodents and primates, but not in artiodactyls (even-toed ungulates / hoofed mammals), there have been independent expansions of the Psg gene family, with all members expressed exclusively in placental trophoblast cells. For the mouse Psg genes, we sought to determine the genomic organisation of the locus, the expression profiles of the various family members, and the evolution of exon structure, to attempt to reconstruct the evolutionary history of this locus, and to determine whether expansion of the gene family has been driven by selection for increased gene dosage, or diversification of function. Results We collated the mouse Psg gene sequences currently in the public genome and expressed-sequence tag (EST) databases and used systematic BLAST searches to generate complete sequences for all known mouse Psg genes. We identified a novel family member, Psg31, which is similar to Psg30 but, uniquely amongst mouse Psg genes, has a duplicated N1 domain. We also identified a novel splice variant of Psg16 (bCEA). We show that Psg24 and Psg30 / Psg31 have independently undergone expansion of N-domain number. By mapping BAC, YAC and cosmid clones we described two clusters of Psg genes, which we linked and oriented using fluorescent in situ hybridisation (FISH). Comparison of our Psg locus map with the public mouse genome database indicates good agreement in overall structure and further elucidates gene order. Expression levels of Psg genes in placentas of different developmental stages revealed dramatic differences in the developmental expression profile of individual family members. Conclusion We have combined existing information, and provide new information concerning the evolution of mouse Psg exon organization, the mouse Psg genomic locus structure, and the expression patterns of individual Psg genes. This information will facilitate functional studies of this complex gene family. ==== Body Background In mammalian pregnancy the interaction between the maternal uterine tissues and foetal trophoblasts is regulated by a wide variety of cellular and endocrinological mechanisms. These mechanisms underpin trophoblastic invasion and remodelling of maternal tissues, placental angiogenesis, and the modulation of maternal immune responses. Central to these processes is the production by trophoblast of a variety of hormones that are found in abundance in the maternal bloodstream during pregnancy [1]. The pregnancy-specific glycoproteins (PSG) are the most abundant foetal proteins in the maternal bloodstream in late pregnancy [2]. They are synthesised in the syncytiotrophoblast of the human placenta and giant cells and spongiotrophoblast in the rodent placenta [3-5]. The PSG family of glycoproteins belongs to the carcinoembryonic antigen (CEA) family, which also includes the CEA-related adhesion molecules (CEACAMs). The CEA family is itself part of the immunoglobulin (Ig) superfamily [6]. The Ig domain structure of the human and rodent PSGs differs. Containing both V-like Ig domains (N), C2-like Ig domains (A and B) and relatively hydrophilic tails (C), domain arrangements in human PSGs are type I (N-A1-A2-B2-C), type IIa (N-A1-B2-C), type IIb (N-A2-B2-C), type III (N-B2-C) and type IV (A1-B2-C) [7]. In contrast, rodent PSGs are typically comprised of 3, and in a few cases of 5 or 7 N-domains followed by an A-domain [8]. In the primate / rodent ancestor, the initial duplication of the CEACAM / PSG primordial gene has been estimated to have occurred about 90 Myr ago [9], approximately at the time of human-rodent divergence. The most probable PSG ancestor in rodents and primates is a CEACAM15-like molecule based on the organisation of N and A domains. CEACAM15 is not classified as a PSG because comparisons of N and A domain sequence identity clearly delineate members of the CEACAM and PSG subfamilies (Roland Zebhauser, WZ, AM, TM, to be published elsewhere). It has been suggested that human and rodent PSG multigene families evolved independently via further gene duplication and exon shuffling events [10]. There are 11 members of the PSG family in humans that are encoded by genes clustered on chromosome 19q13.2 [11,12]. PSG proteins have a similar domain structure to the CEACAMs, but lack a membrane anchor and are therefore secreted. However, a few variants have been described that are retained within the cell. Conversely, a small number of human and mouse CEACAM variants lack a membrane anchor and are secreted. Membrane-anchored CEACAMs are widely expressed during embryonic development and in adult tissues, and are implicated in carcinogenesis, angiogenesis and regulation of immune functions [13,14]. In contrast, PSGs and some CEACAMs are expressed almost exclusively in trophoblasts of the haemochorial placenta of rodents and primates [4,5,15]. The biochemical properties and physiological functions of the PSGs remain to be fully elucidated, although functional experiments and clinical observations are beginning to provide some clues. Low PSG levels in the maternal circulation are associated with threatened abortions, intrauterine growth retardation and foetal hypoxia [16-19]. The importance of PSGs for the maintenance of pregnancy is also underlined by the observation that the application of anti-PSG antibodies or vaccination with PSG induces abortion in mice and monkeys, respectively, and reduces the fertility of non-pregnant monkeys [20,21]. The majority of PSG functional studies have focussed on determining whether PSGs are able to modulate the maternal immune system to prevent rejection of the allotypic foetus. Early studies with complex PSG mixtures isolated from placenta indicated an inhibitory effect on phytohaemagglutinin or allogeneically stimulated lymphocytes [22,23]. In further experiments it was shown that human monocytes secreted anti-inflammatory cytokines in response to PSG exposure. Moreover, recombinant mouse PSG18 was found to induce the production of interleukin (IL)-10 in the mouse macrophage cell line RAW 264.7 [24]. Human PSG1, PSG6 and PSG11 all induced secretion of IL-10, IL-6 and transforming growth factor (TGF)-β1 [25]. Whilst IL-10 and TGF-β1 are anti-inflammatory [26], IL-6 is usually considered to be a proinflammatory cytokine. However IL-6 does have some well-described anti-inflammatory properties [27]. Furthermore, IL-6 has been shown to indirectly promote trophoblast growth by upregulation of human chorionic gonadotropin (hCG) release by the trophoblast, and induction of granulocyte-macrophage-colony stimulating factor (GM-CSF) [28,29]. Further evidence implicating PSGs in immune modulation arises from PSG mediated suppression of T cells in purulent septic complications of abortion [30] and elevated circulating PSG levels are correlated with improved symptoms of rheumatoid arthritis [31]. PSG induction of alternative monocyte activation is of particular importance as it implies a PSG-mediated switching of the immune system from a predominantly TH1 response to a predominately TH2 response which is more compatible with a successful pregnancy [32]. The only PSG receptor identified to date is the integrin-associated CD9 receptor, which was found to bind the N1 domain of both PSG17 [33] and PSG19 (unpublished data). Additionally, the presence of the conserved tripeptide motif Arg-Gly-Asp (RGD) on a solvent-exposed loop in the N-terminal Ig domain in the majority of human and some lower-primate PSGs implicates a function that involves integrin-related receptors [34]. Thus it has been speculated that the RGD domain may enable some PSGs to disrupt cell-matrix interactions [35]. However, no rodent PSG isolated to date possesses an RGD domain. Evidence supporting the hypothesis that the RGD domain may be involved in receptor binding was provided by the discovery that a peptide containing the RGD motif, from human PSG9, bound to a receptor on the surface of a promonocytic cell line [36]. In common with integrin interactions, this was dependent on the presence of divalent cations and showed sensitivity to cytoskeletal signalling. However, the expected sizes of the receptor subunits differed from those of known integrins, therefore, the identity of the receptor remains elusive. Much current work has focussed on human PSGs due to their possible relevance to disorders of pregnancy. However, the study of rodent PSGs is important because, the evident differences between primate and rodent PSG protein domain structures notwithstanding, there appears to be considerable conservation in terms of expression in trophoblast, independent gene family expansions in mammalian lineages with haemochorial placentation, and postulated immune functions during pregnancy. Moreover, the application of gene targeting and mutagenesis in the mouse is likely to be informative with respect to elucidating the cellular and physiological functions of PSGs. Such experiments will require an accurate genomic map of the mouse Psg locus, which we undertook to produce in the work described herein. It is also pertinent to ask whether the independent expansions of PSG gene families in different mammalian lineages reflect selection for increased gene dosage or for diversification of function mediated through different protein structures or developmental expression patterns. We therefore undertook to examine and correlate protein domain evolution and expression profiles of the various mouse Psg genes to attempt to address this question. Our results suggest that different family members have very different expression levels at different stages of development, which we consider may be supportive of the hypothesis that mouse Psg genes may have evolved divergent functions in mammalian pregnancy. However, mutagenesis of individual family members will be necessary to rigorously test this hypothesis. Results Identification of novel mouse Psg genes For comparative studies of the human PSG family it is relatively easy to compare coding sequences (CDS) and peptide sequences because complete sequence information is available. However the data available for mouse PSGs is not complete, making such analyses difficult. Thus, we firstly collated the currently available public data, and we then attempted to identify sequences for PSGs that were not completely resolved in the databases. Full-length cDNA sequences of Psg17, Psg18, Psg19, Psg21, Psg23, Psg28 and Psg30 were identified via basic name searches of the RefSeq RNA database. Their identity was then verified by comparison to cDNA fragment sequences, which were obtained during the course of this work and deposited in GenBank [37], as misnaming of genes is commonplace in the databases. The cDNA sequence of Psg22 was then identified via BLAST analysis of the mouse RefSeq RNA database using the GenBank partial sequence referenced in Beauchemin et al. [37]. Psg31 was identified by BLAST analysis of the same database using the full-length Psg30 sequence and found to be the XM_355864.1 predicted transcript. However, there was a discrepancy between the predicted transcript and the sequences of EST clones CK032208 and CN694284. Comparison of these EST sequences with genomic contig NT_039395.2, using pairwise BLAST analysis, revealed that there had been a duplication of the N1 domain exon. We refer to the two N1 domains of Psg31 as N1 and N1* hereafter. The gene and full-length cDNA coding sequences of the remaining mouse genes (Psg20, Psg24, Psg25, Psg26, Psg27 and Psg29) were deduced manually by systematic BLAST analysis of the mouse genome database as described in Methods. None of these predicted cDNAs were observed in the mouse EST database, although all except Psg20 were observed in the Trace Archive EST sequences. A novel splice variant of Psg16 was also found. BLAST analysis of the mouse High Throughput Genomic Sequences (HTGS) database identified contig AC148976.2, which appears to contain the whole Psg16 gene. An alternative exon 1 was discovered upstream of the previously described initiating exon by a pairwise BLAST comparison of this contig with full-length Psg17 coding sequence. The use of this alternative exon 1 produced a transcript that encodes a typical PSG polypeptide complete with a predicted secretory-peptide signal sequence and cleavage site. Multiple hits identified from subsequent BLAST analysis of the mouse EST and Trace Archives EST databases provided evidence that this novel splice variant was placentally expressed in vivo. In contrast, only one hit was obtained by identical analysis using the coding sequence of the brain-specific transcript described in Chen et al. [38]. This transcript (BC030357) was derived from a retinal cDNA library. The brain-expressed splice variant is generated from an alternative initiation site within exon 2 of the dominant placentally-expressed form of the gene. Alternative promoter usage would explain the brain and placenta-specific expression patterns of these variants of Psg16. Unlike the brain-specific variant, the placentally-expressed variant possesses a predicted secretory signal peptide at the N-terminus, like most other Psg gene family members. The comparison of the brain derived Psg16 coding sequence with the genomic sequence (AC148976.2) also revealed differences in the encoding of the A-domain. The placental transcript is predicted to be encoded by 5 exons, as are the majority of mouse Psg mRNAs. However, a weak splice donor signal sequence within the fifth exon permits splicing to a strong splice acceptor sequence downstream of the sixth exon, as seen in the brain-expressed transcript. Trace Archive EST data reveals multiple hits to sequences from placental cDNA libraries using the 3' end of the placental Psg16 coding sequence as bait. This confirms the existence of our predicted transcript. Conversely, similar analysis using the brain-expressed variant yielded no hits. The sixth exon is present on a separate randomly ordered gene fragment within the AC148976.2 contig. Psg-ps1 was previously considered to be a pseudogene, based on a point deletion at nucleotide position 30, downstream from the canonical Psg translational start site [8]. However, despite this frame shift, the open reading frame of this unusual Psg continues 105 bp upstream of the site of the mutation to an alternative ATG. Inspection of the sequence revealed a Kozak consensus, and BLAST analysis of the public EST and Trace Archive EST databases yielded many mRNA clones that contain this region in addition to downstream exons. Hence, this gene is clearly expressed, and we now propose to rename Psg-ps1 as Psg32 hereafter. We note that this mutation and amino terminal extension abolishes the canonical PSG secretory signal and peptide cleavage site. We therefore suggest that if Psg32 is indeed translated, the resulting protein is retained within the cytoplasm. To determine if the deletion observed in BALB/c mice was also present in other murine strains, we amplified and sequenced a 146 bp fragment by PCR using a set of primers specific for the 5'-untranslated region and the leader peptide of Psg32. The deletion observed in the Psg32 cDNA is also present in the genomic DNA of A/J, C57BL6/J, YBR/Ei, and SWR/J inbred mouse strains (data not shown). The nomenclature (past and current) and accession numbers of nucleotide sequences of all the murine PSGs are documented in Table 1. The genome sequence and predicted CDS and translation products for Psg16, Psg20, Psg26 and Psg31 are listed in Additional file 1. The complete CDS data for all known mouse Psgs (except the brain-specific splice variant of Psg16) are listed in Additional file 2. The complete protein primary sequences for all known mouse Psgs (except the brain-specific splice variant of Psg16) are listed in Additional file 3. Table 1 Summary of mouse PSG nomenclature and sequence accession numbers Current Name Previous Names Accession Numbera Commentb Psg16 bCEA AC148976.2 (RC 40000–60000) predicted CDS: join (1878–1941, 4115–4462, 7291–7650, 8750–9109, 11758–12041); bCEA is a splice variant of Psg16 Psg17 Cea2, mmCGM5 NM_007677 Psg18 Cea3, mmCGM6 NM_011963 Psg19 Cea4 NM_011964 Psg20 Cea7 AC079497.1 (113793–127892) predicted CDS: join (1770–1836, 2989–3345, 4997–5356, 6587–6937, 13114–13397) Psg21 Cea8 NM_027403 Psg22 Cea9 NM_001004152.1 Psg23 Cea11 NM_020261 Psg24 Cea12 AC079526 (115000–131000) predicted CDS: join (1648–1696, 2771–3130, 5965–6324, 9351–9710, 10943–11302, 12844–13191, 14196–14479) Psg25 Cea13 NW_000292.1 (RC 890000–910000) predicted CDS: join (4905–4968, 7121–7480, 10406–10765, 11988–12347, 15508–15791) Psg26 Cea14 join (CAAA01217140.1 {RC 1–6315}, CAAA01213459.1 {557–4715}, CAAA01175422.1 {155–2891}) predicted CDS: join (2148–2211, 3292–3651, 5836–6195, 7507–7866, 10823–11106) Psg27 Cea15 AC087156.1 (RC 139366–153050) predicted CDS: join (240–303, 2037–2393, 5271–5630, 6669–7028, 10039–10322) Psg28 Cea16 NM_054063 Psg29 Cea17 AC079526 (183285–194009) predicted CDS: join (1459–1522, 2658–3005, 6275–6634, 8128–8487, 9428–9700) Psg30 XM_145406 GNOMON prediction in NCBI Psg31 AC134475.3 (10000–70000) predicted CDS: join (3923–3986, 5262–5621, 19366–19725, 34382–34741, 36822–37172, 40760–41119, 42413–42763, 47310–47669, 49090–49443, 50473–50756) Psg32 Psg-ps1 XR_000250 GNOMON prediction in NCBI a Where nucleotide start and end positions are shown in parenthesis after accession numbers, they refer to the start and end positions of the genomic sequence excerpt (encompassing the PSG exons) that is included in Additional file 1. RC indicates that the sequence in Additional file 1 is the reverse complement. b Where we have predicted the full CDS of a PSG (based on common structure and splice sites), the numbers shown refer to exon start and end positions within the excerpted sequence included in Additional file 1. Domain structure of mouse PSG proteins A schematic representation of the mouse Psg domain structures is shown in Fig. 1. Of the seventeen mouse Psgs, thirteen encode a common structure of three Ig variable (IgV)-like domains (N-domains) and a single Ig constant (IgC)-like domain (A-domain). Psg24, Psg30 and Psg31 have an expanded structure created by the duplication of IgV-like domains. An unrooted phylogenetic tree indicates three main branches of IgV-like domain evolution (Fig. 2). There is a group consisting of N1 domains, a group of N2 domains and N2-derived domains, and a group of N3 domains and N3-derived domains. Therefore, in agreement with the most common structure observed in Fig. 1, the ancestral mouse Psg would be expected to have had an N1-N2-N3-A arrangement of domains. The expansion of Psg24, Psg30 and Psg31 has occurred mostly through duplications of the N2 and N3 IgV-like domains, with the exception of Psg24 N5 and Psg31 N1 domains. Figure 1 Domain organization of mouse PSGs. Mouse PSGs are composed of 3 – 8 IgV-like N domains and one IgC-like A domain. The relative position of potential N-glycosylation sites (consensus amino acid sequence: asparagine-X-threonine / serine; X any amino acid except proline) were identified using the NetNGlyc 1.0 Server online software and indicated by lollipops. Although PSG32 is probably not routed through the endoplasmic reticulum, the putative N-glycosylation sites are shown for comparison. Of the two PSG16 splice variants, only the variant expressed in the placenta is shown. Figure 2 Evolutionary relationships between mouse PSG IgV-like domains. An unrooted evolutionary tree based on ClustalX amino acid sequence alignments showing the relationships between all mouse PSG N-domains. The three main groups N1, N2 and N3 have been ringed for clarity. The scale bar represents 0.1 amino acid substitutions per site. In order to characterise the evolution of the mouse Psgs with expanded domain numbers, Neighbor-Joining (NJ) trees with bootstrap values of 1000 were prepared (Fig. 3A) and ClustalW amino acid sequence alignments (Fig. 3B) were studied to identify the origin of the novel IgV-like domains in these three exceptional Psgs. From examination of the data in Fig. 3A(i) it was not apparent from which progenitor domain the Psg24 N5 domain evolved due to lack of confidence in the branch on which it lies. However, using the alignment identities in Fig. 3B(i) it can be seen that, although generally poorly conserved, the best match of 51.2% was obtained by alignment with the N2 domain. Therefore, our evolutionary model assumes that the Psg24 N5 domain arose from an early duplication of the N2 domain. Also, based on agreement of the data in Fig. 3A(i) and 3B(i), the N2 domain duplicated again more recently to yield the N3 domain. This latter duplication explains why the Psg24 N4 domain is N3-like. The order of these events is shown schematically in Fig. 3C(i). Figure 3 Domain expansion of Psg24, Psg30 and Psg31. A. NJ-trees based on ClustalX amino acid sequence alignments showing: (i) the evolution of PSG24 IgV-like domains compared to those of PSG17; (ii) the evolution of PSG30 IgV-like domains compared to those of PSG17; (iii) the evolution of PSG31 IgV-like domains compared to those of PSG17. The trees were rooted using an outgroup consisting of the N-domain amino acid sequences of human PSG1, PSG2 and PSG3. Alignments were bootstrapped 1000 times yielding node values which are represented as follows < 50%: no mark; 50–74%: marked ; 75–94%: marked ; ≥ 95%: marked *. The scale bar represents 0.1 amino acid substitutions per site. B. The arrangement of domains represented by boxes shaded: cyan for leader (L) peptides; light pink for the N1-domains; dark pink for N2 and N2-like domains; red for N3 and N3-like domains; blue for A-domains. (i) Comparison of Psg17 and Psg24 exon arrangement including identities of amino acid sequence alignments. (ii) Comparison of Psg30 and Psg31 exon arrangements including identities of amino acid sequence alignments. C. Predicted model of IgV-like domain expansion by exon duplications in (i) Psg24 and (ii) Psg30 and Psg31. Using a similar analysis we propose a model for the expansion of domains within Psg30 and Psg31 (Fig. 3C(ii)). We suggest that the N4 and N6 domains of Psg30 and Psg31 are derived from a progenitor N2 domain. Similarly, the N5 and N7 domains are derived from a progenitor N3 domain. Expansion is predicted to have occurred in 2 or 3 separate events in a common ancestor of Psg30 and Psg31. In the first instance the progenitor N3-like and N2-like domains were duplicated, either at different points in evolution or at the same time. The final step was a duplication of both of these daughter domains to create Psg30 and the precursor of Psg31. The precursor of Psg31 then underwent another duplication, this time of the N1 domain. Expression of Psg genes in mouse placenta at different developmental stages On the basis that all mouse Psg genes originated from a common ancestor, and expanded into a multigene family by duplication and subsequent divergence, the question as to whether the expression patterns have also diversified is relevant to determining the selective forces underlying Psg gene family expansion. As Psg genes are expressed predominantly in the placenta, cDNA was prepared from total RNA extracted from mouse placenta at four stages of development between E10.5 and E17.5. Psg cDNA sequences were then amplified with PCR primers designed to amplify Psg16 – Psg29 inclusive. Size fractionation of PCR products on an ethidium bromide-stained agarose gel, indicates that mouse Psg genes are predominantly expressed from around E15.5, increasing in expression through to at least E17.5 (Fig. 4). However, after blotting the products onto nylon membranes and hybridising radiolabelled oligonucleotide probes specific for individual Psg genes (Table 2), we observed significant differences in expression profiles of different genes during development. This method is probably semi-quantitative at best but does give some indication of relative expression levels. We observed that Psg16 and Psg26 are weakly expressed at E15.5 but strongly expressed at E17.5. In contrast, Psg17, Psg18, Psg21 and Psg23 are expressed strongly at E15.5, further increasing by E17.5. Psg27 shows a similar expression pattern to these four Psgs, but at a relatively low level. Very weak expression was observed on E17.5 for Psg19, Psg20, Psg24, Psg25 and Psg29, whereas Psg22 and Psg28 were undetectable. Psg30, Psg31 and Psg32 domain structures had not been finalised and therefore their expression was not analysed in this experiment. Figure 4 Expression of Psg mRNAs during placental development. Total RNA (1 μg) from day 10.5, 12.5, 15.5 and 17.5 BALB/c placentae was reverse transcribed using an oligo (dT) oligonucleotide (reverse PCR primer). After addition of the degenerate Psg-all oligonucleotide (forward PCR primer), which anneals to the cDNA of all known members of the mouse Psg family, Psg cDNAs were amplified by PCR (see schematic diagram depicting generalised mouse Psg cDNA amplification). Aliquots were size-separated by agarose gel electrophoresis. a, PCR products were visualised by ethidium bromide staining. b-o, the amplification products were blotted onto nylon membranes and individual blots were hybridised with single gene-specific 32P-labelled oligonucleotides from the N1 domain regions (Table 2). The location of the primers used for amplification of the Psg cDNAs and the region from which the sequences of the gene-specific oligonucleotides were derived are shown together with a schematic representation of mouse Psg mRNA. The 5'- and 3'-untranslated regions are shown as bold lines. L, leader; N1-N3, IgV-like domains; A, IgC-like domain. Table 2 Oligonucleotides used in this study Oligo Sequence Position Comment Psg17A5' 5'-CTTGCCACACAGCCCGTCAT-3' Psg17 A domain Psg17A3' 5'-TCATCACAGCCAGGATGACT-3' Psg17 A domain mPsg-5' 5'-AWCCTSYTGSYTCCTGC-3'a N1 domain binds to several mouse Psg cDNA sequences mPsg-3' 5'-TGMARGWAYAKGGATGT-3'a N1 domain binds to several mouse Psg cDNA sequences PsgN1-F 5'-GAAGATCTAGCCTCCMTYTTDDCCT-3'a Bgl II intron 1/N1 exon for the amplification of all known Psg N1 exons (except Psg32) PsgN1-R 5'-CCATCGATTACTTACWGTWSACVTRVA-3'a ClaI N1 exon/intron 2 for the amplification of all known Psg N1 exons (except Psg32) Psg32N1-F 5'-GAAGATCTAGCTTTTCTTTTAACCTC-3' Bgl II N1 domain Psg32-exon1 5'-GAGGTGTCCTTGGTGCTTCTC-3' exon 1 Psg32-specific oligo (dT) 5'-TTCTAGAATTCAGCGGCCGC(T)30 VN-3'a poly(A) tail Psg-all 5'-CCTCCMTYTTDDCCTRCTGS-3'a N1 domain binds to all known Psg cDNA sequences except Psg32 bCEAN/2 5'-GCAAATGTACAGTGGTAG-3' N1 domain Psg16-specific Psg17N 5'-GTGGAATTCTTACCTCCC-3' N1 domain Psg17-specific Psg18N 5'-GGCTGTACTACTATAGTG-3' N1 domain Psg18-specific BK07 5'-AAAGTGCCACCCGGGAA-3' N1 domain Psg19-specific Psg20N 5'-TGCCAAGGTCACTATCCA-3' N1 domain Psg20-specific Psg21N 5'-GCTCTGCATTTTCTGGAC-3' N1 domain Psg21-specific 35N 5'-GTCTGGTATAGAGGGGTG-3' N1 domain Psg22-specific 53N 5'-GCTGTGTATTTACTGGAC-3' N1 domain Psg23-specific 9.3N1 5'-ATAGCAGAGGTGTGACG-3' N1 domain Psg24-specific 11.2N1 5'-ATCTTCTAGGCCTTGCC-3' N1 domain Psg25-specific 189N 5'-CATTCGCTGTACTATAGTG-3' N1 domain Psg26-specific 214N 5'-CGAGTCACCATCCATTCA-3' N1 domain Psg27-specific 2128N 5'-GCACTATAGTTTAACAGCG-3' N1 domain Psg28-specific 9140N 5'-TGCAGTGGTGTCTGACTT-3' N1 domain Psg29-specific Psg-ps1N 5'-TTAGTGCCACCACAAGTG-3' N1 domain Psg32-specific a Standard IUB/IUPAC nucleic acid codes codes have been used to indicate degeneracy where: R = G/A; Y = T/C; K = G/T; M = A/C; S = G/C; W = A/T; B = G/T/C; D = G/A/T; H = A/C/T; V = G/C/A; N = A/C/G/T. To supplement the PCR-based Psg expression studies, we performed 'virtual northern' analysis in silico by screening the public EST database for sequences matching Psg N1 or A-domains and counting the numbers of matches (Fig. 5). There was generally good concordance of the virtual data with the RT-PCR data; notably, Psg21 and Psg23 are highly represented in both datasets. However, disagreements were also evident e.g. Psg16 expression was low in the RT-PCR data, but high in the virtual data. A random sample of twenty of the large number of Psg16 EST sequences in the database indicated that all were of placental origin, ruling out contamination with brain-derived sequences as an explanation for the disparity between RT-PCR and virtual analysis. There was also generally good agreement with the results from screening the EST database with N1 and A domain sequences, although the numbers of A-domain hits were 4–5 fold lower than the N1-domain hits. The only exception to this observation was that Psg30 and Psg31 sequences were identified in 2-fold greater abundance when screened with the A domain compared with the N1 domain. Despite some discrepancies, therefore, the combined RT-PCR and virtual Northern data demonstrate that developmental onset of expression, and maximum expression levels, vary considerably within the Psg family. Figure 5 Virtual Northern analysis of the mouse Psg genes. The nucleotide sequences of the Psg exons encoding the N1 or the A domains were used in NCBI-BLAST searches of the GenBank mouse EST database (March 16, 2004) for the presence of Psg transcripts (virtual Northern analysis). A hit was registered when a 100% match for a sequence > 150 bp was observed. Obvious mismatches such as unidentified nucleotides (N) or single nucleotide insertions or deletions (especially at the end of a sequence run) were ignored. Mouse Psg locus genomic organisation The published mouse Psg gene locus is contained on contig NT_039395. However, the complement of Psg genes is incomplete and the majority of gene sequences within the contig are unordered. We therefore decided to determine the organisation of Psg genes within the locus by screening BAC, YAC and cosmid clones using hybridisation with gene-specific oligonucleotide probes. We defined two separate contigs (subclusters) within which the order of Psg genes was determined to the fullest extent possible. The orientation of the two subclusters with respect to each other and the chromosome 7 centromere was determined by fluorescent in situ hybridisation (FISH) analysis. These data are summarised in Fig. 6. All of the known mouse Psg genes are located within cytobands A1 and A2 on proximal chromosome 7 and are interspersed with other genes, particularly Ceacams, as determined by comparison with the published mouse genomic sequence on contig NT_039395. We did not observe any obvious correlation between the relative positions of the Psg genes at the locus and their domain arrangements or expression patterns. Figure 6 Physical map of mouse Psg gene locus. A. The order of the Psg genes was inferred from the presence of the various genes on overlapping cosmid, BAC and YAC clones. The position of Psgs represented by filled boxes is unequivocal, whereas the position of those represented by open boxes is ambiguous. Arrows between pairs of genes indicate that their order remains unresolved. The distances between individual genes are not shown to scale. Chimeric YACs mapping to separate chromosomes are indicated by stippled and solid lines. The solid lines correspond to chromosome 7 regions containing the Psg genes indicated above. The locations of the non-chromosome 7 regions are not known. Only the sizes of non-chimeric YACs have been determined and are shown (size bar corresponds to 100 kb). The centromere (cen) / telomere (ter) order and the relative orientation of the two Psg gene subclusters were resolved by FISH mapping. B. Two-colour FISH prophase mapping of relative orientation of the two Psg gene subclusters using mouse m5S cells and C57BL/6CrSlc mouse lymphocytes. (i) FISH pattern representative of 38 experiments where BAC 310D2 in subcluster 1, labelled with rhodamine (R), is centromeric to BAC 600E2 from subcluster 2, labelled with fluorosceine (F). (ii) FISH pattern representative of 38 experiments where BAC 310D2 in subcluster 1, labelled with rhodamine, is centromeric to YAC F10104 from subcluster 2, labelled with fluorosceine. (iii) Orientation of subcluster 2 determined by relative positions of BAC 572D4, labelled with rhodamine, which is telomeric to YAC F10104, labelled with fluorosceine. There is a discrepancy with respect to the distance between the two subclusters. The currently poorly resolved data covering this region in the the Ensembl assembly implies the presence of a gap between Psg29 and Psg32. However, we determined that the subclusters are fused between Psg32 and Psg30/Psg18. YAC F10104 (which is non-chimeric) is about 460 kb long and contains only two Psg genes which indicates the presence of a non-Psg genomic region. We estimate the gap between the subclusters to be approximately 400 kb based on the size of cosmids containing two Psg genes (ca. 40 kb). Discussion The human PSG genomic data in the public databases are relatively complete. For each PSG gene, there are annotated RefSeq resources comprising information on genomic structure, transcripts and translation products. The nomenclature is also standardised [37]. Further, there are accurate chromosome 19 locus assignments allowing complete visualisation of the PSG locus and surrounding genes. In contrast, a substantial quantity of mouse Psg genomic data in the public domain is fragmented, incomplete and somewhat unreliable. We sought to collate the existing genomic data, to present novel data to fill in gaps, and to provide a coherent resource of mouse Psg genomic data. To determine whether the existing set of mouse Psg genes was complete we performed systematic BLAST searches of a variety of public DNA sequence databases. This analysis revealed the existence of a novel expressed Psg gene, which we name Psg31 in line with the accepted nomenclature convention [37]. Psg31 apparently evolved from a duplication of the whole of the Psg30 gene followed by a subsequent internal duplication of the N1 domain. We were also able to predict the complete coding sequences of four Psg genes for which previously only partial fragments were described. The gene, CDS and protein sequences of these predictions, coupled with a complete reference of all known mouse Psg CDS and primary protein sequences are provided in three attached Additional Files. Using the full CDS information obtained for the complete set of mouse Psg gene sequences, domain structures for all family members were predicted. All of the PSG proteins possess previously described arrangements of Ig-like domains. Except for two members, discussed below, all are predicted to encode N-terminal secretory signal sequences. Our predicted novel splice variant of Psg16 has a complete N1 domain and secretory signal peptide sequence. Trace Archive EST database BLAST analysis confirmed that this variant is expressed in the placenta. In contrast, the brain-expressed variant [38] has only a partial N1 domain and no secretory signal peptide. The previously described Psg-ps1 pseudogene [8] was found to be expressed in the placenta using Trace Archive EST database BLAST analysis and possesses an excellent Kozak sequence at the predicted translational initiation site. This evidence therefore indicates that this gene, which we rename Psg32, is not a pseudogene but a bona fide expressed Psg gene family member. The Psg32 transcript may encode a protein that is retained within the cytoplasm. We note that a precedent in the human exists in the form of a non-secreted splice variant of PSG11 [7]. Psg31 has the unusual N1-N1-N2-N3-N4-N5-N6-N7-A domain structure. This newly characterised Psg gene has evolved from a duplication of the entire Psg30 gene followed by an internal duplication of the N1 domain. There may be functional significance associated with the N1 domain duplication. The complex nature of Psg gene evolution, including putative gene conversion and recombination events between family members [34], makes it difficult to analyse their evolution. Despite this, the data generated from ClustalX alignments and NJ trees enabled us to generate trees that allow prediction of the order of events of domain duplications in Psg24, Psg30 and Psg31. We note that the apparent route to domain number expansion differed between Psg24, and Psg30 and Psg31. The extra N-domains of Psg24 are derived from two duplications of the N2 domain. However, for Psg30 and Psg31, independent duplications of each of the N2 and N3 domains were probably followed by a secondary duplication of the daughter domains, possibly as a single event. Gene expression data from RT-PCR of placental RNA and EST database analysis revealed considerable differences in the expression levels of different Psg genes. In this analysis Psg21 and Psg23 were the most abundant, consistent with a previous report of abundant Psg23 expression [39]. Whilst there was generally good agreement between the two methods of expression analysis we cannot determine, based on current data, whether Psg gene expression differences reflect selection for divergent functions, or increased gene dosage for enhancement of an existing function, because expression levels were uniformly low for many family members, and there was a general trend of increased expression during gestation. Psg transcripts are found from day 6.5 of embryonic development onward in primary trophoblast giant cells, later (from day 10.5) in spongiotrophoblast cells and, to a lesser extent, in a cell population in the deciduas basalis at day 14.5 [40]. At present, it is unclear whether the various Psg genes exhibit different cellular expression patterns, which might indicate divergent functions of the various PSGs. There are interesting parallels between the expansion of the Psg gene family and similar expansions of other placentally-expressed gene families such as the prolactin and growth hormone families [1], and the aspartic and cysteine proteases [41]. Such duplications may be a manifestation of parent-offspring conflict or inter-sibling rivalry over maternal investment [42]. Having collated all known mouse Psg gene protein coding sequences and protein domain structures, the mouse Psg genomic locus on chromosome 7 remained to be determined to complete a comprehensive resource for the analysis of Psg function. The NCBI build 32 composite mouse assembly data revealed that only four Psgs had been mapped. Other Psgs on contig NT_039395 are currently unordered. We therefore screened cosmid, YAC and BAC libraries, and orientated Psg-containing clones to identify, where possible, the order of Psg genes within the locus. We were not able to resolve all ambiguities in gene order on our map; however, where public database information is available, our data are in good agreement. We found no clear relationship between gene location and gene expression level suggesting that, within the Psg locus, each Psg gene is autonomously regulated. Conclusions The evolution and physiological functions of the relatively understudied mouse Psg gene family are poorly understood. This is a feature shared with other placentally-expressed, multigene families such as the prolactin and growth hormone genes [1]. In order to provide a comprehensive resource to facilitate functional studies of mouse Psg genes, including the generation of mouse mutants with modified Psg gene expression profiles, we have collated the entire set of mouse Psg genes, their predicted encoded proteins, and their evolutionary histories. The complete CDS data will enable the cloning, over-expression, and gene targeting of individual or multiple mouse Psg genes. This will facilitate the elucidation of their function and, by extrapolation, their human homologues, which may be involved in diseases of pregnancy. Methods Isolation of cosmid, YAC and BAC clones Cosmid libraries in pWE15 which were made from liver DNA of BALB/c and C57BL/6 mice were obtained from Dr. Edwin N. Geissler, Boston, MA, USA, and Stratagene (Heidelberg, Germany), respectively. They were screened for the presence of Psg gene-containing cosmids using a 32P-labelled, full-length Psg cDNA (2.1 kb KpnI/XbaI fragment of pCea2b [8]) as a probe. The final wash was in 4x NaCl/Cit (1x NaCl/Cit is 0.15 M NaCl, 0.015 M sodium citrate pH 7.0), 0.1% SDS at 65°C. Psg gene-containing YAC clones were identified by hybridisation of DNA from YAC clones which were spotted at high density onto nylon membranes [43] with the same Psg cDNA probe under medium stringency conditions with a final wash in 4x SSPE (1x SSPE is 180 mM NaCl, 10 mM sodium phosphate pH7.4, 1 mM EDTA), 0.1% sodium dodecyl sulfate (SDS) at 65°C. The membranes with arrayed YAC clone DNAs were kindly provided by Dr. H. Lehrach, Max-Planck-Institut für Molekulare Genetik, Berlin. Two libraries were screened, 902 and 903, both in the vector pYAC4 [44] composed of 9216 clones each, containing spleen DNA of C3H and C57BL/6 mice, respectively. Filters with arrayed BAC clones containing genomic DNA from the embryonic stem cell line CJ7 (129/Sv strain) (CloneRanger™ BAC Human CTC, Invitrogen, Karlsruhe, Germany) were screened by hybridisation with a 32P-labelled probe consisting of the N1 domain exon sequences of 14 mouse Psg genes (Psg17–Psg29 and Psg32). The N1 domain exons were amplified individually by PCR using the degenerate primer pair PsgN1-F/PsgN1-R or Psg32N1-F/PsgN1-R (4 mM each) for Psg32 (Table 2) and cosmid clones (10 ng) with individual Psg genes as template in the presence of 1 U Taq polymerase and 4 mM MgCl2 in a total volume of 30 ml (annealing: 50°C, 30 s). N1 exons of Psg28 and Psg29 were released by digestion with SalI and KpnI from pUC18 (see below). Southern blot analysis of cosmid and YAC DNAs DNA from YAC clones was isolated by CsCl equilibrium density gradient centrifugation in the presence of ethidium bromide, essentially as described [45], except that spheroblast formation was achieved by incubation for 90 min with 0.17 mg/ml lyticase (approx. 6,000 U/mg) from Arthrobacter luteus (Sigma, Deisenhofen, Germany). Two mg of cosmid or 0.5 mg of YAC DNA were digested with restriction endonucleases, size fractionated by electrophoresis on 1% agarose gels and blotted onto positively charged nylon membranes. To identify N1 and A domain exon-containing DNA fragments, the digested DNAs on the membranes were hybridised with 32P-labelled N1 (cosmid DNA blots only) and A domain probes from Psg17 and washed under medium stringency conditions (4x SSPE, 0.1% SDS, 65°C). The Psg17 N1 and A domain cDNA fragments used as probes were obtained by PCR (denaturation: 94°C, 15 s; extension: 72°C, 3 min; 30 cycles) using the mPsg-5'/mPsg-3' (annealing: 50°C; 30s) and Psg17A5'/Psg17A3' (annealing: 60°C, 30 s), primer pairs respectively, and the Psg17 cDNA clone pCea2b as template (Table 2; [8]). Identification of new Psg genes from YAC clones N1 exons from unknown Psg genes were amplified by PCR (annealing: 52°C, 30 s; 30 cycles) in a total volume of 100 μl using 200 ng of YAC clone DNA as template, 1 U Taq polymerase, 3 mM MgCl2 and 4 mM each of PsgN1-F and PsgN1-R degenerate oligonucleotides (Table 2) which bind to the N1, but not N2 and N3 exons of all known mouse Psg genes (except Psg32). The product was purified by electrophoresis on a 1.8% agarose gel and subcloned into pUC18 after blunt-ending (SureClone ligation kit: Pharmacia, Freiburg, Germany). The N1 exons from two of the 10 newly identified Psg genes (Psg28, Psg29) were analysed by sequencing recombinant plasmids which did not hybridise with oligonucleotide probes specific for known Psg genes (Table 2). Mapping of the Psg locus The presence of the different Psg genes within YAC, BAC and cosmid clones was first determined by PCR followed by hybridisation with oligonucleotides specific for individual Psg genes. DNA from Psg-containing YAC (100 ng) and cosmid clones (10 ng) were used to amplify the N1 domain exons of all known Psg genes in a total volume of 60 μl as described above. The N1 exon of Psg32 was amplified in a separate reaction using Psg32N1-F and PsgN1-R (Table 2) as primers under the same conditions used for the amplification of the other N1 exons. For the analysis of BAC clones, PCR was performed directly from the BAC-containing bacterial clones according to the supplier's protocol. Aliquots (3.5 μl) from the various PCR reactions were alkali-denatured, dot-blotted onto nitrocellulose and hybridised with individual 32P-labelled (final concentration: 0.3–1.2 × 106 dpm/ml), gene-specific oligonucleotides (Table 2) in 0.5 M sodium phosphate pH 7.2, 7% SDS, 1 mM EDTA over night at 40°C. The filters were washed twice for 20 min each in 2x SSPE at room temperature, followed by two washes in 6x SSPE, 0.1% SDS at a temperature 4°C below the calculated melting temperature of the hybrids [46]. Oligonucleotides containing at least 3 mismatches in comparison with the corresponding sequences of all known Psg and Cea subgroup members were designed using the computer program Primer [47]. The only exception is the Psg19-specific oligonucleotide which exhibits only 2 mismatches to the Psg22 sequence. However, the stringency of the post-hybridisation washes only allowed binding of oligonucleotides with a maximum of one mismatch. The specificity of the oligonucleotides and the hybridisation conditions was demonstrated on cosmid DNAs containing individual Psg genes. The identity of the Psg genes was verified by sequencing. No cross-hybridisation with other Psg genes was observed. The size of the YACs was determined by pulsed field gel electrophoresis followed by Southern blot hybridisation with the Psg17 cDNA clone pCea2b (see above) essentially as described previously [48]. Fluorescence in situ hybridisation (FISH) analyses The chromosomal location and chimerism of YAC clones were determined by FISH analyses, using B1-PCR of YAC DNA for probe preparation essentially as described [49]. Orientation and order relative to the chromosome 7 centromere and to each other of the two Psg gene subclusters was defined by FISH analysis using probes described in Fig. 6. FISH was performed essentially as described [50] on m5S cells [51] and concanavalin A-stimulated lymphocytes [52] from the C57BL/6CrS1c mouse strain. RNA isolation, RT-PCR and specific detection of Psg cDNAs BALB/c mice were mated overnight, and the next day plugged females were designated as day 0.5 of gestation. Pregnant females were killed by cervical dislocation and placentae were dissected free of maternal tissue, immediately frozen in liquid nitrogen and stored at -70°C. Total RNA was extracted by the acid phenol method [53]. The expression of individual Psg genes was studied by RT-PCR followed by hybridisation of the products with gene-specific oligonucleotides. Total RNA (1 μg) from placentae of different gestational stages was reverse transcribed in a total volume of 10 μl by avian myoblastosis virus (AMV) reverse transcriptase (Promega, Mannheim, Germany) in the presence of 6 U/μl RNasin (Promega) using a degenerate oligo (dT)30 oligonucleotide (1 μM) as primer (Table 2). The reaction mix was adjusted to 1x Taq buffer (20 mM Tris-Cl, 16 mM (NH4)2SO4, pH 8.6), 3 mM MgCl2 and 0.4 mM dNTPs in a total volume of 100 μl. Amplification of all known Psg cDNAs (except for the cDNA of Psg32 (Cea6), which at the time of the experiment was presumed to be a pseudogene [8]) was achieved by PCR (denaturation: 94°C, 15 s; annealing: 58°C, 30 s; extension: 72°C, 3 min; 30 cycles) using Taq polymerase after addition of 400 pmoles of Psg-all (Table 2) and 50 pmoles of the oligo (dT) oligonucleotide as 5'- and 3'-primer, respectively. Ten μl aliquots each were size fractionated by electrophoresis on a 1% agarose gel, blotted onto a positively charged nylon membrane (Roche Diagnostics, Mannheim, Germany) and hybridised with individual 32P-labelled, gene-specific oligonucleotides (Table 2) as described above. DNA sequence determination Nucleotide sequences were determined on both strands with flanking universal and internal oligonucleotides as primers using a T7 polymerase sequencing kit (Pharmacia) or a Taq Dye Deoxyterminator cycle sequencing kit (PE Applied Biosystems, Foster City, CA, USA). Assessment of availability of full-length mouse Psg sequences in the public databases All bioinformatics searches described below used the online software and databases available at the NCBI . Where fully annotated, Psg cDNA sequences were identified by name searches of the RefSeq RNA database. Attempts were then made to identify remaining known Psg cDNA sequences via BLAST analyses of the mouse RefSeq RNA database using the GenBank partial-sequences referenced in [37]. Any PSG cDNA sequences that could still not be identified by this method were determined by BLAST analysis of the mouse genome database using known fragments of the sequence to be determined. This identified genomic contigs that could be interrogated for the 'missing' exonic sequences by pairwise BLAST analysis using the Psg17 cDNA sequence, or fragments thereof, as a probe. A similar procedure was applied to situations where the existence of alternatively spliced exons was suspected to reside within in a Psg gene-containing contig. In the cases where Psg mRNA sequences were built from genomic sequence, or splice variants were predicted, evidence for the existence of such mRNA species in vivo was tested by BLAST analysis using the mouse EST and Trace Archive EST databases. Bioinformatic analysis of the mouse PSG Ig domains The coding sequences of the PSG domains were aligned using Clustal X [54]. For the production of rooted neighbour-joining (NJ) evolutionary trees, alignments were bootstrapped 1000 times. Evolutionary trees were constructed from the alignments using TreeView . Analysis of the Psg32 exon 1 sequence Four hundred nanograms of DNA obtained from four inbred mouse strains (A/J, C57BL6/6J, YBR/Ei, and SWR/J) (Jackson Laboratory, Bar Harbor, Maine, USA) were amplified using the oligonucleotide primers 5'-AAGGAAGGACAGCAAAT and 5'- AGCTGTGAGCAGAAGAC (denaturation: 94°C, 30 s; annealing: 50°C, 30 s; extension: 72°C, 30 s; 30 cycles) with Pfu DNA polymerase (Stratagene) following the manufacturer's instructions. The 146 bp PCR products were subcloned into PCR-Script (Stratagene). Clones that hybridized to a Psg32-specific oligonucleotide, Psg32-exon 1, which binds to a sequence internal to the PCR primers were sequenced. Authors' contributions ASM performed bioinformatics relating to Psg locus organisation, phylogenetic analyses, Psg expression studies, and drafted the manuscript. BF characterized Psg genomic clones and performed Psg expression studies. GD isolated the Psg BAC clones and analysed Psg32 exon 1 sequences in mouse strains. MB and FW contributed to Psg expression studies and YAC fragment assembly. TH and KO performed FISH analysis for identification of orientation of Psg subclusters. TM co-conceived the project and coordinated work performed in Cork. WZ co-conceived the project, coordinated all work performed in Germany, and performed bioinformatics relating to Psg phylogeny and expression. Supplementary Material Additional File 1 The gene sequence and predicted CDS and primary protein sequences for Psg16 (placental transcript), Psg20, Psg24, Psg25, Psg26, Psg27, Psg29 and Psg31 A basic text file containing the primary genome data (with source HTGS or WGS information). Predicted CDS sequence is included along with translations. Reverse complement is abbreviated 'RC' in the text. Click here for file Additional File 2 Complete set of mouse Psg CDS sequences A basic text file in FASTA format containing CDS sequences for all known mouse Psgs (not including the brain-specific variant of Psg16). Click here for file Additional File 3 Complete set of mouse PSG primary protein sequences A basic text file in FASTA format containing primary protein sequences for all known mouse Psgs (not including the brain-specific variant of Psg16). Click here for file Acknowledgements We thank Dr. Hameister, Ulm for help with the chromosomal localisation and determination of chimerism within the YAC clones. We acknowledge support from the Deutsche Krebshilfe and the Irish Higher Education Authority Program for Research in Third Level Institutions (HEA PRTLI1 & 3), funded under the National Development Plan. 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==== Front BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-6-11564711510.1186/1471-2350-6-1Research ArticleGenome-wide and Ordered-Subset linkage analyses provide support for autism loci on 17q and 19p with evidence of phenotypic and interlocus genetic correlates McCauley Jacob L [email protected] Chun [email protected] Lan [email protected] Lana M [email protected] Genea [email protected] Kimberly [email protected] Susan E [email protected] Jonathan L [email protected] James S [email protected] Center for Human Genetics Research, Departments of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA2 Biostatistics, Vanderbilt University, Nashville, TN, USA3 Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA2005 12 1 2005 6 1 1 18 10 2004 12 1 2005 Copyright © 2005 McCauley et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Autism is a neurobehavioral spectrum of phenotypes characterized by deficits in the development of language and social relationships and patterns of repetitive, rigid and compulsive behaviors. Twin and family studies point to a significant genetic etiology, and several groups have performed genomic linkage screens to identify susceptibility loci. Methods We performed a genome-wide linkage screen in 158 combined Tufts, Vanderbilt and AGRE (Autism Genetics Research Exchange) multiplex autism families using parametric and nonparametric methods with a categorical autism diagnosis to identify loci of main effect. Hypothesizing interdependence of genetic risk factors prompted us to perform exploratory studies applying the Ordered-Subset Analysis (OSA) approach using LOD scores as the trait covariate for ranking families. We employed OSA to test for interlocus correlations between loci with LOD scores ≥1.5, and empirically determined significance of linkage in optimal OSA subsets using permutation testing. Exploring phenotypic correlates as the basis for linkage increases involved comparison of mean scores for quantitative trait-based subsets of autism between optimal subsets and the remaining families. Results A genome-wide screen for autism loci identified the best evidence for linkage to 17q11.2 and 19p13, with maximum multipoint heterogeneity LOD scores of 2.9 and 2.6, respectively. Suggestive linkage (LOD scores ≥1.5) at other loci included 3p, 6q, 7q, 12p, and 16p. OSA revealed positive correlations of linkage between the 19p locus and 17q, between 19p and 6q, and between 7q and 5p. While potential phenotypic correlates for these findings were not identified for the chromosome 7/5 combination, differences indicating more rapid achievement of "developmental milestones" was apparent in the chromosome 19 OSA-defined subsets for 17q and 6q. OSA was used to test the hypothesis that 19p linkage involved more rapid achievement of these milestones and it revealed significantly increased LOD* scores at 19p13. Conclusions Our results further support 19p13 as harboring an autism susceptibility locus, confirm other linkage findings at 17q11.2, and demonstrate the need to analyze more discreet trait-based subsets of complex phenotypes to improve ability to detect genetic effects. ==== Body Background Autism (OMIM # 209850) is a neurobehavioral disorder involving deficits in language and social abilities and patterns of repetitive behaviors, restricted interests and resistance to change. The most recent estimate of population prevalence for the broader autism spectrum indicates a rate of 34/10,000 (~1/300) [1], with a male: female ratio of 4:1 [2,3]. Evidence from various studies indicates idiopathic autism has a complex genetic etiology. Twin studies show a concordance of 60% among monozygotic (MZ) twins and 0% among dizygotic (DZ) pairs for classic autism, but this increases to 92% for MZ pairs and 10% for DZ pairs when a broader phenotype of related social and language abnormalities is included [4,5]. The sibling recurrence risk is suggested to be ~3–10% but may be underestimated as a result of "stoppage rules" [6-8], and the relative risk is thus 30–100 times that in the general population [5,7]. Heritability is estimated at 90%, which is among the highest for psychiatric disorders. While the data do not strongly endorse any one model for inheritance, twin and family studies support a multilocus etiology with as many as 10–20 loci (reviewed in [9-11]). Genome-wide screens of multiplex autism families for susceptibility loci [12-22] have identified a few genomic regions in common across multiple studies; 7q and 2q have received the greatest attention [17,19,20,23-28], with support from chromosomal abnormalities affecting these regions in idiopathic autism (reviewed in [29]). Genetic studies of autism are substantially complicated by clinical and locus heterogeneity, and it is possible that epistatic or epigenetic mechanisms may play important roles in genetic etiology [9,30]. Analytical strategies that address the latter concerns are limited, and most studies to date have focused on analysis of main effects using a global autism diagnosis to define affection status. Moving forward, more sophisticated approaches are being proposed in which trait-based subsets of the broader autism phenotype are used in genetic analyses. Similarly, given the interdependence of genes and their protein products within biological systems, analytical approaches that address potential interaction between susceptibility loci will also be critical to characterizing gene-phenotype relationships in autism. We report a second generation 10-cM microsatellite-based genomic screen of multiplex autism families. The dataset for this screen includes 71 families recruited by the Tufts/New England Medical Center, a well-characterized set of 85 families from the Autism Genetics Resource Exchange (AGRE), and 2 families from Vanderbilt University. Several sites of suggestive linkage are identified, although none meet criteria for genome-wide significance. The loci with greatest support for linkage were 17q11.2 and 19p13; the latter site demonstrated significantly increased allele-sharing when the Ordered-Subset Analysis (OSA) algorithm was employed using a quantitative trait-based autism phenotypic subset related to specific "developmental milestones" as a covariate to rank families. Methods Sample and Demographics The demographics for the 158 family dataset comprising the studies in this report are shown in Table 1. Families were recruited through three sites: (a) 71 families from the Tufts/NEMC site, (b) 2 families from the Vanderbilt University site, and (c) the remainder of families (85) were chosen from the AGRE repository based on our own recruitment criteria. Multiplex families (mostly affected sibling-pairs) had one affected individual who met full criteria for autistic disorder based on Autism Diagnostic Interview-Revised (ADI-R; [31-33])) algorithm scores, while the second individual either met criteria or in some cases was under the cut-off by only one or two points. Exclusion criteria included dysmorphic features, abnormal karyotype, diagnosis of fragile X syndrome, and other genetic disorders of known etiology. Individuals were assessed by the respective groups using the ADI-R at a developmental age >18 months; Tufts/NEMC and Vanderbilt groups included individuals between the ages of 4 and 22; in cases in which ADI-R interviews were performed initially at <4 years, they were repeated when the probands reached 4 years of age. All individuals were additionally assessed using the Autism Diagnostic Observation Schedule [32,34] and the Vineland Adaptive Behavior Scales – Interview Edition [35,36]. Genotype data and statistical analysis DNA from Tufts and Vanderbilt samples was obtained from peripheral blood or immortalized lymphoblastoid cell lines using the PureGene Kit (Gentra Systems). While a minority of families from the Tufts/NEMC cohort had been genotyped previously [13], both new and previously genotyped families were genotyped by deCODE (Reykjavik, Iceland) using their 500 marker (~8 cM intermarker spacing) panel and corresponding genetic map [37]. Genotype data were obtained from the AGRE website [38] for families whose samples were purchased from the AGRE repository and included in this study. Clinical procedures and genotyping for the AGRE sample has been described previously [18,39]. AGRE samples and corresponding genotype data had a distinct but overlapping panel of markers compared to the Tufts and Vanderbilt families. AGRE genetic markers were placed on the deCODE map, with order and spacing properly insured through exhaustive comparisons between genotyped markers, available genetic maps, and physical DNA sequence assemblies in both public and Celera databases. Genotype data for each chromosome underwent thorough error detection and genotype confirmation. Initially, data were tested for Mendelian inconsistencies using PEDCHECK [40] and RELPAIR [41], followed by SIMWALK2 [42] for haplotype construction to detect genotyping errors reflected by unlikely double recombinants. In the event of a highly improbable genotype, the data for that marker were excluded for the family. Allele frequencies were estimated using genotype data from all unrelated individuals in the combined dataset, consisting of more than 300 chromosomes. Allele frequencies were compared with available data from other Caucasian populations, and no significant differences were observed (data not shown). The LAPIS program of the PEDIGENE system [43] was used to output appropriate analysis files for the different programs. Linkage was analyzed using both model-dependent and model-independent methods. For autosomes, two-point and multipoint heterogeneity LOD (HLOD) scores were calculated under both dominant and recessive models using Allegro [44]. Disease allele frequency was estimated to be 0.01 and 0.1 for dominant and recessive models, respectively. Phenotypic status was only considered for affected individuals, and other family members were designated as having an unknown phenotypic status. Nonparametric allele-sharing LOD* values were calculated using affected relative pair data based on an exponential model using the Spairs scoring function as recommended by McPeek [45]. NPL scores and corresponding P-values were also calculated by Allegro. Data from the X chromosome were analyzed using ASPEX [46] and FASTLINK [47] to calculate two-point and multipoint MLOD scores. Peak parametric (HLOD) or nonparametric LOD* scores ≥1.5 were considered as "suggestive" evidence for linkage and listed in Table 2, along with corresponding peak marker, deCODE cM location, and chromosomal band position. The nonparametric genome-wide significance threshold [48,49] for linkage at the P = 0.05 level was determined by conducting simulations using Merlin [50] with the current dataset. The Simulate option in Merlin was used to produce 1000 random datasets that preserve the properties of the original data for marker informativeness, spacing and missing data patterns. An empirical significance threshold was determined by using the 95th percentile of the resulting distribution. OSA [51] identifies genetically more homogeneous subsets of the overall data by ordering families according to covariate trait values in ascending or descending order. OSA takes the first family and calculates an allele-sharing LOD* score. In an iterative process, OSA successively adds families, re-calculating LOD* scores with each addition, and it identifies the division in the dataset at which maximum linkage is obtained on the chromosome being analyzed. Permutation testing is used to determine the empirical significance of the observed results. OSA has been applied with success to identify or increase evidence in support of linkage to complex disease susceptibility loci [52-54]. To explore potential genetic interaction or other genetic correlations between sites of main effect (i.e. suggestive linkage), OSA was applied using family-specific LOD scores as the covariate trait. Families were ranked according to the family-specific LOD score at peak sites demonstrating LOD scores ≥1.5. Allele-sharing analysis was performed for the other six chromosomes using the OSA algorithm. For instances of empirically significant increases in evidence for linkage, we explored the nature of the genetic correlation to ask whether it reflected clinical correlations in the respective subsets. We employed ADI-based factor subsets, identified by principal components analyses of ADI/ADI-R items, to represent putative phenotypic subsets in autism [55,56]. The ADI-based variable clusters correspond to "(1) language, (2) social intent, (3) developmental milestones, (4) rigid-compulsive behaviors, (5) savant skills, and (6) sensory aversion", as determined by Folstein and colleagues [55]; and (7) "insistence on sameness" as described by Cuccaro and colleagues [56]. We thus compared the seven ADI-based factor score means (both the mean of family means and the mean of affected individuals) using a t-test for the families above and below the OSA-determined split in the dataset resulting in maximal linkage. Subsequent analysis involved specific examination of the "developmental milestones" cluster. The milestones factor indexes on the following ADI items: "(1) To walk unaided; (2) to sit unaided on flat surface; (3) age of first single words; (4) age of first phrase; (5–6) acquisition of bladder control: daytime, night; (7) acquisition of bowel control." Analysis of the "developmental milestones" factor as a potential phenotypic subset related to the autism linkage correlations was performed by applying the OSA algorithm. We used "developmental milestones" family means, normalized via SAS and Box-Cox transformation procedures, as an ascending ranking covariate. LOD* scores were calculated according to the OSA algorithm, and the resulting increase in linkage achieved with the OSA-determined family subset was analyzed through permutation testing. Approval for these studies was granted by the respective Institutional Review Boards at Tufts University School of Medicine/New England Medical Center and Vanderbilt University Medical Center. Additionally, all studies were performed with informed consent provided by the families participating in the research. Results Seven chromosomes revealed one or more regions of linkage with a model-dependent or model-independent LOD score ≥1.5 (Figure 1). No locus reached the empirically derived genome-wide significance level of 2.92. These suggestive loci include 3p25, 6q23, 12p12, 16p12-p13, 17q11, 17q21 and 19p13 (Table 2). Data provide the most compelling support for 17q11.2 and 19p13 as harboring autism susceptibility loci. For 17q11.2, peak linkage was observed at 53 cM, corresponding to marker D17S1294 (Table 2), at which we see a multipoint HLOD of 2.85. Nonparametric multipoint analysis revealed an allele-sharing LOD* score of 2.13 and an NPL score of 2.84 (P = 0.0024). A second telomeric peak can be distinguished on 17 at ~69 cM, corresponding to 17q21.2. Marker D17S1299 at this site yielded a HLOD of 1.9, a LOD* of 1.66 and an NPL score of 2.26 (P = 0.012). The more proximal peak at ~53 cM lies in close proximity (~150 kb) to the serotonin transporter (SLC6A4) locus, long considered to be an attractive functional candidate gene for autism and other neuropsychiatric conditions. Figure 2 shows multipoint LOD score plots for both dominant and recessive parametric (HLOD) and nonparametric allele-sharing LOD* values for chromosomes 17 and 19. The second most significant result was observed on 19p13, where peak linkage was detected at marker D19S930, revealing a multipoint HLOD of 2.55 at ~40 cM (Table 2 and Figure 2). Nonparametric analyses detected a LOD* of 1.92 and a corresponding NPL of 2.77 (P = 0.003). As with chromosome 17, the multipoint analyses show a second more telomeric peak, corresponding to marker D19S113. The recessive HLOD at this site was 2.20, with model-independent LOD* and NPL values of 1.39 and 2.10 (P = 0.018), respectively. To address the possibility of gene-gene interaction, we applied the OSA approach with family-specific LOD scores as the ranking trait. Families, almost all of which are affected sib-pairs, were ranked in both ascending and descending order using family-specific LOD scores. The three most significant correlations are presented in Figure 3. Using chromosome 19 LOD scores as the covariate, the results on chromosome 17q, while non-significant (P = 0.1), showed an increase in linkage at the more distal peak on 17q21.1 from a LOD* of 1.7 to 3.6 and identified an optimal subset of 52 families. Applying the same covariate, a significant increase was seen on chromosome 6q, with a smaller, completely overlapping, 30-family optimal subset. This subset resulted in an increase in LOD* values from 1.0 to 3.6 at ~164 cM (P = 0.004). Another significant finding involves the 7q region, possibly representing the most replicated site of linkage in autism (reviewed in [9,29]). Given a substantial focus on this region over several years, we lessened our criteria to examine any other chromosome demonstrating a LOD score >1. Application of OSA using chromosome 7q linkage data, again ranking families based on LOD scores in a descending manner, lead to a significant increase in linkage on 5p at ~41 cM from a LOD* of 1.1 to 3.3 in a 41-family subset. Thus, in these three cases, notwithstanding the nonsignificance of the 19p13/17q21 result, there is a positive correlation of linkage in varying but overlapping subsets of the data between these respective pair-wise locus combinations. To further explore the basis of the observed results, we tested the hypothesis that underlying phenotypic correlates might explain genetic correlations. We compared the mean values for the seven factor traits in the optimal subsets compared to the means of the remaining families using a t-test, both under assumption of equal and unequal variances. This comparison for all seven available factors revealed a nominally significant differences in the chromosome 19 optimal subsets identified from OSA analysis of chromosomes 17 (52 families) and 6 (30 families) for the "developmental milestones" cluster. The families in the optimal OSA subset have lower scores and therefore are more rapidly achieving developmental milestones. A similar procedure for the chromosome 7-based subset revealed no obvious differences in any of the factors (data not shown). To directly test the hypothesis that chromosome 19 linkage was related to reduced affection for the "developmental milestones" factor, we performed an OSA analysis in which families were ranked in ascending order based on mean values for the milestones factor score. Figure 2 shows the results from this analysis, which generated increased evidence for linkage to 19p13 with peak LOD* scores increasing from 1.9 to 3.4. Permutation testing revealed this increase to be empirically significant (P = 0.04), thus further supporting 19p13 as harboring a genetic risk factor for autism. Discussion We have presented evidence in support of autism susceptibility loci on chromosomes 17q and 19p. Our results suggest that the 19p locus is related to a phenotypic profile involving a more rapid achievement of particular "developmental milestones". Features indexed in this ADI-based factor are: (1) ability to walk unaided; (2) ability to sit unaided on a flat surface; (3) age of first single words; (4) age of first phrase; (5–6) acquisition of bladder control: daytime and night; and (7) acquisition of bowel control. Analyses leading to this conclusion also showed positive genetic correlations between optimal OSA-defined subsets contributing to linkage at 19p13 and increases in linkage at loci on 17q21 and 6q23. A similar positive genetic correlation was shown for chromosomes 7q and 5p, however this observation lacks evidence of an underlying phenotypic relationship based on available ADI variable clusters. While the increase in linkage at 17q21 was not empirically significant, the differences in "milestone" score means between the optimal chromosome 19 subsets seen for both chromosomes 17 (52 families) and 6q (30 families) were significant. These exploratory data led to the significant finding of increased linkage in the single direct test of our hypothesis concerning the phenotypic correlation related to chromosome 19 linkage. Despite the significance of the final results on 19, we remain cautious in the interpretation of the overall results. As with a number of other genomic screens in autism, no single main effect locus achieved genome-wide significance. Support for a number of these loci, particularly at 17q11.2 and 19p13 comes from similar suggestive linkage in other genomic screens for autism. Although not all screens detect these loci (not an uncommon finding in linkage studies for complex genetic disorders), the evidence is strong regarding an effect at 19p, within 10 cM of our peak: (1) Shao et al reported an MMLS = 1.21 and an MLOD = 1.38 [14]; (2) the Paris Autism Research International Sibpair Study (PARIS) an MMLS = 1.37 [12]; the International Molecular Genetic Study of Autism Consortium (IMGSAC) reported an MLS of 1.16 [15]; the Mt. Sinai group reported an NPL of 1.56 which increased to 2.31 when only families with obsessive-compulsive behaviors were considered for this region [22]. Similarly, several groups have reported evidence for linkage at 17q11. The recently published AGRE follow-up genomic screen identified an MLS of 2.83 near SLC6A4 [21]. A genome scan for attention deficit/hyperactivity disorder (ADHD) identified an MLS of 2.98 near this locus [57]. An IMGSAC follow-up screen for autism [27] reported a maximum multipoint LOD score of 2.34 at HTTINT2 in the SLC6A4 gene on chromosome 17q11.2. Our own more preliminary analysis of linkage in this region with a highly overlapping dataset to that in the current study, revealed very similar results [58]. Given our inclusion of some AGRE families, it is not completely unexpected that 17q11.2 linkage is similar to that seen the larger AGRE 2nd-stage screen [21], however AGRE families only represented about half of the overall dataset. Families recruited from the Tufts/NEMC site are clearly contributing to this linkage based on the LOD score-based optimal family subset compositions. The 17q21 locus is worth further consideration. Our data support the premise that the adjacent linkage peaks represent distinct loci and are not an artifact of primary linkage at 17q11.2. The evidence for linkage at 17q21, while weaker than that at 17q11.2 only 16 cM centromeric, specifically showed an, albeit nonsignificant, interlocus correlation with 19p13 linkage. Linkage at 17q11.2 in this subset of families actually decreases slightly. Of particular interest is the fact that the distal region harbors the integrin β3 (ITGB3) locus, which was identified recently from a genome-wide quantitative trait locus (QTL) association screen for platelet serotonin levels [59]. We see nominal evidence of linkage to autism at this site, and ~20–25% of individuals with autism have elevated levels of circulating serotonin. The other "suggestive" (LOD ≥ 1.5) loci reported here have also been detected in other genome-wide scans for autism loci. A broad region of 7q has been detected in most screens [17,20,23,25,27,28]. The 16p region has been identified by IMGSAC, and others [15,18,22,27]. Chromosomal abnormalities have also been reported for several of these regions in cases of autism (reviewed in [11]). Linkage at 3p was reported by at least two groups [14,17]. Linkage has also been reported at our 6q locus by at least one other group [12]. Thus, while not significant, the replication of these linkage observations provides support for the likelihood that many of these loci represent true sites of main effect in autism. The application of OSA to detect putative interlocus correlations between the 19p13 and 17q21, 19p13 and 6q23, and between 7q35 and 5p are limited to some degree in significance by their highly exploratory and hypothesis-generating nature. Given the number of comparisons between loci, and the number of comparisons between optimal subset pairs (on 19p or 7q) for the traits means, the potential for type I error is increased. Therefore our interpretation must be cast alongside appropriate caveats. Nevertheless, the multiple exploratory comparisons generated a hypothesis: that linkage to 19p13 was related to a more rapid achievement for specific milestones. We tested this hypothesis with a single analysis revealing an empirically significant increase in linkage at 19p13. Our results of autism linkage and its increase using an ascending milestone score covariate in OSA, taken in the context of replicated observations of suggestive linkage by other groups, strengthens support for the presence of an autism gene at this site. In the end, ultimate interpretation will rely upon replication of these phenomena with independent samples to confirm these observations. Finally, our results highlight the utility of using trait-based subsets of autism to identify putative susceptibility loci for this complex disorder. We and others have hypothesized a likely increased specificity of individual risk genes and corresponding alleles for traits or subphenotypes comprising the broader autism spectrum. Therefore methods such as OSA with power to identify more homogeneous samples and QTL (quantitative trait locus) linkage and association analyses should provide greater sensitivity in the discovery of disease genes in the context of locus and clinical heterogeneity. Additionally, OSA or other forms of conditional linkage analyses, have the ability to uncover potential interactions between loci, an important concept since the inherent interdependence of proteins in common pathways or networks acting during development and normal neuronal function could be easily imagined to act genetically in concert with one another. Conclusions We report evidence to support linkage of autism to 17p11.2 and 19p13. Exploratory analyses to test for correlations between suggestively-linked loci, using the OSA method revealed positive correlations of linkage (i.e. in overlapping families) between 7q and 5p, 19p and 6q, and possibly 19p and 17q22, distal to peak linkage at 17q11.2. Comparing mean scores for ADI-derived factor traits from families above and below the OSA-defined split maximizing linkage, suggested a positive correlation between 19p13 linkage and a more rapid achievement of "developmental milestones" as measured by items in this cluster of ADI variables. We tested this hypothesis by applying OSA with descending "developmental milestone" scores as the ranking covariate, and detected an empirically-significant increase in linkage to 19p13. These findings further support evidence for an autism susceptibility locus in 19p13 and underscore the utility in applying trait subsets in complex disorders to identify genetic risk factors. Competing interests The authors declare that they have no competing interests. Authors' contributions JLM and LMO were responsible for conducting the genome-wide linkage analyses; JLM conducted all OSA analyses, was largely responsible for coordinating and executing the bulk of the reported studies, was key to drafting and editing the manuscript text, and in preparing the figures. CL and JLH provided input into the design and interpretation of statistical results, and provided guidance for conducting the genome-wide simulations required to determine the significance threshold. JLH established the Core-based infrastructure in the Center for Human Genetics Research that facilitated this work and provided very helpful input into the development of the manuscript. SEF developed, with her group, the ADI-based clusters so crucial to examining phenotypic subsets in this paper, which she helped to edit. SEF was responsible for overseeing recruitment and the phenotypic assessment of families from her research group, then at Tufts/NEMC. GC is the clinical coordinator at the Vanderbilt site and oversees ascertainment, recruitment and detailed phenotypic assessment of affected individuals. KG is the Vanderbilt data coordinator and is responsible for management and oversight of family information, pedigree data, and status of DNA, blood or cell line samples for family members. She is directly responsible for preparation of Table 1 of this paper. JSS and JLH are Principal Investigator (PI) and co-PI, respectively, of the current study, and together conceived of and implemented the experimental strategy in close consultation with all other co-authors. JSS initially drafted the manuscript and with JLM incorporated changes suggested by co-authors. JSS was responsible for preparation of all final versions of figures from earlier versions provided by co-authors. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements We acknowledge the important contributions to this work from staff in the Vanderbilt Center for Human Genetics Research, including the DNA Resources, Data Analysis, Family Ascertainment, and Bioinformatics Cores. We also thank the invaluable contributions of Brian Winkloski, Beth Rosen-Sheidley C.G.C., and Dr. Michael Dowd for their hard work at the Tufts/NEMC site and contributions to the early phases of this work. Funding for this work was supported in part by NIH grants MH61009 to JSS, MH55135 to SEF (PI) and JSS (Co-PI); a Vanderbilt Kennedy Center Hobbs Discovery Research Award to JSS and a National Alliance for Autism Research (NAAR) predoctoral fellowship for JLM, and NIH grant NS26630 to Margaret Pericak-Vance (Duke Center for Human Genetics) and JLH. Some of this work was also supported through the Vanderbilt General Clinical Research Center (RR 00095). We especially wish to acknowledge the families who participated in this research, including the AGRE families and resources provided by the AGRE consortium, without whose contribution, none of this work would be possible. Figures and Tables Figure 1 Genome-wide nonparametric linkage analysis in 158 multiplex families for autism loci. Individual plots show allele-sharing LOD* scores calculated for autosomes using Allegro and MLOD scores for the X chromosome calculated using ASPEX. Figure 2 Multipoint linkage analysis under all models for chromosomes 17 (A) and 19 (B). Multipoint parametric HLOD plots for both dominant (blue) and recessive (red) models, and nonparametric allele-sharing LOD* values (black) are displayed across the respective chromosomes. OSA analysis using ascending "developmental milestones" factor scores to order families is shown for chromosome 19, for which a 92-family optimal subset was identified and used to calculate allele-sharing LOD* scores (dashed black line). Figure 3 OSA using family-specific LOD scores as the ranking covariate. Families were ordered based on descending LOD scores at peak linkage for 19p13 and allele-sharing LOD scores calculated in the optimal subset for (A) chromosome 17 or (B) chromosome 6. Families were also ranked based on descending LOD scores at peak linkage on chromosome 7q (C), and LOD scores calculated for chromosome 5. Solid lines reflect multipoint LOD scores corresponding to the entire dataset for the chromosome being analyzed, while dashed lines represent analysis of the optimal subset (above the dataset division in all cases) identified from OSA; these were 52 families for chromosome 17, 30 for chromosome 6 and 41 families for chromosome 7. Table 1 Sample Demographics Families in Linkage Screen 158 Tufts/NEMC 71 AGRE 85 Vanderbilt 2 Affected Individuals 333 Males 257 Females 76 Tufts/NEMC Total 148 Males 117 Females 31 AGRE Total 181 Males 137 Females 44 Vanderbilt Total 4 Males 3 Females 1 Age at ADI (range) 2–46.7 Tufts/NEMC* 2–46.7 AGRE 2–38.0 Vanderbilt 6.2–9.2 Ethnicity (Individuals) Caucasian (242) 73.0% Tufts/NEMC (130) 87.8% AGRE (108) 59.7% Vanderbilt (4) 100.0% Hispanic-Latino (14) 4.2% AGRE (14) 7.7% African-American (8) 2.4% Tufts/NEMC (6) 4.1% AGRE (2) 1.1% Asian (8) 2.4% Tufts/NEMC (2) 1.4% AGRE (6) 3.3% Multi-ethnic (14) 4.2% AGRE (14) 7.7% Other (2) 0.6% Tufts/NEMC (2) 1.4% Unknown (45) 13.5% Tufts/NEMC (8) 5.4% AGRE (37) 20.4% I.Q. estimate distributions Tufts/NEMC (148) <30 13 30–49 21 50–69 37 70+ 17 Unknown 60 AGRE* (181) <30 17 30–49 27 50–69 15 70+ 13 Unknown 109 Vanderbilt*** (4) <30 1 30–49 1 50–69 0 70+ 2 Unknown 0 ADI-Rs performed <4 yrs were repeated at 4 yrs for Tufts/NEMC families IQ estimates are based on the Vineland Daily Living standard scores *IQ estimates are based on overall Vineland Adaptive Behavior standard scores Table 2 Linkage Data for Loci with LOD Scores >1.5 Chromosome deCODE cM Marker HLOD LOD 3p25.3 25 D3S3691 1.76R 2.22 6q23.2 131 D6S1656 1.61R 0.62 7q35 152 D7S2195 1.65R 1.14 12p12.1 45 D12S1591 1.50R 1.43 16p13.2 15 ATA41E04 1.64D 1.38 16p13.12 33 D16S3062 1.87D 1.60 16p12.3 43 D16S490 1.80D 1.49 17q11.2 53 D17S1294 2.85D 2.13 17q21.2 69 D17S1299 1.90D 1.60 19p13.11 40 D19S930 2.55R 1.92 19p13.11 56 D19S113 2.20R 1.39 R: recessive; D: dominant ==== Refs Yeargin-Allsopp M Rice C Karapurkar T Doernberg N Boyle C Murphy C Prevalence of autism in a US metropolitan area JAMA 2003 289 49 55 12503976 10.1001/jama.289.1.49 Volkmar FR Szatmari P Sparrow SS Sex differences in pervasive developmental disorders J Autism Dev Disord 1993 23 579 591 8106301 McLennan JD Lord C Schopler E Sex differences in higher functioning people with autism J Autism Dev Disord 1993 23 217 227 8331044 Folstein S Rutter M Infantile autism: a genetic study of 21 twin pairs J Child Psychol Psychiatry 1977 18 297 321 562353 Rutter M Macdonald H Le Couteur A Harrington R Bolton P Bailey A Genetic factors in child psychiatric disorders – ll. 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==== Front Nutr JNutrition Journal1475-2891BioMed Central London 1475-2891-4-11564414110.1186/1475-2891-4-1ResearchIn vitro digestion and lactase treatment influence uptake of quercetin and quercetin glucoside by the Caco-2 cell monolayer Boyer Jeanelle [email protected] Dan [email protected] Rui Hai [email protected] Department of Animal Science, Cornell University, Ithaca, New York 14853-7201, USA2 Institute of Comparative and Environmental Toxicology, Cornell University, Ithaca, New York 14853-7201, USA3 Department of Food Science, Stocking Hall, Cornell University, Ithaca, NY 14853-7201, USA2005 11 1 2005 4 1 1 28 11 2004 11 1 2005 Copyright © 2005 Boyer et al; licensee BioMed Central Ltd.2005Boyer et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Quercetin and quercetin glycosides are widely consumed flavonoids found in many fruits and vegetables. These compounds have a wide range of potential health benefits, and understanding the bioavailability of flavonoids from foods is becoming increasingly important. Methods This study combined an in vitro digestion, a lactase treatment and the Caco-2 cell model to examine quercetin and quercetin glucoside uptake from shallot and apple homogenates. Results The in vitro digestion alone significantly decreased quercetin aglycone recovery from the shallot digestate (p < 0.05), but had no significant effect on quercetin-3-glucoside recovery (p > 0.05). Digestion increased the Caco-2 cell uptake of shallot quercetin-4'-glucoside by 2-fold when compared to the non-digested shallot. Despite the loss of quercetin from the digested shallot, the bioavailability of quercetin aglycone to the Caco-2 cells was the same in both the digested and non-digested shallot. Treatment with lactase increased quercetin recovery from the shallot digestate nearly 10-fold and decreased quercetin-4'-glucoside recovery by more than 100-fold (p < 0.05), but had no effect on quercetin recovery from apple digestates. Lactase treatment also increased shallot quercetin bioavailability to the Caco-2 cells approximately 14-fold, and decreased shallot quercetin-4'-glucoside bioavailability 23-fold (p < 0.05). These Caco-2 cells had lactase activity similar to that expressed by a lactose intolerant human. Conclusions The increase in quercetin uptake following treatment with lactase suggests that dietary supplementation with lactase may increase quercetin bioavailability in lactose intolerant humans. Combining the digestion, the lactase treatment and the Caco-2 cell culture model may provide a reliable in vitro model for examining flavonoid glucoside bioavailability from foods. ==== Body Background Cardiovascular disease and cancer are the two most common causes of death in the United States and most industrialized nations. A diet high in fruits and vegetables has been correlated with a reduced risk for both cancer and heart disease [1,2]. It is thought that the phytochemicals found in fruits and vegetables may be responsible in part for these health benefits [3]. Phytochemicals from fruits and vegetables may inhibit cell proliferation, protect against oxidative stress, influence cell-signaling pathways, and reduce inflammation. Because these compounds appear to have such beneficial effects, interest has been raised in examining the bioavailability of these compounds. A phytochemical of particular interest is quercetin, a strong antioxidant that is widely consumed in many fruits and vegetables. Quercetin has potential protective effects against both cancer and heart disease. Briefly, quercetin has been found to down regulate expression of mutant p53 in breast cancer cells, arrest human leukemic T-cells in G1, inhibit tyrosine kinase, and inhibit heat shock proteins [4]. Quercetin has been shown to decrease lipid peroxidation, inhibit cell proliferation, induce apoptosis, and inhibit platelet aggregation [5-8]. Because quercetin exhibits such a wide array of positive health effects, it is especially important to understand quercetin bioavailability from whole foods. Two widely consumed, good food sources of quercetin are apples and onions [9-13]. In most foods, quercetin does not exist in the aglycone form, but is, instead, conjugated. The type of sugar moiety to which quercetin is bound affects quercetin bioavailability. For example, quercetin in the apple is bound mainly to galactosides, rhamnosides, and arabinosides, and these quercetin conjugates are not well absorbed by the small intestine. The onion contains mainly quercetin glucosides, which are well absorbed by the small intestine [14]. More work is needed to understand the bioavailability of quercetin and other flavonoids from foods. The Caco-2 cell culture model is a well-established in vitro technique, extensively used to study intestinal cell absorption of compounds such as pharmaceuticals and nutrients; it is an excellent in vitro tool to study bioavailability of specific compounds. We have previously used the Caco-2 cell culture model to examine the uptake of quercetin from apple and onion extracts [15]. Using this model, we found that absorbed quercetin from onion extracts was significantly greater than from apple extracts, as expected [15]. Others have used the Caco-2 cell culture model to evaluate cell transport and/or accumulation of pure phytochemicals such as quercetin, quercetin glucosides, chrysin, flavone, epicatechin, proanthocyanidin, and carotenoids [16-23]. To further understand quercetin bioavailability, it is important to examine the effects of digestion on foods prior to intestinal uptake. An in vitro digestion has been paired successfully with the Caco-2 cell culture model to study iron and carotenoid bioavailability [23,24]. Use of the in vitro digestion with the Caco-2 cell culture model could be quite useful in more specifically analyzing quercetin bioavailability from foods. At this time there is little information available describing the effects of digestion on flavonoids from foods. Vallejo et al. [25] found that over 80% of total flavonoids were lost during an in vitro digestion of broccoli. In a study of ileostomy patients, Walle et al. estimated that the intestine might absorb 65–81% of major forms of dietary flavonoids after enzymatic hydrolysis [26]. A good in vitro model would aid in evaluating bioavailability of phytochemicals from foods by offering a simple method to screen for factors that may affect intestinal absorption of quercetin and quercetin glucosides, such as the food matrix, food processing, digestion, and interactions with other foods. Human and animal models can be expensive and time consuming, while a cell culture model allows for rapid, inexpensive screenings. The Caco-2 model has the potential to be a good model to measure quercetin absorption, however there are some drawbacks. Caco-2 cells have been shown to express significantly less lactase phlorizin hydrolase (LPH) than the average human small intestine [27]. Since this enzyme is most likely responsible for the first step in the metabolism of quercetin glucosides [28], this deficiency would clearly limit the ability of the Caco-2 cells to metabolize and absorb quercetin from quercetin glucosides. Caco-2 cells used in our lab have expressed greater LPH activity (3 mU/mg protein) than other Caco-2 cells (0.3 mU/mg protein) [15], resulting in lactase activity similar to that expressed by enterocytes from a lactose intolerant human (2–10 mU/mg protein)[27]. The compound forskolin induced lactase phlorizin hydrolase activity four-fold in Caco-2 cells [29]. In weanling rats, lactose consumption increased lactase activity in the jejunum by three-fold [30]. Thus, we hypothesized that treating Caco-2 cells with either forskolin or lactase may raise lactase expression to rates comparable to humans, making the Caco-2 cell model a more valid model for screening quercetin glucoside bioavailability from food. If lactase activity cannot be induced in Caco-2 cells, treating food samples with lactase following the digestion procedure and prior to cell bioavailability assays may give more comparable results to humans. The objectives of this study were (1) to develop an optimized in vitro digestion method for examining quercetin and quercetin glucoside recovery from digestates using onions and apples; (2) to examine the effect of lactase on shallot digestates; and (3) to examine Caco-2 cellular uptake of quercetin and quercetin glucosides from digested and lactase treated shallot. Methods Chemicals and materials Shallots and onions (Northern Yellow) were obtained from a local grocery store. Apples (Red Delicious and Cortland varieties) were obtained from the Cornell Orchards (Cornell University, Ithaca, NY). Porcine pepsin, bile extract, pancreatin, lactase (beta-galactosidase, from Kluyveromyces lactis, activity of 3000 units/mL), quercetin, and quercetin-4'-glucoside were purchased from Sigma Chemical Company (St. Louis, MO). Quercetin-3-glucoside was purchased from Indofine Chemical Company, Inc (Hillsborough, NJ). Caco-2 cells were obtained from the American Type Culture Collection (Rockville, MD) and were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco Life Technologies, Grand Island, NY) supplemented with 5% fetal bovine serum (Gibco Life Technologies, NY), 10 mM HEPES, 50 units/mL penicillin, 50 μg/mL streptomycin, and 100 μg/mL gentamicin, and were maintained at 37°C in 5% CO2. In vitro digestion Two hundred grams of each food sample were chopped, blended for 5 min with 200 mL saline (140 mM NaCl, 5 mM KCl) using a Waring blender, and then homogenized using a Virtis 45 homogenizer. The total homogenates were aliquotted in 15 mL centrifuge tubes and stored at -20°C until use. For the digestion treatment, 2 g aliquots of the food sample were placed in a centrifuge tube with an equal amount of saline. The pH was decreased to 2.0 by drop-wise addition of 1M HCl, and porcine pepsin was added to a final concentration of 1.3 mg/mL. The digestate was incubated in a shaking water bath at 37°C for 30 minutes. The pH of the digestate was then increased to 5.8 with the drop-wise addition of 1M NaHCO3. Porcine bile extract and pancreatin were added to a final concentration of 1.1 and 0.175 mg/mL, respectively. The pH was increased to 6.5 by drop-wise addition of 1M NaHCO3, and the samples were incubated for 1 hour in a water bath at 37°C. Following digestion the pH was decreased to 2 by addition of HCl and the digestates were stored at -80°C for further analysis. To examine and optimize the effects of digestion time and pH on the recovery of compounds from the digestate, the above parameters were varied. To examine the effects of pepsin digestion time, the pepsin digestates were incubated for 0, 30, 60 or 90 minutes and then incubated with the intestinal digestion enzymes for 60 minutes. To examine the effects of intestinal digestion, the samples were incubated with pepsin for 30 minutes, then incubated with pancreatin and bile for 0, 30, 60, or 90 minutes. The effects of 100 μM ascorbic acid and a nitrogen environment on quercetin and quercetin glucoside recovery from onion and apple digestates were examined. Following homogenization and prior to digestion, the food samples were mixed 1:1 with saline containing 200 μM ascorbic acid, leaving a final sample concentration of 100 μM ascorbic acid. During the digestion procedure described above, the samples were flushed constantly with nitrogen. The effect of pH of either 6.5 or 7.0 during intestinal digestion on quercetin and quercetin-3-glucoside recovery was compared. The effect of the storage pH was examined by comparing digested samples having either a final pH of 2.0 or 6.5. The effect of storage pH on 20 μM pure quercetin and 20 μM quercetin-3-glucoside was examined by comparing recoveries from samples stored at pH = 2.0, 3.5, 5.0 or 7.0. All samples were stored overnight at -80°C. Prior to HPLC analysis samples were thawed and extracted 4 times with acidified ethyl acetate (pH 2.0), evaporated to dryness and reconstituted in 2 mL acidified methanol (pH 2.0). Lactase digestion Doses of lactase (0.5 units to 3000 units per gram sample) were applied to 1 gram shallot extract and incubated for 15 minutes at 37°C. The final pH was brought to 2.0 and the samples were stored at -80°C. The time kinetics were examined by incubating 1 gram shallot extract with 100 units of lactase for 0, 15, 30, 60, 90, 120, 240, and 720 minutes. The effect of both lactase and digestion were examined by digesting the samples with pepsin and pancreatin as described above, then incubating the samples with 100 units of lactase for 30 minutes at 37°C. Samples were stored overnight at -80°C. Prior to HPLC analysis, all digestate samples were thawed and extracted 4 times with acidified ethyl acetate (pH 2.0), evaporated to dryness and reconstituted in 2 mL methanol. Uptake of quercetin-4'-glucoside and quercetin from shallot digestates by Caco-2 cells Caco-2 cells were seeded at a density of 5 × 105 cells per well in a collagen coated 6-well, flat bottom plate and incubated at 37°C in a 5% CO2 environment. Caco-2 cells were used between passages #10–25, and the cells reached confluence approximately 5 days post seeding. Culture media was changed three times a week. On day 14 post seeding, the DMEM was removed and the cells were rinsed three times with phosphate buffered saline (PBS). Shallot homogenates were digested as previously described and were placed directly on the 14 day old Caco-2 cells, or the samples were diluted 1:2 or 1:4 in HBSS (Hank's Balanced Salt Solution). Cells were also incubated with non-digested shallot homogenates for comparison. For each treatment, two wells of cells were used for each sample, and each treatment was repeated in triplicate. To examine the effect of lactase on quercetin uptake from shallots, shallots were digested then incubated with lactase (50, 100, 300, and 1000 units/g shallot) for 20 minutes at 37°C. The digested shallot homogenates and the digested plus lactase treated shallot homogenates were diluted 1:2 in HBSS and placed on the cells. In all experiments, Caco-2 cells were incubated with treatment for 30 minutes at 37°C in 5% CO2. The shallot treatment and HBSS was removed and the cells were rinsed three times with 20% methanol in PBS. Cells were scraped in acidified methanol (pH = 2.0) and the wells were rinsed three times with methanol. The scraped cells were sonicated for 15 minutes, centrifuged at 1600 g for 5 minutes, and the methanol supernatant was collected. The cells were rinsed three more times with methanol, the supernatants were collected and the methanol extracts were evaporated to dryness under nitrogen and reconstituted in 400 μl acidified methanol for HPLC analysis. Induction of lactase activity in Caco-2 cells Caco-2 cells were seeded at a density of 5 × 105 cells per well in a 6-well flat bottom plate. The cells were cultured in DMEM spiked with different doses of either lactose (10, 50, 100, 500, and 1000 μM), or forskolin (1, 10, 50, 100, and 200 μM). Media was changed every two days, and cells were harvested and lactase activity was measured at 14 days post-seeding. Lactase activity of Caco-2 cells was measured using a method adapted from Dahlqvist [31]. Cells were trypsinized, collected, centrifuged and resuspended in homogenization buffer (50 mM sodium phosphate; 1 mM EDTA; 10 mM dithiothreitol; protease inhibitor cocktail, Sigma Chemical Co., St. Louis, MO). Cells were homogenized 5 times for 30 seconds with 1 minute of cooling between bursts using a benchtop homogenizer. Homogenates were treated with 56 mM lactose and incubated at 37°C for 60 minutes. Glucose oxidase, peroxidase, and o-dianisidine were applied to the cell homogenates and the final colored products were measured at 420 nm using a spectrophotometer [31]. The results were compared to a glucose standard curve to determine the amount of glucose released by lactase in the Caco-2 cell monolayer. Protein was determined from crude cell homogenates colorimetrically using the Lowry method with comparisons to a bovine serum albumin standard curve. Results are expressed as milliunits/mg of protein, and one unit is defined as the lactase activity that hydrolyzes 1 μmole of lactose per minute at 37°C. HPLC analysis Quercetin and quercetin-3-glucoside content of untreated homogenates, digestates, and Caco-2 cell extracts were determined using an RP-HPLC procedure with a Supelcosil LC-18-DB column (150 mm × 4.6 mm, and 3 μm pore size). Waters 515 HPLC pumps (Waters Corp., Milford, MA) and a Waters 2487 dual wavelength absorbance detector (Waters Corp., Milford, MA) set at 370 nm were used for all HPLC analysis. Quercetin, quercetin-3-glucoside, and quercetin-4'-glucoside were used as standards. For the analysis of quercetin, quercetin-3-glucoside, and quercetin-4'-glucoside in the apple peel extracts, shallot extracts, and digestate extracts, the solvent system used was (A) acidified water (pH 2.0; triflouroacetic acid) and (B) acetonitrile. The gradient method was the following: 0.0 min, flow rate = 1.4, (A) 90% and (B) 10%; 53 min, flow rate = 1.5, (A) 80% and (B) 20%; 58 min, flow rate = 1.7, (A) 65% and (B) 35%; 64 min, flow rate = 1.4, (A) 90% and (B) 10%. Twenty μL injections were made for each sample. Quercetin, quercetin-3-glucoside, and quercetin-4'-glucoside concentrations in the apple peel extracts, shallot extracts, and in the digestates were extrapolated from the pure quercetin and quercetin-3-glucoside standard curves. Statistical analysis All data were reported as means ± SD for three replicates of each treatment. An analysis of variance (ANOVA) was used to compare results between treatment groups, and pairwise multiple comparisons were performed using Fisher's LSD with an individual error rate of 0.05. The statistical analysis was completed using Minitab Release 12 software (State College, PA). Results In vitro gastrointestinal digestion The total pepsin digestion time and pancreatin/bile digestion time had little to no effect on recovery of both quercetin and quercetin-3-glucoside from apple and onion homogenates when treated for up to 60 minutes (data not shown). After 90 minutes of pepsin digestion and 90 minutes of pancreatic digestion, quercetin and quercetin 3-glucoside in both the apple and the onion decreased slightly. Based on these results we chose to use a 30-minute pepsin digestion and a 60-minute pancreatin/bile digestion. In past studies, quercetin from onion and quercetin from quercetin-4-glucoside supplements reached the plasma in less than an hour following consumption by human volunteers [32]. Therefore, long in vitro digestion times were not necessary to mimic human digestion of quercetin compounds from apples and onions. The presence of ascorbic acid and nitrogen had no effect on quercetin or quercetin-3-glucoside recoveries from the digestates (data not shown). Quercetin and quercetin-3-glucoside recoveries from digested samples treated with ascorbic acid, nitrogen or both ascorbic acid and nitrogen were not different from the recoveries from the untreated digested samples. The factor that had the greatest effect on recovery was pH (Figure 1A). Quercetin is less stable at higher pH, therefore the effect of pH during intestinal digestion was examined. Recoveries of quercetin and quercetin-3-glucoside after intestinal digestion at pH 7.0, when compared to pH 6.5, were not significantly different (p > 0.05). However, following overnight storage at -80°C, the samples stored at pH 2.0 had significantly greater quercetin and quercetin-3-glucoside recoveries than samples stored at pH 6.5 and 7.0 (p < 0.05). Pure quercetin and quercetin-3-glucoside were also more stable at lower storage pH following digestion (Figure 1B). At pH 2.0, the recoveries for pure quercetin and quercetin-3-glucoside were 74.8% and 86.2% when compared to the control. At the highest pH (7.0), recoveries for quercetin and quercetin-3-glucoside were 46.5% and 13.9%, respectively. Figure 1 The effects of intestinal digestion pH and acidic storage on quercetin and quercetin-3-glucoside recovered from digested onions (A) and the effects of storage pH on digested pure quercetin and quercetin-3-glucoside (B). Samples were digested for 30 minutes with pepsin, 60 minutes with pancreatin and bile, and then stored at -80°C. Each point represents the mean ± standard deviation of triplicate observations within the same experiment. Different letters indicate significantly different observations within each compound (p < 0.05). Based on these results, optimal digestion conditions were decided to be: pepsin digestion at pH 2.0 for 30 minutes, pancreatin/bile digestion at pH 6.5 for 60 minutes, and a final storage at pH 2.0. Ascorbic acid and nitrogen treatments were not continued. Using these conditions, the effect of digestion on quercetin and quercetin-3-glucoside recoveries from apples, onions, and pure quercetin and quercetin-3-glucoside was examined (Figure 2). Following digestion, recoveries of pure quercetin-3-glucoside, apple quercetin-3-glucoside, and onion quercetin-3-glucoside were similar to recoveries from non-digested samples (p > 0.05). Quercetin-3-glucoside recoveries from the digested apple, onion, and pure compound were 87.7, 89.5, and 86.4%, respectively. Quercetin recovery was significantly reduced in digested pure quercetin and digested onion samples, when compared to non-digested samples (p < 0.05). Quercetin recovery was lower than quercetin-3-glucoside recovery and tended to vary more, depending on the food matrix: 52.5% and 74.3%, from the onion and pure compound, respectively. There was no significant difference in quercetin recovery between to the non-digested and digested apple homogenates. There was only trace or no quercetin in non-digested apple homogenates, so the appearance of any quercetin following digestion resulted in a net increase. Figure 2 The effects of digestion on pure quercetin and quercetin-3-glucoside and quercetin and quercetin-3-glucoside from apple and onion. Each point represents the mean ± standard deviation of triplicate observations within the same experiment. An asterisk indicates a significant difference between the control and the digestate (p < 0.05). Uptake of quercetin-4'-glucoside and quercetin from shallot digestates by Caco-2 cells Quercetin-4'-glucoside and quercetin were absorbed by Caco-2 cells following treatment with both digested shallot and non-digested shallot homogenates (Figure 3). Quercetin-3-glucoside was not detected in any sample. Quercetin-4'-glucoside uptake by the Caco-2 cells increased by approximately 2-fold following digestion (p < 0.05). Caco-2 cells treated with shallot homogenate absorbed approximately 2.9 ± 0.65 nmol of quercetin-4-glucoside, and Caco-2 cells treated with digested shallot absorbed 5.4 ± 0.04 nmol. Quercetin aglycone recovery from the digested shallot extract was only 47% that of the non-digested homogenate (Figure 3B insert), however quercetin uptake from the digested samples was similar to the non-digested samples (p > 0.05). Caco-2 cells absorbed 2.8 ± 0.4 nmol and 2.7 ± 0.2 nmol quercetin from the non-digested and digested shallot homogenates, respectively. Absorption of both quercetin-4-glucoside and quercetin from digested shallot followed a dose response. The Caco-2 cells absorbed quercetin 4'-glucoside and quercetin incrementally less from the digested samples that were diluted 1:2 or 1:4 in HBSS. Figure 3 Caco-2 uptake of quercetin-4-glucoside (A) and quercetin (B) from digested and non-digested shallot homogenates. Shallot homogenates were digested for 30 minutes with pepsin at pH 2.0 and for 60 minutes with pancreatin/bile at pH 6.5. Digestates were directly placed on cells or diluted 1:2 or 1:4 in HBSS and then placed on cells. Cells were incubated with digestates for 30 minutes at 37°C. The imbedded graph in (B) shows quercetin recovery from shallots following the digestion procedure only. Each bar represents the mean ± standard deviation of triplicate observations within the same experiment. Different letters indicate significantly different observations within each compound (p < 0.05). Induction of lactase activity in Caco-2 cells Addition of lactose and forskolin, a specific inducer of lactase [29], to Caco-2 cells did not significantly increase lactase activity of Caco-2 cells. The lactase activity of all cells ranged from 2–4 mU/mg protein in all treatments. Lactase Digestion Treatment with 100 units lactase/g sample had a significant effect on both quercetin and quercetin-3-glucoside recoveries from the shallot (p < 0.05; Figure 4). Quercetin recovery from shallot digestates increased 5.5 fold, from 47.5 ± 7.6 μg/g sample in the untreated digestate to 262.2 ± 17.6 μg/g sample in the lactase treated sample. The lactase plus digestion treatment resulted in a non-significant decrease in quercetin recovery compared to the lactase only treated samples. Quercetin-3-glucoside from shallot digestates also increased approximately 5 fold, from 17.3 ± 1.7 μg/g sample to 80.0 ± 10.3 μg/g sample following the lactase treatment. The effect of lactase on the apple samples was not as great. Quercetin-3-glucoside recovery decreased slightly, while changes in quercetin levels were not significant (p > 0.05). Figure 4 The effects of lactase and a combined lactase and digestion treatment on quercetin and quercetin-3-glucoside recovery from shallot (A) and apple (B) homogenates. Each point represents the mean ± standard deviation of triplicate observations within the same experiment. Different letters indicate significantly different observations within each compound (p < 0.05). Because treating shallots with lactase increased quercetin recovery so greatly without significantly decreasing quercetin-3-glucoside recovery, the effect of lactase on quercetin-4'-glucoside recovery was also examined. More quercetin-4'-glucoside is found in shallots when compared to quercetin-3-glucoside. As the dose of lactase increased, quercetin-3-glucoside recovery increased from 18.2 ± 3.7 up to 175.5 ± 48.1 μg/g shallot at the 1000 unit dose and then decreased to 60.2 ± 2.0 μg/g at the 3000 unit dose (Figure 5A). As the dose of lactase increased, quercetin recovery increased and quercetin-4'-glucoside decreased (Figure 5A). The increase in quercetin was quite dramatic. Recovery of quercetin from untreated shallot samples was 93.7 ± 2.2 μg quercetin per gram sample, and at the highest lactase dose, recovery of quercetin from shallot samples was 958.8 ± 76.1 μg quercetin per gram shallot. Quercetin-4'-glucoside recovery decreased from 518.2 ± 10.7 μg/g shallot from the untreated sample to 3.2 ± 0.8 μg/g shallot at the highest treatment dose. A similar trend was seen for all compounds in the kinetic experiment. As the incubation time increased, recoveries of quercetin increased and quercetin-4'-glucoside decreased (Figure 5B). Quercetin-3-glucoside increased through two hours, and then decreased following four and eight hours of incubation with lactase. In both the dose response and kinetic experiments, increases in quercetin recoveries were greater than decreases in quercetin-3-glucoside or quercetin-4'-glucoside recoveries. Figure 5 Dose response (A) and kinetics (B) of lactase on quercetin and quercetin glucoside recovery from shallots. Homogenized shallots were incubated 60 for minutes with 10, 50, 100, 300, 500, 1000, or 3000 units of lactase/mL sample. Homogenized shallots were incubated with 100 units lactase/mL sample for 15, 30, 60, 90, 120, 240, and 720 minutes. Each point represents the mean ± standard deviation of triplicate observations within the same experiment. Caco-2 cell uptake of quercetin and quercetin glucosides following lactase treatment The addition of lactase following the pepsin and pancreatin/bile digestion significantly increased the amount of quercetin absorbed by the Caco-2 cells with a significant decrease in the amount of quercetin-4'-glucoside absorbed by the Caco-2 cells (p < 0.05, Figure 6). Quercetin uptake increased from 0.98 ± 0.67 nmol from the digested sample up to 14.1 ± 1.6 nmol from the digested plus 1000 units lactase treated sample. Quercetin-4'-glucoside uptake by Caco-2 cells decreased as the dose of lactase increased, however the increase in quercetin was more dramatic than the decrease in quercetin-4'-glucoside. Quercetin-3-glucoside uptake was not detected. Figure 6 Caco-2 uptake of quercetin-4-glucoside and quercetin from digested shallot and digested plus lactase treated shallot. Shallot homogenates were digested for 30 minutes with pepsin at pH = 2.0 and for 60 minutes with pancreatin/bile at pH = 6.5. Digested shallots were then treated with either 50, 100, 300, or 1000 units lactase/mL sample for 20 minutes. Samples were diluted 1:2 and then placed on the cells for 30 minutes. Each point represents the mean ± standard deviation of triplicate observations within the same experiment. Discussion In vitro digestion Previously, our laboratory determined that our Caco-2 cells had the potential to be used as a model to study quercetin bioavailability from onions and apples [15]. In the present study, we modified an in vitro digestion procedure and combined it with the Caco-2 cell model to give a more comprehensive examination of quercetin and quercetin glucoside bioavailability in Caco-2 cells. The digestion procedure modified in these experiments resulted in quercetin-3-glucoside recoveries that ranged from 78.9 to 89.5% and quercetin recoveries that ranged from 47.3 to 74.3%. In both cases, the lowest digestate recoveries were from the shallot. Interestingly, quercetin recovery from both the onion and shallot was considerably lower than from the pure compound. This difference is not yet explained, but could potentially be due to interactions with other compounds found in the onion and shallot. In all cases, quercetin recovery from digestates was lower than quercetin-3-glucoside. The glucoside moiety may lend stability to quercetin-3-glucoside during digestion, and could contribute to its greater bioavailability in vivo as well. Quercetin aglycone may be more susceptible to oxidation or other degradation during exposure to both the digestive enzymes and the variations in pH in the stomach and intestine. Ascorbic acid and nitrogen, both added to help decrease oxidation, had no effect on quercetin or quercetin-3-glucoside recoveries from digested onions. The factor that had the greatest effect on both quercetin and quercetin-3-glucoside stability in the digestate was pH. Recoveries of digested pure compounds and digested compounds from the onion homogenates were significantly less if stored at a pH of 6.5 or 7.0 than if stored at pH 2.0 at -80°C (Figure 1). Vallejo et. al [25] used an in vitro digestion method to measure the effect of digestion on a variety of compounds from broccoli. Following the pepsin and pancreatic digestion, they recovered only 16% of total flavonoids, and the main flavonoids found in the broccoli were quercetin and kaempferol glycosides. The recovery of quercetin and quercetin-3-glucosides, flavonoids common to the foods in our study, was much higher than 16% from the digested shallot, onion and apple. It has been estimated that quercetin glucoside bioavailability may be as high as 80% in humans and quercetin bioavailability may range from 35–53% [26,33]. Our results would appear to be more reasonable estimates, if indeed, quercetin and quercetin glucoside bioavailability lies within the approximated ranges found by Walle et. al [26,33]. Effect of digestion on quercetin and quercetin glucoside uptake by Caco-2 cells Digestion of the shallot resulted in decreased recoveries of both quercetin and quercetin-3-glucoside, therefore it was expected that digestion might decrease the bioavailability of these compounds as well. Following digestion, quercetin aglycone in the shallot was decreased by approximately 50%; however, quercetin bioavailability was unchanged following digestion compared to the non-digested samples. This means that the digestion procedure must degrade quercetin, and simultaneously enhances the bioavailability of quercetin, bringing the cellular uptake back to comparable levels with the non-digested samples. Quercetin-4'-glucoside uptake by the Caco-2 cells from the shallot increased by approximately 2-fold following the in vitro digestion. We hypothesize that the digestion procedure may have released more compounds from the food matrix leaving them more available for uptake by the Caco-2 cells. The digestion procedure may also have improved solubility of the compounds, increasing their absorption by the Caco-2 cells. Quercetin-3-glucoside was not detected in the Caco-2 cells following treatment with shallot. Quercetin-3-glucoside is a minor compound in the shallot, and it is believed that the levels were below our detection limit. In the past, we found that Caco-2 cells did absorb trace amounts of quercetin-3-glucoside from shallot extracts [15]. In the previous experiments, shallots were first extracted with ethanol and ethyl acetate, and finally reconstituted and concentrated in methanol. This procedure produced more concentrated shallot extracts for cell treatment than with our current procedure. In the current study, shallot homogenates and digestates were too dilute to detect small changes in initial concentrations or in cellular uptake of quercetin-3-glucoside, which was below the detection limit. Strong evidence suggests that quercetin glucosides are more bioavailable in humans than the quercetin aglycone, however it has not yet been determined why this is the case. Based on the digestion data, quercetin glucoside is more stable following the in vitro digestion conditions than quercetin and is therefore more likely to reach the intestine intact. Quercetin-4'-glucoside bioavailability in Caco-2 cells was increased nearly 2-fold following digestion and quercetin absorption was not changed (Figure 3). It has been hypothesized that absorbed intact quercetin glucosides are then quickly hydrolyzed by cytosolic β-glucosidase to quercetin aglycone [34]. It has also been hypothesized that the major pathway for quercetin glucoside absorption begins with hydrolysis by LPH. Deglycosylation of quercetin glucosides at the brush border membrane positions the resulting aglycone in a prime position for diffusion across the brush border. The deglycosylation of the quercetin glucoside would result in a higher concentration of aglycone at the apical enterocyte membrane and potentially increase the rate of absorption [35]. Effect of lactase on quercetin and quercetin-4'-glucoside uptake by Caco-2 cells The potential pathways for quercetin glucoside and quercetin metabolism and absorption can be seen in Figure 7. Quercetin aglycone passively diffuses across the apical membrane and is then glucuronidated. Evidence strongly suggests that quercetin glucosides are first hydrolyzed by the lactase site of lactase phlorizin hydrolase prior to diffusion across the apical membrane [36]. Quercetin glucosides may also be transported into the cell by the sodium-dependent glucose transporter1 (SGLT1) and then hydrolyzed by the cytosolic beta-glucosidase. Quercetin-3-glucoside is not a good substrate for cytosolic beta-glucosidase [37]. Since research has shown that both quercetin-3-glucoside and quercetin-4'-glucoside are similarly bioavailable in humans [14,38], this could be an indication that hydrolysis by LPH and the subsequent passive diffusion of quercetin into the cell is the main pathway for quercetin glucoside absorption across the brush border. Following hydrolysis and incorporation into the cells, quercetin aglycone is then glucuronidated. Quercetin glucosides and possibly quercetin glucuronides are then transported back into the lumen by multidrug resistance protein 2 (MRP2). Conjugated quercetin metabolites also eventually reach circulation, but the transporter involved in transporting them across the basolateral side is still unknown. Figure 7 Potential mechanism of quercetin and quercetin glucosides uptake by enterocytes. LPH, lactase phlorizin hydrolase; SGLT1, sodium-dependent glucose transporter 1; CBG, cytosolic B-glucosidase; MRP2, multi-drug resitance protein 2; UDP-GT, UDP glucuronosyl transferase; QUE, quercetin. Since lactase is an important enzyme in the metabolism and subsequent absorption of quercetin glucosides, lactose intolerant individuals may have a reduced capability to hydrolyze quercetin glucoside for further absorption across the small intestinal wall. Many lactose intolerant people use commercial lactase to break down lactose. Not only might this enzyme help improve digestibility of lactose, but it may also increase bioavailability of quercetin from foods. Initial lactase treatments were applied both to shallot and apple homogenates and digestates. Lactase had little to no effect on apple samples. Apples contain quercetin bound mainly to galactosides, rhamnosides, and xylosides, conjugates that would not be readily hydrolyzed by lactase. However, lactase treatment had great effects on shallot and onion digestates. The shallot and onion are high in quercetin glucosides, mainly quercetin-4'-glucoside, quercetin-3-glucoside, and quercetin-3,4'-diglucoside, compounds readily hydrolyzed by lactase. Treatment with lactase in the range of 15 units/mg sample up to 1000 units/mg sample, significantly increased both quercetin and quercetin-3-glucoside recovery in shallot homogenates and digestates. The increase in quercetin-3-glucoside is most likely a result of deglycosylation of quercetin-3,4'-diglucoside. Rhodes et al. [39] found that over time quercetin-3,4'-diglucoside in chopped onion will autolyze to monoglucosides, and within 24 hours the diglucoside will completely disappear. This may also explain the increase in quercetin-3-glucoside over time. Quercetin-4'-glucoside decreased following treatment with lactase as expected. Results from this work suggest that quercetin-4'-glucoside is utilized by lactase prior to quercetin-3-glucoside. Interestingly, the increase in total quercetin was greater than the decrease in quercetin-4'-glucoside. Digestion with lactase may release quercetin from the food matrix as well, making it more available for absorption. Following hydrolysis of quercetin glycosides, it has been hypothesized that quercetin is quickly glucuronidated, and quercetin glucuronides are then found circulating in the plasma [40]. In the current studies, these Caco-2 cells showed no signs of glucuronidating quercetin following quercetin absorption, but do express some LPH activity as was evident by the increased quercetin uptake from shallots and from the lactase activity assays [15]. Thus, these Caco-2 cells have the potential to be a good model of quercetin absorption, but not of further metabolism. A good model of quercetin glucoside bioavailability should incorporate lactase activity. The Caco-2 cells used for these experiments expressed lactase activity similar to that of a lactose intolerant human (2–10 mU/mg protein). These cells had approximate lactase activity of between 2–4 mU/mg protein, consistent with what we previously reported [15]. Lactose tolerant humans tend to have intestinal lactase activity that ranges from 20–80 mU/mg protein [41]. Forskolin and lactose did not induce lactase activity in our Caco-2 cells. The lactase activity of our cells was nearly 10 times higher than previously reported values for Caco-2 cells of 0.3 mU/mg protein of lactase activity [27], and it is quite possible that the lactase enzyme in our Caco-2 cells is already expressed to the fullest extent. Since lactase activity could not be increased in the Caco-2 cells, we combined a lactase treatment with digestion to provide an intestinal uptake model that is more comparable to a lactose tolerant human, or a lactose intolerant human ingesting a lactase digestive aid to help improve lactose digestion. Not only did lactase increase the amount of quercetin in digested shallot homogenates, but it also increased the amount of quercetin taken up by the Caco-2 cells from the digested shallot extracts. This suggests that a lactase containing digestive aid may increase absorption of quercetin from onions in lactose intolerant humans. Combining the Caco-2 cell model with an in vitro digestion, simulating stomach and small intestinal digestion, and a lactase digestion may provide a more useful model to examine and screen for bioavailability of flavonoid glucosides from common foods (Figure 8). Figure 8 Caco-2 cell culture model for examining quercetin bioavailability from foods. Conclusions Following an in vitro stomach and small intestinal digestion, the recovery of quercetin and quercetin-3-glucoside is optimized at a storage pH at or below 3.5. Storage at pH above 3.5 results in loss of most of the compounds. The in vitro digestion increased the uptake of shallot quercetin-4'-glucoside by the Caco-2 cells. Quercetin uptake by the Caco-2 cells was similar between digested and non-digested samples, despite the fact that approximately 50% of shallot quercetin is lost during digestion. Treating shallot digestates with lactase increased the recovery of quercetin aglycone 10-fold and decreased the recovery of quercetin-4'-glucoside. Lactase treatment increased the bioavailability of quercetin aglycone 14-fold and decreased the bioavailability of quercetin-4'-glucoside to the Caco-2 cells. Combining pepsin, pancreatin/bile, and lactase digestions with the Caco-2 cell culture monolayer may results in a useful model for studying flavonoid bioavailability from foods. Many advances have been made in understanding flavonoid bioavailability, however many questions still remain unanswered. Food processing, interactions with other compounds as well as with other foods are all factors that may affect bioavailability of flavonoids. A simple and inexpensive screening model would be beneficial as a first step in examining many of these factors. The Caco-2 cell model has the potential to be a good model for analyzing intestinal uptake of quercetin, quercetin glucoside and other flavonoids. While the Caco-2 cell model will never be an exact replica of a human small intestine, it could provide a valuable tool for initial screenings of large quantities of samples relatively quickly and inexpensively. In this study, it was found that a simple digestive aid such as lactase might increase quercetin bioavailability. This may be of importance to lactose intolerant people. 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112 1504 1514	15644141	PMC546214	CC BY	2021-01-04 16:39:31	no	Nutr J. 2005 Jan 11; 4:1	utf-8	Nutr J	2,005	10.1186/1475-2891-4-1	oa_comm
==== Front BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-5-11563893310.1186/1471-2261-5-1Research ArticleQuality of care for hypertension in the United States Asch Steven M [email protected] Elizabeth A [email protected] Liisa [email protected] John [email protected] Jennifer [email protected] Alison [email protected] Roland [email protected] Pablo [email protected] Eve A [email protected] West LA VA, Mail Code 111G, 11301 Wilshire Bl, Los Angeles, CA 90073, USA2 RAND Health, 1776 Main Street, Santa Monica, CA 90407, USA3 Global Epidemiology and Outcomes Research, Pharmaceutical Research Institute, Bristol-Myers Squibb Company, PO Box 4000, Princeton, NJ 08543-4000, USA4 VA Center for Practice Management and Outcomes Research and the Department of Medicine, University of Michigan, Ann Arbor, (11H), 2215 Fuller Road, Ann Arbor, MI 48105, USA2005 7 1 2005 5 1 1 6 7 2004 7 1 2005 Copyright © 2005 Asch et al; licensee BioMed Central Ltd.2005Asch et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Despite heavy recent emphasis on blood pressure (BP) control, many patients fail to meet widely accepted goals. While access and adherence to therapy certainly play a role, another potential explanation is poor quality of essential care processes (QC). Yet little is known about the relationship between QC and BP control. Methods We assessed QC in 12 U.S. communities by reviewing the medical records of a randomly selected group of patients for the two years preceding our study. We included patients with either a diagnosis of hypertension or two visits with BPs of ≥140/90 in their medical records. We used 28 process indicators based on explicit evidence to assess QC. The indicators covered a broad spectrum of care and were developed through a modified Delphi method. We considered patients who received all indicated care to have optimal QC. We defined control of hypertension as BP < 140/90 in the most recent reading. Results Of 1,953 hypertensive patients, only 57% received optimal care and 42% had controlled hypertension. Patients who had received optimal care were more likely to have their BP under control at the end of the study (45% vs. 35%, p = .0006). Patients were more likely to receive optimal care if they were over age 50 (76% vs. 63%, p < .0001), had diabetes (77% vs. 71%, p = .0038), coronary artery disease (87% vs. 69%, p < .0001), or hyperlipidemia (80% vs. 68%, p < .0001), and did not smoke (73% vs. 66%, p = .0005). Conclusions Higher QC for hypertensive patients is associated with better BP control. Younger patients without cardiac risk factors are at greatest risk for poor care. Quality measurement systems like the one presented in this study can guide future quality improvement efforts. ==== Body Background Hypertension affects approximately 58 million Americans [1]. Lowering diastolic blood pressure (BP) by 10 mm Hg can reduce the number of strokes by as much as 56% and the incidence of coronary heart disease by 37% [2]. In addition, it has been shown that lowering systolic BP to 150 mm Hg decreases the incidence of all types of strokes [3]. Although treatment reduces mortality, morbidity and costs, nearly half of all people with hypertension go untreated and only 23% control their BP to the recommended level [4]. The high prevalence of uncontrolled hypertension is due in part to a lack of awareness: 32% of people with the disease do not know they have it [4]. However, over 40% of diagnosed hypertensive patients remain uncontrolled [4]. One potential explanation for uncontrolled hypertension is suboptimal quality of care. Although the U.S. Joint National Committee on Prevention, Detection, and Treatment of High Blood Pressure (JNC-VII) [5] has codified standards for clinical processes in hypertension in the United States, studies dating back to the 1970s have shown that many patients fail to receive this essential care [6-8]. On the other hand, many of these studies have not established a relationship between these care processes and BP control. One exception is a recent study of U.S. Veterans Administration patients in five facilities, which found a correlation between aggressive treatment and better-controlled BP [9]. Still, most studies examining the link between care processes and controlled hypertension generally have been confined to single delivery systems, a limited number of facilities, or a relatively small set of indicators of hypertensive quality. In a previous study, we examined general measures of hypertensive quality (including treatment, diagnosis, and follow-up indicators) and found that these care processes were associated with BP control in young women participating in a single health plan [10]. Studies with more generalizable target populations are lacking. If deficits in process quality are indeed related to BP control, then which patients are failing to receive optimal care? The literature suggests that ethnic minorities and older patients are less likely to have controlled hypertension [4], but we do not know what clinical factors may be affecting patient care. For instance, physicians may be targeting higher-risk patients and administering better care to those with diabetes, coronary artery disease (CAD), and tobacco abuse. Likewise, providers may be delivering better care to older patients as they are also at higher risk, though limited evidence suggests the opposite is true [11,12]. We hypothesized that overall process quality for hypertensive care is associated with better BP control. We developed indicators of hypertensive care and determined whether patients had received the indicated care by reviewing medical records for a national sample of patients receiving care in a variety of settings. We also investigated whether patients with other cardiac risk factors received better hypertensive care and had better BP control. Methods Design and sampling Our examination of hypertensive care was part of a larger study called the Community Quality Index Study (CQI), a cross-sectional study that assessed effectiveness of care for 32 different clinical conditions by examining medical records for patients in 12 randomly selected communities with populations greater than 200,000 (Boston, Cleveland, Greenville, Indianapolis, Lansing, Little Rock, Miami, Newark, Orange County, Phoenix, Seattle, and Syracuse) [13]. The methodology and overall results from CQI are presented elsewhere, and are summarized briefly here [14]. Between October 1998 and August 2000, study participants were selected by random digit dialing and asked permission to obtain copies of their medical records from all providers they had seen in the previous two years [14]. Of the 20,158 persons in the starting sample, we excluded 2,091 (10%) because they had moved out of the area, passed away, or become incapacitated in some manner that left them unable to participate in the study. Of the 17,937 adults who were eligible for the study, 74% (13,275) completed the telephone survey. Of the 12,412 participants who reported having at least one health care visit during the previous two years, 84% (10,404) agreed to medical record review and 7,528 signed consent forms. We obtained at least one record for 89% (6,712) of the respondents who consented, and we received 84% of the total records for which we had consent forms. Non-respondents were more likely to be female, older, and to have used health care services during the study period (p < .001). Development of quality indicators We developed process indicators for hypertensive quality of care by reviewing the scientific literature and clinical practice guidelines [15] pertaining to hypertensive care [16,17]. The indicators represented clinical processes across the spectrum of hypertensive care and were based closely on JNC-VI [15]. A diverse expert panel of nine physicians reviewed the indicators and supporting evidence using a modified Delphi method. They rated each indicator's feasibility and validity using a 9-point Likert scale. Indicators were accepted if their median validity score was 7 or higher and their median feasibility score was 4 or higher. The final 28 quality-of-care indicators included 1 screening indicator, 14 diagnostic indicators, 8 treatment indicators, and 5 follow-up indicators. Eight of the indicators were supported by randomized controlled trials and 20 by JNC-VI or other expert guidelines. The full list of indicators is presented in Table 1. Table 1 Performance of recommended hypertensive care indicators Indicator Eligible (n) Adherence to Indicators (%) Standard Error (%) 1. Systolic and diastolic blood pressure should be measured on patients otherwise presenting for care at least once each year. 1,953 72 1.0 2. All patients with average blood pressures of Stage 1 or greater as determined on at least 3 separate visits should have a diagnosis of hypertension documented in the record. 823 73 1.8 3. Patients with a new diagnosis of Stage 1–3 hypertension should have at least 3 or more measurements on separate visits with a mean SBP > 140 or a mean DBP > 90. 185 21 3.6 4. Medication and substance abuse: Personal history of tobacco abuse, alcohol abuse, or taking of medications that may cause hypertension; 183 35 5.0 5. Physical examination: examination of the fundi 199 14 2.8 6. Examination of heart sounds 199 71 4.1 7. Examination of abdomen for bruits 199 49 4.6 8. Examination of peripheral arterial pulses 199 32 4.1 9. Examination of neurologic system 199 35 4.6 10. Initial laboratory tests should include at least 5 of the following: Urinalysis; 241 30 4.3 11. Serum, plasma, or blood glucose; 241 65 4.2 12. Serum potassium; 241 59 4.4 13. Serum creatinine; 241 62 4.2 14. Serum cholesterol; or 241 58 4.2 15. Serum triglyceride. 241 60 4.4 16. First-line treatment for patients in risk group HN-A or HN-B, is lifestyle modification. The medical record should indicate counseling for at least 1 of the following interventions prior to initiating pharmacotherapy: 27 31 10.2 - weight reduction if obese; - increased physical activity if sedentary; or - low sodium diet. 17. First-line treatment for patients with Stage 1A hypertension, is lifestyle modification. The medical record should indicate counseling for at least 1 of the following interventions prior to initiating pharmacotherapy: 25 25 10.4 - weight reduction if obese; - increased physical activity if sedentary; or - low sodium diet. 18. Treatment for Stage 1B and 1C, and Stages 2 and 3 hypertension should include lifestyle modification. The medical record should indicate counseling for at least 1 of the following interventions: 149 40 5.3 - weight reduction if obese; - increased physical activity if sedentary; or - low sodium diet. 19. Stage 1B hypertensives whose blood pressure remains Stage 1 after 6 months of lifestyle modification recommendation should be offered pharmacotherapy. 113 20 4.5 20. Stage 1A hypertensives whose blood pressure remains Stage 1 after 12 months of lifestyle modification recommendation should be offered pharmacotherapy. 22 14 7.6 21. Patients in any risk group with Stage 2–3 hypertension should be offered pharmacotherapy. 359 64 3.4 22. Patients in Risk group HN-C should be offered pharmacotherapy. 277 67 3.9 23. Patients in Risk group C with stage 1 hypertension should be offered pharmacotherapy. 332 75 2.9 24. Hypertensive patients should visit the provider at least once each year. 1,681 94 0.7 25. Newly diagnosed Stage 1 patients should be evaluated by the provider within 4 months of their initial visit. 111 76 5.1 26. Newly diagnosed Stage 2 patients should be evaluated by the provider within 2 months of their initial visit. 56 66 7.6 27. Newly diagnosed Stage 3 patients should be evaluated by the provider within 2 weeks of their initial visit. 18 33 12.9 28. Hypertensive patients with consistent average SBP > 140 or DBP > 90 over 6 months should have one of the following interventions recorded in the medical record: 853 77 1.8 - Change in dose or regimen of antihypertensives; or - Repeated education regarding lifestyle modifications. Overall 72 1.0 Explanation of staging system used in Table 1 Risk group A indicates no CAD risk factors or target organ damage or CAD. Risk group B indicates CAD risk factors, but no target organ damage or CAD or DM. Risk group C indicates target organ damage, DM or CAD. HN high-normal indicates 130–139 or 85–89. Stage 1 hypertension indicates 140–159 or 90–99. Stage 2 hypertension indicates 160–179 or 100–109. Stage 3 hypertension indicates ≥180 or ≥110. Data collection After a two-week training course, twenty nurse abstractors used a computer-based abstraction tool with branching logic to abstract from the medical records we collected the following data for the two-year study period: a diagnosis of hypertension; a diagnosis of co-morbid disease, including CAD, diabetes, and hyperlipidemia; BP readings; laboratory results, including serum creatinine, cholesterol, trigylcerides, sodium, potassium, and urinalyses; prescriptions for anti-hypertensive agents; and counseling for lifestyle modification. The Kappa statistic for inter-rater reliability for indicator eligibility was .75 and, given agreement on eligibility, the kappa statistic for the indicated care was .91. Definitions 86% of patients with a visit in the last 13 months of the study period had a blood pressure measured. Patients were included in the study if their provider noted a diagnosis of hypertension in their medical record or if they had BP readings greater than 140/90 on two occasions at least two weeks apart. However, patients were only classified as having a new diagnosis of hypertension if this was specifically noted in their medical records. Per JNC-VI, we defined Stage 1 hypertension as BP 140/90–159/99 and Stage 2 hypertension as BP > 160/100; we determined stage by averaging a patient's BP over the study period, regardless of notation of diagnosis or treatment regimen. We defined uncontrolled hypertension as a last systolic BP ≥ 140 or a last diastolic BP ≥ 90. Analytic methods We determined both individual and overall quality scores for each patient. We calculated individual scores by determining a patient's eligibility for an indicator and whether s/he received the indicated care. We calculated overall quality scores by adding the number of instances in which a patient was eligible for and received the indicated care and dividing this number by the total number of instances for which s/he was eligible for the indicated care; this score was expressed as a percentage. We defined optimal quality as patients having received all of the indicated care for which they were eligible. We adjusted the scores for non-response using multivariate models that weighted respondents to be representative of the population from which they were drawn. We used the bootstrap method to directly estimate standard errors for all of the individual indicator scores [18]. Statistical comparisons at the bi-variate level were reported as t-tests for continuous variables and chi-square tests for categorical discontinuous variables. A multivariate regression model was constructed to explain BP control using clinical and demographic variables available from the medical record. Results Of the 6,712 adult patients for whom we analyzed medical records1,953 (29%) had a diagnosis of hypertension noted in their medical record or repeated BP readings indicative of hypertension. Of these, 241 (12%) were newly diagnosed during the study period; 1,102 (57%) had hypertension noted in the medical record, but the diagnosis was not new; 610 (31%) had repeated BPs ≥ 140 systolic and/or 90 diastolic evidencing hypertension, but no diagnosis in the chart. The sample included 1,070 women (55%), 1,326 patients over age 50 (68%), and 918 (47%) patients with CAD, hyperlipidemia, or diabetes noted in their medical records. The average last systolic BP was 139.2 and the average last diastolic BP was 82.0. Of the 1,368 patients (70%) who were receiving pharmacological treatment for hypertension, 25% were prescribed beta-blockers, 37% diuretics, 30% calcium channel blockers, and 35% angiotensin converting enzyme inhibitors or angiotensin II receptor antagonists. Patients with a diagnosis of hypertension noted in their records were more likely to have received pharmacotherapy during the study period than those with elevated BP readings but no notation of a diagnosis (88% vs. 31%, p < .0001). On average, patients were eligible for 3.8 indicators (range: 1–21 and visited their providers 13 times during the study period. Table 1 shows the performance of the 28 indicators among the sample. Indicator #1 had the largest number of eligible patients (n = 1,953); Indicator # 27 had the fewest number of eligible patients (n = 20). Indicator scores ranged from a low of 14% for Indicators #5 and #20 to a high of 94% for Indicator #24. Indicator #28 required a change in regimen in response to persistently elevated blood pressure; 80% of patients satisfied this requirement with a change in medication and 20% with repeated counseling on nonpharmacologic means of BP control. The mean patient overall quality score was 72%, and 57% of patients (1,113) received optimal quality of care (i.e., they received all recommended care for which they were eligible). The overall quality score was higher for patients with a diagnosis of hypertension noted in their chart (86% vs. 41%, p < .0001). The last BP reading for 42% (825) of patients indicated good control (<140/90). Table 2 shows the proportion of patients with well-controlled BP who received optimal and sub-optimal care by subgroup. Overall, patients with optimal quality care were more likely to have their BP under control (44.9% vs. 35.1%, p < .0006). This held true for all age groups, and for men, but was not statistically significant for women. Patients with optimal quality scores who did not have diabetes, CAD, or hyperlipidemia were more likely to have their BP controlled than those with sub-optimal scores. Table 2 Proportion of patients with well controlled BP by level of quality of care received Group N % <140/90 (Standard error) P-value Optimal quality Sub-optimal quality All 1,953 44.9 (2.0) 35.1 (2.0) .0006 Female 1,070 41.2 (2.5) 37.5 (2.7) .3209 Male 883 48.9 (2.8) 32.7 (3.0) .0001 Age <= 50 627 49.5 (3.4) 39.1 (3.0) .0234 Age > 50 1,326 43.4 (2.4) 32.2 (2.7) .0020 Diabetes 372 48.5 (3.7) 43.9 (5.4) .4797 No diabetes 1,581 44.0 (2.2) 33.5 (2.2) .0008 CAD 337 43.6 (3.9) 38.4 (6.3) .4835 No CAD 1,616 45.3 (2.3) 34.8 (2.1) .0007 Hyperlipidemia 604 47.4 (3.3) 39.7 (4.2) .1496 No hyperlipidemia 1,349 43.5 (2.4) 33.7 (2.3) .0033 CAD, DM, or hyperlipidemia 918 46.2 (2.7) 41.9 (3.5) .3237 Smoker 352 47.6 (4.7) 37.9 (4.1) .1218 Nonsmoker 1,601 44.4 (2.1) 34.3 (2.4) .0015 A logistic regression (not shown) revealed that the relationship between higher quality scores and BP control persisted when controlling for number of physician visits, baseline blood pressure control, gender, age, smoking status, and presence of CAD, diabetes, or hyperlipidemia. Other than quality, only baseline BP control was significantly associated with BP control at the end of the study. The relationship between quality and BP control also persisted when we restricted the study sample to those with at least two BP readings 12 months apart (n = 1,321). Likewise, there were no changes in direction or significance when we controlled for BP at 160/100 or used the continuous measures of last systolic or diastolic BP reading as the outcome. Figure 1 shows the relationship between quality scores and various demographic and clinical characteristics. There was no significant difference between quality for men and women; however, patients over age 50 received better care than their younger cohorts (76% vs. 63%, p = <.0001), as did patients with diabetes (77% vs. 71%, p = .0038), CAD (87% vs. 69%, p = <.0001), or hyperlipidemia (80% vs. 68%, p = <.0001). In addition, smoking was associated with lower quality of care (66% vs. 73%, p = .0005). Figure 1 Overall process quality scores by demographic and clinical subgroups Discussion Our data suggest that quality of care for hypertensive patients falls short of the ideal. Overall, patients received about 72% of recommended essential care processes, and 77% of patients with persistently elevated blood pressure had some change in therapy noted in the medical record over the course of two years. Though still concerning, these rates are higher than in some previous studies in more limited populations [9,19]. Some of the differences may be due to differences in the study population, and some may be due to more liberal definitions of what constitutes a change in therapy. We allowed longer time periods for the change to occur, and counted switching to different but possibly equipotent regimens and repeated nonpharmacologic interventions as therapeutic changes. Patients with additional risk factors for cardiovascular events were more likely to receive recommended care, suggesting that providers may be targeting patients at highest risk for hypertensive complications. Alternatively, these patients may have had better BP control because they were already engaged in treatment for other conditions. Smoking was the exception to this pattern. Smokers received lower quality of care even though they are at higher risk for cardiac complications from hypertension. This is particularly surprising because cigarette smoking is the only measured risk factor that directly and immediately affects BP, though its effect on long-term hypertension is unclear [20]. Further research is needed to identify possible explanations, but we hypothesize that physicians provide lower quality of care to smokers because they perceive them as unwilling to participate in their own care or less likely to comply with medical therapy. As in previous studies of hypertensives who have visited their physician, just under half of hypertensive patients had controlled BP. However, patients who received optimal care (i.e., they received all indicated care for which they were eligible) were more likely to have controlled BP at the end of the study, supporting the hypothesis that a broad range of hypertensive process quality is associated with better intermediate outcomes. The study was not powered to analyze which specific subcomponents of hypertensive care were most associated with BP control. However, because controlled hypertension is correlated with fewer myocardial infarctions and cerebrovascular accidents, it also seems likely that better overall care processes would eventually be associated with fewer complications from hypertensive care, although we could not directly test for this. This process-outcome relationship between hypertensive care and BP control was observed in almost all subgroups, but differences in quality were statistically significant primarily in patients without other cardiac risk factors [4]. Unfortunately, our study could not detect small differences in the relationship between quality of care and BP control within cardiac risk factor subgroups, and this is likely to partially explain this finding. However, for patients with CAD or diabetes, another potential explanation could be a "ceiling effect": diabetic patients received 77% of recommended care and patients with CAD received 87%, leaving little variation in quality to explain differences in BP control. In addition to poor process quality, there are other possible explanations for the high rates of uncontrolled BP observed in this study. Even among patients with at least one medical visit in the last two years, impaired access to care might be a factor, and this problem was likely worse in the 863 patients with no visits during the study period who were not included in the analysis [4]. Among those included in the study, however, impaired access was likely a small factor since the average number of provider visits in the two-year study period was 14 for patients with controlled BP and 12 for those with uncontrolled BP. In addition, hypertensive quality of care is no better in Canada despite the presence of universal health insurance in Canada and high prevalence of access problems in the U.S. [21], suggesting that sub-optimal clinical care processes are as important as access in explaining our results. Patient noncompliance has also long been recognized as an important predictor of poor BP control, but we did not measure it directly [22]. Our study is the first to examine the relationship between the quality of hypertensive care and outcomes among a national population; however, some study limitations exist beyond those inherent in retrospective medical record reviews. First, in constructing the indicators, our expert panel was instructed to rate indicators highly only if documentation of the process in the medical record was common and required for good quality care. Nonetheless, it is possible that documentation differences rather than true quality differences explain some of the observed variations in process quality or outcomes. A previous study found that differences in process scores were 10% lower for medical record abstraction compared to standardized patients with audiotapes of encounters [23]; thus, our study may underestimate actual quality of care by that amount. Second, we chose 140/90 as the threshold for poor BP control based on expert guidelines in force at the time of the study, but this threshold could potentially affect our results. However, sensitivity analyses using a higher threshold (160/100) and continuous distribution did not change our results. Later guidelines have suggested lower thresholds for diabetics; our findings remained consistent when we used 130/85 for this population. Third, providers' failure to measure the blood pressure could have caused us to erroneously exclude some patients from the analysis, though the vast majority of the studied patients had regular blood pressure measurement. Fourth, our response rate could have biased our results, particularly our estimates of overall quality. To account for this problem, we used standard techniques to adjust for non-response. Moreover, responders were more likely to be older and have chronic conditions than non-responders. Since we found higher quality of care among these subgroups, it is likely that our estimates represent upper bounds. Finally, our study does not take into account the lower threshold for pre-hypertension that was recently published in JNC-VII [5], since the care we evaluated was delivered prior to this recommendation. Future research will need to investigate whether the new standards are associated with better control. Quality assessment in hypertension is only useful if it is linked with efforts to improve care. There is some evidence that quality improvement (QI) programs can lead to BP control among hypertensive patients. Godley has recently showed that a QI program in a group-model managed care organization increased BP control from 37% to 49% [24]. Our results can help providers focus on the processes most likely to improve control and avoid adverse outcomes. Conclusions We found that the average hypertensive patient in the United States did not receive one in four essential care processes, and those with sub-optimal care had worse BP control. Our data indicate that improvements in processes of care may lead to better outcomes. While it appears that providers appropriately focus attention on patients with additional cardiovascular risk factors, they are under-treating other hypertensive patients who (as previous research would suggest) are nonetheless at increased risk for the same adverse outcomes. Future research should test if the application of routine measurement of hypertensive process quality in provider groups or plans leads to improved processes and BP control. Competing interests Drs. Asch, McGlynn, Adams, and Kerr declare they have no competing interests. Ms. Hiatt, Hicks, and DeCristofaro declare they have no competing interests. Drs. Chen and LaPuerta are employees and own shares of Bristol-Myers Squibb Company. Authors' contributions SA and BM served as principal investigators of this study and were involved in all aspects, as were EK and LH. PL and RC helped arrange partial funding and were involved in the analysis and writing of the manuscript. JA led the analysis, AD and JH were involved in supervising the data collection and participated in the analysis. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements Initial development of the indicators was funded by the Heath Care Financing Administration and the Agency for Healthcare Research and Quality. Bristol Myers Squibb funded an updated literature review and hypertension-specific expert panel. The California Healthcare Foundation funded development of the chart abstraction tool. The Robert Wood Johnson Foundation funded data collection and analysis. Drs. Asch and Kerr were funded by VA HSR&D Career Development Awards during the period of this project. We gratefully acknowledge the invaluable contributions of Joan Keesey, Landon Donsbach, Karen Ricci, Peggy Wallace, Belle Griffin, Jo Levy, and Laural Hill at RAND, Kevin Dombkowski at Vector Research Incorporated, and the RAND Hypertension Expert Panel. ==== Refs Hajjar I Kotchen TA Trends in prevalence, awareness, treatment and control of hypertension in the United States, 1988–2000 JAMA 2003 290 199 206 12851274 10.1001/jama.290.2.199 MacMahon S Peto R Cutler J Collins R Sorlie P Neaton J Abbott R Godwin J Dyer A Stamler J Blood pressure, stroke, and coronary heart disease. 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==== Front BMC Med Res MethodolBMC Medical Research Methodology1471-2288BioMed Central London 1471-2288-5-11563663810.1186/1471-2288-5-1Research ArticleStructural equation model testing and the quality of natural killer cell activity measurements Hayduk Leslie A [email protected] Hannah [email protected] Greta G [email protected] Merry-Jo D [email protected] Melanie A [email protected] Department of Sociology, University of Alberta, Edmonton, Alberta, T6G 2H4 Canada2 University Centre for Neuroscience, University of Alberta, Edmonton, Alberta, T6G 2S2 Canada3 Faculty of Nursing, University of Alberta, Edmonton, Alberta, T6G 2G3 Canada2005 6 1 2005 5 1 1 22 6 2004 6 1 2005 Copyright © 2005 Hayduk et al; licensee BioMed Central Ltd.2005Hayduk et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Browne et al. [Browne, MacCallum, Kim, Andersen, Glaser: When fit indices and residuals are incompatible. Psychol Methods 2002] employed a structural equation model of measurements of target cell lysing by natural killer cells as an example purportedly demonstrating that small but statistically significant ill model fit can be dismissed as "negligible from a practical point of view". Methods Reanalysis of the natural killer cell data reveals that the supposedly negligible ill fit obscured important, systematic, and substantial causal misspecifications. Results A clean-fitting structural equation model indicates that measurements employing higher natural-killer-cell to target-cell ratios are more strongly influenced by a progressively intrusive factor, whether or not the natural killer cell activity is activated by recombinant interferon γ (rIFN γ). The progressive influence may reflect independent rate limiting steps in cell recognition and attachment, spatial competition for cell attachment points, or the simultaneous lysings of single target cells by multiple natural killer cells. Conclusions If the progressively influential factor is ultimately identified as a mere procedural impediment, the substantive conclusion will be that measurements of natural killer cell activity made at lower effector to target ratios are more valid. Alternatively, if the individual variations in the progressively influential factor are modifiable, this may presage a new therapeutic route to enhancing natural killer cell activity. The methodological conclusion is that, when using structural equation models, researchers should attend to significant model ill fit even if the degree of covariance ill fit is small, because small covariance residuals do not imply that the underlying model misspecifications are correspondingly small or inconsequential. ==== Body Background Browne, MacCallum, Kim, Andersen and Glaser [1] employed a measurement model of natural killer cell lysis as an example of testing structural equation models. Their model failed to fit the data, though the authors judged the degree of covariance ill fit to be "negligible from a practical point of view"[1]. One of us (Hayduk) was engaged in a SEMNET [2] discussion of model fit testing, and objected to the close-yet-failing structural equation model being described as adequate. We re-examined the relevant measurement procedures and subsequently located a cleanly fitting model which provided evidence of important systematic effects coordinated with the effector to target ratios used during the measurement of natural killer (NK) cell activity. This article summarises the Browne et al. [1] data, discusses the clean-fitting model, and investigates alternative models in an attempt to better characterise the factor that produces the progressive measurement interference. Methods The immune system measurements Browne et al. [1] analysed the correlation matrix for eight measures of immune system function of 72 females with breast cancer, recorded during investigation of the physiological consequences of a psychological intervention [3,4]. Four 51Cr-release measures of natural killer cell lysis were obtained using effector (NK cell) to target cell (K562 human myeloid cell) ratios of 100:1, 50:1, 25:1 and 12.5:1. Following Browne et al. [1] we designate these measures by their effector to target (E:T) ratios, NK100, NK50, NK25 and NK12 respectively. Similarly, natural killer cell lysis measured in the presence of recombinant interferon gamma (rIFNγ) using E:T ratios of 50:1, 25:1, 12.5:1 and 6.25:1, are designated IFN50, IFN25, IFN12, and IFN6 respectively. Lower E:T ratios are used in the presence of rIFNγ because rIFNγ increases NK cells' ability to rupture target cells. The correlations reported in Browne et al.'s [1] Table 1 indicate that the four NK measures correlate highly with one another (average r = 0.852), and that the four rIFNγ enhanced NK measures also correlate highly with one another (averaging 0.960). However, the low correlations between the sets of NK and rIFNγ measurements (averaging only .111) indicate that the two sets of measurements reflect relatively distinct aspects of natural killer cell functioning. Browne et al. [1] viewed this as justifying the use of an exploratory two-factor model (Figure 1) which, unfortunately, was significantly inconsistent with the data (χ2 = 103.59, degrees of freedom (df) = 13, and probability p < 10-15). The small but significant residual differences between the data correlations and the correlations implied by the two-factor model were dismissed by Browne et al.[1] as "negligible from a practical point of view". SEMNET discussion of this model prompted Hayduk to investigate whether some unrecognized measurement feature was producing the significant, even if seemingly slight, ill fit. Table 1 Maximum likelihood estimates for the Browne et al [1] two-factor, and the progressive impact, models Browne et al.[1] Model++ Progress ive Impact Model NK Activity Factor IFN Activity Factor Indicator R2+++ NK Activity Factor IFN Activity Factor NK Progressive Factor IFN Progressive Factor Indicator R2 Proportion of indicator variance explained by NK Activity Factor Proportion of indicator variance explained by IFN Activity Factor NK100 .842 .003 .709 .705 -- -80.1 -- .958 .50 -- NK50 .936 -.005 .876 .851 -- -50.0+ -- .918 .72 -- NK25 .943 .015 .892 .920 -- -25.0+ -- .874 .85 -- NK12 .964 -.013 .927 .922 -- -12.5+ -- .995 .98 -- IFN50 .030 .942 .893 -- .897 -- -50.0+ .972 -- .80 IFN25 -.019 .996 .988 -- .977 -- -25.0+ .996 -- .95 IFN12 .005 .995 .990 -- .988 -- -12.5+ .988 -- .98 IFN6 -.018 .991 .977 -- .944 -- -6.25+ .990 -- .99 Factor Variance 1.0+ 1.0+ 1.0+ 1.0+ .000069§ .000069§** + a fixed coefficient * beyond 2 standard errors ** beyond 3 standard errors § constrained to be equal ++ Identifying Browne et al's [1] exploratory two-factor model requires excluding one indicator from loading on each factor. Repeated emails to both Browne and MacCallum were unable to elicit a statement of precisely which two loadings had been set to zero. There are 16 different ways of excluding one IFN indicator from loading on the NK activity factor and simultaneously excluding one NK indicator from the INF activity factor (see dashed loadings in Figure 1). The loadings in column 1 and 2 are the average of the estimated loadings calculated across these 16 exclusion possibilities. Each of the 16 models provided a χ2 = 103.8, df = 13, p = 0.000, with the slight difference from the reported fit being easily attributed to the three figure accuracy of the correlations published in Browne et al.[1]. +++ These values are for the version of the Browne et al. [1] model that excludes the effects leading from the NK latent to IFN6 and from the IFN latent to NK100 for identification of the model. Figure 1 The Browne, MacCallum, Kim, Andersen and Glaser (2002) two-factor model The dashed arrows correspond to weak effects. Statistical identification of the model's coefficients requires exclusion of one dashed arrow from each factor as explained in Table 1. Andersen, Farrar, Golden-Kreutz, Kutz, MacCallum, Courtney & Glaser [3] provide a description of the reasonably standard procedures used to obtain the Browne et al. [1] data. Peripheral blood leukocytes (PBLs) were obtained from 60 mL of venous blood, counted so that a known number of PBLs could be suspended in medium and incubated with either additional medium or additional medium plus rIFNγ. K562 target cells (a human myeloid cell line sensitive to NK cell activity) were labelled with 51Cr and aliquoted with the effector cells (either the NK, or the rIFNγ activated NK cells) in the ratios reported above. The cell mixture was centrifuged to ensure cell surface contact, and incubated to provide an opportunity for the NK cells to bind and rupture the target cells, thereby releasing the radioactive target cell cytoplasm. Gamma radioactivity of the supernatant collected from a second centrifuging indicated the effectiveness of the NK or rIFNγ-activated-NK cells at lysing the target cells, with larger measurements corresponding to more effective NK cell activity. An alternative model Browne et al. [1] modelled the measurements made at the various E:T ratios as replicate measurements. Hayduk suspected that the progressively varying E:T ratios might have introduced systematic measurement interference. Higher E:T ratio measurements might result in systematically less NK cell effectiveness, not because of differential NK activity but because of some progressive complication subsumed within the measuring procedures. For example, higher E:T ratios might decrease the ability of NK cells to contact and lyse target cells due to competition for cell surface contact area. Or multiple NK (or other leukocyte) cells might block some NK cells from attaining sufficient surface contact with the K562 cells, and thereby render them seemingly ineffective – not due to lack of potency, but as a result of competition for surface contact. Alternatively, the lysing of a single target cell by multiple attached NK cells, which becomes more likely at higher E:T ratios, might make the NK cells appear comparatively ineffective on a "per cell" basis. The amount of target cell cytoplasm released per effector cell would be disproportionately small because multiple NK cells might have to "share the credit" for participating in lysing a single target cell, and not because of lower NK cell effectiveness. Competition for attachment sites, and multiple simultaneous NK attacks on single targets, would increase as the effector NK cells more radically outnumbered target cells, and hence should be more pronounced at higher E:T ratios. These considerations led to the model of E:T-progressive interference depicted in Figure 2. This model postulates two latent factors, paralleling the factors in the Browne et al. [1] model (an NK activity factor causing the NK indicators' values, and an rIFNγ activity factor causing the IFN indicators' values), plus two progressively interfering factors (one spanning the NK indicators, the other spanning the IFN indicators). The effects of the interfering factors are progressive in proportion to the E:T cell ratios, and negative because we anticipated progressive reduction in the per-cell radioactivity readings, as discussed above. The negative signs are purely for ease of expression, since progressive positive values result in an equivalent model that merely interchanges the high and low ends of the underlying factor's scale. One progressive factor is postulated as acting within each measurement series, and these factors are postulated as being independent of one another, and also independent of the true scores on the NK and rIFNγ-enhanced activity factors. The variances of the two methods factors were constrained to be equal because the procedural similarity in the measurement series initially led us to suspect that routine within-series laboratory variations might propagate proportionally. We originally saw no reason to anticipate that rIFNγ would alter the mechanisms initially postulated as providing the progressive interference. Later consideration of multiple potential mechanisms led us to investigate the possibility of variance differences, as reported below. Figure 2 The model with progressively influential factors Results This model contains 18 estimates: four loadings of the NK indicators on the NK activity factor, four loadings of the IFN indicators on the rIFNγ activity factor, the correlation between the two activity factors (whose variances are fixed at 1.0), eight measurement error variances (one per indicator), and the single variance applied or assigned to both the separate interfering factors. This model fits, but a negative measurement error variance estimate for NK100 suggested a ceiling had been reached for the largest E:T ratio. Freeing the loading for the NK100 indicator results in clean fit (χ2 = 11.97, df = 17, p = 0.802) and an estimate of -80.1, rather than a strictly proportional value of -100. The alternative of constraining the offending measurement error variance to be non-negative while maintaining the -100.0 loading, also results in a fitting model (χ2 = 14.72, df = 18, p = 0.681) having very similar estimates, so whether the interfering effects are "nearly proportional" or "strictly proportional" is equivocal. The progressive and nearly-proportional model (see Figure 3 for program details) provides the estimates in Table 1. The clean fit of this model convinces us that something is indeed interfering with the NK cell activity measurements, and "that something" is acting progressively and nearly in proportion to the E:T ratios. Figure 3 LISREL (Joreskog and Sorbom [6]) program syntax for the Figure 2 progressive factors model Further characterising the interfering entity Additional models were estimated in attempts to further characterize the entity providing the progressive interference. The lowest inter-item correlations, and the greatest ill-fit of the Browne et al. [1] model, appeared for the NK measurements, so we checked whether a single methods factor spanning only the NK measures would be consistent with the data. This model is similar to Figure 2 model, except that the progressive methods factor spanning the IFN measures is eliminated. This model fails significantly (χ2 = 62.5, df = 17, p < 0.001) and thereby informs us that even the seemingly cleaner IFN measurements are influenced by some progressively interfering factor. Are two similar, yet separate, factors required for the NK and IFN measurement series, or might it be possible that one E:T-coordinated progressive factor spans all eight measurements? That is, could the progressive interfering factor be something common to an individual, rather than something set for each individual once within the NK measurement series and reset independently (or be some other interference) within the IFN measurement series? To check this, we specified a model having a single progressive methods factor leading to all eight indicators. The same E:T ratio dictated loadings were used, and the NK100 loading freed. This model also fails convincingly (χ2 = 75.5, df = 17, p < 0.001) and thereby speaks against the progressive entity being something connected to each case as a whole. That is, no single feature common to the full set of measurements (e.g. the time of blood sampling, or the person's age, or their cancer progression, or mistaken cell counts), could be progressively applied across both measurement series to account for the data. Such factors might constitute the entity spanning the items within one series, but the other measurement series would have to be progressively influenced by something else. Together, these two failing models require that the entities providing the progressive interference are features connected exclusively to either the NK series or the IFN measurement series, or are something that is set independently within each of the NK and IFN measurement series for any given case. We next attempted to check the requirement for equal progressive-factor variance incorporated in the Figure 2 model. Attempting to estimate a separate variance for each progressive factor resulted in signs of under-identification, and hence these data should be heard as being consistent with, but not necessarily as requiring, equal variances for the progressively interfering factors. In response to the comments of Reviewer-2 (Professor Mulaik), we attempted to check whether the progressive methods factors were necessarily independent of the corresponding true NK activity factors, by freeing the corresponding covariances (which were constrained to be equal). This also resulted in signs of underidentification. Hence these data should be heard as being consistent with, but not necessarily as demanding, the independence of the methods factors from the corresponding true NK activity factors. Forcing even a modest correlation between true NK activity and progressive methods factors results in substantial suppression of effects, and standardized effects exceeding 1.0 – which is not "impossible" but which would certainly "confront" anyone inclined to postulate a coordination between true NK activity and the progressive methods factors on substantive rather than "exploratory-statistical" grounds. It might be tempting to interpret these underidentified models as signs of insufficient power due to the rather small N of 72 provided by Browne et al. [1] but we think it would be more reasonable to see these underidentified models as artifacts of the limited variety of variables in the Browne et al. [1] data. The N of 72 provided sufficient power to speak strongly against the Browne et al. model, and sufficient power to speak strongly against several of the alternative models we considered above. The models that became underidentified did so largely because the structure of these models resulted in the freed coefficients having no unique (freed-coefficient dependent) implications which could potentially be found to be more/ less consistent with the data. Anyone wishing to investigate the ideas contained in the underidentified models would be better advised to add a wider variety of variables into their data and model structure, rather than merely increasing N while maintaining the current style of measurements and models. The parameter estimates We have basically two sets of estimates to consider: the estimates for the failing Browne et al. [1] two-factor model (Figure 1, and Table 1 columns 1, 2, and 3), and the estimates for the fitting progressive measurement interference model (Figure 2, and Table 1 columns 4 through 10). The loadings of the measures on the NK-activity and IFN-activity factors, namely the estimated effects of "true" NK-activity and "true" IFN-activity on their respective sets of four measures, differ importantly between these models. The Browne et al. [1] estimates are relatively uniform and large, in contrast to the loadings for our fitting model which display a definite progression from smaller to larger loadings as one moves from higher to lower E:T ratio measurements. It is no coincidence that the weakest loading estimates appear where the progressive interference is the greatest, namely for the highest E:T ratio measurements. As more variance in a measure is accounted for by the progressively interfering factor, less variance is left to be accounted for by the true NK or IFN activity factor. According to the fitting progressive model, only about half the variance in the NK100 indicator arises from true variability in NK activity, while the "other half" of the variance arrives primarily from the progressive methodological factor, with a minimal amount of error variance contributed by features unique to the NK100 measurement. Given that the latent variables have variance 1.0, the variance the NK activity factor contributes to an NK indicator can be calculated as the square of the appropriate loading. The Browne et al. [1] model, therefore, claims true NK activity contributes .71, .88, .89 and .93 to the variance in NK100, NK50, NK25 and NK12 respectively. In contrast, squaring the effects leading from our NK activity factor to the indicators provides values of .50, .72, .85 and .98. These values make it clear that Browne et al.'s [1] overlooking of the progressive methodological interference results in their model claiming that too large a portion of the variance in the high E:T ratio measures arises from true NK activity, while too small a portion of the variance in the lowest E:T ratio measure arises from true NK activity. (A similar, but less pronounced, pattern appears if corresponding calculations are made for the contribution of IFN activity to the IFN indicators.) That is, the bias in the Browne et al. [1] estimates systematically obscures the substantial and progressively stronger measurement of true NK activity by the lower E:T ratio measurements, whether viewed from the perspective of the estimates themselves or the variance accounted for by those estimates. The squared multiple correlation coefficient R2 (column 3 of Table 1) is usually interpreted as a "proportion of explained variance" but the above observations require that we reconsider this for the Browne et al. model. The Browne et al. model fails to fit with the data, and hence confronts evidence of causal misspecification, and it also confronts evidence of bias in its estimates. Is it reasonable to claim that a misspecified model containing biased estimates "explains" or "accounts for" variance in the indicators? Even biased estimates can be put through the mathematical formula providing model-implied variances and R2 (see Hayduk [5] pages 106–116, 184; and notice that the first four entries in column 3 correspond closely to the model-implied variance contributions reported in the preceding paragraph), but can mathematically-clean manipulations of biased, non-world-matching, coefficients be reasonably described as providing an "account of" or an "explanation for" indicator variances? That is, if biased estimates from a wrongly specified model are put through the perfectly-adequate mathematics providing variance implications, are the resultant variances "explained" or "accounted for"? Our view is that claims to "explaining variance" and "accounting-for variance" are rendered unconvincing if there is evidence indicating the model that supposedly provides the "explanation or account" fails to correspond to a proper representation of the external world. Hence, we view the R2 values in column 3 of Table 1 as properly calculated, yet fundamentally dubious, because the calculations are based on biased estimates from a misspecified model. These R2 values constitute "dubiously explained or accounted-for proportions" of indicator variances. Our Figure 2 model does not confront evidence of misspecification, and hence it would seem that the R2 values in column 8 of Table 1 could be more comfortably described as proportions of explained variance. But these R2 values have a different kind of uncertainty attached to them because the identity of the progressive latent variable is currently unascertained, as we discuss in the next section. The final two columns of Table 1 provide the proportions of variance in the indicators that are most confidently "explained" because these values come from a model that fits the data, and report the proportions of variance originating in latent variables whose identity is most confidently known. Let us next consider the loading estimates from the perspective of the correlations between two pairs of indicators, specifically the correlation between the NK100 and NK50 measurements (0.902) and the correlation between NK25 and NK12 (0.930). The Figure 2 model accounts for the 0.902 NK100-NK50 correlation via the action of two common causes: the true NK activity factor which contributes (0.705)(0.851)(1.0) (namely, the product of two loadings and the variance of the relevant common factor; Hayduk [5] pages 26, 106), and the progressively interfering factor which contributes (-80.1)(-50.)(0.0000695), for an overall correlation of 0.600 + 0.278 = 0.878 (with the remaining 0.023 residual being within the range of sampling fluctuations). The 0.930 NK25-NK12 correlation is similarly accounted for by a true NK activity contribution (0.920)(0.992)(1.0) and a progressive methods factor contribution (-25.)(-12.5)(0.0000695), for a total of 0.912 + 0.022 = 0.934 (which leaves a residual of -0.004). Notice that while the correlations are not radically different (0.902 vs 0.930) the contribution to the correlation provided by the causal actions of true NK activity differ substantially (0.600 versus 0.912). A substantial portion of the correlation between the NK100 and NK50 measurements is being provided by the progressively interfering factor, and when this is taken into account, there is a substantial reduction in the degree of coordination that can be attributed to both these measures causally responding to true NK activity. This is the classic distinction between reliability and validity. The NK100 and NK50 measures seem to possess substantial reliability (the basic 0.902 correlation) but much less validity since a substantial portion of the stability, or inter-measure reliability, is arising from a stable, and in this instance progressively-influential interfering entity. The small variance estimate for the progressively interfering factor (0.0000695) is partially an artifact of the large absolute values used in setting the proportional methods effects (-100, -50, -25, etc.). If each of these effects is rescaled by dividing by 100, the effects become -1.0, -0.5, -0.25, -0.125 for NK and -0.5, -0.25, -0.125 and -0.0625 for IFN, and the proportionality of the effects is preserved but the estimated variance of the progressive factor is increased 1002 fold, to 0.695, while the other estimates remain unchanged. One additional model estimate is worth noting. The Figure 2 model permits a correlation between the NK and IFN activity factors, but the corresponding estimate is small (0.090) and insignificant. The insignificance of this correlation implies that it is reasonable to view all four of the factors in Figure 2 as being basically independent of one another. Two independent entities account for the NK measurements while two additional entities that are independent of one another and also independent of the NK-measurement-producing entities account for the IFN measurements. What is producing the progressive interference? Let us first consider features capable of producing progressive interference within each series. Multiple simultaneous lysings of a single target cell provide several possibilities. With higher E:T ratios it becomes progressively more likely that any given target cell will be simultaneously attacked by more than one NK cell. The 51Cr "credit" for having lysed a target cell will be shared among the multiple attacking NK cells, and hence will reduce the seeming per-NK-cell effectiveness of the NK cells. Individual differences in the mechanisms of cell recognition, strength of attachment, delay in NK cytoplasmic reorganization, or energy supply, which are separate from whatever rate-limit constitutes "true NK cell activity", could provide individual differences constituting the variance in the "progressive factor". From this perspective, the independent progressive factor within the rIFNγ series might constitute a rIFNγ induced switch to a different rate-limiting component associated with multiple NK lethal attachments. Alternatively, the progressive interference might arise from the blocking of some effector NK cells by physical presence of scrimmage-line NK or lymphocyte cells. If an NK cell is obstructed or delayed in making contact with a target cell by: a) the physical obstruction created by other cells between this NK cell and the target, or b) the NK cell wasting time discovering that the adhered cell is merely another NK or lymphocyte cell rather than a valid target, this progressively reduces the apparent 51Cr-producing effectiveness of that cell – again a phenomena which should coordinate with the E:T ratio. The "multiple simultaneous attacks" and "blocking" scenarios might be supplemented by individual differences in the ability of NK cells to lyse multiple sequential targets. At higher E:T ratios fewer pristine targets are available and hence fewer NK cells have the opportunity to deliver second-lethal-doses, or may end up sharing their second-dose credits. Or, if lethal doses from multiple NK cells reduce the time to ion-gradient-induced cell membrane rupture, multiple-simultaneous-NK activity might instantiate the positive-valued model reported above. Yet other possibilities arise from NKT-cells and T-cell suppression of NK cells. At higher E:T ratios, there may be greater suppression of NK cells by higher concentrations of suppressor chemical signals. Similarly, NKT-cells may become progressively activated or deactivated by E:T-concentration-dependent signals from other T cells in the medium. If an individual's NK cells are not uniformly active, but rather display a within-individual gradient of activity (some NK cells being more active than others), and if this gradient is set independently of the features underlying "true NK activity" this would provide another form of explanation. Yet another possibility arises from the uptake of 51Cr by NK cells or other lymphocytes following its release from lysed target cells. At higher E:T ratios more cells are present to re-uptake 51Cr released from lysed target cells, and hence less 51Cr will appear in the centrifuged supernatant. Clearly, there are multiple possibilities for what might be providing the E:T ratio coordinated variations in lysing ability, and any independent pairing of these possibilities potentially constitute the interference in the NK and IFN measurement series. Discussion and Conclusions This study was prompted by fortuitous use of NK cell activity measurements in a debate over the testing of structural equation models. According to Browne et al. [1], even though the two-factor model of NK and rIFNγ activity they proposed (Figure 1) was significantly at odds with their correlation data, the residual differences were small enough to be "negligible from a practical point of view". Our view was that the small size of the correlation residuals did not imply that the reason for the ill fit was correspondingly small or unimportant, and this prompted our reexamination of what might be producing the ill fit. These reconsiderations led to the Figure 2 model in which the measurements reflect both the "true" degree of NK or rIFNγ induced NK cell activity along with the influences of features that progressively impact these measurements in proportion to the E:T ratios. Introducing a progressive, and nearly proportional, interfering factor within each measurement series resulted in a cleanly fitting model whose residuals are small enough to be easily attributable to chance sampling fluctuations, and whose estimates imply that true NK or rIFNγ-induced activity is most accurately measured at low E:T ratios. The impact of the progressively interfering feature is sufficiently pronounced that at the highest E:T ratio of 100 only half of the variance in the NK measurement can be attributed to "true" NK activity. The "other half" of the variance in this measurement seems to arise primarily from the progressive factor. Thus the small residuals of the Browne et al. [1] model seem to have obscured major influences in the data. Consideration of the methodology underlying the NK and rIFNγ measurements locates several possible identities for the progressively effective feature, including multiple simultaneous lysings, cell occlusion or blockage, within-individual NK activity gradients, and 51Cr reuptake. One important consequence of the fitting model is that it provides evidence indicating the lowest E:T measures provide the most valid measures of NK and rIFNγ induced lysing activity. The NK or rIFNγ measurements made at higher E:T ratios correlate highly with one another, but a substantial portion of these correlations appears to result from the progressive interfering factor and not "true" NK and rIFNγ activity. The higher E:T ratio measures remain reliable in the sense of being stable, but they are not as valid as measurements of "true" lysing activity. Given that nearly half the variance in the NK100 measurements is connected to the progressive factor, we have encountered something that is substantial and probably routinely noticed in practice, and is just being mislabeled or overlooked. A second important consequence of the Figure 2 model is that it suggests that there may exist some "third-causal-source" of lysing ability. If we think of natural lysing ability as a first source, and rIFNγ activation as a second source of lysing ability, the progressive factor may constitute a third and independent causal source. That is, just as rIFNγ-induced NK cell activity can therapeutically supplement NK activity, whatever constitutes the progressive factor may also be able to therapeutically supplement both the NK and rIFNγ-induced activities. If the interfering factors turn out to be something like blocking of access to the target cells by other cell bodies, this will be viewed as merely "the reason" lower E:T ratios provide more trustworthy measurements. But if the interfering factor turns out to be something connected to a chemical concentration (e.g. magnesium stores) then this could constitute a potential third and independent causal route to therapeutic enhancement of killer cell activity. The fact that the progressive factors are tightly connected to E:T ratios makes differential NK activity at various E:T ratios an obvious point of investigative departure. The fact that one of the progressive factors contributes about half the variance in the highest E:T ratio NK measurements implies we are not confronting issues at the limits of measurement, but rather are confronting issues of measurement confounding. Incorporating measures of variables connected to the "candidate explanations" in an expanded version of Figure 2 could effectively screen the explanatory options. Competing interests The author(s) declare that they have no competing interests. Authors' contributions LAH developed and ran the structural equation models, and prepared multiple drafts of the manuscript. HPR discussed, and suggested changes to, the various manuscript drafts; prepared the figures, and arranged for consultation with outside experts. GGC discussed, and suggested changes to, the various drafts; and arranged for consultation with outside experts. MJDL discussed, and suggested changes to, the various drafts; and prepared the table. MAB discussed, and suggested changes to, the nearly final manuscript drafts. All the authors read and approved the final version of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements We thank Dr. Feldzgeritta Pazderka, Dr. Xuejun Sun, Dr. Debbie Burschtyn, Dr. Andrew Shaw, F. Margaret Milner, and Kostyantyn Grygoryev for their discussions of technical points related to this article. ==== Refs Browne MW MacCallum RC Kim CT Andersen BL Glaser R When fit indices and residuals are incompatible Psychological Methods 2002 7 403 421 12530701 10.1037//1082-989X.7.4.403 SEMNET The Structural Equation Modeling Discussion Network Andersen BL Farrar WB Golden-Kreutz D Kutz LA MacCallum R Courtney ME Glaser R Stress and immune responses after surgical treatment for regional breast cancer J Natl Cancer Inst 1998 90 30 36 9428780 10.1093/jnci/90.1.30 Andersen BL Kiecolt-Glaser JK Glaser R A biobehavioral model of cancer stress and disease course American Psychologist 1994 49 389 404 8024167 10.1037//0003-066X.49.5.389 Hayduk LA Structural Equation Modelling with LISREL: Essentials and Advances 1987 Baltimore, Johns Hopkins University Press Joreskog KG Sorbom D LISREL 8: User's Reference Guide 1996 Chicago, Scientific Software International	15636638	PMC546216	CC BY	2021-01-04 16:32:52	no	BMC Med Res Methodol. 2005 Jan 6; 5:1	utf-8	BMC Med Res Methodol	2,005	10.1186/1471-2288-5-1	oa_comm
==== Front BMC Blood DisordBMC Blood Disorders1471-2326BioMed Central London 1471-2326-5-21565690510.1186/1471-2326-5-2Research ArticleLymphocyte subsets in hemophilic patients with hepatitis C virus infection with or without human immunodeficiency virus co-infection: a nested cross-sectional study Zhang Mingdong [email protected]'Brien Thomas R [email protected] William C [email protected] James J [email protected] the Multicenter Hemophilia Cohort Study [email protected] Viral Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD; USA2 SAIC Frederick, Inc., Frederick, MD, USA3 Other collaborators and institutions of the Multicenter Hemophilia Cohort Study are in the Appendix2005 18 1 2005 5 2 2 7 9 2004 18 1 2005 Copyright © 2005 Zhang et al; licensee BioMed Central Ltd.2005Zhang et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background With chronic infection, hepatitis C virus (HCV) RNA can be detected in B cells and associated with B-cell disorders, but these are not well defined. Methods The relationship between HCV infection and lymphocyte subpopulations was evaluated rigorously in 120 asymptomatic hemophilic patients, randomly selected from a prospective cohort study. CD4+ T cells, CD8+ T cells, CD19+ B cells, and CD56+ NK cells were quantified by flow cytometry using cryopreserved peripheral blood mononuclear cells of 24 hemophilic patients in each of five age-matched groups [uninfected; chronic HCV with or without human immunodeficiency virus (HIV); and cleared HCV with or without HIV]. Results As expected, patients with HIV had significantly reduced CD4+ and increased CD8+ T cells. Irrespective of HIV, patients with chronic HCV infection had approximately 25% fewer CD19+ B cells than those without chronic HCV infection. Conclusions These data support the hypothesis that asymptomatic patients with chronic HCV infection have an altered B-lymphocyte population. ==== Body Background The natural history of hepatic C virus (HCV) infection may include B-cell diseases. HCV can be detected in peripheral blood mononuclear cells (PBMCs) and may also replicate in these cells [1-3]. Type II cryogloblinemia and some non-Hodgkin lymphomas have been linked to HCV [4], but links between B cells and asymptomatic HCV infection are poorly defined. One study reported that hemophilic patients co-infected with HCV and human immunodeficiency virus (HIV) had reduced B cells and CD4+ T cells [5]. To further characterize PBMC immunophenotypes in patients with chronic or cleared HCV infection, we quantified proportions of CD4+ T cells, CD8+ T cells, B cells, and natural killer (NK) cells in PBMCs of carefully selected, well characterized hemophilic patients. Methods Study subjects Patients with hemophilia or another congenital coagulation disorder (herein referred to as "hemophilia") enrolled in the Multicenter Hemophilia Cohort Study [6,7] were categorized into five groups: no HIV or HCV infection (HIV-/HCV-); chronic (viremic) HCV infection without HIV (HIV-/HCV RNA+); chronic (viremic) HCV infection with HIV (HIV+/HCV RNA+); cleared (non-viremic) HCV infection without HIV (HIV-/HCV RNA-); and cleared (non-viremic) HCV infection with HIV (HIV+/HCV RNA-). From each of the five categories, 24 age-matched subjects were randomly selected. Non-viremic subjects were considered to have cleared HCV spontaneously, as they were selected from among all those who, without specific therapy, were negative for HCV RNA in two specimens collected at least 6 months apart. Viremic subjects were selected from among all those with an HCV RNA level above the 67th percentile of all HCV-positive MHCS patients (described below), all of whom had received HCV-contaminated plasma products many years earlier and thus were considered to have chronic HCV infection. All samples were taken before 1996, the year when highly active antiretroviral therapy (HAART) became available for treatment of HIV infection. HCV and HIV assays HIV status was defined by antibody testing with licensed immunoassays and immunoblot confirmation. HCV antibody status was determined with a commercially available second- or third-generation enzyme immunoassay, with most of the reactive samples confirmed by recombinant immunoblot assay (HCV RIBA2.0 or 3.0, Chiron Corp., Emeryville CA). HCV RNA was detected and viral load was determined with branched-DNA technology [Quantiplex HCV RNA 2.0 Assay (bDNA), Chiron Corp., Emeryville CA] with a lower limit of sensitivity of 200,000 genome equivalents/mL, which is 31,746 international units (IU)/mL. HIV-1 viral load was determined with the HIV Amplicor Monitor (Roche Molecular Diagnostics, Branchburg, NJ). Flow cytometry A vial of 5 million cryopreserved PBMCs, stored in vapor phase LN2 until testing, was analyzed by flow cytometry for proportions of CD4+, CD8+, CD19+, and CD56+ cells. In a biological safety cabinet, vials were thawed in a 37°C water bath with agitation. To each, 300 units of DNase I (Rnase-free, Roche Molecular Biochemicals) was added. The contents were transferred to a 15 ml polypropylene tube. Thawed cells were slowly diluted with RPMI-1640 media supplemented with 20% fetal calf serum, mixed frequently, then pelleted at 1000 rpm in a Sorvall RT6000 refrigerated centrifuge. The liquid was decanted. Cells were mixed in residual volume by gentle shaking, washed a second time with 4.0 ml of thawing media, then resuspended in 2.0 ml calcium- and magnesium-free Dulbecco's phosphate buffered saline (PBS) containing 4% heat-inactivated human AB serum to block high affinity Fc receptors. After 10 minutes the cells were pelleted in the centrifuge, then resuspended in 0.6 ml of PBS/2% BSA. 100 μl of cell suspension was added to each of five 12 mm × 75 m polypropylene tubes containing three-color combinations of fluorescently tagged monoclonal antibodies (CD45/CD14/CD3, CD45/CD4/CD3, CD45/CD8/CD3, CD45/CD19/CD3 and CD45/CD56/CD3). The lymphocyte gate was defined with light scatter properties and CD45/CD14/CD3. A Coulter XL flow analyzer was used with stops set to collect 5,000 CD3+ lymphocytes in each sample, if possible. Listmode files were analyzed offline using Coulter System II software (version 3.0). Quality control criteria included purity of gated lymphocytes; percentage recovery of lymphocytes in the gate; reproducibility of CD3 between tubes (range <4%); and sum of CD3+, CD3-/CD19+, CD3-/CD56+ (range 90–110%). Statistical analysis The primary analysis was performed on proportions, rather than absolute levels, of each PBMC subset, to reduce variance that results from counting total lymphocytes in peripheral blood [8]. The Kruskal-Wallis and Wilcoxon rank sum tests were used to compare the lymphocyte subsets between groups, particularly chronic versus cleared HCV. Statistical analyses were done with the Statistical Analysis System version 6.0 (Cary, NC). Results Characteristics of study subjects Of the 120 patients, 78 had hemophilia A (factor VIII deficiency). Because patients with hemophilia A generally required more intensive clotting factor replacement therapy as well as product (Factor VIII concentrate) that was more infectious for HIV than was Factor IX concentrate used for patients with hemophilia B, hemophilia A was significantly more common in the two groups with HIV co-infection (n = 19 with chronic HCV and n = 21 with cleared HCV) than the three groups without HIV co-infection (n = 11 to 14, p = 0.01). By design, the five groups had similar ages (mean 26.5 years, range 3.4 – 54.8 years, Table 1). HCV RNA levels were higher with HIV co-infection (median 4.6 × 106 IU/mL) than with HCV only (median 2.4 × 106 IU/mL, P < 0.01). Among 48 HIV-infected subjects, 6 (25%) with cleared and 7 (29%) with chronic HCV had clinically defined AIDS (P = 0.75) when their PBMCs were collected for this study. HIV viral load did not differ between the nine with cleared and the eight with chronic HCV who were tested on the date PBMCs were isolated (P = 0.89). Ten of 120 subjects were chronically infected with hepatitis B virus, and the prevalence of chronic HBV infection did not differ among 5 groups (P = 0.12). Only two subjects, both HIV-infected but one with cleared HCV infection and one with chronic HCV infection, had developed ascites, a manifestation of hepatic decompensation, at the time that their PBMCs were collected for this study. Table 1 Age and peripheral blood mononuclear cell subsets (median, interquartile range) by viral infection status. Viral Infection Statusa Age and Cell Type HIV- HCV- HIV+ HCV+ HCV RNA- HIV+ HCV+ HCV RNA+ HIV- HCV+ HCV RNA- HIV- HCV+ HCV RNA+ Pb (n = 24) (n = 24) (n = 24) (n = 24) (n = 24) Age (years) 24.4 (10.8–38.7) 26.7 (18.6–34.0) 25.6 (20.9–31.0) 22.5 (15.8–30.4) 30.0 (17.0–34.7) 0.80 CD4+ (%) 40.4 (35.5–49.2) 12.4 (2.7–27.0) 15.3 (2.3–24.4) 40.1 (34.4–45.7) 41.4 (36.2–45.8) <0.0001 CD8+ (%) 26.3 (24.4–33.0) 52.3 (42.6–59.5) 55.1 (49.2–62.6) 26.0 (20.4–32.8) 29.1 (22.3–37.6) <0.0001 CD19+ B (%) 15.5 (12.6–21.7) 15.4 (10.5–22.2) 11.4 (4.7–15.7) 17.8 (13.7–22.2) 13.3 (10.7–19.5) 0.04 CD56+ NK (%) 9.0 (6.8–12.9) 7.0 (3.7–9.7) 6.5 (4.1–13.8) 9.7 (6.7–13.5) 8.4 (6.5–12.4) 0.22 a HIV, human immunodeficiency virus; HCV, hepatitis C virus; RNA, presence or absence of HCV viremia, as detected by branched DNA assay (median HCV viral loads 4.6 × 106 and 2.4 × 106 IU/mL in HIV+ and HIV- subjects, respectively). b ANOVA test for comparison of age and Kruskal-Wallis test for comparisons of cell proportions. CD4+ T cells As expected, subjects with HIV had lower proportions of CD4+ T cells whether they had cleared HCV (12.4% vs. 40.1%) or chronic HCV (15.3% vs. 41.4%, P#0.0001; Table 1). Patients with cleared versus chronic HCV, however, had similar proportions of CD4+ T cells, regardless of HIV status (P∃0.82). CD8+ T cells CD8+ T cells differences mirrored those of CD4+ T cells (Table 1). Subjects with HIV had higher proportions of CD8+ T cells whether they had cleared HCV (52.3% vs. 26.0%) or chronic HCV (55.1% vs. 29.1%, P#0.0001). Regardless of HIV, subjects with cleared and with chronic HCV had similar proportions of CD8+ T cells (P∃0.30). CD19+ B cells Proportions of CD19+ B cells differed significantly among the five groups (Kruskal-Wallis P = 0.04; Table 1). In pairwise comparisons, CD19+ B cells were significantly lower with HIV and chronic HCV infections compared to uninfected subjects (11.4% vs.15.5%, P = 0.03). CD19+ B cells also were lower, albeit not significantly, with chronic HCV than with cleared HCV infection in both HIV-uninfected (13.3% vs. 17.8%, P = 0.09) and HIV-coinfected (11.4% vs. 15.4%, P = 0.08) groups. CD19+ B-cell levels did not differ between uninfected subjects and those with cleared HCV infection (P = 0.63). CD19+ B-cell proportions did not correlate with HCV viral load (Spearman R = 0.06, P = 0.78). CD56+ NK cells Proportions of CD56+ NK cells did not differ significantly among the five groups (Kruskal-Wallis P = 0.22; Table 1), suggesting that HCV infection status of hemophilia patients had limited effect on CD56+ NK cells. Discussion Among HIV-infected subjects, we found that CD19+ B-cell proportion was statistically significantly lower, from 15.5% to 11.4%, with chronic HCV infection, a fractional reduction of one-quarter [(15.5–11.4)/15.5 = 0.26]. We found subjects with chronic HCV without HIV co-infection had a reduction of the same magnitude, albeit of marginal statistical significance. HIV infection was more frequent with hemophilia A than with other coagulation disorders [6], but this did not confound the association of HCV chronicity with lower B-cell proportions. In addition, we observed an approximately one-quarter reduction (25%) in estimated absolute B-cell count in chronic HCV, despite higher variance (data not presented) [8]. Overall, our results corroborate and are of similar magnitude to those reported from Japan by Yokozaki et al [5]. Infection of B cells by HCV is controversial. Both B cells and hepatocytes express CD81, a possible receptor for HCV [9]. If HCV can replicate in B cells, as implied by detection of negative-strand RNA [10,11], then one might postulate HCV-related pathogenesis or apoptosis, as observed in B-cell lines [12]. B-lymphocytopenia also could occur during initial HCV infection and contribute to HCV chronicity by impairing neutralizing antibody response to HCV envelope 2 (E2) protein's hypervariable region-1 [13]. We found no differences in proportions of CD56+ NK cells with HCV infection, but this does not negate possible impairment of NK cell function with HCV infection. In vitro, binding of HCV E2 to CD81 inhibits the functions of cross-linked NK cells [14,15]. CD56+ NK cell proportion was reduced among our subjects who cleared HCV despite HIV co-infection, an observation that has no ready explanation and may have appeared by chance. Our study is limited by its cross-sectional design and analysis of PBMCs collected long after primary HCV and HIV infections had occurred. We cannot define sequential PBMC changes in relation to critical virologic events, such as spontaneous clearance of HCV. Furthermore, the cells that we detected in cryopreserved PBMCs may not be representative of those in fresh PBMCs. Still, we did observe the expected associations of CD4+ and CD8+ T cells with HIV infection. These findings, our careful flow cytometry methods, and our rigorous study design, with frequency matching on age and random selection of subjects from a large, well characterized cohort, probably offer a reliable "snapshot" of PBMCs in hemophilic patients infected with HCV alone or co-infected with HIV. Conclusions We found no association of cleared or chronic HCV infection with altered levels of CD4+ or CD8+ T cells, although this does not negate the likely functional importance of HCV-specific T-cell subpopulations in clearance of HCV [16,17]. Subjects with high level, chronic HCV viremia, but not those with cleared HCV infection, had reduced CD19+ B-cell levels. Further studies need to clarify the temporal sequence of B-cell changes with HCV chronicity and associated clinical conditions. Appendix: Collaborating investigators (and institutions) in the Multicenter Hemophilia Cohort Study M. Zhang, J.J. Goedert, T.R. O'Brien, P.S. Rosenberg, C.S. Rabkin, E.A. Engels, D. Whitby (National Cancer Institute, Rockville and Frederick MD); M.E. Eyster (Division of Hematology and Oncology, Pennsylvania State University Medical Center, Hershey PA); B. Konkle (Cardeza Foundation Hemophilia Center, Philadelphia PA); M. Manco-Johnson (Mountain States Regional Hemophilia and Thrombosis Program, University of Colorado, Aurora CO); D. DiMichele, M.W. Hilgartner (Hemophilia Treatment Center, New York Presbyterian Hospital, New York NY); P. Blatt (Christiana Hospital, Newark DE); L.M. Aledort, S. Seremetes (Hemophilia Center, Mount Sinai Medical Center, New York NY); K. Hoots (Gulf States Hemophilia Center, University of Texas at Houston, Houston TX); A.L. Angiolillo, N.L.C. Luban (Hemophilia Center, Children's Hospital National Medical Center, Washington DC); A.Cohen, C.S. Manno (Hemophilia Center, Children's Hospital of Philadelphia, Philadelphia PA); C. Leissinger (Tulane University Medical School, New Orleans LA); G.C. White II (Comprehensive Hemophilia Center, University of North Carolina, Chapel Hill NC); M.M. Lederman, S. Purvis, J. Salkowitz (Case Western Reserve University School of Medicine, Cleveland OH); C.M. Kessler (Georgetown University Medical Center, Washington DC); A. Karafoulidou, T. Mandalaki (Hemophilia Center, Second Regional Blood Transfusion Center, Laikon General Hospital, Athens, Greece); A. Hatzakis, G. Touloumi (National Retrovirus Reference Center, Athens University Medical School, Athens, Greece); W. Schramm, F. Rommel (Medizinische Klinik Innerstadt der Maximilian, Universitaet Muenchen, Munich, Germany); P. de Moerloose (Haemostasis Unit, Hôpital Cantonal Universitaire, Geneva, Switzerland); S. Eichinger (University of Vienna Medical School, Vienna, Austria); K.E. Sherman (University of Cincinnati Medical Center, Cincinnati OH); and B.L. Kroner (Research Triangle Institute, Rockville MD). Disclaimer "The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organization imply endorsement by the U.S. Government." Competing interests The author(s) declare that they have no competing interests. Authors' contributions MZ and JG conceived of the study and drafted the manuscript. MZ performed the random sampling and statistical analyses. JG provided advice on the sampling and statistical analyses, obtained funding, managed the parent cohort, and supervised the virologic testing. TOB provided advice on the design. WK provided advice on and performed the FACS analyses. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgments The authors are grateful to Wendell Miley for running the HCV and HIV viral load testing and especially to the study subjects and staff members for their contributions to the Multicenter Hemophilia Cohort Study. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. N01-CO-12400. ==== Refs Zignego AL Macchia D Monti M Thiers V Mazzetti M Foschi M Maggi E Romagnani S Gentilini P Brechot C Infection of peripheral mononuclear blood cells by hepatitis C virus J Hepatol 1992 15 382 386 1332999 Wang JT Sheu JC Lin JT Wang TH Chen DS Detection of replicative form of hepatitis C virus RNA in peripheral blood mononuclear cells J Infect Dis 1992 166 1167 1169 1328405 Laskus T Radkowski M Piasek A Nowicki M Horban A Cianciara J Rakela J Hepatitis C virus in lymphoid cells of patients coinfected with human immunodeficiency virus type 1: evidence of active replication in monocytes/macrophages and lymphocytes J Infect Dis 2000 181 442 448 10669324 Ferri C Pileri S Zignego AL Goedert JJ Hepatitis C virus, B-cell disorders, and non-Hodgkin's lymphoma Infectious Causes of Cancer: Targets for Intervention 2000 Towota, NJ: Humana Press, Inc 349 368 Yokozaki S Takamatsu J Nakano I Katano Y Toyoda H Hayashi K Hayakawa T Fukuda Y Immunologic dynamics in hemophiliac patients infected with hepatitis C virus and human immunodeficiency virus: influence of antiretroviral therapy Blood 2000 96 4293 4299 11110704 Goedert JJ Eyster ME Lederman MM Mandalaki T De Moerloose P White GC 2ndAngiolillo AL Luban NL Sherman KE Manco-Johnson M Preiss L Leissinger C Kessler CM Cohen AR DiMichele D Hilgartner MW Aledort LM Kroner BL Rosenberg PS Hatzakis A End-stage liver disease in persons with hemophilia and transfusion-associated infections Blood 2002 100 1584 1589 12176875 Goedert JJ Hatzakis A Sherman KE Eyster ME Lack of association of hepatitis C virus load and genotype with risk of end-stage liver disease in patients with human immunodeficiency virus coinfection J Infect Dis 2001 184 1202 1205 11598846 Taylor JM Fahey JL Detels R Giorgi JV CD4 percentage, CD4 number, and CD4:CD8 ratio in HIV infection: which to choose and how to use J Acquir Immune Defic Syndr 1989 2 114 124 2495346 Pileri P Uematsu Y Campagnoli S Galli G Falugi F Petracca R Weiner AJ Houghton M Rosa D Grandi G Abrignani S Binding of hepatitis C virus to CD81 Science 1998 282 938 941 9794763 Morsica G Tambussi G Sitia G Novati R Lazzarin A Lopalco L Mukenge S Replication of hepatitis C virus in B lymphocytes (CD19+) Blood 1999 94 1138 1139 10454799 Zehender G Meroni L De Maddalena C Varchetta S Monti G Galli M Detection of hepatitis C virus RNA in CD19 peripheral blood mononuclear cells of chronically infected patients J Infect Dis 1997 176 1209 1214 9359720 Sung VM Shimodaira S Doughty AL Picchio GR Can H Yen TS Lindsay KL Levine AM Lai MM Establishment of B-cell lymphoma cell lines persistently infected with hepatitis C virus in vivo and in vitro: the apoptotic effects of virus infection J Virol 2003 77 2134 2146 12525648 Farci P Shimoda A Wong D Cabe zon T De Gioannis D Strazzera A Shimizu Y Shapiro M Alter HJ Purcell RH Prevention of hepatitis C virus infection in chimpanzees by hyperimmune serum against the hypervariable region 1 of the envelope 2 protein Proc Natl Acad Sci U S A 1996 93 15394 15399 8986822 Crotta S Stilla A Wack A D'Andrea A Nuti S D'Oro U Mosca M Filliponi F Brunetto RM Bonino F Abrignani S Valiante NM Inhibition of natural killer cells through engagement of CD81 by the major hepatitis C virus envelope protein J Exp Med 2002 195 35 41 11781363 Tseng CT Klimpel GR Binding of the hepatitis C virus envelope protein E2 to CD81 inhibits natural killer cell functions J Exp Med 2002 195 43 49 11781364 Chang KM Thimme R Melpolder JJ Oldach D Pemberton J Moorhead-Loudis J McHutchison JG Alter HJ Chisari FV Differential CD4(+) and CD8(+) T-cell responsiveness in hepatitis C virus infection Hepatology 2001 33 267 276 11124845 Rosen HR Miner C Sasaki AW Lewinsohn DM Conrad AJ Bakke A Bouwer HG Hinrichs DJ Frequencies of HCV-specific effector CD4+ T cells by flow cytometry: correlation with clinical disease stages Hepatology 2002 35 190 198 11786976	15656905	PMC546217	CC BY	2021-01-04 16:31:42	no	BMC Blood Disord. 2005 Jan 18; 5:2	utf-8	BMC Blood Disord	2,005	10.1186/1471-2326-5-2	oa_comm
==== Front BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-5-11563163710.1186/1471-2407-5-1Research ArticleIncreased expression of integrin-linked kinase is associated with shorter survival in non-small cell lung cancer Takanami Iwao [email protected] First Department of Surgery, Teikyo University School of Medicine, Tokyo, Japan2005 5 1 2005 5 1 1 12 8 2004 5 1 2005 Copyright © 2005 Takanami; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Integrin-linked kinase (ILK) promotes tumor growth and invasion. Increased ILK expression is correlated with progression of several tumor types, but the expression of ILK has not been investigated in patients with non-small cell lung cancers (NSCLCs). Methods We investigated ILK expression in patients with NSCLC by means of immunohistochemistry. Results ILK expression was significantly associated with tumor grade, T status, lymph node metastasis and stage. (p = 0.0169 for tumor grade; p = 0.0006 for T status; p = 0.0002 for lymph node metastasis; p < 0.0001 for stage). The 5-year survival rates for patients with strong and weak or no ILK expression levels were 20% and 59%, respectively: the difference was statistically significant (p < 0.0001). A multivariate analysis of survival revealed that ILK expression, T status, N status and vascular invasion were statistically significant prognostic factors (p = 0.0218 for ILK; p = 0.0046 for T status; p < 0.0001 for N status; p < 0.0001 for vascular invasion). Conclusions Our study demonstrates that increased expression of ILK is a poor prognostic factor in patients with NSCLC. ==== Body Background Interaction of cells with the extracellular matrix regulates many physiological and pathological processes. These interactions are mediated by a large family of cell surface receptors known as integrins, which recognize several extracellular matrix proteins, including fibronectin, collagens, and vitronectin [1]. Integrins act as the bridge between extracellular matrix components and the cytoskeleton and other proteins, regulating cell survival, proliferation, differentiation, and migration [1]. Integrins are important mediators of tumor invasion and metastasis through interaction with extracellular matrix. All integrins are heterodimers composed of one copy each of two subunits, α and β. Many studies examined the association between integrins and clinicalpathology or prognosis in lung cancer. Reduced integrin α3β1 expression was reported to be a factor of poor prognosis in pulmonary adenocarcinoma [2]. Increased expression of integrin β1 was reported to be a poor prognosis in small cell lung cancer [3]. Integrin α5β1 was reported to be associated with lymph node metastasis of non-small cell lung cancer (NSCLC) [4], or to be a prognostic factor in node-negative NSCLC [5]. Integrin-linked kinase (ILK) interacts with cytoplasmic domain of both β1 and β3 integrins and is activated by cell- extracellular matrix interactions [6]. Overexpression of ILK in epithelial cells results in anchorage-independent cell growth with increased cell cycle progression [7]. Furthermore, increased ILK expression is correlated with progression of several tumor types, including prostate [8], gastric [9], and colon carcinomas [10]. However, to our knowledge, the expression of ILK has not been investigated in patients with NSCLC. Thus, we investigated ILK expression in series of 134 cases of curatively resected NSCLC by means of immunohistochemical assays to evaluate its clinical significance. Methods Patients A total of 134 patients (88 men and 46 women; mean age, 65 years; age range, 28 to 80 years) with NSCLC were studied (consecutive cases). All patients in this study had undergone curative tumor resection in our department between 1995 and 1998. Patients who died within 30 days after surgery and those who underwent exploratory thoracotomy were excluded from the study. Patients with a past history of another type of cancer were also excluded. With regard to histological type, 75 were adenocarcinomas, 54 were squamous cell carcinomas and 5 were large cell lung carcinomas. The lesions of these 134 patients were staged on both operative and pathologic findings according to the UICC TNM classification [11] (1997). There were 36 patients (27%) with stage Ia, 37 (28%) with stage Ib, 3 (2%) with stage IIa, 18 (13%) with stage IIb, and 40 (30%) with stage IIIa. None of the study subjects received pre- or postoperative chemotherapy and median follow-up of the patients was 75.3 months (range 60–96 months) and their outcomes were known. Immunohistochemical staining Resected tissue specimens were fixed in formalin, embedded in paraffin, and cut into 3-μm serial sections. The sections were subjected to hematoxylin-eosin staining and immunohistochemical analysis with ILK. A mouse anti-ILK monoclonal antibody (Santa Cruz Biotechnology, CA, USA) was used, and immunohistochemical staining was based on the avidin-biotin-peroxidase complex method and was performed with a Vestastatin kit (Vector, Burlingame, CA). Negative control sections were treated using nonspecific IgG in the primary antibody. Determination of expression of ILK Immunoreactivity was graded as from - to +++, according to the number of cells stained. Grades was defined as follow as: -, no positive cells; ±, less than 5% of tumor cells showed immunoreactivity; +, 5–50% of tumor cells showed immunoreactivity; ++, 50–80% of tumor cells showed immunoreactivity; +++, over 80% of tumor cells showed immunoreactivity. Grades ++ and +++ were regarded as strong expression, and grades ± and + were regarded as weak expression. The results of immunohistochemical staining were evaluated independently by three observers with no prior knowledge of patients' clinical data. The evaluation was suitable for 96% of the samples. The other specimens (4%) were re-evaluated independently, then classified according to the classification given most frequently by the observers. In this study, we compared the group of tumors of strong ILK expression with the group of tumors of weak or no ILK expression. Statistical analysis The association between ILK expression and clinical data was statistically evaluated by using Mann-Whitney U test or the chi-square test. Survival curves were calculated by the Kaplan-Meier method and were compared using the log-rank test. The correlation of variables with survival was analyzed by multivariate analysis using a Cox proportional hazards model. All statistical analyses were performed using the StatView software package (Abaracus Concepts, Berkeley CA). A P value of <0.05 was considered statistically significant. Results Expression of ILK and clinicopathologic parameters In non-neoplastic lung tissue, ILK was not detected in epithelial cells, while ILK expression was found in stromal cells including fibroblasts and infiltrating lymphocytes (Figure 1). In the cancer cells of many patients, ILK expression was detected in both cytoplasm and nuclei (Figure 2, and 3). Of the 134 patients, 6 (4%) were classified as -, 34 (25%) as ±, 53 (40%) as +, 25 (19%) as ++, and 16 (12%) as +++. Patients with ILK ++ and +++ were placed together in a strong ILK group and were compared with weak or no ILK group. Tumor cells expressed ILK protein in most NSCLC cases, and strong expression was detected 31% (41 of 134) of the cases. Stromal cells in cancer lesion were also positive to ILK, and rate of positive cells and intensity of the staining were not different from those of stromal cells in non-neoplastic lesion. As shown in Table 1, there were no significant differences between ILK expression status and clinical factors of age, gender, histology, lymphatic invasion and vascular invasion. However, the expression of ILK protein was significantly associated with tumor grade, T status, lymph node metastasis and stage (p = 0.0169 for tumor grade; p = 0.0006 for T status; p = 0.0002 for lymph node metastasis; p < 0.0001 for stage). Relationship between ILK expression and overall survival The 5-year survival rates for the groups with strong, and weak or no for ILK expression in their tumors were 20% and 59% respectively: the difference was statistically significant (p < 0.0001) (Figure 4). The univariate survival analysis revealed that tumor grade, T status, N status, stage, lymphatic invasion, vascular invasion and ILK expression all were statistically significant prognostic factors (p = 0.0003 for tumor grade; p < 0.0001 for T status; p < 0.0001 for N status; p < 0.0001 for stage; p < 0.0001 for lymphatic invasion; p < 0.0001 for vascular invasion; p < 0.0001 for ILk expression) (Table 2). However, age, gender, and histology were not significant factors. The multivariate survival analysis revealed that T status, N status, vascular invasion and ILK expression were statistically significant prognostic factors (p = 0.0046 for T status; p < 0.0001 for N status; p < 0.0001 for vascular invasion; p = 0.0218 for ILK expression) (Table 3). Discussion Recent studies have indicated that ILK facilitated the phosphorylation of AKT, which is required for AKT activation [12]. Activation of AKT upregulates vascular endothelial growth factor expression, and AKT is known to induce angiogenesis and suppress apoptosis [12]. By regulating, the activity of AKT as well as glycogen synthase kinase 3, ILK facilitates the assembly and activity of the β-catenin/LEF-1 transcription complex, and suppresses expression of the invasion suppressor E-cadherin [13,14]. Overexpression of ILK in epithelial cells results in anchorage-independent cell growth with increased cell cycle progression, and constitutive up-regulation of cyclin D and cyclin A expression [7]. Inhibition of ILK elicits cell cycle arrest and induces apoptosis [15]. Overexpression of ILK in epithelial cells induces tumorigenicity in nude mice, indicating that ILK can act as a proto-oncogene [16]. ILK is associated with a highly invasive phenotype of certain tumors [7]. In the current study, we detected ILK expression using immunohistochemistry on tumors from patients with NSCLC. ILK was strongly expressed in 31% of tumor samples, whereas there was no ILK expression in noncancerous pulmonary tissue samples from the same patients, except for fibroblasts and infiltrating lymphocytes. These finding suggest that ILK expression may serve as a useful marker of NSCLC. ILK is very low in healthy cells. In cancer cells, however, ILK activity is often increased, possibly as a result of a malfunctioning of upstream components in the integrin and growth factor signaling pathways. We found a significant correlation between strong expression and advanced T status, N status and stage. No correlation was found age, gender or histology. These results suggest that ILK expression is correlated with tumor progression in NSCLC. Cases with strong ILK expression were reported to be significantly more frequent in advanced gastric carcinoma [9]and advanced melanoma [17]. ILK-mediated pathway that may enhance tumor progression is its regulation on MMP expression [18]. During tumor progression, MMPs facilitate the pathological processes of tumor invasion, angiogenesis, and metastasis by breaking down the extracellular matrix. The overexpression of ILK has been shown to be result of increased MMP-9 expression [18]. We also demonstrated that the expression of ILK correlated with tumor grade. Recent studies have also linked ILK expression to tumor grade of prostate [7], gastric [9] or ovarian carcinomas [19]. The overall prognosis of patients with strong ILK expression was significantly poorer than that of patients with weak or no expression. For stage I or stage II/IIIa patients, prognosis was poorer for those with strong ILK expression than for those with weak or no ILK expression, although the differences were not significant (data not shown). Strong expression of ILK protein was reported to be significantly associated with presence of nodal metastasis [9,17]. The univariate survival analysis revealed ILK expression was a significant prognostic factor as well as T status, N status, stage, lymphatic invasion and vascular invasion. The status of ILK expression might be dependent on the status of lymph node metastasis or other variables. So the multivariate analysis for survival was performed. In the current study, the multivariate analysis revealed that ILK expression was picked up for its independent level of prognostic significance. Our results also showed that the tumors with a strong expression of ILK were associated with an increased recurrence, which suggests that patients with strong ILK expression may be prone to metastasis, or may already have occult systemic diseases. ILK expression level plays one of the key roles in the biology of NSCLC and defines a more aggressive tumor phenotype of NSCLC. Preoperative adjuvant therapy in NSCLC is designed to improve survival and reduce local recurrence. Recent reports have shown that preoperative adjuvant therapy has led to an increase in overall survival of NSCLC patients [20,21]. It is possible that this preoperative adjuvant treatment modality may be important for patients whose tumors have strong ILK expression. In the future, small molecule antagonists of ILK may be used to interfere with recurrence in tumor patients. Conclusion Our study demonstrates that ILK expression is a poor prognostic factor in patients with NSCLC. Thus, the utility of the expression of ILK could open up a new window for the molecular marker and the treatment of NSCLC. Competing interests The author(s) declare that they have no competing interests. Pre-publication history The pre-publication history for this paper can be accessed here: Figures and Tables Figure 1 Immunohistochemical studies of ILK in non-neoplastic pulmonary tissue and in NSCLC tissue. a non-neoplastic pulmonary tissue: ILK was not detected in epithelial cells, while ILK expression was found in many stromal cells. Figure 2 well differentiated adenocarcinoma: ILK protein was localized in both cytoplasms and nuclei. (Magnification, ×40 (Figure 1), ×16 (Figure 2), and ×40 (Figure 3)). Figure 3 well differentiated adenocarcinoma: ILK protein was localized in both cytoplasms and nuclei. (Magnification, ×40 (Figure 1), ×16 (Figure 2), and ×40 (Figure 3)). Figure 4 Overall survival curves for patients classified according to expression of ILK. Five-year survival rates were 20% and 59% for patients in the strong ILK and weak or no ILK groups, respectively. A log-rank test revealed a significant difference between the two groups (p < 0.001). Table 1 Clinicopathologic characteristics of patients with NSCLC classified according to ILK Characteristic Expression of ILK P-value weak or none (n = 93) strong (n = 41) Age MEAN ± SD 64.6 ± 11.0 64.5 ± 9.3 0.3412 Gender Male 57 31 Female 36 10 0.1077 Histology Adenocarcinoma 54 21 Squamous cell ca. 37 17 Large cell ca. 2 3 0.4771 Tumor grade Well differentiated 38 9 Mode differentiated 37 15 Poorly differentiated 18 17 0.0169 T status T1 34 6 T2 49 20 T3 10 15 0.0006 Nodal status N0 69 18 N1 7 1 N2 17 22 0.0002 Stage Ia/Ib 62 11 IIa/IIb 14 7 IIIa 17 23 <0.0001 Lymphatic invasion Negative 42 15 Positive 51 26 0.3548 Vascular invasion Negative 56 19 Positive 37 22 0.1360 Table 2 Univariate Analysis of Variables that Affected the Overall Survival Rate Determined by Cox Proportional Hazards Model Variable Relative risk 95%CI p-value Age (<65 yrs/≦65 yrs) 1.070 0.699–1.710 0.080 Gender (male/female) 1.537 0.866–2.383 0.1605 Histology (SCC/others) 1.174 0.688–2.005 0.5560 Tumor grade (mode., poor/well) 1.802 1.130–2.478 0.0003 T status (T2, T3/T1) 2.669 1.878–3.794 <0.0001 N status (N1, N2/N0) 5.911 3.600–9.705 <0.0001 Stage (stage II, III/Stage I) 8.143 4.851–12.255 <0.0001 Lymphatic invasion (positie/negative) 3.634 2.074–6.367 <0.0001 Vascular invasion (positive/negative) 3.198 1.974–5.181 <0.0001 ILK (strong/weak or no) 2.926 1.827–4.686 <0.0001 Table 3 Risk Factors that Affected the Overall Survival Rate Determined by the Cox Proportional Hazards model Variable Relative risk 95%CI p-value Tumor grade (mod., poor/wel) 1.238 0.858–1.786 0.2543 T status (T2, T3/T1) 1.845 1.208–2.818 0.0046 N status (N1, N2/N0) 3.897 2.241–6.775 <0.0001 Vascular invasion (positive/negative) 2,981 1.733–5.127 <0.0001 ILK (strong/weak or no) 1.844 1.093–3.112 0.0218 ==== Refs Giancotti FG Ruoslahti E Integrin signaling Science 1999 285 1028 1032 10446041 10.1126/science.285.5430.1028 Adachi M Taki T Huang C Higashiyama H Doi O Tsuji T Miyake M Reduced integrin alpha3 expression as a 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==== Front BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-5-41565690010.1186/1471-244X-5-4Research ArticleThe stability of life satisfaction in a 15-year follow-up of adult Finns healthy at baseline Koivumaa-Honkanen Heli [email protected] Jaakko [email protected] Risto J [email protected]äki Heimo [email protected] Markku [email protected] Department of Psychiatry, University of Kuopio, Kuopio, Finland. Department of Psychiatry, Kuopio University Hospital, Kuopio, Finland2 Finnish Twin Cohort Study, Department of Public Health, University of Helsinki, Finland3 Department of Mental Health and Alcohol Research, National Public Health Institute, Helsinki, Finland4 Research Institute of Public Health, University of Kuopio, Kuopio, Finland5 Department of Public Health; University of Turku, Turku, Finland2005 18 1 2005 5 4 4 3 3 2004 18 1 2005 Copyright © 2005 Koivumaa-Honkanen et al; licensee BioMed Central Ltd.2005Koivumaa-Honkanen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background While physical health has improved considerably over recent decades in Finland, the disease burden of mental health, especially that of depression, has become increasingly demanding. However, we lack long-term data on the natural course of subjective well-being in the general population. The aim of this study was to investigate the long-term course of self-reported life satisfaction. Methods This was a 15-year prospective cohort study on a nationwide sample of adult Finnish twins (N = 9679), aged 18–45 and healthy at baseline, who responded to postal questionnaires in 1975, 1981 and 1990 including a 4-item life satisfaction scale (happiness/easiness/interest in life and feelings of loneliness). Life satisfaction score (range: 4–20) was classified into three categories: satisfied (4–6), intermediate (7–11) and dissatisfied group (12–20). The associations between life satisfaction scores during the follow-up were studied with linear/logistic regression. Results Moderate stability and only a slight effect of age or birth-cohort on mean life satisfaction score (LS) were detected. In 1990, 56% of all and 31% of the dissatisfied remained in the same LS category as at baseline. Only 5.9% of the study subjects changed from being satisfied to dissatisfied or vice versa. Correlations between continuous scores (1975, 1981 and 1990) were 0.3–0.4. Baseline dissatisfaction (compared to satisfaction) predicted dissatisfaction in 1981 (OR = 10.4; 95%CI 8.3–13.1) and 1990 (5.6; 4.6–6.8). Multiple adjustments decreased the risk only slightly. Conclusions Life satisfaction in adult Finns was moderately stable during 15 years. Among an identifiable group (i.e. the dissatisfied) life dissatisfaction may become persistent, which places them at a greater risk of adverse health outcomes. ==== Body Background While physical health has improved considerably during recent decades in Finland, the disease burden of mental health – especially that of depression – has become increasingly demanding for the health care services and society. When the global disease burden – including both fatal and non-fatal outcomes – has been assessed, major depression has been shown to be one of its leading causes [1]. However, not only diagnosed depression but also incomplete recovery from depression and subthreshold depressive symptoms have adverse consequences and chronic courses [2-6]. In general, poor mental health affects somatic health and subjective well-being, but poor subjective well-being might also develop into mental disorder and create a loss of functional capacity, if not a sign of an undiagnosed mental disorder already at baseline. Life satisfaction and happiness are some of the concepts that have previously been viewed as indicators of subjective well-being [7,8]. Life dissatisfaction, even reported by seemingly healthy subjects, is associated with several indicators of poor health or health risk factors, but especially with depressive symptoms [9-11]. Longitudinally, it predicts poor health outcomes such as morbidity, mortality and premature work disability – due to both somatic as well as psychiatric causes – among the healthy but dissatisfied subjects [9,11-15]. When dissatisfaction is repeatedly reported over years the risk of a poor health outcome increases even more [13-15]. Due to these adverse health outcomes and shortened life expectancy among the identifiable group from the healthy general population (i.e. the dissatisfied), more attention should be paid to the natural course of life dissatisfaction in the general population. However, good mental health is also an area that should be studied in psychiatry. It is something more than the absence of symptoms. It is a mental state that is objectively desirable, indicating for example maturity, emotional and social intelligence, resilience and subjective well-being according to Vaillant [16]. While the possibilities to directly assess mental health at the level of the general population are limited, subjective well-being at the population level can be measured. Subjective well-being has mainly been investigated in cross-sectional settings and among the elderly, while follow-ups may have been brief and data from the general population have been sparse. Even if subjective well-being in general population has been suggested to be quite stable [9,17-23], it has also been pointed out that the apparent stability should not be due to the insensitivity of measurements to change or due to fact that most people report satisfaction with life [17,20]. However, this is not the case with psychiatric patients and life satisfaction, among whom life satisfaction has been shown to be lower than in any other patient group [9] and to improve markedly concurrently with their recovery from depression [10]. Mental health policy plays an increasingly recognized role in society, but it needs both epidemiological data as well as experts on the field of mental health to monitor the populations and trends [24]. Thus, in psychiatry, we need information on the natural long-term course of subjective well-being in the general population. This study aimed to examine the long-term course of life satisfaction in healthy adults and to determine how strongly self-reported life dissatisfaction predicts future life dissatisfaction. Methods This prospective cohort study with a follow-up from 1975 to 1990 was based on the Finnish Twin Cohort, a nationwide sample of all Finnish same-sex twin pairs born before 1958 with both members alive in 1975. A baseline health questionnaire was sent in 1975 to twin candidates [25]. The follow-up questionnaires in 1981 and 1990 were sent only to verified twins. Furthermore, the 1990 questionnaire was sent only to twins from pairs born in 1930–1957 with both co-twins alive and residing in Finland. The overall response rates to the questionnaires were 89% in 1975, 84% in 1981 and 77% in 1990. The study procedure has been presented in detail elsewhere [12,25]. The questionnaires included a four-question scale for life satisfaction, which was modified from a questionnaire developed for measuring the quality of life for research purposes in Nordic countries [26]. It has been used among all adult age-groups [9,12] as well as among psychiatric patients [9,10,27]. The study subjects were asked to rate aspects of life satisfaction: interest in life, happiness, ease of living and loneliness (very interesting/happy/easy/not at all lonely = 1, fairly interesting/happy/easy = 2, fairly boring/unhappy/hard/lonely = 4, very boring/unhappy/hard/lonely = 5). Missing data and the response 'cannot say' were scored as 3. If three or four items were missing, the sum score was recorded as 'missing'. Thus, the total score (LS) ranged from 4–20, with increasing scores indicating a decrease in life satisfaction. On the basis of the distribution of the sum score (LS), subjects were categorized into the satisfied (LS:4–6), the intermediate group (LS:7–11) and the dissatisfied (LS:12–20) [12]. The intermediate group consisted of those with an LS score within one standard deviation from the mean [9]. At baseline, responses to all four items were provided by 95.8% (N = 22,416) and at least two items, enabling LS to be calculated, by 99.2% (N = 23,212) of all respondents aged 18–45 years. Thus, one or two missing values were recoded as '3' values for 3.4% of subjects. The criteria for inclusion in the present study were the availability of baseline life satisfaction data, an age of 18–45 years on 1 January 1976 and being a twin (N = 19,973), since only twins were eligible to receive follow-up questionnaires, as well as being healthy at baseline (N = 16,496, see below for criteria). Moreover, the questionnaire was sent in 1990 only to those whose twin partner was alive. Thus, from these eligible subjects, study subjects were those with all three life satisfaction scores available (N = 9679). They consisted of 4466 (46.1%) male and 5213 (53.9%) female twins (Table 1). The mean age (SD) at baseline was 28.8 years (7.5) for men and 28.1 years (7.6) for women. Those subjects who had incomplete follow-up data (N = 6817) were compared with study subjects. Their life satisfaction data was available as follows: 1) LS 1975 and 1981 (n = 4930); 2) LS 1975 and 1990 (n = 385); 3) LS 1975 only (n = 1502). Table 1 Baseline characteristics of the study subjects and those with incomplete follow-up data on life satisfaction. Study subjects Subjects with incomplete follow-up p-value Baseline characteristics N (9679) column % N (6817) column % Sex <0.0011) Men 4466 46.1 3932 57.7 Women 5213 53.9 2885 42.3 Age-group <0.0012) 18 – 25 4121 42.6 3100 45.5 16 – 35 3513 36.3 2431 35.6 36 – 45 2045 21.1 1286 18.9 Social class <0.0013) Upper 565 5.8 361 5.3 Intermediate 2695 27.8 2121 31.1 Lower 6419 66.4 4335 63.6 Marital status <0.0014) Cohabiting 5386 55.7 3443 50.6 Living alone 4291 44.3 3366 49.4 Smoking cigarettes daily <0.0015) Non-smoker 6698 69.3 3991 58.7 1 – 19 2344 24.2 2976 30.5 > 19 627 6.5 737 10.8 Pure alcohol g/month <0.0016) None 1361 14.1 807 11.9 1 – 99 4077 42.2 2334 34.3 100 – 399 2626 27.1 2076 30.5 400 – 799 1083 11.2 939 13.8 ≥ 800 524 5.4 648 9.3 Physical activity/month <0.017) < 1 1138 12.2 888 13.7 1 – 5 4585 49.3 3045 46.9 ≥ 6 3577 38.5 2563 39.4 Life satisfaction in 1975 <0.0018) 4 – 6 2214 22.9 1370 20.1 7 – 11 6239 64.4 4303 63.1 12 – 20 1226 12.7 1144 16.8 Life satisfaction data not available from 1981 and/or 1990. 1) F(1, 10007) = 165; 2) F(2, 10006) = 7.02; 3) F(2, 10006) = 9.17; 4) F(1, 10002) = 36.1; 5) F(2, 9995) = 94.0; 6) F(4, 9995) = 45.0; 7) F(2, 9786) = 5.50; 8) F(2, 10006) = 29.0 The criteria for baseline health were based on a health questionnaire (Q) and three nationwide registries: the Hospital Discharge Registry (H), the Registry of Specially Refunded Medication (M) and the Cancer Registry (C). Thus, those with symptoms or diseases covering cardiovascular disease, diabetes, chronic obstructive pulmonary disease or malignant cancer, those who used medications for 37 selected chronic somatic or psychiatric diseases, as well as those who were on a work disability pension due to any cause or had an inpatient admission between 1972 and April 1976, were excluded [12]. The specific exclusion criteria for psychiatric disorders covered work disability (Q), inpatient treatment due to psychiatric causes (ICD-8: 290–309) (H), the right to free medication for psychosis before 1977 (M) and use of hypnotics/tranquilizers for more than 10 days in the preceding year (Q). It has previously been reported that 4-item life satisfaction is associated with a lower age, female sex, cohabiting, an upper social class, non-smoking, lower alcohol consumption and physical activity [9,12,13]. Thus, the multivariate model included baseline variables such as age (18–24/25–34/35–45), sex, marital status (married or cohabiting/single, divorced or widowed), social class (lower/intermediate/upper group), physical activity (at least 30 minutes of exercise < 1/1–5/ ≥ 6 times a month), current smoking status (non-smoker/1–19/ > 19 cigarettes daily) and alcohol consumption (none/1–99/100–399/400–799/ ≥ 800 g pure alcohol/month) [12]. The upper social class consisted of those with at least 13 years of education and sedentary work, while the lower social class consisted of those with less than 10 years of education and work involving at least standing and walking. Data analysis was carried out using STATA (version 7.0). Since a study subject could be an age- and sex-matched twin sibling of another study subject, not all the observations were necessarily independent. Therefore, correct standard errors were computed by treating each pair of twins as a single unit (i.e. cluster sampling). The statistical significance of differences was tested by estimates of means (SVYMEAN and SVYLC procedure) for continuous variables and by the chi-squared test for categorical variables (SVYTAB procedure), corrected for clustered data and converted into F-statistics. The stability of life satisfaction over time was examined by computing Pearsonian correlation coefficients between continuous variables. To study how former life dissatisfaction predicts later life dissatisfaction, linear and logistic regression for clustered data was used. Results The baseline characteristics of the study population and those whose follow-up data on life satisfaction was not available at all three data collection times is presented in Table 1. Study subjects were more often women, cohabiting, non-smokers and used less alcohol than those whose follow-up data on life satisfaction was incomplete. There were also slight differences in social class and physical activity. Furthermore, study subjects were somewhat more satisfied (mean LS 8.23; 95%CI 8.18–8.29 vs. LS 8.61; 8.54–8.68) and slightly older (mean age 28.4; 28.2–28.6 vs. 27.9; 27.7–28.1) than those with incomplete follow-up data (Table 1). In the study population with complete follow-up data on life satisfaction, no marked differences were observed in mean LS scores between age or gender groups or measurement times (Table 2). There was only a slight decrease in mean life satisfaction during the follow-up of 15 years. The main decrease took place in women during 1981–1990. When birth cohorts were studied, only those born during 1940–49 showed a trend of decreasing satisfaction throughout the follow-up, regardless of gender, but they were also the most satisfied group at baseline, being then 26–35 years of age. Young men aged 18–25 at baseline were and remained the most dissatisfied group throughout the follow-up. Table 2 Mean (95% CI) life satisfaction (LS) according to sex, birth cohort and current age among 9679 Finnish adults in 1975, 1981 and 1990. Subjects (n) LS 1975 (n) LS 1981 (n) LS 1990 All (9679) 8.23 (8.18 – 8.29) (9679) 8.26 (8.20 – 8.31) (9679) 8.35 (8.29 – 8.41) Men (4466) 8.30 (8.22 – 8.38) (4466) 8.33 (8.25 – 8.41) (4466) 8.38 (8.30 – 8.46) Women (5213) 8.18 (8.10 – 8.25) (5213) 8.19 (8.12 – 8.27) (5213) 8.32 (8.24 – 8.40) Birth cohorts, all 1950 – 57 (4121) 8.46 (8.36 – 8.55) (4121) 8.31 (8.23 – 8.40) (4121) 8.45 (8.36 – 8.54) 1940 – 49 (3513) 7.98 (7.90 – 8.07) (3513) 8.13 (8.05 – 8.22) (3513) 8.32 (8.22 – 8.41) 1930 – 39 (2045) 8.22 (8.11 – 8.32) (2045) 8.35 (8.23 – 8.46) (2045) 8.19 (8.08 – 8.31) Current age, all 18 – 23 (3254) 8.50 (8.40 – 8.61) - - - - 24 – 32 (3608) 8.04 (7.96 – 8.13) (4520) 8.28 (8.20 – 8.36) - - 33 – 45 (2817) 8.17 (8.08 – 8.26) (4101) 8.19 (8.11 – 8.27) (6236) 8.39 (8.31 – 8.46) 46 – 51 (1058) 8.39 (8.23 – 8.55) (1679) 8.37 (8.23 – 8.50) 52 – 60 - - (1764) 8.18 -(8.06 – 8.30) In terms of 3-category LS scores (Table 3), more than half (56%) of the study subjects in 1990 scored in the same category as they did in 1975, but the relationship was less stable for the satisfied (36%) and the dissatisfied (31%) than the intermediate group (69%). However, only 5.9% of the study subjects were satisfied (LS 4–6) at one of the three data collection times but dissatisfied (LS12-20) at one of the other time points. Table 3 The distribution of study subjects according to their self-reported life satisfaction (LS) in 1975 and 1990. LS 1990 Satisfied Intermediate Dissatisfied TOTAL N row % N row % N row % N row % LS 1975 Satisfied 785 35.5 1266 57.2 163 7.4 2214 100 Intermediate 1088 17.4 4293 68.8 858 13.8 6239 100 Dissatisfied 122 10.0 728 59.4 376 30.7 1226 100 TOTAL 1995 20.6 6287 65.0 1397 14.4 9679 100 Life satisfaction score: satisfied (LS 4–6); intermediate (LS 7–11); dissatisfied (LS 12–20). The correlation was 0.30 between continuous life satisfaction scores in 1975 and 1990, 0.38 between 1975 and 1981 scores and 0.40 between 1981 and 1990 scores. These coefficients were similar for men and women, but lowest among those aged 18–25 (0.26, 0.34 and 0.39, respectively) and highest among those aged 36–45 (0.36, 0.47 and 0.42, respectively). For the total study population the annual auto-correlation was estimated as 0.92 during 1975–90. Baseline life dissatisfaction predicted future life dissatisfaction (Table 4). This was also true after adjusting for all the covariates as well as when the categories of each covariate were separately investigated. The same pattern was shown both with categorical and continuous life satisfaction scores. The predictive ability was expectedly stronger for the shorter follow-up (1975–1981) than for the total follow-up (1975–1990). When the odds ratios were compared with those which could be calculated for the subjects with incomplete life satisfaction follow-up data, no significant differences were found. Table 4 Prediction of future dissatisfaction (LS 12–20) according to baseline dissatisfaction. Risk (OR with 95%CI) for the dissatisfied (LS 12–20) compared to the satisfied (LS 4–6) at baseline. COMPARISON OF LS SCORES BETWEEN 1975 & 1981 1975 & 1990 Subjects (n) OR (95 % CI) OR (95 % CI) All † 9679 10.42 (8.28 – 13.10) 5.56 (4.55 – 6.80) Adjusted ‡ 9283 9.79 (7.68 – 12.47) 5.23 (4.24 – 6.46) Men † 4466 14.81 (10.19 – 21.51) 6.11 (4.47 – 8.35) Adjusted ‡ 4298 12.86 (8.74 – 18.91) 5.62 (4.05 – 7.80) Women † 5213 7.93 (5.89 – 10.68) 5.29 (4.06 – 6.90) Adjusted ‡ 4985 7.94 (5.78 – 10.92) 4.98 (3.78 – 6.56) Risk of future dissatisfaction : LS 12–20 vs. LS 4–12 † Adjusted for age ‡ Adjusted simultaneously for age, sex, marital status, social class, alcohol consumption, current smoking and physical activity (cf. method section). Discussion Life satisfaction was moderately stable in healthy adult Finns during a 15 year period. Age or the birth-cohort had only a slight effect on mean life satisfaction. One third of those dissatisfied at baseline remained the same after the 15-year follow-up. The ability of baseline life satisfaction to predict future life satisfaction was strong, but decreased during the follow-up period, which is a trend that has also been suggested previously [23]. Previous studies have indicated that depression and depressive symptoms may have a chronic course [2-6]. On the other hand, in non-patient samples subjective well-being has also been suggested to be stable [17-22]. Concomitant anxiety or personality traits might play a role in this [28-32]. However, our results concerning the possible chronic course of life dissatisfaction are now based on a very long follow-up and a large sample of adults who reported or were found to have no indication of sickness at baseline. Although regression towards the mean in life satisfaction score was shown in the follow-up and greater instability among the dissatisfied and the satisfied than in the intermediate group, a complete shift from one extreme to another was rare. In Finland the number of new work disability pensions due to depression has strongly increased [33]. However, at the population level, subjective well-being seems not to have decreased correspondingly according to a comparison of two separate cross-sectional national surveys in 1980 and 2000 using the 12-item General Health Questionnaire [34]. Our cohort study with the 4-item life satisfaction scale measured three times during a 15-year follow-up on the same population strengthens these findings. On the other hand, during these years the physical health of the Finnish population has improved in many objectively assessed ways [34], but an improvement has not been seen in mental health indicators or in life satisfaction at population level. Thus, objectively assessed better somatic health or strong national economic growth (an increase of 48% in inflation-adjusted gross national income per capita from 1975 to 1990), which has also taken place during these years, seems not to guarantee better subjective well-being in a population that is globally speaking already quite well-off. This kind of trend has also been suggested previously [16,32,35]. On the contrary, the mean level of subjective well-being, which was previously sufficient to maintain work ability, seems not to meet the requirements of today's working life. Our results with respect to predictions seemed not to be overestimations. Those subjects whose dissatisfaction might have led to the most adverse result, i.e. death, were excluded from our study. The response to follow-up surveys was somewhat lower among the dissatisfied, but the observed risks among the study subjects did not differ statistically significantly from those obtained from the subjects with incomplete follow-up data. Similarly, when adjusting for follow-up health behavior instead of baseline health behavior variables, these predictions strengthened slightly, but not significantly. The 4-item life satisfaction scale is easily administered and well accepted. Its sum score was available for 96% (with imputed scores for 99%) of all respondents at baseline [12,13]. This might be due to the low number of items and its ability to tap the positive pole of subjective well-being [7], even if its sum score is also strongly associated with scores obtained by the 21-item Beck Depression Inventory [10,11,13,36]. In the general population, life dissatisfaction predicts both fatal and non-fatal poor long-term health outcomes [11-15]. Thus, it is worthwhile to assess subjective well-being. In general, if only poor subjective well-being is detected, our long follow-up suggests that there seems to be time to intervene. To prevent a process from leading to more adverse outcomes, subjects should acknowledge their situation and use their own personal resources, if available. According to a panel of experts, the concept of mental health can be regarded as a developmental process providing an individual or a group with the necessary resources to cope with the demands of life without the simultaneous appearance of negatively experienced moods of longer duration [37]. Society and health care services should support the growth of personal resources and start to intervene when these resources are inadequate and poor subjective well-being persists. In psychiatry, however, according to Vaillant, "since primary prevention is clearly superior to treating disease once it has occurred, we need to study also individuals with positive mental health the way that agronomists study wheat that is resistant to drought and blight" [16]. Our large nationwide sample with a high response rate and a long follow-up period enabled an examination of the long-term course of life satisfaction. The exclusion criteria for baseline health disorders were comprehensive and based on both self-reports and several national registries with high coverage and validity [38-41]. Although these analyses were performed on individuals drawn from a twin cohort, the results should be applicable to the general population. Being a twin does not affect the predictive ability of life satisfaction for mortality or suicide [12,13], and there is at most only a modest contribution of genetics to inter-individual differences in life satisfaction [42]. However, the potential influence of twinship was taken into account in the statistical analysis. The arbitrary exclusion of those twins who did not have a living twin partner in 1990, required in the composition of the Twin Finnish Cohort data, enabled us to control for the loss of a twin sibling. Conclusions Life satisfaction among healthy adult Finns was moderately stable in a 15-year follow-up. Since the dissatisfied, one third of whom consistently rated themselves as dissatisfied, can be identified from the general population, and since dissatisfaction places them at a risk, and repeatedly reported dissatisfaction at even greater risk, of adverse health outcomes, assessing subjective well-being should be encouraged both in surveys and in clinical practice in order to identify those in need of further evaluation of their mental health. Competing interest The author(s) declare that they have no competing interest. Authors' contributions KJ participated in composing the Finnish Twin Cohort data, in planning, commenting on, revising and approving the final manuscript. KM participated in composing the Finnish Twin Cohort data, in planning, commenting on, revising and approving the final manuscript. HR participated in planning, commenting on, revising and approving the final manuscript. HV participated in planning, commenting on, revising and approving the final manuscript. KHH planned the study, performed the statistical analyses and was the main author. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements This study was supported by the Academy of Finland (grant 27380). ==== Refs Michaud CM Murray CJ Bloom BR Burden of disease – Implications for future research JAMA 2001 285 535 539 11176854 10.1001/jama.285.5.535 Wells KB Stewart A Hays RD Burnam MA Rogers W Daniels M Berry S Greenfield S Ware J The functioning and well-being of depressed patients. Results from the Medical Outcomes Study JAMA 1989 262 914 919 2754791 10.1001/jama.262.7.914 Wells KB Burnam MA Rogers W Hays R Camp P The course of depression in adult outpatients. 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==== Front Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-4-11564932910.1186/1476-072X-4-1ResearchGeographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors Klassen Ann Carroll [email protected] Martin [email protected] Frank [email protected] Faculty of Social and Behavioral Sciences, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, Room 745, Baltimore, Maryland 21205, USA2 Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 113 Brookline Avenue, 6th Floor, Boston, Massachusetts, 02215, USA3 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA2005 13 1 2005 4 1 1 16 12 2004 13 1 2005 Copyright © 2005 Klassen et al; licensee BioMed Central Ltd.2005Klassen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Spatial variation in patterns of disease outcomes is often explored with techniques such as cluster detection analysis. In other types of investigations, geographically varying individual or community level characteristics are often used as independent predictors in statistical models which also attempt to explain variation in disease outcomes. However, there is a lack of research which combines geographically referenced exploratory analysis with multilevel models. We used a spatial scan statistic approach, in combination with predicted block group-level disease patterns from multilevel models, to examine geographic variation in prostate cancer grade and stage at diagnosis. Results We examined data from 20928 Maryland men with incident prostate cancer reported to the Maryland Cancer Registry during 1992–1997. Initial cluster detection analyses, prior to adjustment, indicated that there were four statistically significant clusters of high and low rates of each outcome (later stage at diagnosis and higher histologic grade of tumor) for prostate cancer cases in Maryland during 1992–1997. After adjustment for individual case attributes, including age, race, year of diagnosis, patterns of clusters changed for both outcomes. Additional adjustment for Census block group and county-level socioeconomic measures changed the cluster patterns further. Conclusions These findings provide evidence that, in locations where adjustment changed patterns of clusters, the adjustment factors may be contributing causes of the original clusters. In addition, clusters identified after adjusting for individual and area-level predictors indicate area of unexplained variation, and merit further small-area investigations. ==== Body Background Ideally, contextual analysis allows for consideration of both attributes that are generalizable across multiple settings, and geographically referenced relationships – influences that occur in context with each other. However, a tension exists between geographic variation analysis, which identifies the location and nature of the variation, and non-spatial analysis, which may identify characteristics of environments or individuals associated with variation, but does so without spatially specific models. Spatial variation in disease characteristics occurs, and multiple statistical methods have been developed to determine whether patterns of variation occur by chance alone, or whether variation is unlikely to have happened at random[1]. One type of variation analysis is cluster detection analysis, which specifically examines geographic clustering – spatial groups of outcomes that are statistically unlikely to occur by chance alone, given the overall distribution of the outcome of interest across the entire space being examined. Examples might be the occurrence of the disease itself [2,3], or distributions of factors of interest, such as characteristics of the disease, intermediate events such as extent of the disease at time of diagnosis [4] and receipt of certain treatments [5], or outcomes such as mortality related to the disease [6]. However, if clustering of an outcome is identified and determined to occur non-randomly, there is still little information on which to act, because the reasons for these clusters remain hidden. Conversely, conventional non-spatial analysis methods may be used to identify important influences on individual or area-level disease variation. For example, hierarchical or multilevel regression can be used to simultaneously examine individual and area-level characteristics which are associated with variation in disease incidence, characteristics, or outcome [7]. However, these methods usually consider areas as discrete, even when they are contiguous, without examining relationships between the largest units of analysis. If areas in analyses are geographically related, after building multilevel models, it is still necessary to examine the data for spatial dependence, and to determine whether the model fully accounts for geographic patterns, or whether there is remaining unexplained variation that is spatially dependent – including, but not limited to, geographic clustering. The study of disease patterns in prostate cancer, for example, can be informed by geographic analyses. Prostate cancer is a disease with strong geographic variation, both internationally and also within individual countries or regions [8]. Like most cancers, the development of prostate cancer typically occurs over a long period of time. Both age of onset and disease course vary enormously, but it has been demonstrated through autopsy study that most men will develop some degree of prostate cell abnormality in older age. It is likely that many factors contribute to its development; from inherited genetic risk, to lifestyle patterns in diet, use of substances such as tobacco and alcohol, exercise and body size and composition, to environmental exposures to a range of protective and detrimental agents [9]. Furthermore, although much is still unknown about prostate cancer etiology and development, there is sufficient information to argue that prostate cancer is most likely caused by a complex combination of factors, rather than a single explanatory risk. Beyond simple incidence, outcomes such as stage at diagnosis, tumor biology and histologic grade, receipt of standard-of-care treatment, and high quality survivorship are also geographically patterned. When considering the utility of a geographic approach to prostate cancer influences, it may be useful to think of three broad categories of factors. There are factors which may be, at first consideration, purely non-geographic in influence. An example of this might be the influence of the biological characteristics of the cancer on the disease course, such as the relationship between histologic grade of tumor on the stage or extent of disease at diagnosis [10]. This relationship is considered important and tumor characteristics such as grade are almost always included when modeling outcomes. Yet we can consider this influence to be relatively non-geographic, because we might speculate that this relationship does not change under local geographic influences. Other factors, such as age, might be considered to be pseudo-geographic in influence. The age distribution of the male population would vary across almost any geographic area under consideration, and there is also a strong age-disease relationship in prostate cancer, with the risk of the disease increasing with age. However, the age-disease relationship is not likely to be primarily driven by geography. Adjusting for the distribution of age within a population of interest is often desirable, in order to remove the confounding caused by age, and simulate the geographic variation we would expect to see if we had populations with identical age distributions. A third and more complex category of influences are those for which geographic context is critical to their causal pathway, and thus these variables may be only partially understood outside of their geography. Examples might be individual social or behavioral characteristics such as ethnicity or race, income, insurance or education, occupation, diet or body size. For example, the consistently greater risk for prostate cancer among men of African ancestry compared to all other ethnic groups in the world suggests fundamental biologic causes that supersede geographic influences. However, substantial geographic variation within the US African-American population, as well as international variation between African, Afro-Caribbean, and US men of African ancestry suggests complex multigenerational social and geographic influences [11]. Even influences that we may confidently classify as so fundamental as to be geographically immutable, such as the relationship between tumor biology and disease progression, could be influenced by geographic variation in access to care or medical practices, dietary, occupational, or environmental agents, or individual variation in behaviors such as tobacco use, exercise, or body size. Therefore, the extent to which any factor's influence on a cancer outcome varies by context or location offers tremendous insight into the mechanisms of influence. The purpose of this research was to combine cluster detection analysis techniques with multilevel modeling of area-level influences on disease patterns, in order to examine the relationship between social-environmental influences and spatial patterning. We used data from the Maryland Cancer Registry on incident cases of prostate cancer occurring in Maryland from 1992 to 1997, and examined variation in two disease characteristics which contribute significantly to overall disease burden: histologic grade of tumor, and stage of disease at time of diagnosis. The use of geographic analysis of prostate cancer outcomes of interest, in combination with modeling of known risk factors, may prove useful in understanding how much of the strong geographic patterns in prostate cancer can be explained by individual and area-level influences, and how much remains, as of yet, unexplained. For each of our two outcomes of interest, higher tumor grade and later stage of disease at diagnosis, we first modeled the "crude" or unadjusted variation in these outcomes across the entire State. This was done by calculating a block group-specific expected rate of each outcome, based simply on the number of cases within the block group and the overall rate of the outcome across the State, and comparing the ratio of observed to expected cases with the given outcome at the block group level. We then used estimates from multivariate models to refine our estimates of the expected number of higher grade or later stage cases, and recalculated, at the blockgroup level, the ratio of observed to expected cases with the outcome of interest. Throughout each set of three analyses, the observed number of cases remained the same, and the expected number (the denominator) varied with each adjustment. Therefore, if an independent variable in a regression model was positively associated with excess risk for the outcome of interest, it increased the regression-estimated expected number of such cases, and thus decreased the observed-to-expected ratio in areas where it was observed. Factors which were negatively associated with risk for the outcome, when adjusted for, reduced the number of such cases expected, and, in turn, increased the observed-to-expected ratio. The methods used are explained in greater detail in the methods section. Results Table 1 describes the overall population of prostate cancer incident cases reported to the Maryland Cancer Registry during 1992–1997, as well as the population used for each analysis. Cases ranged in age from 16 to 106, with a median age of 69. Among cases retained for analysis, 26% were African-American. Overall, in Maryland during the time period 1992–1997, 23% of cases whose record contained histologic grade information had a tumor grade of 3 (poorly differentiated) or 4 (non-differentiated), and 21% staged cases had their disease detected after it had spread outside the prostate gland (stage 2 through 7). Table 1 Characteristics of prostate cancer cases in Maryland, 1992–1997 Registry Population N = 23993 Stage Analysis N = 19223 Grade Analysis N = 18947 Age Group n % n % n % 16–49 403 2 352 2 325 2 50–69 11777 49 10228 53 9868 52 70–79 8739 36 6833 36 6853 36 80–106 3002 13 1810 9 1901 10 Missing 72 1 0 0 0 0 Race/Ethnicity White 16565 69 14255 74 14114 74 Black 5779 24 4968 26 4833 26 Asian 11 1 0 0 0 0 Native American 12 1 0 0 0 0 Other, Not Specified 343 1 0 0 0 0 Missing 1283 5 0 0 0 0 SEER Summary Stage at Diagnosis 0 80 1 0 0 0 0 1 15679 65 15233 79 13798 73 2 2250 9 2190 11 2000 10 3 263 1 255 1 220 1 4 170 1 165 1 152 1 5 150 1 145 1 127 1 7 1274 5 1235 7 945 5 Missing 4127 17 0 0 1705 9 Grade at Diagnosis 1 2505 10 2042 10 2289 12 2 13112 55 11301 59 12335 65 3 4425 18 3786 20 4199 22 4 128 1 113 1 124 1 Missing 3823 16 1981 10 0 0 Figure 1 and table 2 provide information on the four-item county-level social resource index used in the multilevel analysis. The six suburban counties surrounding Washington, D.C. have the highest scores on this index, with Baltimore City and the rural areas of western Maryland and the Eastern Shore of the Chesapeake Bay region having the lowest scores. Both low and high scoring counties contribute substantial numbers of African-American cases to the analysis. Figure 1 Maryland counties, ranked by county resource index score. Maryland's 24 counties ranked from lowest (Baltimore City) to highest (Howard County), based on combined score on four 1990 US Census population characteristics (table 2). Table 2 County resource index score and subcomponents, Maryland 1990 Census County County Resource Index Score1 Index Subcomponents: 1990 Census # Cases % Cases Who Are Black % High School Graduate2 % Employed3 % Moved in last 5 years4 Median Household Income ($1000)5 1. Balto. City -1.58 61 91 42 24 3645 61 2. Garrett -1.57 68 93 34 23 109 1 3. Somerset -1.54 61 92 42 23 100 33 4. Allegany -1.48 71 92 36 22 480 1 5. Dorchester -1.41 65 94 38 25 202 33 6. Caroline -1.01 67 96 42 28 161 24 7. Washington -.72 69 96 45 30 517 3 8. Kent -.68 71 97 43 30 104 32 9. Worcester -.57 71 95 50 28 247 21 10. Wicomico -.47 72 95 51 29 300 25 11. Cecil -.37 72 95 45 36 256 7 12. Talbot -.34 77 98 44 32 286 15 13. Queen Anne's -.07 77 96 45 39 199 18 14. Balto. Co. -.06 78 96 43 39 3890 11 15. St. Mary's .11 77 96 51 37 202 21 16. Carroll .11 79 97 43 42 605 4 17. Frederick .36 80 97 49 41 497 7 18. Harford .38 82 97 49 42 760 9 19. Calvert .45 79 97 47 48 197 20 20. Anne Arundel .52 81 97 49 45 1671 13 21. Prince Geo .55 83 96 51 43 2457 52 22. Charles .55 81 97 49 46 350 33 23. Montgomery 1.41 91 97 53 54 3077 11 24. Howard 1.59 91 98 57 54 618 19 1. County resource index scores were calculated by summing the raw score of four measures (percent high school graduates, percent employed, percent moved in last 5 years, and median household income), subtracting the mean of the raw composite scores, and dividing by the standard deviation of the raw composite scores. 2. Percent of persons 25 years or older who have received a high school diploma or its equivalent (e.g. GED) or higher (e.g. college or professional school). 3. Percent of persons 16 years old and over in the labor force who are currently employed. 4. Percent of residents age 5 and older who were not living in the same dwelling five years ago.5. Summed incomes (in thousands of dollars) of household members 15 years of age and older. Cluster detection results – higher grade of tumor Figures 2, 3, and 4, and the related table 3, show the block group-level patterns of tumor histology across the State. Of the 3670 Maryland 1990 Census block groups, 3313 (90%) contained cases used in this analysis; the number of cases per block group ranged from 1 to 99 with a median of 4. Figure 2 Observed vs. expected block group rates of high grade tumors, and significant clusters. Proportion of prostate cancer cases with histologic grade of 3 or 4, compared to proportion expected based on overall Maryland rate, Maryland Cancer Registry, 1992–1997, N = 18949. A spatial scan statistic was used to identify non-overlapping clusters of statistically significant high or low rates (table 3). Figure 3 Observed vs. expected block group rates of high grade tumors, adjusted for case characteristics, and significant clusters. Proportion of prostate cancer cases with histologic grade of 3 or 4, compared to proportion expected based on case characteristics of age, race and year of diagnosis. Figure 4 Observed vs. expected block group rates of high grade tumors, adjusted for case and area-level characteristics, and significant clusters. Proportion of prostate cancer cases with histologic grade of 3 or 4, compared to proportion expected based on case characteristics of age, race and year of diagnosis, and area-level Census characteristics. Table 3 Cluster Analysis of Higher Grade* Prostate Cancer Cases – Maryland Cancer Registry, 1992–1997 Radius (km) # Block groups in Cluster # Higher Grade Cases Expected # Higher Grade Cases Observed Relative Risk P Value Map 2. Unadjusted Analysis Cluster 1 5.99 550 522.5 669 1.28 .001 Cluster 2 44.93 201 305.3 210 0.69 .001 Cluster 3 10.34 173 253.9 176 0.69 .004 Cluster 4 5.93 38 93.8 49 0.52 .017 Map 3. Adjusted Analysis Cluster 1 14.81 292 487.3 362 0.74 .001 Cluster 2 54.65 162 247.6 164 0.66 .003 Cluster 3 8.27 80 99.6 156 1.56 .013 Map 4. Adjusted Analysis * Cluster 1 6.02 554 444.0 643 1.45 .001 Cluster 2 48.62 1181 1825.4 1587 0.87 .001 Cluster 3 30.88 99 155.8 94 0.60 .004 * Among cases with a histologic grade, those cases graded as 3 or 4 vs. 1 or 2. Expected Rate Adjusted for Race, Age, Year of Diagnosis. * Expected Rate Adjusted for Age, Race, Year of Diagnosis, and Area-Level Census Characteristics. Figure 2 shows that most block groups vary from the expected proportion of high grade cases (23%); block groups with lower proportions of high grade cases are displayed in blue, and those with greater than expected rates of high grade cases are shown in red. (Block groups contributing no cases to the analysis are identified in white on the maps.) Much of this variation is random, and not statistically different than we would expect by chance. Furthermore, because a block group's spatial size is inversely proportional to population density, large individual block group areas of deep color, although striking to the eye, are unlikely to include a substantial proportion of the case population, and therefore would not constitute a statistically significant area of variation on their own. However, figure 2 identifies four non-overlapping clusters with statistically significant (p < .05) higher or lower rates of aggressive grade. The most likely cluster is a geographically small densely populated area in Baltimore City, with a relative risk (RR) of 1.28 (p = .001). The second most likely non-overlapping cluster is a large area in the center of the Eastern Shore of the Chesapeake Bay, with a significantly lower rate of high grade tumors in men with prostate cancer (RR = 0.69, p = .001). Two small areas of lower rates in the suburban areas outside of Baltimore City were identified, one to the north of the City (RR = 0.69, p = .004) and one to the southwest (RR = 0.52, p = .017). Figure 3 shows that adjustment for individual case characteristics (older age, black race, and earlier year of diagnosis) changes the number and location of statistically significant clusters of high and low rates of aggressive grade. The most likely cluster is an area of lower risk for aggressive grade located between Baltimore City and Washington DC (RR = 0.74, p = .001); this area overlaps with the area contained in cluster 3 in figure 2. Similarly, a large area of the Eastern Shore is again identified as the second most likely cluster with a lower relative risk for higher grade tumors (RR = 0.66, p = .003). There are no longer significant non-overlapping clusters in Baltimore City or northwestern Baltimore County. However, a previously non-significant area of excess risk in Anne Arundel County is now identified, based on the number of cases expected from individual case risk characteristics, as having statistically significant excess risk (RR = 1.56, p = .013). This cluster was identified but not reported due to borderline statistical significance (p = .09) in the unadjusted analysis (figure 2). Figure 4 shows results of a cluster detection analysis comparing the observed and expected numbers of cases of high grade tumor in each block group, based on individual case characteristics, and also block group and county-level population characteristics (block group median household income, as well as the composite index of county-level high school attainment, employment, income, and residential mobility). Adjusting for these area-level social influences changes both the number and location of block groups found to have higher or lower rates than expected by chance. The most likely cluster in this analysis is an area of higher than expected rates of aggressive tumor among cases, located to the west of Baltimore City (RR = 1.45, p = .001). This small area was previously identified as the most likely cluster in figure 2, but with a lower relative risk, and was not identified as having higher rates than expected in the analysis adjusting for individual characteristics (figure 3). The second most likely cluster in this analysis is a large area of lower than expected rates (RR = 0.87, p = .001), located in several counties to the north and west of Washington DC. This area includes small clusters 3 from figure 2 and cluster 1 from figure 3, but the majority of block groups in this cluster were not previously included in the clusters found in the previous analyses. The third most likely cluster in this analysis is located on the Eastern Shore, and although it includes areas identified in the two previous clusters detected on the Eastern Shore, it is both smaller in area and has lower estimate of relative risk for aggressive disease among cases in this area (RR = 0.60, p = .004). Cluster detection results – stage at diagnosis Figures 5, 6, and 7, and the related table 4, display results of the cluster detection analysis for later stage diagnosis. Cases were located in 90% (3313/3670) of Maryland's 1990 Census block groups; cases per block group ranged from 1 to 90 with a median of 4. Figure 5 Observed vs. expected block group rates of later stage at diagnosis, and significant clusters. Proportion of prostate cancer cases with stage of disease at diagnosis of 2 to 7, compared to proportion expected based on overall Maryland rate, Maryland Cancer Registry, 1992–1997, N = 19223. A spatial scan statistic was used to identify non-overlapping clusters of statistically significant high or low rates (table 4). Figure 6 Observed vs. expected block group rates of later stage at diagnosis, adjusted for case characteristics, and significant clusters. Proportion of prostate cancer cases with stage of disease at diagnosis of 2 to 7, compared to proportion expected based on case characteristics of age, race, tumor grade, and year of diagnosis. Figure 7 Observed vs. expected block group rates of later stage at diagnosis, adjusted for case and area-level characteristics, and significant clusters. Proportion of prostate cancer cases with stage of disease at diagnosis of 2 to 7, compared to proportion expected based case characteristics of age, race, tumor grade, and year of diagnosis, and area-level Census characteristics. Table 4 Cluster Analysis of Later Stage* Prostate Cancer Cases – Maryland Cancer Registry, 1992–1997 Radius (km) # Block groups in Cluster # Later Stage Cases Expected # Later Stage Cases Observed Relative Risk P Value Map 5. Unadjusted Analysis Cluster 1 85.32 1436 1481.0 1743 1.18 .001 Cluster 2 20.72 88 93.8 182 1.94 .001 Cluster 3 41.92 291 512.7 366 0.71 .001 Cluster 4 10.61 316 455.8 325 0.71 .001 Map 6. Adjusted Analysis Cluster 1 24.71 96 98.3 191 1.94 .001 Cluster 2 69.96 326 372.5 533 1.43 .001 Cluster 3 39.82 1208 1633.4 1398 0.86 .001 Cluster 4 4.12 286 248.4 329 1.32 .029 Map 7. Adjusted Analysis * Cluster 1 20.65 95 104.9 188 1.79 .001 Cluster 2 69.96 326 394.8 533 1.35 .001 Cluster 3 47.90 676 1014.0 831 0.82 .001 * Among cases receiving staging, those cases diagnosed at Stages 2–7 vs. Stage 1. Expected Rate Adjusted for Race, Age, Grade, and Year of Diagnosis. * Expected Rate Adjusted for Age, Race, Grade, Year of Diagnosis, and Area-Level Census Characteristics. Statistically significant clusters of high or low rates were identified in four geographic areas in the unadjusted analysis (figure 5). As described in detail in table 4, the most likely cluster is the largest, covering most of the Eastern Shore and some of the adjacent Western Shore of the Chesapeake Bay region of Maryland, with cases in this area have a modestly elevated relative risk of later stage diagnosis (RR) = 1.12, p = .001). A smaller geographic area in the western area of the State was identified as the second most likely cluster, with a relative risk of 1.94 (p = .001). Two relatively affluent areas of the State were identified has having lower probability of later stage diagnosis: Montgomery County, a suburb of Washington D.C. (RR = 0.71, p = .001), and the suburban and rural areas to the north and west of Baltimore City (RR = 0.71, p = .001). Figure 6 shows that, after adjusting for individual case attributes associated with late stage (black race, younger age, aggressive or missing tumor grade, and earlier year of diagnosis), the relationship between the observed number of later stage cases and the expected number changes in many block groups across the State. Although the visual pattern remains similar, the location and size of statistically significant clusters, as well as the relative risk of late stage diagnosis within those clusters, changes. The most likely cluster is now in western Maryland, with a relative risk which is essentially unchanged by adjustment for individual case characteristics (RR = 1.94, p = .001). The largest cluster has now been reduced in size and includes primarily the lower Eastern Shore, but the estimate of relative risk for later stage diagnosis in this area has increased (RR = 1.43, p = .001). The area of lower risk for cases in suburban Washington DC has grown to include much of the suburban area between Washington and Baltimore (RR = 0.86, p = .001), and a new area, centered in Baltimore City, has been identified as having greater risk for later stage diagnosis (RR = 1.32, p = .029). Figure 7 displays results of a cluster detection analysis for later stage diagnosis, comparing actual counts to those expected when considering both individual men's age, race, year of diagnosis, and tumor biology, as well as their immediate neighborhood and county level of social resources – including occupation, education, employment, poverty and residential mobility. These additional adjustments change both the visual patterning of higher and lower rates, as well the location and estimates of relative risk for the statistically significant clusters identified. Two clusters of higher than expected rates of later stage diagnosis remain, covering essentially the same areas as in figure 6. The relative risk for later stage diagnosis in western Maryland has been reduced only slightly, from 1.94 to 1.79 (p = .001), and the relative risk for the secondary cluster on the Eastern Shore has been reduced from 1.43 to 1.35 (p = .001). Both the Baltimore City cluster and the suburban Washington DC clusters seen in the first two maps are no longer identified as statistically significant. However, a large area in the north central part of the State has been identified as having lower than expected rates of later stage diagnosis, with a relative risk of 0.82 (p = .001). Discussion These geographic analyses provide information on both biological influences on cancer, as well as those more closely influenced by patterns of medical care. For tumor biology, the results of the unadjusted analysis suggest that one primarily rural area of the State, as well as two affluent suburban areas, appear to offer protection from high grade tumor histology. On the other hand, the urban Baltimore area has higher than expected rates of high grade tumors among men diagnosed in the time period 1992–1997. Individual case characteristics change this picture dramatically, but do not "explain away" all variation in this important disease characteristic. For example, black race is an important risk factor for aggressive tumor biology; therefore, it is reasonable to speculate that area differences in the proportion of African-American men in the case population may have accounted for some of the clustering in figure 2, with clusters in primarily white northern Baltimore County and primarily black Baltimore City no longer statistically significant with race adjustment. Figure 3 shows that, despite individual case differences accounted for with adjustment, there are still three areas of the State with unusually high or low rates of aggressive disease. Figure 4 shows some impact of further adjustment for social resource composition within small areas (block groups) and larger areas (counties). The interpretation of this adjustment is more speculative than confirmatory, but suggests some avenues for further research. Large areas of the rural Eastern Shore of Maryland are no longer identified as being contained inside non-overlapping areas of statistically significant lower risk for aggressive tumor biology, with the protected area being narrowed from a radius of 54.65 kilometers to 30.88 kilometers. Conversely, the small protective area in affluent Howard County between Washington and Baltimore has now grown from a radius of 14.81 kilometers in figure 3 to 48.62 kilometers in figure 4. Anne Arundel County is no longer at excess risk but Baltimore City is. The influence of area level social resources on high grade of tumor was complex: the men with the lowest risk for aggressive tumors were white men living in small areas of greater income, nested within counties of overall low social resources. Therefore, clusters remaining in figure 4 are those whose rates are either higher or lower than expected given their social characteristics. The cluster in Baltimore City reflects the fact that Baltimore City does not fit the overall model of low resource counties as protective. Baltimore City is the single urban county in the lowest range of the index; the rest of the lower resource counties are predominantly rural. Therefore, moving from figure 3 (only individual adjustment) to figure 4 (area-level adjustment) identified that Baltimore City's low social resources are not protective, to same effect as in rural counties. This difference may be caused by any number of lifestyle differences between urban and rural low income communities. Although individual case race is not likely to be driving this difference, it may be that area-level racial composition is another piece of this puzzle, given that Baltimore City's racial composition differs so dramatically from the other low resource counties. Conversely, the protective clusters are found in counties with high social resource index scores, centered in Montgomery County in the Washington, D. C. suburbs, and in an area with slightly low scores, Talbot and Queen Anne's counties on the Eastern Shore. For the D.C. suburbs, their rate in figure 2 is neither high nor low; however, their high social resource index score would predict high rates; therefore they create a lower- than-expected cluster. For the Eastern Shore, the lower rate of aggressive disease has been consistent across all three cluster analyses. For low social resource counties such as Dorchester, the adjusted predicted rate in figure 4 is now consistent with expected low rates, and therefore this area is no longer part of a cluster. However, the rate is lower than expected in counties with slightly higher resources, and therefore the most affluent Eastern Shore counties (Talbot and Queen Anne's) continue to be identified as lower than expected. Finally, Anne Arundel County, which had higher than expected rates in the individually adjusted analysis, is now no different than expected, arguing that the relatively high social resource index score for this county led to a closer approximation of expected proportion of cases with aggressive disease. When considering the geographic patterning of later stage at diagnosis for men with prostate cancer in Maryland during the time period 1992 to 1997, it appears from the unadjusted analysis that men in certain rural areas were much more likely to come into treatment with more advanced disease than those in the suburban, more affluent areas of the State. Individual characteristics of the patients appear in some ways to have masked these geographic differences, in that the clusters generally remain or become more important once the case population mix of characteristics such as age, race, tumor biology, and year of diagnosis is taken into account (figure 5 versus figure 6). Additionally, an area of Baltimore City, which has a greater proportion of young, African American men than the rest of the State, became significantly more likely to have later stage cases, after adjustment for age and race. This suggests that men in Baltimore are specifically disadvantaged in terms of early detection of disease, beyond what would be predicted by their age, race, or tumor biology. From figure 7, we see evidence that area-level socioeconomic resources may contribute to these patterns. The relative risk for late stage diagnosis in the two rural clusters has been reduced somewhat, and the cluster of lower risk in the north-central part of the State is less different than in the unadjusted map. Baltimore City and the Washington suburbs no longer differ significantly from the rest of the State, supporting a socioeconomic influence on the previous clusters. Prostate-specific antigen (PSA) testing was widely available in Maryland during the entire time period of this study (1992–1997). This suggests that more global barriers to health care, rather than differential access to this specific diagnostic tool, were more important in creating these patterns of late stage diagnosis. Conclusions Although there is no statistical test to evaluate the proportion of variation explained in a multilevel model, because of the inclusion of random effects, it is reasonable to state that, overall, our adjustment methods did account for substantial variation in rates of aggressive disease and late stage diagnosis, by considering important influences – characteristics of the men themselves, as well as characteristics of their environments. In spite of this, variation in these cancer characteristics remained substantial across the State. In fact, whether the measure used is the number of cases, number of block groups, or geographic area, figures 4 and 7 identify as much, if not more, deviation from the predicted pattern than the unadjusted figures 2 and 5 respectively. Many areas are included in clusters across all three analyses of a specific outcome. Given that the exact boundaries of clusters are always approximate, and would be expected to vary from analysis to analysis, it is important to note similarities as well as differences within each outcome-specific set of maps. This suggests that there are underlying causal influences that remain, despite the important relationships with the measures used for adjustment. An additional caveat in the interpretation of these cluster detection analyses is related to the choice of criteria used for reporting clusters. In consideration of the large amount of data being examined, both in terms of geographic area and number of cases, we chose to report clusters only if they had a statistical significance of p < .05, and contained no geographic overlap with a more significant cluster. These restrictions meant that a given geographic area could possibly be described as part of a cluster of excess or reduced risk in one analysis, and not in other, based on small changes in expected number of cases, or based on the identification of a more significant cluster nearby. These findings have implications on both a practical cancer control level, as well as for further research in prostate cancer. For state and local health agencies, trends in area-level patterns of cancer outcomes over time can be used to monitor change, whether to evaluate the effectiveness of geographically distributed interventions such as screening or treatment programs, or identify population changes which may increase need for services. Unadjusted cluster analyses provide valuable information for cancer control planners who need to address areas of greatest need, regardless of the cause. However, adjusted analyses identify geographically unique situations, such as the persistently elevated rates of later stage diagnosis in two rural areas of Maryland. For researchers, analytic techniques which identify both explained and unexplained geographic variation may provide information about the multilevel synergistic factors influencing cancer patterns, or, at a minimum, identify areas and populations meriting further study. Methods Data and data sources More detailed information on these data and methods has been reported previously [10]. With IRB approval from the Johns Hopkins School of Public Health and the Maryland Department of Health and Mental Hygiene, and a data agreement between the two institutions, we obtained all incident cases of prostate cancer reported to the Maryland Cancer Registry during the years 1992–1997 (n = 24,189). Based on case residence address, we geocoded cases to latitude and longitude coordinates. For cases unable to be geocoded, we assigned cases to a coordinate location within their zipcode using a weighted imputation algorithm, based on 1990 US Census race-, age-, and gender-specific population distributions within their zipcode [12]. We thus assigned each nongeocoded case to a Census block centroid, based on the best-known distribution of men like himself within his zipcode. Of 24,189 cases, 23,993 had verifiable Maryland addresses. Ninety-one percent (21,904) were successfully geocoded, and nine percent (2,089) were assigned to an imputed location within their zipcode by algorithm. An additional 3063 cases were not used, due to missing demographic or clinical data, or because their race was neither African-American or white, leaving a final analysis population of 20,928. We used individual case characteristics from the Registry record, including age at diagnosis, race, year of diagnosis, tumor stage, and tumor histologic grade. Based on case residence, we added to each case record selected 1990 US Census characteristics of three nested geographic units surrounding the case location – the Census block group, Census tract, and county. Our record for each case therefore contained individual demographic and clinical characteristics based on the Cancer Registry data, point location of residence, and Census measures for the case's block group, tract and county of residence. We created seventeen possible area-level social indicators for each block group, tract, and county from the 1990 US Census STF-3 file [13]: three measures of housing resources (median sale price, percent of owner occupied units, and housing value percentile rank, based on both rental and sale values, weighted by the proportions of rental and owner-occupied housing in an area), three income measures (median household income, median family income, and median per capita income), four population composition measures (percent white, percent black, percent born outside the US, and percent age 5 or older not living in the same residence for at least five years), two social class measures (percent high school graduates among persons age 25 and older, and percent employed in white collar jobs, defined as Census job classifications of managerial, professional, technical, sales, and administrative support) and five material deprivation measures (percent households without a car, percent households without a telephone, percent persons 16 and older who were unemployed, percent persons living in "crowded" residences, defined as more than one person per room, and percent of persons living in poverty). For continuous variables (case age and census measures), we compute standardized measures to reduce collinearity, by centering each case value at the population mean, and dividing by the standard deviation. For year of diagnosis, we centered the values at 1994, a midvalue in our six year time window. We chose two outcomes of interest which are associated with differences in prostate cancer disease severity and longterm survival for patients – histologic grade, or degree of cell differentiation, of the tumor, and stage, or extent, of disease at time of diagnosis. For each outcome, we dichotomized the data and examined the likelihood of the more negative outcome. For tumor grade, we compared tumors staged as 3 or 4 (poorly differentiated or undifferentiated) to those graded as 1 or 2 (well or moderately well differentiated). For stage at diagnosis, we used the Surveillance, Epidemiology and End Stage (SEER) summary stage [14], and compared cases diagnosed at stages 2 through 7 (regional to distant metastases) to those diagnosed at stage 1 (localized disease). Cases only missing staging information were retained for analyses of grade, and cases only missing grade were used in the analysis of stage at diagnosis. Because tumor histology is an important predictor of rapidly spreading disease, tumor grade was used as an independent predictor in modeling late stage disease. Cluster detection methods In order to explore the geographic patterns of our two outcomes of interest, we used the spatial scan statistic [15] to detect and evaluate the statistical significance of any geographic clusters of each outcome. This method imposes a very large number of overlapping circles of different location and size on the map, each of which is a potential cluster, and adjusts for the multiple testing inherent in the many circles considered. Our cluster detection method identified clusters of both high and low rates, with a maximum scanning window size to include up to 50% of the population at risk. Secondary clusters were reported if they had no geographic overlap with more likely clusters. P-values were derived from 999 simulated Monte Carlo replications under the null hypothesis of spatial randomness of outcomes of interest. We conducted three separate cluster detection analyses for each of the two prostate cancer outcomes: higher histologic grade of tumor, and later stage at diagnosis. In the unadjusted analysis, under the null hypothesis, the expected number of more aggressive grade or late stage cases in a block group was calculated by multiplying the total case population of the block group by the statewide rate of the outcome of interest. Thus, in the unadjusted analysis, a block group would be expected to have the same rate or proportion of late stage or high grade cases in its case population as the State. In the two adjusted analyses, the expected number of aggressive grade or later stage cases was calculated from a regression model containing individual case characteristics, or from a regression model with both individual and area-level covariates. Based on the expected counts, the number of aggressive grade and later stage cases in each block group was modeled as a Poisson distribution. For the unadjusted analyses, we also used a Bernoulli model to compare the distribution of so-called "cases" (those with aggressive grade or late stage) to "controls" (less aggressive grade or early stage) based on point location of each residential address, rather than rates within block groups. This was useful to compare the sensitivity of the Poisson model assumption for aggregated data to that of the unaggregated Bernoulli method. No major differences in results were found, and to allow proper comparison between the adjusted and unadjusted analyses, the Poisson model results are presented for all three types of analyses. For each cluster identified, we list the radius, number of block groups in the cluster, the observed versus expected number of late stage or aggressive grade cases, the relative risk and the p value. The relative risk is the risk of the respective outcome within the cluster, compared to the population's risk. We report clusters with statistical significance p < .05 that do not overlap with another reported cluster with a lower p-value. Calculations were done using the freely available SaTScan v4.0 software . Sources of expected population counts We used the results of two multivariate modeling methods to calculate the expected count of aggressive grade and late stage cases in each block group. In prior work [10] we built multivariate hierarchical logistic regression models [16,17] to identify individual and area-level factors significantly associated with aggressive grade and late stage among cases, and these findings, summarized below, served as the basis for calculating our expected population in each block group. In logistic regression models including only individual level predictors, our final model included the following statistically significant associations with higher histologic grade of tumor: older age (Odds Ratio (O.R.) 1.17, 95% Confidence Interval (C.I.) 1.13, 1.21), black race (O.R. 1.46, 95% C.I. 1.35, 1.57), more recent year of diagnosis (O.R. 0.92, 95% C.I. 0.90, 0.94), and an interaction between age and year of diagnosis (O.R. 1.06, 95% C.I. 1.04, 1.08). To build the multilevel models, we tested each of the 17 area-level indicators at each level, starting with block group, and also tested for interactions at each level and between levels. To avoid unstable models, when we found multiple significant Census predictors, we computed and tested simple indices by summing relevant Census measures. We also tested for random effects, to account for additional variability. In a multilevel logistic regression model of aggressive tumor grade, each of the above individual level variables remained significant. In addition, two area-level indicators were significant in the final model: block group median household income (O.R. 0.92, 95% C.I. 0.87, 0.96), with an interaction between black race and income (O.R. 1.12, 95% C.I. 1.02, 1.223), and a standardized county resource index, composed of four summed county-level measures: percent high school graduates, percent employed, percent moved within the past five years, and median household income (in $1000 units) (O.R. 1.23, 95% C.I. 1.16, 1.31). Random intercept terms were found to be significant at the block group and county level. In logistic regression models including only individual level predictors, our final model included the following statistically significant associations with late stage at diagnosis: older age (O.R. 0.85, 95% C.I. 0.82, 0.90), black race (O.R. 2.97, 95% C.I. 1.35, 1.59), higher tumor grade (O.R. 2.97, 95% C.I. 1.35, 1.59), missing tumor grade (O.R. 5.56, 95% C.I. 2.77, 3.17), more recent year of diagnosis (O.R. 0.83, 95% C.I. 0.78, 0.88) and interactions between age and black race (O.R. 1.18, 95% C.I. 1.09, 1.27), grade and year of diagnosis (O.R. 1.06, 95% C.I. 1.02, 1.10), and missing grade and year of diagnosis (O.R. 1.19, 95% C.I. 1.10, 1.30). In the multilevel logistic regression model of late stage at diagnosis, each of the above individual level variables remained significant. In addition, two area-level indicators were significant in the final model: block group percentage of white collar workers among the employed population (O.R. 0.93, 95% C.I. 0.89, 0.98), and the standardized county resource index (O.R. 0.94, 95% C.I. 0.89, 0.98). A statistically significant interaction existed between county resource score and older age (O.R. 0.95, C.I. 0.92, 0.99), and random intercept terms were found to be significant at the block group and county level. Calculation of block group-specific predicted populations The models described above were used to calculate an expected count of aggressive grade and late stage cases, respectively, for each block group. This was accomplished by taking the inverse logit transform of the expected linear predictor in each logistic regression model, yielding a set of estimated probabilities for each outcome. These probabilities were then aggregated to the block group level, providing expected block group-specific counts of later stage and aggressive grade cases. By definition the expected linear predictors includes only estimates from fixed effects in each multilevel logistic regression model. The random effects at the block group and county level, although influential on parameter estimation, were not included in these calculations. Compared to the unadjusted results, geographic patterns shown in the adjusted analyses could be interpreted as those existing after controlling for individual and area-level factors, respectively. In essence, this approach explores residual geographic variation. For this reason, information from the random effects is not included in determining expected outcome counts. Authors' contributions AK obtained funding and data for this research, and was PI on the project. She conceptualized and conducted the analysis, and drafted the manuscript. MK directed the development and interpretation of the spatial analysis. FC worked with AK to develop and interpret the multilevel models used, as well as the methodology for imputation. All authors participated in the preparation and approval of the final version of the manuscript. Acknowledgements This work was supported in part through a Cooperative Agreement between the Centers for Disease Control and Prevention(CDC) and the Association of Schools of Public Health (ASPH), and by the Maryland Cigarette Restitution Fund of the State of Maryland. Its contents are the responsibility of the authors, and do not necessarily reflect the official views of the CDC, ASPH, or the Maryland Department of Health and Mental Hygiene. We gratefully acknowledge the assistance and support of the Maryland Cancer Registry of the Maryland Department of Health and Mental Hygiene, and Stacey Neloms, MPH, Director. ==== Refs Lawson AB Statistical Methods in Spatial Epidemiology 2001 Chichester, John Wiley and Sons Sheehan JT DeChello LM Kulldorff M Gregorio DI Gershman S Mroszczyk M The Geographic Distribution of breast cancer incidence in Massachusetts 1988 to 1997, adjusted for covariates Int J Health Geogr 2004 3 17 15291960 10.1186/1476-072X-3-17 Gregorio DI Kulldorff M Sheehan TJ Samociuk H Geographic distribution of prostate cancer incidence in the era of PSA testing, Connecticut, 1984–1998 Urology 2004 63 78 82 14751353 10.1016/j.urology.2003.08.008 Gregorio DI Kulldorff M Barry L Samociuk H Geographic differences in invasive and in-situ breast cancer incidence according to precise geographic coordinates, Connecticut, 1991–1995 Int J Cancer 2002 100 194 8 12115569 10.1002/ijc.10431 Gregorio DI Kulldorff M Barry L Samocuik H Zarfos K Geographic differences in primary therapy for early-stage breast cancer Ann Surg Oncol 2001 8 844 849 11776501 10.1245/aso.2001.8.10.844 Jemal A Kulldorff M Devesa SS Hayes RB Fraumeni JF Jr A geographic analysis of prostate cancer mortality in the United States, 1970–1989 Int J Cancer 2002 101 168 74 12209994 10.1002/ijc.10594 Snijders TAB Bosker RJ Multilevel Analysis 1999 London, Sage Publications Parkin DM Whelan SL Ferlay J Teppo L Thomas DB Cancer Incidence in Five Continents Lyon, International Agency for Research on Cancer, IARC Scientific Publications No 155 2003 Signorello LB Adami H Adami H, Hunter H, Trichopoulos D Prostate Cancer Textbook of Cancer Epidemiology 2002 Oxford, Oxford University Press 400 428 Klassen AC Curriero FC Hong JH Williams C Kulldorff M Meissner HI Alberg A Ensminger ME The role of area-level influences on prostate cancer grade and stage at diagnosis Prev Med 2004 39 441 448 15313082 10.1016/j.ypmed.2004.04.031 Angwafo FF Migration and Prostate Cancer: An International Perspective J Natl Med Assoc 1998 90 S720 723 9828589 Geolytics Inc CensusCD Blocks 1998 Geolytics Inc Census CD + Maps 1998 Young JL JrRoffers SD Ries LAG Fritz AG Hurlbut AA (eds) SEER Summary Staging Manual-2000: Codes and Coding Instructions 2001 Bethesda, MD: National Cancer Institute, NIH Pub No. 01-4969 Kulldorff M A spatial scan statistic Communications in Statistics: Theory and Methods 1997 26 1481 1496 Rabe-Hesketh S Pickles A Skrondal A GLLAMM Manual Technical Report 2001 Department of Biostatistics and Computing, Institute of Psychiatry, King's College, University of London Stata Corporation Stata Statistical Software, Release 70 2001	15649329	PMC546220	CC BY	2021-01-04 16:39:06	no	Int J Health Geogr. 2005 Jan 13; 4:1	utf-8	Int J Health Geogr	2,005	10.1186/1476-072X-4-1	oa_comm
==== Front Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-4-21564710710.1186/1476-4598-4-2ResearchStable expression of constitutively-activated STAT3 in benign prostatic epithelial cells changes their phenotype to that resembling malignant cells Huang Hosea F [email protected] Thomas F [email protected] Ping [email protected] Arnold B [email protected] Beverly E [email protected] Division of Urology, Department of Surgery, UMDNJ-New Jersey Medical School,185 S. Orange, Ave., Newark NJ, 07103 USA2 Department of Microbiology & Molecular Genetics, UMDNJ-New Jersey Medical School, 185 S. Orange, Ave., Newark NJ, 07103 USA2005 12 1 2005 4 2 2 14 7 2004 12 1 2005 Copyright © 2005 Huang et al; licensee BioMed Central Ltd.2005Huang et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Signal transducers and activators of transcription (STATs) are involved in growth regulation of cells. They are usually activated by phosphorylation at specific tyrosine residues. In neoplastic cells, constitutive activation of STATs accompanies growth dysregulation and resistance to apoptosis through changes in gene expression, such as enhanced anti-apoptotic gene expression or reduced pro-apoptotic gene expression. Activated STAT3 is thought to play an important role in prostate cancer (PCA) progression. Because we are interested in how persistently-activated STAT3 changes the cellular phenotype to a malignant one in prostate cancer, we used expression vectors containing a gene for constitutively-activated STAT3, called S3c, into NRP-152 rat and BPH-1 human benign prostatic epithelial cells. Results We observed that prostatic cell lines stably expressing S3c required STAT3 expression for survival, because they became sensitive to antisense oligonucleotide for STAT3. However, S3c-transfected cells were not sensitive to the effects of JAK inhibitors, meaning that STAT3 was constitutively-activated in these transfected cell lines. NRP-152 prostatic epithelial cells lost the requirement for exogenous growth factors. Furthermore, we observed that NRP-152 expressing S3c had enhanced mRNA levels of retinoic acid receptor (RAR)-α, reduced mRNA levels of RAR-β and -γ, while BPH-1 cells transfected with S3c became insensitive to the effects of androgen, and also to the effects of a testosterone antagonist. Both S3c-transfected cell lines grew in soft agar after stable transfection with S3c, however neither S3c-transfected cell line was tumorigenic in severe-combined immunodeficient mice. Conclusions We conclude, based on our findings, that persistently-activated STAT3 is an important molecular marker of prostate cancer, which develops in formerly benign prostate cells and changes their phenotype to one more closely resembling transformed prostate cells. That the S3c-transfected cell lines require the continued expression of S3c demonstrates that a significant phenotypic change occurred in the cells. These conclusions are based on our data with respect to loss of growth factor requirement, loss of androgen response, gain of growth in soft agar, and changes in RAR subunit expression, all of which are consistent with a malignant phenotype in prostate cancer. However, an additional genetic change may be required for S3c-transfected prostate cells to become tumorigenic. ==== Body Introduction Signal transducers and activators of gene transcription (STATs) are, as their name suggests, proteins that regulate gene expression by affecting transcription. They are part of the signal transduction pathway used by many growth factors and cytokines, and are activated by phosphorylation of tyrosine and serine residues by up-stream kinases [1]. For example, signaling by IL-6 and other members of this cytokine family generally induces phosphorylation of STAT3 [1,2]. In the example given in Figure 1, IL-6-induced binding to its receptor leads to homodimerization of the receptor, which in turn leads to autophosphorylation of the cytosolic domain of gp130; this in turn causes the phosphorylation of one of 3 kinases, JAK1, JAK2, or Tyk 2. The activated up-stream kinase phosphorylates STAT3, which allows for dimerization of STAT3 although this concept is currently being revisited, since it has been shown in hepatic cells under inflammatory stress, there is evidence for STAT3 association on lipid rafts prior to phosphorylation [3,4] in association with chaperone proteins such as Hsp90 (reviewed in [5]); however only the dimer form of STAT3 can translocate and bind to DNA at specific binding sites, thereby directing transcription of target genes. In benign cells, the signaling by STAT3 is under tight regulation, so that the signal delivered to the cell is transient. However aberrant signaling by STAT3 has been noted in many types of malignancies, such as myeloma, head and neck cancer, breast cancer, and prostate cancer [6-9]. Such persistent signaling by IL-6 leading to aberrant activation of STAT3 is thought to play a role in neoplastic progression of prostate cells [10]. Importantly, we and others have shown that malignant prostate cells expressing persistently-activated STAT3 become dependent upon this transcription factor for survival, resulting in apoptosis [11-13]. Thus, persistently-activated STAT3 fulfills the criteria of a proto-oncogene [14,15]. Figure 1 An example of cytokine-mediated activation of STAT3. In this example, IL-6-induced binding to its receptor leads to homodimerization of the receptor, which in turn leads to autophosphorylation of the cytosolic domain of gp130; this in turn causes the phosphorylation of one of 3 kinases, JAK1, JAK2, or Tyk 2. The activated up-stream kinase phosphorylates STAT3, which allows for dimerization of STAT3; only the dimer can translocate and dock to DNA at target genes, thereby directing transcription. Prostate cancer (PCA) is the second most frequently diagnosed non-cutaneous malignancy in American males, affecting approximately 35% of them according to recent data [16,17]. This translates into approximately 35,000 deaths last year in the United States alone; 189,000 new cases were diagnosed in 2002 and over 220,000 cases were projected for 2003 [18,19]. Moreover, in a recent report the authors claimed that 30% of male mortality overall may be due to prostate cancer [20]. For the most effective therapy with the fewest side-effects, a thorough understanding of the genes involved in the neoplastic process is essential. Androgens are known to play a critical role in the tumorigenic process, with activity mediated by the androgen receptor. Initially, prostate cancers are androgen-sensitive (that is, they cease growing when deprived of androgens or when treated with androgen receptor antagonists, such as flutamide or bicalutamide), and therefore most patients respond to androgen ablation therapy. However, there are side-effects to this therapy that make it unpleasant for the patient [21]. Even with androgen ablation therapy, the disease often recurs and when it does, it usually becomes androgen-insensitive or hormone-refractory [22]. There is evidence that STAT3 activation via IL-6 plays a role in the conversion of normal prostate cells to prostate cancer cells, and from androgen-responsive to the androgen insensitive phenotype [10,23,24]. The progression to androgen-independence has been found to be associated with IL-6, with c-myc expression, and with insulin-like growth factors, all of which can signal through the activation of STAT3 [25-28]. STAT3 is negatively regulated by a retinoid-sensitive protein, GRIM-19, which may explain the positive effects retinoids show against prostate cancer cells in vitro [29-31]. Retinoid therapy for the treatment of prostate cancer is currently being tested, due to the ability of these compounds to rapidly induce apoptosis [32]. Indeed, the recent addition of Taxotere to the pharmacopeia for prostate cancer may well be due to its demonstrated effect on retinoid receptors [33]. The regulation of the expression of the 3 retinoid receptors type A (RAR-α, -β, and -γ) in the progession to prostate cancer has been partially addressed by Richter, et al., who showed the differential effects of all-trans retinoic acid in human prostate cancer lines [34,35] To this end we are studying the oncogenic role of STAT3 activation in rat prostate epithelial cell lines NRP-152 [36] and human benign prostatic hyperplasia line BPH-1 [37,38]. Our main hypothesis is that constitutively-activated STAT3 (cSTAT3) plays an essential role in the development of PCA and the maintenance of the malignant phenotype. Because prostate epithelial cells become hypertrophic, but rarely malignant, they are useful for studying the progression to neoplasia to see how a relatively transformation-resistant cell type becomes neoplastic through cSTAT3. We previously determined that STAT3 was constitutively phosphorylated (hence activated) in malignant NRP-154 but not in NRP-152 cells, even when the NRP-152 cells were treated with testosterone [10]. We hypothesized that cSTAT3 may account for the tumorigenicity of NRP-154 cells, and therefore may play a determining role in the progression from hyperplasia to neoplasia. To test our hypothesis, we transfected a plasmid containing a mutated gene for STAT3 known as S3c, in which a Cys residue was substituted for an Ala residue, thereby allowing the dimerization of the mutated STAT3, which can then translocate across the nuclear membrane and effect gene transcription in much the same way as the phosphorphylated wild-type STAT3 gene product [14,39] into NRP-152 and BPH-1 cells. We then examined the phenotype of the selected transfected cells after cloning by limit dilution. Our results, indicating that NRP-152 and BPH-1 cells underwent changes in phenotype consistent with that of malignant cells, are presented here. Results Selection of Transfected NRP-152 and BPH-1 Cells Two weeks after transfection with either pIRES or pIRES-S3c and selection with G418, no surviving cells were observed in the wells that received Clonfectin only. Growth of cells was observed in all wells that received either of the plasmids plus Clonfectin. Transfected cells were expanded for further analysis in complete medium. A summary of cells and clones and what their phenotypes were is given in Table 1. To summarize briefly, since the full results will be discussed in this section, we observed the following changes: Table 1 Summary of transfected cells Growth Factor G418 FLAG EGFP Growth In Cell Plasmid Dependence Sensitivity Epitope Expression Simple Medium NRP-152 none x x - - - 152-pIRES pIRES-EGFP x - - x - 152-S3c pIRES-S3c - - x x x BPH-1 none n/a x - - n/a BPH-pIRES pIRES-EGFP n/a - - x n/a BPH-S3c pIRES-S3c n/a - x x n/a The table summarizes the cells used or made in the experiments described. Cells were transfected with the indicated plasmid, as described in Materials & Methods. G418 was added at 400 μg/ml. Growth factors were those added to 152 medium, but not to 154 medium, as described in Materials and Methods, for NRP-152 cells and transfectants. X = presence of characteristic; --- = absence of characteristic. NRP-152 cells require a variety of growth factors and additives in their medium (see Materials and Methods Section; [36]); 152-pIRES cells (NRP-152 cells transfected with pIRES-EGFP) required the same medium as NRP-152 cells. But 152-S3c cells grew in DMEM/Ham's F12 supplemented only with 10% newborn calf serum. Moreover, 152-S3c cells expressed EGFP (as did 152-pIRES, which was expected since they were transfected with pIRES-EGFP) and the FLAG epitope, which is part of the S3c gene [40]. Both 152-pIRES and 152-S3c cells grew in the presence of G418. BPH-1 cells grow in RPMI-1640 supplemented with bovine serum; therefore this line does not have growth factor dependence to begin with. BPH-pIRES and BPH-S3c cells, aside from exhibiting G418 resistance, expressed EGFP, but only BPH-S3c expressed the FLAG epitope of the S3c gene. The evidence for these observations given in Table 1 is presented in the rest of this section. Expression of FLAG and EGFP in 152-S3c and BPH-S3c Cells Was Observed After Transfection and Selection with Antibiotics After no viable cells were observed following antibiotic treatment, we analyzed transfected cells for the presence of the markers flanking the S3c gene on the plasmids used, FLAG and EGFP. The analyses were done by flow cytometry on a FACScan; also by Western blot using specific Abs, and the results are presented in Figure 2. In Panels A through D, the mean fluorescence intensities of representative clones of 152-S3c and BPH-S3c cells stained with monoclonal Ab to FLAG plus fluorescent goat anti-mouse F(ab2)', as well as the enhanced green fluorescent protein fluorescence intensities of transfected cells, are shown. Panel A displays the anti-FLAG fluorescence intensity of 1 representative clone of 152-S3c (thin line) compared to untransfected NRP-152 cells (thick line); approximately 95% of the 152-S3c cells stained with the anti-FLAG antibody. Similary, Panel B shows the fluorescence intensity of anti-FLAG-stained BPH-1 cells (thin line) compared to anti-FLAG-stained BPH-S3c clone (thick line), where approximately 76% of the BPH-S3c cells stained with the anti-FLAG antibody. Panels C and D display the EGFP fluorescence for clones of 152-S3c and BPH-S3c cells, compared with untransfected cells, respectively. In Panel C, the thick line shows the fluorescence intensity of EGFP in 152-S3c and the thin line shows the lack of EGFP fluorescence in the untransfected NRP-152 cells. Approximately 67% of the 152-S3c cells showed EGFP fluorescence. In Panel D, the thin line shows the EGFP fluorescence intensity of BPH-S3c cells, while the thick line shows it for untransfected BPH-1 cells. Approximately 45% of the BPH-S3c cells showed fluorescence due to EGFP. We concluded that in addition to antibiotic resistance, the transfected cells expressed markers flanking the S3c gene, and therefore we could attribute any change in phenotype of the cells to the expression of the S3c, in comparison to the vector-transfected cells. Panel E shows the results of immunoprecipitation with anti-FLAG Ab, followed by Western blot to detect EGFP. We used anti-FLAG Ab for the immunoprecipitation because (1) a S3c-specific Ab is not available, and (2) because all cells express STAT3. Thus, because expression of FLAG equates with expression of S3c specifically, immunoprecipitating with anti-FLAG would reveal the S3c-expressing cells. As seen in Figure 2E, the bands corresponding to 27 kD EGFP are visible only in the lanes from 152-S3c and BPH-S3c cells, while no EGFP bands are visible in the bands from the parental lines NRP-152 and BPH-1 cells. Since the EGFP gene is 3' to the S3c gene in the pIRES-S3c plasmid we constructed (the plasmid codes for a bicistronic message with 1 promoter for EGFP and S3c), these results confirm the flow cytometry data shown in Panels A through D. Figure 2 FLAG and EGFP expression in representative NRP-152 and BPH-1 clones transfected with either pBABE-S3c or pIRES-S3c. NRP-152 and BPH-1 cells were transfected with pBABE-S3c or pIRES-S3c, which bear the FLAG epitope on the S3c gene. Clones were derived by limit dilution, as described in Materials & Methods. Panels A–D: In all histograms, the marker M1 sets the region of positively fluorescent cells for determining the percent positive cells. Panels A & B: Fixed cells were permeabilized and stained with anti-FLAG M1 Ab (Sigma), as described in Materials & Methods. Controls for staining were included, as described. Panel A: Transfected NRP-152 cells. Thin line = 152-S3c; thick line = NRP-152. Panel B: Transfected BPH-1 cells. Thin line = BPH-1; thick line = BPH-S3c. Panels C & D: NRP-152 and BPH-1 cells transfected with pIRES-S3c were analyzed for EGFP fluorescence, following selection. Panel C: Transfected NRP-152 cells. Thin line = NRP-152; thin line = 152-S3c. Panel D: Transfected BPH-1 cells. Thick line = BPH-1; thin line = BPH-S3c. Panel E: Immunoprecipitation followed by Western blot showing EGFP expression in transfected NRP-152 and BPH cells. Note the lack of EGFP bands for parental lines NRP-152 and BPH-1, whereas EGFP was detected using EGFP-specific Ab (Pharmingen) in lanes for 152-S3c and BPH-S3c. Methods: NRP-152, 152-S3c, BPH-1, and BPH-S3c cells were lysed in buffer. Equal amounts of protein in cell lysates were pre-cleared with Protein A/G beads, then precipitated with anti-FLAG AB plus Protein A/G beads with rotation in the cold. The pelleted beads plus proteins were separated on 12% SDS gels, transferred to PVDF membranes, then blotted with Ab to EGFP. Enhanced chemifluorescence was used to reveal the 27 kD bands corresponding to EGFP. 152-S3c Cells Grew in the Absence of Exogenous Growth Factors To demonstrate that 152-S3c cells grew in the absence of growth factors required by untransfected NRP-152 cells, transfected and untransfected NRP-152 cells were grown in microtiter wells. Proliferation was quantified by the oxidation of MTT after 48 hr. Figure 3 shows the results of these experiments. NRP-152 and 152-pIRES cells grew more slowly in unsupplemented 154 medium than they did in 152 medium. However, 152-S3c cells (3 representative clones, D5, A12, and H4, are shown) grew nearly as well in 154 medium as in 152 medium, and grew significantly better in 154 medium than either NRP-152 or 152-pBABE cells (p < 0.001; Figure 3A). Therefore, clones of 152-S3c cells, stably transfected with pBABE-S3c, grew in vitro as if they lost the requirement for additional growth factors in the cell culture medium. Figure 3 Growth of NRP-152, NRP-154, 152pBABE, and 152-S3c clones on 154 medium compared to growth on 152 medium. 103 cells were seeded in microtiter wells, in the indicated medium. After incubation for 48 hr, MTT (15 μl at 25 μg/ml) were added to each well, and incubation was continued for 4 hr more. The formazan was dissolved in 0.1% SDS, and the absorbance was quantified on a DynaTech plate reader at 570 nm. Unpaired Student t-tests (InStat3 software) were performed to assess the statistical significance of the growth of S3c-transfected cells relative to pBABE transfected and untransfected NRP-152 cells. Panel A: Comparison of growth as measured by MTT absorbance at 48 hours; Panel B: Comparison of growth rates over 72 hours. Stable Expression of S3c in BPH-1 Cells Resulted in STAT3-Dependence for Survival In order to show that the persistent expression of activated STAT3 was required for the survival of the transfected cells, as we have previously shown for hormone-refractory prostate cancer cells lines [11,12], we transfected pIRES-S3c into human BPH-1 cells [38] for studies with antisense STAT3 oligonucleotides. We used BPH-1 cells and transfected lines only for these experiments, because the antisense oligonucleotide was designed for use in human cells, and we wanted to maximize the efficacy of the antisense oligonucleotide. Figure 4 shows that transfection of 125 nM of sense STAT3 oligonucleotide decreased viability by only 5% at 48 hours, whereas transfection of the same amount of antisense STAT3 oligonucleotide decreased viability to 18% at 48 hours. Furthermore, transfection of antisense STAT3 oligonucleotide into untransfected BPH-1 cells did not decrease viability any more than did transfection of sense oligonucleotide. Figure 4B shows that 24 hours after transfection with 125 nM of antisense STAT3, BPH-S3c cells displayed a 66% reduction in intracellular STAT3 protein levels. We concluded from these experiments that the S3c expressed in BPH-S3c cells was functionally active, and that BPH-S3c cells were dependent upon continued STAT3 expression for their very survival, just like hormone-refractory prostate cancer cell lines [11,13]. These data are more evidence for a profound difference in phenotype between BPH-1 cells and BPH-S3c cells. Figure 4 Functional activity of STAT3 in S3c-transfected cells. Panel A: To show the functional activity of STAT3 expressed by the S3c gene, BPH-1 cells stably transfected with pIRES-S3c were treated with either 125 nM sense or antisense STAT3 oligonucleotide. Percent viability over time was determined by staining with propidium iodide, then quantifying fluorescence on a FACScan flow cytometer. Panel B: Treatment with 125 nM antisense STAT3 oligo reduced the amount of intracellular STAT3 protein in the clone of BPH-S3c cells shown in 3A. Twenty-four hours after transfection, BPH-S3c cells were harvested, fixed, and permeabilized, then stained with antibody to STAT3, as described in Materials and Methods. Quantification was performed on a FACScan flow cytometer. The black line indicates the amount of intracellular STAT3 in BPH-S3c cells treated with sense STAT3, while the grey line shows the amount of STAT3 in BPH-S3c cells given antisense STAT3. STAT3 expression was reduced by 66% in this experiment. 152-cS3 Cells Have Decreased Expression of RAR-β and -γ mRNA, and Increased Expression of RAR-α mRNA In prostate cancer cell lines and archived specimens, we previously found that RAR-β and -γ have decreased mRNA levels, while RAR-α mRNA increased, relative to non-malignant prostate cell lines and the normal margins of the same specimens [34,35]. This finding is also true of NRP-152 and NRP-154 cells: the expression of RAR-β and -γ is decreased in NRP-154 cells relative to NRP-152 cells. In order to see if the same change in retinoic acid receptor subunit expression occurred when S3c is expressed, which is consistent with the malignant phenotype, we did the following experiments. For these, we used 152-S3c and 152-pIRES cells, so that we could compare the RAR levels with those of NRP-154 and parental NRP-152 cells, because these 2 related cell lines are believed to represent two stages in the progression and development of prostate cancer [36,41]. Figure 5 depicts the northern blot hybridization results for RAR-β (Figure 5A) and -γ (Figure 5B) in transfected and untransfected cells. Lane 1 in both panels shows the hybridized mRNA for untransfected NRP-152 cells, while both lanes 2 show the hybridized band for NRP-154 cells. Note the decreased amount of RAR-β and -γ in lanes 2 (from NRP-154 cells, the prostatic carcinoma line) relative to the amount in lanes 1, obtained from NRP-152 cells, the benign prostatic hyperplasia line. Lanes 3 show the hybridized mRNA obtained from NRP-152 cells transfected with the vector, pIRES-EGFP, while the bands displayed in both lanes 4 shows that when NRP-152 cells were transfected with pIRES-S3c, the hybridization of RAR-β and -γ decreased similarly to what is observed in lanes 1 and 2. Figure 5C compares RAR-α mRNA expression in the 4 cell lines: lane 1 again is NRP-152 and lane 2 is NRP-154; there is more mRNA hybridized in lane 2 than in lane 1, and the band appears as a doublet in lane 2 as well. Lane 3 shows the results from NRP-152 cells transfected with pIRES-EGFP, while lane 4 shows the results from NRP-152 transfected with pIRES-S3c: note the similar pattern to that of lanes 1 and 2 – lane 4 shows more hybridization and a doublet band for RAR-α as well. We concluded from these results that transfection of NRP-152 cells with pIRES-S3c, but not pIRES-EGFP, induced a change in RAR mRNA expression that is often observed in prostate cancer cell lines and archived specimens. Figure 5 S3c expression inhibited RAR-β and -γ expression and increased expression of RAR-α in NRP-152 cells. Panel A: Effect of S3c on RAR-β mRNA levels. Panel B: Effect of S3c on RAR-γ mRNA levels. Panel C: Effect of S3c on RAR-α mRNA levels. NRP-152, NRP-154, NRP-pBABE, and 152-S3c cells were grown to confluence, and RNA was harvested as described in Materials & Methods. Electrophoretic separation of RNA was followed by transfer to nitrocellulose, then hybridization with 32P-labeled probe, followed by autoradiography. Lane 1 = NRP-152 (rat benign prostatic hyperplasia line); lane 2 = NRP-154 (rat prostatic carcinoma line); lane 3 = 152-pBABE; lane 4 = 152-S3c. The comparison to 18S RNA is shown for each. BPH-S3c Cells Were Androgen-Insensitive In many human prostate cancers, overexpression of the androgen receptor has been noted [42,43]. Therefore, the development of the hormone-refractory state apparently occurs even when there is no disruption of the expression of the androgen receptor, at least in some prostate cells. To clarify these contradictory data and to check for the development of functional androgen-insensitivity, we examined the growth rate of human BPH-1 and BPH-S3c cells in the presence and absence of dihydrotestosterone (DHT), and also DHT in the presence of the antagonist flutamide (F). Our results, presented in Table 2, show that while BPH-1 cells respond to DHT and are blocked by F, the same is not true of BPH-S3c. Thus, the persistent expression of S3c in BPH-1 cells resulted in a functionally androgen-insensitive state for these cells. Table 2 Androgen-Insensitivity is conferred by S3C expression in BPH-1 cells Cell nM DHT %Stimulation μM F + nM DHT %Inhibition BPH 10 200 1 10 97 BPH-pIRES 10 250 1 10 99 BPH-S3c 10 2 1 10 -4 DU145 10 3 1 10 3 Cells were grown in 96-well plates for 72 hr, in the presence or absence of drugs (DHT = dihydrotestosterone; F = flutamide, bolded and underlined) as indicated. The MTT assay was used to assess proliferation. %Enhancement = (absorbance + drug/absorbance - drug) × 100; % Inhibition = 1-(absorbance + drug/absorbance - drug) × 100. The responses of DU145 cells, a human prostate cancer cell line that is androgen-insensitive and resistant to flutamide, is shown for comparison. 152-S3c Cells Lost Sensitivity to the JAK2 Inhibitor AG490 In non-malignant cells, the activation of STAT3 is effected by a specific upstream kinase, JAK1 or JAK2 or sometimes Tyk2. Previously we had shown that the constitutive activation of STAT3 in NRP-154 cells rendered those cells insensitive to apoptosis induced by the JAK2 inhibitor AG490 [10]. In order to see if insensitivity to AG490 was conferred on 152-S3c cells, we added AG490 to cells and assessed apoptosis 48 hr later by annexin V binding and PI inclusion. Table 3 shows the data we obtained. Whereas NRP-152 and 152-pIRES cells were 45 ± 10% and 38 ± 5% apoptotic, respectively, 48 hr after treatment with 100 μM AG490, only 6.3 ± 3% of 152-S3c cells and 7.5 ± 4% of the NRP-154 cells were apoptotic after 100 μM AG490 treatment. We conclude from these experiments that S3c expression in NRP-152 cells decreased their sensitivity to AG490, which is consistent with what we observed in malignant NRP-154 cells. Table 3 NRP-152 Cells Transfected with S3c lost sensitivity to JAK2 inhibitor AG490 Cell S3c? Rx μM % Apoptotic +/- SD NRP-154 YES AG490 0 8 ± 4 100 7.5 ± 5 5152-S3c YES AG490 0 7.5 ± 4 100 6.3 ± 3 152-pIRES NO AG490 0 11 ± 2 100 38 ± 5* NRP-152 NO AG490 0 7.5 ± 4 100 45 ± 10* Cells were placed in 60 mm wells for 48 hr with compound at the concentrations indicated. Zero concentration of the compound is the vehicle (DMSO) control. At the end of the incubation period, cells were harvested, washed, and stained with FITC-annexin V, to demonstrate apoptotic cells. Quantification of fluorescence was performed on a Becton-Dickinson FACScan flow cytometer using CellQuest software. * p < 0.005 by Student t-test, compared to vehicle-treated cells. 152-S3c Cells Grew in Soft Agar As an in vitro indication of tumorigenic potential, soft agar cloning assays were performed as described [44]. S3c-transfected cells were compared to NRP-152 and to pIRES-EGFP-transfected cells in these experiments. We observed that 152-S3c cells grew significantly better (p < 0.0001 by 2-tailed Student t-test) in soft agar than either untransfected NRP-152 or pIRES-transfected NRP-152 cells (Table 4). We conclude from these experiments that 152-S3c cells have the potential to form tumors in vivo, whereas it has previously been established that NRP-152 cells are not tumorigenic [36], and we would not expect 152-pIRES cells to be tumorigenic either. Table 4 NRP-152 Cells transfected with S3c grew in soft Agar CELL S3c? #WELLS #COLONIES +/- SEM NRP-152 NO 12 2.6 ± 0.9 152-pIRES NO 12 5.8 ± 1.8 152-S3c YES 12 35 ± 4.5* Cells were placed in 60 mm wells in soft agar for 10 days. Colonies of more than 1 mm in size were counted were counted using an inverted phase contrast microscope. * p < 0.001 by Student t-test Expression of S3c Did Not Confer Tumorigenicity on Benign NRP-152 Cells Based on our previous data, especially the soft agar cloning data, we expected that 152-S3c cells would form tumors in SCID mice. However, in 3/3 experiments (in two them, Matrigel was used to enhance tumorigenicity of the cells), an average of 1/5 mice developed tumors; these were 1 mm in diameter or less. We chose to use only transfected NRP-152 cells for these experiments, because in certain in vivo environments, untransfected BPH-1 cells have been observed to form tumors [38]. We conclude that while persistent S3c expression altered the phenotype of 2 different benign prostatic hyperplasia lines in ways consistent with the development of the malignant phenotype, an additional change in gene expression may be required for tumorigenicity in prostate cancer development. Discussion We have demonstrated that NRP-152 and BPH-1 cells transfected with a constitutively-activated form of the STAT3 gene, S3c, gained a phenotype which more closely resembled that of NRP-154 cells. Specifically, the transfected cells expressed resistance to the antibiotic G418, and also expressed the FLAG epitope, as revealed by intracellular flow cytometry following staining with anti-FLAG Ab in Figure 2B–C, while Figure 2A shows the FLAG expression in mock transfected cells. As additional evidence of S3c expression, we looked for EGFP expression in 152-pIRES cells, since the bicistronic message from this vector (pIRES-EGFP) places the S3c gene 3' to the EGFP, so that S3c would have to be translated before EGFP is translated. Figure 2D shows the EGFP expression in the same clone whose FLAG expression is shown in Figure 2C. These results were confirmed by immunoprecipitation/Western blot analysis, which is shown in Figure 2E, whereupon cell lysates were precipitated with Ab to the FLAG peptide on the S3c gene, then blotted with anti-EGFP Ab. Only the transfected and selected 152-S3c and BPH-S3c cells revealed EGFP bands, not the parental lines. After obtaining these results, we characterized the phenotype of the transfected cells. Parental NRP-152 cells are fastidious in their growth factor requirement, whereas NRP-154 cells and 152-S3c clones grew in medium supplemented only with serum (Figure 3). Therefore, we assessed the change in growth of transfected NRP-152 cells by comparing their growth in unsupplemented medium. We found that clones of 152-S3c cells grew nearly as well as NRP-154 cells in simple medium, whereas NRP-152 and 152-pIRES cells grew poorly in the absence of growth factors included in the medium (Figure 3). The change in growth factor requirement is one often observed for neoplastic cells, and is consistent with the role of STAT3 as a proto-oncogene with the capability of transforming benign cells into malignant cells [15,45]. As for dependence on survival of constitutively-activated STAT3, which has been observed in NIH-3T3 transfected with S3c [40] and in hormone-refractory prostate cancer cell lines [11], BPH-S3c cells treated with 125 nM antisense STAT3 oligonucleotides died over time, going from 100% viable to less than 20% viable 48 hours after transfection (Figure 4A); the reduction in viability could be attributed to the effect of antisense STAT3 on STAT3 protein expression, which was reduced by 66% at 24 hours after transfection (Figure 4B). These data mean that like hormone-refractory prostate cancer cells, BPH-1 cells transfected with S3c became dependent upon the continued expression of S3c for their survival. As for RAR expression, we observed decreased mRNA levels of RAR-β and -γ, but increased RAR-α expression in S3c-transfected NRP-152 cells; the results shown in Figure 5 are consistent with the expression levels of these receptor mRNAs previously observed by us in prostate cancer lines [34] and in prostate cancer patient specimens [35]. These findings are echoed in those of Yang, et al., who observed that IL-6-induced STAT3 signaling in lung epithelial cell lines lead to increased RARα expression, which was abrogated when the STAT3 DNA-binding domain was substituted by the corresponding STAT1 domain [46]. The importance of our results with respect to prostate cancer is that this disease is often refractory to retinoid therapy, the molecular basis for which is not known at this time. Our results gives possible insight into the mechanism of retinoid insensitivity, and might also indicate that treatment of prostate cancer with STAT3 inhibitors and with retinoids may be beneficial. In terms of androgen receptor function, S3c expression in BPH cells changed their response to androgens so that BPH-S3c cells were no longer stimulated by DHT, and the growth of BPH-S3c cells was not inhibited by flutamide treatment (Table 2). These findings with respect to the androgen receptor and responses to DHT and flutamide are especially important, as it may be the one of the first indications of a direct effect of STAT3 on androgen receptor responses, and may indicate a possible molecular mechanism for the development of the hormone-refractory state in prostate cancer patients. The progression to androgen-independence has been found to be associated with IL-6, with c-myc expression, and with insulin-like growth factors, all of which can signal through the activation of STAT3 [25-28]. It has been postulated that cross-talk between STAT3 and the androgen receptor plays a role in the development and maintenance of the hormone-refractory state in prostate cancer [47]; our data indicate that persistently-activated STAT3 may obviate the need for expression of the androgen receptor, since the androgen receptor did not respond to either DHT or F in S3c-transfected BPH-1 cells (Table 2). Further work is warranted in this area. Prior to performing in vivo tumorigenicity experiments, we wanted to see if S3c-transfected cells could grow in soft agar as clones. We observed that S3c expression in NRP-152 cells allowed them to grow as clones in soft agar (Table 4). However, even though 152-S3c cells grew in soft agar, a phenotype usually consistent with tumorigenicity, in 3 out of 3 experiments we failed to observe tumors in more than 20% of the mice, and these tumors were not more than 1 mm in diameter (data not shown). Therefore, we concluded from these data that persistent expression of activated STAT3 alone was not sufficient to produce tumorigenicity in prostatic epithelial cells, although it had been sufficient in NIH-3T3 cells, as previously reported [40]. Furthermore, recent observations by Zhang and coworkers point to an important function for STAT3 in both tumorigenesis and metastasis formation in leiomyosarcoma [48], due to signaling by hepatocyte growth factor/scatter factor. Among the candidate genes regulated by STAT3 in this regard are matrix metalloproteinase-2, which is essential for tumor invasion and metastasis formation [49]. Perhaps STAT3 cooperates with another factor regulated by hepatocyte growth factor/scatter factor, which is not expressed by either NRP-152 or BPH-1 cells. Only more experiments will reveal whether this is the case. Indeed, we are planning experiments to see what genes are regulated by S3c, to gain insight into the phenotypic changes induced by S3c expression. For example, very recently it was reported that STAT3 and the microphthalmia-associated transcription factor were both required for optimal upregulation of c-fos, and subsequent tumorigenicity, in NIH-3T3 cells [50]. Whether the prostatic lines NRP-152 or BPH-1 express microphthalmia-associated transcription factor has not been determined; the levels of c-fos in S3c-transfected lines can be determined. As well, Dechow and coworkers reported that transfection of S3c into mammary epithelial cells rendered those cells tumorigenic in irradiated SCID mice [51]; whether our results are an indication of a difference between mammary epithelial cellls and prostatic epithelial cells or a reflection of irradiated vs. non-irradiated SCID mice remains to be elucidated. As more information is revealed about gene expression changes that accompany the progression of prostate cancer from the benign to the hormone-refractory state, the other genetic changes needed for tumorigenicity of S3c cells should be revealed. Conclusions Our data indicate that transfection of NRP-152 and BPH-1 prostatic epithelial cells with a gene for persistently-activated STAT3, S3c, changed the phenotype of the cells into one resembling a malignant phenotype, thereby giving even more importance to the role of activated STAT3 in the transformation of normal cells into neoplastic cells. Importantly, we found that cells expressing S3c depended on its continued expression for survival. Two kinds of evidence are presented: first, S3c-transfected cells became sensitive to the effect of antisense STAT3 oligonucleotide. When transfected with antisense STAT3, both BPH-S3c and 152-S3c underwent apoptosis. Second, the S3c-transfected cells were not sensitive to the commonly-used STAT3 inhibitors, which are really JAK inhibitors, because activation of STAT3 by the upstream JAK is not required when S3c is expressed. We observed that growth factor-dependent NRP-152 cells grew without growth factor supplementation when transfected with S3c gene, whereas the medium for vector-transfected NRP-152 cells still required supplementation with growth factors. Moreover, we observed that 152-S3c cells grew in soft agar, whereas neither vector-transfected nor untransfected NRP-152 cells did. Furthermore, we observed that the expression of RAR subunits in 152-S3c cells was different from vector-transfected and untransfected NRP-152 cells, and that the changes were consistent with what we previously observed in specimens from prostate cancer patients, as well as in primary prostatic epithelial cells compared with prostate cancer cell lines [34,35]. These data may have implications for the relative lack of sensitivity of PCA to retinoid therapy. As for BPH-1 cells, which do not require growth factor supplementation, we observed that when transfected with S3c, this cell line lost its responses to testosterone and to the testosterone antagonist flutamide. Neither of these changes was observed in vector-transfected BPH-1 cells. However, neither S3c-transfected cell line was tumorigenic when injected into SCID mice, leading us to conclude that additional genetic changes are possibly needed for tumorigenicity in prostate cells. Methods Cell Lines NRP-152 and NRP-154 cells were the gift of Dr. David Danielpour, Ireland Cancer Center, University Hospitals, Cleveland, OH [36]. Growth factor-dependent NRP-152 cells were grown in DMEM/Ham's F12 medium (1:1; GIBCO) supplemented with 10% newborn bovine serum (Hyclone), 2 mM glutamine (GIBCO), epidermal growth factor (20 ng/ml), insulin (5 μg/ml), dexamethasone (0.1 μM) and cholera toxin (10 μg/ml; all, Sigma), pH 7.3 (152 medium). NRP-154 cells were grown in DMEM/Ham's F12 medium plus 10% newborn calf serum (154 medium). Growth factor-independent BPH-1 cells [37,38] were the gift of Dr. Simon Hayward, Vanderbilt University, Nashville, TN. They were grown in RPMI-1640 medium supplemented with 10% newborn bovine serum. For transfections, cell were seeded into wells of 6-well plates and grown until 50–80% confluent monolayers of cells were present, as assessed by observation under inverted phase-contrast microscopy. Transfections Derivation of the pBABE-S3c plasmid containing a constitutively-activated STAT3 gene, S3c (gift of Dr. Jacqueline Bromberg, Memorial Sloan-Kettering Cancer Institute) has been previously described [14,45]. The S3c gene was excised along with its FLAG tag, and inserted into pIRES-EGFP (Clontech), resulting in the plasmid called pIRES-S3c. For stable transfections, Clonfectin reagent (Clontech) was mixed with plasmid DNA (6 μl Clonfectin and between 1 and 3 μg plasmid), according to the manufacturer's instructions. The complete medium was removed from the plates of cells and replaced with 1.8 ml IMDM (Invitrogen). The Clonfectin-plasmid mixtures (100 μl) were added to the cells; replicate cultures of cells received Clonfectin only at the time of transfections. The plasmid-Clonfectin mixtures were left on the cells in the incubator for 4 hr, at which time the supernatant fluids were aspirated and replaced with 5 ml/well pre-warmed complete medium. Twenty-four hr following transfections, G418 (Invitrogen) was added at a final concentration of 800 μg/ml. The medium plus G418 was replaced 3 times/wk until no surviving cells were observed on the Clonfectin-only wells, usually 2 weeks. At that time, G418 was added at 100 μg/ml to maintain the transfected cells. When the transfected cells reached confluence, they were used for further analyses. Table 1 gives a summary of transfected cells and phenotypes obtained. For transient transfections, LipoFectamine 2000 in Opti-MEM I medium (both, Invitrogen) was used according to the manufacturer's directions. For subconfluent (~50%) cells, 2 μl of LipoFectamine 2000 was used with varying amounts of antisense or sense STAT3 oligonucleotide (gift of Dr. James Karras, ISIS Pharmaceuticals). The oligonucleotides were left on the cells for 6 hours before cell culture medium supplemented with 30% was added to each well. Cells were incubated until assays were performed. Limit-Dilution Cloning In order to analyze clonal populations of cells, transfected cells (pIRES-S3c or pIRES-EGFP) were harvested, diluted to 10 cells/ml in complete medium, and seeded into microtiter plates at 100 μl/well. The total volume of each well was brought to 200 μl with additional medium, and the plates were incubated until growth of seeded cells was observed, usually at 10 days to 2 weeks. Determination of Stable Transfection by Expression of FLAG in 152-S3c and BPH-S3c Cells by Intracellular Flow Cytometry Expression of the FLAG epitope engineered onto the constitutively-activated STAT3 gene in transfected NRP-152 cells was performed by intracellular flow cytometry, as described [52]. Briefly, 152-S3c or BPH-S3c cells were harvested, washed, and fixed in 4% paraformaldehyde/PBS (Pharmingen) for 30 min on ice. Fixed cells were washed and permeabilized with 0.1% sapononin (Pharmingen) for 15 min at room temperature, then washed. Mouse monoclonal Ab M1 to FLAG (Sigma) was added (1 μg/106 cells/100 μl permeabilization buffer) to the cells for 1 hr on ice. The cells were washed 3 times, then incubated with phycoerythrin (PE)-labeled goat anti-mouse F(ab2)' (Caltag) for 1 hr on ice in permeabilization buffer. After washing 3 times, cells were resuspended in 1 ml PBS and analyzed on a Becton-Dickinson FACScan. CellQuest software was used to acquire and analyze the fluorescence. The Kolmogorov-Smirnov 2-sample test was used to determine the level of significance of the change in fluorescence intensity between control-stained (F(ab2)'-stained only) and Ab-stained populations of cells, thereby ascertaining that the populations observed in the histograms were truly separate populations of cells [53]. Immunoprecipitation/Western Blot Studies For immunoprecipitation, cells lysed in Lysis Buffer (10 mM PBS, pH 7.4, 1% NP-40, 0.5% sodium deoxycholate, 0.1% sodium dodecylsulfate (SDS), 1 mM sodium orthovanadate, 1 mM phenylmethyl-sulfonyl fluoride, 40 μg/ml aprotinin) were precleared with Protein A/G agarose (Santa Cruz Biotechnology), then precipitated with 1–5 μg rabbit Ab (Cell Signaling or Pharmingen) plus Protein A/G (Santa Cruz Biotechnology) agarose overnight. After washing, the beads were eluted by heating in Laemmli buffer for 5 min at 95°C, followed by electrophoretic separation on 12% SDS-polyacrylamide gels (Novex Nu-PAGE pre-cast gels). Transfer of separated protein species to nylon membrane (Millipore) was followed by blocking in 10% non-fat dry milk in TBST (50 mM Tris HCl, pH 7.4, 150 mM NaCl, 0.3% Tween 20). Incubation of the membrane with rabbit Ab was followed by incubation with alkaline phosphatase-linked goat anti-rabbit antibody (Amersham ECF kit). After addition of substrate from the kit, the membranes were read by the Typhoon imager, with ImageQuant software for resolution of images (Molecular Dynamics). Measurement of In Vitro Growth of Cells NRP-152, NRP-154, BPH-1, and transfected cells were seeded at 103cells/well in microtiter plates in appropriate medium, as indicated. After 48 hr, 15 μl MTT (Sigma; 25 μg/ml) was added to each well for 4 hr, then the resulting formazan was dissolved in 0.1% SDS. Absorbance was determined at 570 nm on a Dynatech microplate reader. Statistical determinations of significance were performed by unpaired Student t-test for multiple independent assays, using GraphPad software. Determinations of Androgen Insensitivity and Presence of Retinoid Receptors The effect of dihydrotestosterone (DHT) as growth agonist, and the effect of flutamide (F) as growth antagonist, was assessed by use of the MTT assay described above. DHT and F were obtained from Boeringer-Mannheim, and cells were treated with 1 or both drugs at concentrations ranging from 1 to 100 nM for DHT, and 0.1 to 3 μM for F. These are within the published ranges of efficacy for these drugs [34,54]. Vehicle controls were included. Replicate plates were harvested at 24, 48, 72, and 96 hrs after treatment. Northern blot hybridizations to detect the retinoid receptors RARα, RARβ, and RARγ were performed as previously published [34]. In brief, RNA was isolated from cells using RNAEasy (Qiagen) and quantified spectrophotometrically. RNA was separated by size on agarose gels, then transferred to nitrocellulose membranes (Schleucher & Schuell). The probe was labeled with 32P-dCTP (New England Nuclear), then allowed to hybridize to the blot overnight in hybridization buffer. After washing, hybridization was detected by use of a PhosphoImager (Molecular Dynamics). Apoptosis Assays Forty-eight hr after transient transfection, cells were harvested using Enzyme-Free Cell Dissociation Buffer. After two washes with PBS, they were stained with FITC-annexin V (5 μl/106 cells; Caltag) for 15 min at room temperature. Apoptotic cells (cells staining with FITC-annexin V) were quantified by measuring green fluorescence in FL1 on the flow cytometer. In some experiments, cells were also stained with propidium iodide (PI), which is detected by the FL3 detector. CellQuest software was used to acquire and analyze the data on a Becton-Dickinson FACScan flow cytometer. For studies using the tyrphostin JAK2 inhibitor AG490 (Calbiochem), the dissolved compound was added to subconfluent cells, as described [11]. A vehicle control was included for the 0 μM concentration. Forty-eight hrs later, cells were harvested and processed for quantification of apoptosis by annexin V binding and PI incorporation. Assay for Growth in Soft Agar Transfected cells were subjected for growth in soft agar to assess their change in phenotype with regards to colony formation. After selection and cloning, 104 cells were trypsinized and washed in Ca2+/Mg2+-free PBS (Life Technologies) and plated in 1 ml of medium plus serum without supplements containing 0.3% (w/v) Noble Agar (Difco/Becton-Dickinson) over a 2 ml layer of the same medium with 0.6% agar in six-well plates. The number of colonies was counted using low magnification microscope (4×) after 10 days. In Vivo Tumorigenicity Studies Our protocol was reviewed and approved by the Institutional Animal Care and Use Committee of UMDNJ. Severe-combined immunodeficient (SCID) mice (Charles River Laboratories) were obtained at 5 weeks of age, and acclimatized in the barrier vivarium for 1 week. At that time they were injected subcutaneously with 8 × 106 S3c or vector-transfected (pIRES-EGFP) control cells. Each group consisted of 5 animals. In some experiments, the cells were mixed with Matrigel (Collaborative Research) prior to injection. Tumor growth was monitored weekly using engineer's caliper's to measure the 2 perpendicular diameters, over the course of 12 weeks. List of abbreviations STAT signal transducer and activator of transcription cSTAT3 constitutively-activated STAT3 JAK Janus activated kinase PCA prostate cancer S3c constitutively-activated STAT3 gene having a Cys substitution FLAG an immunogenic peptide fused to gene of protein to be expressed for identification and/or purification purposes SCID severe combined immunodeficient PBS phosphate-buffered saline DMEM Dulbecco's modifcation of Eagle's medium IMDM Iscove's modification of Eagle's medium FITC fluorescein isothiocyanate PE phycoerythrin PI propidium iodide FL1, 2, or 3 fluorescence detectors on a flow cytometer that collect fluorescence data within a set range of wavelengths, FL1 being the lowest and FL3 being the highest EGFP enhanced green fluorescence protein RAR retinoic acid receptor DHT dihydrotestosterone F flutamide Authors' contributions HFH conceived of the retinoic acid receptor subunits experiments, and perofrmed the northern blot hybridizations. TFM performed the growth factor dependence and growth rate experments, performed some of the in vivo experiments, and prepared cells for flow cytometry. PS performed some of the transfections, participated in the in vivo experiments, the western blots, and prepared cells for flow cytometry. ABB made the pIRES-S3c plasmid from pBABE-S3c, and performed the soft-agar cloning experiments. BEB conceived of the project, performed most of the transfections, performed some of the in vivo experiments, and performed all flow cytometry acquisitions and analyses. All authors read and approved the manuscript. Acknowledgements We'd like to acknowledge the kind gift of the pBABE-S3c plasmid from Dr. Jacqueline Brmoberg, Memorial Sloan-Kettering Cancer Institute. Also, Dr. Robert Wieder, Department of Medicine, Division of Medical Oncology, New Jersey Medical School, provided valuable help in teaching us the soft agar colony assay. 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==== Front J CarcinogJournal of Carcinogenesis1477-3163BioMed Central London 1477-3163-4-21565200010.1186/1477-3163-4-2CommentarySingle nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events Hemminki Kari [email protected]örsti Asta [email protected] Bermejo Justo [email protected] Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany2 Department of Biosciences at Novum, Karolinska Institute, 141 57 Huddinge, Sweden2005 17 1 2005 4 2 2 30 7 2004 17 1 2005 Copyright © 2005 Hemminki et al; licensee BioMed Central Ltd.2005Hemminki et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Single nucleotide polymorphisms (SNPs) are extensively used in case-control studies of practically all cancer types. They are used for the identification of inherited cancer susceptibility genes and those that may interact with environmental factors. However, being genetic markers, they are applicable only on heritable conditions, which is often a neglected fact. Based on the data in the nationwide Swedish Family-Cancer Database, we review familial risks for all main cancers and discuss the evidence for a heritable component in cancer. The available evidence is not conclusive but it is consistent in pointing to a minor heritable etiology in cancer, which will hamper the success of SNP-based association studies. Empirical familial risks should be used as guidance for the planning of SNP studies. We provide calculations for the assessment of familial risks for assumed allele frequencies and gene effects (odds ratios) for different modes of inheritance. Based on these data, we discuss the gene effects that could account for the unexplained proportion of familial breast and lung cancer. As a conclusion, we are concerned about the indiscriminate use of a genetic tool to cancers, which are mainly environmental in origin. We consider the likelihood of a successful application of SNPs in gene-environment studies small, unless established environmental risk factors are tested on proven candidate genes. genotypinginteractionsgenomicsmultiple comparisonsmolecular epidemiology. ==== Body Introduction Genetic association studies on complex diseases have become very popular and most of them are case-control studies using single nucleotide polymorphisms (SNPs) as markers. There has been concern about the poor reproducibility of the results and the reasons for such discrepancies have been discussed [1-5]. However, the theoretical underpinnings of such studies have attracted less attention, apart from the use of SNPs as mapping tools, an application which we will not discuss in the present article [6-8]. Heritable etiology in many common diseases may not be overwhelming, and the use of genetic tools to dissect disease causation may thus be questionable. For cancer, all useful etiological measures, such as incidence changes upon time and migration, and aggregation of cancer among twins and families, point to a predominant environmental contribution to cancer causation [9-15]. However, in these contexts, the environment is anything that is not inherited, including variables that can be measured in epidemiological studies, in addition to un-measurable and random, stochastic events. The fact that only a minority of smokers are diagnosed with lung cancer is often cited as evidence for inherited differences in susceptibility to tobacco carcinogenesis. There are also other possible reasons, such as time-dependent stochastic effects, which in inbred animals are the likely reason that only some animals develop cancer when exposed to a constant level of a carcinogen. How feasible is it then to carry out SNP studies on cancer, particularly when the subjects are overwhelmingly unselected cases, among whom familial cases are rare. It is worrisome that genotyping is now almost a required standard component in epidemiological studies, without consideration of the expected heritable influence. Formal sample size and power calculations are irrelevant if there are no data on the assumed heritable component in the causation of a particular cancer. It is also worrisome that the fundamental differences between purely genetic and gene-environment studies regarding control populations and multiple testing problems are not appreciated. In this contribution, we first review study designs and aims of SNP studies, then we give data on familial risks for the main types of cancer, to give an idea of the upper bounds for the risks that can be expected in genotyping studies. Finally, we will calculate familial risks resulting from variants of assumed genotype relative risk and allele frequency. Such data are useful in the assessment of study designs and in the evaluation of obtained results. We use the term 'familial' to denote cancers in two or more first-degree relatives and 'heritable' when an inherited gene defect is known or inferred due to a high risk [16]. Instead of 'genotype relative risk', we refer to 'odds ratio' (OR), consistent with terminology of most association studies. Study designs and aims In the simplest form, a SNP with a known or assumed function is selected, and the genotypes are determined in cases and controls to test for association. Most studies published on cancer are of this kind, purely "genetic studies" testing the effect of the genotypes on the risk of cancer, without considering any other variables [17,18]. In addition to genotype effects, some studies have incorporated the effects of haplotypes and data on functional effects of the studied SNPs [19-21]. Population stratification has been an issue in association studies and it is important that the control subjects are drawn from the same ethnic and geographic population [2,5]. However, there is no need for individually based matching (age, gender etc.), typical of epidemiological case-control studies, as long as the genotypes of the control population follow the Hardy-Weinberg equilibrium [22,23]. Multiple testing is an issue in "genetic studies" but solutions are available, for example using the Bonferroni adjustment [2,5]. Most studies have selected SNPs with assumed functional effects or they test the effects of haplotypes. For some genes, null or truncating alleles exist and homozygotes would then lack a functional protein. However, for missense types of SNPs the functional effects may be small or nil when tested in in vivo systems [21]. Unfortunately, for many genes an in vivo functional test cannot be easily devised. Drug metabolism genes are a fortunate exception in this regard, because aberrant responses in humans have lead to the characterization of the underlying gene variants. One common feature of almost all the published studies is that patients have been collected without regard of a family history, thus sacrificing statistical power but attempting to compensate with a large sample size [24,25]. Using familial cases would be advantageous statistically, and some effects, such as that of CHEK21100delC, have only been detected among familial cases [26]. A variant of the candidate gene approach is the "gene-environment study", which is founded on the assumption that in complex diseases environmental factors interact with heritable factors, and a strong effect can be detected when both are present [27-29]. In epidemiology, interactions (also called effect modifications) are best described for multiple exposures, which may be additive, multiplicative or mixed [30]; these probably also apply to gene-environment interactions, but there are few bonefied examples on quantified gene-environment interactions [29]. It is conceptually appealing to assume that environmental factors interact with the genetic make-up to cause a differential susceptibility to cancer. However, examples are needed in order to verify this concept and its magnitude in cancer causation. The SNP component has become a favored adjunct to epidemiological studies, promoted with the hypothesis that the small, perhaps insignificant effects noted between exposure and cancer can be salvaged by incorporation genetic host factors into the study. These studies always include multiple comparisons, firstly, including the epidemiological variables in various classes, which may add up to thousands of cells (a small study with 5 variables in 5 strata each results in 55 = 3125 unique cells), and, secondly, the genes that are selected for analysis, are taken from a pool of tens or hundreds of potential candidate genes. No solutions have been found for this "two-dimensional" multiple testing problem. However, important for the present discussion, gene-environment interactions only exist if there is a heritable component in the particular cancer, and the likelihood of observing an effect is larger if the heritable component is large. In populations of random mating, it may be plausible that gene-environment interactions of epidemiologically measurable magnitude exit in the absence of a measurable familial risk. In the case of many exposures, some with harmful and others with protective effects, interacting with many genes, it may be possible that the familial risks are missed in spite of true gene-environment effects. Similarly, non-conventional dose-effect relationships, such as those suggested for blood vitamin D levels and prostate cancer [31], would be difficult to reconcile in terms of any genetic models. According to the complex disease paradigm, many relatively common alleles, interacting with environmental factors, cause susceptibility to common diseases [28,32]. Such a "non-Mendelian" inheritance may not cause an appreciable familial risk because the penetrance is so low that the likelihood of several family members being affected would be small. Twin studies should be able to assess the contribution of polygenic heritability [33], and the heritability estimates derived for colorectal (35% heritability), breast (27%) and prostate (42%) cancers, the only significant ones among site-specific cancers, encompass the total (broad) heritability, as definable using the twin model [11]. The much smaller heritability estimates, generated from family studies, were thought to result in part because of inability to consider such polygenic effects [13]. Familial risks and proportions Familial risk of a disease is a measure of its clustering in family members. Commonly, familial risk is defined between those who have a relative (e.g., parent or sibling) with cancer compared to those whose relatives are free from cancer, given as a familial relative risk or familial standardized incidence ratio (SIR). The SIRs shown below have been obtained from the Swedish Family-Cancer Database, the largest dataset of the kind in the world [34]. Familial SIRs have been adjusted for age, socio-economic status, period and region, and for women, for reproductive parameters. Table 1 shows familial SIRs for 0 to 68 year old offspring whose parents had the same cancer [35]. Table 1 also shows the number of observed cases, 95% confidence intervals (95%CIs) for the SIRs and the familial proportions, i.e., the percentage of all affected offspring who have an affected parent. A total of 4938 concordant familial cancers were found, with an overall SIR of 2.02. All the site-specific familial risks were significantly increased, except those for connective tissue. Hodgkin's disease showed the highest SIRs of 4.91, followed by testicular (4.35) and non-medullary thyroid cancers (3.26). Esophageal and ovarian cancers and multiple myeloma had SIRs in excess of 3.00. Among common cancers, the SIRs were increased for female breast (1.84), prostate (2.45) and colorectal adenocarcinoma (1.86), and the number of familial pairs ranged between 681 and 1779 for these common cancers. Apart from colorectal, breast and ovarian cancer, hardly anything is known about the genetic bases of these neoplasms, which should be good news to those who want to test the effects of candidate genes [36,37]. Table 1 SIR for cancer in offspring by parental concordant cancer Cancer site O SIR 95%CI Familial proportion, % Breast 1779 1.84 1.76 1.93 8.5 Prostate 922 2.45 2.29 2.61 15.4 Colorectum (adenocarcinoma) 681 1.86 1.73 2.01 10.1 Lung 365 2.09 1.88 2.32 6.6 Melanoma 166 2.62 2.23 3.05 2.5 Urinary bladder 117 1.75 1.45 2.10 3.9 Nervous system 112 1.75 1.44 2.11 1.8 Ovary 97 3.15 2.56 3.85 3.3 Endometrium 83 2.47 1.97 3.07 2.9 Stomach 82 2.17 1.72 2.69 5.5 Skin, squamous cell 77 2.52 1.99 3.15 4.1 Non-Hodgkin's lymphoma 74 1.82 1.43 2.29 2.1 Kidney 64 1.87 1.44 2.39 2.8 Leukemia 55 1.88 1.42 2.45 1.7 Pancreas 46 1.87 1.37 2.49 3.0 Upper aerodigestive tract 39 1.71 1.22 2.35 1.9 Cervix 39 1.82 1.29 2.49 1.7 Endocrine glands 38 2.22 1.57 3.06 1.5 Liver 37 1.66 1.17 2.28 2.6 Myeloma 23 3.32 2.10 5.00 2.6 Thyroid gland, nonmedullary 12 3.26 1.68 5.71 1.0 Testis 10 4.35 2.07 8.04 0.5 Esophagus 8 3.14 1.34 6.22 1.3 Hodgkin's disease 8 4.91 2.10 9.73 0.7 Connective tissue 4 1.87 0.49 4.84 0.5 All 4938 2.02 1.97 2.08 5.5 Bold type: 95%CI does not include 1.00. Familial proportion: % of affected offspring with affected parent Familial cases constituted 15.4% of all prostate cancers, which was the largest proportion by far. The proportion was 10.1% for colorectal adenocarcinoma and 8.5% for breast cancer, but only 0.5% for connective tissue and testicular tumors. The proportions are generally higher for common cancers. Overall familial cancers (same cancer in offspring and parents) constituted 5.5% of all cancers. We have tried to estimate the degree of environmental contribution to the familial risk by comparing cancer risks betweens spouses. Spouse concordance, which does not generally exceed an SIR of 1.3, can be noted only for cancers with known strong environmental risk factors: lung and genital cancers and early onset gastric and pancreatic cancers and melanoma [38,39]. Spouse correlation does not consider environmental sharing early in the life; this has been estimated by comparing cancer risks between siblings with a small or large age difference, respectively [40]. For most sites, including the breast and the colorectum, heritability is likely to be the main contributor to familial cancer [41,42]. Environmental factors are probably a large contributor to the familial aggregation of cervical, lung and upper aerodigestive tract cancers, and a minor contributor to familial risks for melanoma and squamous cell skin cancer [43,44]. If environmental causes of familial clustering have been quantified or excluded, familial SIRs and proportions give estimates on the heritable effects for cancer at the level of nuclear families (here between parents and offspring). Because of low penetrance, familial proportions underestimate true heritable effects. On the other hand, the twin model assumes that the shared environmental effects of monozygotic and dizygotic twins are identical, which may not be true. If monozygotic twins share more than dizygotic twins, the estimated heritability is exaggerated. Thus, the heritability estimates for cancer are still unreliable, and, due to possible interactions, a dichotomous classification into heritable and environmental components is conceptually inaccurate [45]. Moreover, the current models for twin studies do not allow the existence of interactions, a condition probably violated for many cancers. Nevertheless, the available data suggest that the heritability is low for most cancers, and even for prostate, breast and colorectal cancer it contributes a small etiological proportion. Familial risks from snps Results from a successful SNP study can imply that the particular variant contributes to a familial risk of the particular cancer. The resulting familial risk depends on the allele frequency of the SNP, observed OR and the mode of inheritance, i.e., on the relative risks of heterozygotes compared to homozygotes. In the dominant model, the risk of heterozygotes equals that of the variant homozygotes; in the recessive model, the risk of heterozygotes equals that of the wild type homozygotes. In the additive model, the risk of heterozygotes is the mean of the two homozygotes; in the multiplicative model, the risks between the genotypes differ by a constant multiplier. The methods for the calculation of familial risks to offspring of affected parents (comparable to SIRs of Table 1), based on allele frequency and OR of the genotype are presented elsewhere [46]. According to Table 2, the calculated familial risk is negligible at very low and very high allele frequencies when ORs are below 10, and at any allele frequency when OR is 2 or less. Most SNP studies are carried out on variants with frequencies at 5% or higher, and then substantial familial risks may be caused by a single gene with a high OR. Familial risk of breast cancer was 1.84 in Table 1; however, because the known genes, including BRCA1/2, ATM, p53 and CHEK2, explain about 25% of the risk [47], the unexplained familial risk is about 1.6. In Table 2 we have fold-faced SIRs that are incompatible with the empirical data for breast cancer (risk 1.60 or more), i.e., the resulting familial SIRs would be too high. If a single dominant gene would explain all the remaining familial risk of breast cancer, the allele frequency should be 0.2 and OR about 15; with allele frequency of 0.01, OR should be about 10. In the more likely scenario, many genes contribute to the familial risk, but their joint effect cannot exceed the above values. Table 2 Familial risk to offspring of affected parents assuming define allele frequencies and their effects according various genetic models OR Allele frequency 1.5 2 5 10 20 Dominant model 0.001 1.00 1.00 1.02 1.08 1.33 0.01 1.00 1.01 1.13 1.57 2.84 0.05 1.01 1.04 1.36 1.99 2.90 0.1 1.02 1.05 1.38 1.80 2.24 0.2 1.02 1.06 1.28 1.46 1.60 0.3 1.02 1.05 1.18 1.27 1.33 0.4 1.01 1.03 1.11 1.15 1.18 0.5 1.01 1.02 1.06 1.08 1.10 Additive model 0.001 1.00 1.00 1.00 1.02 1.09 0.01 1.00 1.00 1.04 1.17 1.63 0.05 1.00 1.01 1.13 1.46 2.13 0.1 1.01 1.02 1.18 1.50 1.97 0.2 1.01 1.03 1.20 1.41 1.63 0.3 1.01 1.03 1.17 1.31 1.42 0.4 1.01 1.03 1.14 1.23 1.29 0.5 1.01 1.03 1.11 1.17 1.20 Recessive model 0.001 1.00 1.00 1.00 1.00 1.00 0.01 1.00 1.00 1.00 1.00 1.00 0.05 1.00 1.00 1.00 1.01 1.04 0.1 1.00 1.00 1.01 1.06 1.23 0.2 1.00 1.01 1.08 1.28 1.75 0.3 1.00 1.02 1.16 1.47 1.93 0.4 1.01 1.03 1.23 1.52 1.85 0.5 1.01 1.04 1.25 1.48 1.68 Multiplicative model 0.001 1.00 1.00 1.00 1.00 1.01 0.01 1.00 1.00 1.01 1.04 1.11 0.05 1.00 1.01 1.06 1.18 1.42 0.1 1.00 1.01 1.11 1.28 1.60 0.2 1.01 1.02 1.16 1.36 1.67 0.3 1.01 1.03 1.17 1.36 1.61 0.4 1.01 1.03 1.16 1.32 1.51 0.5 1.01 1.03 1.15 1.27 1.40 Because the prevalence has no effect on the calculated familial risks, Table 2 can be used for any cancers of variable prevalences. The familial SIR for lung cancer was 2.09 (Table 1). However, judged from the spouse correlation, probably a large but undefined proportion of familial risk for lung cancer can be explained by environmental factors, and the unexplained heritable component may be not very different from breast cancer. For upper aerodigestive tract cancers, the familial SIR was 1.71, but tobacco smoking and other environmental factors probably contribute to familial clustering and the heritable component is likely to be relatively small in this cancer. It is of interest to examine the magnitude of familial risks which would be predicted from the published ORs for candidate genes. In a review of 34 polymorphisms in 18 different genes tested for breast cancer, a large proportion of the associations were not significant and the ORs were below 2.0 [17]. Even many significant ORs were below 2.0 and the resulting familial risk is negligible. However, there were some exceptions; in one study, TNF-alpha with an allele frequency of 0.2 showed an additive risk of about 10 (homozygote/homozygote). According to Table 2, the resulting familial risk would be about 1.4, i.e., if the effect were true, this gene would explain half of all familial risk for breast cancer; in that respect it would be two times more important than BRCA1 and BRCA2 combined. The effects of metabolic polymorphisms on various cancers have been reviewed in an IARC publication [48]. Among many genes, CYP2D6 has been analyzed in many studies as a risk factor for lung cancer, although it is not expressed in lung tissue [49,50]. Some genotyping studies have reported ORs between 5 and 15 for poor metabolizer genotypes. Assuming an allele frequency of 0.02 and a dominant OR of 10, Table 2 gives a familial risk of about 1.8, which, if true, would account for all familial risk of lung cancer not explainable by environmental factors. Conclusions The poor reproducibility of candidate gene studies has most commonly been associated to small sample size, population stratification and low prior probability, i.e., poor selection of genes or SNPs; a SNP with small functional effect would also imply a low prior probability for an effect [5,32]. We agree that the low prior probability is an important factor but we would like to widen the scope of the query from the right gene to the right tool: is the genomic tool generally applicable to a disease that is mainly environmental? It is likely that some successes will continue to come in associating new genes with cancers of a reasonable heritable component, such as that of CHEK21100delC in breast cancer, and populations of familial cancers will be important either in finding the initial association or in confirming the effect. In spite of the unsolved multiple testing problems, we consider plausible that gene-environment interactions will be established between demonstrated risk factors and proven candidate genes, for which tobacco-induced lung cancer would appear an obvious choice; however, even the candidate genes for lung cancer are still being searched. Testing of unproven genes and/or unproven environmental factors for gene-environment interactions is the high-risk design for a multiple testing outcome. It is worrisome to the field of gene-environment interactions that no such proof-of-principle has yet been demonstrated. It is commendable that all available molecular and environmental data are being used in attempts to understand the mechanisms of human cancer [51-53]. With increasing understanding of the cellular mechanisms more useful tools will become available. Even though these cellular systems are governed by heritable genes, variants in these genes may not have an impact large enough to predispose to heritable cancer. With current technological resources there is a growing danger that technology rather than biology is becoming the driving force in population studies [3]. Although the new technologies will allow benefits for the analysis of multiple SNPs and haplotypes in genetic pathways rather than in individual genes, they will not change the fact that cancer is mainly an environmental disease, expressed as somatic alterations on a heritable background. Forcing unproven genetic paradigms into all epidemiological studies is risky, and the likelihood of contradictory results may increase, leading to a gradual erosion of credibility. Acknowledgements Supported by Deutsche Krebshilfe, The Swedish Cancer Society and the EU LSHC-CT-2004-503465. 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==== Front Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-61565507410.1186/1477-7525-3-6ResearchEffect of method of administration on longitudinal assessment of quality of life in gynecologic cancer: An exploratory study Gil Karen M [email protected] Heidi E [email protected] Michael P [email protected] Eric L [email protected] Gruenigen Vivian E [email protected] Department of Obstetrics and Gynecology, Akron General Medical Center, Akron, Ohio, USA2 Northeastern Ohio Universities College of Medicine, Rootstown, Ohio, USA3 Department of Obstetrics and Gynecology, University Hospitals of Cleveland, Cleveland, Ohio, USA2005 17 1 2005 3 6 6 10 12 2004 17 1 2005 Copyright © 2005 Gil et al; licensee BioMed Central Ltd.2005Gil et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Longitudinal assessments of quality of life are needed to measure changes over the course of a disease and treatment. Computer versions of quality of life instruments have increased the feasibility of obtaining longitudinal measurements. However, there remain occasions when patients are not able to complete these questionnaires. This study examined whether changes measured using a computer version of the Functional Assessment of Cancer Therapy – General (FACT-G) on two occasions would be obtained if patients completed a paper version on one of the two occasions. Methods Gynecologic oncology patients completed a computer version of the FACT-G pre-operatively and at six months. Patients were given the option of using the paper version instead of the computer at either time point. Repeated measures analysis of variance was used. Results One hundred nineteen patients completed the FACT-G at both time points. Seventy-one (60%) patients used the computer at both visits, 26 (21.8%) used the computer followed by the paper version, 17 (14.3%) used the paper version followed by the computer version, and five patients (4.2%) used the paper version at both visits. Significant effects over time were obtained in the physical, functional, and emotional well-being domains, and in total scores, but there were no effects of method of administration of the questionnaires and no interaction between method of administration and changes over time. Conclusions These data indicate that women are responding to the content of the questionnaire and not method of data collection. Although using the same method of administration of instruments over time is desirable, using alternate methods is preferable to forgoing data collection entirely. Large scale studies should be conducted to determine if the multiple methods of data collection that are becoming increasingly available are producing interchangeable information. ==== Body Background Measurement of changes in quality of life (QoL) has become a standard outcome variable in evaluating different therapeutic regimes in cancer [1-3]. Standardized, validated and reliable questionnaires are available for the measurement of changes in QoL [4-7]. Additionally, the use of these instruments by clinicians caring for patients is being explored [8-11]. Assessing changes in QoL as patients progress through the course of disease and treatment increases the need for longitudinal assessment. Computer versions of these questionnaires have become available and can be used for longitudinal assessments [12-17]. These systems are well accepted by patients [12,16-19] and allow for the collection of data without transcription errors [12,17,18]. Comparison of data collected at one point in time by computer versus paper suggest that the method of collecting the information does not have a large effect on the data collected [19], although some differences are obtained. Formatting of the questions has been found to have an effect [20], and there may be a tendency of patients to give more positive responses with the computer, especially if the format is simplified [12,20]. A potential barrier to longitudinal measurements is that compliance may decrease over time. Patients may be initially willing to answer questions on the computer, but be less willing to do so on subsequent visits [14,17]. Reasons for this may include time constraints due to office and patients' scheduling needs as well as patients feeling unwell. One method to deal with these realities of daily clinical practice is to offer patients the choice of taking home the questionnaires to complete if they state they do not have time or do not want to complete the questionnaires on the computer at that time. This would introduce two principal differences. The method of data collection would be different (paper vs. computer), and the location of completing the forms would be different (home vs. office). Asking patients about their QoL over the past several days would reduce the effect of answering the questions in the office or at home. If the instruments are measuring significant changes in life due to major events such as diagnosis of serious disease, surgery, chemotherapy, and remission, then the location and method of administration of the instrument should have minimal effect on responses. Women attending a gynecologic oncology practice were enrolled in a longitudinal study of QoL. Women completed a computer version of a QoL questionnaire pre-operatively and again at six months. They were given the option of using the paper version at either time point, and the effect of this choice was examined. An additional issue examined was whether use of the paper version was widespread or sporadic. The goal of the study was to compare changes over time obtained when women used a touch-screen computer on two occasions with changes obtained when women used a paper version of the questionnaire on one of the occasions. Methods Patients who were scheduled to undergo surgery for endometrial cancer, ovarian cancer or an adnexal mass were invited to participate in a long term study of QoL, complementary medicine use and diet. Women at two gynecologic oncology offices in Northeast Ohio were recruited from 2001 – 2003. Informed consent was obtained for participation in this IRB approved study. Private office records and hospital discharge records were reviewed to abstract demographics and final pathology diagnosis. Baseline demographics were ascertained by interview with a research assistant pre-operatively. Patients completed the questionnaire pre-operatively and again at six months. Computer kiosks with a 15 inch monitor were programmed with the Functional Assessment of Cancer Therapy-General questionnaire (FACT-G) along with an additional fatigue module [21]. The FACT is a 27-item questionnaire consisting of four domains: physical, emotional, social and functional well-being. Patients are asked how true each statement has been for them over the past seven days. Each domain is comprised of six to seven questions scored by use of a Likert-type scale ranging from 0 (not at all) to 4 (very much). Each domain appeared on one screen and patients touched their response to each individual question. Patients could change their answers by touching an alternate response on that screen but could not return to a previous screen. All questions had to be completed before the computer continued to the next screen. The touch screen computer was designed so that the format of the questions closely matched the format of the questions on the paper form. Patients utilized the computer kiosk independently during their office visit although the research assistant was available to answer questions. Patients were given the option of completing the questionnaire using the paper version at any time. Statistical analyses Patients were categorized into four groups; those who completed the FACT-G on the computer on both occasions (CC), those who completed the initial assessment by computer and used paper format at six months (CP), patients who completed the initial assessment on paper and the six-month via computer (PC) and patients who completed both assessments on paper (PP). Analysis of variance or chi-square statistic was used to compare baseline demographic variables between patients who always used the computer and those who utilized the paper version at either time point. Repeated measures analysis-of-variance was used to analyze change in the domain score from baseline to six months (time effect), whether there was an effect of group (CC, CP, PC and PP) and whether there was an interaction between group and time. Significance was set at p < 0.01 due to multiple comparisons. SPSS version 10.0 was used for analysis (Chicago, IL). Results A total of 187 patients were asked to participate in this longitudinal study and 151 agreed (81%). Following completion of the initial assessment, 32 patients were lost to follow-up, moved, missed the second appointment entirely or refused to complete the questionnaire the second time (16 patients with benign adnexal mass, 8 with endometrial cancer and 8 with ovarian cancer). A total of 119 patients (79% of patients who agreed to participate in the study) completed the FACT-G assessments at both time points. Forty patients had endometrial cancer, 40 had ovarian cancer and 39 had a benign adnexal mass. Twenty of the cancer patients had Stage III or IV disease. Virtually all of the patients were Caucasian (96.6%). Patients returned the questionnaire by mail within a few days of their scheduled visit. Seventy-one (60%) patients used the computer at both visits (CC), 26 (21.8%) used the computer initially followed by the paper version at six months (CP), 17 (14.3%) used the paper version initially followed by the computer version (PC), and five patients (4.2%) used the paper version at both visits (PP). Patients in the PP group were excluded from statistical analyses as the numbers in that group were small (n = 5). There were no differences in the age (F = 0.225, p = 0.80) or level of education (χ2 = 2.75, p = 0.60) between the CC, PC and CP groups (Table 1). Approximately 60% of the patients within each diagnosis group used the computer at both time points (Table 1). A slightly higher percentage of patients with a benign adnexal mass used the paper version of the FACT-G at the six months visit (χ2 = 11.07, p = 0.026) as they were more likely to decline to come in for an office visit and request the FACT-G be sent home than were the patients with a cancer diagnosis (Table 1). Four of the five patients in the PP group had ovarian cancer. Mean age of those in the PP group was similar to the other groups (61.2 years) and all had some college or were college graduates. Table 1 Patient Demographics by Group CC (n = 71) CP (n = 26) PC (n = 17) Age (mean ± SEM) 58.3 ± 1.5 56.4 ± 2.6 57.4 ± 2.9 Diagnosis Benign (n = 39) 24 (61.5%) 14 (35.9%) 1 (2.6%) Endometrial CA (n = 39) 25 (64.1%) 5 (12.8%) 9 (23.1%) Ovarian CA (n = 36) 22 (61.1%) 7 (19.4%) 7 (19.4%) Education HS or less 31 (43.7%) 14 (53.8%) 11 (64.7%) Some college 14 (19.7%) 4 (15.4%) 2 (11.8%) College grad or higher 26 (36.6%) 8 (30.8%) 4 (23.5%) Physical well-being domain scores were significantly higher at six months than at baseline (Figure 1, F = 8.849, p = .004) and there was no effect of group (CC, CP, PC; p = 0.480) and no interaction between time and group (p = 0.457). Functional well-being scores were also higher at six months (Figure 2, F = 14.024, p < 0.001) and there was no effect of group (p = 0.453) and no interaction effect (p = 0.583). Emotional well-being scores were significantly higher at six months (Figure 3, F = 24.334, p < 0.001) and there was no effect of group (p = 0.943) and no interaction between group and time (p = 0.865). Social well-being scores did not increase with time (Figure 4, p = 0.14) and there was no effect of group (p = 0.185). There was a significant interaction between group and time (F = 5.671, p = 0.005) as the CP group had a higher score at baseline. There was no effect of time, group or interaction on fatigue scores (data not shown). Total scores were significantly higher at six months (Figure 5, F = 12.174, p = 0.001) and there was no effect of method (p = 0.756) and no interaction effect (p = 0.392). Figure 1 Scores on the Physical Well-Being domain of the Functional Assessment of Cancer Therapy (FACT-G) Figure 2 Scores on the Functional Well-Being domain of the Functional Assessment of Cancer Therapy (FACT-G) Figure 3 Scores on the Emotional Well-Being domain of the Functional Assessment of Cancer Therapy (FACT-G) Figure 4 Scores on the Social Well-Being domain of the Functional Assessment of Cancer Therapy (FACT-G) Figure 5 Total scores on the Functional Assessment of Cancer Therapy (FACT-G) Discussion Physical, functional, emotional well-being, and total scores, improved significantly between baseline and six months. In all cases, there was no effect of group and no interaction between group and time, indicating that the women were not affected by the method of data collection. There were also no significant effects of group even when there was no change in the scores over time (social well-being, fatigue). The one significant interaction effect was observed with the social well-being domain, which appeared due to a high baseline score in the CP group. At baseline, the CP group was the same as the CC group (they all used the computer) so it is not clear why there would be a high baseline score in the group that would use a paper version six months later. It is possible that with the number of tests conducted, one spurious finding would be obtained. The trend across all the tests is very strong, however. There are clear and significant changes with time but not with the method of obtaining the data. Given the choice between using the computer version and the paper version, a small number of women chose the paper version. Of the 238 total measurements, the paper version was used a total of 53 times (22%). Reasons for not using the computer included not wanting to come in to the physician's office at all and patient preference but also instances beyond the patients' control such as scheduling complications and researcher unavailability on a small number of occasions. Designing strategies to increase computer availability may result in further reductions in patient use of the paper versions. If patients can log onto the computer using a unique identifier and complete the questionnaires on their own in the waiting room, the number of women who have to take questionnaires home or forgo completing them should decrease even further. The second assessment occurred six months following major surgery for all women. The majority of women with ovarian cancer received chemotherapy, but were not receiving it at six months. This time point therefore allows a relatively stable point to assess changes in QoL relative to pre-operative scores in these groups of women. It is possible that differences in method of data collection would be obtained if women were acutely ill at the time of measurement, however the time frame of seven days used in the FACT-G reduces the likelihood that a separation in time of a day or two between using the computer in the office or the paper version at home will result in different responses. The time frame used in the FACT-G, and the relatively stable time point chosen may therefore contribute to the lack of measurement effect obtained in these groups of women. A limitation of this study is the lack of minority representation which may reduce the generalizability of these results. Additionally, 19% of patients refused to participate in the study. Of the patients who did participate, 21% did not complete the second assessment, although this figure includes 16 women with a benign adnexal mass who may have returned to their referring physician, and women with cancer who moved or transferred their care. Nonetheless, the women who remained on study may differ from those who did not agree to participate or who did not complete the second assessment. They may, for example, have a greater degree of commitment to the research process. A second limitation is that women were not randomly assigned to use either the computer or the paper versions. This is a preliminary examination of existing data to determine whether there appeared to be a selection bias, or major effect, of using the paper version. Women with QoL scores that differed markedly from the norm, for example, might have chosen to take the paper version home. This did not appear to be the case, however, as highly significant effects of time were observed, but group and interaction effects were markedly non-significant. Related limitations include the remote, but possible, explanation that the first method of administration had an effect on participants at the second time point. Additionally, patient choice itself may have had an unmeasured effect. For example, women with benign adnexal mass were more likely to forego the second office visit and complete the questionnaire at home. Disease and questionnaire administration are therefore confounded. These limitations may have influenced group choice, as well as responses on the second measurement. These exploratory data suggest that women are responding to questionnaires presented on a computer in the same manner as questionnaires on paper. This study therefore differed somewhat from studies that found differences in method of administration [12,20]. An important consideration may be maintaining the same format of the questions in the two methods of administration. In this study, each domain was presented on one large screen so that all questions were listed together. The similarity of the format may have contributed to the finding that modes of administration are interchangeable, however larger scale studies, which include randomization and assessing women at different stages of treatment, should be conducted to verify these findings. Conclusions Longitudinal measurements of health- related QoL are increasingly used in cancer patients. This study examined whether two different methods of measuring QoL (computer and paper) would provide interchangeable data. It appears that patients are dealing with issues of significant concern and they are responding to the content of the questions and not the method of data collection. It is clearly desirable to standardize the method of data collection and have conditions remain constant across time. The results of this study, however, demonstrate that valid data are obtained with alternate methods of data collection and this is preferable to foregoing data collection entirely. Authors' contributions KG and VVG conceived of the study, and participated in its design and coordination. HF, MH, EJ and VVG implemented the study and were responsible for day to day conduct of the study. KG and HF analyzed the data. KG, HF, VVG drafted the manuscript; JE and MH provided critical review. All authors read and approved the final manuscript. Acknowledgements Supported by the Irene H. Smith Memorial Fund of The Stark Community Foundation, the Akron General Development Foundation and the Northeastern Ohio Universities College of Medicine Foundation. The authors would like to acknowledge the contributions and suggestions made by this journals' reviewers. ==== Refs Schwartz CE Sprangers MA An introduction to quality of life assessment in oncology: the value of measuring patient-reported outcomes Am J Manag Care 2002 S550 S559 12512979 Boling W Fouladi RT Basen-Engquist K Health-related quality of life in gynecological oncology: instruments and psychometric properties Int J Gynecol Cancer 2003 13 5 14 12631213 10.1046/j.1525-1438.2003.13051.x Osoba D What has been learned from measuring health-related quality of life in clinical oncology Eur J Cancer 1999 35 1565 1570 10673963 10.1016/S0959-8049(99)00192-6 Cella DF Tulsky DS Gray G Sarafian B Linn E Bonomi A Silberman M Yellen SB Winicour P Brannon J The Functional Assessment of Cancer Therapy scale: development and validation of the general measure J Clin Oncol 1993 11 570 579 8445433 Basen-Engquist K Bodurka-Bevers D Fitzgerald MA Webster K Cella D Hu S Gershenson DM Reliability and validity of the functional assessment of cancer therapy-ovarian J Clin Oncol 2001 19 1809 1817 11251013 Aaronson NK Ahmedzai S Bergman B Bullinger M Cull A Duez NJ Filiberti A Flechtner H Fleishman SB de Haes JC The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology J Natl Cancer Inst 1993 85 365 376 8433390 Kemmler G Holzner B Kopp M Dunser M Margreiter R Greil R Sperner-Unterweger B Comparison of two quality-of-life instruments for cancer patients: the functional assessment of cancer therapy-general and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 J Clin Oncol 1999 17 2932 2940 10561373 Detmar SB Muller MJ Schornagel JH Wever LD Aaronson NK Health-related quality-of-life assessments and patient-physician communication: a randomized controlled trial JAMA 2002 288 3027 3034 12479768 10.1001/jama.288.23.3027 Taenzer P Bultz BD Carlson LE Speca M DeGagne T Olson K Doll R Rosberger Z Impact of computerized quality of life screening on physician behaviour and patient satisfaction in lung cancer outpatients Psychooncology 2000 9 203 213 10871716 Velikova G Brown JM Smith AB Selby PJ Computer-based quality of life questionnaires may contribute to doctor-patient interactions in oncology Br J Cancer 2002 86 51 59 11857011 10.1038/sj.bjc.6600001 Velikova G Booth L Smith AB Brown PM Lynch P Brown JM Selby PJ Measuring quality of life in routine oncology practice improves communication and patient well-being: a randomized controlled trial J Clin Oncol 2004 22 714 724 14966096 10.1200/JCO.2004.06.078 Velikova G Wright EP Smith AB Cull A Gould A Forman D Perren T Stead M Brown J Selby PJ Automated collection of quality-of-life data: a comparison of paper and computer touch-screen questionnaires J Clin Oncol 1999 17 998 1007 10071295 McLachlan SA Allenby A Matthews J Wirth A Kissane D Bishop M Beresford J Zalcberg J Randomized trial of coordinated psychosocial interventions based on patient self-assessments versus standard care to improve the psychosocial functioning of patients with cancer J Clin Oncol 2001 19 4117 4125 11689579 Wright EP Selby PJ Crawford M Gillibrand A Johnston C Perren TJ Rush R Smith A Velikova G Watson K Gould A Cull A Feasibility and compliance of automated measurement of quality of life in oncology practice J Clin Oncol 2003 21 374 382 12525532 10.1200/JCO.2003.11.044 Carlson LE Speca M Hagen N Taenzer P Computerized quality-of-life screening in a cancer pain clinic J Palliat Care 2001 17 46 52 11324185 Buxton J White M Osoba D Patients' experiences using a computerized program with a touch-sensitive video monitor for the assessment of health-related quality of life Qual Life Res 1998 7 513 519 9737141 10.1023/A:1008826408328 Allenby A Matthews J Beresford J McLachlan SA The application of computer touch-screen technology in screening for psychosocial distress in an ambulatory oncology setting Eur J Cancer Care (Engl) 2002 11 245 253 12492461 10.1046/j.1365-2354.2002.00310.x Drummond HE Ghosh S Ferguson A Brackenridge D Tiplady B Electronic quality of life questionnaires: a comparison of pen-based electronic questionnaires with conventional paper in a gastrointestinal study Qual Life Res 1995 4 21 26 7711686 10.1007/BF00434379 Taenzer PA Speca M Atkinson MJ Bultz BD Page S Harasym P Davis JL Computerized quality-of-life screening in an oncology clinic Cancer Pract 1997 5 168 175 9171553 Boyes A Newell S Girgis A Rapid assessment of psychosocial well-being: are computers the way forward in a clinical setting? 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==== Front World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-471560692210.1186/1477-7819-2-47ResearchThe prognosis of women with stage IB1-IIB node-positive cervical carcinoma after radical surgery Cheng Xi [email protected] Shumo [email protected] Ziting [email protected] Meiqin [email protected] Muquan [email protected] Rongyu [email protected] Department of Gynecologic Oncology, Cancer Hospital, Fudan University, Shanghai, 200032, P.R. China2004 18 12 2004 2 47 47 1 10 2004 18 12 2004 Copyright © 2004 Cheng et al; licensee BioMed Central Ltd.2004Cheng et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Pelvic lymph nodes metastasis is an important prognostic factor for patients with cervical carcinoma. However, the relationships between the number of positive nodes, site of metastases nodes, adjuvant therapy and the prognosis is controversial. The purpose of this study was to investigate the influence of positive lymph nodes on the prognosis of Chinese women with stage IB1-IIB cervical carcinoma. Patients and methods Between January 1992 and December 1997, 398 women with International Federation of Gynecology and Obstetrics (FIGO) stage IB1-IIB cervical carcinoma underwent radical surgery in Cancer Hospital, Fudan University. Of these sixty-six patients (16.6%) who were histologically confirmed to have positive pelvic lymph nodes were analyzed retrospectively. The survival was estimated using Kaplan-Meier method. The differences in survival were compared with Log-rank test. Multivariate analyses were performed with the Cox proportional hazard model. Results The 5-year survival of the patients with pelvic lymph nodes metastases was 40.7%. Cox proportional hazard model analysis showed that cellular differentiation, the number of positive nodes and adjuvant therapy to be the independent prognostic factors (P < 0.05). The 5-year survival of patients with one positive node was higher than that of those with two or more positive nodes (56.5% vs. 36.4%, P < 0.05). The distant metastasis rate in the former group (5.9%) was lower than the latter's (32.7%) (P = 0.05). However, there was no significant difference of pelvic recurrence between the two groups (P > 0.05). The number of positive nodes positively correlated with the level of positive nodes (P < 0.01). The 5-year survival of the patients who had no adjuvant therapy (12.6%) was much lower than that (53.7%) of those with adjuvant therapy (P < 0.05). However, there was no obvious difference between adjuvant radiotherapy, chemotherapy and chemo-radiotherapy (P > 0.05). Conclusions The prognosis of patients with stage IB1-IIB node-positive cervical carcinoma who underwent radical surgery alone was very poor. Adjuvant therapy increases the survival rate, decreases the pelvic recurrence and distant metastasis. ==== Body Background Although radical radiotherapy (RT) and radical surgery can be the proper choices for patients with early stage cervical cancer, most of the patients in China prefer the radical surgery to RT. Hence, in China the radical surgery has been widely used as first-line therapy for this group of women. Some poor prognostic subgroups have been identified, among these the pelvic lymph node status has been considered as the most important prognostic factor. Radical hysterectomy with bilateral pelvic lymphadenectomy produces an expected 85–90% survival in women with stage IB and IIA cervical carcinoma without lymphatic spread. However, once tumors involve regional lymph nodes, 5-year survival has been reported to be only 30–60% [1]. In most of the studies the presence of pelvic lymph node metastases has been associated with increased pelvic recurrence and distance metastases, and a decrease in overall survival [2-7]. However, many questions such as the relationship between the numbers, the site of positive nodes, the modality of postoperative multidisciplinary therapy and the prognosis is not yet clear. This study investigated the factors that could predict the prognosis of the patients with stage IB1-IIB node-positive cervical carcinoma. Patients and methods Between January 1992 and December 1997, 398 women with International Federation of Gynecology and Obstetrics (FIGO) stage IB1-IIB cervical carcinoma underwent radical surgery at the Department of Gynecologic Oncology, Cancer Hospital of Fudan University. Of these 66 patients who had undergone Wertheims-Meigs' surgery (radical hysterectomy and pelvic lymphadenectomy) and were histologically confirmed to harbor positive pelvic lymph node were included in this study. The median age at diagnosis was 49 years (range 21 to71). Out of 66, 8 patients were in stage IB1 (12.1%), 37 patients (56.1%) in stage IIA and 21 patients (31.8%) in stage IIB. Histologically 41 women (62.1%) had squamous carcinoma, 20 (30.3%) had adenocarcinoma, 4 (6.1%) adenosquamous carcinoma and 1 patient (1.5%) had small cell carcinoma. The tumors in 4 patients (6.1%) were well differentiation, 46 cases (69.7%) moderately differentiated and 16 (24.2%) poorly differentiated. The average lymph nodes resected were 14.8 per patient while the average positive lymph nodes resected were 3.7 (1~28) per patient. The average diameter of the cervical tumors was 3.6 cm (1~7 cm). The details of the patients' clinical characteristics are listed in Table 1. Table 1 Clinico-pathologic characteristics of patients with node-positive cervical carcinoma after radical surgery Factors n Percentage (%) Age(yrs) <40 15 22.7 ≥40 51 77.3 Stage IB 8 12.1 IIA 37 56.1 IIB 21 31.8 Tumor size(cm) <4 31 47.0 ≥4 35 53.0 Histology Squamous 41 62.1 Adenocarcinoma 20 30.3 Adenosquamous 4 6.1 Others 1 1.5 Differentiation Poor 16 24.2 Moderate 46 69.7 Well 4 6.1 Pelvic lymph node metastases 1 17 25.8 ≥2 49 74.2 Parametrial extension Negative 58 87.9 Positive 8 12.1 Vaginal margin involved Negative 64 97.0 Positive 2 3.0 Depth of stromal invasion ≤2/3 19 28.8 ≤2/3 47 71.2 Lymphvascular permeation Negative 47 71.2 Positive 19 28.8 Sixty four of these women (97.0%) had brachytherapy in either three or four fractions with a total dose of 15~20 Gy at point A, two weeks prior to radical surgery because of bulky tumor or vaginal vault involvement. Intra-arterial chemotherapy was administered to 11 patients (16.7%) before surgery because of bulky tumor or parametrial extension. The regimen based on cisplatin (CDDP) + 5-Fluorouracil(5-FU) with 2~3 cycles at 3-weeks intervals was used. All patients underwent Wertheims-Meigs' procedure that included radical hysterectomy and bilateral pelvic lymphadenectomy. Three patients had para-aortic lymph node (PALN) sampling because there was suspicion of metastasis. Postoperative external beam irradiation was delivered to 18 patients (27.3%) with 6 MV X-ray routine anterior and posterior portal at a dose of 35~45 Gy at point B with 1.8 Gy daily fraction. Two patients with positive vaginal margin were given extra brachytherapy with 15 Gy 0.5 cm below the vaginal mucosa. 12 cases with positive common iliac node and 3 with PALN metastasis received an additional 40 Gy to the PALN chain area. 10 patients (15.2%) were given postoperative chemotherapy. The regimen consisted CDDP + 5-FU + Cyclophosphamide (CTX) for squamous carcinoma, and CDDP + 5-FU + Mitomycin (MMC) for adenocarcinoma with 2~6 cycles at 3~4-weeks intervals. A total of 19 patients (28.8%) received adjuvant radiotherapy and chemotherapy as mentioned above. Follow-up Patients were evaluated every two months for the first two years and then six monthly for the additional years by clinical interview, or telephone, or letters. Disease-free survival (DFS) was defined as the time from the date of surgery to local or nodal recurrence or metastasis. Overall survival (OS) was calculated from the date of surgery to the date of death. Recurrences were defined as local if they were detected in the pelvis or vagina and distant metastases as detected in extra-pelvic locations. The median follow-up time was 32 months (range 2~108 months). Statistical analysis Data analysis was performed with Statistical package for social sciences (SPSS) version 10.0 statistical package. The survival was calculated by Kaplan-Meier method. The differences in survival were compared with Log-rank test. Multivariate analyses were performed with the Cox proportional hazard model. The correlation analysis was performed by Kendall's method. Pearson's chi-square or Fisher's exact test was used to compare the difference of proportions. A probability value of P < 0.05 was considered significant. Results The 5-year overall survival of the patients with pelvic lymph node metastasis was 40.7%. The 5-year survival of patients with one positive node (56.5%) was higher than that (36.4%) of those with two or more positive nodes (P < 0.05). The former's distant metastasis rate (5.9%) was lower than the latter's (32.7%) (P = 0.05). However, there was no significant difference of pelvic recurrence between them (P > 0.05) (Table 2, Figure 1) Table 2 Relationship between the number of positive nodes and prognosis Number of positive nodes n 5-year survival (%) P Recurrence rate (%) P Metastasis rate (%) P 1 17 56.5 0.033 23.5 0.807 5.9 0.050 ≥2 49 36.4 26.5 32.7 Figure 1 Overall survival according to one vs. two or more positive lymph nodes. The 5-year survival (33.3%) of the patients with positive common iliac node and paraaortic lymph node (PALN) was less than that (43.1%) of patients with lower than common iliac node involvement. However, the difference was not significant (P > 0.05). The former's pelvic recurrence rate (13.3%) was lower than the latter's (29.4%) (P > 0.05). However, the patients with common iliac or paraaortic nodes had significantly higher distant metastasis (53.3%) than that of the patients with lower nodes (17.6%) (P < 0.01). The number of the positive lymph nodes was closely correlated with the level of lymph node metastasis. The relative coefficient was 0.557 (P < 0.01) (Table 3). Table 3 Relationship between the site of positive nodes and prognosis Site n 5-year survival rate (%) P Recurrence rate (%) P Metastasis rate (%) P Common iliac or above 15 33.3 0.086 13.3 0.318 53.3 0.005 Lower than common iliac 51 43.1 29.4 17.6 The 5-year survival (12.6%) of the patients who had no adjuvant therapy was much lower than that (53.7%) of those with adjuvant therapy (P<0.05). The 5-year survival rates of the adjuvant radiotherapy, adjuvant chemotherapy, and adjuvant radio-chemotherapy groups were 53.5%, 49.2% and 56.1% respectively (P > 0.05). The pelvic recurrence (42.1%) and distant metastasis (31.6%) of the surgery alone were higher than those with adjuvant therapeutic groups. However, the differences were not significant (P > 0.05) (Table 4, Figure 2) Table 4 Relationship between adjuvant therapy and prognosis n 5-year survival (%) Recurrence rate (%) Metastasis rate (%) No adjuvant therapy 19 12.6 42.1 31.6 Radiotherapy 18 53.5 22.2 22.2 Chemotherapy 10 49.2 20.0 10.0 Radiochemotherapy 19 56.1 15.8 21.1 Figure 2 Overall survival according to adjuvant therapy [radiotherapy (RT) vs. chemotherapy (CT) vs. radiochemotherapy (RT+CT) vs. no adjuvant therapy (No)]. Multivariate analysis of prognostic factors Cox proportional hazard model analysis showed that cellular differentiation, number of positive nodes and adjuvant therapy to be the independent prognostic factors for survival (P < 0.05) (Table 5) Table 5 Cox proportional hazard model analysis of variables in predicting overall survival Factors Coefficient RR 95%CI P Age -0.031 0.970 0.932~1.010 0.136 Stage 0.283 1.327 0.379~4.644 0.658 Tumor size 0.332 1.394 0.984~1.975 0.061 Histology -0.035 0.966 0.563~1.659 0.900 Differentiation 0.787 2.196 1.104~4.370 0.025* Number of positive nodes 0.076 1.079 1.006~1.158 0.034* Parametrial extension 0.484 1.623 0.584~4.508 0.353 Vaginal margin involved -1.007 0.365 0.029~4.655 0.438 Depth of stromal invasion 0.423 1.526 0.856~2.722 0.152 Lymphvascular permeation 0.270 1.311 0.500~3.437 0.582 Nerve invasion 0.101 1.106 0.092~13.337 0.937 Adjuvant therapy -1.684 0.186 0.075~0.459 0.000* * P < 0.05 Discussion Cervical cancer remains the third common cancer in women around the world, although its incidence is on the decline in North American and in Europe. It is estimated that there will be 10,370 new cases in 2005 in North America [8]. In many developing countries, not only is cervical cancer the most frequently occurring cancer among middle-aged women, but it is also a leading cause of death, partly due to the poor access to medical care and the unavailability of routine screening in many of these countries [9]. In Shanghai, the biggest city in China, there were 150 new cases in 2000. The standard incidence rate was 2.9 per 100 000 [10]. Radical surgery has been found to be very effective in patients with early stage (IB-IIA) cervical carcinoma [11-13]. However, the 5-year survival has been lingering at 50%-90% during the past twenty years [14]. The reported risk factors of cervical carcinoma included clinical stage, bulky tumor, histological types, cellular differentiation, deep stromal invasion, parametrial extension, vaginal margin involved, lymphvascular permeation, lymph node metastasis, race and age [13,15-18]. In most studies the presence of pelvic lymph node metastases has been associated with increased pelvic recurrence and distance metastases, and a decrease in overall survival. Our Cox proportional hazard model analysis showed cellular differentiation, number of positive nodes and adjuvant therapy to be the independent prognostic factors (P < 0.05). The 5-year survival of patients with one positive node (56.5%) was higher than that (36.4%) of those with two or more positive nodes (P < 0.05). There was no significant difference of pelvic recurrence between them. However, the former's distant metastases rate (5.9%) was lower than the latter's (32.7%) (P = 0.05). In the analysis of the site of lymph node metastases, the 5-year survival (33.3%) of the patients with positive common iliac node and paraaortic lymph node (PALN) was lower than that (43.1%) of patients with lower than common iliac node involvement. Unlike the series of Tsai the difference in our series was not statistically significant [19]. This is probably due to limited number of cases with paraaortic or common iliac nodes. The number of positive nodes correlated with the height of positive nodes (P < 0.01). The result suggested that in patients with more positive pelvic nodes there is higher chance of nodal metastasis at higher level node basins, which supported the external irradiation of PALN chain area for the multiple pelvic nodes involvement. The distant metastasis rate was 32.7% in patients with multiple positive lymph nodes, which showed the limitation of adjuvant radiotherapy and theoretically supported the postoperative combined chemotherapy. The prognosis of patients with positive lymph node was poor because of local recurrence and distant metastasis. How to improve the prognosis of these patients has been the focus of gynecological oncology. Stock et al [7] compared postoperative whole pelvic irradiation with those treated with radical hysterectomy alone, and showed a significantly improved pelvic control rate, 5-year disease-free survival (DFS) and overall survival (OS) (78% Vs 45%, 65% Vs 41%, 58% Vs 46%, respectively). Peters' study [20] demonstrated that postoperative radio-chemotherapy could greatly improve the 4-year DFS and OS compared to the adjuvant radiotherapy alone. In our study, adjuvant therapy improved the 5-year survival than the surgery alone (53.7% vs. 12.6%), decreased the pelvic recurrence and distant metastasis, which suggested the clinical importance of adjuvant therapy in patients with positive nodes. In this study the 5-year survival of adjuvant radio-chemotherapy arm was higher than adjuvant radiotherapy alone or chemotherapy alone. The pelvic recurrence (42.1%) and distant metastasis (31.6%) of the surgery alone were higher than the other three therapeutic arms. However, the differences were not significant and the limited number of cases in each arm could be the reason. It is necessary that these results are verified in prospective randomized control trials. Several randomized clinical trials [20-25] performed by the Gynecologic Oncology Group (GOG), the Radiation Therapy Oncology Group (RTOG) and the Southwest Oncology Group (SWOG) have demonstrated a significant advantage both in DFS and OS when cisplatin-based chemotherapy was administered during radiation for advanced stages of cervical cancer and early stage disease with poor prognostic factors. Green et al [26] did a systemic review of all known randomized controlled trials published between 1981 and 2000, 2865–3611 patients were available for analysis. The findings suggested that concomitant chemotherapy (CT) and radiotherapy (RT) improves OS and DFS, and reduces local and distant recurrence. Based on results, the US National Cancer Institute (NCI) released a clinical announcement supporting the concurrent use of cisplatin-based chemotherapy with RT for high-risk early stage and locally advanced stage cervical cancer [9,27]. Recently a prospective randomized trial performed by the National Cancer Institute (NCI) of Canada [25] failed to show benefit with the use of chemoradiation compared with RT alone. The potential inclusion of paraaortic nodes positive patients and the significant difference in anemia raises question about this trial result [28]. The therapy of patients with stage IIB cervical carcinoma is still a controversy [29]. Although radical radiotherapy (RT) is the proper choice for patients with stage IIB cervical cancer in general, the therapeutic effect is not as good as expected if the tumor is too bulky or is histologically adenocarcinoma. Hence a few gynecology oncologists tried brachytherapy and/or neoadjuvant chemotherapy to decrease the lesion, then performed radical surgery for some stage IIB patients with bulky tumor (>4 cm) or slight (less than 1/2) parametrial extension [30,31]. Postoperative adjuvant therapy would then be recommended according to risk factors [9,17,20,32]. In our study, the 5-year survival of the patients with stage IIB cervical carcinoma was only 54.3%, which was not at all satisfactory compared with the reported 58.9%–80.1% [33-35]. So we advocate that radical surgery should be taken cautiously for this group of patients. Any attempt to improve their prognosis by means of adjuvant therapy is not recommended if the parametrium can not be thoroughly dissected from the pelvic wall. Competing interests The author(s) declare that they have no competing interests. Authors Contributions XC collected the data, patients' follow-up and drafted the article. SC, ZL, MT, MX collectively designed the study, participated in collection of data and revising the article. RZ participated in collection and analysis of data, revising the article Funding Source None ==== Refs Monk BJ Cha DS Walker JL Burger RA Ramsinghani NS Manetta A Disaia PJ Berman ML Extent of disease as an indication for pelvic radiation following radical hysterectomy and bilateral pelvic lymph node dissection in the treatment of stage IB and IIA cervical carcinoma Gynecol Oncol 1994 54 4 9 8020837 10.1006/gyno.1994.1157 Hopkins MP Morley GW Stage Ib squamous cell cancer of the cervix: Clinicopathologic features related to survival Am J Obstet Gynecol 1991 164 1520 1527 2048598 Hsu CT Cheng YS Su SC Prognosis of uterine cervical cancer with extensive lymph node metastases: Special emphasis on the value of pelvic lymphadenectomy in the surgical treatment of uterine cervical cancer Am J Obstet Gynecol 1972 114 954 962 4645136 Inoue T Morita K The prognostic significance of number of positive nodes in cervical carcinoma stage Ib, IIa, IIb Cancer 1990 65 1923 1927 2372764 Lanza A Re A 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==== Front J NeuroinflammationJournal of Neuroinflammation1742-2094BioMed Central London 1742-2094-2-31565690910.1186/1742-2094-2-3ResearchEffects of the cyclooxygenase-2 inhibitor nimesulide on cerebral infarction and neurological deficits induced by permanent middle cerebral artery occlusion in the rat Candelario-Jalil Eduardo [email protected] Noël H [email protected]ález-Falcón Armando [email protected]ía-Cabrera Michel [email protected]ñoz Eduardo [email protected]ón Olga Sonia [email protected] Bernd L [email protected] Department of Pharmacology, University of Havana (CIEB-IFAL), Havana 10600, Cuba2 Neurochemistry Research Group, Department of Psychiatry, University of Freiburg Medical School, Hauptstrasse 5, D-79104 Freiburg, Germany3 Departamento de Biología Celular, Fisiología e Inmunología. Universidad de Córdoba, Avda Menéndez Pidal s/n. 14004, Córdoba, Spain4 VivaCell Biotechnology GmbH, Ferdinand-Porsche-Str. 5, D-79211 Denzlingen, Germany2005 18 1 2005 2 3 3 17 12 2004 18 1 2005 Copyright © 2005 Candelario-Jalil et al; licensee BioMed Central Ltd.2005Candelario-Jalil et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Previous studies suggest that the cyclooxygenase-2 (COX-2) inhibitor nimesulide has a remarkable protective effect against different types of brain injury including ischemia. Since there are no reports on the effects of nimesulide on permanent ischemic stroke and because most cases of human stroke are caused by permanent occlusion of cerebral arteries, the present study was conducted to assess the neuroprotective efficacy of nimesulide on the cerebral infarction and neurological deficits induced by permanent middle cerebral artery occlusion (pMCAO) in the rat. Methods Ischemia was induced by permanent occlusion of the middle cerebral artery in rats, via surgical insertion of a nylon filament into the internal carotid artery. Infarct volumes (cortical, subcortical and total) and functional recovery, assessed by neurological score evaluation and rotarod performance test, were performed 24 h after pMCAO. In initial experiments, different doses of nimesulide (3, 6 and 12 mg/kg; i.p) or vehicle were administered 30 min before pMCAO and again at 6, 12 and 18 h after stroke. In later experiments we investigated the therapeutic time window of protection of nimesulide by delaying its first administration 0.5–4 h after the ischemic insult. Results Repeated treatments with nimesulide dose-dependently reduced cortical, subcortical and total infarct volumes as well as the neurological deficits and motor impairment resulting from permanent ischemic stroke, but only the administration of the highest dose (12 mg/kg) was able to significantly (P < 0.01) diminish infarct volume. The lower doses failed to significantly reduce infarction but showed a beneficial effect on neurological function. Nimesulide (12 mg/kg) not only reduced infarct volume but also enhanced functional recovery when the first treatment was given up to 2 h after stroke. Conclusions These data show that nimesulide protects against permanent focal cerebral ischemia, even with a 2 h post-treatment delay. These findings have important implications for the therapeutic potential of using COX-2 inhibitors in the treatment of stroke. ==== Body Background The brain is highly sensitive to disturbance of its blood supply. Stroke is a devastating disease and is the third most common cause of death, and the most common cause of motor and mental disability in adults, in developing countries [1]. Complex pathophysiological events occur in brain during ischemic processes, and these are considered responsible for cell damage leading to neuronal death (for review see [2,3]). However, it is now generally accepted that the mammalian brain may be more resistant to ischemia than previously thought. This raises the possibility of therapeutic intervention before brain damage has become irreversible. A number of interacting and sequentially evoked events tend to reinforce the initial ischemic insult. A key role in these processes is played by post-ischemic inflammation. The Ca2+-related activation of intracellular second messenger systems, the increase in reactive oxygen species formation, as well as hypoxia itself triggers the expression of a large number of pro-inflammatory genes following cerebral ischemia. Thus, mediators of inflammation such as platelet-activating factor (PAF), tumor necrosis factor α (TNFα), interleukin 1β (IL-1β), chemokines (IL-8, monocyte chemoattractant protein-1) and other pro-inflammatory factors are produced by the ischemic brain tissue [3]. In addition, the expression of adhesion molecules with the subsequent infiltration of polymorphonuclear leukocytes occurs following ischemic stroke. Results from several studies also suggest that the marked and sustained expression of inflammation-related enzymes such as inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) plays an important role in the secondary events that amplify cerebral injury after ischemia [4-12]. Nimesulide (N-(4-nitro-2-phenoxyphenyl)-methanesulfonamide) is a non-steroidal anti-inflammatory drug with potent effects. It shows a high affinity and selectivity for COX-2 with a COX-2/COX-1 IC50 selectivity ratio of 0.06 (whole blood assay) [13]. Nimesulide readily crosses the intact blood-brain barrier in both humans and rodents [13,14]. Several recent studies have demonstrated a marked neuroprotective effect of nimesulide on chronic cerebral hypoperfusion [15], kainate-induced excitotoxicity [16], quisqualic acid-induced neurodegeneration [17], diffuse traumatic brain injury [18,19], glutamate-mediated apoptotic damage [20] and induction of the expression of the B subunit of endogenous complement component C1q (C1qB) in transgenic mice with neuronal overexpression of human COX-2 [21]. Recently, we have found a significant neuroprotective effect of nimesulide both in global cerebral ischemia [10,22], a type of injury that mimics the clinical situation of cardio-respiratory arrest, and in a rat model of ischemic stroke induced by the transient (1 h) occlusion of the middle cerebral artery [12]. Since most cases of human ischemic stroke are caused by permanent occlusion of cerebral arteries [23-26], the present study was conducted to assess whether nimesulide would also show neuroprotective efficacy on the cerebral infarction induced by permanent middle cerebral artery occlusion (pMCAO) in the rat, a clinically relevant model of ischemic stroke. The effects of the COX-2 inhibitor nimesulide had not been previously investigated in a model of permanent ischemic stroke. Methods Animals Male Sprague-Dawley rats (CENPALAB, Havana, Cuba) weighing 280–340 g at the time of surgery were used in the present study. Our institutional animal care and use committee approved the experimental protocol (No. 02/67). The animals were quarantined for at least 7 days before the experiment. Animals were housed in groups in a room whose environment was maintained at 21–25°C, 45–50 % humidity and 12-h light/dark cycle. They had free access to pellet chow and water. Animal housing, care, and application of experimental procedures were in accordance with institutional guidelines under approved protocols. Induction of permanent focal cerebral ischemia in the rat Rats were anesthetized with chloral hydrate (300 mg/kg body weight, i.p.). Once surgical levels of anesthesia were attained (assessed by absence of hind leg withdrawal to pinch), ischemia was induced by using an occluding intraluminal suture as described previously [27-29]. Briefly, the right common carotid artery (CCA) was exposed by a ventral midline neck incision and ligated with a 3-0 silk suture. The pterygopalatine branch of the internal carotid artery was clipped to prevent incorrect insertion of the occluder filament. Arteriotomy was performed in the CCA approximately 3 mm proximal to the bifurcation and a 3-0 monofilament nylon suture, whose tip had been rounded by being heated near a flame was introduced into the internal carotid artery (ICA) until a mild resistance was felt (18–19 mm). Mild resistance to this advancement indicated that the intraluminal occluder had entered the anterior cerebral artery and occluded the origin of the anterior cerebral artery, the middle cerebral artery (MCA) and posterior communicating arteries [27]. After the advancement of the nylon suture, the ICA was firmly ligated with a 3-0 silk suture. The incision was closed and the occluding suture was left in place until sacrificing the animals. The duration of surgery did not exceed 12 min in any case. The animals were allowed to recover from anesthesia and to eat and drink freely. The body temperature was strictly controlled during and after ischemia. To allow for better postoperative recovery, we chose not to monitor physiological parameters in the present study because additional surgical procedures are needed for this monitoring. Nevertheless, we performed separate experiments to investigate the effects of nimesulide on major physiological variables such as mean arterial blood pressure, blood glucose, rectal temperature, hematocrit, blood pH and blood gases (pO2 and pCO2). The effects observed with nimesulide in the present study were not related to modification of physiological variables since these parameters did not differ between nimesulide-treated and vehicle-treated rats (data not shown). These findings are in agreement with our previous results [10,12], suggesting that nimesulide does not significantly change major physiological variables. Neurological evaluation An unaware independent observer performed the neurological evaluations prior to the sacrifice of the animals according to a six-point scale: 0= no neurological deficits, 1= failure to extend left forepaw fully, 2= circling to the left, 3= falling to left, 4= no spontaneous walking with a depressed level of consciousness, 5= death [30,31]. Assessment of functional outcome Motor impairment in this study was assessed with the use of the accelerating rotarod (Ugo Basile, Varese, Italy, Model 7750). Rats were given 2 training sessions 10 minutes apart before surgery. Rats were first habituated to the stationary rod. After habituation they were exposed to the rotating rod. The rod was started at 2 rpm and accelerated linearly to 20 rpm within 300 sec. Latency to fall off the rotarod was then determined before ischemia (presurgery) and before sacrificing the animals. Animals were required to stay on the accelerating rod for a minimum of 30 sec. If they were unable to reach this criterion, the trial was repeated for a maximum of five times. The two best (largest) fall latency values a rat could achieve then were averaged and used for data analysis. Rats not falling off within 5 min were given a maximum score of 300 seconds [32,33]. A sham-operated group was also included (n = 8). The investigator performing the rotarod test did not know the identity of the experimental groups until completion of data analysis. Quantification of brain infarct volume The method for quantification of infarct volume was performed exactly as reported by others [34,35]. Briefly, the animals were sacrificed under deep anesthesia and brains were removed, frozen, and coronally sectioned into six 2-mm-thick slices (from rostral to caudal, first to sixth). The brain slices were incubated for 30 min in a 2% solution of 2,3,5-triphenyltetrazolium chloride (TTC) (Sigma Chemical Co.) at 37°C and fixed by immersion in a 10% phosphate-buffered formalin solution. Six TTC-stained brain sections per animal were placed directly on the scanning screen of a color flatbed scanner (Hewlett Packard HP Scanjet 5370 C) within 7 days. Following image acquisition, the image were analyzed blindly using a commercial image processing software program (Photoshop, version 7.0, Adobe Systems; Mountain View, CA). Measurements were made by manually outlining the margins of infarcted areas. The unstained area of the fixed brain section was defined as infarcted. Cortical and subcortical uncorrected infarcted areas and total hemispheric areas were calculated separately for each coronal slice. Total cortical and subcortical uncorrected infarct volumes were calculated by multiplying the infarcted area by the slice thickness and summing the volume of the six slices. A corrected infarct volume was calculated to compensate for the effect of brain edema. An edema index was calculated by dividing the total volume of the hemisphere ipsilateral to pMCAO by the total volume of the contralateral hemisphere. The actual infarct volume adjusted for edema was calculated by dividing the infarct volume by the edema index [36-38]. Infarct volumes are expressed as a percentage of the contralateral (control) hemisphere. The investigators who performed the image analysis were blinded to the study groups. Experimental design Time course of lesion development after pMCAO At various times after pMCAO (4, 8, 12, 24 and 48 h, n = 6–8 per group) the animals were sacrificed and the brains were quickly removed, sectioned and stained as previously described in order to calculate the infarct volume. Evaluation of nimesulide's effects: dose-response experiment In order to evaluate the effect of nimesulide administration on rat focal cerebral ischemia, three different doses of nimesulide (3, 6 and 12 mg/kg) were given to rats by intraperitoneal administration 30 min before the onset of pMCAO (n = 7–9 animals per group). Additional doses were given at 6, 12 and 18 h after stroke. This treatment schedule and dosage range was based on the pharmacokinetic profile of nimesulide [39] and on our previous experience with this compound in models of cerebral ischemia [10,12]. We also studied the effect of a single dose of nimesulide (12 mg/kg; i.p.) given 30 min before ischemia (n = 8). A single injection vehicle-treated group was also included (n = 7). Assessment of the therapeutic time window for the neuroprotective effect of nimesulide in pMCAO After investigating the dose-response relationship, we studied the effect of nimesulide (12 mg/kg; i.p.) when administered 0.5, 1, 2, 3 or 4 h after ischemia (n = 8–11 animals per group). The corresponding vehicle-treated groups were included as controls (n = 7–10 rats per group). Three additional doses were given every 6 h after the first treatment with nimesulide or vehicle exactly as described before for the repeated treatment schedule in the dose-response experiment. After completing the neurological evaluation and rotarod performance at 24 h after permanent focal cerebral ischemia, animals were sacrificed and the brains were removed to calculate the infarct size. Data analysis Data are presented as means ± S.D. Values were compared using t-test (two groups) or one-way ANOVA with post-hoc Student-Newman-Keuls test (multiple comparison). Neurological deficit scores were analyzed by Kruskal-Wallis non-parametric ANOVA followed by the Dunn test (multiple comparison) or Mann-Whitney test for analysis of individual differences. Rotarod performance was expressed as a percentage of pre-surgery values for each rat and analyzed by ANOVA for repeated measures followed by the Student-Newman-Keuls test. Differences were considered significant when p < 0.05. Results Time course of the development of cerebral infarction and neurological deficits after pMCAO The temporal evolution of the lesion volumes is presented in Fig. 1A as the cortical and subcortical components of the infarction. Subcortical injury was evident in TTC-stained coronal sections as early as 4 h after permanent stroke (see insets of TTC-stained sections at different times after stroke in Fig. 1A). Subcortical lesion was maximal between 8 and 12 h after pMCAO, although there was a slight but significant increase between 8 and 24 h when the overall comparison was performed (one-way ANOVA, followed by Student-Newman-Keuls test). Nevertheless, the Student's t-test analysis failed to detect any significant increase between 12 and 24 or 48 h post-injury, thus indicating that the subcortical damage reached maximal values by 12 h after the insertion of the occluding filament (Fig. 1A). On the other hand, cortical damage progressed more slowly; it was detected at 4 h after pMCAO, and by 8 h there was an increase of the infarct but this was not statistically significant as compared to that at 4 h. On the contrary, there was a significant (p < 0.05) increase in the lesion when the infarction at 12 h is compared with that at 4 or 8 h, and a more dramatic increase of damage is seen at 24–48 h after stroke, a time at which the cortical infarct volume is maximal in this model as shown in Fig. 1A. Figure 1 Temporal development of focal cerebral infarction induced by permanent middle cerebral artery occlusion (pMCAO). (A): Evolution of cortical and subcortical infarct volumes after pMCAO in rats. Representative TTC-stained sections at different times after stroke are shown in the insets. (B) and (C): Time course of the increase of neurological deficits and motor impairment induced by pMCAO. Infarct volumes are expressed as a percentage of the contralateral (control) hemisphere. Bars represent the group mean ± SD. * p < 0.05 with respect to subcortical infarct volume at 4 h. &p < 0.05 with respect to subcortical infarct volume at 8 h. # p < 0.05 with respect to cortical infarct volume at 8 h. p < 0.05 with respect to cortical infarct volume at 12 h. § p < 0.05 with respect to 4 and 8 h. The horizontal bar in Panel B shows the median neurological score. With regard to the neurological deficits and motor impairment induced by pMCAO (assessed by the neurological score and accelerating rotarod test), it is important to emphasize the fact that these parameters were maximal by 12 h after stroke and the animals did not show any further increase in the neurological deficits or motor impairment after 24 or 48 h of the occlusion, as depicted in Fig. 1B and Fig. 1C. Based on these results, we decided to evaluate the effects of nimesulide after 24 h of pMCAO. Effects of different doses of nimesulide on infarct volume and functional outcome after pMCAO Repeated treatments with nimesulide dose-dependently reduced cortical, subcortical and total infarct volumes in the permanent model of stroke, although only the administration of the highest dose (12 mg/kg) was able to significantly (P < 0.01) diminish brain damage (Table 1). There was a trend towards a reduction in lesion volumes in animals treated with nimesulide 6 mg/kg, but this effect was not confirmed by the statistical analysis of the data. Unlike the long-term treatment paradigm, the administration of a single dose of nimesulide (12 mg/kg) 30 min before pMCAO failed to significantly reduce total infarct volume, though a modest neuroprotective effect was seen in the subcortical areas as shown in Table 1. Table 1 Effect of different doses of the cyclooxygenase-2 inhibitor nimesulide on total, cortical and subcortical infarct volumes in a rat model of permanent focal cerebral ischemia. Treatment Total infarct volume (%) Cortical infarct volume (%) Subcortical infarct volume (%) Repeated doses Vehicle (n = 9) 56.1 ± 11.4 41.6 ± 10.3 12.6 ± 4.5 Nimesulide 3 mg/kg (n = 7) 54.9 ± 14.9 38.1 ± 17.2 14.3 ± 2.4 Nimesulide 6 mg/kg (n = 8) 41.4 ± 12.3 31.8 ± 9.3 9.7 ± 3.5 Nimesulide 12 mg/kg (n = 9) 34.1 ± 13.8 24.6 ± 11.2 ** 7.1 ± 3.9 * Single dose Vehicle, single dose (n = 7) 55.2 ± 15.5 43.9 ± 10.9 13.2 ± 4.2 Nimesulide 12 mg/kg, single dose (n = 8) 49.5 ± 11.7 39.4 ± 11.8 9.1 ± 3.1& Data are mean ± S.D. * P < 0.05 and ** P < 0.01 compared to vehicle. One-way ANOVA followed by Student-Newman-Keuls post-hoc test. &P < 0.05 compared to vehicle single dose (Student's t-test). Interestingly, repeated treatments with 6 and 12 mg/kg of nimesulide were similarly effective in reducing the neurological deficits and the motor impairment resulting from pMCAO (Table 2). This effect was not accompanied by a significant reduction in infarct volume in the case of the dose of 6 mg/kg (Table 1). No neuroprotective effect of nimesulide was observed on the neurological score or rotarod performance when this COX-2 inhibitor was administered as a single dose (12 mg/kg) before the onset of ischemia (Table 2). Table 2 Effect of different doses of nimesulide on neurological deficits and functional outcome (evaluated using the rotarod test) following permanent middle cerebral artery occlusion in the rat. Treatment Neurological Score Rotarod performance (% of presurgery levels) Sham-operated control (n = 8) 0 128 ± 21 Repeated doses Vehicle (n = 9) 3 (3–5) 49 ± 18 Nimesulide 3 mg/kg (n = 7) 3 (2–4) 64 ± 13 Nimesulide 6 mg/kg (n = 8) 2 (1–5) * 89 ± 20 Nimesulide 12 mg/kg (n = 9) 2 (1–4) 84 ± 14 ** Single dose Vehicle, single dose (n = 7) 3 (3–5) 43 ± 21 Nimesulide 12 mg/kg, single dose (n = 8) 3.5 (2–5) 52 ± 19 Values show the median and range (neurological score) and means ± S.D. (rotarod performance). For the analysis of neurological score data, the Kruskal-Wallis nonparametric ANOVA followed by Dunn test (multiple comparison) or Mann-Whitney test for analysis of individual differences were used. For the statistical analysis of rotarod performance results, ANOVA followed by Student-Newman-Keuls post-hoc test was employed. * P < 0.05 and ** P < 0.01 compared to vehicle. Therapeutic time window for nimesulide protection in rats subjected to pMCAO In this experiment we investigated the effect of nimesulide (12 mg/kg) in a situation in which its first administration was delayed for 0.5–4 h after the ischemic challenge. A significant reduction in subcortical infarct volume was observed when the treatment was delayed until 0.5–1 h after pMCAO, but this protective effect of nimesulide was not evident when administered after 2–4 h of the onset of permanent occlusion (Fig. 2A). In the case of cortical infarction, nimesulide diminished lesion volume when treatment was delayed until 2 h after the ischemic insult (Fig. 2B). Similar results were found for total infarct volume as shown in Fig. 2C, though as expected, an overall decline of the neuroprotective effect with post-treatment time was observed. Figure 2 Reduction of subcortical (A), cortical (B) and total (C) infarct volumes by the cyclooxygenase-2 inhibitor nimesulide (12 mg/kg; i.p.) when its first administration was delayed for several hours after the onset of permanent stroke. Nimesulide reduced the infarct size in animals treated at 0.5 (n = 8), 1 (n = 9) and 2 h (n = 9), but not at 3 (n = 11) and 4 h (n = 9) after pMCAO, compared to vehicle-treated and time-comparable control groups (n = 7–9 per group). Infarct volumes are expressed as a percentage of the contralateral (control) hemisphere and the data are represented as the mean ± SD. * p < 0.05 and p < 0.01 with respect to vehicle (Student's t-test). Of interest is the finding that nimesulide not only reduced infarct volume but also enhanced functional recovery when the first treatment is given 2 h after permanent ischemic stroke. Post-ischemic treatment with nimesulide significantly reduced neurological deficits and increased the fall latencies to remain on the accelerating rotarod as compared to those rats given only the vehicle (Table 3). However, this protective effect was lost when the first administration is delayed until 3–4 h after the occlusion of the middle cerebral artery as presented in Table 3. Table 3 Effect of delayed administration of nimesulide (12 mg/kg; i.p.) on neurological deficit score and rotarod performance after permanent middle cerebral artery occlusion (pMCAO) in rats. Vehicle or nimesulide was administered 0.5, 1, 2, 3, or 4 h after stroke. Neurological Score Rotarod performance (%) Time after stroke (h) Vehicle Nimesulide Vehicle Nimesulide 0.5 3 (2–5) 2 (1–3) 44 ± 17 81 ± 18 1 3 (2–5) 2 (1–4) 40 ± 21 85 ± 22 ** 2 3 (3–5) 2 (1–5) * 50 ± 11 73 ± 13 * 3 3 (2–5) 3 (2–5) 47 ± 16 60 ± 15 4 3.5 (2–5) 3 (2–5) 52 ± 23 59 ± 14 Values represent the median and range (neurological score) and means ± S.D. (rotarod performance). P < 0.05 and *P < 0.01 compared with the corresponding vehicle-treated group. Discussion The present study was prompted by our previous encouraging results with nimesulide in a model of transient focal cerebral ischemia, which show that this COX-2 inhibitor is able to potently reduce infarct volume and improve functional recovery [12]. These neuroprotective effects are also observed when treatment is delayed until even 24 h after the onset of ischemia [12]. Since we believe that it is very important to perform thorough, multifactorial and well-designed pre-clinical studies before assuming definitive conclusions on the neuroprotective effect of any compound, and considering that in stroke patients a very early spontaneous recanalization of an obstructed brain vessel is, unfortunately, only rarely found, we conducted the present investigation to shed more light into the effects of nimesulide on ischemic damage using a permanent stroke model in the rat considering that this model might be more relevant to the clinical situation of stroke, as suggested previously [23-26]. The core findings of this study are: (i) administration of clinically relevant doses of nimesulide confers protection against the damage induced by permanent focal cerebral ischemia in two modalities (reduction of infarct size, and improvement of functional outcome) and (ii) nimesulide's neuroprotection is still evident when the first administration is delayed until 2 h after the onset of stroke. Depending on the experimental conditions, the temporal evolution of ischemic damage may vary considerably [30,40,41]. Thus, it is very important to characterize the time course of brain damage, especially if one wants to interpret correctly the effects of a given compound using delayed treatment schedules. Our results showed that in the permanent model of stroke induced by the occlusion of the middle cerebral artery using an intraluminal suture, the infarct size progresses very fast in the subcortical areas (mainly striatum) and much slower in cortical areas, but in general the evolution of damage is relatively quick, reaching maximal values by 24 h after the insertion of the filament (Fig. 1A). These findings are in line with those published previously in this model of stroke [42,43]. Although infarct size continues to increase between 12 and 24–48 h of ischemia (Fig. 1A), the neurological deficits and motor impairment reached their maximum by 12 h, and the animals did not showed any further deterioration of their neurological functions (Fig. 1B and 1C). This might reflect the fact that unlike ischemic injury to many other tissues, the severity of disability is not predicted well by the amount of brain tissue lost. For example, damage to a small area in the medial temporal lobe may lead to severe disability, while damage to a greater volume elsewhere has little effect on function [2]. There is not always a direct correlation between the lesion size and the severity of neurological deficits as demonstrated before in animal models [29,44] and in stroke patients [45]. For that reason, it is essential to evaluate the neuroprotective effects of agents by combining both histological and functional measures. The present study offers a good example of this: even when the lowest doses of nimesulide did not reduce infarct volume in pMCAO (Table 1), one can not minimize the beneficial effects of these doses since a significant reduction in neurological deficits and an improvement of rotarod performance were observed (Table 2). Thus, further studies would be required to better characterize the effects of the lowest doses of nimesulide (3 and 6 mg/kg) in models of cerebral ischemia. Repeated treatments with nimesulide afforded a more remarkable neuroprotection than the administration of a single dose given before the insult (Tables 1 and 2). These data show the importance of continuous long-term administration after ischemic damage in clinical trials to achieve the maximal beneficial effects of neuroprotection by nimesulide. Unfortunately, a large number of promising neuroprotective compounds identified from preclinical experiments have failed in clinical trials in stroke patients [3,45-47]. Although several factors may contribute to these disappointing results, an important issue is the 'therapeutic time window of protection', defined as the time period after the onset of ischemia during which administration of treatment is effective [48,49]. Most of the agents that confer protection in experimental animal models of stroke when given before or a short period after cerebral ischemia have failed in clinical studies [45,50]. Thus, the assessment of the therapeutic time window of protection is of paramount importance in pursuing future therapies to treat stroke victims. Therefore, our next experiments were conducted to evaluate the effects of nimesulide when administered in a delayed treatment schedule in order to establish the therapeutic time window of protection of this COX-2 inhibitor in pMCAO, thus increasing predictive outcome in the clinic. Interestingly, reduction in infarct size and neurological deficits and improvement of rotarod performance were still observed when nimesulide treatment was delayed until 2 h after ischemia (Fig. 2A, Table 3). It is important to compare our present results in pMCAO with those previously obtained in transient ischemia [12]. In the model of transient focal ischemia, the time window of nimesulide's neuroprotection extends over a 24 h period [12], and in other models of cerebral ischemia, the time window of protection of nimesulide is similarly wide [10,22,51]. These results have been also obtained with other COX-2 inhibitors (e.g., NS-398, SC58125 and rofecoxib) in models of transient ischemic stroke [4,52] and global cerebral ischemia [53,54]. These studies suggest that although the protective effects of COX-2 inhibitors are more beneficial when administered early after the ischemic insult, COX-2 selective inhibitors show a wide therapeutic window for the prevention of neuronal death in both focal and global ischemia. However, our present results suggest that in permanent stroke, COX-2 inhibition by nimesulide is not as protective as in transient models (39 % of infarct reduction with pre-treatment in pMCAO vs. 60 % lesion reduction in transient ischemia with immediate treatment) and the therapeutic time window is narrower as compared to temporary occlusion models (2 h in pMCAO vs. 24 h in transient ischemia) as the present results (Tables 1 and 3, Fig. 2) and our recent studies indicate [12]. The vascular inaccessibility of nimesulide into the ischemic/infarcted region could be a plausible explanation for these findings considering that, unlike transient ischemia, in permanent ischemic stroke the protective effects of any drug/agent depend, in large part, on the ability of the compound to reach the ischemic areas mainly through passive diffusion. Although these findings on nimesulide's effects on stroke tempted us to conclude that COX-2 selective inhibitors are less protective in permanent than in transient stroke models, these results should be interpreted with caution since a structurally similar COX-2 inhibitor (NS-398) reduced permanent stroke damage in mice when the treatment started 24 h after MCA occlusion [55] and the same COX-2 inhibitor also reduced lesion size when administered starting 6 h after pMCAO in another previous study [5]. Apparent discrepancies between our present results and these two reports [5,55] might be due to different methods to induce pMCAO (intraluminal vs. distal MCAO involving craniectomy), the specific COX-2 inhibitor used, treatment paradigm or animal species. This emphasizes the importance of conducting more preclinical studies with COX-2 selective inhibitors before these agents could be used in clinical trials in stroke patients. Another issue that needs urgent consideration in future studies with COX-2 inhibitors in cerebral ischemia is the effect of long-term treatment since anti-inflammatory interventions could interfere with nervous regeneration/plasticity and recovery as demonstrated in some types of neuronal injury [56,57]. Conclusion In summary, the present study has evaluated for the first time the neuroprotective effects of the COX-2 inhibitor nimesulide in permanent focal cerebral ischemia, showing beneficial effects on reduction of infarct volume and improvement of functional recovery. This ability of nimesulide to diminish permanent ischemic damage is observed even when the first treatment was delayed 2 h after the ischemic episode. Taken together, these results have important implications for the therapeutic potential of using the COX-2 selective inhibitor nimesulide in the treatment of cerebral ischemia. List of abbreviations used COX-2, cyclooxygenase-2; pMCAO, permanent middle cerebral artery occlusion; MCA, middle cerebral artery; TTC, 2,3,5-triphenyltetrazolium chloride; ANOVA, analysis of variance Competing interests The author(s) declare that they have no competing interests. Authors' contributions ECJ carried out the surgical procedures to induce stroke, participated in the design of the study and in the statistical analysis, reviewed the data and drafted the manuscript. NHM performed the evaluation of neurological deficits and rotarod performance. AGF and MGC performed the calculation of the infarct volumes and participated in the statistical analysis of the data. OSL, EM and BLF participated in the design and coordination of the study, reviewed the data, provided consultation and helped to draft the manuscript. OSL and BLF share senior authorship. All authors read and approved the final manuscript. Acknowledgements The authors are grateful to Dr. Mayra Levi (Gautier-Bagó Laboratories) for kindly providing nimesulide for these studies. ECJ was supported by a research fellowship from the Alexander von Humboldt Foundation (Germany). ==== Refs Hankey GJ Stroke: how large a public health problem, and how can the neurologist help? 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==== Front Theor Biol Med ModelTheoretical Biology & Medical Modelling1742-4682BioMed Central London 1742-4682-2-21564711310.1186/1742-4682-2-2ResearchConstruction of predictive promoter models on the example of antibacterial response of human epithelial cells Shelest Ekaterina [email protected] Edgar [email protected] Dept. of Bioinformatics, UKG, University of Göttingen, Goldschmidtstr. 1, D-37077 Göttingen, Germany2 BIOBASE GmbH, Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany2005 12 1 2005 2 2 2 16 9 2004 12 1 2005 Copyright © 2005 Shelest and Wingender; licensee BioMed Central Ltd.2005Shelest and Wingender; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Binding of a bacteria to a eukaryotic cell triggers a complex network of interactions in and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections by interacting with the pulmonary epithelial cells. We use this example for examining the ways of triggering the response of the eukaryotic cell(s), leading us to a better understanding of the details of the inflammatory process in general. Results Considering a set of genes co-expressed during the antibacterial response of human lung epithelial cells, we constructed a promoter model for the search of additional target genes potentially involved in the same cell response. The model construction is based on the consideration of pair-wise combinations of transcription factor binding sites (TFBS). It has been shown that the antibacterial response of human epithelial cells is triggered by at least two distinct pathways. We therefore supposed that there are two subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set. We developed the concept of complementary pairs, i.e., two mutually exclusive pairs of TFBS, each of which should be found in one of the two complementary subsets. Conclusions We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole (+)-training set, as well as for complementary pairs. Applying this method, we came up with a promoter model of antibacterial response genes that consists of one TFBS pair which should be found in the whole training set and four complementary pairs. We applied this model to screening of 13,000 upstream regions of human genes and identified 430 new target genes which are potentially involved in antibacterial defense mechanisms. ==== Body Background Promoter model construction is a way to utilize information about coexpressed genes; this kind of information becomes more and more available with the advent of gene expression mass data, mainly from microarray experiments. Having a promoter model at hand, one has (i) an explanatory model that and how the coexpressed gene may be coregulated, and (ii) a means to scan the whole genome for additional genes that may belong to the same "regulon". The field of searching for regulatory elements in silico and promoter modeling is already well-cultivated. In spite of numerous sophisticated approaches devoted to this subject [1-9], we still lack a standard method which would enable us to produce promoter models. This may indicate that the existing approaches have their distinct shortcomings and that, thus, the field is still open for new ideas. The biological system we consider in this work is the transcriptional regulation of the response of lung epithelial cells to infection with Pseudomonas aeruginosa. Binding of bacteria to a eukaryotic cell triggers a complex network of interactions within and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections affecting pulmonary epithelial cells [10,11]. We use this example for examining the ways in which the response of the eukaryotic cell(s) is triggered, leading us to a better understanding of the details of the inflammatory process in general. After adhesion of P. aeruginosa to the epithelial cells, the response of these cells is triggered by at least two distinct agents: bacterial lipopolysaccharides [12] and/or bacterial pilins or flaggelins [13]. Both pathways lead to the activation of the transcription factor NF-κB. It has also been shown that transcription factors AP-1 and C/EBP participate in this response [14,15]; pronounced hints on the participation of Elk-1 [16] have been reported as well. However, it is a commonly accepted view that transcription factors which are involved in a certain cellular response cooperate and in most cases act in a synergistic manner. Therefore, their binding sites are organized in a non-random manner [2,3,8,9]. We use this consideration as a basis for constructing a predictive promoter model. We searched for combinations of potential transcription factor binding sites (TFBS), considering those transcription factors (TFs) that are known to be involved in antibacterial responses. Some of the found combinations could be predicted from the fact that they may constitute well-known composite elements, like those containing NF-κB and C/EBP or NF-κB and Sp1 binding sites [TRANSCompel, [17]]. We start with a search for pairwise combinations of TFBS in a set of human genes published to be induced during antibacterial response, considering that combinations of the higher orders can be constructed from them later on. We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole training set, and combinations of mutually exclusive pairs (complementary pairs). The idea of starting the analysis with a "seed" of sequences allows a very biology-driven way of initial filtering of information.To enhance the statistical reliability and to get additional evidence in TFBS combination search, we applied the principal idea of phylogenetic footprinting (using orthologous mouse promoters), yet proposing a different view on applicability of this approach. Finally we came up with a promoter model which we applied to screening of 13,000 upstream regions human genes. We identified 430 new target genes which are potentially involved in antibacterial defense mechanisms. Results Development of the approach In every step of our investigations we tried to combine purely computational approaches with the preexisting experiment-based knowledge, as it is represented in corresponding databases and literature, and with our own biological expertise. To develop a promoter model, the first task is to select those transcription factors, the binding sites of which shall consitute the model. The overwhelming majority of methods and tools estimating the relevance of predicted TF binding sites in promoter regions are based on their over- and underrepresentation in a positive (+) training set in comparison with some negative (-) training set. If, however, a binding site is ubiquitous, or very degenerate, so that it can be found frequently in any sequence, the comparison with basically any (-)-training would not reveal any significance for its occurrence. That tells nothing about their functionality in any specific case, which may be dependent on some additional factors and/or other conditions. Therefore, basing the decision about the relevance of a transcription factor for a certain cellular response solely on whether its predicted binding sites are overrepresented in the responding promoters may lead to a loss of important information. Thus, we did not rely on this kind of evidence but rather chose the candidate transcription factors according to available experimental data. We found 5 factors reported in literature as taking part in anti-bacterial or similar responses and selected them as candidate TFs [11,12,15,18-29]. Not all of these candidate TFs are overrepresented in the (+)-training set used in this analysis (Table 1; see also Methods). For instance, no overrepresentation has been found for important factors such as NF-κB, AP-1 and C/EBP. Nevertheless, these factors were included in the model, because not the binding sites themselves, but their combinations may be overrepresented. Table 1 The genes of the (+)-training set (without orthologs). Marked with asterisks are those included in the "seed" set. No Gene name Accessin no. And LocusLinkID Experimental evidence Additional information Participation in anti-Pseudomonas response 1 Monocyte chemoattractant protein-1, MCP-1* EMBL: D26087 Microarray [66], other experiments [20,21,38] Is well know as expressed in antibacterial response 100% 2 β-defensin* LocusLinkID: 1673 [15,18,19,39,40] Is well known as expressed in antibacterial response; important target gene in innate immunity 100% 3 Interferon regulatory factor 1, IRF-1* LocusLinkID: 3659 Microarray [66] Known to be expressed in epithelial cells probable 4 Equilibrate nucleoside transporter 1, SLC29a1 LocusLinkID: 2030 Microarray [66] 5 Proteinkinase C η type, PKCη* LocusLinkID: 5583 Microarray [66] TRANSPATH® Important link in Ca2+-connected pathways probable 6 Folypolyglutamate synthase, FPGS Ensembl : ENSG00000136877 Microarray [66] 7 RhoB* LocusLinkID: 388 Microarray [66] is induced as part of the immediate early response in different systems probable 8 Origin recognition complex subunit 2, hORC2L LocusLinkID: 4999 Microarray [66] 9 Transcription factor TEL2* LocusLinkID: 51513 Microarray [66] Transcription factor probable 10 Interleukin 8, IL8* EPD: EP73083LocusLinkID: 3576 [10,11,26,44,45] Is well know as expressed in antibacterial response 100% 11 Transcription factor ELF3* LocusLinkID: 1999 Microarray [66] Transcription factor probable 12 Mucin 1(mouse gene), MUC1* RefSeq: NM_013605 [17,27,28,36,47] Different mucins are shown as expressed in antibacterial response 100% 13 NF-kappaB inhibitor alpha, IkBa* LocusLinkID: 4792 EPD: EP73215 Microarray [66] NF-kB inhibitor, the main link in NF-kB-targeting pathways Very high 14 Tissue Factor Pathway Inhibitor 2, TFPI LocusLinkID: 7980 EPD: EP73430 Microarray [66] 15 Urokinase-type plasminogen activator precursor, PLAU LocusLinkID: 5328 Microarray [66] 16 c-jun* Microarray [66] Transcription factor probable 17 Cytochrom P450 dioxin-inducible* LocusLinkID: 1545 Microarray [66] Stress-inducible probable 18 Dyphtheria toxin resistance protein, DPH2L2 EPD: EP74285 Microarray [66] On the other hand, some of the factors, which have also been mentioned in literature as potentially relevant (e.g., SRF [30]) or might be of a certain interest because of their participation in relevant pathways (CREB, according to the TRANSPATH database [31]) were not included in the model because we could not adjust the thresholds for their detection according to our requirements (see Methods). SRF were of special interest, because it is known that it tends to cooperate with Elk-1 [30], but to identify 80% of TP we had to lower the matrix similarity threshold to 0.65, which is unacceptably low and would provide too many false positives. Finally, we constructed our promoter model of binding sites of 5 TFs (NF-κB, C/EBP, AP-1, Elk-1, Sp1), considering their pairwise combinations and some combinations of higher order (complementary pairs, see below). In several steps of the model construction we had to estimate overrepresentation of a feature in the (+)-training set compared with the (-)-training set. We operated with the number of sequences that possess the considered feature, in our case a pair of TFBS, at least once. Otherwise, mere enrichment of a feature in the (+)-training set may be due to strong clustering in a few members of that set which would not lead to a useful prediction model. At the first step the T-test has been performed (the normality of distribution has been demonstrated before (data no shown)), but it appeared to be a weak filter: for example, we could find several pairs which showed, if estimated with T-test, a remarkable overrepresentation (p < 0.001), but with a difference of 97% in the (+)-training set versus 85% in the (-)-training set, which is of no practical use to construct a predictive model, since it is also important to have minimal occurrence of a discriminating feature in the (-)-training set. In the further work we considered all pairs with p < 0.005, but as this did not reasonably restrict the list of considered pairs, we had to apply an additional filtering approach. For this purpose we used a simple characteristic such as the percentage of sequences in (+)- and (-)-training sets. By operating directly with percentages we could easily filter out those pairs which would identify too many false positive sequences, thus getting rid of a substantial part of useless information. This procedure allows to estimate immediately the applicability of the model to identify further candidate genes that may be involved in the cellular response under consideration (see Methods). The main problem of promoter model construction are the numerous false positives. Developing our approaches we applied some anti-false-positives measures : • distance assumptions • identification of "seed" sequences • phylogenetic conservation • subclassification into complementary sequence sets. In the following, we will comment on each item in more details. Distance assumptions The commonly accepted view that functionally cooperating transcription factors may physically interact with each other triggered us to introduce certain assumptions concerning the distances between the considered TFBS. Transcription factors can interact either immediately with each other or through some (often conjectural) mediator proteins (co-factors). Principally there can be many ways of taking this into account, since our knowledge about the mechanisms of interaction is limited. In this work we used two different approaches to consider distances in the promoter model development. In the first case we based our assumptions on the structure of known composite elements. We assumed that the binding sites of interacting TFs should occur in a distance of not more than 150 bp to each other (which is the case for most of the reported composite elements [17]; 150 bp is even an intended overestimation). To be on the safe side and not to overlook some potentially interesting interactions we allowed the upper threshold of 250 bp. Also by analogy with composite elements, for which it is relevant that the pair occurs not at a certain distance, but within a certain distance range, we considered the pairs occurring in segments of a certain length. The second approach was based on more abstract considerations. Thinking of TF interaction, we can imagine three different situations: (a) Directly interacting factors should have the binding sites at a close distance. (b) The factors interacting through some co-factor may have binding sites on some medium distance, depending on the size and other properties of the co-factor (and the factors themselves). (c) We can also expect direct interaction of another type, when the two factors are not located in the nearest neighborhood, but their interaction requires the DNA to bend or even to loop. This means that the distance is no longer a close one, although we cannot estimate the distance range for this case; thus, we allowed different ranges of distances, excluding only the closest ones. We searched for pairs in three distance ranges, roughly called "close", "middle" and "far", all with adjustable borders, so that moving them we could get the best proportion of percentages in (+)- and (-)-training sets. We used the search in the distance ranges as a starting point, but some of the found pairs required optimization of the borders, so that they finally did not fit into any of the predefined ranges. The initial "close" range was taken as 5–20 bp, to exclude the overlapping of the sites, but to allow close interaction; however, the border had to be shifted in many cases up to 50 bp. The initial "middle" range was chosen from 21 to 140 bp (the number of nucleotides wrapping around the core particle of the nucleosome); the "long" range had its upper border at 250 bp. "Seed" sequences Initially the idea of "seed" sequences was exploited because of the desire to make use of preexisting biological knowledge about the expressed genes and also because of doubts in the reliability of the available data set. Different experimental approaches differ in their reliability. The microarray analysis is not absolutely reliable [31,34-36], so we could expect that not all of the reported genes may be relevant for the antibacterial response. On the other hand, some genes are already known to be relevant according to additional published evidence. We thus decided to search for distinguishing features first in these "trustable" genes, and then to spread the obtained results to the whole set. Therefore, we started our analysis with a group of "seed" sequences, which we considered for distinct reasons more reliable and preferable. Choosing a seed group, we took into consideration two kinds of evidence; the first was the source of information, i. e. the methods with which the gene has been shown to participate in the response. We took the promoter sequences of those genes which have been reported by other methods but microarray analysis [11,13,15,18-22,27-29,38-47,47], and which have been independently reported by at least two different groups. The second kind of evidence was whether we could find any additional biological reasoning for the gene to participate in this kind of reply. For instance, a well-known participant of the NF-κB-activating pathway such as IκBα, or participants of different pathways which are likely to be triggered here as well, like c-Jun or PKC, were estimated as the first candidates for the "seed" group. Finally, the "seed" contained 12 human sequences (Table 1). We could retrieve all mouse orthologs constituting a separate mouse "seed". We then run our analysis in either "seed" separately and in the combined human/mouse "seed" and compared the results. First, we identified all TFBS pairs that are present in all sequences of this "seed" group (see Methods) (Fig. 1, step 2). Further on, we searched for the found pairs in the whole (+)-training set (Fig. 1, step 3). In the next step we made a search in the (-)-training set for those pairs that were found in at least 80% of the (+)-training set (Fig. 1, step 4), choosing only those which showed the lowest percentages in the (-)-training set (Fig. 1, step 6). Figure 1 Algorithm of the search for common pairs using seed sets. Step 1. Selection of a "seed" set. Step 2. Identification of all pairs in the "seed" set; only those, which are found in 100% of the "seed" sequences, are taken into further consideration. Step 3. Search for the selected pairs in the whole (+)-training set. Step 4. Only those which are found in more than 80% of sequences of the (+)-training set are taken for into the further consideration. Step 5. Search for the "survived" pairs in the negative training set. Only those which are present in less than 40% of sequences are left. Step 6. The list of the common pairs is ready for the next analysis. Using this approach, we could avoid being drowned by a flood of pairs, most of which would be of minor importance. The huge number of nearly 37,000 pairs in different intervals which can be found in the whole (+)-training set was reduced by at least two orders of magnitude: depending on the "seed" the number of considered pairs varied from 50 to 400. In the next steps this number was reduced by another order of magnitude (Table 2). Table 2 Stepwise filtering of pairs. Pairs found on different steps of the search No of found pairs Pairs found in the whole training set in all distance intervals ~37000 Pairs found in the "seed" set in all distance intervals (step 2 on the fig. 1) ~400 "Seed" pairs in more than 80% of the training set (step 4 on the fig. 1) ~180 "Seed" pairs in more than 80% of the training set and less than 40% of the negative training set (step 6 on the fig. 1) 4 Each "seed" is characterized by its own set of pairs. To ensure the robustness of the obtained results, we undertook the "leave-one-out" test, removing consecutively one sequence of the "seed" set (for the combined "seed" sets which included human and mouse orthologs we excluded simultaneously both orthologous sequences). This has been repeated for each sequence (or ortholog pair). Only the robust pairs have been taken into further consideration. Phylogenetic conservation Evolutionary conservation of a (potential) TFBS is generally accepted as an additional criterion for a predicted site to be functional (phylogenetic footprinting; [49-52]). However, some recent analysis of the human genome reported by Levy and Hannenhalli [50,53] and our own observations made for short promoter regions have shown that only about 50% [50], 64 % [53] or 70 % (Sauer et al., in preparation) of the experimentally proven binding sites are conserved. Missing between 30 and 50 % of all true positives may seem to be acceptable when analyzing single TFBS, but if one constituent of a relevant combination of TFBS belongs to a non-conserved region, we will loose the whole combination from all further analyses. The observed fact is that functional features are not necessarily bound to conserved regions, as long as we speak about primary sequence conservation. Dealing with such degenerate objects as TF binding sites, one should not expect an absolute conservation of their binding sequences. From the functional point of view, it seems to be more reasonable to expect that not the sequences, but the mere occurrence of binding sites and/or their combinations as well as (perhaps) their spatial arrangement would be preserved among evolutionarily related genomes. That is the approach that we use in the present work, completely refraining from sequence alignments. We search for those pairs of TFBS which can be found in human and corresponding mouse orthologous promoter regions, considering the promoter as a metastring of TFBS. We took a feature (the pair of TFBS) into account only if we could identify it in both orthologous promoters, not taking into consideration in what region of the promoter it appeared; we also did not try to align metastrings of TFBS symbols, since they may be interrupted by many additional predicted TFBS (no matter whether they are true or false positives). While this work was in progress, we found a very similar approach in the work of Eisen and coworkers [54,55], who searched for conserved "word templates" in the transcription control regions of yeast. We believe that switching from primary sequence preservation to the conservation of higher-order features like clusters of TFBS is the next step in development of the approaches of comparative genomics. Complementary pairs (pairs of pairs) The idea that combinations or clusters of regulatory sites in upstream regions provide specific transcriptional control is not new [1,8,56]. Nevertheless, the problem of detecting such combinations is still under active development. As mentioned before, due to the complexity of the regulatory mechanisms in eukaryotes the computational prediction of functional regulatory sites remains a difficult task, and the spatial organization of the sites is the problem of the next level of complexity. To facilitate the search for combinations we tried to exploit the concept that subsets of principally co-regulated promoters may be subject to differential regulation. If the response of the cell is mediated through at least two distinct pathways, it is logical to suppose that there are subsets of promoters activated by each of them. The subsets may not be obvious from the expression data or from any other observations, but in some cases (as in ours, when we have two different pathways triggering the same response) one can presuppose the existence of two or more subsets, each of them possessing an own combination of TFBS. These combinations will be complementary in the sense of their occurrence in the set (Fig. 2). For simplicity we considered only pairs of TFBS, but the search for combinations of higher order would make the model more specific. Moreover, detection of complementary pairs enables to identify corresponding complementary subsets of sequences, thus to shed light on some features of the ascending regulatory network. Figure 2 Complementary pairs A, B, C and D are transcription factor binding sites, which form two sorts of pairs (A-B and C-D). These pairs are complementary in the sense of occurring in complementary subsets of the whole set. Formalization of the approach In the following, we will formalize our approach and describe the logics of our investigation. All procedures are described for the example of pairwise combinations, but principally all of them can be applied to combinations of higher orders. We restricted our attempt to pairs for sake of computational feasibility. Identification of pairs We consider all possible pairwise combinations of TFBS in each sequence, as described in Methods. A pair is taken into account if it has been found in a sequence at least once. Let us consider two TFBS m and n located in a distance range from r1 to r2 (where r1 ≤ r2) on either strand of DNA (+ or -). We can denote the sets of sequences containing pairs in different relative orientation as, . To allow inversions of DNA segments containing pairs, we consider three classes of combinations (Fig. 3): Figure 3 Pair classes When grouping different combinations of transcription factor binding sites according to mutual orientation, we allow inversions of the whole module. This gives rise to a total of three classes as shown. In more general form for i = 1,...3 represents the set of sequences with a pair of i-th class m, n(i) (r1, r2). Let be a fraction of the sequences in the (+)-training set, and the fraction of sequences in the (-)-training (control) set. We have to solve now the optimization problem to maximize the difference by choosing appropriate values for m, n, i and r1, r2. Also, we are interested only in pairs, which are present in at least a minimum fraction of (+)training sequences (C1) and in a defined maximum fraction of (-)-training sequences (C2). They can be filtered in advance. Thus, we search for such for which where 0 ≤ C1,2 ≤ 1 are adjustable parameters. For single pairs we chose C1 = 0.8 and C2 = 0.4. We could not find pairs which would satisfy more stringent parameters, i. e. either higher C1 or lower C2; on the other hand, requirement (1) was found to be satisfied by a lot of different combinations which gave rise to the same Pt and Pc. To make the analysis more specific, we can consider combinations of pairs instead of single pairs. For sake of simplicity, we will omit furtheron (r1, r2) from the expression (but it should be kept in mind that is always a function of (r1, r2)). Each possible type of pair is determined by values of m, n and i. We can list all types of pairs and assign a number j to each pair in this list. Then each type of pair is characterized by mj, nj, ij: Then the sequences with the pair can be represented as . For simplicity, let us call For two different j1 and j2 (j1 ≠ j2) we can identify and , which appear in the (+)training set simultaneously: A triple or a combination of a higher order can be represented in the same way. Defining complementary pairs (pairs of pairs) The antibacterial response of the cell is triggered by at least two distinct pathways, and it may be therefore supposed that there are subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set (Fig 2). Complementary pairs were searched first in a "seed" subset of the (+)-training set of sequences (Fig 4, step 1). It comprises those 12 human genes for which the most reliable evidence is available that they are involved in the antibacterial response (as discussed in the subsection Seed sequences; Table 1). We considered all possible pairs which could be found in this subset (Fig. 4, step 2). Further on, we considered all pairwise combinations, calling pairs complementary, if: Figure 4 Algorithm of the search for complementary pairs using "seed" sets Step 1. Selection of a "seed" set; Step 2. Selection of complementary pairs in the human "seed"; every combination is checked in the (-) training set and only those, which are found in less than 40% of sequences, are taken into further consideration. Step 3. Selection of complementary pairs in the "seed" of orthologs or in the joint "human + orthologs" "seed". (Step 2 may be omitted and substituted by Step 3) Step 4. Search for the selected pairs in the whole (+)-training set. After that the final choice is made. (a) they together cover the whole subset (C1 is therefore always set to 1, ); (b) each of them can be found in not more and not less than a certain number of sequences (defined by adjustable parameters C3 and C4, see below), with an allowed overlap (defined by the parameter C5). Thus, the requirement for complementary pairs is: where 0 ≤ C3,4,5 ≤ 1 are adjustable parameters. We chose C3 = 0.3, C4 = 0.7 and C5 = 0.2. As we had no means to estimate the expected proportion of complementary pairs in the subsets, we started with these rather unrestrictive parameter settings. Finally the chosen pairs were found in the proportion 0.4/0.6 for C3/C4. In the next step we repeated the search including the orthologous sequences to the "seed" set (Fig. 4, step 3). We looked for those pair combinations which were found in the first step (in the human "seed" sequences). (The second and the third steps may be combined in one). In the last step we repeated the search in the whole (+)-training set of 33 sequences, looking only for the combinations found in the second step (i.e., in the 12 "seed" and their orthologous sequences) (Fig. 4, step 4). The percentage of the pair occurrence in the (-)-training set has been counted on the first step with the subsequent filtering of pairs. Results of the pair search A rather large number of combinations satisfied the requirements described in the previous section. However, when we selected those that were robust in a "leave-one-out" test for the "seed" sets, the final list of potential model constituents was shortened down to only 2 ubiquitous and 12 complementary pairs. We found one satisfactory pair which should be found in all promoters of target genes: AP - 1, NF - κB(1)(10,93) (AP-1, NF - κB, class 1, distance from 10 to 93 bp; see Fig. 3 for pair classes). The search for the combination of two or more pairs, which should be found in the whole set simultaneously, did not give any significant improvement of the results. Among the complementary pairs we found, several of them appeared to be interchangeable: each pair of pairs or any combination of them resulted in the selection of the same subsets from the (+)-training set (52%) (Fig. 5). Fig. 5 shows only those pairs which have been chosen for the final model, but there were several more which identified the same subset of the (+)-training set. The large number of complementary pairs may indicate that they are parts of more complex TFBS combinations, consisting of 4, 5 or more TFBS. Figure 5 Seven pairs, which are combined in four complementary combinations, and the results of their simultaneous application Each of the complementary pairs searches for nearly the same portion of the training set, while in the negative training set their intersection appears to be very small. Here, only those pairs are shown that have been chosen for the final model, but there were several more, which searched for the same subset of the training set and gave altogether 1,7% in the negative training set. Note that the circles are not exactly drawn to scale. The false positive rate depended on the number of applied pairs; when we used all of them together, they gave only 1.7% of FP (i. e., only 1.7% of the sequences in the (-)-training set revealed the presence of all pairs under consideration). But the simultaneous usage of all the pairs could overfit the model, so we did not apply them all, sacrificing a bit of specificity for sake of a higher sensitivity. Finally, we came up with 4 complementary pairs (Fig. 5) composed of 7 different TFBS pairs. Four of these TFBS pairs together are indicative for one subset of sequences, the remaining three for the other. As it has been mentioned before, the discovery of complementary pairs entails automatically the discovery of the corresponding subsets of sequences. We analyzed the distribution of the constituents of the found complementary pairs across the (+)-training set, which enabled us to assign the genes either to one or to the other subset, or to both (Table 3). Note that one of the subsets (subset 1) is in good agreement with the experimental data: MCP1, IL-8, β-defensin and MUC1 are known to be regulated by LPS, whereas IκBa is an important participant of this pathway; thus, these genes could be expected to belong to one pathway and, therefore, to one subset. Here, they all belong to the subset 1. This observation provides good support for the concept of complementary pairs which we applied here. Table 3 Assignment of training sequences to two subsets. Genes marked with asterisk are known to be activated through LPS-dependent pathway; note that they all belong to one subset. Subset 1(LPS-dependent pathway) Subset 2 Complementary pairs Elk-1, NF-κB(2) (11–124) Elk-1, Sp1(1) (14–96) C/EBP, Sp1(2) (22–87) C/EBP, NF-κB(1) (4–97) AP-1, Elk-1(3) (28–39) NF-κB, Sp1(2) (86–219) Regulated genes (in the training set) MCP1* IL8* β-Defensin* MUC1* ELF3 cytochrome p450 IkBa* PKC, proteinkinase C TEL2 c-jun(?) TFPI-2 RhoB, PLAU, IRF-1, hORC2L Not assigned SLC29, DPH2L2, FPGS, In order to avoid the overfitting of the model and to demonstrate the significance of our results, we performed a permutation test. For that, we conducted 2000 iterations of random permutation of (+) and (-) labels in the training sets and tried to rebuild the model using the procedure described above. The rate of correct classification on this random selection was estimated. The cases of common and complementary pairs were considered separately. The analysis was made for different C1, C2 (0.7<C1<0.8, 0.4<C2<0.5) for common pairs; for complementary pairs we considered the case with C3 = 0.3 C4 = 0.7 C5 = 0.2. The probability to find by chance a "seed" of 12 sequences which would produce at least one pair common for the random selection of 33 sequences (including the "seed") depends on the chosen C1, C2 and is found to vary between p < 0.0005 (C1 = 0.8, C2 = 0.4, the parameters used for our model construction) and p = 0.02 (C1 = 0.7, C2 = 0.4). We failed to find any complementary pairs after 1000 iterations of the permutation test with the parameters used for the "real" (not permuted) model construction. These results suggest that the success of the model construction based on the search for combinations of TFBS is strictly dependent on the selected training set (thus, on our prior biological knowledge) and that the significance of the findings, depending on the correct choice of the adjustable parameters, is high enough to claim their non-randomness. Thus, we can say that in the described case the pairs found in the given (+)-training set with the given parameters are the real characteristics of this set. Promoter model The model consists of two kinds of combinations of pairs: ubiquitous pairs (which should be found in all promoters of the target genes), and complementary pairs. We can divide the model into two modules, one for each kind of combination. Let M1 and M2 be modules comprising ubiquitous pairs and complementary pairs, respectively. Module M1 comprises the pair AP-1, NF-κB(1)(10,93). Module M2 comprises all complementary combinations listed in the Fig. 5. Each complementary pair can be taken as a submodule (m) in M2. To apply the model means to search for sequences containing all these combinations. Let us call S(M) the set of sequences which possess the whole model M; then we can also consider S(M1) and S(M2) (the sets possesing the modules M1 and M2, respectively), and S(m) – the set with a submodule m. Then Module M2 consists of submodules (m); in this case we consider four submodules, so the sequences containing M2 can be found as: S(M2) = S(m1) ∩ S(m2) ∩ S(m3) ∩ S(m4), where the set with each submodule we must consider as a union of sequence sets containing the complementary pairs: The final result of application of the model M can be presented as S(M) = S(M1) ∩ S(M2) The model gives 3.4% of false positives and re-identifies 52% of the whole (+)-training set, but these 52% comprise all most reliable sequences of the set (remember that we must allow for some reduction because the set is not absolutely reliable). Identification of potential target genes Applying our promoter model to screening of 13000 upstream regions from a collection of human 5'-flanking sequences [57], we identified about 580 genes as harboring this combination of TFBS. After erasing all those that encode hypothetical products, we came up with a list of 430 potential target genes, which can be checked for plausibility. More than 60% of these genes encode different representatives of the immune system, which can be expected to participate in the cells' response, as well as transcription factors and other regulatory proteins. Some of the most interesting potential target genes are shown on the Table 4. The whole data set one can find in the Additional files. Table 4 Selection of candidate genes identified by the promoter model. The whole list one can find in Additional files. TNFRSF14 tumor necrosis factor receptor superfamily TNFAIP6 tumor necrosis factor, alpha-induced protein 6 PPP3CA protein phosphatase 3 (calcineurin A) NLI-IF nuclear LIM interactor-interacting factor WISP1 WNT1 inducible signaling pathway protein 1 IL8 interleukin 8 TFPI2 tissue factor pathway inhibitor 2 DEFB2 defensin, beta 2 POU2F1 POU domain, class 2, transcription factor 1 MAP2K1IP1 mitogen-activated protein kinase kinase 1 interacting protein 1 CSF2 colony stimulating factor 2 (granulocyte-macrophage) TAF2F TATA box binding protein (TBP)-associated factor RNA polymerase II, F, 55 kD ABT1 TATA-binding protein-binding protein CALN1 calneuron 1 TRAF1 TNF receptor-associated factor 1 FPGS folylpolyglutamate synthase RENT2 regulator of nonsense transcripts 2 CYP26A1 cytochrome P450, subfamily XXVIA EHF ets homologous factor, MAP3K11 mitogen-activated prot. kinase kinase kinase 11 IRAK-M interleukin-1 receptor-associated kinase M ARHGDIA Rho GDP dissociation inhibitor (GDI) alpha HSY11339 GalNAc alpha-2, 6-sialyltransferase I, long form HCNGP transcriptional regulator protein CYP4F11 cytochrome P450, subfamily IVF IRF3 interferonregulatory factor 3 ICAM3 intercellular adhesion molecule 3 PPARA peroxisome proliferative activated receptor, alpha IKBKG inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma ELK1 ELK1, member of ETS oncogene family STK31 serine/threonine kinase 31 SERPING1 serine (or cysteine) proteinase inhibitor GPR4 G protein-coupled receptor 4 RAB5B RAB5B, member RAS oncogene family RAB7 RAB7, member RAS oncogene family NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells NFKBIB nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, beta CEBPE CCAAT/enhancer binding protein (C/EBP), ε ELK1 ELK1, member of ETS oncogene family EHF ets homologous factor 15 Zinc finger proteins small inducible cytokine subfamily A (Cys-Cys), members 5,11, 20 and 23 Interleukins: IL1, IL1delta, IL8, IL12A, IL12B, IL13, IL23, Discussion We have proposed some approaches to promoter model construction and show how these approaches work in the particular case of antibacterial response of a eukaryotic cell, namely the reaction of human lung epithelial cell to P. aeruginosa binding. One of the results of our work is a list of potential target genes, enriched with different regulatory proteins, including transcription factors and known participants of the ascending pathways. This theoretical result must have two practical consequences: first, it allows to restrict further experimental research to a manageable number of candidate genes; second, it enables to understand or to clarify some uncertain details concerning the triggering pathways, and thus to make some new predictions based on this information. There is a number of published tools for searching for regulatory modules (i.e., "sequence elements that modulate transcription", following the definition given by Bailey and Noble [1] following [7,58]) [7,53,59-63]. The used algorithms may be devided in three classes (sliding window approach, hidden Markov models, discriminative technique), as briefly reviewed in [1]. Any of the approaches, independent of which algorithm it is based on, encounters the same problems arising from the biological nature (and extreme complexity) of the object: (i) scarcity of knowledge about exact location of promoters and enhancers and of experimentally proven binding sites (information used for constructing (+)-training sets); (ii) the fact that statistical significance of a feature (TFBS or a cluster of them) does not necessarily tell anything about the biological functionality of this feature; analogously, the insignificance can not be taken as a proof of the lack of function; (iii) usually weak reasoning for grouping genes (their promoters) in sets according to their function, co-regulation, functional occurrence in the same cell types, etc. The latter has some lucky exceptions, like sets of muscle genes [58] or cell-cycle regulated genes [64], and the situation will obviously improve with further development of microarray technique. In the present work we tried to address the listed problems. We could not, of course, improve the situation with the paucity of experimental data, only endeavored to make our data searches as accurate and exhaustive as possible. In principle we developed our approaches basing them, whenever possible, on biological reasoning. We find it extremely important to use as much experimental evidence as it is available at the moment. In our approach we alternated two different kinds of steps – expanding the data and restricting it: exhaustive data search – "seed" and distance constraints – exhaustive enumeration of all possible pairs – complementary pair constraints. To avoid the problem of low confidence in the (+)-training set (which may occur not only in our specific case), we developed the approach of "seed" sequences. The difference from the "seeds" used in cluster analysis is that in our approach the choice of the "seed" is biologically based. Although the "seed" approach is, obviously, a restrictive measure, moreover, a pre-process restriction, which may result in missing potentially relevant additional sequence features, we find it useful and appropriate when the choice of the "seed" is made on a solid biological basis. After having applied the restrictive "seed" technique and distance assumptions, we undertake an exhaustive, complete enumeration of all possible pairs of potential TF binding sites that can be found in the (+)-training set, which in turn reveals a large number of combinations. This list of all found pairs is processed under a new kind of constraints imposed by the search for complementary pairs. The search for complementary pairs is a completely new approach, which supplies us with a new kind of information. It enables to identify subsets of the (+)-training set which possess different regulatory modules, thus suggesting their triggering by different regulatory pathways. This kind of information becomes extremely important in two cases: (i) when two or more pathways are presupposed to be triggered in the cellular response, like in the case considered in this work; (ii) when the (+)-training set consists of not really co-regulated, but of co-expressed genes, without precise information about which of them are regulated by the same mechanism. The identification of complementary pairs and, consequently, groups of sequences enables to better define the co-regulated genes thus providing a partial, although only predicted, confirmation of the co-regulation, and at the same time to better understand the ascending pathways. The final result of our search supported the idea of complementary pairs. There is a lot of evidence in literature that interleukin 8, β-defensin, monocyte chemoattractant protein and different mucins are regulated through LPS-triggered pathway(s) [12,15,38]. On the other hand, it is also well-known that LPS is one of the "gates" through which the antibacterial response is triggered [24,65]. We know, that in the particular case of interaction with P. aeruginosa this pathway is not the only one [13], but we do not know in advance which of the genes in the (+)-training set belongs to which pathway (except for several genes as listed above). We had no means to include our pre-knowledge in the search. With the complementary pair approach we could re-identify the LPS subset in good agreement with our expectations (Table 3), confirming the efficiency of the method. Our approach, as any other, has its limits. It has been shown for the genuine composite elements of certain types (for instance, NF-AT and AP-1) [66] that one of the two constituents of a composite element could be rather degenerate, as compared with its canonical consensus sequence or when scored with a positional weight matrices (PWM). This means, that our requirement for all binding sites to be found with rather high PWM thresholds may be too restrictive. We are running risk to overlook those constituents of pairs which possess weak consensi. We could not find a solution to this problem. We have no information about which of the TFs could be represented by such low-threshold consensus, and if we take from the very beginning the lower thresholds for all considered matrices, we will be drowned in potential binding sites, nearly all of them probably being false positives. Nevertheless, we find that the PWM approach is better than string identification, which even with allowed mismatches can not provide the same flexibility as PWMs. The next source of limitations we see in the preselection of factors according to published data. Obviously, we can not expect that the experimental data is exhaustive; some of the transcription factors may be not reported just because their participance in a certain process has not yet been investigated. On the other hand, statistical overrepresentation, as it has already been mentioned before, can not be taken by itself as proof of biological functionality or its lack; some TFBS cannot be overrepresented due to their degenerate nature. We had no other idea of how to take into account those TFBS which are not overrepresented, but to rely on published experimental data. We find that the usual methods based on statistical overrepresentaion are even more restrictive, but maybe the best solution could be found in merging both approaches – i.e., using the experimental evidence along with statistical ones, for instance using Bayesian techniques. We see the perspectives of this work in two different fields: further investigation of regulatory networks triggered by P. aeruginosa binding, and further development of the methodological approaches, making them more flexible and applicable to any similar task. The list of predicted target genes has to be evaluated experimentally, but may have its value for further research already on the present step. The future work on reconstructing the intracellular pathways triggering the genetic program of the antibacterial cell response will be well supported with the information picked up from this list. It may give some hints for the next steps of experimental research, for instance providing information about the first candidates to be checked. The information about the complementary subsets of regulated genes helps to better understand the triggering pathways, and the complementarity of their function is a subject for further consideration. The methodological approaches presented in this paper can be, of course, applied to other objects. In this work we focused on the experimentally proven basis for the initial choice of transcription factors. This kind of evidence is stronger than any prediction, but it can work only when this information is available, which may be not the case for some other sets of genes or cellular situations. In the next step of development we would like to allow also an exhaustive computational search through the whole list of known TFs for potential constituents of the models. The usage of Bayesian techniques, as mentioned in the previous paragraph, would be also appropriate for this kind of predictions. Conclusions We suggest a methodology for promoter model construction based on the search of TFBS pairs and show how it works in the particular case of antibacterial response of human lung epithelial cells. We show that the method allows to identify and predict subsets of target genes potentially triggered by different regulatory pathways and thus possessing different regulatory modules. The methodology is easily applicable to any similar task and does not depend on the number of included TFs and/or number of investigated sequences, which only should not be too low for statistical reasons. Methods Databases Eukaryotic Promoter Database , release 77-1. DBTSS, the database of transcription start sites , release 3.0 TRANSCompel® Professional release 7.1 TRANSFAC® Professional release 7.1 TRANSPATH® Professional release 4.1 Training sets The positive (+) training set comprises: 1. Promoters of human genes shown to be expressed in epithelial cells after interaction with P. aeruginosa by means of: a. microarray analysis [67], b. other methods [11,13,15,27,28,37,38]. (Table 1) 2. Orthologous mouse promoters. The sequences were derived either from Eukaryotic Promoter Database , or from DBTSS, the database of proven transcription start sites . The length of the sequences was 600 bp (-500/+100). This region comprises most of then known upstream elements and corresponds to the upstream region used by Davuluri et al. as "proximal promoters" for promoter recognition [69], plus a 100 bp proximal downstream region which also contains many known regulatory elements documented in the TRANSFAC database [70]. The "seed" set is a subset of the positive training set selected for highest experimental reliability (see Table 1). The negative (-) training set was composed of randomly chosen 5'-upstream sequences derived from the TRANSGENOME information resource of annotated human genome features [57]. The set was manually cleaned from all genes which potentially could be involved in the same or similar cellular responses. The set comprised 2040 sequences. Defining the set of transcription factors (potential constituents of the model) We based our selection of TFs on experimental evidence. For that we undertook an extended literature search, looking for the TFs which have been shown to take part either directly in the response of epithelial cells to P. aeruginosa binding or in the pathways triggered during similar responses. The search revealed 5 candidate factors: NF-κB [11,12,15,18,21,23,24,26], C/EBP [21,24,25,27], AP-1 [24,25], Elk-1 [16,24] and Sp1 [28,29,48]. Including C/EBP and Sp1 in the list was additionally reasoned by the fact that these factors are known to be second constituents in the most frequent NF-κB-containing composite elements as they are compiled in the TRANSCompel® database [17]. Moreover, these are the types of composite elements known to participate in different kinds of immune response. Search for the potential transcription factor binding sites We made this search with the weight matrix approach using the Match™ tool [68]; the matrices were chosen from the library collected in TRANSFAC® [70]. For the model construction, the thresholds for the matrix search have been defined individually for each matrix and in such a way that (i) it should yield not less than 80% TP (true positive set, here the set of experimentally proven TFBS from TRANSFAC®); (ii) at least one hit for every searched transcription factor could be found in every sequence of the (+)-training set. The lower border for the thresholds was predefined as 0.80/0.79 (core similarity/ matrix similarity). Identification of pairs We considered all the coordinates (with strand information) of all potential TF binding sites found by Match™ for each transcription factor. Further on, we examined all possible combinations of the coordinates, thus revealing all possible pairs in the sequence. We worked under two different kinds of distance assumptions as described in Formalization of the approach, choosing the most promising results achieved with either of them. We considered all pairs of TFs within these segments. All the pairs of one type found within one distance range were merged. We considered a pair only if it appeared in the sequence at least once (within a certain distance), not taking in account the number of pairs in each sequence. Authors' contributions ES developed the methodological approaches as well as statistical analysis and conducted the data analysis. EW conceived the study and participated in its design and coordination. Both authors drafted the manuscript. Both authors read and approved the manuscript. Appendix 1 Estimation of the validity of model construction algorithm The question is, if we choose by chance a subset of sequences, will our algorithm be able to define a model, specific to such a random subset? In other words, will this algorithm allow to make a model of anything, without dependence on the preselection of the sets ((+)-training set and/or the "seed" set)? We tried to prove the validity of the algorithm theoretically. Our algorithm is based on the definition of biologically relevant "seed" sets, in which we search for the candidate pairs (normal and complementary ones). Therefore, in order to answer the question, it is reasonable to estimate the probability to come across a "seed" set of k sequences, 100% of which possess the required common feature: a pair, a combination of pairs or complementary pairs, just by chance. Note that this estimation is written not for the whole model construction process, but only for the first step of it, where we consider only the "seed" sequences. Let us consider the frequencies of predicted single sites (f) of the TFs included in the model and the frequencies of all possible pairs (F), constructed of these sites. If the frequencies of single sites and the pairs of them satisfy the equation Fij = fi fj, (1) we can interpret Fij as the probabilities of independent events, which is a prerequisite for the following formalism. We measured the frequencies of predicted single sites and the frequencies of all possible pairs in the (-)-training set (see Methods). We did not take into consideration distances and orientations; the probability estimated for the general case will decrease further with the addition of new constraints. The frequencies fi and Fij of single sites and pairs, respectively, were measured directly as fi = mi / N Fij = Mij / N where N is the number of all sequences of the (-)-training set, mi is the number of sequences possessing the i-th site, and Mij is the number of sequences possessing pairs of the i-th and j-th sites. Fij was then calculated as (1) and compared with the measured value. For all cases investigated in this work, the difference between the calculated and measured values did not exceed standard deviation (σ), only in one case getting to 1,5 σ (data not shown). This confirms the correctness of using pair frequencies as probabilities in this case. Let us estimate the probability Ppair to find a set of k sequences in N with any (at least one) pair, same in all k. We can enumerate all possible pairs of sites of the considered TFs, considering only the cases of the independent sites (i<j). Let U be the number of all possible pairs, then we can call Fij = Fu, u ∈ {1,..., U} It is easy to show, that the probability Ppair can be calculated as: Let us estimate the probability P2pairs to find k sequences with any common pairwise combination of pairs (pair of pairs). The pairs of pairs may consist either of 3 (when one site is shared) or of 4 different sites (thus leaving out combinations of identical pairs); their probabilities therefore are: and where fi, fj, fl, fo are the frequencies of the single sites of the considered TFs, i<j<l<o. We can enumerate all possible pairs of pairs (notating them as Q): Let V be the number of all possible pairs of pairs, V = t + s. Analogously to (2), the probability to find k sequences each possessing a pair of pairs of one type, is: where v ∈ {1,..., V}. Let us estimate the probability to find k sequences with any complementary pair (complementary combinations). We consider pairs as complementary, if two of them are found in the seed set in not more than 60% of the sequences and not less than 40 %, the allowed overlap being 20%. The two complementary pairs together must cover the whole seed set. In the case studied here, comprising the 12 sequences of the seed set, we fixed that each of the pairs should be present in at least 5, but not more than 7 sequences, and they are allowed to co-occur in 0–2 sequences. The probability that we choose 12 sequences, possessing any one pair of complementary pairs in accordance with these requirements can be calculated as: where u, w ∈ {1,..., U}, and are the binomial coefficients (note that this formula implies that Pcompl reaches the maximum when the frequencies of both pairs are 0.5). All the probabilities were calculated for the (-)training set of 2040 5'-upstream sequences and for the set of 5 selected transcription factors (see Methods). The results are: Ppair = 0.44 ± 0.02 P2pairs = 0.13 ± 0.01 Pcompl = 0.013± 0.003 We have estimated the simplest variant, considering each time only one feature (1 pair, 2 pairs, or complementary pairs). In this case it can be seen that the simultaneous occurrence of 1 or 2 pairs in 12 randomly chosen sequences has a rather high probability, and thus we can not base our model construction on the search of only these features. (An increase of the number of "normal" pairs in the search will not dramatically improve the situation: the formula (3) describes the probability to find any combination of 3 or 4 sites, therefore, up to 6 pairs;obviously, the simultaneous search of more than 6 pairs will definitely overfit the model, so we do not consider this case). The probability to find 12 sequences sharing complementary pairs is much lower, so the consideration of a complementary combination makes the model much more specific, and the probability of finding a model with a complementary pair "by chance" is sufficiently low for us to claim that the proposed algorithm is valid. Note that this is a very rough estimation, considering only the upper borders; we would like to emphasize once more, that the probabilities were calculated without considering orientation and distance constraints, and that this is the estimation made for only the very first step of analysis: choosing of a seed set with needed properties. Obviously, this value depends on the number of the sequences in the "seed". Note that when we spread our requirements for simultaneous search on the whole (+)-training set (which is the next step of the model construction) the probability of constructing a model "by chance" will drop dramatically. Supplementary Material Additional File 1 The whole list of genes found with the promoter model when applying it to the collection of 13000 human 5'-upstream sequences. This list is not cleaned from hypothetical genes. Click here for file Additional File 2 The list of genes (found with the promoter model when applying it to the collection of 13000 human 5'-upstream sequences) cleaned from hypothetical genes. Click here for file Acknowledgements The authors would like to thank Ingmar Reuter, Ellen Goessling and Dmitri Tchekmenev for technical help and Alexander Kel for helpful discussions. 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==== Front J Exp Clin Assist ReprodJournal of Experimental & Clinical Assisted Reproduction1743-1050BioMed Central London 1743-1050-2-11564932210.1186/1743-1050-2-1EditorialArticle processing charges, funding, and open access publishing at Journal of Experimental & Clinical Assisted Reproduction Sills Eric Scott [email protected] Tina Thibault [email protected] Gianpiero D [email protected] Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Atlanta Medical Center, Atlanta, Georgia USA2 Journal of Experimental & Clinical Assisted Reproduction, Atlanta, Georgia USA3 Cornell Institute for Reproductive Medicine, Weill Medical College of Cornell University, New York, New York USA2005 13 1 2005 2 1 1 3 1 2005 13 1 2005 Copyright © 2005 Sills et al; licensee BioMed Central Ltd.2005Sills et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Journal of Experimental & Clinical Assisted Reproduction is an Open Access, online, electronic journal published by BioMed Central with full contents available to the scientific and medical community free of charge to all readers. Authors maintain the copyright to their own work, a policy facilitating dissemination of data to the widest possible audience without requiring permission from the publisher. This Open Access publishing model is subsidized by authors (or their institutions/funding agencies) in the form of a single £330 article processing charge (APC), due at the time of manuscript acceptance for publication. Payment of the APC is not a condition for formal peer review and does not apply to articles rejected after review. Additionally, this fee is waived for authors whose institutions are BioMed Central members or where genuine financial hardship exists. Considering ordinary publication fees related to page charges and reprints, the APC at Journal of Experimental & Clinical Assisted Reproduction is comparable to costs associated with publishing in some traditional print journals, and is less expensive than many. Implementation of the APC within this Open Access framework is envisioned as a modern research-friendly policy that supports networking among investigators, brings new research into reach rapidly, and empowers authors with greater control over their own scholarly publications. ==== Body Introduction Journal of Experimental & Clinical Assisted Reproduction is a scientific and clinical journal established in September 2004, offering rapid peer review of research of the advanced reproductive technologies. Content is administered by two chief editors with offices in New York and Atlanta, with peer review supported by an international editorial board. Importantly, all published manuscripts are freely available to a global audience in full text format to facilitate sharing of investigative insights, laboratory methods, and surgical techniques. No other journal presently has these objectives. The journal considers submissions of the following types: Original research, Reviews, Book reviews, Case reports, Commentaries, Debate articles, Hypotheses, Methodology articles and Short reports. All manuscripts published by Journal of Experimental & Clinical Assisted Reproduction are included by PubMed at the National Library of Medicine (United States) [1], as well as by the national libraries of the Netherlands [2], Germany [3], and France [4]. The evolution of modern academic publishing Creation of the "Open Access" publishing model represented a watershed moment in how research manuscripts are processed, paid for, provided, and protected. Historically, it was the end consumer (the library or reader) who was charged for access to medical, scientific and technical literature. Such payment was traditionally in the form of subscriptions or by access fees levied in exchange for controlled access to particular articles via internet. In contrast, Open Access publishing asks the author to offset the cost of access. The advent of internet technology facilitated the indexing of medical literature with a speed and degree of sophistication impossible before the world wide web. Nevertheless, once an article was located via computer database search, generally only the 250 word abstract could be viewed – even though full text was often required. As no library can offer each issue of every journal, the literature collection/review process increasingly has become a multi-institutional endeavor. Unfortunately the high cost of maintaining multiple subscriptions has actually caused some libraries to provide fewer journals for users [5,6]. More to know, but less to read? With journal holdings at some libraries trending down, smaller academic publishers were early casualties of the reduced demand. Many were lost to consolidation. This resulted in a once diverse publishing community being controlled by only a few organizations with little incentive to change. Meanwhile the number of specialty journals multiplied to keep pace with the growing complexities of medical research. Even as modern research became more multidisciplinary, academic publishers did not make it easy to share findings across dissimilar journals without the reader paying a price. Thus, a paradoxical albeit unintended contraction of access to published medical research resulted. Within the world of academic publishing, limited competition may have registered profits for some but the worldwide impact on readers' access to scholarly literature has been distinctly negative [7]. It was against this "restricted access" background that the Open Access model of academic publishing was conceived, with a view to maximize rapid collaboration among researchers using a relatively new resource with tremendous potential – the world wide web. Open Access publishing incorporates internet technology which frees the investigator from the limitations of a particular institution's journal holdings as well as the delay of the interlibrary loan. Open Access and the future of academic publishing Implementation of the Open Access publishing model has several important benefits for the scientific community. First, published works are available full text via internet thus making them rapidly accessible to a global audience. Since authors maintain the copyright to their own work, they are free to reproduce it on their own website, link it to related sites, or distribute it according to their own needs without obtaining the publisher's permission first. The only requirement is to acknowledge the article's source. "Reprint requests" are obviated since full text manuscripts are available for free via internet. Such networking has already been shown to boost article citations and impact since these manuscripts are easy to find [8,9]. Second, investigators need only an internet connection and a computer to access every published article with open access – no longer are they limited by their library's journal subscription list. The Open Access publishing model is subsidized by authors (or their institutions/funding agencies) in the form of a single £330 article processing charge (APC), collected at the time of manuscript acceptance for publication. Payment of the APC is never a condition for any manuscript's formal peer review and does not apply to articles rejected after peer review. Additionally, this fee is not assessed for authors whose institutions are BioMed Central members or where genuine financial hardship exists. Such waiver requests are considered by the chief editors on a case-by-case basis. Since the journal exists as an internet resource there is no page limit to the number of color photographs, diagrams, or figures attached to a particular article. Movie files (<10 MB) accompanying a manuscript may also be published using the journal's electronic publishing platform, a feature particularly suited for articles demonstrating new operative or laboratory techniques. Given the full range of publishing possibilities at Journal of Experimental & Clinical Assisted Reproduction, it is hoped that the £330 charge will be affirmed as a good value, especially since subsequent costs associated with reprint fees or page charges assessed by many traditional print journals can easily exceed this amount. Conclusion While several journals have moved to offer free internet access to selected articles, this should not be confused with Open Access [10]. Free access is typically associated with a 6–12 month delay between publication and actual availability of a manuscript, and even when full-text articles are provided copyright restrictions limit the reader's distribution and reproduction of the work. In contrast, the APC assessed by Journal of Experimental & Clinical Assisted Reproduction guarantees Open Access to all published work and permits the author to keep the copyright to their own data, thus benefiting all who are interested in advancing research in the assisted reproductive technologies. Abbreviations APC = article-processing charge. Competing interests The author(s) declare that they have no competing interests. ==== Refs BioMed Central Open Access Charter Tamber PS Is scholarly publishing becoming a monopoly? BMC News and Views 2000 1 1 PubMed Central e-Depot Potsdam INIST Lawrence S Free online availability substantially increases a paper's impact Nature 2001 411 521 11385534 10.1038/35079151 Velterop J Should scholarly societies embrace open access (or is it the kiss of death)? Learned Publishing 2003 16 167 169 10.1087/095315103322110932 Bethesda Statement on Open Access Publishing BioMed Central Institutional Members Schlimgen JB Kronenfeld MR Update on inflation of journal prices: Brandon/Hill list journals and the scientific, technical, and medical publishing market J Med Libr Assoc 2004 92 307 14 15243636 Graczynski MR Moses L Open access publishing – panacea or Trojan horse? Med Sci Monit 2004 10 ED1 3 14704638 Wulff JL Nixon ND Quality markers and use of electronic journals in an academic health sciences library J Med Libr Assoc 2004 92 315 22 15243637 Satyanarayana K Open access publication in biomedical research: implications for developing countries Indian J Med Res 2004 120 67 9 15347853 McLellan F Publishers face backlash over rising subscription costs. High prices have led some US institutions to cancel subscriptions to, or even boycott, scientific journals Lancet 2004 363 44 5 14727612 10.1016/S0140-6736(03)15248-8	15649322	PMC546227	CC BY	2021-01-04 16:40:17	no	J Exp Clin Assist Reprod. 2005 Jan 13; 2:1	utf-8	J Exp Clin Assist Reprod	2,005	10.1186/1743-1050-2-1	oa_comm
==== Front RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-2-31565691010.1186/1742-4690-2-3ResearchEvidence for preferential copackaging of Moloney murine leukemia virus genomic RNAs transcribed in the same chromosomal site Kharytonchyk Sergey A [email protected] Alla I [email protected] Anna B [email protected] Igor K [email protected] Laboratory of Cellular and Molecular Immunology, Institute of Hematology and Blood Transfusion, 223059 Minsk, Republic of Belarus2 Present address: Department of Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN37232, USA2005 18 1 2005 2 3 3 15 11 2004 18 1 2005 Copyright © 2005 Kharytonchyk et al; licensee BioMed Central Ltd.2005Kharytonchyk et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Retroviruses have a diploid genome and recombine at high frequency. Recombinant proviruses can be generated when two genetically different RNA genomes are packaged into the same retroviral particle. It was shown in several studies that recombinant proviruses could be generated in each round of HIV-1 replication, whereas the recombination rates of SNV and Mo-MuLV are 5 to 10-fold lower. The reason for these differences is not clear. One possibility is that these retroviruses may differ in their ability to copackage genomic RNAs produced at different chromosomal loci. Results To investigate whether there is a difference in the efficiency of heterodimer formation when two proviruses have the same or different chromosomal locations, we introduced two different Mo-MuLV-based retroviral vectors into the packaging cell line using either the cotransfection or sequential transfection procedure. The comparative study has shown that the frequency of recombination increased about four-fold when the cotransfection procedure was used. This difference was not associated with possible recombination of retroviral vectors during or after cotransfection and the ratios of retroviral virion RNAs were the same for two variants of transfection. Conclusions The results of this study indicate that a mechanism exists to enable the preferential copackaging of Mo-MuLV genomic RNA molecules that are transcribed on the same DNA template. The properties of Mo-MuLV genomic RNAs transport, processing or dimerization might be responsible for this preference. The data presented in this report can be useful when designing methods to study different aspects of replication and recombination of a diploid retroviral genome. ==== Body Background Retroviruses are a family of RNA viruses which replicate through a DNA intermediate [1]. The unique property of retroviruses is that their virions contain two identical genomic RNA molecules noncovalently linked near the 5' ends forming a dimer [2,3]. Thus, the retroviral genome is diploid. The presence of two RNA molecules in each virion seems to be necessary for recombination because there is no pool of viral replicative intermediates in the cells infected by retroviruses [4,5]. Recombination is thought to contribute to the genetic variability of retroviruses and to repair breaks in genomic RNA. It can not be excluded that both RNA molecules are necessary for synthesis of proviral DNA. Reverse transcription entails two DNA strand transfers during minus and plus DNA synthesis. Since the retroviral virion contains two molecules of the viral RNA, the first DNA transfer might be either intramolecular, transferring to the same template, or intermolecular, transferring to the other template. In the model of Spleen necrosis virus (SNV) it was found that the minus-strand DNA transfer is exclusively intermolecular [6], while another study demonstrated the almost complete preference for intramolecular minus-strand transfer [7]. However, recombinant proviruses can undergo both interstrand and intrastrand transfers in equal proportions [7-9]. The rate of recombination in these reports was 4% per kilobase per replication cycle [4,8] and it was not significantly increased when the marker distance was extended to the size of the retroviral genome, suggesting that recombination is limited to only a subpopulation of retroviruses [10]. On the other hand, Human immunodeficiency virus type 1 (HIV-1) was shown to undergo approximately two to three recombination events per genome per cycle of replication [11] and, similar to the recombinant SNV proviruses, the first DNA strand transfer was either intra- or intermolecular [12,13]. A reason why there are differences in the rates of recombination between HIV-1 and gammaretroviruses (SNV and Mo-MuLV) is not known. It has been suggested that these differences may be associated with the differences in the template switching frequencies of retroviral reverse transcriptases [11]. A recent study has shown that the rates of intramolecular template switching for HIV-1 and Mo-MuLV (Moloney murine leukemia virus) were very similar, indicating that the replication properties of HIV-1 and Mo-MuLV RTs may not differ [14]. However, it is not clear whether the same conditions are required when both genomic RNAs are used as the template during reverse transcription. The other possibility is that gammaretroviruses may copackage genomic RNAs produced at different chromosomal loci by nonrandom chance [15]. In this case, the sizes of heterodiploid and recombining subpopulations of viruses may coincide. In this study, we have investigated whether there is a preference in the formation of homodiploid virions during the mixed retroviral infection. To explore this possibility, we have used the forced recombination system which included two Mo-MuLV-based retroviral vectors containing different selectable markers and one of the vectors having a deletion of the PBS region. These vectors were introduced into the packaging cell line using two different methods, cotransfection, to provide tandem integration, or sequential transfection, and the frequencies of recombination for the vectors have been compared. Results Experimental approach To study whether there was a preference for the formation of homodiploid virions in the mixed retroviral infection we have used two different methods, cotransfection and sequential transfection, to introduce genetically different retroviral vectors into the host cells. Since plasmid DNA transfected into eucariotic cells is usually tandemly integrated in a chromosome [16-19], it is expected that cotransfected vectors will be localized in the same locus of chromosome and RNA transcribed from these templates will form a general pool of molecules. In this case, two genetically different populations of RNA molecules will ideally overlap. On the other hand, it is unknown whether the same conditions exist for reassortment of RNA molecules transcribed at different chromosomal locations. The study of recombination frequencies for retroviral vectors that are introduced by the cotransfection or sequential transfection can help to answer this question. Comparative study of recombination frequencies for retroviral vectors with the same and different chromosomal locations In this study Mo-MuLV-based retroviral vectors were used as partners for recombination. These vectors contained the Mo-MuLV sequences as follows: the 5' and 3' LTRs, ψ region, a part of gag-sequences before XhoI site (position 1560 [20]), and 140 bp including the polypurine tract before 3' LTR (Figure 1). To selectively introduce vectors into the packaging cell line, pDHEneo contained the neo gene that was expressed by transcripts initiated from the long terminal repeat, while pDΔpbsSVpuro contained the puro gene under control of SV40 early promoter region. In addition to the differences in selectable markers, the pDΔpbsSVpuro vector was replication defective due to the deletion of entire PBS. Figure 1 Structures of Mo-MuLV-based retroviral vectors used in this study. U3, R, U5, regions of long terminal repeat; SV, simian virus 40 early promoter region; ψ+, extended packaging signal; Neo, neomycin phosphotransferase gene; Puro, puromycin N-acetyltransferase gene. Δpbs and ΔEP indicate that the entire primer binding site and enhancer-promoter sequences from the U3 region are deleted. Since pDΔpbsSVpuro RNA is impaired at the initiation of reverse transcription, this function can be restored when the cDNA initiated on the copackaged pDHEneo RNA is transferred to the puro RNA template during the first jump; minus-strand synthesis continues through the puro gene, and the template shift occurs within the leader region. Thus, the restoration of retroviral vector containing the puro gene is possible via homologous recombination with the neo-containing construct at the sequence identity in the leader region of the genome. The experimental scheme employed in this study is outlined in Figure 2. Retroviral vectors pDΔpbsSVpuro and pDHEneo were introduced into GP+envAM12 packaging cells by either the cotransfection or sequential transfection procedure. For sequential transfection pDΔpbsSVpuro was first introduced into helper cells. The transfected cells were placed on puromycin selection and the resistant cell clones were picked. Viral titers generated from these clones were analyzed using NIH3T3 cells. None of the cell clones analyzed produced detectable level of puromycin titer. Two clones were further used for transfection of pDHEneo and the G418 resistant clones were selected. For cotransfection the equal quantities of vector DNA was used for transfection of helper cells. The cells were first placed on G418 selection and the resistant cell clones were further obtained via puromycin selection. After drug selection, the double-resistant helper cell clones were isolated. Figure 2 Experimental scheme to study recombination frequencies for retroviral vectors located in the same or different chromosomal sites. It was expected that plasmid DNA of retroviral vectors pDHEneo and pDΔpbsSVpuro cotransfected into the packaging cell line would be tandemly integrated into the host genome. To study the integration of plasmid DNA, the PCR analysis was performed with the primers hybridizing to the 3' end of neo gene (T1, direct, for pDHEneo) and to the SV40 early promoter region (T2, reverse, for pDΔpbsSVpuro). Using these primers, the specific PCR products could be obtained if the pDHEneo and pDΔpbsSVpuro are located in the same chromosomal site. On the other hand, PCR products could be generated with only one of the primers when identical molecules of plasmid DNA were integrated in the opposite orientation. However, the efficiency of amplification in this case seems to be very low because such sequences will contain inverted repeats. The PCR analysis was performed using chromosomal DNA prepared from different cell clones generated after cotransfection or sequential transfection of vectors. PCR products were separated by gel electrophoresis, transferred onto nylon membrane and hybridized with 3' neo specific probe. An example is presented in Figure 3 which shows that specific PCR products of different size were obtained only for the cell clone generated after cotransfection of two vectors. These data are in agreement with early observations [16-19] and demonstrate that plasmid DNA transfected into the packaging cells is cointegrated into the cellular DNA. Figure 3 PCR analysis of plasmid DNA transfected into the packaging cell line GPenv-AM12. A. Analysis of tandemly integrated plasmid DNA. Amplification was performed with a 5' primer specific to neo sequences (T1, unique for pDHEneo) and a 3' primer specific to SV40 early promoter region (T2, unique for pDΔpbsSVpuro). Membrane was hybridized with 3' neo specific probe generated from a 150 bp SalI-ClaI fragment of pDHEneo. ST is GPenv-AM12 virus-producing cell clone ST2-1 generated by sequential transfection of pDHEneo and pDΔpbsSVpuro, and CT is cell clone CT2 generated by cotransfection of the same vectors. Molecular size markers are indicated on the right of the Southern blot. Similar results were obtained when four cell clones were analyzed. B. Control of amplification. Primers specific to the 5'- and 3'-end of neo gene (CND and CNR, respectively) were used to generate PCR products (1.63 kb) from ST and CT DNA samples. Membrane was hybridized with the same probe as in A. PCR products obtained from 200 and 40 ng of ST DNA sample (line 1 and 2); PCR products obtained from 200 and 40 ng of CT DNA sample (line 3 and 4). The result shows that specific PCR products could be amplified both from ST and CT DNA samples with this set of primers. We also used RT-PCR-based assay to examine the ratios of retroviral virion RNA molecules for cell clones generated by different methods of transfection. Since retroviral vectors differed by localization of EcoRI sites in the leader regions, these restriction sites were used as markers to distinguish the two coamplified PCR products obtained with primers specific to this region (Figure 4). EcoRI digestion generated 453- and 148-bp fragments from the pDΔpbsSVpuro PCR products that were readily distinguishable from the 515- and 98-bp fragments generated from the pDHEneo PCR products. Since the only differences between the neo- and puro-containing RNAs are nineteen bases that lie within the polymerized region (PBS was replaced with EcoRI in pDΔpbsSVpuro and one nucleotide was substituted in the leader region of pDHEneo to introduce EcoRI site), these two templates will amplify with equal efficiency. PCR products obtained from virion RNA for the two cell clones generated by sequential transfection and two clones generated by cotransfection of retrovital vectors were digested with EcoRI and the ratio of corresponding DNA fragments was examined. This analysis showed that ratios of retroviral RNAs for different cell clones ranged from 1.6 to 2.5 (pDΔpbsSVpuro/pDHEneo) and were the same for two variants of transfection (Figure 4). Figure 4 RT-PCR analysis of virion RNAs. A. Plasmid structures of retroviral leader regions. L1 and L2, primers used for PCR amplification; sizes of DNA fragments and positions of EcoRI sites are indicated. B. Leader sequences in virion RNAs were PCR amplified and analyzed by restriction digestion. PCR products obtained from virion RNAs of ST2-1 and ST2-2 packaging cell clones (lines 1 and 3); PCR products obtained from virion RNAs of CT1 and CT2 cell clones (lines 2 and 4); M, molecular weight markers. The ratios of puro/neo retroviral RNAs for ST2-1, ST2-2, CT1, and CT2 cell clones were 1.8, 2.0, 1.6, and 2.5, respectively. Viral titers generated from three helper cell clones obtained after sequential transfection and four cell clones obtained after cotransfection are shown in Table 1. In the first case the G418 titers varied from 5.0 × 103 to 6.3 × 104 CFU/ml and puromycin titers from 5.1 × 101 to 8.0 × 102 CFU/ml. In the cotransfection experiment, the G418 titers varied from 3.1 × 104 to 1.1 × 105 CFU/ml and puromycin titers from 1.4 × 103 to 3.6 × 103 CFU/ml. The frequency of recombination was calculated from the puromycin- and G418-drug-resistant colony titers (Table 1). For the sequential transfection experiment the recombination frequencies ranged from 1 to 1.3 %, with an average of 1.1 %, while recombination frequencies for the cotransfection experiment ranged from 3.3 to 4.5 %, with an average of 3.9 %. Table 1 The comparative study of recombination frequencies for cotransfected and sequentially transfected retroviral vectors Method of introduction Clone Viral titer (CFU/ml) Recombination frequency* (%) Puromycin G418 Sequential Transfection: pDΔpbsSVpuro + pDHEneo ST1-1 5.1 × 101 5.0 × 103 1.0 ST2-1 4.2 × 102 4.2 × 104 1.0 ST2-2 8.0 × 102 6.3 × 104 1.3 Mean ± SE 1.1 ± 0.1 Cotransfection: pDΔpbsSVpuro + pDHEneo CT1 3.6 × 103 1.1 × 105 3.3 CT2 1.4 × 103 3.1 × 104 4.5 CT3 2.0 × 103 5.5 × 104 3.6 CT4 3.1 × 103 7.4 × 104 4.2 Mean ± SE 3.9 ± 0.3 pDΔpSVpuro + pDHEneo CR1 2.5 × 101 1.0 × 105 0.03 CR2 2.5 × 101 4.8 × 104 0.05 CR3 0.9 × 101 2.9 × 104 0.03 Mean ± SE 0.04 ± 0.01 *The frequency of recombination was calculated as the ratio of puromycin- to G418-drug-resistant colony titer. The restriction enzyme marker differences in the leader regions of vectors provided a means to analyze the nature of recombinants in NIH 3T3 cells examined by PCR assay. Cellular DNA was analyzed from eight Puror NIH 3T3 cell clones obtained after infection with viruses produced by ST2-1 helper cell clone and eight cell clones obtained after infection with viruses produced by CT1 helper cell clone. This assay showed that all analyzed proviruses were recombinants between parental viruses, three of which were generated by template-switching in the 300 nt DLS region, and thirteen which were generated by template-switching in the 1038 nt region of 3' DLS (data not shown). These experiments demonstrated that the frequency of recombination between vectors localized in the same chromosomal site was about four-fold higher than that of vectors with different chromosomal locations. These data suggest that there might be a preference for the formation of diploid retroviral genome from RNA molecules that are transcribed on the same DNA template. On the other hand, it could not be completely excluded that the high frequency of recombination for retroviral vectors in the cotransfection experiments occurred during or after transfection procedure. The use of retroviral vector with the inactivated promoter To study the possibility of recombination between cotransfected vectors during or after transfection, we used the defective vector in which the 5' LTR promoter was deleted. This vector, pDΔpSVpuro, is almost completely homologous to pDΔpbsSVpuro with the exception of 194 bp in the U3 region (Figure 1). The efficiency of recombination during cotransfection for pDΔpSVpuro and pDHEneo was expected to be similar to that of pDΔpbsSVpuro and pDHEneo. However, the restoration of pDΔpSVpuro during reverse transcription will be limited by the basal level of cellular transcription since this vector is transcriptionally defective. Thus, the use of vector with the inactivated promoter could distinguish between recombination at the level of DNA and RNA in our experimental system. The introduction of viral vectors into the packaging cell line, GP+envAM12, allowed selection and propagation of individual cellular clones under conditions similar to those in the previous experiments. The resulting viral titers are shown in Table 1. For three helper cell clones generated after the cotransfection with pDΔpSVpuro and pDHEneo the G418 titers varied from 2.9 × 104 to 1.0 × 105 CFU/ml, with an average 5.9 × 104 CFU/ml, and the puro titers varied from 0.9 × 101 to 2.5 × 101 CFU/ml, with an average 2.0 × 101 CFU/ml. The frequency of recombination for these vectors was 0.04 %. Thus, these results clearly demonstrated that recombination during cotransfection in our experimental system was a rare event and the majority of recombinations between cotransfected vectors occurred during the reverse transcription. Discussion In the present work we have examined whether there was a preference in the formation of homodiploid genomes when two genetically different retroviral vectors were located in the different regions of the host genome. Since plasmid DNA transfected into eucaryotic cells is usually tandemly integrated [16-19], we have compared the frequencies of recombination for two Mo-MuLV-based retroviral vectors introduced into the helper cell line by either cotransfection or sequential transfection. Our results showed that cotransfection yielded about four-fold higher frequency of recombination comparing to sequential transfection, indicating that diploid retroviral genome is mainly formed from RNA molecules transcribed on the same DNA template. To exclude the possibility that recombination between vectors occurred during the cotransfection or/and the integration of plasmid DNA into the helper cell genome, we used a retroviral vector with the deletion of promoter-enhancer sequences as a partner for recombination. The 100-fold lower frequency of recombination for transcriptionally deficient vector, compared to that of the identical retroviral vector with the intact promoter, indicated that recombination during cotransfection was a rare event relative to recombination during reverse transcription. Recent studies using the Moloney murine leukemia virus and the Spleen necrosis virus based vectors demonstrated that the recombination rate did not increase linearly with the increasing of marker distance and the multiple recombination events were observed much more often than could be expected from the frequency of recombination [10,15,21,22]. From these data it was postulated that the rate of retroviral recombination is restricted by the size of the recombining subpopulation [10,15,21]. On the other hand, the rate of recombination obtained for HIV-1 was about two to three crossovers per genome per replication cycle [11,12]. High rate of HIV-1 recombination was also observed in the experimental system where target sequences and experimental conditions for recombination were the same as in Mo-MuLV- and SNV-based studies [23]. While the rates of intermolecular recombination for HIV-1 and gammaretrovoruses were different, their intramolecular template switching frequencies were similar [14,24]. The preferential formation of homodimers in the mixed retroviral infection can explain the existence of the recombining subpopulation found for avian and murine retroviruses because, in this case, the amount of heterodiploid virions will be less than expected from the randomly distributed genomic RNA. Our demonstration of about 4-fold differences in the frequencies of recombination for the cotransfected and sequentially transfected retroviral vectors seems to agree with the data showing that the maximal recombination rate for Mo-MuLV was 20 % per genome per replication cycle [10,22]. These data also indicate that the difference in the recombination frequencies for gammaretroviruses and HIV-1 could mainly be associated with the ability of these viruses to copackage two different genomic RNAs. The possible mechanism explaining the preferential formation of homodimers, as suggested earlier [15], may be a local transport of RNA transcribed in the same locus of chromosome from the nucleus to their destination in the cellular cytoplasm. In the cytoplasm, RNA could be quickly bound by viral proteins before two different pools of RNA molecules transcribed in different chromosomal sites will be equally distributed. The gammaretroviruses and HIV-1 could differ in the properties of their RNA transport and distribution in the cellular cytoplasm. For example, HIV-1 encodes the virus-specific protein Rev which selectively transports the unspliced viral RNAs from the nucleus to cytoplasm [25]. Moreover, unspliced HIV-1 RNAs form a general cytoplasmic pool of molecules which can further participate in the translation of viral proteins and/or be packaged in the virions [26]. It was recently shown that translation of HIV-1 viral RNAs could precede their packaging [27]. In this case, the translation of genomic RNAs can provide more time for reassortment of two different viral RNAs. As an alternative, it can be suggested that the dimerization of genomic RNAs of gammaretroviruses occurs immediately after transcription in the cell nucleus and heterodimerization involves only minor populations of RNA molecules left in a monomeric form and/or unstable homodimers. The diploidy of retroviral genome supposes that two molecules of RNA could be necessary for replication of virus. However, it is also possible that diploidy is important for recombination and evolution of virus since retroviruses do not have a pool of replicative intermediates that can undergo recombination [5]. The preferential copackaging of genetically identical retroviral RNAs further argues in favour of the hypothesis that both RNA molecules are required in each round of retroviral replication. This assumption is also in agreement with the results of previous studies showing the utilization of both HIV-1 RNAs during reverse transcription [11,12]. It can be suggested that two genomic molecules of RNA are necessary to repair frequent breaks in RNAs [28] or the synthesis of provirus requires involvement of cis-acting elements present in both RNA molecules. Upon completion of our manuscript, an article was published concluding that dimerization of Mo-MuLV genomic RNAs is carried out by nonrandom chance [35]. There are several differences in these two studies. In the cited report, the RNA dimers were examined in the viruses that were generated by transiently cotransfecting two vectors or were produced by cell clones containing retroviral vectors integrated in different chromosomal sites. A model of nonrandom dimerization has been proposed, where Mo-MuLV genomic RNAs may undergo dimerization cotranscriptionally. In our study, the frequencies of recombination were directly compared for cell clones where retroviral vectors were integrated in the same or different chromosomal sites. While retroviral vectors integrated in the same chromosomal site were expressed as independent transcriptional units, the efficiency the heterodimer formation was increased about four-fold compared to that of retroviral vectors with different chromosomal locations. This argues that dimerization of Mo-MuLV genomic RNAs during cotranscription is not the main reason for the preferential formation of homodiploid genomes in Mo-MuLV. In spite of substantial differences in the methods, the estimations of the efficiency of homodimer formation were similar in both studies. The experimental system presented in our report could be used to study cellular and viral factors that are responsible for the preferential copackaging of genetically identical retroviral RNAs. Conclusions The results of this study provide evidence that the Mo-MuLV genome is mainly formed from RNA molecules synthesized on the same DNA-provirus. This property of Mo-MuLV may explain why only small subpopulations of gammaretroviruses produce recombinants. In this context, the differences in the frequencies of recombination between HIV-1 and Mo-MuLV may reflect differences in the ability of these viruses to randomly copackage genetically distinct RNAs. The preferential formation of homodiploid genomes in Mo-MuLV also implies that both molecules of RNA might be required for replication of the retroviral genome. Methods Plasmid constructions pMOV9 containing the complete copy of Mo-MuLV provirus and retroviral vectors pDneo and pDSVpuro have been described earlier and were used as the progenitor for all the constructions [29,30]. pDneo and pDSVpuro contain upstream long terminal repeat (LTR) and ψ+ region before position 1560 of Mo-MuLV sequences [20], neomycin phosphotransferase gene or puromycin N-acetyltransferase gene under control of Simian virus 40 (SV40) early promoter region, and the Mo-MuLV sequences from position 7674 including downstream long terminal repeat. The nucleotides are numbered for the Mo-MuLV sequences starting from the beginning of R region [20]. To generate pDΔpbsSVpuro, we first constructed pLTRΔpbs which contains the LTR and the leader region before position 564 of pMOV9 with the deletion of PBS region. For this purpose we used the PCR to amplify two overlapping fragments after joining of which the PBS region was substituted with the EcoRI site. The first PCR fragment was generated with the primers: U3 SalI 5'-CGCGTCGACAGAAAAAGGGGGGAA-3' (sense, positions 7803–7821) and Rir EcoRI 5'-GCGCGAATTCAATGAAAGACCCCCG-3' (antisense, positions 130–144); the second PCR fragment was generated with the primers: 3'PBS EcoRI 5'-GCGCGAATTCCGGGAGACCCCTGCC-3' (sense, positions 164–178) and L2 5'-GACAAATACAGAAAC-3' (antisense, positions 599–613). PCR fragments were digested with EcoRI, ligated, and further digested with SalI and PstI, and cloned into pBluescript KSII+ (Stratagene). The amplified region of pLTRΔpbs was analyzed by sequencing. The resulting construct, pDΔpbsSVpuro, was generated by exchanging the KpnI-PstI (nucleotide positions 32 to 564) fragment of pDSVpuro with the corresponding fragment of pLTRΔpbs. pDHEneo is identical to pDneo except the point mutations in the sequences flanking the DLS region. These mutations converted the Mo-MuLV sequences in this region into new restriction sites for HindIII and EcoRI. A description of the cloning steps performed to generate this vector is available upon request. To produce pDΔpSVpuro, the enhancer-promoter sequences of U3 region in pDΔpbsSVpuro were deleted. For this purpose the 3.4 kb SacI-BamHI fragment containing 36 bp of 5' U3 region starting from SacI site and including all other vector sequences of pDΔpbsSVpuro was inserted into the SacI and BamHI sites of pTZ18 plasmid. All DNA manipulations were performed by standard procedures [31]. Analysis of integrated plasmid DNA Genomic DNA purification and hybridization were performed by standard molecular techniques [31]. DNA prepared from double-drug-resistant cell clones was used as a substrate for PCR. Integrated plasmid DNA was amplified using a 3' neo-specific sense primer T1 (5'-AGTGCAAATCCGTCGGCAT-3') and an antisense primer T2 (5'-GAGGCGGCCTCGGCCTC-3') within the SV40 early promoter. The sequences of neo gene in proviral DNA were PCR amplified using primers CND (5'-CACGCTGCCGCAAGCACTCA-3') and CNR (5'-TGGGTGGTGAGCAGCTCGCC-3'). PCR was performed in 10 mM Tris (pH 8.3), 50 mM KCl, 2 mMMgCl2, 200 μM each dNTP, 1 % DMSO, 100 nM primers for 20 cycles (94°C 1 min, 50°C 1 min, 72°C 8 min). The products were separated on 0.8 % agarose gel, transferred onto nylon membrane (Hybond-N, Amersham), and hybridized with neo-specific probe (150 bp SalI-ClaI fragment of pDHEneo). Probes were generated by the random-primer method with [α32P] dATP [32]. RT-PCR analysis Virion RNA was purified from filtered culture medium from transfected cells and used in RT-PCR assays [31]. Briefly, RNA samples were reverse-transcribed in a 20-μl reaction with Superscript II (Life Technologies), using an antisense gag-specific primer (L2) beginning at nt 613 (5'-CAAAGACATAAACAG-3'). A third of the resultant cDNA was subjected to PCR (94°C for 1 min, 50°C for 1 min, 72°C for 1 min, for 30 cycles) with AmpliTaq DNA polymerase (Perkin-Elmer), using the same primer that was used in the RT reaction and paired with a sense R-specific primer (L1) beginning at nt 1 (5'-GCGCCAGTCCTCCGA-3'). PCR products were digested with 10 units of EcoRI (Fermentas) according to the manufacturer's recommendations and analyzed by 2 % agarose gel. A GelDoc™ EQ system (Biorad) with SigmaGel v.1.0 software (Jandel Scientific) was used to quantitate the ethidium bromide fluorescence intensity of each band. Cells, DNA transfection, and virus propagation NIH3T3 (murine cell line) and GP+envAM12 (amphotropic 3T3-based packaging cell line with MLV Gag + Pol and Env genes) [33] were grown in Dulbecco's modified Eagle's medium supplemented with 10 % fetal calf serum. The cell clones producing transfected vectors were established by transfecting GP+envAM12 cells with vector plasmids using the dimethyl sulfoxide-polybrene method [34]. Puromycin-resistant cells were selected in 2.5 or 1.5 μg/ml puromycin (Sigma) for GPenv-AM12 or NIH3T3-derived cells, respectively. Geneticin selection was performed at 800 μg/ml (GP+envAM12) or 600 μg/ml (NIH3T3) of G418 (Gibco). Viral infection was performed immediately after harvesting the virus. The supernatants were harvested from 90 % confluent stable producer cell clones after 16 hour intervals and filtered through the 0.45 μm filters. Infections were performed in the presence of 8 μg/ml polybrene (Sigma) for two hours at 37°C. Puromycin- and G418-resistant cfu titers were determined using the infection of NIH3T3 cells by end-point dilution. Competing interests The authors declare that they have no competing interests. Authors' contributions SAK carried out most experiments and made substantial contributions to conception and design. AIK and ABO carried out analysis of integrated plasmid DNA by hybridization and participated in the works with cell cultures. IKF conceived of the study, participated in the design and coordination, and drafted the manuscript. All authors read and approved the final manuscript. Acknowledgments We thank Dr. Nikolai N. Voitenok (Fund for Molecular Hematology & Immunology, Moscow, Russia) for helpful discussion and review of the manuscript, and Dr. Sol M. Resnick (Dow Chemical Company, San Diego, USA) for critical reading of the manuscript. 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==== Front Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-4-41564933310.1186/1475-2875-4-4ResearchHabitat characterization and spatial distribution of Anopheles sp. mosquito larvae in Dar es Salaam (Tanzania) during an extended dry period Sattler Michael A [email protected] Deo [email protected] Michael [email protected] Zul [email protected] Marcel [email protected] Gerry F [email protected] Christian [email protected] Swiss Tropical Institute, P.O. Box, 4002 Basel, Switzerland2 Ministry of Regional Administration and Local Government, Dar es Salaam, Tanzania3 Muhimbili University College of Health Sciences, Institute of Public Health, Dar es Salaam, Tanzania4 Ifakara Health Research and Development Center, P.O. Box 53, Ifakara, Kilombero, Morogoro, Tanzania2005 14 1 2005 4 4 4 14 8 2004 14 1 2005 Copyright © 2005 Sattler et al; licensee BioMed Central Ltd.2005Sattler et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction By 2030, more than 50% of the African population will live in urban areas. Controlling malaria reduces the disease burden and further improves economic development. As a complement to treated nets and prompt access to treatment, measures targeted against the larval stage of Anopheles sp. mosquitoes are a promising strategy for urban areas. However, a precise knowledge of the geographic location and potentially of ecological characteristics of breeding sites is of major importance for such interventions. Methods In total 151 km2 of central Dar es Salaam, the biggest city of Tanzania, were systematically searched for open mosquito breeding sites. Ecologic parameters, mosquito larvae density and geographic location were recorded for each site. Logistic regression analysis was used to determine the key ecological factors explaining the different densities of mosquito larvae. Results A total of 405 potential open breeding sites were examined. Large drains, swamps and puddles were associated with no or low Anopheles sp. larvae density. The probability of Anopheles sp. larvae to be present was reduced when water was identified as "turbid". Small breeding sites were more commonly colonized by Anopheles sp. larvae. Further, Anopheles gambiae s.l. larvae were found in highly organically polluted habitats. Conclusions Clear ecological characteristics of the breeding requirements of Anopheles sp. larvae could not be identified in this setting. Hence, every stagnant open water body, including very polluted ones, have to be considered as potential malaria vector breeding sites. ==== Body Background Urban malaria Urbanization is progressing fast worldwide. By the year 2030, more than 50% of the African population will live in urban areas [1]. It is anticipated that infectious disease problems including vector-borne diseases such as malaria might also increase in urban areas [2]. Keiser et al. [3] estimated that 24.8–103.2 million clinical malaria episodes occur annually in urban settings endemic for malaria. Urban malaria is generally characterized by: low transmission intensity, lack of immunity in the population and higher mortality rates in older age groups [4]. The distribution pattern of malaria transmission intensity is dependent on the degree of urbanization and on the distance from vector breeding sites [5,6]. Consequently, a high heterogeneity of transmission intensity is characteristic for urban malaria [7]. Increasing urban agriculture is thought to play a major role in increasing malaria in urban areas. However, this is not yet demonstrated, as it increases breeding sites for Anopheles sp. mosquito larvae, but it also raises the economic status of the population, allowing improved malaria protection [8]. Malaria is not only a personal health problem, but also an intolerable economic burden [9]. Investments in fighting malaria would result in substantial economic gains [10]. Therefore lowering the malaria burden in urban areas with a high economic development potential could result in improved economic development and serve as an example for other areas [7]. In urban areas, relatively few breeding sites are found per unit population [7]. In addition, the high human density usually allows a better access to curative and preventive health services. As a result, a number of malaria control options such as environmental control, including vegetation clearance, modification of river boundaries, draining swamps and insecticide application to open water bodies become possible and cost-effective [7,11,12]. With prompt access to antimalarials and other vector control interventions such as insecticide-treated bednets, integrated malaria control becomes realistic in African cities with a well developed municipal service delivery infrastructure [3,7,13]. Mapping of breeding sites and mosquito ecology as basis for integrated malaria control Every campaign to reduce malaria transmission aims to be as effective as possible given existing resources. Malaria control methods are affected by many setting-specific factors such as endemicity, vector species and behaviour, seasonality, disease patterns, health service factors and more. Since these factors are not distributed equally in space, accurate and timely information is required before malaria control can be planned and resources allocated properly. With regard to transmission reduction, attention must be paid to the areas of greatest vector abundance and precise risk maps of Anopheles sp. breeding sites are of major importance. Computerized maps have proven to be useful for this purpose, helping to understand malarial epidemiology and guiding interventions [14-18]. Besides geographic location, knowledge of ecological features of mosquito breeding sites is a potential key element for implementing efficient and effective larvae control measures. Such measures have been shown to be an important tool to reduce malaria endemicity [19,20]. In an urban environment Anopheles sp. mosquitoes adapt to new breeding sites created by urbanization, and hence their ecology might differ from rural settings [21]. Most African studies on Anopheles sp. mosquito larvae ecology have been conducted in rural settings [7,22], and findings from these studies might not be applicable to urban settings without adaptation. The spatial distribution of Anopheles sp. and Culicine larvae in open breeding sites in Dar es Salaam, Tanzania, were examined during an extended dry period and correlations between biological and physicochemical environmental factors and the abundance of mosquito larvae were determined. This work was carried out in order to collect baseline information for a larval control-based mosquito abatement programme implemented by the three municipalities of Dar es Salaam. Materials and Methods Study site Dar es Salaam is the biggest and economically most important city of Tanzania, with a population of more than three million people and an area of 1450 km2. The present study area covers 151 km2. in the central area of the city (Figure 1). Although highly urbanized, the study area is dotted with swamps, ponds and lakes and many additional water bodies are created for agricultural purposes. Combined with the hot and humid climate, this results in excellent breeding opportunities for Anopheles sp. mosquitoes. In Dar es Salaam the main malaria vectors are: Anopheles gambiae s.s., Anopheles arabiensis, Anopheles funestus and Anopheles merus [23-25]. Figure 1 Breeding sites of Anopheles sp. larvae in Dar es Salaam, Tanzania. Study period The data collection was conducted from March 1 to May 29, 2003. Even though the study period was meant to be during the rainy season, the actual rainfall (236 mm) was less than half the long-term average from 1961–1990 (576 mm) [26]. Furthermore, the previous short periods of rainfall also failed, so this represents part of an extended drought period. Each area was visited only once in a cross-sectional survey of breeding sites. Mosquito identification To distinguish between A. gambiae s.l. (gambiae s. s., arabiensis, merus) and A. funestus, larvae collected from the productive breeding sites were raised and hatched in the laboratory so that the adults could be identified morphologically by light microscopy using the key of Gillies and De Meillon [27]. Anopheles gambiae s.l. were not further identified to sibling species by PCR because such a methodology is not practical, affordable or particularly useful in the context of routine operations of a sustainable mosquito abatement program in Dar es Salaam. Breeding site identification and recording of ecological parameters Each ward within the study area was searched for open water bodies. To locate areas of potential breeding sites, the search was lead by municipal malaria experts with a good knowledge of the area. Furthermore, one-year-old geo-referenced digital aerial pictures (0.5 m ground resolution, produced by Geospace International, Pretoria, South Africa) were examined visually on the computer and all suspected open water areas were checked on the ground by extracting coordinates from the digital picture and locating the area in the field using a global positioning system (GPS) (eTrex©, Garmin International Inc., Olathe, US). In addition, 10 year-old breeding site maps from the Japan International Cooperation Agency (JICA) were consulted [24]. All open bodies of water were taken as potential breeding site and geo-referenced by GPS. The presence of larvae at high or low densities was determined by dipping. From every potential breeding site up to 10 dips were taken with a standard white 350 ml dipper. If Anopheles sp. could be seen without dipping or nearly every dip contained Anopheles sp. larvae, the site was defined as having a high Anopheles sp. density. Sites where only one or two dips out of 10 contained Anopheles sp. larvae were defined as having a low Anopheles sp. density. Sites where no Anopheles sp. larvae could be found in ten dips were recorded as empty. Pupae were not recorded as they cannot be differentiated from non-Anopheles species in the field. To attempt to quantify the relative mosquito larvae productivity of each breeding site, a productivity score was assigned to each breeding site according to its size and mosquito larvae density (Table 1). Sites which were dry at the time of visit were excluded. Table 1 Classification of larvae productivity in potential breeding sites Perimeter of site Perimeter < 1 m Perimeter 1–10 m Perimeter > 10 m Larval density Absent 0 0 0 Low 1 1 2 High 1 2 3 0 = No larvae productivity, 1 = Low larvae productivity, 2 = Medium larvae productivity 3 = High larvae productivity The same definition was also used to characterize Culicine mosquitoes (mainly consisting of the genera Culex, Aedes and Mansonia) density in the same sites. To record habitat type, every site was categorized as one of the following: stream, small drain, large drain, swamp, rock pool, puddle, footprints, tyre track, artificial hole, concrete hole, artificial container and other. Further biological and physicochemical factors were measured, as shown in Table 2. All visual classifications were done by the same person to maintain consistency. In addition, the general setting was qualitatively described, mentioning special features and general impressions. Table 2 Key parameters measured in the 327 sites that contained water, Dar es Salaam, Tanzania. Characteristics # % Characteristics # % Type of breeding site: Filamentous algae present 138 42.2 Stream 2 0.6 Large drain 29 8.9 Single-celled algae present 18 5.5 Small drain 16 4.9 Swamp 92 28.1 Predators present 118 36.1 Rock pool 0 0 Puddle 22 6.7 Shade Foot/hoof print 4 1.2 0–25% 250 76.5 Tyretrack 1 0.3 26–50% 58 17.7 Artificial hole 82 25.1 51–75% 16 4.9 Concrete hole 9 2.8 75–100% 3 0.9 Artificial container 0 0 Other 69 21.4 Maximum depth more than 0.5 m 108 32.7 General surrounding Size High density housing 236 71.6 Perimeter < 1m 17 5.2 Fields 57 18.0 Perimeter 1–10 m 128 39.4 Industry 19 5.8 Perimeter > 10 m 182 55.4 Other 15 4.6 Turbidity Distance to nearest inhabited house Clear 137 41.9 <10 m 179 54.7 Turbid 160 48.9 10–100 m 127 38.8 Very turbid 30 9.2 >100 m 21 6.4 Salinity mS/cm (rounded to full numbers) Distance to nearest potential resting site 0–1 138 42.3 <10 m 327 100 2–3 145 44.5 10–100 m 0 0 4–5 22 6.7 >100 m 0 0 6–7 4 1.2 8–9 3 0.9 Anopheles larvae density 10–14 4 1.2 Absent 125 38.2 15–19 2 0.6 Low density 64 19.3 >20 8 2.5 High density 138 42.5 Temperature °C (rounded to full numbers) Culicine larvae density 25–26 9 2.8 Absent 139 43.1 27–28 34 10.4 Low density 43 13.1 29–30 92 28.2 High density 145 43.7 31–32 71 21.7 33–34 63 19.3 Anopheles and Culicine pupae density 35–36 39 12.0 Absent 247 75.5 37–38 12 3.7 Low density 37 11.3 39–40 5 1.5 High density 43 13.1 pH (rounded to full numbers) Grass in the middle of site present 134 41.0 6 3 0.9 7 111 34.1 Grass along the edges present 244 74.6 8 169 52.0 9 46 12.3 Floating plants present 65 19.9 10 2 0.6 Floating algae present 41 12.5 Statistical analysis Statistical analysis was carried out with SPSS V 11.0 (SPSS Inc., Chicago, US). Logistic regression analysis was used to determine the importance of key factors for explaining the different mosquito larvae densities. For each group of mosquitoes (Anopheles sp. and Culicines) two logistic regression models were fitted: one model was used to determine the key factors influencing whether larvae were present or absent, and one model was used to determine the key factors influencing whether larvae were present at low or high density. For the second model only data from sites that contained at least one larva were used. All measured parameters were included in the logistic models. The three categories "stream", "foot/hoofprint" and "tyre track" were included in the baseline category "other" as they had only four or less records. No "artificial containers" or "rockpools" were found, consequently these categories are not presented in the analysis. Salinity, temperature and pH were measured with an electronic device (HANNA© HI 98130 Combo pH&EC, Hanna Instruments Inc., Kehl am Rhein, Germany). Conductivity measured in milliSiemens/cm was the indicator to measure salinity. The continuous variables "salinity" "temperature" and "pH" were categorized into quartiles for ease of interpretation. As this study is focused on Anopheles sp. larvae, only the significant parameters are presented for the Culicine models. To reduce the risk of α errors given the large number of variables that where investigated, only variables with p-values below 0.01, or those showing a consistent trend in both regression models with p-values below 0.05, were considered in the discussion. Ethics This study did not involve human subjects and permission was obtained from the National Institute of Medical Research in Tanzania (NIMR/HQ/R.8a/Vol.lX/l 12, No. 2003-110-CC-2003-63). Results Open breeding sites in Dar es Salaam A total of 405 potential mosquito breeding sites were examined and mapped. 19% were dry at the time of visit. Of the 327 sites that contained water, 62% were productive for Anopheles sp. and 57% were productive for Culicine mosquitoes. From all 150 adult mosquitoes reared from the larvae samples, only A. gambiae s.l. could be found. In one productive breeding site salinity was above 1%, suggesting that these larvae were Anopheles merus [28]. The 202 sites productive for Anopheles sp. were made up of 69% low larvae density sites and 31% high larvae density sites. Of the 186 sites productive for Culicine larvae 23% had low larvae density and 77% had high larvae density. Breeding sites of Anopheles sp. and Culicines and their productivity are shown in Figures 1 and 2. Figure 2 Breeding sites of Culicine larvae in Dar es Salaam, Tanzania. Key factors determining the presence/absence or low/high density of mosquito larvae The frequencies of the different parameters are shown in Table 2. Presence/absence of Anopheles sp. larvae (Table 3): Large drains, swamps and puddles were much less likely to contain Anopheles sp. larvae than other habitats. Very turbid water diminished the chance that Anopheles sp. larvae were present. Breeding sites with a size of less than one meter were more likely to contain Anopheles sp. larvae. Table 3 Output of logistic regression model with presence versus absence of Anopheles sp. larvae as outcome and ecological parameters as explanatory variables. Variable Odds Ratio 95% Confidence Interval for OR P-Value Lower Upper Type of breeding site Other 1 Large drain .145 .046 .459 .001 Small drain .492 .111 2.188 .352 Swamp .139 .055 .346 .000 Puddle .196 .051 .752 .018* Artificial hole .481 .186 1.245 .132 Concrete hole 1.407 .156 12.702 .761 General surrounding High density housing 1 .118 Other .812 .210 3.131 .762 Industry .211 .055 .808 .023* Fields .502 .207 1.216 .127 Distance to nearest inhabited house <10 m 1 10–100 m 1.889 .473 7.541 .368 >100 m 1.839 .940 3.596 .075 Culicine density high 1 low .856 .369 1.988 .718 absent .687 .356 1.328 .265 Size of site Perimeter > 10 m 1 Perimeter 1–10 m 1.932 .928 4.020 .078 Perimeter < 1 m 22.825 2.094 248.843 .009** Turbidity of water clear 1 turbid .469 .231 .954 .037* very turbid .167 .059 .473 .001** Salinity measured by conductivity [mS/cm] (Quartiles) 2.6–20.0 1 1.7–2.5 .522 .228 1.195 .124 1.1–1.6 1.418 .576 3.490 .447 0.1–1.0 .728 .313 1.696 .462 Temperature (°C) (Quartiles) 25.1–29.2 1 29.3–31.3 1.758 .676 4.571 .247 31.4–33.4 .731 .306 1.744 .480 33.5–40.0 1.148 .507 2.597 .741 pH of water (Quartiles) 8.1–10.1 1 7.7–8.0 1.773 .711 4.426 .220 7.4–7.6 .825 .330 2.059 .680 6.1–7.3 .439 .180 1.070 .070 Predators present 1.931 1.031 3.619 .040* Sun-lit (less than 50% of surface shaded) 1.913 .575 6.363 .290 Depth >0.5 m .750 .393 1.434 .384 Grass present .952 .601 1.508 .834 Floating vegetation present .935 .570 1.534 .790 Submersed vegetation present 1.201 .679 2.124 .529 * = p < 0.05 ** = p < 0.01 High/low density of Anopheles sp. larvae (Table 4): When Anopheles sp. larvae were present in large drains, swamps and puddles, these sites were much less likely to contain high densities of Anopheles sp. larvae. Table 4 Output of logistic regression model with high versus low Anopheles sp. larvaedensity as outcome and ecological parameters as explanatory variable. Variable Odds Ratio 95% Confidence Interval for OR P-Value Lower Upper Type of breeding site other 1 large drain .124 .021 .739 .022* small drain .768 .109 5.389 .791 swamp .182 .053 .629 .007** puddle .165 .033 .832 .029* artificial hole .712 .198 2.556 .602 concrete hole .348 .032 3.727 .383 General surrounding high density housing 1 other .358 .056 2.290 .278 industry 2.675 .326 21.950 .359 fields .925 .275 3.112 .899 Distance to nearest inhabited house <10 m 1 10–100 m .658 .261 1.656 .374 >100 m .409 .054 3.069 .384 Cuttcine density high density 1 low density .728 .221 2.396 .602 absent 1.078 .432 2.688 .872 Size of site perimeter > 10 m 1 perimeter 1–10 m 1.565 .576 4.255 .380 perimeter < 1 m .907 .166 4.950 .911 Turbidity of water clear water 1 turbid water 1.653 .692 3.951 .258 very turbid water 2.116 .372 12.030 .398 Salinity measured by conductivity [mS/cm] (Quartiles) 2.7–20.0 1 1.7–2.6 1.638 .479 5.599 .431 1.2–1.6 .818 .251 2.671 .740 0.1–1.1 .687 .227 2.079 .507 Temperature (°C)(Quartiles) 34.1–40.0 1 31.6–34.0 1.004 .315 3.203 .994 29.4–31.5 1.312 .356 4.830 .683 25.1–29.3 .619 .176 2.184 .456 pH of water (Quartiles) 8.2–10.1 1 7.8–8.1 .882 .228 3.419 .856 7.5–7.7 .777 .200 3.027 .716 6.5–7.4 .793 .198 3.182 .744 Predators present 1.322 .530 3.302 .550 Sun-lit (less than 50% of surface shaded) 1.035 .184 5.832 .969 Depth >0.5 m .460 .174 1.219 .118 Grass present .801 .272 2.362 .688 Floating vegetation present .352 .130 .952 .040* Submersed vegetation present 1.745 .725 4.197 .214 * = p < 0.05 ** = p < 0.01 Presence/absence of Culicine larvae (Table 5a): Turbid water was clearly associated with the presence of Culicines. Breeding sites with pH values of 7.6 or less were also more likely to contain Culicine larvae. Table 5 Output from a logistic regression model with (a) presence versus absence of Culicine and (b) high versus low density of Culicine larvae as outcome, and ecological parameters as explanatory variable. The same variables as in tables 2 and 3 were included in the model, but only significant results are shown. 5a Variable Odds Ratio 95% Confidence Interval for OR P-Value Lower Upper General Surrounding High density housing 1 Other .646 .170 2.453 .520 Industry .177 .041 .771 .021* Fields .692 .302 1.585 .384 Turbidity of water Clear 1 Turbid 3.620 1.872 7.000 .000** Very turbid 2.689 .981 7.369 .054 pH of water (Quartiles) 8.1–10.1 1 7.7–8.0 1.986 .845 4.669 .116 7.4–7.6 2.916 1.252 6.792 .013* 6.1–7.3 2.586 1.109 6.030 .028* 5b Variable Odds Ratio 95% Confidence Interval for OR P-Value Lower Upper Type of breeding site Other 1 Large drain 4.207 .575 30.801 .157 Small drain .836 .054 12.894 .898 Swamp 1.811 .507 6.467 .361 Puddle 2.797 .345 22.650 .335 Artificial hole 1.137 .288 4.486 .855 Concrete hole .059 .005 .733 .028* pH of water (Quartiles) 8.0–9.0 1 7.6–7.9 2.291 .748 7.014 .146 7.4–7.5 2.485 .707 8.740 .156 6.4–7.3 4.024 1.230 13.158 .021* High/low density of Culicine larvae (Table 5b): A pH value of less than 7.3 was associated with high Culicine larvae density. Productive Anopheles sp. gambiae s.l. mosquito breeding sites in Dar es Salaam: unexpected results Contrary to the conventional thinking that A. gambiae s.l. only breeds in rather clean and clear water [28,29], such larvae were found in habitats organically polluted by rotting vegetation, human faeces or oil. Such sites were the drain of an oil refinery, one organically polluted swamp used as garbage dumping area and one sewage pond with organic pollution from human faeces (Figures 3 and 4). Except for the breeding site type "tyretrack", for which only one potential habitat was identified, An. gambiae s.l. could be found in all types of habitats recorded during the study. Figure 3 Sewage pond in Temeke municipality, Dar es Salaam, Tanzania. Figure 4 Dipper content from sewage pond in Temeke municipality (Figure 3). Socio- and geo-ecological environments with high mosquito breeding site density In our study six environments were defined where larval control interventions are of high priority due to the large number of highly productive breeding sites for Anopheles sp. Most environments identified as potentially suitable for mosquito breeding in Dar es Salaam could be described by a close interaction between geo-ecological settings and the influence of humans. Slopes on riverbeds, riverbeds, borders of swamps, stagnant drains and rivers, areas with restricted access and sites along railway lines made up the six high productivity environments (for further details see: Sattler [30]). As Dar es Salaam is highly populated, houses have been built nearly everywhere. Only swampy areas, riverbeds and steep slopes are not, or only sparsely, populated due to the danger of flooding or landslides. Further, housing is forbidden on military property or other restricted areas such as the international airport. All areas defined as potentially productive have a very high water table, as found in most parts of Dar es Salaam. As a result, these areas represent also suitable land for small-scale farming. The type of agriculture using "matuta" is most likely to lead to mosquito breeding. Matuta is a type of agriculture where plants are grown on top of little ridges, while between the ridges deep furrows are dug. These furrows are often filled with a little water, forming shallow pools perfectly suitable for Anopheles sp. breeding (Figure 5). Further, rice fields, shallow wells and irrigation channels were also found to be very productive for Anopheles sp. in the focus areas. Figure 5 "Matuta" type of agriculture in Dar es Salaam, Tanzania. Discussion Main limitations of the study This study represents a snapshot of a highly dynamic system and presents the first results of a larger malaria control program. More intensive longitudinal studies are currently being undertaken to complement these results. The study was implemented during an exceptionally dry year and, although the study took place during the rainy season, it actually rained very little [26]. As a result, it is unclear whether the breeding site structure found during the three months of our sampling period was representative of the structure found in years with normal rainfall. Similarly, the measured ecological parameters might be different during more normal years, as the selectivity of mosquitoes for oviposition sites can be greatly diminished during dry periods because of the limited availability of aquatic habitats [27]. In years with normal rainfall, it is very likely that many habitats that contained larvae during the study period would be unproductive due to a flushing effect, while other small productive sites might appear. However, for the purpose of reducing mosquito density by larvae abatement, the time when the mosquito population is most vulnerable is the dry period. A. gambiae s.s., A. arabiensis and A. funestus survive during the dry period in discreet habitats, making them an easier target for control interventions [31]. Dry season larval control is for example the rule in South Africa [32]. Very small breeding sites could not be detected by aerial pictures and were, therefore, largely excluded from the study. This could result in the omission of factors potentially important for mosquito control interventions. Further, mosquitoes do not necessarily lay eggs every day in each potential breeding site, thus the reason why a site did not contain larvae could simply be because no eggs were deposited within the last week. One of the key objectives of the study was to detect ecological factors determining Anopheles sp. larvae productivity. But within a city, the influence of humans can never be neglected. The pollution from waste such as oil, soap or industrial by-products is important in potential breeding sites in Dar es Salaam. Larvae may have been absent due to pollution by elements that could not be detected in the frame of this study. Anopheles sp. mosquitoes were not classified down to species level. The goal of the study was to characterize important breeding sites of Anopheles sp. mosquitoes and, consequently, potential foci of malaria transmission, regardless of the species. This is because in the context of sustainable operations in a routine mosquito abatement programme, municipal staff cannot be expected to identify all Anopheles larvae samples to species level without rendering sampling procedures prohibitively laborious and expensive. To achieve a satisfactory impact, exhaustive targeting of all potential vector species is necessary anyway. Furthermore, community acceptance of vector control programmes in Dar es Salaam has been shown to require suppression of all mosquito species, rather than only malaria vectors [25]. Ecological factors influencing mosquito larvae density The abundance of Anopheles sp. and Culicine larvae seems to be influenced differentially by ecological parameters, as none of them had a significant effect in all four regression models. Only one factor influenced the presence or absence of both Anopheles sp. and Culicine larvae. In turbid breeding sites Culicine larvae were much more likely to be present, whereas Anopheles sp. larvae were much more likely to be absent. Bates [29] supports this finding of Anopheles sp. breeding in rather clear water, but Gimnig et al. [33] found increasing A. gambiae s.l. larvae densities with increasing turbidity. Robert et al. [34] found a clear water preference by A. ambiensis breeding in wells in urban Dakar. A study by Ye-Ebiyo et al. [35] found that the production of A. arabiensis was favoured in moderately turbid water, while excessive turbidity limited the production of larvae. The proximity to flowering maize with pollen as food source compensated for the development failure induced by excessive turbidity. Clearly, the simple definition of "turbidity" might not be precise enough. Water which is turbid from particles not edible for Anopheles sp. larvae could disfavour the production of larvae, while water turbid from food particles represents a very suitable habitat. The preference of Culicine mosquitoes for turbid water is coherent with their known breeding site preferences, as they breed successfully in rather polluted environments such as blocked drains and septic tanks [36,37]. Key factors influencing the density of Anopheles sp. larvae In small breeding sites with diameters less than 1 m, Anopheles sp. were more likely to be present than in larger habitats. This is consistent with the general description of breeding sites for A. gambiae s.l., but not with those for A. funestus [27,36]. However, this finding could be biased by the fact that rain was lacking for several weeks, evaporating the bigger pools and concentrating the larvae, hence making them easier to detect. Further, larval density of small breeding sites might be increased due to a higher sampling intensity per unit area. Anopheles larvae were less likely to be present in swamps, and when so, they were found in low densities. This finding matches the known preference of A. gambiae s.l. for temporary sites [36,38]. Even though swamps were less likely to harbour Anopheles sp., the importance of these habitats should not be underestimated because of their great size and their role in supplying water for irrigation ditches, rice fields and various agricultural activities. "Puddle" as a type of breeding site was not favoured by A. gambiae s.l. larvae, and when Anopheles sp. larvae were present there, they were likely to be present in low densities. This finding is not consistent with established habitat descriptions for Anopheles sp. larvae [25,39]. Large drains were likely to have low Anopheles sp. densities. This result agrees with findings by Yamagata [24]. Large drains were often organically polluted with waste water and, therefore, less suitable for Anopheles sp. breeding [21,28,38]. Key factors only influencing the density of Culicine larvae A pH value of less than 7.3 favoured high Culicine densities and a value of less than 7.6 favoured their presence. These results showed clearly that Culicine larvae favoured a pH-neutral environment. Anopheles sp. breeding sites: unexpected findings The findings of this study revealed that A. gambiae s.l. was found in a sewage pond (Figure 3 and 4) and in one swamp extremely polluted with organic matter. These findings from Dar es Salaam, together with other studies, could indicate a change of Anopheles sp. breeding requirements in urban settings. In Lahore, Pakistan, Anopheles sp. mosquitoes were found in the waste water system [40]. In Accra, Ghana, data collections since 1911 indicate that A. gambiae s.l. adapted to breeding in organically polluted water habitats [21]. As most studies of Anopheles sp. larval ecology have been conducted in rural settings, it is likely that unpolluted breeding sites are found and described far more often [7,22], giving a biased impression. One of the main problems is the term "polluted habitat", as it has never been clearly defined. The findings of this study show that Anopheles sp. larvae can breed in nearly every kind of water accumulation. For every ecological factor identified as enhancing or reducing Anopheles sp. larvae productivity, at least one breeding site was found that contradicted these findings. Hence, all water bodies in an urban environment should be considered as potential breeding places and a target for larval control. Defined socio- and geo-ecological environments with high mosquito breeding sites density Slopes to riverbeds, riverbeds, borders of swamps, stagnant drains and rivers, areas with restricted access and areas along railway lines represented environments where most breeding sites were found in Dar es Salaam. These sites where mostly associated with agriculture activities. Afrane et al. [8] stated that areas of high mosquito density tended to follow valleys, where breeding sites were most persistent. Also, in Brazaville the valleys with vegetable gardens and crops were identified as some of the most suitable places for Anopheles sp. breeding [5]. In Dakar, one big marshy area was responsible for the production of nearly all adult Anopheles sp. mosquitoes within a distance of one kilometer [6]. Similar results were found by Staedke et al. [41]. Ponds close to the embankments of a railway line are presented as potential dry-season refugia for mosquito by Charlwood et al. [31]. The increasing urbanization of Dar es Salaam will probably help to reduce risk areas in the long-term, as open spaces get rare and swampy areas are filled to regain building land, but unplanned city growth along the city edges could increase the number of breeding sites [22]. In Brazaville, areas with the lowest malaria transmission intensity corresponded to the oldest and most densely populated districts [5]. Furthermore, high density housing has been shown to reduce breeding sites more than medium-density housing in Kisumu and Malindi, Kenya [22]. Conclusions Mapping of malaria risk on the basis of breeding sites plays an important role for urban malaria control programs. Also, initial risk mapping of breeding sites, combined with improved knowledge of mosquito ecology and their interactions with humans, is crucial to understand the epidemiology of urban malaria. The present study, in accordance with other studies of Anopheles sp. on urban larval ecology, showed that Anopheles sp. mosquito larvae are not restricted to clearly defined habitats. Therefore, malaria control interventions, such as environmental measures or insecticide application, have to consider all open water bodies as potential breeding sites. Specific malaria control interventions are currently developed and tested by the three municipalities of Dar es Salaam. Authors' contributions MS designed and implemented the study, analyzed the data and drafted the manuscript. DM, MK and ZP were involved in designing and implementing the study and analyzing its data. MT participated in the study design, data analysis and drafting of the manuscript. GK and CL assisted in the study design and implementation, data analysis and writing of the manuscript. All authors read and approved the final manuscript. Acknowledgements We would like to thank the people of Dar es Salaam and their District and Ward authorities for their excellent cooperation. A special thanks is extended to Shoo Brycon (Health Officer, Ilala District) for his help in collecting the data, Ms M. Kinenekejo (Malaria Coordinator Temeke) and Mr. R. Kipesha (Malaria Coordinator Kinondoni) for their inputs and support in coordinating the field work. We would also like to thank Dr. J. Minjas (Muhimbili University College of Health Sciences) for his assistance in entomological issues. This work was supported financially by the Swiss Tropical Institute, Switzerland, the Bill & Melinda Gates Foundation and the Environmental Health Project (Grant No: HRN-1-00-99-00011-00). 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==== Front Cost Eff Resour AllocCost effectiveness and resource allocation : C/E1478-7547BioMed Central London 1478-7547-3-11565924510.1186/1478-7547-3-1ResearchFree does not mean affordable: maternity patient expenditures in a public hospital in Bangladesh Khan Suhaila H [email protected] Department of International Health and Development, Tulane School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2200, New Orleans, LA 70112, USA2005 19 1 2005 3 1 1 29 7 2004 19 1 2005 Copyright © 2005 Khan; licensee BioMed Central Ltd.2005Khan; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective This study investigated a) the amount and types of out-of-pocket expenditures by patients for nominally free services in a large public hospital in Bangladesh, b) the factors influencing these expenses, and c) the impact of these expenses on household income. Methods Eighty-one maternity patients were interviewed during their hospitalization in the Dhaka Medical College Hospital. Patients were selected by quota sample to match the distribution of maternity patient categories in the hospital. Patients were interviewed with a semi-structured, in-depth questionnaire. Results All interviewees incurred substantial out-of-pocket expenditures for travel, hospital admission fees, medicine, tests, food, and tips. Only two of the expenditures, travel expenses and admission fees, were not supposed to be provided free of charge by the hospital. The median total per-patient expenditure was $65 (range $2–$350), equivalent to 7% (range 0.04%–225%) of annual household income. Half of all patients reported that their families had to borrow to pay for care at interest rates of 5%–30% per month. A third of these families reported selling jewelry, land or household items to moneylenders. The rural patients reported more difficulty in paying for care than the urban patients. Factors increasing the expenditures were duration of hospitalization, rural residence, and necessary (e.g. C-section, hysterectomy) and unnecessary (e.g. episiotomy) medical procedures. Conclusion Free maternity services in Bangladesh impose large out-of-pocket expenditures on patients. Authorities could reduce the burden by reducing the duration of hospital stays, limiting use of medical procedures, eliminating tips, and moving routine services closer to potential users. Fee for service could reduce unofficial expenditures if the fee were lower than and replaced typical unofficial expenditures, otherwise adding service fees without reform of current hospital practices would lead to even more burdensome expenditures and inequities. ==== Body Background In developing countries governments often subsidize services at public health care facilities and provide them free of charge to users. However, evidence suggests that users still incur large expenditures using the 'free' services for such things that are supposedly provided without charge. Studies have found that patients incurred substantial out-of-pocket expenditures for medicine, food and travel from the use of 'free' public health facilities [1-3]. A study in Vietnam found that out-of-pocket payments can cause serious equity problems such as the poor becoming poorer without greatly affecting the non-poor [4]. Household difficulty in payment of health care expenses can result in the 'distress sale' of property, delay or abandonment of treatment, and sacrifice expenditures on food and education [3,5]. Other studies have found that introducing or increasing user fees negatively affect the utilization of public health facilities [6-9]. Three previous studies have explored issues related to patient expenditures in Bangladesh [3,10,11]. Nahar et al. enumerated the patient expenditures and affordability of free maternity services for normal delivery and caesarean section. Killingsworth et al. explored the linkage between official and unofficial fees in public health facilities, and concluded that these fees had income and equity effects. Stanton et al. reviewed literature on user fees and pointed out the need to further investigate the factors and practices causing patient expenses before institutional implementation of user fees. Thus, this study examined the type, amount and household financial results of out-of-pocket expenditures by patients for nominally free services in a large government hospital in Dhaka. The study also identified the factors and medical practices producing and influencing the out-of-pocket expenditures. Plans to begin fees for service in Bangladesh make it important to document the amount of money actually being paid by the patients under the present system. If current expenditures are large, fee for service may have serious negative impacts on utilization and on the economic well-being of Bangladeshi households. If current expenditures are modest, it is possible that such fees will have a lesser impact. Methods Study site The study was conducted in the Department of Obstetrics and Gynaecology (ObGyn) of the Dhaka Medical College Hospital (DMCH). DMCH is the largest teaching hospital in Bangladesh with 850 beds located in the capital city. DMCH is government funded and provides a wide range of out- and in-patient services. Public hospitals have two payment categories for in-patients: non-paying and paying. Patients first go to an out-patient unit for diagnosis where they are categorized as out- or in-patient. Those categorized as in-patient are then classified as paying or non-paying by observing the clothes and general appearance of the woman and any accompanying relatives. Non-paying patients pay only the hospital admission fee. Paying in-patients are charged the fees for hospital admission, bed, and surgery. The various fees are: hospital admission fee: $0.23, bed fee: $1.34–3.50 per day, surgery fee: $12.50–125. Taka was converted into US dollars using the 1994 exchange rate of US$1.00 = Taka 40.00. Neither patient category is supposed to pay for medicine, tests, food, nursing and other support services during hospitalization; these commodities and services are theoretically provided free by the hospital. Study population, sampling and sample size The study interviewed 81 non-paying in-patients hospitalized for reproductive health conditions (about two thirds were for maternity conditions). Patients were selected by quota sample matching the distribution of the patient categories in the hospital i.e. the selected medical conditions accounted for the greatest number of ObGyn admissions reported for the hospital, and also reflect the causes associated with high maternal mortality and morbidity in Bangladesh [12]. These included normal vaginal delivery (NVD), caesarean section (C-section), abortion, and hysterectomy. NVDs included cases with episiotomy, without episiotomy, and with eclampsia. C-sections included elective and eclamptic cases. Abortion included non-septic and septic abortions. Hysterectomies included abdominal and vaginal hysterectomies for treating fibroid, prolapsed uterus, and pelvic inflammatory disease. Table 1 illustrates the distribution of the selected cases for this study. Table 1 No. of in-patients surveyed by medical condition No. of patients Normal Vaginal Delivery 19 with episiotomy 5 without episiotomy 5 with eclampsia 9 Caesarean section 20 elective 10 eclamptic 10 Abortion 20 non-septic 10 septic 10 Hysterectomy 22 prolapsed uterus 10 fibroid 9 pelvic inflammatory disease 3 Total 81 Variables Information was collected on various characteristics of the study participants. Demographic characteristics included age, education, marital status, and residence. Socio-economic characteristics included occupation and annual household income. Information was also collected on underlying medical condition. Out-of-pocket expenditure related information included types and amounts of expenses incurred during hospitalization such as those for travel, medicine, food, fees, etc. Factors influencing expenditures included type of treatment received and duration of hospitalization. Sources of funds included amount borrowed and interest charged for borrowed amount. Data collection tools and technique Data were collected from patients and their relatives with semi-structured open-ended questionnaires between January – June 1994. The interviewers were physicians employed in DMCH. The interviewers selected the cases by diagnosis from patient admission records. To minimize possible selection bias the first case was selected randomly from the records and then every third case was selected. The selected patients were interviewed a minimum of three times to minimize recall error. Recall error was also minimized as information was collected while patients were still hospitalized. During the first interview demographic and socio-economic information was collected with structured questions. During the second and third interviews information related to expenditures was collected with open-ended questions. To illustrate the data collection process a description of an interview with a typical C-section patient follows. C-section patients are usually hospitalized for two weeks in DMCH. On the first day of hospitalization an interviewer collected information on patient's age, education, marital status, etc. On the eighth day of hospitalization the second interview collected information on treatment received, treatment related out-of pocket expenditures, annual household income, amount of money borrowed to pay for treatment, source of borrowed money, and interest rate charged. On the fourteenth day the third interview collected more monetary information on out-of-pocket expenditures, and on expected expenditures immediately after leaving the hospital. This survey did not cover the expenditures for the full course of the treatment. Expenditure estimates were derived for the duration of the current hospitalization only, i.e. from the day of admission until the day of discharge. Expenditures immediately before admission and after discharge from the hospital included only travel expenses to and from the hospital for the patient and her accompanying relatives. Results Socio-demographic characteristics of study participants The median age of the study participants was 26 years (range 15–60 years). The majority (88%) of the patients were married, the rest were separated (4%), divorced (2%), and unmarried (1%). Forty-four percent of the patients lived in rural areas. The median annual household income was $750 (range $3–$6000) per respondent. The annual household income was higher for the urban (median $900; range $150–$6000) than the rural (median $615; range $3–$6000) respondents. Patient out-of-pocket expenditures All 81 patients interviewed reported incurring substantial out-of-pocket expenditures during their hospitalization. These out-of-pocket expenditures were for travel, hospital admission fee, medicine, tests, food, tips, and other items. As expected there were expenditures related to travel and admission fees which the hospital is not supposed to subsidize. But there were also expenditures for medicine, tests, food, tips, and other items which were supposed to be provided free from the hospital but were not. The median total expenditure for hospitalization was $65 (range $2.15–$350) per patient. On average, 61% of these expenditures ($49) were for services and commodities that were supposed to be provided free from the hospital but were not. The per patient median expenditure for the various expense categories were: medicine $26, tests 0, tips $1.25, food $1.25, other items $4.38, travel $22.25, and hospital admission fee $0.25. On average, medicine constituted 42%, travel 38%, tests 5%, food 4%, tips 2%, admission fees <1%, and others 8% of the total expenditures. C-section and hysterectomy cases had the highest median expenditures. Table 2 illustrates the out-of-pocket expenditures by items not supposed to be provided free by the hospital and items supposed to be given free from the hospital. A description of the expenses follows. Table 2 Distribution of the out-of-pocket expenditures by medical condition (in US$) in 1994 Expenditures on items NOT supposed to be provided from hospital Expenditures on items supposed to be provided free from hospital Travel Fee Medicine Food Tips Other Tests Total NVD (n = 19) median 12.50 0.25 11.25 0.88 1.25 3.88 0.00 62.50 mean 29.72 0.23 18.35 2.24 2.11 5.99 3.42 62.04 range 1–127 0.10–0.33 1–70 0–23 0.75–5 0–25 0–23 6–225 C-section (n = 20) median 36.70 0.10 51.88 2.50 2.06 11.25 0.00 118.75 mean 44.32 0.20 63.52 4.25 2.56 16.79 1.88 133.50 range 5–150 0.10–0.75 25–160 0.50–16 0.25–7 2–75 0–25 41–350 Abortion (n = 20) median 3.08 0.18 12.06 0.84 0.63 0.00 0.00 15.56 mean 11.67 0.17 18.93 1.40 0.77 0.00 0.00 32.94 range 1–63 0–0.30 1–75 0–5 0–2 0.00 0.00 2–125 Hysterectomy (n = 22) median 23.25 0.25 36.25 2.50 1.25 5.00 0.00 75.50 mean 32.04 0.38 30.89 4.84 2.38 4.02 10.15 84.70 range 1–86 0.10–4 1–50 0–19 0–10 0–10 0–75 2–178 Total (N = 81) median 22.25 0.23 26.25 1.25 1.25 4.38 0.00 65.25 mean 29.50 0.24 33.05 3.23 1.96 6.64 4.02 78.65 range 1–150 0–4 1–160 0–23 0–10 0–75 0–75 2–350 NVD: normal vaginal delivery Expenditures on items supposed to be provided free from hospital Medicine All patients were supposed to be provided required medicines free from the hospital but were not. Medicines included antibiotics, analgesics, syringe, catheter, blood, and so forth. Medicine was usually bought when patients were admitted at night. The medicine required for treatment is ordered by the on-duty physician but it takes several hours for the hospital management to process the order. Thus, no free medicine is available immediately. To start the treatment, the on-duty physician requests the patient's relatives to buy the medicine which is purchased from nearby private pharmacies. Tests All tests (e.g. pathology, radiology) are supposed to be provided by the hospital but sometimes the patients had the tests done in a private laboratory because waiting time for tests is very long in the DMCH due to the high patient load. Food Food is provided by the hospital but the interviewees found the hospital food of poor quality or totally lacking (liquid food such as soup or horlicks had to be bought for patients who had undergone surgery since these were not provided by the hospital). Relatives usually stayed with the patient in the hospital because of lack of ayahs (cleaning ladies) or nurses to provide necessary services. Thus, food was usually bought from a vendor or brought from home for both patient and relatives. Tips Tips (bakshish) are payments made to ayahs and guards. Ayahs were given tips for routine services such as pushing the patient's trolley to and from the labour/operation room, shaving the patient before delivery/surgery, giving enemas, etc. Guards at the gates were tipped each time a relative came to visit the patient during non-visitor hours. However, ayahs and guards are salaried hospital employees and are supposed to provide these services free of charge. The patients were reluctant when talking about the tips probably because they were still hospitalized and depended on these employees for access to certain services. Other items The other expenditures included items for the patient (e.g. hot water, bucket for hot water) and the newborn baby (e.g. blanket) that were supposed to be provided by the hospital free of charge but were not. Expenditures on items not supposed to be provided by the hospital Travel Travel expenses are not supposed to be provided by the hospital. Travel expenses consisted of travel to and from the hospital by the patient and any accompanying relatives, and travel expenditures of relatives during hospitalization for purchasing medicine and food for the patient. The patients came to DMCH because they expected 'free' and 'affordable' services compared to private clinics, or they were referred from a primary/secondary level facility, or to get better treatment here. Patients from rural/peri-urban areas took longer to reach DMCH than those from urban Dhaka (range half an hour to two days). Hospital admission fee This expense is also not supposed to be covered by the hospital. The official price of admission was $0.23, but it was zero for two patients and more than the official price for half of the patients interviewed. The study could not elicit the reason the patients paid more than the official price. When probed the patients could not or would not elaborate beyond the amount paid. The patients were very reluctant when talking about paying more than the official price for the admission fee. Factors increasing out-of-pocket expenditures Duration of hospitalization and rural residence of the patients increased the out-of-pocket expenditures. Rural residence increased the travel expenses and thus the total expenditures. Longer duration of hospitalization increased virtually all expenditures. The median duration of hospitalization was 8 days (range 1–34 days) per patient. Duration of hospitalization was the longest for hysterectomies followed by C-sections. Duration of hospitalization was related to severity of medical condition (e.g. eclampsia), necessary medical procedures (e.g. hysterectomy), and unnecessary medical procedures (e.g. episiotomy). One day of extra hospitalization increased expenditures by $2.30 per patient. Choice of medical procedures increased the patient expenditures. Episiotomy increased expenditures as patients were hospitalized for a longer duration and resulted in the purchase of more medicine. Episiotomy increased expenditures for both uncomplicated NVD (by 37%) and eclamptic NVD (by 84%) compared to cases where no episiotomy was performed (data not shown). The medical reason for performing episiotomies is the prevention of perineal tearing but because of a high case load at DMCH physicians perform episiotomies to reduce the length of delivery time, effectively turning hospital expenditures into patient expenditures. Eclampsia increased the expenditures for NVD by 180% (data not shown). Eclampsia is not under the control of the health system or the patient, and procedures used for treating eclampsia are unavoidable. C-sections caused higher patient expenditures compared to NVDs (median $119 and $63 respectively) because C-sections had a longer duration of hospitalization and required more medicine. Elective C-sections and eclamptic C-sections incurred similar expenses because elective C-sections were hospitalized for a longer duration even though there were no complications. Vaginal hysterectomies were 25% less expensive than abdominal hysterectomies because they required less invasive procedures, used local anaesthesia, and had a shorter duration of hospitalization. Sources of funds for patient expenditures The respondents said that they were willing to pay for care. However, rural households reported more difficulty in paying for care than urban households. Difficulty was inferred from the number of households who borrowed to pay for care, and the ratio of the amount borrowed to the annual household income. The median patient expenditure was equivalent to 7% (range 0.04%–225%) of annual household income, and was higher for rural (median 10%; range 1%–225%) than urban (median 7%; range 0.04%–78%) respondents. Half (n = 40) of the households reported borrowing to pay for care. The patient who spent 225% of her annual household income was a rural patient who had a hysterectomy for prolapsed uterus. Surgical patients like her are usually hospitalized for a month as they require more tests than non-surgical patients. This patient's total expenditures were not higher than the others who also had a hysterectomy, however, her annual household income was much lower than that of the others. More urban (n = 23) households borrowed than rural (n = 17) households but the amount borrowed was higher for rural households. The median amount borrowed per household was $38 and was equivalent to 8% (range 0.58%–208%) of the annual household income. On average, the rural households (median 14%; range 2%–208%) borrowed almost double the amount than the urban households (median 6%; range 0.58%–28%). Most often (n = 30) money was borrowed from friends and relatives without interest. When borrowing was from money-lenders (n = 10), households reported interest rates of 5%–30% per month. Three households put up security such as jewelry, land and household goods when borrowing money from moneylenders. The highest reported percentage of money borrowed to income for a rural household was 208% compared to 28% for an urban household. Finally, greater amounts of money were borrowed by C-section and hysterectomy patients than the other categories of patients. Table 3 illustrates the duration of hospitalization, total patient expenditures, annual household income, and amount borrowed by type of residence. Table 3 Distribution of median duration of hospitalization, expenditures, income, and amount borrowed by residence Urban Rural % of patients 56% (n = 45) 44% (n = 36) Duration of hospitalization (day) 7 (1–33) 9 (1–34) Total patient expenditures (US$) 59.25 (2.15–350) 79.25 (2.40–250) Annual HH income (US$) 900 (150–6000) 615 (2.50–6000) Borrowed amount (US$) 37.50 (6.25–250) 52.50 (12.50–200) Parenthesis shows range HH: household Discussion and conclusions The study findings indicate that all the surveyed patients incurred substantial out-of-pocket expenditures for a one time hospitalization. The median per patient expenditure was $65, and two-thirds of these expenditures were for commodities and services that were supposed to be provided free by the hospital but were not. Half the households borrowed to pay for care since they did not have the ability to pay (a finding similar to those found by Nahar et al.). A third of these households sold jewelry, land or household items to moneylenders. The rural households reported more difficulty in paying for care than the urban households. The rural patients had lower income but incurred higher expenditures and borrowed larger amounts than the urban patients. The study data are a decade old but worth presenting because Bangladesh is only recently beginning health sector reform and fee for service. Also, the hospital practices with regard to providing maternity care remain the same, in the hospital studied and in other public hospitals in Bangladesh. Other limitations of the study are its small sample size and that all the interviewees came from only one hospital. The small sample size is the norm when doing in-depth interviews. When a paper is qualitative not quantitative no statistical tests are customarily done. The results are striking despite the limitations. The costs related to NVD and C-section were much higher in this study than that estimated by Nahar et al. (NVD: $62 and $32 respectively; C-section: $133 and $118 respectively). Possible reasons for the differences are: a) recall error higher in the Nahar et al. study as they interviewed post-partum mothers, whereas, this study interviewed patients during hospitalization; b) the Nahar et al. study included only uncomplicated cases, whereas, this study included both uncomplicated and complicated cases. The annual household income was lower in this study compared to the Nahar et al. study ($750 and $1476 respectively). This may be attributable to having rural patients in this study whose income is much lower than urban patients, whereas, Nahar et al. studied only urban patients. Changing current practices regarding the length of hospitalization and medical procedures (e.g. episiotomy) will reduce patient expenditures. This can be achieved by having shorter duration of hospitalization for elective C-section and elective hysterectomy, and limiting the use of episiotomy. Limiting these practices may also lead to lower provider expenditures. Medicine constituted almost half of the total patient expenditures. However, lack of access to medicine resulted from an inefficient management system not from unavailability. Taking less time to process orders would make medicine available quicker to patients and so reduce their expenditures. Hospital management needs to ensure that patients pay only the official rate of admission fees. Hospital management also needs to ensure that patients do not pay tips to salaried hospital staff for routine services. The hospital could arrange for liquid food and hot water for surgical patients in addition to the other services it already provides, i.e. provide a more comprehensive hotel service. Alternatively the hospital could subcontract out its hotel services to those employees who extract tips from patients to provide such services. This may act as an incentive for eliminating the unofficial fees from tips. Making some services such as eclampsia and hysterectomy available at secondary level facilities will benefit the rural patients by reducing the travel expenses. Government hospitals can generate revenue by introducing or increasing some fees for medicine and tests for the paying category of patients but clearly exempting the poorest. How would the system determine who was the poorest? One option would be to let all rural women use hospital for free and have all urban women pay some fee. For service fees not to become a serious barrier to use current systems will have to eliminate or reduce practices that already result in high user expenditures from unofficial payments. Otherwise the added fees may lead to more borrowing from moneylenders, putting lands and goods at risk, and potential impoverishment of more households. A fee for service system needs to be based on information, and more than just setting a price. This can be facilitated by knowing what the patients were already spending in the current system and who were the most at risk of impoverishment. The challenge is to focus on realistic, short term changes that can reduce patient expenditures and inequities. Most of the recommendations of this study depend more on the will of the physicians and the hospital administrators than on the infusion of resources per se. Free maternity services in Bangladesh impose large out-of-pocket expenditures on patients. Authorities could reduce the burden by reducing the duration of hospital stays, limiting use of medical procedures, eliminating tips, and moving routine services closer to potential users. Fee for service could reduce unofficial expenditures if the fee were lower than and replaced typical unofficial expenditures, otherwise adding service fees without reform of current hospital practices would lead to even more burdensome expenditures and inequities. Competing interests The author(s) declare that they have no competing interests. Acknowledgments The author cordially thanks the Dhaka Medical College Hospital administration for the opportunity to conduct this study. Many thanks to Pranesh Chowdhury and Aftab Khan for their assistance. The author is grateful to Jim Foreit for comments on earlier drafts. ==== Refs Abel-Smith B Rawal P Can the poor afford 'free' health services? A case study of Tanzania Health Policy and Planning 1992 7 329 341 Levin A Dmytraczenko T McEuen M Ssengooba F Mangani R Vandyck G Costs of maternal health care services in three Anglophone African countries International Journal of Health Planning and Management 2003 18 3 22 12683270 10.1002/hpm.690 Nahar S Costello A The hidden cost of 'free' maternity care in Dhaka, Bangladesh Health Policy and Planning 1998 13 417 422 10346033 10.1093/heapol/13.4.417 Wagstaff A Paying for health care: quantifying fairness, catastrophe, and impoverishment, with applications to Vietnam 1993–98 2001 The World Bank, Washington DC, USA Russell S Ability to pay for health care: concepts and evidence Health Policy and Planning 1996 11 219 237 10160370 Blas E Limbambala ME User-payment, decentralization, and health service utilization in Zambia Health Policy and Planning 2001 16 19 28 11772987 Collins D Quick J Musau S Kraushaar D Hussein I The fall and rise of cost sharing in Kenya: the impact of phased implementation Health Policy and Planning 1996 11 52 63 10155878 Mwabu G Mwanzia J Liambila W User charges in government health facilities in Kenya: effect on attendance and revenue Health Policy and Planning 1995 10 164 170 10143454 Yoder RA Are people willing and able to pay for health services? Social Science and Medicine 1989 29 35 42 2740926 10.1016/0277-9536(89)90125-1 Killingsworth J Hossain N Hedrick-Wong Y Thomas S Rahman A Begum T Unofficial fees in Bangladesh: price, equity and institutional issues Health Policy and Planning 1999 14 152 163 10538718 10.1093/heapol/14.2.152 Stanton B Clemens J User fees for health care in developing countries: a case study of Bangladesh Social Science and Medicine 1989 29 1199 1205 2588047 10.1016/0277-9536(89)90363-8 Dhaka Medical College Hospital Activities of Obstetrics and Gynaecology Department 1993 Dhaka Medical College Hospital, Dhaka, Bangladesh 1 16	15659245	PMC546230	CC BY	2021-01-04 16:39:14	no	Cost Eff Resour Alloc. 2005 Jan 19; 3:1	utf-8	Cost Eff Resour Alloc	2,005	10.1186/1478-7547-3-1	oa_comm
==== Front Microb Cell FactMicrobial Cell Factories1475-2859BioMed Central London 1475-2859-3-171561056110.1186/1475-2859-3-17ResearchDifferential gene expression in recombinant Pichia pastoris analysed by heterologous DNA microarray hybridisation Sauer Michael [email protected] Paola [email protected] Brigitte [email protected] Minoska [email protected] Michael [email protected] Danilo [email protected] Diethard [email protected] Institute of Applied Microbiology, Department of Biotechnology, University of Natural Resources and Applied Life Sciences, Muthgasse 18, A-1190 Vienna, Austria2 Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, I-20126 Milan, Italy2004 20 12 2004 3 17 17 26 11 2004 20 12 2004 Copyright © 2004 Sauer et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Pichia pastoris is a well established yeast host for heterologous protein expression, however, the physiological and genetic information about this yeast remains scanty. The lack of a published genome sequence renders DNA arrays unavailable, thereby hampering more global investigations of P. pastoris from the beginning. Here, we examine the suitability of Saccharomyces cerevisiae DNA microarrays for heterologous hybridisation with P. pastoris cDNA. Results We could show that it is possible to obtain new and valuable information about transcriptomic regulation in P. pastoris by probing S. cerevisiae DNA microarrays. The number of positive signals was about 66 % as compared to homologous S. cerevisiae hybridisation, and both the signal intensities and gene regulations correlated with high significance between data obtained from P. pastoris and S. cerevisiae samples. The differential gene expression patterns upon shift from glycerol to methanol as carbon source were investigated in more detail. Downregulation of TCA cycle genes and a decrease of genes related to ribonucleotide and ribosome synthesis were among the major effects identified. Conclusions We could successfully demonstrate that heterologous microarray hybridisations allow deep insights into the transcriptomic regulation processes of P. pastoris. The observed downregulation of TCA cycle and ribosomal synthesis genes correlates to a significantly lower specific growth rate during the methanol feed phase. ==== Body Background The methylotrophic yeast Pichia pastoris is well established as expression host for heterologous proteins (reviewed by [1] and [2]). However, despite the high technological impact of P. pastoris, the physiological and genetic information is still rather scarce. The genome sequence has not been published, and in fact less than 100 complete gene sequences have been deposited with GenBank by the time of writing. Consequently, as for most other non-model species, no DNA microarrays are being manufactured. Hence, one of the most powerful tools for the investigation of changes in expression patterns is not available for this yeast species. To circumvent this problem, heterologous hybridisation to commercially available DNA microarrays might be conceivable. In fact, the successful non-homologous probing to microarrays has been reported recently. These studies cover a wide variety of organisms, including bacteria [3], a yeast [4], but also plants [5] and metazoan organisms [6-9]. The highest number of cross-hybridisation experiments has been performed with human microarrays. Chismar et al. [10] report, for instance, that heterologous probing of human cDNA arrays allows to gain useful information about gene expression in various primates. Moody et al. [11] compared, furthermore, the reproducibility of species-specific and cross-species hybridisations by evaluation of microarray hybridisations of porcine and human samples to human cDNA arrays. They reported that results generated by heterologous hybridisation were as reproducible as by homologous hybridisation, and the correlation between data derived from porcine and human hybridisations was strong. As judged from EST sequences of the porcine genome, the authors propose, that stretches of at least 100 bp with high similarity to the human homologue are sufficient for hybridisation. Renn et al. [9] compared the performance of cDNA microarrays from an African cichlid fish (Astatotilapia burtoni) for the heterologous hybridisation with cDNAs from eight different fish species, some of them closely related (other cichlids) and others more distantly related (among them Atlantic salmon and guppy). They conclude that significant results are obtained even with distantly related species, however, the number of positive spots declines with the phylogenetic distance, as strikingly does the degree of measured regulation. While yeasts share many morphological and physiological similarities, they represent a very heterogeneous group of fungal organisms, and a high degree of gene sequence similarity cannot be assumed a priori. When cDNA of the non-conventional yeast Zygosaccharomyces rouxii was probed by cross-hybridisation to Saccharomyces cerevisiae GeneFilters, only 155 ORFs out of the Z. rouxii genome could be reproducibly detected [4]. Anyhow, 86 genes out of these showed altered expression patterns between non-stressed and salinity-stressed Z. rouxii cells and 38 genes behaved differently than the S. cerevisiae homologues, indicating that the information gained is limited but nevertheless useful. As judged from 26S ribosomal DNA sequences, Z. rouxii is assumed to be more closely related to S. cerevisiae than P. pastoris, but still, all three belong to the hemiascomycetes [12]. As there are not many genes characterised for P. pastoris, there is no simple way to assess the degree of gene sequence similarity between P. pastoris and S. cerevisiae. However, in many of the few genes sequenced, stretches of high similarity (score >75% over a length of at least 250 bp and more) can be identified. Most of the known genes belong to the carbon and energy metabolism or contribute to amino acid or protein synthesis. Another group of P. pastoris genes with known sequence belongs to pathways specific for methylotrophic yeasts. For these genes there are no homologues present in S. cerevisiae. Evidently, this respective fraction of the P. pastoris genome would remain unevaluated by heterologous hybridisation. The main aims of this work are to verify whether a heterologous DNA array approach allows to obtain useful information for P. pastoris, and to identify genes that are specifically regulated upon a shift from glycerol to methanol as carbon and energy source. This shift is of particular interest since one of the specific features of methylotrophic yeasts is the tightly regulated methanol metabolism, which has been utilised for the construction of strong and tightly controlled expression vectors. The methanol induced promoter of the alcohol oxidase 1 (AOX1) gene, which is repressed by many carbon sources such as glucose, glycerol or ethanol, is widely used for heterologous gene expression in P. pastoris. Accordingly, methanol is often used as the carbon source that induces the production of heterologous proteins. In addition to heterologous protein induction, the shift of the carbon source to methanol causes major structural and physiological changes within the cell. The enzymes for methanol metabolism are synthesised de novo and some of them are translocated into peroxisomes. Strikingly, peroxisomes can fill most of the cellular volume and AOX1 alone can account for up to 35% of the total soluble protein [13]. Additionally, heterologous protein production and environmental conditions like low fermentation pH have been proven to exert stress in recombinant P. pastoris [14]. Hence, for a first study of the transcriptomic regulations of recombinant P. pastoris, we used a strain expressing human trypsinogen under control of the AOX1 promoter, under conditions that strongly influence the physiology of the host cells, as previously described [15,16]. A series of microarray hybridisations was performed as depicted in table 1, first to qualify the feasibility of cross-species hybridisation, and secondly to analyse the effects of the substrate change in fed-batch fermentations. Results and discussion 1. Qualification of heterologous hybridisation Before analysing differential gene expression data, it was our intention to verify whether the heterologous hybridisation of S. cerevisiae DNA microarrays with P. pastoris cDNA results in significant data. Obviously, the intensity of a signal will depend both on the amount of the specific mRNA in the sample, and the sequence similarity with the respective gene of S. cerevisiae. Therefore, we compared the signals obtained from four microarrays hybridised with P. pastoris cDNA with four microarrays hybridised with S. cerevisiae cDNA (as a control), obtained from shake flask cultures. To estimate the overall potential to obtain data, and the degree of loss of information, the total number of genes giving significant values, and those determined to be under a given threshold were compared (table 2). In average, 66 % of all genes present on the microarray were either only weakly transcribed, not similar enough to produce a significant signal or not present at all in P. pastoris. In contrast, by hybridisation with S. cerevisiae cDNA about 46 % of all genes remained undetected. We analysed those genes of P. pastoris for which sequences were deposited in the GeneBank database for sequence similarities to the S. cerevisiae genome, and determined the number of significant spots on 6 microarrays. 66 % of the signals derived from genes with high similarity (score > 75 % along stretches longer than at least 250 bp) were significant, while of the moderately to weakly similar genes only 28 % of the signals were significant. This indicates that a high sequence similarity of a sub-sequence within a gene is sufficient for efficient hybridisation. Considering that the signal intensity will depend on sequence similarity and length, but also on mRNA abundance, it becomes obvious, however, that a distinct minimum threshold of similarity cannot be defined. It was expected that the number of positive spots would be lower for heterologous hybridisation as compared to homologous hybridisation, but the relatively high number of significant values obtained in our experiment is very promising to achieve useful and new information from this technique. Nevertheless, we sought statistical evidence for the biological significance of hybridisation signals obtained with P. pastoris cDNA. First of all, data obtained from microarrays that were hybridised with identical but differentially labelled samples (yellow experiment) were evaluated, showing very high correlation coefficients of 0.97 and 0.98, for S. cerevisiae and P. pastoris respectively (table 3 and Fig. 1A). The correlation of data from identical samples on different microarrays is somewhat lower (r = 0.86 – 0.92), due to different relative intensities on different chips. Since usually the data of two samples on one microarray are to be compared, it was important to evaluate the reproducibility, expressed as the standard deviations of both values of each spot on microarrays hybridised with identical samples. Fig. 1B displays the standard deviations plotted against the mean relative intensities of all significant spots of such a P. pastoris experiment, showing that the standard deviations do not vary over a wide range of signal intensities, which is in concordance with the results of Moody et al. [11]. 96 % of the signals have a relative standard deviation (s. d. divided by mean) below 0.2. Furthermore, we evaluated the correlation between the signal intensities obtained from P. pastoris cDNA with that from S. cerevisiae cDNA (cells grown under the same conditions). A highly significant correlation (r = 0.72) was observed (Fig 1C). Considering expectable differences in gene expression, different sequence similarities and the fact that signals from two microarrays were compared, such a high correlation is remarkable and suggests that the data obtained are biologically meaningful. The results obtained are in line with the data published by Moody et al. [11] who showed a strong correlation between porcine and human samples on human microarrays. Our data also support Renn et al. [9] who demonstrated that both the number of significant spots and signal correlation of different fish samples on A. burtoni microarrays depended on the phylogenetic distances between sample species and test species. To evaluate the significance level for up- and downregulation, the signals from yellow experiments were plotted one against the other (Fig. 1A). These values should obviously fall in the unregulated range. A threshold regulation factor of 1.5 (illustrated by the dotted lines) includes 99.1 % of all significant spots as not significantly different, which means that such a threshold would yield false positives for less than 1 % of the significant genes. Finally, the global regulation pattern of P. pastoris and S. cerevisiae between pH 5.0 and 3.5 was compared by correlating all genes downregulated under acidic conditions (Fig. 1D). The correlation is highly significant (p < 0.01) with a correlation coefficient r = 0.51. Clearly, different gene regulation, as well as different degrees of sequence similarities contribute to a reduction of this correlation. Interestingly, the average fold signal change of regulated genes is lower for P. pastoris than for S. cerevisiae (correlation slope = 0.8). A similar observation was made for more distantly related fish species [9]. 2. Analysis of gene regulation in P. pastoris As an example for differential gene expression in this study, the level of transcripts during the methanol induction phase of a lab scale fermentation was compared to that during the glycerol feeding phase, both at pH 5.0, and at pH 3.0. Figure 2 shows the development of biomass over time along with the pH and indications of feed changes, and furthermore depicts the time points when the analysed samples were taken. Of all genes that gave significant signals, we report those that showed significant differences upon the shift from glycerol to methanol under at least one condition (pH), and that have a defined function in S. cerevisiae. Unsurprisingly, genes involved in the core carbon metabolism show a significantly different expression during methanol and glycerol metabolism (table 4). Almost no differences between the samples obtained at pH 5.0 and 3.0 were found. Fructose-1, 6-bisphosphatase was significantly upregulated at least in one sample, whereas fructose 1, 6-bisphosphate aldolase, glyceraldehyde-3-phosphate dehydrogenase, enolase, and pyruvate kinase were found to be downregulated on methanol. Transketolase expression was enhanced while the transaldolase transcript level was relatively reduced. Of these enzymes, fructose-1, 6-bisphosphatase and transketolase (among others) are needed for biomass synthesis on methanol. Some genes related to ATP production were found to be downregulated which appears plausible as the rate of energy consumption is lower at the significantly lower specific growth rate when methanol is the carbon source. The beta subunit of pyruvate dehydrogenase was significantly downregulated at pH 5.0, further indicating that methanol metabolism decreases the TCA cycle flux. The upregulation of pyruvate decarboxylase under at least one condition comes somewhat unexpected, because an increase of the flux to ethanol production appears questionable for cells growing on methanol. Table 5 displays a list of ribosomal genes that are essentially all downregulated on methanol. Considering the decreased specific growth rate, a decrease of total RNA is generally expected due to a decreased overall protein synthesis. Interestingly, four histone genes that gave significant signals were not regulated at pH 5.0, but induced at pH 3.0. Only a few amino acid biosynthesis genes appeared to be regulated (table 6), indicating that amino acid synthesis is turned on, both on glycerol and methanol, as mineral media were used throughout the experiment. A major exception was the significant regulation of the proteins involved in methionine metabolism. While the homologues to MET3, MET16 and MET17 were upregulated on methanol, the homologues to MET6, SAM1, SAM2 and SAH1 were downregulated. As shown in figure 3, the first group of enzymes catalyses the reduction and fixation of sulphur, while the second group drives the cycle responsible for methyl group donation. The reduction of this pathway in cells grown on methanol would imply a decrease of the flux from the C1-pool to methionine by MET6 (5-methyltetrahydrofolate-homocysteine S-methyltransferase), which catalyses the transport of activated methyl groups from 5-methyl-THF to homocysteine. These methyl groups are then passed on via S-adenosyl-methionine by a variety of S-adenosyl-L-methionine-dependent methyl transferases, many of them being involved in ribosomal subunit biogenesis, rRNA and tRNA-processing, mRNA capping and nuclear export – once again stressing the lower demand for protein synthesis rate upon growth on methanol. Other significantly regulated groups of genes (table 7) belong to the thiamine biosynthesis, all being upregulated, and the so-called snooze genes related to the stationary phase. Two of these genes, SNZ1 and SNZ2, were differently regulated at pH 5 and pH 3, being repressed on methanol at pH 5 and induced at pH 3, while SNZ3 was repressed at pH 5, but did not yield a significant value at pH 3. However, the reported high sequence similarity of the S. cerevisiae SNZ genes will not enable a reliable differentiation of their regulation on microarrays. For S. cerevisiae it is reported that the highly homologous products of the SNZ gene family are involved in vitamin B6 (pyridoxal) synthesis [17]. Zeidler et al. [18] postulated that pyridoxal is a precursor of thiamine in yeast. Accordingly, the SNZ genes have been reported to be induced both by thiamine and pyridoxal depletion [17]. However, with the data obtained in our experiment we cannot interpret the differential behaviour of the SNZ and THI genes, since among the genes utilising thiamine-pyrophosphat (TPP) as cofactor, transketolase (TKL1) and pyruvate decarboxylase (PDC1, PDC5) are upregulated while PDB1 (pyruvate dehydrogenase beta-subunit) and presumably also α-ketoglutarate dehydrogenase (belonging to the TCA cyle) and DHAS (belonging to the methanol utilisation pathway) are downregulated. Thioredoxin-related genes appeared to be regulated upon shift from glycerol to methanol, too (table 7). Those being downregulated at least in one sample (IMP cyclohydrolase, ribonucleotide reductase, and thioredoxin reductase) are involved in ribonucleotide synthesis, again indicating a decreased demand of RNA precursors. Thioredoxin peroxidase, on the other hand, which is involved in the regulation of cell redox homeostasis and response to oxidative stress by reducing H2O2 and peroxide radicals, tended to be upregulated. This was expected due to the higher amount of oxidative stress during methanol utilisation. To verify the data obtained with microarrays with an independent method, a northern blot analysis of the total RNA samples from the culture grown at pH 5.0 was performed, using the respective P. pastoris homologous sequences to produce the probes (Fig. 4). The actin mRNA level was unchanged whereas the human trypsinogen mRNA level (as a positive control) was strongly induced on methanol, both as expected. MET17 and SAH1 exemplify differentially regulated genes identified by the microarray experiment (table 6). The data from the northern blot confirmed the results derived by microarray analysis, further underlining the reliability of the method. Surprisingly, only minor differences in transcriptional regulation were observed between cultures grown at pH 5.0 and pH 3.0. Of course it has to be considered that not the direct effects of a shift in external pH was observed. Still, one could expect a different set of genes to be significantly regulated upon a shift to methanol metabolism at the different pH values. At pH 3.0 a decreased yield in biomass was detected as compared to cultures at pH 5.0, which is consistent with the observation of Hohenblum et al. [16] stating that low fermentation pH decreases the viability of P. pastoris. In order to compare the general behaviour of P. pastoris at different external pH to that of S. cerevisiae, we measured the intracellular pH (pHi) of the cells. The cells of the culture at pH 5.0 showed a pHi of 7.1 at the end of the glycerol phase, and of 7.2 the end of the methanol phase, whereas the pHi of both samples of the cultures at pH 3.0 was 7.3. Thus, no changes of the pHi were observed between cultures grown at the chosen pH values, which is in contrast to the behaviour of S. cerevisiae, where the pHi appears to be more dependent on the external pH [19,20]. Conclusions We could successfully demonstrate that it is possible to obtain new and valuable information about transcriptomic regulation in P. pastoris by probing S. cerevisiae DNA microarrays. Specific regulation upon a shift from glycerol mineral medium to methanol mineral medium under conditions similar to a production process were analysed. As major effects we recognised a downregulation of TCA cycle genes, and a transcriptional decrease of genes related to ribonucleotide and ribosome synthesis. Furthermore, the supply of activated methyl groups via adenosyl methionine was reduced, indicating a decreased ribosome and tRNA synthesis, which is not surprising since the specific growth rate is significantly decreased during the methanol feed phase in comparison to the glycerol feed phase. Correspondingly, a downregulation of the energy metabolism upon methanol induction appears reasonable. Only a few genes were differentially regulated when comparing expression differences between growth on glycerol and growth on methanol of a culture grown at pH 5.0 to one at pH 3.0. Among the few genes found are the SNZ genes and the histone genes, but a plausible hypothesis for the differential pH dependent regulation was not found. Interestingly, also the intracellular pH did not change between the different external conditions, indicating a major difference in pH regulation between P. pastoris and S. cerevisiae. Materials and Methods Unless stated otherwise, all chemicals were purchased from Merck Eurolab, and all enzymes for DNA manipulation were purchased from MBI Fermentas. 1. Strains The expression strain used in this study was P. pastoris strain X33 (Invitrogen), a wild type strain which can grow on minimal media without supplements. The identity of the strain in use was verified by partial 26S ribosomal DNA sequencing (data not shown). The selection mechanism was based on the Zeocin™ resistance of the transformation vector. Transformation of the strain was carried out with a plasmid derived from pPICZαB (Invitrogen), containing the gene for human trypsinogen 1 [21]. pPICZαB utilises the AOX1 promoter of P. pastoris and the α-factor leader sequence of S. cerevisiae for product secretion. The selected strain was of the methanol utilisation positive (mut+) phenotype, which means that it is fully capable to metabolise methanol as the sole carbon source. As a control strain we used S. cerevisiae CEN.PK 113-5D (MATa, ura3) [22]. 2. Shake flask cultivation of P. pastoris and S. cerevisiae Shake flask cultures were performed at 28°C in YPD medium (2% peptone, 2% glucose, 1% yeast extract). The cells were inoculated to an OD660 of 0.3 from a pre-culture grown over night. For the yellow experiment the samples were taken in exponential phase after 5 h of growth (OD660 for S. cerevisiae: 0.89, P. pastoris: 1.2). To assess differentially regulated genes the respective cultures were divided after 5 h of growth. One half was incubated as before, whereas the other half was supplemented with 250 mM acetic acid. Samples were collected after shaking for 1.5 h at 28°C. The pH of the untreated culture was 5. The pH of the acid treated culture was 3.5. 3. Fermentation of P. pastoris Fed batch fermentations were performed with a MBR mini bioreactor with a final working volume of 2 l, essentially as described by Hohenblum et al. [16]. The media were as follows PTM1 trace salts stock solution contained per litre 6.0 g CuSO4• 5H2O, 0.08 g NaI, 3.0 g MnSO4• H2O, 0.2 g Na2MoO4• 2H2O, 0.02 g H3BO3, 0.5 g CoCl2, 20.0 g ZnCl2, 65.0 g FeSO4• 7H2O, 0.2 g biotin and 5.0 ml H2SO4 (95 %-98 %). All chemicals for PTM1 trace salts stock solution were from Riedel-de Haën, except for biotin (Sigma), and H2SO4 (Merck Eurolab). Batch medium contained per litre 23.7 ml H3PO4 (85 %), 0.6 g CaSO4• 2H2O, 9.5 g K2SO4, 7.8 g MgSO4• 7H2O, 2.6 g KOH, 40 g glycerol, 4.4 ml PTM1 trace salts stock solution. Glycerol fed-batch solution contained per litre 632 g glycerol (100 %) and 12 ml PTM1 trace salts stock solution Methanol fed-batch solution contained per litre 988 ml methanol (100 %) and 12 ml PTM1 trace salts stock solution The dissolved oxygen was controlled at DO = 30 % with the stirrer speed (600 – 1200 rpm). Aeration rate was 100 l h-1 air, which was supplemented with oxygen (up to 25 %) after the begin of the fed batch. The temperature was 25°C, and the pH was controlled with NH3 (25 %). Before starting the fermentation, the pH of 1.2 l batch medium was set to 5.0 with NH3 (25 %). The batch phase of approximately 32 h was followed by a 4 h fed batch with glycerol medium (feed rate 15.6 ml h-1), leading to a dry biomass concentration of approximately 40 g l-1. Then, the feed with methanol medium was started with a feed rate of 6.4 ml h-1. The fermentation was terminated 14 h after the methanol feed start. The pH was 5.0 during batch, and either kept at 5.0 throughout the fermentation, or decreased to 3.0 at the beginning of the glycerol fed batch. The final dry biomass concentration was 51.4 g l-1 at pH 5.0, and 46.7 g l-1 at pH 3.0. Samples were taken at the end of the glycerol fed batch phase and at the end of the methanol fed batch phase, respectively, as depicted in figure 2. 4. mRNA preparation The cell pellets were re-suspended in 10 × the volume of TRI-reagent (Sigma) and frozen. The samples were thawed on ice and after addition of acid washed glass beads the cells were homogenised in a Ribolyser (Hybaid Ltd.) for 2 × 20 sec, in between cooling on ice. After addition of chloroform, the samples were centrifuged and the total RNA was precipitated from the aqueous phase adding isopropanol. The pellet was washed 2 × with 70% ethanol, dried and re-suspended in RNAse free water. mRNA was isolated using the MicroPoly(A)Purist mRNA purification Kit (Ambion) according to the manufacturers protocol. 5. Synthesis and labelling of cDNA 5 μg of mRNA and 0.5 μg of oligo dT primer were mixed in 7 μl of water, incubated for 5 min at 70°C and subsequently at 42°C for about 3 min. The following components were added to 5 μl of said reaction mixture: 4 μl reaction buffer (5 x) for SuperScript II reverse transcriptase (Invitrogen), 2 μl dTTP (2 mM), 2 μl dATP, dGTP, dCTP (5 mM), 2 μl DTT (100 mM), 2.5 μl RNasin (40 U, Promega) and 2 μl FluoriLink Cy3-dUTP (1 mM) or 2 μl FluoriLink Cy5-dUTP (1 mM, Amersham Biosciences) respectively, and 1 μl SuperScript II reverse transcriptase (200 U, Invitrogen) to result in a total of 19.5 μl. The mixture was incubated for 1 h at 42°C. After addition of further 200 U SuperScript II reverse transcriptase the mixture was incubated for another 1 h at 42°C. 7 μl of 0.5 M NaOH/50 mM EDTA were added and the mixture was incubated at 70°C for 15 min. The reaction mixture was neutralised by addition of 10 μl Tris-HCl pH 7.5 (1 M). The labelled cDNA of the two corresponding samples were pooled and purified with Qiaquick purification columns (Qiagen) according to the manufacturer's protocol. 6. Chip hybridisation and set-up of microarrays The cDNA microarrays used for this study were Hyper Gene Yeast Chips from Hitachi Software Engineering Europe AG. According to the manufacturer, about 0.1 to 0.3 ng of PCR amplified cDNA (approximately 200 bp to 8000 bp) were spotted onto a poly-L-lysine coated glass slide and fixed by baking, succinic anhydride blocking and heat denaturation. Labelled cDNA was resuspended in about 70 μl of 5 × SSC/0.05% SDS, heat denatured at 95°C for 3 min and cooled on ice. SDS crystals appearing were dissolved by short and slight warming and the mixture was gently applied to a Yeast Chip according to the scheme presented in table 1. The spotted area was covered with a cover glass and the chips were placed in an airtight container with a humidified atmosphere at 60°C for 16 h. The cover glasses were removed in 2 × SSC/0.1% SDS and the chips were washed consecutively for 5–10 min each in 2 × SSC/0.1% SDS, 0.5 × SSC/0.1% SDS, and 0.2 × SSC/0.1% SDS at RT. The chips were centrifuged at 600 rpm for 3 min in order to dry them. The washing conditions were chosen according to the manufacturer's manual. We have tested less stringent washing conditions which led to higher background without increasing the number of positive signals. 7. Data acquisition and processing Images were scanned at a resolution of 50 μm with a G2565AA Microarray scanner (Agilent) and were imported into the GenePix Pro 4.1 (Axon Instruments) microarray analysis software. GenePix Pro 4.1 was used for the quantification of the spot intensities. Each appearing gene spot was averaged. The data set was then imported into GeneSpring 6.1 (Silicon Genetics) for further normalisation and data analysis. All of the values of each channel on each chip were divided by their respective median for normalisation. Subsequently, the median intensity of all 84 TE spots (spotted with buffer, no DNA) deduced from each value, and all spot values less then the standard deviation of said 84 threshold values were considered to be not significant and were set to the value of the standard deviation. To determine induction or repression of gene activity, the normalised signals on each spot were compared, and all genes showing a signal difference exceeding the threshold (2 fold for S. cerevisiae, and 1.5 fold for P. pastoris, see results) on both parallel independent microarrays were judged as significantly regulated. 8. Statistical evaluation of microarray data After normalisation and background deduction, pairwise correlations of all significant values were calculated using Pearson's correlation coefficient. To evaluate the variability of data derived from both dyes on one chip, the standard deviations of all significant spots hybridised with two identical samples were plotted against the respective normalised mean intensity value. To judge the correlation of gene regulation between S. cerevisiae and P. pastoris, the regulation factors of all of genes that were significantly downregulated in S. cerevisiae upon a difference of pH 5.0 to 3.5, were correlated to the respective P. pastoris regulation factor upon the same media difference, using the Pearson's correlation coefficient. The significance of all correlations against randomly distributed values were evaluated by a t-test, applying a significance level p < 0.01. To exclude an effect of data clustering (as the majority of the values are rather low) on correlation, Spearman's correlation coefficients were calculated as well. As these differed only slightly from Pearson's coefficients, they are not shown. Linear regression analysis was performed with the regulation intensities of P. pastoris against S. cerevisiae in order to compare the average fold change observed for both yeasts. 9. Determination of the intracellular pH (pHi) The pHi was determined as described by Valli et al. [19]. Essentially, samples were centrifuged and resuspended in McIlvaine buffer [23] at pH 3.0, containing 20 μM carboxy SNARF-4F AM (Molecular Probes). Loaded cells were analysed on a FACS Calibur (Becton Dickinson, Franklin Lakes, NJ USA) with a 488 nm argon-ion laser. 104 cells were measured per analysis, using PBS as the sheath fluid. Carboxy SNARF-4F fluorescence emission was measured through a 585/21 BP filter (FL2) and a 670 LP filter (FL3). Threshold settings were adjusted so that cell debris were excluded from data acquisition. The ratio of the two fluorescence intensities is a measure for the internal pH. Calibration was performed with amphotericin B (Sigma) perforated cells as described in Valli et al. [19]. 10. Northern blot analysis Northern blot analysis was essentially performed as described by Sambrook et al. [24]. In short, total RNA prepared as described above was fractionated on a denaturing formaldehyde containing gel, capillary blotted onto a nylon membrane (Nytran Supercharge, Schleicher & Schuell) and fixed by baking. The membrane was stained with methylene blue (0.04% in 0.5 M NaOAc, pH 5.2) for quality control and to ensure that equal amounts of RNA had been loaded. The probes were PCR amplified from genomic P. pastoris DNA using the following primers: actin: gttccagccttctacgtttctattca and acggagtactttctttctggtggag; SAH: agctgaacttgattttggacgac and acttgaggcttgatgttgctgac; Met17: tgcatcaatggtcacggtaaca and tggtgagtagagtagtaaggagcaatga. The probe for human trypsinogen was prepared as described in [15]. The probes were DIG labelled using the PCR DIG Labeling Mix (Roche) according to the manufacturer's protocol. Pre-hybridisation and hybridisation were performed in high SDS hybridisation buffer at 42°C. The blots were washed twice at RT with 2 × SSC/0.1 % SDS and two times at 68°C with 0.5 × SSC/0.1 % SDS. Staining of the blots was performed using anti-Digoxigenin-alkaline phosphatase Fab Fragments (Roche) and the CDP Star chemiluminescent reagent (Tropix) according to the manufacturer's protocol. The images were taken with a Lumi imager F1 (Boehringer Mannheim). Authors' contributions MS and PB designed and performed the microarray hybridisation experiments. MS and BG analysed and interpreted the microarray data, and drafted the manuscript. MV performed the pHi determinations. MM ran and analysed the fed batch fermentations. DP participated in the design of this study, and in data interpretation. DM participated in the design of this study, and in data interpretation, performed the statistical analyses, and drafted part of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank Prof. Harald Strelec (Institute of Applied Statistics and Computing, Univ. Nat. Res. Appl. Life Sci. Vienna) for valuable support in the statistical analyses. Thomas Öfferl helped with data conversion and normalisation, and Ksenija Lopandic verified the identity of the P. pastoris strain with 26S sequencing. MV received an Ernst Mach Grant from the Austrian Federal Ministry of Education, Science and Culture. Figures and Tables Figure 1 Statistical evaluations. (A) "yellow experiment": identical cDNA samples of P. pastoris were differently labelled and hybridised to S. cerevisiae microarrays. Normalised data of channel 532 nm (Cy3) are plotted against channel 635 nm (Cy5). The solid line represents the linear correlation. Dotted lines indicate the limits of 1.5 fold differences between two signals on one spot. More than 99% of all values vary less than 1.5 fold from each other. (B) Standard deviations of all value pairs as shown in panel A, plotted against the respective mean normalised intensities. (C) Correlation of spot intensities comparing S. cerevisiae and P. pastoris, grown in identical conditions. (D) Correlation of gene regulation. The correlation between the downregulated genes of S. cerevisiae and P. pastoris upon a shift from pH 5.0 to pH 3.5 is shown. Figure 2 Fed batch fermentations of P. pastoris. The two panels represent the two different fermentations performed: (a) The pH was kept at 5.0 throughout the fermentation. (b) The pH was let drop to 3.0 and was kept constant subsequently. (A) indicates the batch phase, the cells were growing on glycerol. The time scale starts at 25 h. (B) indicates the glycerol fed batch phase and (C) indicates the methanol fed batch phase. Methanol induces heterologous protein production and serves as a carbon source at the same time. The diamonds (◇) show the total yeast dry mass and refer to the left scale. The squares (□) show the pH of the culture broth and refer to the right scale. The arrows indicate the time points, when the samples for microarray analysis were taken. Figure 3 Schematic representation of the observed regulations in the sulfur, methionine and S-adenosyl-methionine metabolism. Gene names are framed. APS: adenylylsulfate, PAPS: 3'-phosphoadenylyl-sulfate, MET3: ATP sulfurylase, MET6: N5-methyltetrahydrofolate homocysteine methyltransferase, MET10: sulfite reductase, MET14: adenylylsulfat kinase, MET16: 3'-phosphoadenylyl-sulfate reductase, MET17: O-acetylhomoserine (thiol)-lyase, SAH1: S-adenosyl-L-homocysteine hydrolase, SAM1: S-adenosylmethionine synthetase, SAM2: S-adenosylmethionine synthetase. (▲) upregulation upon shift from glycerol to methanol; (▼) downregulation upon shift from glycerol to methanol; (ns) no significant values obtained. Figure 4 Northern blot analysis of selected genes. Exemplarily, the RNA of the pH 5.0 experiment was analysed for the expression of four genes. Actin and human trypsinogen were used as controls for an unregulated, and a methanol induced gene, respectively. MET17 (O-acetylhomoserine (thiol)-lyase) and SAH1 (S-adenosyl-L-homocysteine hydrolase) were chosen as strongly down- or upregulated genes, as determined before (table 6). Table 1 Set-up of microarrays. Chip No. Sample labelled with Cy3 Sample labelled with Cy5 Experiment 1 P. pastoris shake flask, pH 5.0 P. pastoris shake flask, pH 5.0 yellow 2 S. cerevisiae shake flask, pH 5.0 S. cerevisiae shake flask, pH 5.0 yellow 3 P. pastoris shake flask, pH 5.0 P. pastoris shake flask, pH 3.5 pH shift 4 S. cerevisiae shake flask, pH 3.5 S. cerevisiae shake flask, pH 5.0 pH shift 5 P. pastoris fed batch, glycerol, pH 5.0 P. pastoris fed batch, methanol, pH 5.0 Shift glycerol to methanol at pH 5 6 P. pastoris fed batch, methanol, pH 5.0 P. pastoris fed batch, glycerol, pH 5.0 Shift glycerol to methanol at pH 5 7 P. pastoris fed batch, glycerol, pH 3.0 P. pastoris fed batch, methanol, pH 3.0 Shift glycerol to methanol at pH 3 8 P. pastoris fed batch, methanol, pH 3.0 P. pastoris fed batch, glycerol, pH 3.0 Shift glycerol to methanol at pH 3 The table indicates the labelling of the samples and which samples are hybridised together on one microarray for which experiment. The chip numbering is arbitrary. "Yellow" experiment indicates that identical samples were labelled with both dyes and hybridised to the same microarray to test reproducibility. Table 2 Comparison of the number of significant values obtained from homologous versus heterologous microarray hybridisations. Significant values Values under threshold P. pastoris 2031 ± 206 3906 ± 206 S. cerevisiae 3086 ± 888 2851 ± 888 Mean values of 4 microarrays each, and their respective standard deviations are shown. Table 3 Pairwise Pearson's correlation coefficients. Sample 1 Sample 2 microarray correlation coefficient P. p. shake flask pH 5.0 P. p. shake flask pH 5.0 same 0.98 S. c. shake flask pH 5.0 S. c. shake flask pH 5.0 same 0.97 P. p. shake flask pH 5.0 P. p. shake flask pH 5.0 different 0.90 S. c. shake flask pH 5.0 S. c. shake flask pH 5.0 different 0.92 P. p. shake flask pH 5.0 S. c. shake flask pH 5.0 different 0.72 P. p. fed batch, glycerol pH 5.0 P. p. fed batch, glycerol pH 5.0 different 0.86 P. p. fed batch, methanol pH 5.0 P. p. fed batch, methanol pH 5.0 different 0.92 P. p. fed batch, glycerol pH 3.0 P. p. fed batch, glycerol pH 3.0 different 0.90 P. p. fed batch, methanol pH 3.0 P. p. fed batch, methanol pH 3.0 different 0.83 Normalised significant signal intensities derived both from the same and from different microarrays were correlated. P. p = P. pastoris, S. c. = S. cerevisiae. Table 4 Differentially expressed genes of the core metabolism and ATP synthesis for which significant values were obtained. Metabolic Pathway Common Gene Name pH 5 pH 3 Core metabolism CDC19 ▼ ▼ Pyruvate kinase ENO1 ▼ ▼ Enolase FBA1 ▼ ▼ Fructose 1, 6-bisphosphate adolase FBP1 ▲ n.s. Fructose-1, 6-bisphosphatase GPM1 — ▼ phosphoglycerat mutase PCK1 — tend. ▼ Phosphoenolpyruvat carboxylkinase PDB1 ▼ ▼ Pyruvate dehydrogenase (beta subunit) PDC1 ▲ n.s. Pyruvat decarboxylase PDC5 ▲ ? Pyruvat decarboxylase TAL1 ▼ ▼ Transaldolase TDH1 ▼ ▼ Glyceraldehyde-3-phosphate dehydrogenase TDH2 ▼ ▼ Glyceraldehyde-3-phosphate dehydrogenase TDH3 ▼ ▼ Glyceraldehyde-3-phosphate dehydrogenase TKL1 ▲ ▲ Transketolase 1 FDH1 ▲ ▲ protein with similarity to formate dehydrogenase FDH2 ▲ ▲ Formate dehydrogenase SFA1 ▲ ▲ formaldehyde dehydrogenase ATP synthesis IDP1 ▼ ▼ isocitrate dehydrogenase IDP2 ▼ n.s. isocitrate dehydrogenase YJL045W ▼ n.s. succinate dehydrogenase (ubiquinone) activity A 1.5 fold change in expression was regarded as significant difference. (▲) upregulated upon shift from glycerol to methanol, (—) expression unchanged upon shift from glycerol to methanol, (▼) downregulated upon shift from glycerol to methanol, (n.s.) no significant values were obtained, (tend.) signifies that one of the two microarrays did not allow to obtain a significant value, or that one of the values is changed less than 1.5 fold, a question mark (?) signifies that both values for this experiment are significant but show the opposite change of expression, the result is therefore not interpretable. Table 5 Differentially expressed ribosomal and histone genes for which significant values were obtained (Symbols are as for table 4). Metabolic Pathway Common Gene Name pH 5 pH 3 Ribosomal Genes RPL2A ▼ ▼ RPL4B ▼ ▼ RPL5 ▼ ▼ RPL6A — ▼ RPL9B — ▼ RPL17A ▼ ▼ RPP0 ▼ ▼ RPS0A ▼ ▼ RPS0B ▼ ▼ RPS1A ▼ ▼ RPS4B ▼ ▼ RPS9B ▼ ▼ RPS12 ▼ ▼ RPS13 ▼ ▼ RPS22A ▼ ▼ RPS26B ▼ ▼ Histones HHF1 — ▼ Histone H4 HHF2 — ▼ Histone H4 HTB1 — ▼ Histone H2B HTA1 — ▼ Histone H2A Table 6 Differentially expressed genes of the biosynthetic pathways of methionine and other amino acids for which significant values were obtained (Symbols are as for table 4) Metabolic Pathway Common Gene Name pH 5 pH 3 Methionine MET3 ▲ ▲ ATP sulfurylase MET16 ▲ ▲ tend. 3'-phosphoadenylsulfate reductase MET17 ▲ ▲ O-acetyl homoserine-O-acetyl serine sulfhydrylase MET6 ▼ ▼ methionine synthase SAM1 ▼ — S-adenosylmethionine synthetase SAM2 ▼ ▼ S-adenosylmethionine synthetase SAH1 ▼ ▼ putative S-adenosyl-L-homocysteine hydrolase Amino acid biosynth. AAT2 ▼ ▲ aspartate transaminase ARG1 — ▼ arginine biosynthesis ILV5 — ▼ branched-chain amino acid biosynthesis LPD1 ▼ — serine biosynthesis Table 7 Differentially expressed thiamine biosynthetic pathway genes, stationary phase, and thioredoxin related genes for which significant values were obtained (Symbols are as for table 4) Metabolic Pathway Common Gene Name pH 3 pH 5 Thiamine biosynthesis THI4 ▲ ▲ protein required for thiamine biosynthesis THI5 ▲ ▲ proteins involved in synthesis of the thiamine THI11 ▲ ▲ precursor hydroxymethylpyrimidine (HMP); THI12 ▲ ▲ members of a subtelomeric gene family THI13 ▲ ▲ including THI5, THI11, THI12, and THI13 Stationary phase SNZ1 ▼ ▲ stationary phase-induced gene SNZ2 ▼ ▲ stationary phase-induced gene SNZ3 ▼ n.s. stationary phase-induced gene Thioredoxin ADE16 ▼ tend. n.s. 5-aminoimidazole-4-carboxamide ribonucleotide(AICAR) transformylase/IMP cyclohydrolase RNR2 ▼ ▼ Ribonucleotide reductase TRR1 ▼ tend. ? 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==== Front Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-4-51565690310.1186/1476-4598-4-5Short CommunicationIdentification of amplified and highly expressed genes in amplicons of the T-cell line huT78 detected by cDNA microarray CGH Meléndez Bárbara [email protected]ínez-Delgado Beatriz [email protected] Marta [email protected]ández Victoria [email protected]íaz-Uriarte Ramón [email protected]ítez Javier [email protected] Human Genetics Department, Spanish National Cancer Centre (CNIO), c/Melchor Fernéndez Almagro 6, 28029-Madrid, Spain2 Bioinformatics Unit, Spanish National Cancer Centre (CNIO), c/Melchor Fernéndez Almagro 6, 28029-Madrid, Spain2005 18 1 2005 4 5 5 30 7 2004 18 1 2005 Copyright © 2005 Meléndez et al; licensee BioMed Central Ltd.2005Meléndez et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Conventional Comparative Genomic Hybridization (CGH) has been widely used for detecting copy number alterations in cancer and for identifying regions containing candidate tumor responsible genes. Recently, several studies have shown the utility of cDNA microarray CGH for studing gene copy changes in various types of tumors. However, no such studies on T-cell lymphomas have been performed. To date T-cell lymphomas analyzed by the use of chromosome CGH have revealed only slight copy number alterations and not gene amplifications. Results In the present study, we describe the characterization of three amplicons of the T-cell line huT78 located at 2q34-q37, 8q23-q24 and 20p, where new amplified and overexpressed genes are found. The use of a cDNA microarray containing 7.657 transcripts allowed the identification of certain genes, such as BCLX, PCNA, FKBP1A, IGFBP2 and cMYC, that are amplified, highly expressed, and also contained in the amplicons on 20p and 2q. The expresion of these genes was analyzed in 39 T-cell lymphomas and 3 other T-cell lines. Conclusion By the use of conventional CGH and CGH and expression cDNA microarrays we defined three amplicons in the T-cell line huT78 and identified several novel gene amplifications (BCLX, PCNA, FKBP1A, IGFBP2 and cMYC). We showed that overexpression of the amplified genes could be attributable to gene dosage. We speculate that deregulation of those genes could be important in the development of T-cell lymphomas and/or in the maintenance of T-cell lines. ==== Body Background Gene amplification plays an important role in the progression and initiation of many solid tumors, as is the case of breast cancer where amplification of the genes ERBB2 (17q12), cMYC (8q24), and CCND1 (11q13) are found in 10–25% of breast tumors. Amplifications are revealed by Comparative Genomic Hybridization (CGH) in small chromosome areas (restricted to 2–10 Mb) where DNA copy number increases from more than 5 to 10-fold. Within these amplicons it is possible to identify critical amplified genes that are also overexpressed: this is the case for cMYC (8q24.12), ERBB2 (17q12-q21), MDM2 (12q14.3-q15) or BCL2 (18q21.3) [1-3]. Recently array-based CGH on cDNA microarrays has been used to investigate the genomic alterations with high resolution. Using this technique exhaustive analysis of the 17q12 and 17q23 amplicons in breast cancer has led to the identification of other genes that are also contained in the amplicons and whose overexpression could also be attributable to gene amplification [4-8]. Other recent studies in prostate cell lines [9] and in neuroblastoma tumors [10,11] have also shown the utility of cDNA microarray CGH in defining amplicon boundaries and in analyzing the complexity of the amplicons. Tumor-related genes contained in amplicons are identified in these works due to the posibility of correlating gene expression to gene dosage data. To date, however, no amplifications have been described in primary tumors or cell lines derived from T-cell lymphomas [12,13]. Methods The cutaneous T-cell line huT78 was obtained from American Type Culture Collection (Rockville, MD) and cells were grown under recommended culture conditions. High molecular weight DNA was extracted and CGH was performed as described previously [12,14]. In order to define regions of high-level amplification and to identify amplified and highly expressed genes contained in the amplicons, we performed cDNA microarray experiments on T-cell line huT78 to obtain expression and genomic profiles of the amplicons. We used the CNIO Oncochip (v1.1a) containing 7.657 different sequence-validated I.M.A.G.E cDNA clones (Research Genetics; Huntsville, AL) -some of them duplicated to reach a total of 11.718 spots- that represent known genes and expressed sequence tags (ESTs) related to the tumoral process, and tissue specific genes. A complete list can be found at . Genomic DNA hybridizations on microarrays were performed using DNA extracted from the T-cell line huT78 and from the blood of a control donor used as a reference. Genomic DNAs were AluI and RsaI digested, labeled with Cy5 (huT78) and Cy3 (control) using BioPrime labeling kit (Life Technologies, Inc., Gaithersburg, MD), and hybridized on microarrays at 50°C for 14–16 h, as previously described [15]. Post-hybridization washes were performed and microarrays were scanned using a GenePix scanner (Axon Instruments, Foster City, CA). Fluorescence ratios Cy5/Cy3 were obtained and normalized by adjusting these ratios to a normalized factor so that the median of the ratios of all spots in the array equals 1. Only measurements with fluorescence intensities higher than two times the sum of the background averages' of both fluorochromes (Cy3 and Cy5) were considered reliable. Logarithms of the fluorescence ratios (log2 values) were calculated and used for the analysis. The CNIO Oncochip contains 7.657 different clones, of which 3.079 are replicated at least twice: thus, we removed and averaged the replicates by using an in-house developed preprocessing tool [16]. Expression data was also obtained from huT78 cells and from magnetically isolated T lymphocytes obtained from the pooled peripheral blood of 5 anonymous donors that were used as a control. T lymphocytes were isolated by using either magnetic microbeads conjugated to monoclonal mouse anti-human CD3 antibodies purchased from Miltenyi Biotec Inc. (Auburn, CA), or magnetic depletion of non-T-cells with a cocktail of antibodies using the Pan T-cell Isolation Kit (Miltenyi Biotec Inc.). Total RNAs were extracted with Tri Reagent (Molecular Research Center, Cincinnati, OH) following the manufacturer's instructions and amplified using a T7-based method, as previously described [17,18]. Briefly, 5 μgr of total RNA were used to produce double-stranded cDNA (Superscript Choice System, Life technologies Inc.) and amplification of mRNAs was performed using the Megascript T7 in vitro transcription kit (Ambion, Austin, TX) following manufacturer's recommendations. A pool of aRNAs obtained from the Universal Human RNA (Stratagene, La Jolla, CA) was used as a standard reference in all hybridizations. Test or reference amplified RNAs (aRNAs) were labeled with fluorescent Cy5 and Cy3, respectively, as reported [18] and hybridized on microarrays at 42°C for 15 hours. Fluorescence ratios (Cy5/Cy3) were normalized and filtered for genomic data. The Cy5/Cy3 ratios obtained in the cell line hybridization were then compared to those obtained in control T lymphocytes hybridization. In order to analyze if the candidate genes contained in the amplicons are altered in primary tumors and other cell lines, we analyzed expression data obtained in a previous study [19] using tumor samples from 39 primary T-cell lymphomas and 3 cell lines (Jurkat, Molt 16 and Karpas 45). Sample description and clinical details are specified in the work from Martinez-Delgado et al. [19]. All the tumors were diagnosed according to the World Health Organization classification criteria, and all individuals had given official consent. Results CGH studies CGH carried out in huT78 cells revealed three high-level gains at 2q34-q37, 8q23-q24 and 20p, showing copy number gains higher than 5 fold (Figure 1). Low-level gains and losses of whole chromosomes/chromosomal regions were also detected in other chromosomes, with the exception of chromosomes 1, 21 and 22 that did not show copy number alterations. Figure 1 CGH and genomic profiles of chromosomes 1, 2, 8 and 20 of the huT78 cell line. Average of the log2 genomic values over 3 neighbouring genes are plotted in the figure as a function of the location of the clones according to EnsEMBL database. On the right of each graph, CGH profiles show the number of chromosomes analysed (n) and the average profile of the metaphases studied with a 99% interval of confidence. Red and green bars at both sides of each ideogram indicate gains or losses. Only 4 chromosomes are shown in the figure, chromosome 1 did not present DNA amplification, neither by CGH nor by microarray experiments, whilst chromosomes 2, 8 and 20 showed high-level DNA amplification at 2q34-q37, 8q23-q24 and 20p. On the bottom of each graph amplicons are represented (green bars) and the gain or loss regions (green and red bars, respectively). p and q arms are also indicated. Expression and copy number profiling Expression and copy number profiling across each chromosome were performed using data from a total of 4.229 tumor-related genes or ESTs that had a map position and an identity confirmed by in-house sequencing. Map positions of the cDNA clones were obtained from the EnsEMBL database . According to this database, clones were ordered along the chromosomes and their expression and genomic values (See additional data file 1 for the raw data used to perform this analysis) were plotted as a function of their location to obtain chromosomal genomic and expression profiles (Figure 2). Figure 2 Genomic and expression profiles of chromosomes 2, 8 and 20 of the huT78 cell line. Chromosomes presenting regions of high level amplification are shown in the figure. Genomic and expression microarray data (averages of log2 values over 3 neighbouring genes) are plotted as a function of the location of the clones. At the foot of each graph amplicons are represented (gross green bars) along with the gain or loss regions (green and red bars, respectively). p and q arms are also indicated. Only thirty genes that showed high-level genomic gains were also highly expressed in the cutaneous T-cell line huT78 (Table 1). Genes were defined as significantly up-regulated (or down-regulated) if the difference in ratio to the control was at least two-fold (log2 [ratio expression data] ≥ +/-1). Cut-off levels for genomic data were defined at more than 1.7, a significantly high value to assure that the gene is gained at least 4-fold (log2 [genomic data] ≥ 0,8) (data from FISH studies). Within these thirty genes that were gained and overexpressed in the cell line, 5 of them were located in amplicons of chromosome 2 (XRCC5, IGFBP2 and PSMB3) and chromosome 20 (FKBP1A and BCL2L1) revealed by conventional CGH. Table 1 Genes highly gained and overexpressed in huT78 cell line. Unigene ID Gene Symbol G E Log2(G) Log2(E) Cytogenetic Location Gene Hs.76884 ID3 1,857 2,343 0,893 1,228 1p36.13-p36.12 inhibitor of DNA binding 3, dominant negative helix-loop-helix protein Hs.84981 XRCC5 2,04 4,044 1,029 2,016 2q35 X-ray repair complementing defective repair in Chinese hamster cells 5 Hs.162 IGFBP2 2,563 4,544 1,358 2,184 2q33-34 insulin-like growth factor binding protein 2 Hs.82793 PSMB3 1,794 3,874 0,843 1,954 2q35 proteasome subunit, beta type, 3 Hs.174007 VHL 3,119 2,044 1,641 1,031 3p26-p25 von Hippel-Lindau syndrome Hs.55173 CELSR3 1,862 2,647 0,897 1,404 3p24.1-p21.2 cadherin, EGF LAG seven-pass G-type receptor 3 Hs.180145 HSPC030 1,777 2,947 0,829 1,559 3 HSPC030 protein Hs.350266 ARGBP2 1,823 2,028 0,866 1,020 4q35.1 Arg/Abl-interacting protein ArgBP2 Hs.179565 MCM3 1,791 4,378 0,841 2,130 6p12 MCM3 minichromosome maintenance deficient 3 Hs.278589 GTF2I 1,886 2,379 0,915 1,251 7q11.23 general transcription factor II, i Hs.167246 POR 2,007 2,609 1,005 1,384 7q11.2 P450 (cytochrome) oxidoreductase Hs.61762 HIG2 1,885 3,152 0,915 1,656 7q32.2 hypoxia-inducible protein 2 Hs.274424 SAS 1,743 4,944 0,802 2,306 9p24.1-p23 N-acetylneuraminic acid phosphate synthase; sialic acid synthase Hs.184793 DKFZP434F195 1,78 2,125 0,832 1,087 9 DKFZP434F195 protein Hs.18910 POV1 1,78 3,702 0,832 1,888 11p11.2-p11.1 prostate cancer overexpressed gene 1 Hs.91877 THRSP 6,491 3,095 2,698 1,630 11q13.5 thyroid hormone responsive Hs.180628 DNM1L 1,758 2,715 0,814 1,441 12p12.3 dynamin 1-like Hs.76294 CD63 1,747 6,165 0,805 2,624 12q12-q13 CD63 antigen (melanoma 1 antigen) Hs.247888 - 2,284 3,201 1,192 1,679 16 Hs.12303 SUPT6H 1,742 2,819 0,801 1,495 17q11.2 suppressor of Ty 6 homolog (S. cerevisiae) Hs.296281 ILF1 1,754 3,146 0,811 1,653 17q25 interleukin enhancer binding factor 1 Hs.75716 SERPINB2 2,358 3,767 1,238 1,913 18q21.3 serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 2 Hs.8372 UQCR 2,046 2,916 1,033 1,544 19p13.3 ubiquinol-cytochrome c reductase subunit Hs.661 NDUFB7 1,845 2,416 0,884 1,272 19p13.12-p13.11 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 7 Hs.36992 - 4,848 3,213 2,277 1,684 19 Hs.250615 CYP2A7 1,778 3,718 0,830 1,895 19q13.2 cytochrome P450, subfamily IIA, polypeptide 7 Hs.356727 FKBP1A 1,918 4,022 0,940 2,008 20p13 FK506 binding protein 1A Hs.305890 BCL2L1 6,941 11,508 2,795 3,525 20q11.21 BCL2-like 1, Bcl-X Hs.273385 GNAS 1,764 2,059 0,819 1,042 20q13.2-q13.3 GNAS complex locus Hs.284380 GGT1 1,784 2,762 0,835 1,466 22q11.23 gamma-glutamyltransferase 1 Relation of 30 genes presenting genomic values ≥ 1.7 [log2(G) ≥ 0,8] and expression values ≥ 2 [log2(E) ≥ 1]; it represents gains above 4-fold and overexpression above 2-fold. In bold, genes located in amplicons. G, Genomic values; E, Expression values. Amplicons on chromosomes 20, 2 and 8 Focusing on chromosome 20, the genomic structure of amplicon at 20p is not continuous, showing a peak of amplification around 30 Mb on the genes ID1 and BCL2L1 (BCLX) located at 20q11 (Table 2, Figure 3a). Both genes are close together (60 Kb apart) and are contained in the same BAC (RP11-243J16). This BAC was labeled and used as a probe for FISH on metaphase spreads of the cell line showing an extremely high amplification of this genomic fragment (figure 3b). Surprisingly, not all genes that are highly gained are also overexpressed when compared to the expression of normal T-lymphocytes. For example, although ID1 and BCLX show amplification (4 and 7 times respectively), only BCLX is highly expressed (Figure 3c). Other genes that are contained in the amplicon (as defined by conventional CGH) and that showed an increased copy number (some of them also overexpressed), such as FKBP1A, CDC25B and PCNA, were also probed by FISH using BACs RP11-314N13, RP5-1009E24 and RP4-746J20, respectively (Figure 3d). These genes are also gained but not in such a high amplification level as BCLX. These data are observed in their genomic values (Table 2), thus FISH with the corresponding BACs confirmed these results. Table 2 Genes in amplicons of chromosome 2, 8 and 20. Genes in amplicon of Chromosome 20 Unigene ID Gene symbol Position (Kb)* Genomic values Expression values Genetic data Expression data 20p13 Hs.356727 FKBP1A - 1,918 4,022 0,940 2,008 Hs.155140 CSNK2A1 451 n.d. 1,678 n.d 0,747 Hs.156114 PTPNS1 1,505 1,635 0,868 0,709 -0,205 Hs.26045 PTPRA 2,832 1,279 0,251 0,355 -1,997 Hs.89648 AVP 3,051 n.d. 1,274 n.d 0,349 Hs.85004 CENPB 3,753 1,703 1,169 0,768 0,226 Hs.153752 CDC25B 3,765 2,122 1,761 1,079 0,811 Hs.80905 RASSF2 4,748 1,25 0,981 0,322 -0,028 Hs.78996 PCNA 5,083 1,694 8,187 0,760 3,033 Hs.2281 CHGB 5,880 n.d. 0,855 n.d -0,226 Hs.73853 BMP2 6,737 2,694 n.d. 1,430 n.d Hs.91143 JAG1 10,606 1,89 0,953 0,918 -0,069 Hs.44296 FLJ22324 13,761 1,543 2,292 0,626 1,197 Hs.82306 ADF 17,538 1,43 0,475 0,516 -1,075 Hs.268281 CRN 20,003 1,458 1,034 0,544 0,048 Hs.2030 THBD 23,016 1,655 0,572 0,727 -0,806 Hs.1872 PCK1 24,899 1,482 0,824 0,568 -0,279 Hs.274264 VSX1 25,044 1,67 1,682 0,740 0,750 Hs.75424 ID1 29,981 3,825 1,640 1,935 0,714 20q11 Hs.305890 BCL2L1 30,040 6,941 11,508 2,795 3,525 Genes in amplicon of chromosome 2 Unigene ID Gene symbol Position (Kb)* Genomic values Expression values Genomic data Expression data 2q34 Hs.54089 BARD1 218,722 0,734 1,646 -0,446 0,719 Hs.84981 XRCC5 220,094 1,823 2,815 0,856 1,341 Hs.162 IGFBP2 220,592 2,563 4,544 1,358 2,184 Hs.107169 IGFBP5 220,635 2,034 0,820 1,024 -0,285 Hs.38125 IFI75 224,000 1,342 1,664 0,413 0,647 Hs.309943 SP140 224,057 1,45 0,646 0,536 -0,631 Hs.83583 ARPC2 226,590 1,058 0,648 0,081 -0,626 Hs.166068 VIL1 226,796 1,025 1,463 0,036 0,549 Hs.82568 CYP27A1 227,154 1,676 0,764 0,745 -0,388 Hs.88049 PRKAG3 227,195 2,091 0,436 1,064 -1,196 Hs.153003 STK16 227,618 1,657 1,272 0,729 0,347 Hs.75318 TUBA1 227,622 1,631 5,622 0,706 2,491 Hs.77768 DNAJB2 227,652 1,82 0,841 0,864 -0,250 Hs.89655 PTPRN 227,662 1,659 1,447 0,730 0,533 Hs.48291 PDE6D 227,901 1,122 0,520 0,166 -0,943 Hs.1734 INHA 228,352 1,187 0,849 0,247 -0,237 Hs.198 PAX3 230,984 1,677 1,504 0,746 0,589 Hs.78946 CUL3 233,252 1,18 0,997 0,239 -0,004 Hs.75498 SCYA20 236,823 1,347 0,293 0,430 -1,769 Hs.91400 HDAC4 243,313 1,26 0,543 0,333 -0,881 Hs.36587 PPP1R7 244,757 0,907 1,859 -0,141 0,895 2q37 Hs.5345 RNPEPL1 245,348 1,488 0,949 0,573 -0,076 2q35 Hs.82793 PSMB3 - 1,794 3,874 0,843 1,954 Genes in amplicon of chromosome 8 Unigene ID Gene symbol Position (Kb)* Genomic values Expression values Genomic data Expression data 8q23 Hs.114218 FZD6 105,144 0,980 0,621 -0,030 -0,717 Hs.86905 ATP6C 105,407 1,067 0,918 0,094 -0,123 Hs.82173 TIEG 105,820 1,277 0,842 0,353 -0,248 Hs.94262 p53R2 106,237 1,324 0,564 0,369 -0,831 Hs.2463 ANGPT1 109,654 1,745 n.d 0,803 n.d. Hs.106673 EIF3S6 111,104 1.170 0.402 0.227 -1.314 Hs.58189 EIF3S3 119,873 1,097 0,566 0,130 -0,885 Hs.184161 EXT1 121,031 1,260 3,592 0,333 1,845 Hs.174185 ENPP2 122,788 1,223 0,891 0,290 -0,167 Hs.12940 ZHX1 127,568 1,212 1,309 0,277 0,389 Hs.61661 - 127,822 1,101 1,318 0,139 0,399 Hs.181107 ANXA13 127,989 1,333 0,655 0,415 -0,611 Hs.344478 KIAA0196 129,211 1,302 n.d 0,381 n.d Hs.79070 MYC 132,293 1,883 1,597 0,853 0,630 Hs.305916 TG 136,747 1,441 1,711 0,527 0,775 Hs.75789 NDRG1 137,117 1,617 1,140 0,693 0,190 Hs.157240 MGC4737 143,611 1,722 0,828 0,784 -0,273 Hs.740 PTK3 144,545 1,896 0,939 0,914 -0,487 Hs.77667 LY6E 146,427 1,595 2,410 0,674 1,269 Hs.301118 CYP11B1 146,498 1,619 1,227 0,695 0,295 Hs.348605 - 146,918 1,309 0,826 0,388 -0,276 Hs.264428 TSTA3 147,413 1,365 0,693 0,449 -0,530 Hs.223241 EEF1D 147,485 1,554 3,142 0,636 1,651 Hs.323834 NFKBIL2 147,600 1,375 4,436 0,459 2,149 Hs.339697 LOC51160 147,616 1,418 1,546 0,504 0,629 Hs.31442 RECQL4 147,653 1,458 3,419 0,544 1,774 Hs.12271 - 147,813 1,640 2,034 0,714 1,024 Hs.92679 - 147,876 1,178 1,510 0,236 0,595 8q24 Hs.331601 - 147,927 2,138 1,077 1,096 0,107 Areas of genomic gain presenting the highest values are shown in bold. Note that in 20p amplicon, the entire region is gained, but only the highest values are indicated. Underlined are gained and overexpressed genes. Map location according to the EnsMBL database (with exception of FKBP1A and PSMB3 genes that are FISH mapped); *Log2 values; n.d. No data. Figure 3 a) Microarray genomic values for chromsome 20. Note that the entire chromosome 20 has been gained and that, as CGH reveals, a peak of amplification is found at 20p. b) FISH with BAC RP11-243J16 (labeled in green) containing ID1 and BCLX genes to confirm genomic data; c) Graphical representation of microarray genomic (blue) and expression (pink) values as a function of gene position; d) FISH with BAC RP11-314N13 (labeled in red) containing FKBP1A gene on chromosome 20. Metaphase spreads from huT78 cells were prepared by standard cytogenetic methods. Gene-specific BAC clones were selected from the EnsEMBL database. Clones were labeled with SpectrumGreen-dUTP or SpectrumOrange-dUTP (Vysis) by nick translation. Dual-color hybridizations were performed at 37°C for 14–16 h and slides were washed and examined using an Olimpus AX60 epifluorescence microscope. The specificity and location of each probe was previously confirmed by FISH on normal metaphases prior to hybridization on huT78 cells. Using CGH, amplicon in chromosome 2 is narrower than amplicon in chromosome 20 (Figure 1). In fact it seems to be restricted to a few genes if genomic data are examined (Table 2). Only two areas, one around 220 and the other around 227 Mb, present the highest genomic values (shadowed in table 2), with 4 genes overexpressed: XRCC5 (2q35) and IGFBP2 (2q33-24) around 220 Mb, TUBA1 (tubulin, alpha 1) around 227 Mb and PSMD3 mapped by FISH at 2q35. Amongst these, IGFBP2 is the most highly overexpressed (Table 2). Amplicon in chromosome 8 is located at 8q23-q24, where the gene cMYC is located. This gene was found as amplified in other tumors and in this study we show that it is also amplified in the cell line huT78. cMYC is not however highly expressed in this cell line (Table 2). Chromosome 8 amplicon affects a wide region, from 132 to 146 Mb (shadowed in table 2), but only one gene located within this area is overexpressed, LY6E (lymphocyte antigen 6 complex, locus E). This gene is upregulated by all-trans-retinoic acid (ATRA), a differentiation inducer capable of causing clinical remission in about 90% of patients with acute promyelocytic leukemia. Other genes located in the amplicon that have not been analyzed in this study could also be important. Analyses of the amplicon candidate genes in primary T-cell lymphomas and cell lines Expression of the relevant genes found in the amplicons that were gained in this T-cell line were examined in a series of 39 primary T-cell lymphomas and 3 T-cell lines (Jurkat, Molt 16 and Karpas 45) [19]. Three genes located in 20q amplicon were overexpressed in the majority of primary tumors and stablish cell lines. FKBP1A and PCNA were overexpressed in 89,7 and 71,8% respectively (35 and 28 out of 39) of the primary tumors and in all the three cell lines analyzed. In addition, BCLX was overexpressed in 64,1% (25/39) of the primary tumors and in Jurkat cell line. Regarding genes in amplicons of chromosome 2 and 8, IGFBP2 was only overexpressed in 15,4% (6/39) of the tumors and cMYC is not highly expressed in any of them. However, both genes were overexpressed in the three cell lines. Discussion Conventional chromosome CGH of the T-cell line huT78 showed genomic patterns of copy number changes affecting most of the chromosomes. Three highly amplified regions were detected using this technique. Nevertheless, resolution of chromosome CGH is no less than 2 Mb for copy number gains and is a function of both amplicon size and copy number gains [20]. In order to precisely define regions of high-level amplification and to identify amplified and highly expressed genes contained in the amplicons of the T-cell line huT78, we performed cDNA microarray CGH and expression profiling of the cell line. For this purpose, we used the CNIO Oncochip containing 7.657 different sequence-validated cDNA clones that represent known genes and expressed sequence tags (ESTs) related to the tumoral process, and tissue specific genes. Recently, several studies have shown the utility of cDNA microarray CGH for studing gene copy changes in various types of tumors, and the usefulness of defining amplicon boundaries at high resolution (gene level) to assist in locating and identifying candidate oncogenes [4-11]. However, to date, no such studies on T-cell lymphomas have been performed. In the present study, we determined the precise locations of gene amplifications in the T-cell line huT78 by the use of array-based CGH on cDNA microarrays. We observed that, although chromosome CGH detected amplicons of different size (as for example the amplicons on chromosomes 20 and 2), the structure of those amplicons is more complex when it is analyzed at high resolution. Amplicon in chromosome 20 extends along all p arm when detected by use of chromosome CGH. Instead, several peaks of amplification, different in magnitude, appear at 3, 5–6, 10 and 29–30 Mbs when analyzed at gene-by-gene level. Amplicon in chromosome 2, although showing a narrower shape by chromosome CGH than that of chromosome 20, also presented a complex structure at high resolution, with two peaks of amplification around 220 and 227 Mbs. By analyzing mRNA levels in parallel, we observed that not all genes that are amplified are also overexpressed. Thus, by analyzing the expression of the genes contained in the amplicons, we selected several genes whose expression seemed to respond to gene copy number gains. We showed that only a few of the genes contained in the amplicon are highly gained and also overexpressed. In this way we could select the candidate genes to study further that could be involved in the tumorogenesis of the T-cell lymphomas. As stated before [7], however, the elevated expression of an amplified gene cannot alone be considered strong independent evidence of a candidate oncogene's role in tumorigenesis. Thus, we examined the expression of the selected candidate genes in a series of 39 primary T-cell lymphomas and 3 T-cell lines (Jurkat, Molt 16 and Karpas 45) [19]. Three genes located in 20q amplicon were overexpressed in the majority of primary tumors and stablish cell lines. FKBP1A, PCNA and BCLX were overexpressed in 90–65% of the primary tumors. FKBP1A is a receptor for rapamicin as well as for FK506, a powerful immunosuppressant in T-cells, and PCNA is involved in the control of eukaryotic DNA replication and may have a role in DNA repair synthesis. BCLX is involved in both positive and negative regulation of programmed cell death. Thus, these three genes, could be significant in the development of T-cell lymphomas. When the candidate genes located in amplicons of chromosome 2 and 8 were examined, we observed that IGFBP2 was only overexpressed in a low proportion of the primary tumors (15%) and cMYC is not highly expressed in any of them. Curiously, both genes were overexpressed in the three cell lines suggesting that they could be of relevance in the maintenance of the cell lines. Conclusions We have identified 3 amplicons by CGH in the T-cell line huT78. By cDNA microarray CGH. We have narrowed down the regions and selected some amplified and overexpressed genes: BCLX, PCNA and FKBP1A. These genes are also overexpressed in 65–90% of a set of 39 T-cell lymphomas and 3 cell lines, while IGFBP2 and cMYC are only overexpressed in T-cell lines. Our data suggest a different role of these genes in such processes. New studies with more clones covering these amplicons can help us to better identify relevant genes in T-cell lymphomas. Authors' contributions BM carried out the genomic and RNA hybridizations of the cell line onto the microarrays, performed FISH experiments, analyzed the results and drafted the manuscript. BM-D participated in the design of the study and in the analysis and discussion of the results. MC performed the amplification of the RNA and hybridization onto the microarray to carry out expression studies of primary tumors. VF performed nucleic acid extractions for hybridizations onto the microarray, grew and label the BAC clones for FISH assays and performed CGH. RD-U carried out statistical analysis of the microarray results. JB conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript. Supplementary Material Additional File 1 This file contains row genomic and expression data for each gene. Each gene is identified by a unique clone identification (Clone ID), the corresponding gene symbol (Gene Symbol) and the Unigene number (Unigene ID). Localization of all clones are also shown (map positions of the cDNA clones were obtained from the EnsEMBL database, as referred in the text). Click here for file Acknowledgements We thank Amanda Wren for her technical assistance. This work was partially supported by the Comunidad Autonoma de Madrid grants CAM 08.6/0005.1/2001 and CAM 08.1/0020.1/00. RD-U was partially supported by a Ramón y Cajal programme from the Spanish Ministry of Science (MCyT) and by Project TIC2003-09331-C02-02 of the Spanish MCyT.MC is a fellow of the Colegio de Farmaceuticos. ==== Refs Knuutila S Bjorkqvist AM Autio K Tarkkanen M Wolf M Monni O Szymanska J Larramendy ML Tapper J Pere H El-Rifai W Hemmer S Wasenius VM Vidgren V Zhu Y DNA copy number amplifications in human neoplasms: review of comparative genomic hybridization studies Am J Pathol 1998 152 1107 1123 9588877 Knuutila S Aalto Y Autio K Bjorkqvist AM El-Rifai W Hemmer S Huhta T Kettunen E Kiuru-Kuhlefelt S Larramendy ML Lushnikova T Monni O Pere H Tapper J Tarkkanen M Varis A Wasenius VM Wolf M Zhu Y DNA copy number losses in human neoplasms Am J Pathol 1999 155 683 694 10487825 Knuutila S Autio K Aalto Y Online access to CGH data of DNA sequence copy number changes Am J Pathol 2000 157 689 10934171 Kauraniemi P Barlund M Monni O Kallioniemi A New amplified and highly expressed genes discovered in the ERBB2 amplicon in breast cancer by cDNA microarrays Cancer Res 2001 61 8235 8240 11719455 Monni O Barlund M Mousses S Kononen J Sauter G Heiskanen M Paavola P Avela K Chen Y Bittner ML Kallioniemi A Comprehensive copy number and gene expression profiling of the 17q23 amplicon in human breast cancer Proc Natl Acad Sci U S A 2001 98 5711 5716 11331760 10.1073/pnas.091582298 Hyman E Kauraniemi P Hautaniemi S Wolf M Mousses S Rozenblum E Ringner M Sauter G Monni O Elkahloun A Kallioniemi OP Kallioniemi A Impact of DNA amplification on gene expression patterns in breast cancer Cancer Res 2002 62 6240 6245 12414653 Pollack JR Sorlie T Perou CM Rees CA Jeffrey SS Lonning PE Tibshirani R Botstein D Borresen-Dale AL Brown PO Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors Proc Natl Acad Sci U S A 2002 99 12963 12968 12297621 10.1073/pnas.162471999 Pollack JR Iyer VR Characterizing the physical genome Nat Genet 2002 32 Suppl 515 521 12454647 10.1038/ng1035 Wolf M Mousses S Hautaniemi S Karhu R Huusko P Allinen M Elkahloun A Monni O Chen Y Kallioniemi A Kallioniemi OP High-resolution analysis of gene copy number alterations in human prostate cancer using CGH on cDNA microarrays: impact of copy number on gene expression Neoplasia 2004 6 240 247 15153336 Beheshti B Braude I Marrano P Thorner P Zielenska M Squire JA Chromosomal localization of DNA amplifications in neuroblastoma tumors using cDNA microarray comparative genomic hybridization Neoplasia 2003 5 53 62 12659670 Chen Q-R Bilke S Wei JS Whiteford CC Cenacchi N Krasnoselsky AL Greer BT Son C-G Westermann F Berthold F Schwab M Catchpoole D Khan J cDNA array-CGH profiling identifies genomic alterations specific to stage and MYCN-amplification in neuroblastoma BMC Genomics 2004 5 70 15380028 10.1186/1471-2164-5-70 Renedo M Martinez-Delgado B Arranz E Garcia M Urioste M Martinez-Ramirez A Rivas C Cigudosa JC Benitez J Chromosomal changes pattern and gene amplification in T-cell non-Hodgkin's lymphomas Leukemia 2001 15 1627 1632 11587222 10.1038/sj.leu.2402248 Struski S Doco-Fenzy M Cornillet-Lefebvre P Compilation of published comparative genomic hybridization studies Cancer Genet Cytogenet 2002 135 63 90 12072205 10.1016/S0165-4608(01)00624-0 Kallioniemi OP Kallioniemi A Piper J Isola J Waldman FM Gray JW Pinkel D Optimizing comparative genomic hybridization for analysis of DNA sequence copy number changes in solid tumors Genes Chromosomes Cancer 1994 10 231 243 7522536 Pollack JR Perou CM Alizadeh AA Eisen MB Pergamenschikov A Williams CF Jeffrey SS Botstein D Brown PO Genome-wide analysis of DNA copy-number changes using cDNA microarrays Nat Genet 1999 23 41 46 10471496 10.1038/14385 Herrero J Díaz-Uriarte R Dopazo J Gene expression data preprocessing Bioinformatics 2003 19 655 656 12651726 10.1093/bioinformatics/btg040 Eberwine J Amplification of mRNA populations using aRNA generated from immobilized oligo(dT)-T7 primed cDNA Biotechniques 1996 20 584 591 8800675 Tracey L Villuendas R Ortiz P Dopazo A Spiteri I Lombardia L Rodriguez-Peralto JL Fernandez-Herrera J Hernandez A Fraga J Dominguez O Herrero J Alonso MA Dopazo J Piris MA Identification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma Am J Pathol 2002 161 1825 1837 12414529 Martinez Delgado B Melendez B Cuadros M Alvarez J Castrillo JM Mollejo M Bellas C Lombardía L Al-Shahrour F Dominguez O Cascon A Robledo M Rivas C Benitez J Expression profiling of T-cell lymphomas differentiates peripheral and lymphoblastic lymphomas and defines survival related genes Clin Cancer Res 2004 10 4971 4982 15297397 Beheshti B Park PC Braude I Squire JA Microarray CGH Methods Mol Biol 2002 204 191 207 12397799	15656903	PMC546232	CC BY	2021-01-04 16:36:36	no	Mol Cancer. 2005 Jan 18; 4:5	utf-8	Mol Cancer	2,005	10.1186/1476-4598-4-5	oa_comm
==== Front J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-3-31565924410.1186/1479-5876-3-3ResearchT cell responses against tumor associated antigens and prognosis in colorectal cancer patients Nagorsen Dirk [email protected] Carmen [email protected] Anne [email protected] Christoph-Thomas [email protected] Heinz-Johannes [email protected] Susanna [email protected] Licia [email protected] Eckhard [email protected] Ulrich [email protected] Medical Department III, Hematology, Oncology, and Transfusion Medicine, Charité University Medicine Berlin, Campus Benjamin Franklin, Berlin, Germany2 Department of Surgery, Charité University Medicine Berlin, Campus Benjamin Franklin, Berlin, Germany3 Department of Oncology and Hematology, University Clinic Eppendorf, Hamburg, Germany4 Istituto Nazionale Tumori, Milan, Italy2005 19 1 2005 3 3 3 22 11 2004 19 1 2005 Copyright © 2005 Nagorsen et al; licensee BioMed Central Ltd.2005Nagorsen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction Spontaneous T cell responses against specific tumor-associated antigens (TAA) are frequently detected in peripheral blood of tumor patients of various histiotypes. However, little is known about whether these circulating, spontaneously occurring, TAA-reactive T cells influence the clinical course of disease. Methods Fifty-four HLA-A2 positive colorectal cancer patients had been analyzed for the presence of T cell responses against epitopes derived from the TAA Ep-CAM, her-2/neu, and CEA either by ELISPOT assay or by intracellular cytokine staining. Then, Kaplan-Meier survival analysis was performed comparing T-cell-responders and T-cell-non-responders. For comparison, a group of T-cell-non-responders was compiled stringently matched to T-cell-responders based on clinical criteria and also analyzed for survival. Results Sixteen out of 54 patients had a detectable T cell response against at least one of the three tested TAA. Two out of 21 patients (9.5%) with limited stage of disease (UICC I and II) and 14 out of 33 patients (42.4%) with advanced disease (UICC III and IV) were T cell response positive. Comparing all T-cell-responders (n = 16) and all T-cell-non-responders (n = 38), no survival difference was found. In an attempt to reduce the influence of confounding clinical factors, we then compared 16 responders and 16 non-responders in a matched group survival analysis; and again no survival difference was found (p = 0.7). Conclusion In summary, we found no evidence that spontaneous peripheral T cell responses against HLA-A2-binding epitopes of CEA, her-2/neu and Ep-CAM are a strong prognostic factor for survival. T cell responseantigencolorectal cancersurvivalprognosis ==== Body Introduction The importance of the immune system in containing tumor growth is supported by animal studies and various observations in humans [1,2]. These include increased prevalence of certain tumors following immunosuppression as well as the demonstration, that the presence of intralesional T cells is correlated with improved clinical outcome in various solid tumors [1,3-6]. In particular in CRC, the presence of CD8+ T cells within the tumor microenvironment was significantly associated with a better survival in several studies [3,7-9]. However, the antigen-specificity of these cells was not determined. T cell responses against specific tumor-associated antigens (TAA) are frequently detected in the peripheral blood of tumor patients [reviewed in [10]] of various histiotypes including colorectal cancer [11], melanoma [12,13], acute myeloid leukemia [14], breast cancer [15], neuroblastoma [16], and head and neck cancer [17]. Data from selected single patients suggest a favorable clinical course in patients with peripheral, spontaneous TAA-directed T cells [18,19]. However, this type of analysis does not allow firm conclusions. The only study comparing clinical outcome of patients with presence of antigen-specific immune responses including natural as well as vaccine-induced antibodies against melanoma antigen GM2 showed an improved survival in favor of immune responders [20]. TAA-directed T cell responses can reliably be induced using various vaccination approaches [reviewed in [21]]. Several recent reports have found a correlation between induction of a TAA-directed T cell response by vaccination and clinical response [22-25]. Preliminary data also suggest a possibly favorable clinical effect of vaccine-induced T cells in adjuvant vaccination [26-29]. Taken together, these data lead to the question, whether the presence of spontaneous TAA-specific T cells might be a positive prognostic factor. So far, however, no study has systematically compared survival data of patients with and without presence of a spontaneous TAA-directed T cell response. In previous studies, we have demonstrated spontaneous T cell responses against the TAA CEA, Ep-CAM, or her-2/neu in peripheral blood of approximately 25% of colorectal cancer patients [11,30]. These cells were identified in functional T cell assays by antigen-induced IFNγ production. More detailed analyses in some samples revealed a CD3+ CD8+ IFNγ+ CD69+ CD45RA+ phenotype [11], indicative of an effector T cell subset that is able to directly mediate tumor cell lysis [19]. Spontaneous TAA-specific T cells with the potential of effector cells should, theoretically, be capable of destroying tumor cells and thereby lead to elimination of residual disease or prevent tumor progression. To investigate whether a peripheral, spontaneous T cell response has an effect on the clinical outcome of tumor patients, we analyzed survival data of CRC patients with a TAA-directed T cell response and compared these data with the clinical course of CRC patients without detectable T cell response. Patients, materials, and methods Patient selection and T cell assays After institutional review board approval and informed consent, peripheral blood mononuclear cells from 132 patients with CRC in all stages of disease had been prospectively collected and frozen for T cell analysis. All analyses have been performed in compliance with the Helsinki Declaration. Fifty-four patients were tested positive for HLA-A2 and were subsequently analyzed for the presence of T cell responses against the HLA-A0201 presented T cell epitopes Ep-CAM p263–271 [31], her-2/neu p654–662 [32,33], and CEA p571–579 [34] either by ELISPOT assay or by intracellular cytokine staining. HLA analysis, ELISPOT, and intracellular cytokine staining were performed as previously described [11,30]. Positive responses were defined as previously described [11,30]. Survival analysis First, we performed a Kaplan-Meier survival analysis comparing all T-cell-responders and all T-cell-non-responders. Additionally, a two-sided log rank test was used to test statistical significance. Then, in an attempt to reduce the influence of external factors, we compiled a patient group from non-responders matching them to responders according to the following criteria: UICC stage of disease, gender, presence of clinically detectable tumor at time of blood draw, duration of disease until blood draw, age at first diagnosis, and previous therapy. Survival in both groups was compared using Kaplan-Meier analysis and by a two-sided log rank test. A level of p < 0.05 was considered significant. SPSS (11.5) software was used. Results Patients, T cell response, survival of responders and non-responders Fifty-four HLA-A2 positive CRC patients who had been analyzed for T cell responses were included in this study [11,30] and retrospectively analyzed for survival. The overall survival rate was 66.7% at a median of 27.5 months follow-up after blood draw for T cell analysis. In 16 out of 54 patients a total number of 26 T cell responses (between 10 and 1110 specific T cell per 106 PBMC) against one of the three TAA Ep-CAM, her-2/neu, and CEA had been detected. Comparing all responders (n = 16) and all non-responders (n = 38), no survival difference was found in a two-sided log rank test (p = 0.4; Fig. 1A). A very slight trend toward better survival was found in favor of non-responders, which is, however, far from being statistically significant. Since we have previously observed that T cell responses occur more frequently in patients with advanced stages of disease, we compared clinical data for all patients; and found that among non-responders only 50% (19 out of 38) had clinical stage III or IV disease while 87.5% (14 out of 16) responding patients had stage III or IV disease. Thus, the data on survival are possibly based on a strong stage-related bias. Therefore, we subsequently used an approach matching non-responders to responders. Figure 1 Kaplan-Meier survival analyses of colorectal cancer patients based on their T cell response state. Dashed lines refer to T-cell-responders, full lines refer to T-cell-non-responders, crosses mark censored cases. Time point 0 refers to the time of blood draw for T cell analysis. A. Two groups of CRC patients (total n = 54) were analyzed for survival after T cell analysis. One group had a spontaneous T cell response against the tumor associated antigens CEA, Ep-CAM, or her-2/neu (n = 16, dashed line). The other group had no T cell response against these antigens (n = 38, full line). No survival difference between the groups was found (log rank, p = 0.4). B. Two matched groups of CRC patients (total n = 32) were analyzed for survival after T cell analysis. One group had a spontaneous T cell response against tumor antigens CEA, Ep-CAM, or her-2/neu (n = 16, dashed line). The other group had no T cell response against these antigens and was selected by having similar clinical characteristics for stage, gender, presence of clinically detectable tumor at time of blood draw, duration of disease, age, and prior therapy (n = 16, full line). No survival difference was found (log rank, p = 0.7). Survival in matched patient groups Sixteen patients from the above group of 38 non-responders were matched with 16 responders to obtain two groups comparable for potentially confounding clinical factors, in particular stage of disease (see table 1). At the time of the survival analysis 13 of the total of 32 (40.6%) patients had died: seven T-cell-responders (n = 1 stage III, n = 6 stage IV) and six T-cell-non-responders (n = 1 stage III, n = 5 stage IV). All deaths were CRC-related. Median time to death among T-cell-responders was 11 months, among T-cell-non-responders 14.5 months. The calculated mean survival time after blood draw for patients without T cell response was 37.0 months (± 4.8 SEM) with a 95% confidence interval 27.5–46.5. Mean survival of T-cell-responders was 40.2 months (± 6.5 SEM) with 95% confidence interval of 27.5–52.9 (Fig. 1B). In a two-sided log rank test, survival did not show a statistically significant difference between responders and non-responders (p = 0.7). Of note, one to two years after blood draw, patients without T cell response had an up to 20% higher survival rate (approx. 80% vs. approx 60%). These results were, however, not significant. In a two-sided test with β = 0.2, a survival difference of 70% could have been considered significant at a level of α = 0.05 in a population of this size. Table 1 Patient characteristics Peripheral spontaneous T cell response against TAA Positive Negative (matched) Negative (total) Number of patients 16 16 38 Age at first diagnosis, mean ± SD 61.2 ± 12.1 59.9 ± 9.1 60.8 ± 11.1 Male/Female 11/5 11/5 19/19 Duration of disease until T cell analysis (mean) 31.8 30.2 27.4 Stage (UICC) I 0 0 1 (2.6%) II 2 (12.5%) 2 (12.5%) 18 (47.4%) III 3 (18.8%) 3 (18.8%) 5 (13.2%) IV 11 (68.8%) 11 (68.8%) 14 (36.8%) Previous therapy CT 9 (56.3%) 12 (75%) 18 (47.4%) RT 3 (18.8%) 4 (25%) 7 (18.4%) Surgery 12 (75%) 14 (87.5%) 30 (78.9%) Patients without evidence of tumor at time of blood draw 4 (25%, 2 II, 2 III) 4 (25%, 2 II, 2 III) 17 (45%, 13 II, 3 III, 1 IV) Time from blood draw until death or last contact, mean ± SD 28.1 ± 19.2 27.4 ± 15.0 25.7 ± 14.0 Abbreviations: SD standard deviation, CT chemotherapy (5-FU + folic acid, in few cases plus Irinotecan), RT radiotherapy Discussion In the present study, we analyzed the clinical course of colorectal cancer patients with or without T cells reactive against HLA-A2-binding epitopes of Ep-Cam, her-2/neu, and CEA. No survival difference between T-cell-responders and T-cell-non-responders was found. This result has to be interpreted cautiously due to the small number of HLA-A2+ patients responding to the above antigens. Obviously, these small numbers cause a high beta error. Thus, this study is only a first indication that the tested spontaneous T cell responses are not important prognostic factors for survival. The second limitation of our study is that the known repertoire of TAA as potentially important T cell targets in CRC grows every year; and our T cell analysis included only a fraction of potential epitopes. Various other CRC-associated antigens, such as MUC1 or p53, and additional MHC class I antigenic epitopes of CEA and her2-neu have been described [summarized in [35]]. T cell responses against antigens in addition to the ones tested here could potentially play a role in immune surveillance of CRC. Immune surveillance is understood as a complex process in which T cells and tumor cells influence each other in several ways ["immunoediting", [1]]. There are various potential factors related to tumor cells as well as T cells which may explain a lack of survival effect by TAA-specific T cells. The frequency of TAA-specific T cells detected in most patients was quite low in the range of 10 to 100 T cells per 106 PBMC. These numbers may be too low to control tumor growth especially in patients with a higher tumor burden. Furthermore, a general T cell dysfunction including anergic T cells and T cells with downregulated CD3-zeta chains has been described in CRC patients [36,37]. It is possible that the specific T cells detected in the present study are functionally unable to destroy tumor cells. This assumption, however, is not supported by our previous finding that TAA-specific T cells have an effector potential as analyzed in selected patients [11]. Since we have analyzed peripheral blood, we do not know if the circulating T cells have the potential to migrate to the tumor site or compartments where CRC cells frequently migrate to including lymph node, liver and bone marrow. Furthermore, tumor cells may not be recognizable by TAA-specific T cells. It has been shown that CRC cell lines secrete immunosuppressive cytokines and that development of T cell responses is impeded due to low HLA expression and lack of intercellular adhesion molecule-1 (ICAM-1) and HLA-DR [38,39]. This is especially relevant considering the fact that TAA-specific T cell responses in peripheral blood are more frequently detectable in advanced stages of CRC [11,40], as well as other tumors [40,41]. These data led to the hypothesis that metastasizing of tumor cells to lymph nodes is a prerequisite for the development of circulating T cell responses [11,42]. Furthermore, the presence of TAA-directed T cell responses may have selected immune escape tumor variants. A broad variety of tumor escape mechanisms, such as antigen loss or loss of HLA expression, is described in various clinical conditions [43]. It is possible that we encounter similar mechanisms in the present study since malignant cells had grown in vivo during the presence of specific T cell responses. Finally, the role of suppressor and regulatory T cells in this specific context is unknown. Taken together, no evidence was found that peripheral, spontaneous T cell responses against HLA-A0201-binding epitopes of CEA, Ep-CAM, or her-2/neu influence survival of CRC patients. Since the low patient number limits the conclusion, further studies should investigate more patients, more detailed function and migratory pattern of spontaneous T cell responses as well as the genetic profile of the tumor; and consider a broader antigen and epitope repertoire. These studies could have implications for vaccination therapy as we learn more about why immune surveillance may fail to control tumors and if the presence of a natural T cell response may impact on the efficacy of a vaccine. Abbreviations CEA carcinoembryonic antigen, CRC, colorectal carcinoma; ELISPOT, enzyme-linked immunospot; IFNγ, Interferon-γ; HLA, human leukocyte antigen; PBMC, peripheral blood mononuclear cells, TAA, tumor associated antigen. Competing interests The author(s) declare that they have no competing interests. Acknowledgments We thank Dr. Dr. W. Hopfenmüller and Prof. Dr. P. Martus, both at the Institute for Medical Informatics, Biometry and Epidemiology, Charité University Medicine Berlin, Germany, for statistical advice. 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Adv Immunol 2000 74 181 273 10605607	15659244	PMC546233	CC BY	2021-01-04 16:39:28	no	J Transl Med. 2005 Jan 19; 3:3	utf-8	J Transl Med	2,005	10.1186/1479-5876-3-3	oa_comm
==== Front Theor Biol Med ModelTheoretical Biology & Medical Modelling1742-4682BioMed Central London 1742-4682-2-11564413510.1186/1742-4682-2-1ResearchHuman embryonal epithelial cells of the developing small intestinal crypts can express the Hodgkin-cell associated antigen Ki-1 (CD30) in spontaneous abortions during the first trimester of gestation Tamiolakis Demetrio [email protected] John [email protected] Maria [email protected] Silva [email protected] Sophia [email protected] Maria [email protected] Dionysios [email protected] Panagiotis [email protected] Athanasios [email protected] Nikolas [email protected] Department of Cytology, General Hospital of Chania, Crete, Greece2 Department of Pathology, Ippokration Hospital of Salonica, Greece3 Department of Histology – Embryology, Democritus University of Thrace, Greece4 Department of Obstetrics and Gynecology, Democritus University of Thrace, Greece2005 11 1 2005 2 1 1 20 9 2004 11 1 2005 Copyright © 2005 Tamiolakis et al; licensee BioMed Central Ltd.2005Tamiolakis et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Ki-1 (CD30) antigen expression is not found on peripheral blood cells but its expression can be induced in vitro on T and B lymphocytes by viruses and lectins. Expression of CD30 in normal tissues is very limited, being restricted mainly to a subpopulation of large lymphoid cells; in particular, cells of the recently described anaplastic large cell lymphoma (ALCL), the Reed-Sternberg (RS) cells of Hodgkin's lymphoma and scattered large parafollicular cells in normal lymphoid tissues. More recent reports have described CD30 expression in non-hematopoietic and malignant cells such as cultured human macrophages, human decidual cells, histiocytic neoplastic cells, mesothelioma cells, embryonal carcinoma and seminoma cells. Results We investigated the immunohistochemical expression of CD30 antigen in 15 paraffin-embedded tissue samples representing small intestines from fetuses after spontaneous abortion in the 8th, 10th and 12th weeks using the monoclonal antibody Ki-1. Hormones had been administered to all our pregnant women to support gestation. In addition, a panel of monoclonal antibodies was used to identify leukocytes (CD45/LCA), B-lymphocytes (CD20/L-26) and T-lymphocytes (CD3). Our findings were correlated with those obtained simultaneously from intestinal tissue samples obtained from 15 fetuses after therapeutic or voluntary abortions. Conclusions The results showed that: (1) epithelial cells in the developing intestinal crypts express the CD30 (Ki-1) antigen; (2) CD30 expression in these epithelial cells is higher in cases of hormonal administration than in normal gestation. In the former cases (hormonal support of gestation) a mild mononuclear intraepithelial infiltrate composed of CD3 (T-marker)-positive cells accompanies the CD30-positive cells. CD30 (Ki-1) antigenhuman intestinal cellsspontaneous abortionsvoluntary or therapeutic abortionsfirst trimester of gestation. ==== Body Introduction CD30 antigen, a member of the tumor necrosis factor (TNF) receptor superfamily [1-3], was originally identified as a cell surface antigen on primary and cultured Hodgkin's and Reed-Sternberg cells by use of the monoclonal antibody Ki-1 [4,5]. CD30 antigen is normally expressed by a subset (15–20%) of CD3+ T cells after activation by various stimuli [6]. Its expression is stimulated by interleukin (IL)-4 during lineage commitment of naïve human T cells [7,8] and is augmented by the presence of CD28 co-stimulatory signals [9]. CD30 also is expressed at variable levels in different non-Hodgkin's lymphomas (NHL) as well as in several virally transformed T and B cell lines [5,10]. In particular, CD30 is a specific marker of a subset of peripheral T cell NHLs known as anaplastic large cell lymphomas (ALCL) [5]. More recently, preferential CD30 expression has been detected on a subset of tissue and circulating CD4+ and CD8+ T cells producing mainly Th2 cytokines in immunoreactive conditions [11-14]. CD30 appears to have an important immunoregulatory role in normal T cell development. Within the thymus, CD30L is highly expressed on medullary thymic epithelial cells and on Hassal's corpuscles [15]. Pallesen and Hamilton-Dutoir [16] were the first to report CD30 expression outside lymphoid tissue in 12 out of 14 cases of primary or metastatic embryonal carcinoma (EC) of the testis, using immunostaining with the monoclonal antibodies (MAbs) Ber-H2 and Ki-1. Subsequently, several investigators have confirmed their results and have detected CD30 in these carcinomas at the protein [17-20] and the mRNA [10] level. Two reports demonstrated CD30 expression in 4/21 and 4/63 cases of testicular and mediastinal seminoma [21] and in the seminomatous components of 7/14 cases of mixed germ cell tumours of the testis [22]. Suster et al. detected the CD30 antigen in 6/25 yolk sac tumours of the testis and mediastinum [22]. CD30 expression has also been reported in other non-lymphoid tissues and cells such as soft tissue tumours [23], decidual cells [24,25], lipoblasts [26], myoepithelial cells [27], reactive and neoplastic vascular lesions [28], mesotheliomas [29], cultivated macrophages, and two histiocytic malignancies [30]. Primitive crypts (epithelial downgrowths into the mesenchyme between the small intestinal villi), appear in the postpharyngeal foregut between the 9th and 12th weeks of embryo development. Goblet cells are present in small numbers after 8 weeks, Paneth cells differentiate at the base of the crypts in weeks 11 and 12, and enteroendocrine cells appear between weeks 9 and 11. The fact that the CD30 molecule can mediate signals for cell proliferation or apoptosis [2] prompted us to perform a systematic investigation of CD30 antigen expression in non-hematopoietic embryonal tissues during the proliferation and differentiation stages, beginning with the epithelial cells of the developing intestinal crypts. Materials and methods Samples representing 15 small intestines from fetuses after spontaneous (involuntary) abortion occurring in pregnant women treated with progesterone (300–600 mg per day until the 12th gestational week), and 15 small intestines from fetuses after therapeutic or voluntary abortion, were obtained in the 8th, 10th and 12th weeks of gestation. The Regional Ethics Committees approved the study. Written informed consent was obtained from all individuals and the procedures followed accorded with institutional guidelines. Small intestines were cut in 3 mm slices and fixed in 10% neutral buffered formaldehyde at 4°C for 24 h, then processed for routine paraffin embedding. Paraffin blocks were available in all cases, and 3 μm thick tissue sections were stained routinely with hematoxylin-eosin, PAS and Giemsa, and subsequently by immunohistochemistry. Immunoperoxidase labeling was performed as follows: sections were deparaffinized in 70% alcohol and endogenous peroxidase was blocked with 3% H2O2 in methanol. The sections were preincubated in 20% serum of the species from which the secondary antibody was raised, and the primary antibody was applied. After overnight incubation at room temperature, the secondary biotinylated antibody was applied for 30 min. Staining was visualized with a Vector Elite System (Vector Laboratories, Burlingame, CA) using diaminobenzidine as the chromogen. The sections were counterstained with dilute hematoxylin. The primary antibodies used were as follows: (CD30/Ki-1) activated lymphoid cells, mouse monoclonal antibody (Novocastra); (CD45/LCA) leukocyte common antigen, mouse monoclonal antibody (Dako); (CD20/L-26) B-lymphocytes, mouse monoclonal antibody (Dako); and (CD3) T-lymphocytes, mouse monoclonal antibody (Dako). We used the high temperature antigen unmasking technique for immunohistochemical demonstration of CD30/Ki-1 on paraffin sections (Novocastra). Control slides were incubated with nonimmunized rabbit serum. An anaplastic lymphoma case-slide (positive control) was run in parallel with the assay. Analysis of CD30/Ki-1 positive cryptae cells For each sample, the CD30/Ki-1 positive population was assessed by enumeration of labeled cells in each tissue compartment for a minimum of five random fields per section viewed at 40-fold magnification through a grid. Cell numbers were calculated per mm2 of tissue section. The counted areas were selected from random tissue sections, taking into account that the ratio of the area of the intestinal stroma to the area of surface epithelium covering the crypts was representative of the entire field. Areas with obvious necrosis or haemorrhages were excluded. Statistical analysis was performed using the ANOVA test. Results Five microscopic fields of the small intestines were evaluated in each case without knowledge of the clinical data (TABLE 1). Two observers examined the sections independently, and positive cellular staining for each antibody was manifested as fine brown cytoplasmic granularity and/or surface membrane expression. Table 1 Expresion of CD30 antigen in fetal intestinal cryptae cells during the first trimester of gestation. Spontaneous abortions Voluntary abortions 8th week 10th week statistics 8th week 10th week statistics CD30(+)cells/mm2 3.61+/0.16 5.27+/-0.19 p < 0.0001 3.42+/-0.17 3.43+/-0.18 p = 0.95 8th week 12th week statistics 8th week 12th week statistics CD30(+)cells/mm2 3.61+/-0.16 5.34+/-0.23 p < 0.0001 3.42+/-0.17 3.41+/-0.17 p = 0.95 8th week of gestation In cases of spontaneous (involuntary) abortion, immunohistochemistry revealed small clusters or scattered, large-sized CD30/Ki-1 positive cryptae cells within the intestine in all settings examined (Fig. 1), with percentages varying from 3.2 to 3.9 (mean ± sd = 3.61 ± 0.16). In the neighbouring intestinal stroma a slight cellular infiltration was observed, consisting of rounded mononuclear cells approximately 10 μm in diameter with eccentric kidney-shaped nuclei and expressing a CD45/LCA and CD3 phenotype. In cases of voluntary or therapeutic abortion, immunohistochemistry showed a smaller number of large-sized CD30/Ki-1 positive cryptae cells in all settings examined (Fig. 2), with percentages varying from 3.1 to 3.7 (mean ± sd = 3.42 ± 0.17). No inflammatory infiltrates or necrosis were noted in the neighbouring intestinal stroma. Figure 1 8th week of gestation (involuntary abortions). Ki-1 (CD30) antigen is expressed by a small number of epithelial cryptae cells. Immunohistochemical stain X 400. Figure 2 8th week of gestation (voluntary abortions). Weak to moderate expression of Ki-1 (CD30) antigen in the developing crypts. Immunohistochemical stain X 400. 10th week of gestation In cases of spontaneous abortion, immunohistochemistry showed a higher number of positive CD30/Ki-1 cryptae cells than at the 8th week of gestation (Fig. 3), with percentages varying from 4.9 to 5.6 (mean ± sd = 5.27 ± 0.19). There were very few inflammatory infiltrates in the intestinal stroma expressing the phenotype CD45/LCA and CD3. In cases of voluntary or therapeutic abortion, the frequency of CD30/Ki-1 positive cryptae cells was similar to that at the 8th week of gestation, with percentages varying from 3.2 to 3.9 (mean ± sd = 3.43 ± 0.18). No inflammatory infiltrates or necrosis were noted in the neighbouring intestinal stroma. Figure 3 10th week of gestation (involuntary abortions). Strong expression of Ki-1 (CD30) antigen in the developing crypts. Immunohistochemical stain X 400 12th week of gestation In spontaneous abortion cases the number of CD30/Ki-1 positive cryptae cells was even higher than at 10th week, with percentages varying from 4.8 to 5.7 (mean ± sd = 5.34 ± 0.23). The number in cases of voluntary or therapeutic abortions was more or less the same as at 8th and 10th weeks, with percentages varying from 3.2 to 3.7 (mean ± sd = 3.41 ± 0.17). No differences in immune reaction were noted in the neighbouring intestinal stroma in cases of either spontaneous or voluntary/therapeutic abortion in comparison to the 8th and 10th gestational weeks. The differences among the numbers of CD30/Ki-1 positive cells at the 8th, 10th and 12th gestational week after spontaneous abortion were statistically significant (p < 0.0001). No significant differences were observed in the numbers of these cells after voluntary or therapeutic abortions (p = 0.95). Discussion The value of the CD30 antigen as a diagnostic marker for Hodgkin's lymphoma and anaplastic large cell lymphoma is well documented [4,5,31]. However, the function of this cytokine receptor in Hodgkin's lymphoma and other CD30-positive diseases is still not clear. CD30 appears to have an important immunoregulatory role in normal T cell development. In normal cells, this transmembrane glycoprotein can be induced on B and T lymphocytes by mitogen stimulation or viral transformation [32-34]. cDNA cloning has revealed that the CD30 protein is a cytokine receptor of the tumor necrosis factor receptor superfamily [1,35], the ligand of which belongs to the tumor necrosis factor family [22,23]. Recent in vitro data indicate that the CD30 receptor-ligand complex can mediate signals for cell proliferation, apoptosis and cytotoxicity in lymphoid cells [20,36,37]. Our results give the first indication that the CD30 antigen is expressed in the epithelial cells of developing intestinal crypts. This observation has a number of important implications. First, our findings are of significance with regard to the accepted origin of R-S cells. Care must be taken when drawing histogenetic conclusions based on the identification of a single marker in different cell types. Shared expression of CD30 antigen does not necessarily relate Hodgkin and R-S cells to activated lymphocytes. The identification of this antigen in cells as apparently disparate as activated lymphocytes, R-S cells and now human epithelial cells of the developing fetal intestinal crypts suggests that previous views about the nature of the Ki-1 antigen must be re-examined. The Hodgkin and Reed-Sternberg cells are indeed lymphocytes as they harbor rearranged immunoglobulin (in more than 90% of cases) and T cell receptors [38]. Although the expression of CD30 antigen may indicate a relationship between these cell types, it is likely to be less straightforward than was previously supposed. Identification of the normal physiological role of CD30 antigen is thus made even more imperative if these relationships are to be understood. Second, these findings indicate that outside the lymphatic system, CD30 antigen expression in the epithelial cells of developing intestinal crypts can mediate signals for cell proliferation and differentiation in a region where other cell types (stem, goblet, Paneth and enteroendocrine) grow throughout life. Third, CD30 expression in the epithelial cells of the developing intestinal crypts is induced by progesterone. This is a novel mechanism of CD30 induction, distinct from neoplastic transformation and viral infection of lymphocytes. The demonstration of large R-S like cells in the developing crypts within a lymphoplasmacytic infiltrate, in the same way that similar R-S like cells are observed in reactive lymph nodes, especially within the parafollicular areas, is evidence that such cells might represent the physiological counterparts of R-S cells. The possibility that CD30 is an oncofetal antigen is supported by our positive findings in fetal intestinal cryptae cells. We have so far been able to investigate only a single tissue from a small number of fetuses of early gestational age. Pallesen and Hamilton-Dutoit [16] examined CD30 expression in normal adult, neonatal and fetal (week 28) testes, as well as other tissues (brain, spinal cord, lung, gut, kidney, erythropoietic tissue, muscle, bone and connective tissue) from fetuses of 11 and 12 weeks gestational age, with negative results. This is the first demonstration of CD30 in epithelial cells in fetal tissue. Although our results require confirmation from frozen sections, they – together with a reported positive staining in placenta [24,25] – suggest that the antigen is expressed by proliferating and differentiating epithelial cells of other than lymphoid origin. Clearly the extent of expression of CD30 antigen in embryonal tissues warrants further investigation. ==== Refs Durkop ABC Latza U Hummel M Eitelbach F Seed B Stein H Molecular cloning and expression of a new member of the nerve growth factor receptor family that is characteristic for Hodgkin's disease Cell 1992 68 421 427 1310894 10.1016/0092-8674(92)90180-K Smith CA Gruss HJ Davis T Anderson D Farrah T Baker E Sutherland GR Brannan CI Copeland NG Jenkins NA Grabstein KH Gliniak B McAlister IB Fanslow W Alderson M Falk B Gimpel S Gillis S Din WS Goodwin RG Armitage RJ CD30 antigen, a marker for Hodgkin's lymphoma, is a receptor whose ligand defines an emerging family of cytokines with homology to TNF Cell 1993 73 1349 1360 8391931 10.1016/0092-8674(93)90361-S Smith CA Farrah T Goodwin RG The TNF receptor superfamily of cellular and viral proteins: activation, costimulation, and death Cell 1994 76 959 962 8137429 10.1016/0092-8674(94)90372-7 Schwab U Stein H Gerdes J Lemke H Kirchner J 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==== Front Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-4-21563163010.1186/1475-925X-4-2ResearchA finite element method model to simulate laser interstitial thermo therapy in anatomical inhomogeneous regions Mohammed Yassene [email protected] Janko F [email protected] Department of Medical Informatics, University of Goettingen, Robert-Koch-Str. 40, D-37075-Goettingen, Germany2 Department of Sciences and Technology, University of Applied Sciences and Arts, von-Ossietzky-Str. 99, D-37085-Goettingen, Germany2005 4 1 2005 4 2 2 2 11 2004 4 1 2005 Copyright © 2005 Mohammed and Verhey; licensee BioMed Central Ltd.2005Mohammed and Verhey; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Laser Interstitial ThermoTherapy (LITT) is a well established surgical method. The use of LITT is so far limited to homogeneous tissues, e.g. the liver. One of the reasons is the limited capability of existing treatment planning models to calculate accurately the damage zone. The treatment planning in inhomogeneous tissues, especially of regions near main vessels, poses still a challenge. In order to extend the application of LITT to a wider range of anatomical regions new simulation methods are needed. The model described with this article enables efficient simulation for predicting damaged tissue as a basis for a future laser-surgical planning system. Previously we described the dependency of the model on geometry. With the presented paper including two video files we focus on the methodological, physical and mathematical background of the model. Methods In contrast to previous simulation attempts, our model is based on finite element method (FEM). We propose the use of LITT, in sensitive areas such as the neck region to treat tumours in lymph node with dimensions of 0.5 cm – 2 cm in diameter near the carotid artery. Our model is based on calculations describing the light distribution using the diffusion approximation of the transport theory; the temperature rise using the bioheat equation, including the effect of microperfusion in tissue to determine the extent of thermal damage; and the dependency of thermal and optical properties on the temperature and the injury. Injury is estimated using a damage integral. To check our model we performed a first in vitro experiment on porcine muscle tissue. Results We performed the derivation of the geometry from 3D ultrasound data and show for this proposed geometry the energy distribution, the heat elevation, and the damage zone. Further on, we perform a comparison with the in-vitro experiment. The calculation shows an error of 5% in the x-axis parallel to the blood vessel. Conclusions The FEM technique proposed can overcome limitations of other methods and enables an efficient simulation for predicting the damage zone induced using LITT. Our calculations show clearly that major vessels would not be damaged. The area/volume of the damaged zone calculated from both simulation and in-vitro experiment fits well and the deviation is small. One of the main reasons for the deviation is the lack of accurate values of the tissue optical properties. In further experiments this needs to be validated. LITTlaser induced thermotherapysimulationtemperature calculationperfusionheat distributionlight diffusiondamage functionvesseltissuehead-neck-regionminimal invasive therapytherapy planningtumour treatment ==== Body Introduction Laser radiation is now used routinely in surgery to incise, coagulate, or vaporize tissues. The laser light power is converted into heat in the target volume with ensuing coagulative necrosis, secondary degeneration and atrophy, and tumour shrinkage with minimal damage to surrounding structures [1]. The use of lasers in surgery introduces some desirable features over normal surgical methods such as increased precision, improved hemostasis, and less tissue manipulation. Laser light power is thereby delivered to the targeted area by an optical fibre. The use of an optical fibre as applicator for interstitial light delivery was demonstrated, among others, by Bown [2] in 1983 as a method of heating and destroying deep-seated tumours [3]. The biological effects of laser energy depend on the laser wavelength, laser power, the duration of irradiance, blood perfusion, and both the optical and thermal properties of the tissue involved. Laser-tissue interaction mechanisms may be thermal, photochemical, or mechanical in nature [4]. Photochemical like PhotoDynamic Therapy (PDT). Mechanical like the effects induced using pulsed lasers (photoacoustic, photodisruptive). The surgical procedures that involve coagulation or ablation of tissue are thermal. Clinical studies have yet to demonstrate that LITT is practical for the palliation of hepatic and nasopharyngeal tumours (e.g. [5-9]). The criteria for the clinical success of the thermotherapy for tumours in homogeneous tissues, for example, in the liver or brain, was described, among others, by Vogl [3], who placed applicator/diffuser at the centre of the tumour, using MRI online monitoring of the thermal changes to control the treatment [6]. In contrast to homogeneous and simple tissues, however, normal anatomical structures are more complicated. Especially for small tumours near main vessels a positioning of the laser applicator at the centre of the lesion is difficult and maybe not the optimum choice. Modelling the laser-tissue interaction is beneficial for the analysis and optimisation of the parameters governing planned laser surgical procedures. Nevertheless, we still lack an adequate model that grants accuracy. Most of the models suggested depend greatly on simplifications of the real problem, either in the geometry they offer or in the system of equations they use. Some models, which use the bioheat equation, neglect the role of the changes in the tissue properties during temperature elevation process [10], which deem such a model unrealistic, especially considering high temperatures. Few modelling methods have simulated the behaviour of LITT in human tissue. Best known is the Monte-Carlo method described, among others, by Roggan et al. [11]. This method can simulate the use of multiple applicators but is limited to symmetric geometries and has not been correlated to real anatomic datasets. Similar to this is the finite difference method described in [12], where the authors did not include the coagulation process with its irreversible changes in optothermal tissue properties. In order to overcome these limitations, we included the damage function as well as perfusion terms in the modelling process, taking dependencies of these parameters into account. This paper describes in detail the bases for a modelling method to simulate the effect of LITT for the treatment in various indications near large vessels, such as the carotid artery in the neck region. We thereby propose the use of LITT, frequently applied in the treatment of liver tumours [6], in more sensitive areas such as the neck region to treat tumours in lymph node metastases or epithelial carcinomas with dimensions of 0.5 cm – 2 cm in diameter. The actual response of tissue to laser irradiation is a time-dependent phenomenon. Initially, there are thermal and possibly photochemical changes of the tissue at the molecular level. Next are changes in tissue perfusion caused by thermally induced vascular relaxation and/or vessel damage. Heat deposited at the application site is transferred to adjacent structures. This may be desirable for coagulation purposes – or it may cause unexpected thermal damage to otherwise viable tissues adjacent to the irradiation site. The rate of heat transfer depends on the composition and organization of tissues involved. Blood perfusion during and after irradiation has significant effects on the size of the damage zone. We discuss in this paper our mathematical approach, its considerations and restrictions. In the main part we present the mathematical and physical backgrounds used to achieve the model. Then we present and discuss the results of our simulation in comparison with the results of our in-vitro experiment. Materials and methods Our model of LITT considers both optical and thermal effects. It is based on calculations describing the light distribution using the diffusion approximation of the transport theory; the temperature rise using the bioheat equation, including the effect of microperfusion in tissue to determine the extent of thermal damage, and the dependence of thermal and optical properties on the temperature and the injury. Injury is estimated using a damage integral, which depends on the temperature elevation history. The order and flow of the modelling steps are described in the following sections in detail. The geometry of the 3D model The head and neck area consists of complex anatomical structures in close proximity. In sonographic 3D volume datasets of the neck area the sternocleidomastoideus muscle and the neck vessels (common carotid artery, internal carotid artery, external carotid artery and the internal jugular vein) serve as leading structures [13]. Because of the almost superficial anatomic position of the vessels and their straight course (Fig. 1a) especially the common carotid artery is easily shown sonographically. Differentiation of inflammed lymph nodes and metastases located parallel to the large neck vessels [14] can be achieved 90% of the time with the help of signal-enhanced colour duplex sonography [15,16], making ultrasound preferable to other imaging techniques. Figure 1 The geometry used. The left carotid artery is shown in this figure labelled with ca. The following letters indicate the orientation: s for superior, i for inferior, p for posterior, a for anterior, l for left, r for right. (a) is a photo of the human anatomy in the neck area. The carotid artery is shown here after moving the vein to the cranial direction. (b) shows the corresponding freehand 3D ultrasound dataset of the human neck region acquired axially. The 3D image in the top of (b) shows the 3D ultrasound volume together with the carotid artery segmented with 3D Slicer software [17]. The 3D model is displayed with 40% transparency. (c) displays the model used in the simulation approximated according to the geometry of the human neck shown in (a) and (b). The segmentation of the 3D ultrasound dataset in (b), is available as a video stream, too, showing the geometry of the carotid artery (see Additional file 1). One can easily segment the carotid artery from 3D sonographical, MRI or CT volume datasets. For the segmentation of our 3D ultrasound dataset (Fig 1b) we used the software package 3D Slicer [17]. The real geometry is complex and needs to be simplified to reflect limited computational capacity. We thus obtained the 3D base model consisting of a cube of 447 cm3 shown in Fig. 1c. The cube contains the blood vessel approximated either by the cylindrical shape or much better by a shape of cone. The applicator is shown as a thin tube of 2.5 cm perpendicular to the vessel (Fig. 1c). Commercially available laser applicator fibres for thermotherapy frequently have a water jacket to cool the surface. The applicator is assumed to be a cylinder, and the cooling effect is implemented as a boundary condition at the diffuser surface. The tissue surrounding the vessel is treated as a homogeneous muscle tissue. According to the geometry described using a mesh is generated to perform a finite element method calculation (Fig. 2). The model was implemented with FEMLAB 2.3 as an add-on to Matlab 6.5 for finite element modelling [18]. Figure 2 The finite element method (FEM) mesh. The values of each variable and of each property value are evaluated at each point of the mesh. In other words, the "simulation loop" in Fig. 3 is performed at each of point of the mesh. The bi-points variables' values are evaluated using an interpolation process. The light distribution equation In most tissues, both absorption and scattering are present simultaneously. A mathematical description of the absorption and scattering characteristics of light can be performed analytically or by using the transport theory. Transport theory has been extensively used when dealing with laser-tissue interactions. Furthermore, experimental results have confirmed its validity in most cases [19]. The photon propagation described using the transport theory has been dealt with already in [4,19-23]. Exact analytical solutions to the radiative transport equation have been found for only few special simple cases. However, when scattering processes dominate absorption in the medium, a high penetration depth of the light is the consequence. This is the case in LITT treatment in human tissue using an Nd:YAG 1064 nm laser or even a diode 830 nm laser: The penetration depth of both types ranges from between 1300 μm – and 1400 μm, whereas the penetration depth of other laser types like argon 514 nm laser or CO2 10600 nm laser is less than 350 μm. This leads us to the possibility of applying light diffusion approximation to the transport theory [4]. Because of the high penetration depth of the Nd:YAG laser in turbid media, diffusion theory provides a relatively accurate description of light propagation. In three dimensions the diffusion equation needs to be solved numerically, because an analytical solution is not possible [4]. FEM is the most practical method; moreover a number of different efficient solutions using FEM are now available. An exact derivation of the light diffusion equation can be found in [4,20]. Here, we give the light diffusion approximation to the transport theory, which is implemented in our model (Fig. 3: Light Distribution Equation): Figure 3 The simulation loop. The figure shows the simulation flow chart as a step in the imagined surgical planning system (left side). The future goal of the surgical planning system is to verify the three parameters governing a laser treatment: the applicator position, the laser power, and the exposure duration. The temperature starting point of the volume is set to normal body temperature. Three input parameters are taken: average blood velocity, laser power, and application time (right side: input). The main part of the simulation is the loop, which calculates the variables in the forward steps, then updates the values of the different properties (parameters) in the backward step according to the results of the forward step. The loop follows the section materials and methods and uses its nomenclature for variables and functions (right side: loop). The output of the solver consists of three parts: the light energy fluence rate φ(r.t), the temperature distribution T(r, t), and the damage Ω(r, t) (right side results). The results are explicity shown in figures Fig. 4 and Fig. 5. The roman numbers (I-VI) refer to the equations in the text. φ being the light fluence rate [W cm-2], D the diffusion coefficient [cm], and Q the source term [W cm-3]. μa is the absorption coefficient and μ's the reduced scattering coefficient in tissue. The roman number (I) indicates the position in Fig. 3. The relationship between the reduced scattering coefficient and the scattering coefficient, μs is described by μ's = μs (1-g), with g being the anisotropy factor incorporating the effects of directionally dependent scattering. The absorption coefficient μa for visible and for near infrared radiation ranges between 0.001 mm-1 < μa < 10 mm-1 for biological tissues. While for the scattering coefficient μs is in the order of 1 mm-1 <μs < 100 mm-1 [20]. The optical properties μa and μ's depend on the tissue, and they change their values during a real treatment. In order to simulate this effect, the optical properties of the tissue are functions of the damage Ω [20] (see section on "damage function"). The damage function Ω describes the pathologic state of the tissue during treatment. In general, for simple geometries like a point-source, the solution of the light diffusion equation will be an exponentially decreasing function with effective attenuation coefficient given by: As an example, the solution of eq. 1 for a light source similar to a medical applicator in a homogeneous medium takes the shape of an ellipsoid [24] as shown in Fig. 4. Figure 4 Solution of the light distribution equation. Left: the energy density colour index. The solution is elliptical as expected. The penetration depth in the vessel is less than in the tissue, because of different scattering and absorption coefficients. The heat distribution equation in tissue The aim of irradiation with laser energy is to produce heat in the targeted tissue. Excess heat is either stored or extracted, leading to changes in the local temperature. The bioheat equation was repeatedly used to describe the heat changes in biological tissue [4,19,20]. The bioheat equation is the realizing of the principle of conservation of the energy applied to tissue volume, (Fig. 3: Heat Distribution Equation, in Normal Tissue), where T is the temperature [°C], ρ the density of tissue [kg cm-3], c the specific heat of tissue [J kg-1 °C-1], k the thermal conductivity of tissue [W cm-1 C-1], r the position vector [cm], t the time [s], Tart the temperature of arterial blood [°C], S the deposited light power [W cm-3], wb [ml/(g.min)] is the tissue average volumetric blood perfusion rate (but because the density of blood ρb is to be considered as a constant value, it is possible to call ρb·wb [kg s-1 cm-3] the average volumetric blood perfusion rate, unfortunately usually denoted in the literature also with wb), and cb the specific heat of blood [J kg-1 C-1]. The coefficients ρ, k and wb are functions of temperature T. As a basis for the optical and thermal parameters for the simulations, we used values published by Mueller et al. [1]. Especially for normal body muscle tissue the physical properties are collected in Table 1. Table 1 Listing of physical parameters. The table shows the physical parameters for native tissue and for coagulated-tissue, as well as for blood [25]. The small letters indicate the references where the material parameters are taken from: a) [20], b) [26], c) [27], d) [28], e) [29]. Muscle Blood Native Coagulated Absorption coefficient, μa (cm-1) a) 0.23 a) 0.22 b) 0.44 Scattering coefficient, μs' (cm-1) a) 1.3 a) 13 b) 2.78 Density, ρ (kg cm-3) c), d) 1.04·10-3 d) 1.06·10-3 Specific heat capacity, c (J kg-1 °C-1) d) 3.64·103 d) 3.89·103 Heat conductivity, k (W cm-1 °C-1) c) 5.18·10-3 e) 5.4·10-3 The heat distribution equation in large vessels 1. The incompressible Navier-Stokes equation In order to make the model adaptable to individual shapes of segmented vessels, we considered the geometry of a large vessel as a volume in which an incompressible fluid (blood) flows. The direction of the blood flow and the initial speed profile are implemented as boundary conditions. The incompressible Navier-Stokes equation for the blood (Newtonian fluid) reads: Here, η is the dynamic viscosity [kg s m-1], ρ the density [kg m-3], u the velocity field, p the pressure [N/m2], and F a volume force field such as gravity. Implementing the Navier-Stokes equation in the model allows us to present a time-periodic change in the blood flow rate, i.e., to simulate the beat cycle effect in the vessel. The main effect here on the result of the simulation lies in the accuracy of the estimated heat elevation in the tissue: A continuous blood flow has a different profile than the cycled flow (laminar and not laminar, or rather complex with four beat phases), which yields a different final cooling effect. For vessels away from the heart, the pumping cycle does not clearly appear; it tends to be a normal laminar flow. In this case (u(r, t) = u(r)) and eq. 5 is reduced to the following: 2. The bioheat equation in large vessels The heat convection between tissue and a large vessel occurs as a direct energy transfer rather than perfusion. The vessel is a heat sink in the treated volume. Therefore, the perfusion term in the bioheat equation has to be modified to consider heat conduction and blood flow. A new term, the so-called enthalpy transport, is added to retain the validity of the bioheat equation. The term serves for interpreting the internal energy flow out of the control tissue volume by means of the blood flow [30]. Considering the blood velocity field () in a large vessel the bioheat equation becomes: The damage function The thermal damage in cells and tissue is described mathematically by a first-order thermal-chemical rate equation, in which temperature history determines damage. Damage is considered to be a unimolecular process, whereby native molecules transform into a denatured/coagulated state through an activated state leading to cell death [4,19]. Damage is quantified using a single parameter, Ω, which ranges on the entire positive real axis and is calculated from the Arrhenius law: where A [s-1] is the frequency factor, Ea [J/mole] the activation energy, R [J mole-1 K-1] the universal gas constant, and T [K] the temperature. C(r, 0) and C(r, τ) are the concentrations of the undamaged molecules at the beginning and at time τ, respectively. Damage Ω (eq. 8, Fig. 3, Damage Function) is dimensionless, exponentially dependent on temperature, and linearly dependant on time of exposure. The activation energy Ea and the frequency factor A are derived from thermodynamic variables. They describe the denaturation process of proteins and other cellular constituents. A ranges from 1040s-1 to 10105s-1, and Ea from 105J/mole to 106J/mole [4]. The equation above indicates that the measure of damage describes the probability for tissue being destructed. It is the logarithm of the ratio of the initial concentration of undamaged tissue to the concentration after damage has accumulated, for the time interval t = 0 to t = τ. Therefore, Ω = 1 corresponds to the reduction in concentration of native molecules to a 37% level for a unimolecular system – an irreversible damage of 100% of the affected cells. However, in terms of the thermal damage to tissue, Ω (r, t) is a function of the observer's definition of damage. In [31] a limit of Ω >0.6 has been discussed as a margin of final tissue destruction (Fig. 5). A value of Ω = 0.6 corresponds to reduction in concentration of native molecules to 50% level. Figure 5 The heat distribution and the damage in the volume. Left: the heat colour index in °C. The damage appears when the value of the damage function Ω (eq. 8) reaches the threshold of 0.6. Here the image shows the results after 200 s. The damage zone is shown in grey. Fig. 5 is available also as a video stream demonstrates the temperature rise inside the tissue. The video stream shows where, how, and when this damage appears (see additional file 1). The dependences of the tissue properties on tissue temperature and damage Heat capacity is assumed to be constant over a wide temperature range. The temperature dependence of thermal conductivity and density is taken into consideration by the following linear approximations [20] (Fig. 3: Properties): On the other hand, the optical properties are influenced directly by the damage Ω. The scattering coefficient of coagulated tissue is much higher than that of native tissue. The optical properties change during the process of tissue coagulation, leading to a higher scattering coefficient and a nearly constant absorption coefficient. This becomes obvious by the change of the colour of the irradiated area (bleaching) and leads to reduction in penetration depth. The actual property set is calculated from the actual damage value as well as the optical properties in the native and coagulated tissue states [20] (Fig. 3, Properties): Here, μs, native and μs, coagulated denote the scattering coefficients of native and coagulated tissue, respectively, g being the anisotropy factor. In literature g is mostly considered constant. Anyhow, some authors [20,31] reported the possibility of different values for g according to the damage state. Model implementation The diagram in Fig. 3 illustrates the flow of the simulation. There are three main parts to the modelling of laser-tissue interaction (Fig. 3, right part): • First, the irradiance distribution in the different tissues is determined by directly applying eq. 1. As shown in equations eq. 11, eq. 12, and eq. 13 the values of the properties depend on damage Ω (eq. 8). In the first loop step Ω is zero, and it starts to increase according to the rise in temperature, i.e., the different optical properties have as their starting point native tissue and as end point coagulated tissue. The actual value lies between both limits as determined according to Ω. • In the second step, the temperature distribution in the tissue caused by laser energy deposition is estimated by solving the two bioheat equations for tissue and large vessel. The source term in both equations is defined by the absorbed energy at each mesh point (Fig. 2) from the distributed light energy calculated in the first step (light-energy to heat-source coupling) using: • A by-step here is the estimation of the blood speed field () from the Navier-Stokes equation (either eq.5 or eq. 6). In our solved model, because we suggested treatment as taking place in the neck near the carotid vessel, we considered to use eq. 6 for obtaining the speed field, which is valid for laminar flow. • In the third and main part, thermal damage is predicted from spatial and temporal temperature distribution (eq. 8). • After estimating the heat distribution and the damage value, we perform a backward step to calculate the new values of the properties according to eq. 9 through eq. 12, which are updated in the equations set for the next loop iteration. For our calculations we used FEMLAB's standard mesh generator with its default settings for modelling [18]. The mesh consists of 5354 nodes, more dense near the applicator and becomes coarser moving towards the walls. The time-dependent equation set described in the previous sections was solved using FEMLAB's time-dependent solver "femtime". The default settings for the solver were used: 0.01 for relative error tolerance and 0.001 for absolute error tolerance. Because of the non-linearity of this problem, the special time stepping algorithm "fldaspk" offered by FEMLAB was used in order to obtain a stable and convergent numerical solution [18]. A normal numerical solution to initial value problem of differential equation generates a sequence of values for the independent variable (time) tn and a corresponding sequence of values for the dependent variable (φ, T, u, Ω, and all other variables depend on them in this case) so that each φn, Tn,... approximates the solution at tn [32]. Modern numerical methods automatically determine the step sizes hn = tn+1 - tn so that the estimated error in the numerical solution is controlled by a specified tolerance [32]. The "fldaspk" solver uses the algorithm of the known DASPK solver written by Linda Petzold, which uses variable-order variable-stepsize backward differentiation formulas (for independent variable, time t in this case) [33]. There is no control on the time step itself, rather on the specified tolerance of each variable. Light is considered to be emitted from an interstitial fibre with a fibre-diameter of 1 mm; it was modelled as an isotropically radiating cylindrical source (Fig. 4). In the real treatment a cooling process using a special cooling catheter is performed to keep the temperature at the surface of the applicator low, preventing damage at its surface. A special boundary condition at the applicator surface should be applied in order to simulate this cooling effect. In the model this is realized by setting the outer surface temperature of the applicator to a constant value (normal body temperature T = 37°C). At the modelled volume surfaces the insulation boundary conditions, optical and thermal, are used, where n is the outward unit vector normal to the surface. This means the gradients of light fluence rate and of temperature vanish at the surface. Even though this condition is more suitable for light fluence rate, as small amount of radiations reach the surface, but in general the temperature and light fluence rate will not be constant. The need to set this condition in this way is because that the numerical solver demands defined fixed boundary conditions, which sometimes do not agree with the real situation. According to the NCRP-data [34] the perfusion rate wb over the entie tissue is set to 1.4·10-6kg of blood s-1 cm-3 for T < 60°C and to 0 when T ≥ 60°C, which is to be considered as a normal result of stopping the blood perfusion according to temperature elevation in the tissue [31]. In order to solve the Navier-Stokes equation, we set the dynamic viscosity, η, to 3.5·103kg·s·m-1. To evaluate the damage, Ω, the activation energy Ea is set to 670000 J/mol and the frequency factor A to 9.4·10104s-1 [22,35]. Damage is considered to appear when Ω reaches the value of 0.6 [31]. The simulation takes around 2 hours on Sun Blade 2000 with Solaris 9 OS, 6 GB RAM and UltraSPARC IIIi processor. Experimental validation We performed a single in-vitro experiment to check our model. The setup is shown in Fig. 6. The experimental work was performed using a fresh piece of porcine muscle tissue. In order to simulate the cooling effect of the blood flow in a vessel, a transparent tube (Polyethylene) and a porcine blood were used. An electronically controlled roller pump system (Storz Endomat LC203303) was used to pump the blood through the tube. We used the online ultrasound sonography to situate the applicator in its right position in front of the tube and to get the 3D ultrasound data analogy to the base geometry shown in Fig. 1. The sample has dimensions of 1265 cm3. The tube inner diameter is 5 mm and the outer diameter is 7 mm. The blood and the laser cooling liquid had the room temperature of 21.4°C while the sample itself 17.6°C. A laser power of 30 W and blood average flow speed of 40 ml·s-1 were used. We measured the exact distance between the laser applicator and the tube edge, 3 mm, at the end of the experiment after performing the cut in the sample. We fixed our application time to 300 s. Figure 6 The experiment setup. 1- laser applicator, 2- transparent tube (considered as a blood vessel), and 3- is ultrasound sonography prob. The tube was introduces by pulling it through a hole made using dipped knife. The online sonography was used to verify the position of the applicator. In the simulation model we omit the perfusion term, as there is no perfusion to be considered in-vitro. We simulated the tube (blood vessel) with diameter of 6 mm. All other experimental conditions are implemented in the model as they are in the experiment. Because of the lack of data, which describe the properties of the porcine tissues, in all literature available to us, we used the same data presented in Table 1 to complete this simulation. The highest temperature and widest damage are reached in front of the centre of the applicator. Taking into account the perpendicular surface to the applicator at centre, which is the most critical in the volume as all effects participate together: applicator, vessel, and blood flow, we complete the comparisons of the results between the experiment and the simulation using this plane. Hence, we made a cut in the probe at the level equivalent to this plane. Coagulation of tissue is immediately apparent and always indicates lethal thermal effect [4]. Anyhow, and in order to comment on, a damage boundaries have to be identified. For most tissues, coagulation can be seen with naked eye as whitening of the tissue associated with turgor and opacity [4]. The damage boundary in the probe cut is determined by applying a grey threshold of 50% on the picture of the cut after changing it to grey scaled image. Away from the applicator position in a certainly undamaged area, applying this threshold led to 92.68% of the pixels to be black and 7.32% white, while in the damaged tissue 6.64% of the pixels were black and 93.3% white. The Matlab 6.5 R13, its Image Processing 4.0 Toolbox, and Paint Shop pro 8 were used to perform these steps. A comparison in the z-direction needs an up-down cut (y-z plane) through the applicator position perpendicular to the tube/vessel, which was not possible after the cut in x-y direction. Results In [25] we presented results from different geometries. Here, we focus on the results from the physical and mathematical points of view. We present also a comparison between the results of our model and the results of the in-vitro experiment. Theoretical results Fig. 4 shows that the light energy is distributed, as expected, in a shape of ellipsoid, because of the absorption in the tissue. It also shows that the light penetration depth in blood is less than in the normal tissue, because of the different values of the absorption and scattering coefficients. Fig. 5 shows the heat elevation. The cooling effect of the blood vessel may be clearly identified here. Fig. 5 is available as a video stream, too. This video stream demonstrates the temperature rise inside the tissue as a result of the irradiation. The video stream shows where, how, and when this damage appears (see Additional file 2). The dimensions of the damage zone, which may be considered the target goal of the simulation, can be calculated directly by producing grided axes in all the 3D and 2D results as well as with routines written especially for this aim. Comparison with experimental results Fig. 7 shows the interpreted development of the damage zone. The calculation shows that the damage starts with an application time of 72 s using laser power of 30 W. It starts at a distance of 2.5 mm from the centre of the applicator in the opposite direction of the vessel. After that, the damage zone will spread to all directions as it is shown in Fig. 7. Fig. 8 shows the solution of the heat and light distribution equations for this model. In the first 60 s there is no noticeable change in the light distribution. In the next 30 s the damage zone appears, which leads to changes in the scattering and absorption coefficients (eq. 11 and eq. 12). Figure 7 The interpreted development of the damage zone as a result of the simulation model using the experimental conditions. The applicator is recognized as a grey point in the middle and is indicated with (App). The damage zone starts with an application time of 72 s. The damage zone is presented here at 73 s, 120 s, 180 s, 240 s, and 300 s of application time as indicated in the figure. Figure 8 Dispersion of heat and light distribution considering the experiment conditions. The figure shows the results at 60 s, 90 s, and 300 s of application time. The isolines show the light distribution, which represent the deposition of the energy irradiated from the laser applicator. The major change occurred to the energy penetration depth happens between 60 s and 90 s of application time. The dashed area presents the damage zone at these times. Fig. 9 shows the overlaid damage zones of both model and experiment. The kidney-shaped lesion is about 21.2 cm2, while our model shows a damaged zone of 2.11.45 cm2. We obtain calculation errors of 5% in the x-axis parallel to the blood vessel, and of 20% in the y-direction perpendicular to the vessel. This deviation happens mainly due to inaccurate optical properties values. Figure 9 The results from simulation and experiment. For the experiment the following settings were used: time of application: 300 s, laser power: 30 W, blood flow rate: 40 ml/s, and applicator-vessel edge distance: 3 mm. Using these conditions a simulation is performed. In the figure the damage zones of both model and experiment are overlaid to show the deviation. The black oval shows the damage zone obtained from the simulation using the above conditions. The two parallel black lines indicate the vessel position and diameter in the simulation. The lesion is about 21.2 cm2, while the calculated damage zone is 2.11.45 cm2. The calculation error ranges from 5% in the x-axis parallel to the blood vessel to 20% in the y-direction perpendicular to the vessel. Both cutting the tissue with scalpel and the opening induced a tissue movement. This movement is a reason for the error in y-direction beside the error obtained from the optical properties, which affect all directions. The discoloration in the top of the image at the opposite side of the artificial vessel is due to the cut and the opening of the probe. Discussion To date most simulation models of LITT have used the Monte-Carlo method (MC) to calculate the light distribution, then combine its results with Finite Difference method (FDM) to calculate the heat distribution. Because of its formulation, this combination fits very well for a radially symmetric problem. A weakness arises, however, when dealing with asymmetric volumes in real human anatomy. Arbitrarily curved surfaces separate the different tissues. Consequently, calculations from the FDM becomes so complex that errors start to appear in the results presented stemming from the dependency of FDM on dividing the volume into voxels. One way to overcome this is to increase the voxels' number. Indeed, this leads to less error at the tissue-separating surfaces, although it increases the resources and calculation steps, making the procedure inconvenient. In principle, combining MC and FEM (instead of FDM) is possible theoretically, and seems to be promising as it overcomes the latter problems, but to our knowledge has not implemented yet. From another perspective, MC solution converges to the exact solution of the transport equation only when the number of traced photons increases infinitely [4,21], which yield time consuming calculations. Because we are dealing with an asymmetric geometry, we chose the FEM. It allows us to define and refine the mesh in the volume of interest in order to obtain more precise results. Furthermore, using a FEM mesh we are able to adapt individually the mesh for each patient's dataset. Never the less, it was not necessary to combine methods, FEM is used for all equations. The model we propose depends on the following considerations: • The coupling of a set of time-dependent equations, which simulate the whole process of the LITT treatment (Fig. 3). The set of equations includes the light diffusion equation, the bioheat equation, the Navier-Stocks equation, the damage function, and the dependencies of the properties on temperature and ensued damage. • We consider the functional dependence of the various tissue properties at the various spatial and temporal points, according either to the tissue type, or temperature, or the damage value – or even a combination thereof. • We take into account the irreversible changes in the tissue stemming from the treatment as they directly affect the solution of the set of equations. Our model remains a mathematical model, meaning errors could appear from the considerations and simplifications made to realize it. Generally, such errors appear because of the following reasons: • The inaccuracy of the optical, thermal, and damage properties are main point in the model's set of equations. In fact, these properties play a key role in the accuracy of the model's results. Many methods have been presented to calculate these properties [4,19,21], but still we see differences in the values presented by the different groups, which reflect the difficulty of measuring these properties. The problem is increased by the dependencies of the properties on the different variables (temperature, damage) over time. This makes the deviation neither linear nor regular. • An error appears because of machine performance limitations: The available memory limits the number of mesh nodes and the degrees of freedom (DOF) used to build the model. This causes a deviation from an otherwise accurate result [18]. On the other hand, it is useless to increase the nodes number or the DOF arbitrarily: This would result in more time consuming calculations, since one would always have to run an interpolation and smoothing process as next to last step. In practice, a suitable number of nodes/DOF should be chosen, so that the interpolation routines estimate smoothly the value of all variables between nodes. Our model is based on what was mentioned above, with FEMLAB's standard refining processes at the critical areas (around the diffuser and vessel). It is important to refine the mesh around such surfaces, something that can be done much more conveniently using FEM rather than FDM. The convenience of the FEM-based modelling may be found in this very point of its ability to have different degrees of refining in the mesh according to how critical the region is. • Absolute tolerance: All numerical methods have an allowed error (absolute tolerance) that reflects the criterion of the convergence. Normally, different solvers use different tolerances. In our model we used the FEMLAB's default tolerance value of 0.01 which leads to a final error of 1%, considered as a reasonable value for modelling. One way to follow these errors and deviations from a real treatment result is to estimate them and to eliminate their effects from the final results of the model. This can be realized and implemented in the model by adding an error-correcting factor from the first degree (or even higher) in the set of equations correcting the result of each equation at each time step. These corrective factors should be measured practically by comparing the results of the model and the results from real experiments on test tissues or probes. Our experiment shows a deviation of 5% in x-direction and 20% in y-direction. As the main reason for this deviation we propose inaccurate values for the optical tissue properties. Fig. 7 and Fig. 8 clearly show the kidney-shaped damage zone caused by the cooling effect of the blood vessel (or the tube in the experiment), which keeps its neighbouring in the native state for longer time. In literature [20,31], the value of the absorption coefficient μa for both native and coagulated different biological tissues are close. Baring that in mind, and knowing that the value of the scattering coefficient μs becomes normally, for biological tissue, 10 times greater than its starting value, i.e. native state, we can judge that as soon as the damage zone appears and the moving from native to coagulated state according to eq. 11, eq. 12, and eq. 13 the deviation in the calculations will increase as well. Thus, accurate values of the different tissue properties, and especially the optical properties are key points in obtaining realistic results from the simulation. One promising technique for determination of optical properties was presented by Dam et al. [36]. There method provides an online values of the optical properties at 660 nm, 785 nm, 805 nm, 974 nm using a cylindrical probe head situated on the skin. It still require further researches. Anyhow, thinking of developing such a method to be able to gather information about optical properties at 1064 nm interstitially using a catheter during the irradiation can be of great value for modelling. Our model gives the possibility to implement such a gathered information directly. Thinking of using it online to predict the damage and controlling the irradiation power needs for sure more researches. Finally, beside the error obtained from the optical properties, which affect all directions, both cutting the tissue with scalpel and the opening induced a tissue movement. This movement is a reason for deviation, especially in y-direction, as we perform the cutting in this direction. Conclusions For several years now LITT has been a well-known and approved therapy system for tumour ablation in the liver and some other anatomical regions. Minimally invasive LITT procedures use a Nd:YAG 1064 nm laser. Therapy planning, however, remains unsolved and is still a challenging issue. Today's simulations are based on symmetric geometries. Without exact therapy planning systems, the usage of LITT is limited to homogeneous tissues or the respective surgeon's experience. The finite element technique proposed in this paper can overcome both limitations. We propose a model to validate in the future the LITT method in other anatomic regions. The model enables the efficient simulation for predicting the damaged zone induced with the diffuser of the LITT. The simulation is performed for tissue ablation near vessels, though obviously FEM is not limited to this. Exemplarily, we implemented the model for tissue ablation near the carotid artery in the neck region using an approximation for the artery shape. We describe the bases necessary to calculate the effects of the temperature rise caused by the absorption of light energy in the tissue, using the bioheat equation and including the cooling effects of vessel blood flow and micro-perfusion in tissue in order to determine the extent of thermal damage. The shape of the carotid artery is derived from a real segmented geometry based on, but not limited to, 3D ultrasound. Experimentally, we performed a laser irradiation in a porcine muscle tissue sample. The results of our model diverge between 5% to 20% from the lesion obtained in the experimental work. From the authors' point of view two major reasons can be identified. The lack of accurate data describing the thermal and optical properties leads definitely to deviations. Furthermore the cut of the probe with scalpel induces a certain tissue shift, especially in the y-direction. Anyhow, more experiments with different conditions are necessary to be able to carry out a statistical study and find the exact origin of the deviation, and, if necessary, define an error correction factors and add them to equation set. But that does not set aside the desire of accurate values for the properties of the tissue. From another hand, still our model practical, it presents a step in using segmented data as basis for much more detailed surgical therapy planning. Combining LITT and adequate planning system could increase both the anatomical application range and the quality of therapy procedures. Supplementary Material Additional File 1 Animated gif file, the Geometry. The animated gif shows the 3D ultrasound volume together with the carotid artery segmented using 3D Slicer software [17]. The movie belongs to Fig. 1b. The gif file can be played using the internet browser. Click here for file Additional File 2 Animated gif file, The heat distribution and the damage zone in the volume. The video stream demonstrates the temperature rise inside the tissue. The video stream shows where, how, and when this damage appears. The damage zone is shown in grey colour. The gif file can be played using the internet browser. Click here for file ==== Refs Mueller G Roggan A Laser-Induced Interstitial Thermotherapy: SPIE-The International Society for Optical Engeneering 1995 Bown S Phototherapy of tumours World J Surg 1983 7 700 709 6419477 Vogl TJ Mack M Straub R Eichler K Engelmann K Roggan A Zangos S Percutaneous interstitial thermotherapy for malignant liver tumors. 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==== Front Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-4-31564212010.1186/1476-511X-4-3ResearchProlonged treatment of genetically obese mice with conjugated linoleic acid improves glucose tolerance and lowers plasma insulin concentration: possible involvement of PPAR activation Wargent Ed [email protected] Matthew V [email protected] Claire [email protected] Andrew E [email protected] Louise [email protected]'Dowd Jacqueline [email protected] Steven [email protected] Alexandra WC [email protected] Inge [email protected] Jonathan RS [email protected] Michael A [email protected] Clore Laboratory for Life Sciences, University of Buckingham, Buckingham, MK18 1EG, UK2 Biosciences division, Unilever Research, Colworth Laboratory, Sharnbrook, Bedfordshire, MK44 1LQ, UK3 Lipid Nutrition, Loders Croklaan BV, PO Box 4, 1520 AA Wormerveer, The Netherlands2005 10 1 2005 4 3 3 21 12 2004 10 1 2005 Copyright © 2005 Wargent et al; licensee BioMed Central Ltd.2005Wargent et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Studies in rodents and some studies in humans have shown that conjugated linoleic acid (CLA), especially its trans-10, cis-12 isomer, reduces body fat content. However, some but not all studies in mice and humans (though none in rats) have found that CLA promotes insulin resistance. The molecular mechanisms responsible for these effects are unclear, and there are conflicting reports on the effects of CLA on peroxisomal proliferator-activated receptor-γ (PPARγ) activation and expression. We have conducted three experiments with CLA in obese mice over three weeks, and one over eleven weeks. We have also investigated the effects of CLA isomers in PPARγ and PPARα reporter gene assays. Results Inclusion of CLA or CLA enriched with its trans-10, cis-12 isomer in the diet of female genetically obese (lepob/lepob) mice for up to eleven weeks reduced body weight gain and white fat pad weight. After two weeks, in contrast to beneficial effects obtained with the PPARγ agonist rosiglitazone, CLA or CLA enriched with its trans-10, cis-12 isomer raised fasting blood glucose and plasma insulin concentrations, and exacerbated glucose tolerance. After 10 weeks, however, CLA had beneficial effects on glucose and insulin concentrations. At this time, CLA had no effect on the plasma TNFα concentration, but it markedly reduced the plasma adiponectin concentration. CLA and CLA enriched with either isomer raised the plasma triglyceride concentration during the first three weeks, but not subsequently. CLA enriched with its trans-10, cis-12 isomer, but not with its cis-9, trans-11 isomer, stimulated PPARγ-mediated reporter gene activity; both isomers stimulated PPARα-mediated reporter gene activity. Conclusions CLA initially decreased but subsequently increased insulin sensitivity in lepob/lepob mice. Activation of both PPARγ and PPARα may contribute to the improvement in insulin sensitivity. In the short term, however, another mechanism, activated primarily by trans-10, cis-12-CLA, which probably leads to reduced adipocyte number and consequently reduced plasma adiponectin concentration, may decrease insulin sensitivity. ==== Body Background The term conjugated linoleic acid (CLA) refers to a mixture of positional and geometric isomers of linoleic acid (cis-9, cis-12-octadienoic acid). The major components of CLA, are the cis-9, trans-11 (c9, t11) and the trans-10, cis-12 (t10, c12) isomers, both of which have biological activities. The t10, c12-isomer is the one primarily responsible for the effects of CLA on weight gain and insulin sensitivity. The CLA used in the present study contains these isomers in roughly equal proportions. CLA may be of benefit in cancer, atherosclerosis and possibly some disorders of the immune system. In addition, it reduces weight gain and fat accretion in rats and mice. Some studies have found that CLA causes fat loss in humans [1-4], and two studies have shown weight loss[2,5], but another did not find any significant effect of CLA on body composition or body weight [6]. More marked effects of CLA on body weight and body composition have been obtained in rats and especially mice, possibly because the rate of energy expenditure relative to energy stores is much higher in rodents than in humans. In previous studies, CLA or t10, c12-CLA has exacerbated glucose tolerance and raised plasma insulin levels in normal and genetically obese (lepob/lepob) mice, despite causing weight loss [7-10]. Both t10, c12-CLA and c9, t11-CLA have also been reported to cause insulin resistance in humans [11-13], although in other studies neither CLA nor its major isomers affected insulin resistance significantly [14-17]. By contrast, CLA or t10, c12-CLA improved glucose tolerance in Zucker fatty lepfa/lepfa and Zucker diabetic fatty lepfa/lepfa rats [18-21]. One difference between the rodent species is that peroxisomal proliferator-activated receptor (PPAR)α-regulated genes are more responsive to CLA in mice than in rats [22], but this would be expected to correlate with improved insulin sensitivity in mice rather than rats [23]. By contrast with this result for PPARα knockout mice, at least one response to CLA depends on expression of PPARγ: the beneficial effect of CLA in a mouse model of colitis was absent in mice that lacked PPARγ in the colon [24]. In view of the variety of reports of CLA's effects on PPARγ activity and expression, it is possible that PPARγ responses may vary between species; the different proportions of isomers in the CLA used in different studies may also be important [25]. Some reports describe activation of PPARγ by CLA or t10, c12-CLA [18,33]; others describe little or no agonist activity [10], but antagonism of rosiglitazone [25,27]. One report describes increased expression of PPARγ mRNA in white adipose tissue of rats [28] and another describes increased expression in liver of mice [10], but studies in isolated adipocytes or of adipose tissue from treated mice show decreased expression [25,27,29,30]. Since PPARγ agonists increase insulin sensitivity but promote adipogenesis, decreased activity of PPARγ in adipose tissue could explain why CLA reduces obesity but increases insulin resistance. Various other mechanisms have also been suggested to explain why CLA exacerbates insulin sensitivity despite causing loss of fat. One of these is increased expression of tumour necrosis factor (TNF)α, since TNFα is associated with apoptosis of adipocytes but causes insulin resistance. TNFα mRNA levels were markedly increased in isolated adipocytes from normal mice that had been fed on CLA for as little as four days [8]. Surprisingly however, serum TNFα levels were reduced by CLA in both normal rats [31] and mice [32]. Moreover CLA reduced the expression of TNFα in mouse macrophages [33]. Although most studies in mice have found that CLA exacerbates glucose tolerance and raises the plasma insulin concentration, in one study treatment of lepdb/lepdb mice with CLA for eleven weeks improved glucose tolerance and reduced plasma insulin concentration during the glucose tolerance test, indicating improved insulin sensitivity. Treatment for five weeks also improved glucose tolerance but plasma insulin was raised [34]. It is sometimes difficult to interpret studies in lepdb/lepdb mice because β-cell failure as well as insulin resistance affects glucose homeostasis, and glycosuria can cause weight loss. Therefore, to investigate the effect of CLA on glucose homeostasis further, we have conducted four experiments in lepob/lepob mice. We have included rosiglitazone as a comparator in one experiment and compared the effects of t10, c12- and c9, t11-enriched CLA with CLA in another experiment. The first three experiments were over 22 days, whereas the fourth was extended to eleven weeks and included measurements of plasma TNFα and adiponectin. An intriguing finding is that whereas CLA initially exacerbated glucose tolerance and raised the plasma insulin level, after ten weeks it began to improve glucose tolerance and lower the plasma insulin levels. We also describe activation of PPARγ by t10, c12 but not by c9, t11-CLA, whereas PPARα was activated by both isomers. Results Weight gain Inclusion of CLA in the diet of genetically obese (lepob/lepob) mice reduced their body weight, or weight gain compared to mice fed on a diet containing a similar amount of sunflower oil (Figure 1). Mice fed on chow supplemented with sunflower oil gained weight at the same rate as mice fed on chow alone (Figure 1b). The effect of CLA on weight gain appears to be primarily due to t10, c12-CLA, since CLA enriched to 90% with this isomer had more effect on weight gain than CLA containing 50% t10, c12-CLA and 50% c9, t11-CLA (Figure 1c). CLA that was enriched to 90% with the c9, t11 isomer had only a small effect on weight gain, and this may have been due to the 10% t10, c12-CLA. Food intake was measured over two days in experiment 4 and was not reduced by CLA (food intake per group: control, 33.1 and 37.5 g; CLA 10 g/kg diet, 35.1, 43.4 g; CLA 25 g/kg diet, 39.9, 39.6). Rosiglitazone (10 mg/kg diet) reduced body weight (Figure 1a) but to a lesser extent than CLA (15 g/kg diet). Figure 1 Weight gain in mice fed on diets that contained (doses per kg diet) (a) experiment 1: sunflower oil (●, 15 g), rosiglitazone (◇,10 mg), CLA (▲, 15 g); (b) experiment 2: chow only (○), sunflower oil (●, 25 g), CLA (▲, 25 g); (c) experiment 3: sunflower oil (●, 25 g), CLA (▲, 25 g), t10, c12-CLA (△, 25 g), c9, t11-CLA (▽, 25 g); (d) experiment 4: sunflower oil (●, 25 g), CLA (10 g) plus sunflower oil (15 g) (■), CLA (▲, 25 g). P < 0.05; P < 0.01: *P < 0.001 compared to sunflower oil group. Tissue weights and body length The weight of the parametrial white fat pads was reduced by CLA and by CLA enriched with t10, c12- but not c9, t11-CLA (Tables 1 and 2). The weight of the interscapular brown fat pad was reduced by CLA in experiment 4 (Table 2) but not in experiment 3 (Table 1). This may be because experiment 4 lasted for 11 weeks, whereas experiment 3 lasted for only 3 weeks and the control brown fat pad was more than three times heavier at the end of experiment 4 than at the end of experiment 3, presumably due primarily to its lipid content. Table 1 Terminal tissue weights and NEFA levels in experiment 3. NEFA levels were measured in 5h-fasted mice on day 13. Termination was on day 22. Supplements were included in the diet at concentrations of 25 g/kg. Sunflower oil CLA t10, c12-CLA c9, t11-CLA White adipose wt (g) 0.95 ± 0.12 0.49 ± 0.05+ 0.39 ± 0.06+ 0.82 ± 0.04 Brown adipose wt (g) 0.21 ± 0.03 0.23 ± 0.02 0.27 ± 0.11 0.18 ± 0.03 Liver wt (g) 2.02 ± 0.15 3.17 ± 0.05+ 3.25 ± 0.10+ 2.76 ± 0.18+ Pancreas wt (g) 0.12 ± 0.02 0.12 ± 0.01 0.11 ± 0.02 0.20 ± 0.02 NEFA (mM) 1.09 ± 0.09 1.13 ± 0.14 0.90 ± 0.07 0.82 ± 0.11 + P < 0.001; n = 6 Table 2 Terminal tissue weights and plasma hormones and NEFA levels in experiment 4. NEFA levels were determined in 5h-fasted mice on the days shown. Other measurements were in fed mice on day 77. Sunflower oil 1% Clarinol A80 2.5% Clarinol A80 Body length (mm) 95.0 ± 1.0 85.7 ± 0.3+ 86.4 ± 1.4+ White adipose wt (g) 2.11 ± 0.15 0.63 ± 0.07+ 0.19 ± 0.02+ Brown adipose wt (g) 0.74 ± 0.07 0.33 ± 0.04+ 0.007 ± 0.005+ Liver wt (g) 4.96 ± 0.17 6.27 ± 0.58 6.77 ± 0.36 Pancreas wt (g) 0.18 ± 0.01 0.20 ± 0.01 0.16 ± 0.02 NEFA (mM) Day 14 3.20 ± 0.17 3.58 ± 0.07 3.27 ± 0.13 Day 35 3.46 ± 0.42 3.93 ± 0.22 2.82 ± 0.40 Day 70 2.90 ± 0.19 3.44 ± 0.13* 1.92 ± 0.09+ Plasma adiponectin (ng/ml) 35.6 ± 7.0 8.2 ± 1.2+ 0.89 ± 0.13+ Plasma TNFα (pg/ml) 34.0 ± 5.7 33.0 ± 2.4 42.3 ± 7.1 * P < 0.05; + P < 0.001; n = 6 Liver weight was increased by CLA and by CLA enriched with either isomer, c9, t11-enriched CLA having the smallest effect. The weight of the pancreas was unaffected by all treatments (Tables 1 and 2). Others have reported that t10, c12- and c9, t11-CLA increase liver weight in lean mice, the effect of the t10, c12 isomer being associated with increased liver lipid [35]. Body length was reduced by CLA (Table 2). Glucose tolerance Rosiglitazone lowered fasting blood glucose, improved glucose tolerance (Figure 2a) and reduced the area under the glucose tolerance curve (Figure 3a). Two weeks treatment with CLA or with CLA enriched with t10, c12-CLA raised fasting blood glucose, exacerbated glucose tolerance (Figures 2b to 2d) and increased the area under the glucose tolerance curve (Figures 3b to 3d) compared to the sunflower oil and chow alone diets. CLA-enriched with c9, t11-CLA had no effect (Figures 2c and 3c). Figure 2 Oral glucose tolerance. Symbols are the same as in Figure 1 and again (a) is experiment 1, (b) is experiment 2 and (c) is experiment 3. (d), (e) and (f) are oral glucose tolerance curve on days 14, 35 and 70 for experiment 4. Figure 3 Areas under the glucose tolerance curves shown in Figure 2. Panel (d) shows areas from Figure 2 (d), (e) and (f). Doses of CLA are shown as g/kg diet. P < 0.05; P < 0.01: *P < 0.001 compared to sunflower oil group. After five weeks of treatment the higher dose of CLA raised glucose levels less than it had after two weeks (Figures 2d, e; 3d, e), and after 10 weeks this dose actually improved glucose tolerance (Figure 2f) and reduced the area under the glucose tolerance curve (Figure 3d). Its effect was similar to that of rosiglitazone over two weeks in experiment 1 (Figures 2a and 3a). The lower dose reduced the peak glucose level after 10 weeks, but it did not reduce the area under the glucose tolerance curve significantly. Insulin The improvement in glucose tolerance in response to rosiglitazone was associated with a reduction in the fasting plasma insulin level, but rosiglitazone did not reduce the terminal fasting insulin level (Figure 4a). The exacerbations of glucose tolerance in response to two weeks treatment with CLA and CLA enriched with t10, c12-CLA were associated with increases in the fasting plasma insulin level. CLA enriched with c9, t11-CLA did not alter the fasting insulin level on the day that it showed no effect on glucose tolerance. However, c9, t11-enriched CLA increased the plasma insulin level in fed mice on day 21 (Figure 4c). Figure 4 Plasma insulin values from (a) experiment 1, (b) experiment 2, (c) experiment 3, and (d) experiment 4. Doses are described in Methods and the legend to Figure 1. In panel (d) the does of CLA are in g/kg diet. Day 13 or 14, day 35 and day 70 values are following a 4.5 h fast; day 22 values are following a 16 h fast; and day 21 values are for fed animals. Doses of CLA are shown as g/kg diet. P < 0.05; P < 0.01: P < 0.001 compared to sunflower oil group. After five weeks both doses of CLA still raised the fasting plasma insulin level, but after ten weeks, both doses reduced the plasma insulin level compared to the control group (Figure 4d). Insulin resistance A useful indicator of insulin resistance can be obtained by multiplying values for fasting blood glucose and plasma insulin concentrations [36]. This is analogous to homeostatic model assessment (HOMA), which has been developed for studies in humans, although the full version, which originally involved dividing the product of glucose (mM) and insulin (mU/ml) concentrations by 22.5 is not suitable for studies in rodents [37]. By multiplying fasting blood glucose (mM) by plasma insulin (nM) we illustrate in Figure 5 how CLA initially exacerbated but subsequently improved insulin resistance. Figure 5 Effect of CLA on insulin sensitivity in experiment 4. The insulin sensitivity index was determined by multiplying the fasting blood glucose concentration (Figure 2d, e, f, 0 min) by the fasting plasma insulin concentration (Figure 4d). Triglycerides and fatty acids CLA raised the fasting triglyceride level in experiments 1 to 3 (Figures 6a to 6c), but in experiment 4 the only statistically significant effect was an increase elicited by the lower dose after two weeks (Figure 6d). CLA enriched with t10, c12-CLA had a similar or greater effect than CLA. Surprisingly on day 13 c9, t11-CLA, but not CLA or t10, c12-CLA caused a significant increase in the plasma triglyceride level, although c9, t11-CLA did not have a significant effect compared to CLA (Figure 6c). Figure 6 Plasma triglyceride values from (a) experiment 1, (b) experiment 2, (c) experiment 3, and (d) experiment 4. Doses are described in Methods and the legend to Figure 1. In panel (d) the does of CLA are in g/kg diet. Day 13 or 14, day 35 and day 70 values are following a 4.5 h fast; day 22 values are following a 16 h fast; and day 21 values are for fed animals. Doses of CLA are shown as g/kg diet. P < 0.05; P < 0.01: P < 0.001 compared to sunflower oil group. CLA and isomer-enriched CLAs had no effect on the fasting plasma non-esterified fatty acid (NEFA) concentration in experiment 3 (Table 1), and CLA had no effect after two and five weeks in experiment 4 (Table 2). After ten weeks, however, the lower dose of CLA raised the plasma concentration of NEFA, whereas the higher dose lowered it (Table 2). TNFα and adiponectin After ten weeks CLA had no effect on the plasma TNFα concentration. The plasma adiponectin concentration, by contrast, was markedly decreased (Table 2). PPAR activation CLA enriched with t10, c12-CLA (50 and 100 μM), but not with c9, t11-CLA, stimulated PPARγ-mediated reporter gene activity (Figure 7a). In contrast, both CLA isomers (100 μM) elicited a significant increase in PPARα-mediated reporter gene expression, and c9, t11-CLA was effective at 10 μM (Figure 7b). Figure 7 Effect of the c9, t11 and t10, c12-enriched CLA on (a) PPARγ- and (b) PPARα mediated gene expression. Cos-7 cells were transiently transfected with the plasmids pPPRE3TK-luc, pRLTK, pRSV/hRXRα and pcDNA3/hPPARγ 1 (a) or pcDNA3/hPPARα (b). Transfected cells were treated for 46 (a) or 24 (b) hours. Cell extracts were assayed for firefly and renilla luciferase activity. Reporter gene activity was determined by normalising firefly luciferase activity against renilla luciferase activity. The CLA isomers were prepared in 0.1% DMSO so that each of the stated isomers was at the given concentration. P < 0.05 and ** P < 0.01; n = 6. Discussion Physiology A number of studies have found that CLA reduces weight gain and fat accretion in both rats and mice. Those that have failed to show such effects are generally those that have used low levels of CLA or CLA that contained low concentrations of t10, c12-CLA [6]. Studies disagree, however, as to whether CLA improves or exacerbates glucose tolerance and insulin resistance in these species: studies in rats show improvements [18-21], whereas studies in mice, with the exception of one study in lepdb/lepdb mice [34], show exacerbations [7-10]. In humans, several studies have shown a loss of fat [1-4], but doubts have been raised about the use of CLA for the treatment of obesity by reports that both t10, c12-CLA and c9, t11-CLA exacerbated insulin resistance in abdominally obese men [11-13], and that t10, c12- (but not c9, t11-CLA) reduced the HDL cholesterol concentration or the HDL:LDL cholesterol ratio [11,12,14]. These are by no means consistent findings, however [13-17]. Our study raises the possibility that an initial exacerbation of glucose tolerance, apparently due to insulin resistance, might, after prolonged treatment with a high dose of CLA, be followed by improved insulin sensitivity and glucose tolerance. We used genetically obese (lepob/lepob) mice as our model of insulin resistance. There is only one previous report of the effect of CLA in lepob/lepob mice [9]. In agreement with other studies in rodents, we found that CLA reduced weight gain and perigenital fat pad weight, and that this effect appeared to be produced by the t10, c12- rather than the c9, t11-isomer. We also found, like other studies in mice, that glucose tolerance was initially exacerbated and the plasma insulin concentration was raised. Again, these effects were primarily due to the t10, c12-isomer. However, in our last experiment we found that after ten weeks glucose tolerance improved and the fasting plasma insulin concentration was reduced by treatment of lepob/lepob mice with CLA. These benefits were achieved faster with the higher (25 g/kg diet, including 40% t10, c12-CLA) than the lower dose (10 g/kg diet) of CLA. Our results are similar to those of Hamura et al [34], insofar as they found that treatment of lepdb/lepdb mice with CLA for eleven weeks improved glucose tolerance and lowered insulin levels during the glucose tolerance test. Hamura et al. found that glucose tolerance was also improved in lepdb/lepdb mice after five weeks of treatment, but this was apparently due to increased insulin secretion rather than improved insulin sensitivity. It is not clear why others have not also found improved glucose tolerance and reduced insulin levels following prolonged treatment of mice with CLA. In the one previous study in lepob/lepob mice, the dose of t10, c12-CLA was only about 5.9 g/kg diet and treatment was for only four weeks [9]. A study in lean C57Bl/6J mice similarly used a dose of 4 g/kg diet of t10, c12-CLA for four weeks [10]. Other studies lasted for twelve weeks and eight months, however. The dose of CLA was only 10 g/kg diet in both studies and neither was conducted in exceptionally obese or insulin resistant mice [7,8]. We therefore suggest that improved insulin sensitivity is most likely to be found when mice are initially markedly obese and insulin resistant, and when treatment is prolonged and results in a major loss of adipose tissue. The relevance of our findings to humans is unclear, but it is interesting that in one 12 month study plasma glucose was raised after two weeks treatment with CLA, but not at any subsequent time [16]. After ten weeks the higher dose of CLA lowered the plasma fasting non-esterified fatty acid concentration, which is consistent with improved insulin sensitivity (Table 2). The lower dose raised the NEFA concentration at this time, despite apparently improving insulin sensitivity, even though it did not raise the NEFA concentration at earlier times when glucose tolerance was reduced and plasma insulin levels were raised. This paradox seems to be largely a consequence of the low control NEFA concentration after ten weeks: the highest NEFA level in the low dose CLA group was after five weeks of treatment and it seems possible that the NEFA concentration in the low dose group would have fallen with further treatment, just as it was falling in the high dose group (Table 2). Hamura et al. [34] found that after twelve weeks CLA reduced the plasma NEFA concentration in lepdb/lepdb mice, suggesting that insulin sensitivity was improved. After six weeks, however, CLA raised the NEFA concentration, suggesting that CLA exacerbated insulin sensitivity at this time. The elevated plasma NEFA concentration may have been partly responsible for the elevated plasma insulin and improved glucose tolerance after six weeks in their study. CLA and its major isomers have little or no effect on plasma NEFA levels in humans [11,12,14]. Mechanism No single mechanism has been identified that can account for the various effects of CLA on lipid and carbohydrate metabolism, let alone its anticarcinogenic and immunomodulatory activities. In part this is because CLA is a mixture of isomers, each with its own balance of activities. The activities of even the most active isomer, t10, c12-CLA, cannot, however, be pinned down to a single mechanism. We can first rule out any possibility that responses to CLA in our experiments were mediated by a fall in leptin levels as has been suggested by others [8], because our study was conducted in lepob/lepob mice. In any event, when fat loss is achieved by reducing energy intake, insulin action and glucose tolerance improve despite a reduction in the plasma leptin concentration. Antagonism of PPARγ by t10, c12-CLA has been suggested to contribute to both decreased adipogenesis and insulin sensitivity [38]. A recent report that c9, t11-CLA exacerbates insulin sensitivity [12] is consistent with a report that it too antagonises PPARγ, albeit a little less effectively than t10, c12-CLA [25]. We found little evidence that c9, t11-CLA exacerbates insulin sensitivity in lepob/lepob mice, however (Figures 3c and 4c), and others have reported no effect [9]. Some workers favour the hypothesis that t10, c12-CLA decreases adipogenesis and insulin sensitivity by a mechanism that involves decreased expression of PPARγ in adipose tissue [38]. Since PPARγ agonists increase PPARγ expression in some situations [39,40], antagonism of PPARγ might decrease PPARγ expression. By contrast with mice, however, in rats CLA increases both PPARγ mRNA expression and insulin sensitivity [28]. Thus it is possible that CLA activates one mechanism that decreases and another that increases insulin sensitivity, and that PPARγ expression reflects the balance of these forces, rather than having a causal role. In any event, our results do not support antagonism of PPARγ as a mechanism of action of CLA. In agreement with some other workers [18,33], we find that t10, c12-CLA, but not c9, t11-CLA activates PPARγ. Activation of PPARγ might contribute to the improvement in insulin sensitivity in lepob/lepob mice following prolonged treatment with CLA. Activation of PPARα might also contribute to improved insulin sensitivity. t10, c12-CLA was more potent as an activator of PPARα than of PPARγ in the particular assays that we used, although it is inappropriate to make precise comparisons of potency in view of the different plasmids, their levels and other differences between the two assays. In contrast to some previous reports [10,41], t10, c12-CLA was, if anything, more potent than c9, t11-CLA as an activator of PPARα. Nevertheless, c9, t11-CLA was sufficiently effective to raise the possibility that it contributed to the improvement in insulin sensitivity due to the prolonged treatment with CLA. Activation of PPARα and peroxisomal proliferation might contribute to the increased liver weight in c9, t11-CLA-treated mice, steatosis also playing a role, at least in the case of t10, c12-CLA [10]. Activation of PPARα does not, however, appear to have a major role in the anti-obesity effect of CLA, because CLA reduced body fat content in PPARα knockout mice [42]. Moreover, some investigators have found that c9, t11-CLA is more effective than t10, c12-CLA as an activator of PPARα [10,41]. Other workers have shown that CLA also activates PPARβ/δ [10,41], but the effect was small in one these studies and c9, t11-CLA was more potent than t10, c12-CLA [10]. We measured plasma TNFα and adiponectin concentrations following prolonged treatment with CLA. Neither hormone contributed to the improved insulin sensitivity at this time: the plasma TNFα concentration was unchanged by CLA, and the plasma adiponectin concentration was reduced rather than increased. Other investigators have reported that t10, c12-CLA but not c9, t11-CLA reduces the level of adiponectin mRNA in white adipose tissue of lean mice [35]. Since adiponectin is released primarily from adipocytes but plasma levels are low in obesity and increased by weight loss [43], the marked reduction in plasma adiponectin in the CLA-treated mice is consistent with their having less, rather than smaller, adipocytes. This is in turn consistent with the view that the anti-obesity effect of CLA and the initial decrease in insulin sensitivity is due to apoptosis of adipocytes. Decreased fat cell number may also account for the tendency of CLA to increase plasma triglyceride levels in lepob/lepob mice, the remaining fat cells being too full to accommodate the triglyceride released by cells that have undergone apoptosis. Other workers have reported that CLA reduces plasma triglyceride levels; but in some cases this reduction was in lean mice with less replete adipocytes [42,26], and in the one other study in lepob/lepob mice, it was an effect of c9, t11-CLA, which presumably causes little apoptosis of adipocytes [9]. Interestingly, CLA has been reported to increase plasma adiponectin levels in Zucker diabetic fatty rats, at the same time decreasing plasma triglycerides and improving insulin sensitivity [21]. We therefore suggest that the main effect of CLA in Zucker diabetic fatty rats is to reduce fat cell size, rather than to promote apoptosis and reduce fat cell number. Conclusions Treatment with CLA initially decreased but subsequently increased insulin sensitivity in lepob/lepob mice. Activation of both PPARγ and PPARα may contribute to the improvement in insulin sensitivity. In the short term, however, another mechanism, activated by t10, c12-CLA but not c9, t11-CLA, which probably leads to reduced adipocyte number and consequently plasma adiponectin concentration, may decrease insulin sensitivity. Methods Animals Female C57Bl/6 lepob/lepob mice were obtained from Harlan Olac (Bicester, UK) and maintained at 23 ± 1°C with lights on from 07:00 to 19:00 h. They were housed in plastic cages with bedding and fed 'rat and mouse standard diet' (Beekay Feed, B & K Universal Ltd., Hull, UK). Six days before the start of the studies they were allocated to treatment groups (6 mice per group), such that each group had a similar mean bodyweight. The CLA and other treatments were mixed with powdered diet and given ad libitum. The mice were killed by cervical dislocation in experiments 1 to 3; in experiment 4 they were anaesthetised with sodium pentobarbitone (Sagatal; 80 mg/kg, i.p.) and exsanguinated through an aortic catheter. All procedures were conducted in accordance with our Home Office, UK project licence under the Animals (Scientific Procedures) Act and as agreed by the University of Buckingham Ethical Review Board. Materials added to diets CLA (in acid form), including isomer-enriched CLA, and sunflower oil were provided by Loders Croklaan, Wormerveer, The Netherlands. The CLA used in experiments 1, 2 and 3 was Clarinol™ A-60, containing 30% c9, t11-CLA and 31% t10, c12-CLA and 25% oleic acid. The CLA used in experiment 4 was Clarinol™ A-80, containing 40% c9, t11-CLA and 40% t10, c12-CLA. Rosiglitazone was synthesised by Dextra Laboratories, Reading, Berks, UK. Experiment 1 At the start of treatment the mice weighed 34.5 ± 4.3 g (mean ± S.D.). The treatment groups were high oleic (83.5%) sunflower oil (control; 15 g/kg diet); rosiglitazone (10 mg/kg diet) plus high oleic sunflower oil (15 g/kg diet); CLA (Clarinol™ A-60: 15 g/kg diet). An oral glucose tolerance test was conducted after 14 days as described below. 30 min prior to giving glucose, when the mice had been fasted for 4.5 hours, blood (100 μl) was taken for the measurement of plasma insulin and triglycerides. Plasma insulin and triglycerides were also measured after 21 days when the mice were feeding ad libitum, and after 22 days when they had been fasted for 16 hours, immediately before termination of the experiment. Experiment 2 At the start of the treatment the mice weighed 44.0 ± 3.2 g (mean ± S.D.). A group fed on powdered chow alone was included. The doses of CLA (Clarinol™ A-60) and sunflower oil in the other two groups were increased to 25 g/kg diet. The oral glucose tolerance test and other measurements were carried out as described for experiment 1, except that the glucose tolerance test was one day earlier (i.e. after 13 days). Experiment 3 At the start of treatment the mice weighed 34.1 ± 2.1 g (mean ± S.D.). The treatment groups were high oleic sunflower oil (25 g/kg diet); CLA (Clarinol ™ A-60: 25 g/kg diet); CLA with the CLA component of Clarinol enriched to 90% with t10, c12-CLA (25 g/kg diet); similarly, CLA enriched to 90% with c9, t11-CLA (25 g/kg diet). The oral glucose tolerance test and other measurements were carried out as described for experiment 2. In addition, the plasma non-esterified fatty acid concentration was measured in the blood taken prior to the glucose tolerance test, and the liver, pancreas, parametrial white adipose tissue depot and interscapular brown adipose tissue depot were weighed at termination. Experiment 4 At the start of treatment the mice weighed 31.6 ± 3.8 g (mean ± S.D.). The treatment groups were high oleate (as glyceride) sunflower oil (25 g/kg diet); CLA (Clarinol A80: 10 g/kg diet) plus high oleic sunflower oil (15g/kg diet); CLA (Clarinol A80: 25 g/kg diet). Oral glucose tolerance tests preceded by blood sampling for the measurement of triglycerides, insulin and non-esterified fatty acids were taken after 14, 35 and 70 days of treatment. After 77 days of treatment the animals were anaesthetised in the fed state and blood was taken from the thoracic aorta for the measurement of plasma TNFα and adiponectin. Tissues were weighed as in experiment 3. Body length was measured from the tip of the nose to the anus. Oral glucose tolerance tests In each experiment oral glucose tolerance was measured after 13 or 14 days. In experiment 4 it was also measured after five and ten weeks. The mice were fasted for five hours before being dosed with glucose (3 g/kg, p.o. in experiments 1 and 2; 2 g/kg, p.o. in experiments 3 and 4). Blood samples (20 μl) were taken from the tip of the tail after applying a local anaesthetic (lignocaine) and immediately before and 30, 60, 90, 120 and 180 min after dosing the glucose. They were mixed with haemolysis reagent and blood glucose was measured in duplicate using the Sigma Enzymatic (Trinder) colorimetric method and a SpectraMax 250 (Molecular Devices Corporation, Sunnyvale, CA, USA). Areas under the glucose tolerance curve (0–180 min) and other manipulations and analysis of the data were carried out using Prism software, version 3.0 (GraphPad Software Inc., San Diego, CA, USA). Other plasma analytes Blood was collected into EDTA-coated microcuvettes (Sarstedt microcuvette, Aktiengsellschaft & Co., Nämbrecht, Germany) for the measurement of plasma insulin, non-esterified fatty acids and triglycerides. Plasma was stored at -80°C. 5 μl plasma samples were assayed using kits for triglyceride (Sigma enzymatic colorimetric method), non-esterified fatty acids (Wako Chemicals, Neuss, Germany) and insulin (mouse standard; Crystal Chemistry Incorporated, Downers Grove, IL, USA). For the measurement of plasma TNFα (Linco; St Charles, MI, USA) and adiponectin (Biosource UK, Nivelles, Belgium), blood was taken into tubes containing 200 units of heparin and plasma was stored at -80°C. Plasmids for reporter gene assays pRSV/hRXRα and pcDNA3/hPPARγ1 were both obtained from Professor VKK Chatterjee (Addenbrooke's Hospital, Cambridge, UK). PPPRE3TK-luc was prepared by replacing the NF-kB enhancer element of pNF-κB-luc (Clontech) with a cassette of 3 PPAR Response Elements (PPREs). A double-stranded PPRE cassette was prepared using the 'Klenow fill-in' technique. An 113 bp oligonucleotide (PPRE3) 5'- GCATTCACGCGTCAAATATAGGCCATAGGTCATTCTCGAGCAAATATAGGCCATAGGTCATTCTCGAGCAAATATAGGCCATAGGTCAGATTCGATCAATATAGGCCATAGGTCACTCGAGGCAACAGATCTTACGCATG -3' containing a triplet of PPREs and appropriate restriction endonuclease sites was used as a template for synthesis of a second DNA strand. This was primed by PPRE3R, 5'-CATGCGTAAGATCTGTTGCC-3', which is complementary to the 3' region of PPRE3. 20 μl annealed PPRE3 and PPRE3R, 1.5 μl 2 mM dNTPs, 1 × Klenow Buffer and 5 units DNA Polymerase I (Klenow fragment), were incubated for 1 hour at 37°C and then 10 minutes at 75°C, purified by ethanol precipitation and resuspended in sterile water. The double-stranded PPRE cassette was digested with MluI and BglII and ligated into pNF-κ B-luc that had been cleaved with the same restriction endonucleases. Ligated DNA was transformed into competent JM109, E. coli cells (Promega). pcDNA3/PPARα was prepared by removing the human PPARα cDNA insert from pUC18/hPPARα as a NruI/BamHI fragment and ligating it into EcoRV/BamHI cleaved pcDNA3.1(-) (Invitrogen). Ligated DNA was transformed into competent JM109, E. coli cells (Promega). PPARγ and PPARα reporter gene assays Cos-7 cells (ECACC No. 87021302) were routinely cultured in DMEM containing 10% FCS, 2 mM L-Glutamine, 100 iu/ml penicillin and 100 μg/ml streptomycin at 37°C/5% CO2. Transient transfections were performed using LipofectAMINE as directed by the manufacturers (GibcoBRL). For the PPARγ assay Cos-7 cells were plated in 24-well plates at a density of 0.375 × 105 cells/well. Cells were transfected in serum-free medium (DMEM containing 2 mM L-glutamine) with pPPRE3TK-luc, pRLTK, pRSV/hRXRα and pcDNA3/hPPARγ 1 at concentrations of 0.4, 0.03, 0.02 and 0.02 μg/well, respectively. Five hours after transfection cells were fed with 250 μl/well of serum-free medium containing various concentrations (0–100 μM) of CLA (either the c9, t11 isomer or t10, c12 isomer prepared in 0.1% DMSO). Cell lysates were prepared after 46 hours using 100 μl 1 × passive lysis buffer (Promega) per well. Firefly and renilla luciferase activities were measured using a Dual Luciferase Assay kit (Promega), as described by the manufacturers. Measurements were performed on an MLX microtitre plate luminometer (Dynex). For the PPARα assay Cos-7 cells were plated in 24-well plates at a density of 0.5 × 105 cells/well. Cells were transfected in serum-free medium with pPPRE3TK-luc, pRLTK, pRSV/hRXRα and pcDNA3/hPPARα at concentrations of 0.4, 0.04, 0.03 and 0.03 μg/well, respectively. Five hours after transfection DMEM supplemented with 2 mM L-glutamine and 20% SBCS (charcoal-stripped bovine calf serum, Sigma) was added to the cells. Following 18 hours incubation at 37°C/5% CO2 the medium was removed and replaced with medium (DMEM supplemented with 2 mM L-glutamine and 10% SBCS) containing various concentrations (0-100 μM) of CLA enriched to 85% with the c9, t11 isomer or 81% with the t10, c12 isomer prepared in 0.1% DMSO. After 24 hours of treatment cell lysates were prepared and luciferase activity measured as described above. Statistics Data were analysed by one-way analysis of variance followed by LSD test with the sunflower oil treatment as the control. Means are of 6 values with SEM. Authors' contributions MC, LB, IM and MS devised the experiments. EW, MS and CS, supported by JO and SW conducted the in vivo experiments and analysed materials from these. AM conducted the in vitro experiments. MS and MC conducted the initial analysis and interpretation of the data. JA reanalysed some of the data and wrote the manuscript with input from the other authors, and in particular from AE and IM with respect to interpretation and perspectives. 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==== Front World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-3-31563894610.1186/1477-7819-3-3Case ReportAn unusual presentation of a malignant jejunal tumor and a different management strategy Samaiya Atul [email protected] SV Suryanarayana [email protected] Sanjay [email protected] Sidhartha [email protected] Sunil [email protected] Dillip K [email protected] Nootan K [email protected] Department of Surgical Oncology, Institute Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India2 Department of Radio-diagnosis, Institute Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India3 Department of Radiotherapy, Institute Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India2005 9 1 2005 3 3 3 22 6 2004 9 1 2005 Copyright © 2005 Samaiya et al; licensee BioMed Central Ltd.2005Samaiya et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Malignant small bowel tumors are very rare and leiomyosarcoma accounts for less than 15% of the cases. Management of these tumors is challenging in view of nonspecific symptoms, unusual presentation and high incidence of metastasis. In this case report, an unusual presentation of jejunal sarcoma and management of liver metastasis with radiofrequency ablation (RFA) is discussed. Case presentation A 45-year-old male presented with anemia and features of small bowel obstruction. Operative findings revealed a mass lesion in jejunum with intussusception of proximal loop. Resection of bowel mass was performed. Histopathological findings were suggestive of leiomyosarcoma. After 3-years of follow-up, the patient developed recurrence in infracolic omentum and a liver metastasis. The omental mass was resected and liver lesion was managed with radiofrequency ablation. Conclusion Jejunal leiomyosarcoma is a rare variety of malignant small bowel tumor and a clinical presentation with intussusception is unusual. We suggest that an aggressive management approach using a combination of surgery and a newer technique like RFA can be attempted in patients with limited metastatic spread to liver to prolong the long-term survival in a subset of patients. ==== Body Background Malignant tumors of the small bowel are rare and accounts for the <2 % of total gastrointestinal (GI) malignancy [1,2]. The age-adjusted incidence of small bowel malignancy is 1 per 100,000 with prevalence of 0.6% [3]. Management of these tumors is challenging because of their rarity, relative inaccessibility for diagnosis and diverse histologic types and nonspecific symptoms [4]. Because of the heterogenous and aggressive nature many of them present with recurrence and visceral metastasis. Radiofrequency ablation has been successfully tried as a minimally invasive but effective local treatment of liver metastasis from a variety of primary sites including small bowel tumors [5]. In this case report, we present an unusual case of small bowel sarcoma, and discuss the clinical presentation and management using combination of surgery and radiofrequency ablation (RFA). Case presentation A 45-year-old male was referred to our center with the diagnosis of suspected non-Hodgkin's lymphoma (NHL) of the bowel in December 1999. He had generalized weakness for 2-years along with recurrent vomiting, occasional constipation and melaena for last 2 months. The diagnosis was considered after an ultrasound (USG) guided fine needle aspiration cytology (FNAC) from the intra-abdominal mass done elsewhere and showed features suspicious of NHL. At presentation, patient's general condition was poor and he was dehydrated and pale. There was no peripheral lymphaedenopathy. Abdominal examination revealed an ill-defined, mobile, nontendor lump in left paraumblical region extending up to left lumber region. There was no hepato-splenomegaly. Examination of chest and cardiovascular system was normal. After initial resuscitation with crystalloids and blood transfusion, patient was further investigated. At presentation his hemoglobin was low (6.4 gm%) but rest of the routine haematological investigations were within normal limit. The chest X-ray was normal. Abdominal ultrasound (USG) showed a left upper abdominal mass lesion suggestive of bowel mass. The upper GI endoscopy was normal. The barium meal follow through examination was suggestive of intussusception of proximal jejunum and a suspected mass lesion at the leading edge of intussusceptum. An USG guided core needle biopsy of mass was performed because of earlier suspicion of NHL, which showed smooth muscle bundles with areas of necrosis. Based on the above findings and biopsy report, patient was taken up for exploratory laparotomy 3 weeks after initial presentation. Operative findings revealed an intussuscepted proximal jejunum loop 12 cm distal to duodeno-jejunal flexure and a vascular polypoidal growth measuring 6.5 × 5 × 3.5 cms on serosal surface of jejunum. Liver and spleen were normal. There was no evidence of mesenteric or retroperitoneal lymphaedenopathy, ascites or peritoneal disease. The resection of involved segment of jejunum with 5 cm margins and an end-to-end anastomosis was performed. The postoperative course was uneventful. Pathological examination of the specimen revealed two mass lesions measuring 4.5 cm & 3 cm in continuity (total 7.5 cm in maximum diameter) located on mucosal and serosal surface of jejunum respectively. Microscopically it showed spindle cell tumor with a mitotic rate of 5/10 high power fields (HPF). The tumor was negative for desmin, S-100, CD 34 and c-kit (CD117) but focally positive for actin on immunohistochemistry (IHC). The resection margins were free. The final diagnosis of malignant spindle cell tumor – leiomyosarcoma of jejunum was made. Due to negative margins and no evidence of disease elsewhere, no adjuvant therapy was planned and patient was kept on regular follow up. He was assessed clinically at three months interval and an USG of abdomen was performed six monthly. Patient remained disease free for 39 months. After that a follow-up USG showed a space-occupying lesion (SOL) in segment VII of liver and a mass lesion in left upper abdomen. The patient was asymptomatic and abdominal examination revealed no abnormality. A computerized tomographic (CT) scan of abdomen was performed which showed a 2 × 2 cms, brightly enhancing SOL in segment VII of liver (Figure 1) and a 3 × 4 cms omental mass lesion abutting the bowel loop in left upper abdomen (Figure 2). Upper GI endoscopy and colonoscopy did not reveal any abnormality. A provisional diagnosis of recurrent leiomyosarcoma was made. Patient was planned for resection of the mass and RFA of liver lesion. Figure 1 CT scan image showing the hepatic metastasis in segment VII. Figure 2 CT scan image showing the omental mass adjacent to bowel loop (circle around the omental mass). Patient was explored and two separate well encapsulated, fleshy mass lesions were found in infracolic omentum, measuring 6 × 7 × 3.5 and 2 × 1 × 1 cms. The liver lesion was not palpable and there was no evidence of disease at primary site or anywhere else in peritoneal cavity. An infracolic omentectomy along with tumor nodules was performed. The histopathological features were suggestive of a highly cellular tumor composed of spindle shaped cells arranged in interlacing fascicles. On immunohistochemistry, multiple sections were negative for CD-34, CD-117, S-100 and desmin but focally positive for actin as in the initial tumor. So a diagnosis of recurrent leiomyosarcoma was made. The patient was taken up for an USG guided RFA of liver metastasis 3 weeks after surgery. The RFA was performed by Cool Tip™ RF machine [Radionics, USA] using single tip probe. The procedure was conducted for 12 minutes at temperature of 65°C and the lesion was ablated with 1 cm margin. Follow-up CT scan done 3 days later showed complete ablation of metastatic lesion (Figure 3). The patient was kept on regular follow-up and nine months after RFA he was asymptomatic and an abdominal CT scan showed no evidence of recurrence. Figure 3 Post radiofrequency ablation CT scan image showing complete resolution of hepatic metastasis. Discussion The small intestine accounts for 75% of total length of GI tract and more than 90% of mucosal surface. However, it is the site of only 6–25% of GI neoplasms and only 2% of GI malignancies [2,6]. Leiomyosarcoma is the fourth most common malignant tumor of small bowel and it's incidence is 1.2 cases/million/year [7,8].The most frequent site of leiomyosarcoma is jejunum, followed by ileum and duodenum [6,8]. The peak incidence is in 6th decade and there is a slight male preponderance [6]. Most of these tumors are slow growing and associated with a long period of symptoms [8]. The most common presentation is GI bleeding and anemia [6]. Abdominal pain and palpable mass is present in 5–50% of patients [6]. Other symptoms include nausea, vomiting and weakness [7]. Nonetheless, there is no specific sign or symptom that defines presentation of smooth muscle tumors [7]. Our patient presented with features of GI bleed, anemia and recurrent subacute intestinal obstruction. Intestinal obstruction is seen in less than 5% patients with smooth muscle tumor and is usually caused by tumor infiltration or malignant adhesions [6,7]. In the present case, the cause of obstruction was intussusception. In adult patients, small bowel intussusception is caused mostly by small benign intraluminal ileal tumors like lipoma and leiomyoma [6,8]. However, in this patient intraluminal component of dumb bell jejunal leiomyosarcoma was causing intussusception. Intussusception is a rare phenomenon in patients with leiomyosarcoma and tumors of ileal region presents with intussusception more often than jejunal location [6]. Review of English literature revealed only one case of jejunal intussusception caused by leiomyosarcoma [9]. The commonly recommended investigations in cases of suspected small bowel tumor are endoscopy, CT scan and contrast studies and most often the final diagnosis is confirmed after laparotomy and histopathology [6] as happened in the present case. The preoperative tissue diagnosis is not routinely recommended except in suspected cases of lymphoma, germ cell tumor and unresectable metastatic disease because of theoretical risk of peritoneal seeding and tumor rupture along with difficulty in obtaining definitive diagnosis [10,11]. Majority of leiomyosarcoma grow extraluminally but infrequently tumor can grow both extra and intraluminally. Blanchard et al in their extensive review of small bowel leiomyosarcoma found that 68% of jejunal tumors grow extraluminally and only 14.4% in dumb bell fashion [6]. Tumor size varied from 1–20 cm and in >3/4th of patients tumor is >5 cm. In the present case, the tumor was 7.5 cms and had grown in a dumb bell fashion. The surgical resection is the mainstay of treatment in these tumors [6,7]. The surgery usually involves the en bloc resection of tumor with wide margins along with adjacent mesentery [6]. The lymphatic spread of leiomyosarcoma to regional lymph nodes occurs in 5%–13% of patients and role of lymph node dissection is controversial. However, routine lymph nodal dissection in the absence of nodal disease is not recommended [7]. Unfortunately, leiomyosarcomas are notoriously radioresistant and attempts at using adjunctive radiotherapy and chemotherapy have failed [12,13]. There is no role of adjuvant therapy and patients with complete resection are kept on close follow-up [6,7,13]. Periodic USG or CT scan is recommended, as biologically these tumors are heterogeneous and aggressive in nature [10]. Similar guidelines were followed in this patient. The differentiation between benign and malignant smooth muscle tumors is difficult and metastasis is the only conclusive evidence of malignancy. Similar to other sarcomas, the most common route of metastasis is hematogenous and common sites are liver and lungs. But unlike other sarcomas peritoneal seeding is more commonly seen [6]. Blanchard et al reported 41.3% incidence of metastasis in jejunal leiomyosarcoma and liver was the most common site followed by peritoneum and mesentery [6]. They also found recurrent tumors in 40.9% of patients in conjunction with metastatic disease. A similar picture was present in our patient. Ranchod and Kempson [14] reported that number of mitosis is the single most criterion for predicting the metastatic potential and tumors with ≥5 mitosis per 10 high power fields (HPFs) behaved more aggressively. A tumor that shows >2 mitosis/HPF is usually considered malignant [6]. The size of tumor is also considered in risk stratification for metastasis and tumors <5 cms are significantly less likely to metastasize [6]. With this criterion, our patient falls into high-risk group for relapse. Overall reported survival rate for GI leiomyosarcoma is 10–48% [6]. But in an analysis of six series, Licht et al [15] reported only 5–20%, 5-year survival for high-grade tumors (tumors with >10 mitosis per 10 HPFs). O'Riordan et al [8] reported 5-year survival rate of 27% for tumors >5 cms. In a series of 21 patients with high grade leiomyosarcomas, Chou et al found that 17 of their cases had liver metastasis or local recurrence [16]. Conlon et al [17] reported 44% recurrence after complete resection, and mean time to recurrence was 9 months (median 7, ranging from <1 to 37 months). Despite the high-risk, our patient had relapse after 40 months of primary surgery and the relapse sites were omentum and liver, without having any evidence of disease at primary site. A few patients benefit from surgical intervention for removal of metastasis in small bowel leiomyosarcoma [7]. Karakousis et al, [18] reported prolongation of survival after metastasectomy in intestinal leiomyosarcoma and 3 year survival of 28%. Kohno et al [19] described 10-years survival after multiple surgeries in a patient of intestinal leiomyosarcoma. Considering the good general condition of our patient, long disease free interval and limited bulk of disease, we planned for resection of omental recurrence and treat the liver lesion with RFA. Most of the experience of liver metastasis management is in colorectal malignancy and there is limited experience of liver resection in noncolorectal cancers [20]. There are different types of therapy for the management of liver metastasis – surgical resection, regional treatment modalities, systemic therapy and local or in situ ablation [21]. Although, the gold standard is surgical resection however only 15–25% of patients with liver metastasis are suitable for hepatic resections [20,21]. In soft tissue sarcoma; in a selected group of patients, metastatectomy is recommended [22]. Due to high morbidity and mortality and limited survival after liver resection, recently, in situ ablation of liver metastasis with radiofrequency ablation is gaining popularity [5,20,21]. RFA is ideally suitable for single lesion <5 cm in size and complication rate of RFA is usually very low [23]. The effectiveness of RFA has been demonstrated in few retrospective studies but long-term results are awaited [24]. Considering the very high-risk of relapse in leiomyosarcoma and a deep-seated liver metastasis requiring major hepatic resection, we planned for the RFA of liver lesion. Until now, there are only a few reports of use of RFA in treatment of liver metastasis in leiomyosarcoma [25,26]. Tepetes et al [25] reported 5-year survival in a case of hepatic metastasis from leiomyosarcoma with combination of liver resection, RFA and systemic chemotherapy. Karakousis et al [18] reported 3 and 5 years survival as 28% and 4% respectively after complete resection of metastasis in intestinal leiomyosarcoma. However, survival was limited to 6 months for high-grade leiomyosarcoma. Despite a high-risk tumor and having local relapse with liver metastasis, our patient is still disease free after 9 months of second surgery and 4 years after first surgery. Conclusion Jejunal leiomyosarcoma is a rare variety of malignant small bowel tumor with diverse presentation, heterogeneous behavior and a high propensity for relapse. Clinical presentation with intussusception is unusual. We suggest that an aggressive management approach using a combination of surgery and a new technique like RFA can be attempted in patients with limited metastatic spread to liver to prolong the long term survival in a subset of patients. Competing interest The author(s) declare that they have no competing interests. Authors' contributions SVSD, NKS: Surgical management and review of manuscript. AS, SH, SK: Review of literature and preparation of manuscript. ST, DKP: Radiofrequency ablation. All authors have read and approved the contents of manuscript. Acknowledgement Patients' written consent was obtained for publication of his case record and photographs of CT scan images. ==== Refs Neugut AI Jacobson JS Suh S Mukherjee R Arber N The epidemiology of cancer of small bowel Cancer Epidemiol Biomarkers Prev 1998 7 243 251 9521441 Barelay TH Schapira DV Malignant tumors of small intestine Cancer 1983 51 878 881 6821853 Attanoos R Williams GT Epethelial and neuroendocrine tumors of the duodenum Semin Diagn Pathol 1991 8 149 162 1925122 Briicher BLDM Roder JD Fink U Stein HJ Busch R Siewert JR Prognostic factors in resected primary small bowel tumors Dig Surg 1998 15 42 51 9845562 10.1159/000018585 Solbiati L Ierace T Goldberg SN Sironi S Livraghi T Fiocca R servadio G Mueller PR Del Maschino A Gazelle GS Percutaneous US-guided radio-frequency tissue ablation of liver metastases: treatment and follow-up in 16 patients Radiology 1997 202 195 203 8988211 Blanchard DK Budde JM Hatch GF Wertheimer-Hatch L Hatch KF Davis GB Foster RS JrSkandalakis JE Tumors of small intestine World J Surg 2000 24 421 429 10706914 10.1007/s002689910067 O'Riordan BG Vilor M Herrera L Small bowel tumors: Overview Dig Dis 1996 14 245 257 8843980 Gill SS Heuman DM Mihas AA Small intestinal neoplasms J Clin Gastroenterol 2001 33 267 282 11588539 10.1097/00004836-200110000-00004 Mingoli A Modini C Sgarzini G Medici F Nardacchione F Marzano M Multifocal leiomyosarcoma of the gastrointestinal tract requiring emergency surgical treatment for small bowel intussusception Ital J Gastroenterol 1993 25 26 28 8428019 Connolly EM Gaffney E Reynolds JV Gastrointestinal stromal tumors Br J Surg 2003 90 1178 1186 14515284 10.1002/bjs.4352 Fernandez-Trigo Sugarbaker PH Sarcomas involving the abdominal and pelvic cavity Tumori 1993 79 77 91 8346570 Lee YT Leiomyosarcoma of the gastrointestinal tract: general pattern of metastasis and recurrence Cancer Treat Rev 1983 10 91 101 6347377 10.1016/0305-7372(83)90007-5 Choi TK Ng A Wong J Doxorubicin, dacarbazine, vincristine and Cyclophosphamide in the treatment of advanced gastrointestinal leiomyosarcoma Cancer Treat Rep 1985 69 443 444 3995513 Ranchod M Kempson RL Smooth muscle tumors of the gastrointestinal tract and retroperitoneum: a pathologic analysis of 100 cases Cancer 1977 39 255 262 832238 Licht JD Weissmann LB Antman K Gastrointestinal Sarcomas Semin Oncol 1988 15 181 188 3285480 Chou FF Eng HL Sheen-Chen SM Smooth muscle tumors of the gastrointestinal tract: Analysis of prognostic factors Surgery 1996 119 171 177 8571202 Conlon KC Casper ES Brennan MF Primary gastrointestinal sarcomas: analysis of prognostic variables Ann Surg Oncol 1995 2 26 31 7834450 Karakousis CP Blumenson LE Canavese G Rao U Surgery for disseminated abdominal sarcoma Am J Surg 1992 163 560 564 1375814 10.1016/0002-9610(92)90556-7 Kohno H Nagasue N Araki S Kato T Ten-year survival after synchronous resection of liver metastasis from intestinal leiomyosarcoma Cancer 1981 47 1421 1423 7226067 Detry O Warzee F Polus M De Roover A Meurisse M Honore P Liver resection for noncolorectal, nonneuroendocrine metastases Acta Chir Belg 2003 103 458 462 14653028 Liu LX Zhang WH Jiang HC Current treatment for liver metastases from colorectal cancer World J Gastroenterol 2003 9 193 200 12532430 Abdalla EK Pisters PW Metastasectomy for limited metastases from soft tissue sarcoma Curr Treat Options Oncol 2002 3 497 505 12392639 Livraghi T Solbiati L Meloni MF Gazelle GS Halpern EF Goldberg SN Treatment of focal liver tumors with percutaneous radio-frequency ablation: complications encountered in a multicenter study Radiology 2002 226 441 451 12563138 Livraghi T Solbiati L Meloni F Ierace T Goldberg SN Gazelle GS Percutaneous radiofrequency ablation of liver metastases in potential candidates for resection: the "test-of-time approach" Cancer 2003 97 3027 35 12784338 10.1002/cncr.11426 Tepetes K Tsamandas AC Ravazoula P Petsas T Bonikos DS Karavias DD Survival for 5 years after repeat liver resections and multimodality treatment for metastatic intestinal leiomyosarcoma: report of a case Surg Today 2002 32 925 928 12376797 10.1007/s005950200184 Tepel J Hinz S Klomp HJ Kapischke M Kremer B Intraoperative radiofrequency ablation (RFA) for irresectable liver malignancies Eur J Surg Oncol 2004 30 551 555 15135485 10.1016/j.ejso.2004.03.010	15638946	PMC546237	CC BY	2021-01-04 16:39:05	no	World J Surg Oncol. 2005 Jan 9; 3:3	utf-8	World J Surg Oncol	2,005	10.1186/1477-7819-3-3	oa_comm
==== Front Nutr JNutrition Journal1475-2891BioMed Central London 1475-2891-4-21564932410.1186/1475-2891-4-2ResearchNutrition education: a questionnaire for assessment and teaching Makowske Mary [email protected] Richard D [email protected] Department of Biochemistry. State University of New York Downstate Medical Center. Brooklyn, NY 11203 USA2005 13 1 2005 4 2 2 28 11 2004 13 1 2005 Copyright © 2005 Makowske and Feinman; licensee BioMed Central Ltd.2005Makowske and Feinman; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It is generally recognized that there is a need for improved teaching of nutrition in medical schools and for increased education of the general population. A questionnaire, derived in part from a study of physician knowledge, was administered to first year medical students in order to assess their knowledge of various aspects of nutrition and metabolism, and as a teaching tool to transmit information about the subject. The performance of first year students was consistent with a generally educated population but there were surprising deficits in some fundamental areas of nutrition. Results of the questionnaire are informative about student knowledge, and immediate reinforcement from a questionnaire may provide a useful teaching tool. In addition, some of the subject matter can serve as a springboard for discussion of critical issues in nutrition such as obesity and markers for cardiovascular disease. A major barrier to improved teaching of nutrition is the lack of agreement on some of these critical issues and there are apparent inconsistencies in recommendations of government and health agencies. It seems reasonable that improved teaching should address the lack of knowledge of nutrition, rather than knowledge of official guidelines. Student awareness of factual information should be the primary goal. ==== Body Background Like many medical schools, SUNY Downstate Medical Center has been trying to improve the teaching of nutrition in the curriculum. The study presented here had two goals. First, we wanted to assess the knowledge of the first year class on the subject and, second, we hoped to use the questionnaire method, with immediate feedback, as a mechanism for imparting information on the areas covered. Whereas some form of nutrition education is part of the curriculum of most medical schools [1], it is generally believed that both medical professionals and the general public have serious limitations in their knowledge of the field [2,3]. Four of the 14 questions presented to the first year medical class were included in a recent survey of physicians published in this journal [4]. That paper was critical of the level of physician knowledge regarding nutrition. Therefore it seemed appropriate to compare performance of students with that of practitioners. In addition, the mechanism of providing information by presenting an anonymous quiz with immediate feedback could address one of the problems cited as a barrier to introducing nutrition into the medical curriculum – that is, that the first year basic science curriculum is already very concentrated, leaving little room for new material. The quiz format provides motivation and, because it is anonymous, does so in a relatively unstressful way. Perhaps the most important problem in introducing nutrition into the medical curriculum is the lack of agreement on what should be taught. There is strong, even contentious debate about the most fundamental issues such as obesity, cardiovascular disease, and diabetes. Thus, concerns about inadequate physician knowledge frequently refer to ability to counsel patients according to standard guidelines [4-6]. Here we suggest that a more appropriate goal would be the understanding of basic nutritional information and that this information is not always in agreement with official guidelines. The details of these problems are left to Results and Discussion since we first want to offer the reader a chance to take the questionnaire as given to students. The quiz is presented twice: first, as given to medical students, and then, a second time, with the answers and the comments given to students immediately upon completion of the quiz. A follow-up email that was sent to students after results were tabulated has also been reproduced. Results and Discussion provides data on student performance and additional discussion. Methods The following is a verbatim reproduction (with addition of references) of the questionnaire given to first year medical students at SUNY Downstate Medical Center. The first four questions were taken from Flynn, et al. [4]. Following Flynn, we did not attempt to define the term "low fat diet" which is usually used without elaboration. No specific time limit was given for the quiz but a class of 111 students finished in about 20 min with a few stragglers. Students were given the answer sheet (shown after the quiz itself) in exchange for their questionnaire. Students were in the middle of the metabolism course, part of a subdivision characterized as Gastrointestinal Block. They had been taught bioenergetics and carbohydrate and lipid metabolism, and would be expected to know the answers to some of the questions based on that material. Other questions tested general nutrition knowledge while questions 12–14 emphasized material taught later in the course. Questionnaire This questionnaire is anonymous and does not affect your grade. Make up a User Name (in case there is a follow-up) that only you know and that is not common, like Sherlock-37) User Name _________________________________________________ The questionnaire is designed to test your general knowledge, not anything you have learned in the course. 1. A good source of monounsaturated fat is: (check all that apply) _____ Butter _____ Canola Oil _____ Corn Oil _____ Flaxseed Oil _____ Olive Oil _____ Safflower Oil _____ Soybean Oil _____ don't know 2. The diet component that is most likely to raise triglycerides is (select one) _____ Fat _____ Carbohydrate _____ Protein _____ don't know 3. In general, what effect does a low-fat diet have on triglycerides? _____ Increase _____ Decrease _____ no change _____ don't know 4. In general, what effect does a low-fat diet have on HDL-c (high density lipoprotein-cholesterol) ? _____ Increase _____ Decrease _____ No change _____ Don't know 5. In the past thirty years, the per cent fat in the American Diet has: _____ Increased _____ Decreased _____ Stayed about the same. 6. The most energy dense food (most calories/gram) is: _____ Carbohydrate. _____ Protein. _____ Fat. 7. High total blood cholesterol can be lowered significantly by: _____ Diet _____ Drugs such as statins _____ Diet or drugs are equally effective _____ Neither 8. The dietary change that is most likely to increase the risk of cardiovascular disease: _____ unsaturated fat → saturated fat (that is, replace unsaturated fat with saturated fat) _____ unsaturated fat → carbohydrate _____ carbohydrate → unsaturated fat _____ carbohydrate → saturated fat _____ saturated fat → carbohydrate _____ saturated fat → unsaturated fat 9. Glycemic Index measures the increase in blood sugar over 2 hours per gram of carbohydrate ingested, compared to glucose (=100). For each food indicate the approximate glycemic index as: H, high (70–100), M, Medium (40–70) or L, Low (< 40). You may enter a number if you think you know or can figure it out: _____ white bread _____ whole wheat bread _____ ice cream _____ carrots _____ sucrose (table sugar) _____ fructose _____ bran muffin _____ banana 10. The substances in the following list that either are themselves or are considered to contain large amounts of complex carbohydrates. _____ white bread _____ whole wheat bread _____ ice cream _____ fructose _____ sucrose (table sugar) _____ corn starch _____ fiber 11. In the first list check the vitamins that are generally considered to have antioxidant activity. In the second list check the vitamins that are precursors for oxidative coenzymes. (check all that apply) ANTIOXIDANTS _____ ascorbic acid (vitamin C) _____ niacin _____ riboflavin _____ thiamine _____ pyridoxal phosphate (Vitamin B6) _____ vitamin B12 _____ vitamin D _____ vitamin E REDOX PRECURSORS _____ ascorbic acid _____ niacin _____ riboflavin _____ thiamine _____ pyridoxal phosphate _____ vitamin B12 _____ vitamin D _____ vitamin E 12. Megaloblastic anemia is a prominent feature of deficiencies of: _____ Vitamin B12 _____ Folic Acid _____ Neither _____ Deficiencies of either 13. Addition of folic acid to the diet can relieve all the symptoms due to deficiencies of: _____ Vitamin B12 _____ Folic Acid _____ Both _____ Neither 14. Vitamin B12 deficiency is most commonly seen in: _____ children due to poor nutrition. _____ children due to poor absorption. _____ the elderly due to poor nutrition. _____ the elderly due to poor absorption. Answers and feedback Answers and comment were given to students immediately in exchange for their questionnaires. Students were polled about the quiz verbally and by Email. There were few responders although all were positive and included comments that they might not have read the material in the answers had they not taken the quiz first. The Answer Sheet is reproduced verbatim below and it should be understood that it contains some comments that are more colloquial than would be included in a more formal document. Answer Sheet The first four questions appeared in a recent paper (Flynn M, et al. Nutr J 2003, 2:19) that reported the results of a questionnaire designed to assess the level of knowledge of physicians. The authors were critical of the responses; the paper was entitled "Inadequate physician knowledge of the effects of diet on blood lipids and lipoproteins." In particular, "Physicians showed a poor understanding of the effects of changing the relative intake of carbohydrates and fats on triglycerides and HDL." The rationale for the study were guidelines recommended by the Third Report of the National Cholesterol Education Program Adult Treatment Program (ATP III, Circulation 2002, 106:3143–3421). The ATP III, compared to previous version, advocates lowering triglycerides as a secondary target to lowering LDL. The major conclusions: • Half of the physicians did not know that canola oil is a good source of monounsaturated fat; 26% did not know that olive oil is also. • Ninety-three percent (84% of cardiologists vs. 96% of internists) did not know that a low-fat diet, in general, would increase blood triglycerides. • Approximately three-quarters (70% of cardiologists vs. 77% of internists) did not know a low-fat diet would decrease HDL; almost half (45%) thought that a low-fat diet would not change HDL. • About one-half (47%; 22% of cardiologists vs. 53% of internists) did not know carbohydrate was the diet component most likely to raise triglycerides. 1. A good source of monounsaturated fat is: (check all that apply) _____ Butter _ X _ Canola Oil _____ Corn Oil _____ Flaxseed Oil _ X _ Olive Oil _____ Safflower Oil _____ Soybean Oil _____ don't know It is generally considered that monounsaturated fats are protective of cardiovascular disease (as in the Mediterranean Diet). The effect of monounsaturated fat explains the anomaly in the so called Seven Countries study [7] which launched low fat recommendations. The two countries among the highest consumers of fat had roughly the highest (Finland) and the lowest (Crete) incidence of cardiovascular disease. This finding immediately led to a change in recommendations to lower saturated fat, rather than fat across the board. The persistence of a recommendation to lower total fat is now controversial. Some people may be surprised when they actually look at the data (Figure 1). High concentrations of monounsaturated fats are found in olive oil (73 %) and canola oil (58 %), but the highest is in avocado oil, and there are fairly high levels in beef tallow and lard (44% and 47 %). Interestingly, beef tallow is what MacDonald's previously used to fry their French fries (which at the time got thumbs up from Julia Child) until pressured to switch to vegetable oil. Also of interest is that most of the saturated fat in beef fat itself is stearic acid which is not considered atherogenic [8]. Figure 1 Fatty acid composition of common dietary fats and oils. Data from Figure 1.9 of reference [44]. You should also know that canola oil is named for CANadian OiL (there is no canola tree) and is a highly processed version of rapeseed oil (which is a plant in the Brassica family) and is the subject of popular controversy because of the possible production of trans-fatty acids during processing. 2. The diet component that is most likely to raise triglycerides is (select one) _____ Fat _ X _ Carbohydrate _____ Protein _____ don't know 3. In general, what effect does a low-fat diet have on triglycerides? _ X _ Increase _____ Decrease _____ no change _____ don't know The phenomenon of carbohydrate-induced hypertriglyceridemia is well established in the literature [9-15]. It is the primary reason we ask patients to fast prior to having blood drawn for serum lipid analysis. It is also one of the arguments for low carbohydrate diets since the most predictable effect of such diets is a dramatic decrease in triglycerides compared with low fat diets [16-22]. Low carbohydrate diets also generally increase HDL (good) cholesterol while low fat diets lower HDL although this is somewhat less reliable than the triglyceride effect. An argument against low carbohydrate diets is that, whereas, on average, LDL frequently goes down, individual results are highly variable and LDL increases for some subjects [16-22]. 4. In general, what effect does a low-fat diet have on HDL-c (high density lipoprotein-cholesterol)? _____ Increase _ X _ Decrease _____ No change _____ Don't know 5. In the past thirty years the per cent fat in the American Diet has: _____ Increased _ X _ Decreased _____ Stayed about the same. This is the major point of attack on current guidelines since the obesity epidemic correlates with a decreased percentage of fat in the diet (for men in some years, there was a decrease in total amount of fat) and increased carbohydrate consumption [23,24]. Defenders of current guidelines maintain it is simply that the wrong kind of carbohydrate (i.e., sugars and refined starches) is being consumed, and that affluence has increased the availability of food and portion sizes, and people are overeating "because it is there." On the other hand, published results of low carbohydrate diets show that they can be effective and therefore there is no great over-consumption even though portion sizes are unlimited. It is also argued that the wrong kind of fat (i.e., saturated and trans fats) is being consumed although no effect of fat per se on obesity, independent of calories, has been found. 6. The most energy dense food (most calories/gram) is: _____ Carbohydrate. _____ Protein. _ X _ Fat. The operational numbers in kcals/g are 4, 4, and 9 for carbohydrate, protein, and fat. This is the basis of traditional recommendations for low fat diets for obesity. The reduction in percentage of dietary fat and the obesity epidemic noted above, however, suggests that this is not a good universal principle. The role of macronutrient composition on satiety, taste, total consumption and effect on weight loss is largely unknown due to the multiple factors and individual differences, and some experimental support can be found for just about any idea. 7. High total blood cholesterol can be lowered most significantly by: _____ Diet _ X _ Drugs such as statins _____ Don't know _____ Diet or drugs are equally effective _____ Neither Drugs such as statins (HMGCoA reductase inhibitors) are very effective at reducing cholesterol. Diet can also be effective but usually less so. Combination diet and drugs, however, may be most effective but there is, again, not universal agreement as to what that diet should be. 8. The dietary change that is most likely to increase the risk of cardiovascular disease: _____ unsaturated fat → saturated fat (that is, replace unsaturated fat with saturated) _ X _ unsaturated fat → carbohydrate _____ carbohydrate → unsaturated fat _____ carbohydrate → saturated fat _____ saturated fat → carbohydrate _____ saturated fat → unsaturated fat In addition to the effect on risk factors, epidemiologic evidence suggests that replacing fat with carbohydrate is deleterious. Replacing unsaturated fat with saturated fat will also increase the risk of cardiovascular disease [8,9,14,25]. Of course, in terms of obesity, reducing calories by removing fat and not replacing it with anything is good. Removing carbohydrate, however, may be better, at least in terms of cardiovascular risk as in references above. 9. Glycemic Index (GI) measures increase in blood sugar over 2 hrs per gram of carbohydrate, compared to glucose (=100). For each food indicate the approximate glycemic index as: H, high (60–100), M, Medium (40–60) or L, Low (< 40). You may enter a number if you think you know it: H (70) white bread M (52) whole wheat bread M (50) ice cream M (47) carrots H (70) sucrose (table sugar) L (20) fructose HM (60) bran muffin M (50) banana The GI is a very rough indicator of rise in blood sugar and is influenced by absorption and the concentration of glucose. Fructose has a low GI (20) indicating slow conversion to glucose in 2 hrs but far from being considered a "good" sugar, at high levels may be very deleterious. The concept of glycemic load (GL) which corrects for the amount of carbohydrate per serving may be a better parameter but runs into problems about serving size. So muffins and candy bars have GL = 15 and carrots only 3 but 80 g of carrots may not be a lot for some people [26-28]. 10. The substances in the following list that either are themselves or are considered to contain large amounts of complex carbohydrates. _ ? _ white bread _ ? _ whole wheat bread _ NO ice cream _ NO fructose _ NO sucrose (Table Sugar) _ ? _ corn starch _ ? _ fiber If you had trouble with this, it is because nobody knows how the term should be used and, in fact, we recommend it not be used at all. The original chemical definition – to some extent still used in organic chemistry – is that a complex carbohydrate is a polysaccharide (not mono- (glucose, fructose) or di- (sucrose, lactose)). Any starch, e.g. corn starch or white bread (poly-glucose: amylose or amylopectin) fits the definition, and it used to be nutritional dogma that, at least in terms of raising blood glucose, complex carbohydrates (that is, polysaccharides) were better than simple sugars. When the dogma was finally tested this turned out not to be true and the concept of glycemic index arose. It is clear from question 9 that white bread is nutritionally similar to pure glucose. Probably because of the evocative nature of the word, the term "complex" is still used. Sometimes it means foods that have a low glycemic index due to poor absorption usually due to the presence of fiber, but it is never precise. When people say complex carbohydrate they usually mean the carbohydrate recommended in their diet and missing from somebody else's diet. Suggested nomenclature is "polysaccharides, starch, high fiber," although fiber itself is a heterogeneous category. 11. In the first list check the vitamins that are generally considered to have antioxidant activity. In the second list check the vitamins that are precursors for oxidative coenzymes. (check all that apply) ANTIOXIDANTS _ X _ ascorbic acid (vitamin C) _____ niacin _____ riboflavin _____ thiamine _____ pyridoxal phosphate (Vitamin B6) _____ vitamin B12 _____ vitamin D _ X _ vitamin E REDOX PRECURSORS _____ ascorbic acid _ X _ niacin _ X _ riboflavin _ X _ thiamine _____ pyridoxal phosphate (Vitamin B6) _____ vitamin B12 _____ vitamin D _____ vitamin E 12. Megaloblastic anemia is a prominent feature of deficiencies of: _____ Vitamin B12 _____ Folic Acid _____ Neither _ X _ Deficiencies of either 13. Addition of folic acid to the diet can relieve all the symptoms due to deficiencies of: _____ Vitamin B12 _ X _ Folic Acid _____ Both _____ Neither 14. Vitamin B12 deficiency is most commonly seen in: _____ children due to poor nutrition. _____ children due to poor absorption. _____ the elderly due to poor nutrition. _ X _ the elderly due to poor absorption. The most obvious symptom of a folic acid deficiency, anemia, is due to a requirement for folic acid in the synthesis of DNA. Deficiencies lead to poor maturation of red blood cells (megaloblasts). Megaloblastic anemia can also be caused by a B12 deficiency, which, indirectly, has the same effect. There are only two reactions in humans requiring vitamin B12. First, vitamin B12 is a cofactor in formation of the amino acid methionine from homocysteine and the folic acid derivative, methyl-tetrahydrofolic acid (methyl-THF): Homocysteine + methyl-THF → Methionine + THF This explains the relation between dietary folic acid and high homocysteine which is a marker for cardiovascular disease and potential birth defects. A deficiency in folic acid or the cofactor, B12, will prevent this reaction from occurring. The effect of B12 deficiency on folic acid is indirect: if methionine synthesis cannot be carried out, methyl-THF will build up ("methyl trap"). This is effectively a folic acid deficiency and anemia is the outcome. The second requirement for B12 involves organic acids and deficiencies can lead to neurologic damage. The anemia in a B12 deficiency may be successfully treated with folic acid, swamping out the methyl trap. Neurologic damage, however, may still occur unless the B12 deficiency is also treated. A dietary deficiency of B12 is rarely seen since little is needed and it is stored well. Deficiency is usually detected in the elderly due to decreased production of intrinsic factor, a protein required for absorption. The considerations above bear on the recommendation to add folic acid to manufactured food. Critics point out that by preventing anemia, a B12 deficiency could be masked. Since the major deficiency is not dietary but absorptive, the problem can't be solved by simply adding B12 as well. Follow up The following analysis was sent as an Email to students after the scores on the quiz were tabulated. Results of Nutrition Questionnaire As indicated in the answer sheet, the first four questions are taken from a recent paper (Flynn, M., et al. (2003) Nutrition Journal 2: 19) that reported the results of a questionnaire designed to assess the level of knowledge of physicians. Their conclusion was that "Physicians showed a poor understanding of the effects of changing the relative intake of carbohydrates and fats on triglycerides and HDL." Interestingly the rationale for the study was determining "Physicians' ability to effectively counsel patients with elevated cholesterol to initiate a Therapeutic Lifestyle Changes Diet (TLC)" (as proposed by the Third Report of the National Cholesterol Education Program Adult Treatment Program (ATP III) which recommends lowering triglycerides as a secondary target to lowering LDL. The TLC Diet recommends total fat as 25–35 % and carbohydrate at 50–60 %. Paradoxically, Flynn, et al.'s assessment of physician knowledge focused on the deleterious effect of carbohydrate on triglycerides. Given this association, it is puzzling that ATP III would council people trying to lower triglycerides to undertake such a diet. In any case, your performance compared to their sample is shown in Figure 2. Figure 2 Performance of physicians and first year medical students on questionnaire. Data on physicians from reference [4]. Two questions of importance: Question 5: In the past thirty years, the per cent fat in the American diet has declined by about 10 %, close to the target level of 30% of total calories set back in the 70's. In order to explain why this has been associated with an obesity epidemic, many have blamed portion-size, the fast-food industry and consumers themselves. In any case, most people did not know that percent fat consumption had gone down. Answers were: Increased: 76 % Decreased 22 % Same 3 % Question 6. Again, it was surprising that so many people did not know the relative energy density. The answers: Carbohydrate 19 % Protein 7 % Fat 74 % Analysis Questionnaires were collected from 111 students, all but 6 of whom answered all the questions. Student answers were tabulated and discrimination coefficients were determined by point biserial correlations [29] using LXR-TEST™ software (Logic eXtension Resources ). The discrimination coefficient varies between -1 and +1 and measures the extent to which performance on a particular question reflects performance on the quiz as a whole, that is, whether a question discriminates high performers from low performers. A typically high value of +0.4 means that the question was answered correctly by most students who did well on the exam and answered incorrectly by most who did not. Because the questions had different goals (knowledge assessment vs. motivation for accepting new information), and because some (e.g. questions 2–4) are interrelated, no measure of performance or statistics were carried out for the quiz as a whole. Results and Discussion Student and physician knowledge of nutrition Questions 1–8 Results from the first eight questions and the data from Flynn, et al. [4] are shown in Tables 1 and 2 and in Figure 2. Flynn, et al. used four questions that succinctly identified both practical and conceptual knowledge bearing on the ability to implement dietary recommendations from the Adult Training Program (ATP III) of the National Cholesterol Education Program (NCEP)[6]. Table 1 Student and physician responses (%) Cardiologists Internists Students discrim coeff 1. A good source of monounsaturated fat Butter 4 4 8 0.39 Canola Oil 43 51 26 0.21 Corn Oil 13 16 22 0.32 Flaxseed Oil 12 10 25 0.32 Olive Oil 82 73 58 0.35 Safflower Oil 24 32 25 0.26 Soybean Oil 18 16 38 0.35 don't know 6 6 16 2. Diet component most likely to raise triglycerides Fat 16 47 63 -0.2 Carbohydrate 78 47 32 0.17 Protein 0.8 0.6 0 don't know 5 5 2 3. Effect of low-fat diet on triglycerides Increase 16 4 14 0.22 Decrease 52 73 68 -0.17 no change 26 26 15 0.01 don't know 6 4 3 4. Effect of low-fat diet on HDL-c Increase 11 24 31 -0.08 Decrease 30 23 32 0.25 no change 52 44 23 -0.18 don't know 7 9 14 Discrimination coefficients indicate discrimination of high and low performers on quiz overall as described in the text. (correct answers in bold). Table 2 Student Responses (%) Students discrim coeff 5. Past thirty years, per cent fat in American diet Increase 76 -0.10 Decrease 22 0.14 Same 3 -0.10 6. Most energy dense food Carbohydrate 19 -0.15 Protein 7 -0.10 Fat 74 0.19 7. High blood cholesterol lowered significantly by Diet 14 0.06 Drugs such as statins 21 0.17 Diet or drugs equal 61 -0.15 Neither 0 Don't know 0 8. Most likely to increase risk of CVD UF t>gF 46 0.02 UF -> CHO 7 0.11 CHO -> UF 3 -0.27 CHO -> SF 23 0.09 SF -> CHO 2 0.00 SF -> UF 12 -0.08 First year students did not do as well as physicians at identifying sources of monounsaturated fats. On the other hand, the good discrimination coefficient indicates that knowledge of fat composition is a good indicator of overall knowledge (at least as assessed by general performance on this quiz). Although a substantial fraction of cardiologists polled by Flynn knew that carbohydrate raised triglycerides (Figure 2), most internists and most medical students did not. Likewise, a very small fraction of first year students or physicians were aware of the association between low fat diets and two markers of CVD, triglycerides and HDL-c. As discussed in the student answers (see Methods), there is some irony in that the questions chosen by Flynn bring out the unfavorable effect of carbohydrate on triglycerides while the ATP III recommendation is to maintain 50 % carbohydrate in the diet. Student responses attest to the success of continued popular and government recommendations favoring low fat diets but the content of the answers raises the question of whether sufficient information is being disseminated. It further raises the question as to whether these recommendations, rather than the basic nutritional knowledge, should be communicated. Along the same lines, our questionnaire went beyond the area covered by Flynn to consider the changes in diet that have accompanied the epidemic of obesity. It has to be considered very surprising that only 22 % of an educated population knew that the per cent fat in the American diet has decreased (Figure 3); for men, in fact, the total amount of fat has decreased, whereas for women there has been a slight increase consistent with the much larger increase in caloric intake among women [24]. The observation of a decrease in fat and an increase in carbohydrate in parallel with the obesity epidemic remains as a serious challenge to traditional dietary recommendations. The reduction in fat in the diet from the 1970s to 1995 has been noted by one author [24] to provide a benefit in reduction in serum cholesterol from 213 to 205 mg/dL! Although more difficult to quantify, a decline in exercise is also a likely contributor to the epidemic, but it seems inappropriate, without further evidence, to ignore the prima facie evidence of the effect of macronutrients. Despite the clear correlation between higher carbohydrate, lower fat and obesity, government and health agencies rarely question the appropriateness of the original guidelines and have continued to recommend still higher carbohydrate and still lower fat [6,30,31]. Such recommendations have to be considered controversial and likely to change. For this reason we feel that one of the points brought out by our quiz and Flynn's is that nutritional facts rather than official recommendations should be the goal of nutrition education. Figure 3 Changes in fat and carbohydrate between 1977 and 1995. Data from USDA as reported in reference [24]. Question 6 We were surprised by some lapses in student knowledge revealed by the questionnaire. The most basic question – which macronutrient is the most energy dense – had only 74 % correct answers. To understand how surprising a response this is, it should be understood that the mean score on exams in this section of the medical course is typically 80 % and, on most exams, several questions are answered correctly by 98–100 % of the class. The National Board of Medical Examiners assumes knowledge of caloric value of macronutrients, and we had expected that it was common knowledge. The questionnaire result indicates that no fact in nutrition is too basic to be excluded from course material. Questions 7–8 – Diet and Cholesterol An overwhelming number of medical students believe that diet is as effective as drugs in lowering total cholesterol. This again attests to the pervasive message that diets control blood cholesterol, an idea continually reinforced by media advertisements, for example, for the cholesterol lowering effect of breakfast cereal. Whereas it is likely that diet is an important influence on CVD, there is, again, the problem of which diet and the question would probably have been better framed, as in questions 2–4, on specific lipid components. It is likely, for example, that many medical students would not know that dietary cholesterol is largely without effect on serum cholesterol. In any case, it is generally acknowledged that, on average, drugs such as statins have a greater impact on cholesterol than currently reported diet interventions. The general effectiveness of statins and the promotion by pharmaceutical companies has, most recently, led to a movement to reinforce the idea that genetics (which can't be controlled by diet) also plays a role. The competing financial interests have produced, in our view, bizarre and unpatriotic (?) television commercials blaming mother and apple pie for high cholesterol. Figure 4 Effect of substitution of 5 % of calories on incidence of cardiovascular disease. Data from Hu, et al. [8] Few students in the first year medical class knew that replacing unsaturated fat with carbohydrate was the most damaging substitution in terms of an association with CVD risk. The data from Hu, et al., [8,25] shown in Figure 4 represent yet another reason to reevaluate low fat recommendations. Similar results have been found in the analysis of risk factors [14]. In other words, whereas everybody agrees that removing fat from the diet as a mechanism of calorie reduction is a good thing, replacing fat with carbohydrate correlates with an increase in CVD risk and is likely worse for weight loss. Questions 9–10 – Glycemic Index and Complex Carbohydrates These two questions (Table 3) point out the confusion that exists in characterizing dietary carbohydrates. The glycemic index (GI), and the glycemic load (GL) which corrects for total carbohydrate in individual foods, are indicators of rise in blood glucose. Glycemic control is a major variable in the analysis of metabolic syndrome and obesity, and dietary strategies based on the glycemic index [28] have the same rationale as low carbohydrate diets: reduce fluctuations in insulin and associated anabolic effects. A low carbohydrate diet might be described as a very low glycemic load diet. Nonetheless, the concepts of GI and GL have become part of the political controversies surrounding dietary strategies and proponents usually urge a low GI diet as an alternative rather than a variation of low carbohydrate diets ([15]) despite the fact that in at least one isocaloric comparison of high GI and low GI meals, the low GI meal was, in fact, lower in carbohydrate [27]. An important limitation on the concept of GI is that fructose and therefore fructose-containing products such as sucrose and high-fructose corn syrup may have low values, although these substances may not be desirable. The atherogenic qualities of fructose [32] is one of the ideas that we bring out in the lectures in the medical school course. Table 3 Student Responses (%) on Carbohydrates 9. Glycemic Index Low Medium High discrim coeff White Bread 5 22 70 0.2 Whole Wheat Bread 39 49 10 0.18 Ice Cream 5 16 77 -0.1 Carrots 68 19 9 0.07 Sucrose 1 10 86 0.16 Fructose 3 22 73 -0.04 Bran Muffin 25 49 23 Banana 20 53 23 0.23 10. Complex Carbohydrates Yes No White Bread 60 38 Whole Wheat Bread 68 32 Ice Cream 31 68 Fructose 11 88 Sucrose 18 81 Corn Starch 48 51 Fiber 43 56 We recommend that the term complex carbohydrates not be used since, in practice, it has lost its original meaning of polysaccharide. It is interesting that, to some extent, student answers followed the original definition. Most students picked both white bread and whole wheat bred as complex although, with a slight preference for picking whole wheat over white bread as many health professionals and the lay public might. Question 11 – Vitamins We credit the popular media with the generally good knowledge about the antioxidant vitamins shown in Table 4. In our view, however, this may be a mixed blessing because it shifts the emphasis from macronutrient composition, a major factor in health, to micronutrients, which, at least for the American population, has to be considered secondary. The relatively low performance and good discrimination coefficient in the question on redox precursors is somewhat discouraging, especially in that students had been exposed to the involvement of the three oxidative coenzymes in glycolysis and the TCA cycle. Moreover, the origin of NAD coenzymes in dietary niacin was explicitly taught. We think this apparent deficiency likely results from a lack of emphasis on integration of nutritional information with biochemistry. Table 4 Student Responses (%) on vitamin question 11. Vitamins with indicated activity. yes discrim coeff ANTIOXIDANTS Ascorbic Acid 77 0.47 Niacin 23 0.39 Riboflavin 19 0.45 Thiamine 9 0.57 Pyridoxal Phosphate (Vitamin B-6) 21 0.31 Vitamin B-12 31 0.41 Vitamin D 16 0.38 Vitamin E 69 0.45 REDOX PRECURSORS Ascorbic Acid 27 0.47 Niacin 51 0.31 Riboflavin 43 0.23 Thiamine 34 0.31 Pyridoxal Phosphate 33 0.28 Vitamin B-12 27 0.45 Vitamin D 11 0.44 Vitamin E 5 0.43 Questions 12–14 – Questionnaires as a teaching method: folate metabolism These questions were presented as a preview for an upcoming lecture on folate metabolism; therefore it was expected that students would not score very highly (Table 5). We identify folate metabolism as one of the critical areas of biochemical nutrition. The importance of homocysteine and use of dihydrofolate reductase inhibitors such as methotrexate are two of the most obvious examples of how biochemistry is a practical part of medicine. At the same time, the biochemical pathways are among the most complex, and because folate spans different areas of metabolism, it is difficult to teach. The key nutritional issues are covered both in lecture and in a case-based learning session. Table 5 Student Responses (%) on folic acid questions Students discrim coeff 12. Megaloblastic anemia: deficiencies of Vitamin B-12 39 -0.11 Folic Acid 31 0.17 Neither 6 0.09 Either 19 -0.09 13. Folic acid: relieve deficiencies of Vitamin B-12 7 -0.1 Folic Acid 49 0.12 Both 38 -0.07 Neither 3 14. Vitamin B-12 deficiency commonly seen in children due to poor nutrition. 32 -0.11 children due to poor absorption. 23 -0.04 the elderly due to poor nutrition. 9 0.1 the elderly due to poor absorption. 25 0.1 Nutrition in the Medical School Many papers have been written on the need for, and the difficulty in implementing, improvements in teaching nutrition in medical schools [2,3,33]. Some of the major problems frequently cited are 1) inflexibility in the curriculum due primarily to time constraints and 2) inability to define what aspects of the subject needs to be taught. There is also considerable disagreement on the best method of teaching the subject. The current study bears on some of these questions. Adding nutritional material to the curriculum With respect to point 1) above, the first year medical school curriculum is undoubtedly very dense in content. Adding new material is difficult, especially if it is of the strictly factual type, e.g. macronutrient composition of particular foods. The "low pressure" quiz used here can, in theory, impart a certain amount of specific knowledge and generate student interest without interrupting the general flow of course work. The quiz provides a venue in which interested students can absorb the information, and become aware of the general area if they need to find the information later. Also, in our view, many subjects taught in basic science courses already have nutritional relevance, e.g., cofactors that come from vitamins, and these ideas should be better emphasized. We point out, when the NAD cofactors are introduced, that one would expect global effects of a deficiency disease because of the number of different enzymes that use these cofactors. Although vitamin deficiencies are rare in the absence of gross malnutrition, the emerging role of hypervitamin therapies [34] has great pedagogical value. The tie-in through the quiz may reinforce the basic biochemistry. What to teach in nutrition. Guidelines on macronutrient recommendations We see the question of what to teach as the most critical problem in introducing or expanding nutrition education in the medical school course. Individual faculty may be resistant to giving up their own interests, but this may depend on how well the case is made for changing to new topics. The original study by Flynn was designed to test physicians' knowledge and expand it to allow them to better implement ATP III recommendations on serum triglycerides. In combination with other questions that we have introduced, the general problem arises as to whether these recommendations or nutritional data should be taught. We feel that official recommendations, such as ATP III, have some inherent contradictions. Given that low fat diets tend to raise triglycerides, the associated recommendations to reduce dietary fat and to raise carbohydrate intake appear somewhat contradictory. The major focus of ATP III, however, is control of cholesterol but again, the literature is not clear-cut. Thus, whereas the association between cholesterol levels and CVD is generally accepted by all but a minority of critics, the effect of diet, especially reduced fat diets, on CVD, or even cholesterol, is far more controversial. The Chapter on "Diet and Coronary Heart Disease (CHD)" in Willett's Nutritional Epidemiology [35] is 40 pages long with more than 300 references and contains more than one disclaimer on the diet-heart hypothesis, e.g. "Even if a change in dietary lipids influences the incidence of CHD in the direction predicted by its effect on total blood cholesterol level, the quantitative relationship between this dietary change and risk of disease is uncertain because of the possibility of many other potential physiologic effects of this dietary manipulation (p. 422), " or "Although substantial indirect evidence supports the classic diet-heart hypothesis, the magnitude of any association is likely to be modest for ranges of diet found within western culture or attainable by realistic dietary changes if the effects predicted by metabolic studies are correct (p. 443)." Finally, papers have been published by respected authors with such titles as: "Dietary fat is not a major determinant of body fat [36]," or "Do high carbohydrate diets prevent the development or attenuate the manifestations (or both) of syndrome X? A viewpoint strongly against [37]" These cautionary reports as well as those of other critics of low fat dietary recommendations [8,19,38-40] are largely ignored by the ATP III and the body of experts who are making current recommendations. The recent demonstration of a beneficial effect of saturated fat and lower carbohydrate in patients on an overall low fat diet [41] has been described in an accompanying editorial as an "American paradox." [42]. The extent to which researchers seek to resolve this paradox remains to be seen. The analysis above also bears on the role of low carbohydrate diets in educating students and physicians. We have previously indicated how such diets can be used to teach basic intermediary metabolism [43] and whereas we do not recommend any particular diet, we feel that the biochemical rationale of carbohydrate restriction makes it increasingly difficult to justify exclusive recommendations for low fat, high carbohydrate guidelines. In summary, what to teach remains very problematic. There are clear inconsistencies in the dietary recommendations of the ATP III and other professional agencies. This has to make one question whether students and physicians should be educated only in currently recommended practice, or whether we should instead emphasize understanding the underlying data. This is especially true, given the disclaimers in the American Heart statement [31] that "These recommendations may require modification, based on the results of ongoing and future dietary therapy studies." and that "The available data suggest that it is unlikely that one approach is appropriate for all patients." Of course, presentation of such controversial questions can be introduced into a problem-based learning session but medical students naturally prefer concrete answers and appropriately expect some guidance. The resolution currently depends on individual instructors and departments. It would be good pedagogically to establish the idea that not everything is known about nutrition and that many people consider that a rush to guidelines on insufficient evidence is to be avoided. Conclusions A questionnaire, derived in part from one previously published to assess physician knowledge, can be used to determine medical student awareness of nutritional facts. At the same time, such a quiz can be employed as a teaching device to reinforce earlier material, provide preview of new material, or expose students to factual information that is not easily incorporated into a formal course. One of the areas chosen, the effect of macronutrients on obesity and cardiovascular disease, can lead to discussion and focus on important current issues. The performance of first year medical students as well as the performance of the physicians in the previous study suggest that improvement is needed in imparting knowledge about some basic ideas in nutrition. We believe that the focus should be on these ideas rather than on official recommendations with which the ideas are sometimes in conflict. Finally, the questionnaire is intended as a practical method. The authors would be grateful for any information on the outcome of its use and/or any suggestions for improving the quiz itself. List of Abbreviations HDL: High Density Lipoprotein LDL: Low Density Lipoprotein TAG: Triacylglycerol THF: Tetrahydrofolic acid TLC: Therapeutic Lifestyle Changes Diet ATP III: Third Report of the National Cholesterol Education Program Adult Treatment Program NCEP: National Cholesterol Education Program GI: glycemic index GL: glycemic load CVD: Cardiovascular Disease Acknowledgements Several people have provided valuable help and suggestions. The authors thank Dr. William C. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1571905610.1371/journal.pbio.0030037Research ArticlePsychologyHomo (Human)Parsing a Cognitive Task: A Characterization of the Mind's Bottleneck Parsing a Cognitive TaskSigman Mariano [email protected] 1 Dehaene Stanislas 1 1Unité INSERM 562, Cognitive NeuroimagingService Hospitalier Frédéric Joliot, CEA/DRM/DSV, Orsay cedexFrancePosner Michael Academic EditorUniversity of OregonUnited States of America2 2005 8 2 2005 8 2 2005 3 2 e372 5 2004 23 11 2004 Copyright: © 2005 Sigman and Dehaene.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Timing the Brain: Mental Chronometry As a Tool in Neuroscience The Bottleneck of Central Processing: Clues from Reaction Times Parsing a mental operation into components, characterizing the parallel or serial nature of this flow, and understanding what each process ultimately contributes to response time are fundamental questions in cognitive neuroscience. Here we show how a simple theoretical model leads to an extended set of predictions concerning the distribution of response time and its alteration by simultaneous performance of another task. The model provides a synthesis of psychological refractory period and random-walk models of response time. It merely assumes that a task consists of three consecutive stages—perception, decision based on noisy integration of evidence, and response—and that the perceptual and motor stages can operate simultaneously with stages of another task, while the central decision process constitutes a bottleneck. We designed a number-comparison task that provided a thorough test of the model by allowing independent variations in number notation, numerical distance, response complexity, and temporal asynchrony relative to an interfering probe task of tone discrimination. The results revealed a parsing of the comparison task in which each variable affects only one stage. Numerical distance affects the integration process, which is the only step that cannot proceed in parallel and has a major contribution to response time variability. The other stages, mapping the numeral to an internal quantity and executing the motor response, can be carried out in parallel with another task. Changing the duration of these processes has no significant effect on the variance. The inability to operate two cognitive processes simultaneously - a mental bottleneck - can be explained by a model in which evidence is accumulated stochastically to reach a decision ==== Body Introduction Even the most simple behaviour involves a chain of computations, which link perception, decision making, and action [1,2,3]. Measurements of response times (RTs) have been used as a major source of information on the organization of these stages [4,5], and more recently these analyses have been combined with neuroimaging data to identify separate processing modules [6,7]. This seemingly simple measure of time to completion of a cognitive operation has several intriguing properties. One of them is its noisy character. Even in very simple tasks, RTs typically vary over a broad range of several hundred milliseconds. Another property is that RT can slow down considerably under some circumstances in which the subject is distracted by another competing stimulus or task. This suggests the existence of at least some stages that act as a bottleneck and can only operate serially, one at a time. Here we set out to relate response variability and the serial versus parallel architecture of processing stages. Do all stages of processing contribute uniformly to this variance? Or are some stages particularly variable in their computation time? And does variability relate in a systematic manner to their parallel or serial nature? When two tasks are presented simultaneously (or sequentially at a short interval), a delay in the execution of the second task has been systematically observed [8,9,10,11]. This interference effect is referred to as the psychological refractory period (PRP) and has been explained by a model that involves three stages of processing: a perceptual component (P component), a central component (C component), and a motor component (M component), in which only the C component establishes a bottleneck [5,9,12,13,14,15]. PRP experiments have associated the C component to “response selection”, the mapping between sensory information and motor action [16]. A separate line of psychological research has investigated how the decision to respond is achieved. The decision-making process has been modelled as a noisy integrator that accumulates evidence provided by the sensory system [17,18,19,20,21,22,23,24,25]. Although many variants have been proposed, the basic idea is that perceptual evidence is stochastically accumulated in time. Decision thus results from a random walk of an internal abstract variable. Indeed, in many circumstances, such a decision mechanism can be optimal in the sense that it maximizes the overall likelihood of a correct classification of the stimuli [26,27]. In the simplest scheme, all the variance in RT is attributed to this integration process. Thus, the integration model establishes a possible parsing of a task into components: a fixed component to transform the sensory information to an abstract variable, the accumulation of evidence itself (the only variable process), and the execution of the response. In the present work, we propose a single assumption that unifies those two lines of research. We postulate that only the integration process establishes a serial bottleneck, while all other stages can proceed in parallel with stages of another task (Figure 1). This model should thus explain both the dual-task interference experiments and the detailed analysis of RT distributions. While extremely simple, the model makes powerful mathematical predictions in experiments in which the order of the presentation of the two tasks and their relative offset in presentation are varied. Moreover, it also makes specific predictions in experiments in which the complexity of one of the tasks is changed. Depending on whether the locus of the change is in the P, C, or M components, the shape of RTs as a function of the delay between stimuli acquires a very different shape. Figure 1 The Model: The Process of Accumulation of Evidence Constitutes the Mind's Bottleneck Each task involves a sequence of three stages of processing. The perceptual and motor stages are fixed and can be carried out in parallel with stages of another task, while the central stage consists of a noisy integration (a random walk) until a decision threshold is reached. The central stage of task 2 cannot start until the central stage of task 1 is finished. Thus, this establishes a bottleneck and characterizes a serial process. The distribution of RTs for the second task is wider than that for the first task, because it combines the intrinsic variance of task 2 (the time to reach threshold) and the variance in onset of the central stage of task 2, which is set by the ending of the central stage of task 1. We designed a behavioural task to test the validity of the model. This number-comparison task involves deciding whether a digit presented on the screen is larger or smaller than 45. Different manipulations of the task render it more difficult, presumably, at different stages of processing. The different task manipulations include notation (whether the number was presented in Arabic digits or in spelled words), distance (the numerical distance between the presented number and 45), and response complexity (whether subjects were asked to tap once or twice to indicate their choice). Previous studies have shown that all of these manipulations change the difficulty of the task: RTs increase when numerical distance decreases and when numbers are presented in spelled words [28,29]. These effects have been shown to be additive and to involve distinct brain regions and components of the event-related potentials [29,30]. Thus, it is likely that they affect different components of processing, making this task a good candidate to explore the validity of the model. Results Subjects were asked to perform a dual task. One of the two tasks was presented visually and involved a number comparison: subjects decided whether a digit presented on the screen was larger or smaller than 45. Hereafter we will refer to it as “number task”. The other was a tone-discrimination task that involved deciding whether the frequency of a single pure tone that was presented for 150 ms was high (880 Hz) or low (440 Hz) (subjects heard both tones repeatedly before the beginning of the experience). Hereafter we will refer to it as “tone task”. Two different populations of subjects performed the task in the two possible orders, tone task followed by number task or vice versa. The number task was our main task of study, and was manipulated using three different factors: notation (whether the number was presented in Arabic digits or in spelled words), distance (the numerical distance between the presented number and 45), and response complexity (whether subjects were asked to tap once or twice to indicate their choice). The tone task was never varied throughout the experiment. The rationale underlying this experimental design is that the tone task is used as a probe to study, through interference experiments, the different stages of processing of the number task. This asymmetry between the two tasks, which might be helpful to keep in mind, was of course not stated to the subjects, who were just asked to attend equally to both tasks. The results section is organized as follows. We first report an analysis of basic measures of central tendency and dispersion. We then address how different manipulations (within the number task or through the interference with the tone task) change the mean RTs and their dispersion. These types of tests allow us to test the additivity of the effects of each factor and, through the interference analysis, whether they affect the perceptual, the central, or the motor stage. A second level of analysis involves a more detailed characterization of the shapes of the distributions of RTs. Fitting the distributions allows us to evaluate the accumulation model of response decision and to relate its components to those identified by their patterns of interference in the first-level analysis. For clarity and as a reference throughout the paper, all the definitions, components of the models, and experimental manipulations are summarized in Table 1. Table 1 Definitions of Notations DOI: 10.1371/journal.pbi0.0030037.t001 Analysis of Mean RTs and Interquartile Range Effect of the different manipulations of the number task The first analysis involved studying the effects of the different manipulations of notation, distance, and response complexity on mean RTs and response dispersion on the number task when it came first. Our model predicted that manipulations that affect separate stages should have additive effects on mean RTs, and that only manipulations that affect the central stage should significantly increase response dispersion. For this analysis (and throughout the paper unless otherwise specified) distance, which is the absolute value of the difference between the presented number and 45, was binned in two groups, close (≤12) and far (>12). Central tendency was measured by estimating the mean RT after trimming for outliers, by discarding responses slower than 1,200 ms. Response dispersion was measured by estimating the interquartile range, i.e., the difference between the 75th percentile and 25th percentile of the distribution of RTs. (Identical results were obtained when using other measures, e.g., median and standard deviation of RTs. Note that, in general, serial stage models predict that factors affecting distinct stages should have additive effects on mean RTs, but not necessarily on median RTs. Our model, however, supposes that factors affecting P and M components do not add to response dispersion, but merely add a constant factor to RTs. Under this hypothesis, factors affecting selectively P, C, and M components should also have additive effects on median RTs. In fact, the effects of perceptual and motor factors should be quantitatively the same on mean and median RTs.) As we expected from several prior experiments [29,30], performing the task for spelled words required more time than for Arabic digits (Figure 2A). We also observed a significant distance effect: RT for close numbers was longer than for far numbers. In addition, these effects were additive, as revealed by ANOVAs with subjects as a random factor and notation and distance as within-subject factors (Table 2, effects of notation; Figure 2A). Similarly, response complexity increased subjects' mean RT, and this effect was additive with the distance manipulation (Table 2, effects of response complexity; Figure 2A). Figure 2 Effects of the Different Manipulations on the Mean and Dispersion of RT (A) Changes in the mean RT of the numeric task when it comes first in the different experimental manipulations. Changing the notation or the response complexity makes mean RT slower, and within each condition, responses are slower for close than for far distances. The difference between far and close conditions is independent of the experimental manipulation, indicating an additive effect that is tested in the ANOVAs (see Table 2). (B) A different pattern is observed for the interquartile range, which provides a measure of dispersion. While distance manipulation results in a major change of the interquartile range, there is not a major effect of notation or response complexity. Table 2 Results of the Different Manipulations of the Number Task on the Mean and Interquartile Range Red indicates a significant effect DOI: 10.1371/journal.pbi0.0030037.t002 Interestingly the effects of the different manipulations on response dispersion did not follow the effects on the mean, indicating that some factors slowed RT but did not significantly increase their dispersion. The distance manipulation resulted in a significant increase of the interquartile range typical of stochastic process, where the dispersion increases with the mean. In contrast, notation and response complexity, while causing an important change in the mean, did not result in a significant increase of the interquartile range (Table 2; Figure 2B). To fully address whether the number-comparison task involves three separate stages with each experimental factor (distance, notation, and response complexity), a complete “additive factors” experimental design is needed, in which the different factors are crossed and thus all the interactions can be tested. However, such a factorial design, if tested in the double-task experiment, would involve an exceedingly large number of conditions, which would be very difficult to test on a subject by subject basis within a single session. Instead, we ran it as an independent experiment, in which a new group of subjects was asked to perform only the number task. The results of this new experiment, summarized in Table 3, confirmed and extended our previous findings. (1) All factors (distance, notation, and response complexity) had a significant main effect on mean RT. (2) All interactions between factors were nonsignificant. In particular, the new experiment allowed us to test two interactions that could not be addressed previously (notation by response complexity and triple interaction), which were also nonsignificant. This additive-factors analysis is thus fully compatible with our hypothesis that the number task involves three successive stages, each selectively influenced by one of the three factors. (3) As expected, those findings held for both analyses of the mean and median RTs, both of which are reported in Table 3. (4) As shown previously, the interquartile range and standard deviation were only affected by the distance manipulation, but did not change significantly with the response complexity and notation manipulations. The size of the confidence intervals was similar in all conditions (Table 3), suggesting that this was not just a matter of statistical power. For instance, response complexity had a major impact on mean RT (249 ± 20 ms), but no effect on its standard deviation (1.4 ± 8.9 ms), while distance had a more modest impact on mean RT (41.2 ± 5.8 ms) and a comparable effect on standard deviation (19.2 ± 6.5 ms). While this result does not necessarily imply that the variance associated with notation- and response-dependent processes is strictly zero, it suggests that those processes have a substantially lower contribution to the variance than the distance-dependent stage. Table 3 Results of the Different Manipulations of the Number Task (in a Single-Task Experiment) on Different Variables In this experiment, the condition Words 2 Taps was included to allow a full factorial design that permits testing all the different double interactions between the three factors and their triple interaction. Same results are obtained for different measures of central tendency (mean and median) and of dispersion (standard deviation and interquartile range). Red indicates a significant effect. Confidence intervals (CI) are reported, with all values in milliseconds DOI: 10.1371/journal.pbi0.0030037.t003 Taken together with the assumption of our model that only the central stage contributes to response variance, our observations suggest that the numerical distance factor affects the C decision component, while notation and response complexity affect noncentral P or M components. Interference by the tone task In addition to tests of additivity, a useful experimental technique to address the separable nature of different components and to understand their organization in time, is the interference analysis, in which the task of study (the number-comparison task) is performed together with a probe task (the tone task). The delay in the onset between the two tasks is controlled experimentally, and to achieve a full separation of the three components, the two tasks must be presented in both possible orders (Figure 3). Under the assumptions of the PRP model, the P and M components can be carried out in parallel with another task, but the central stage is the only one that provides a bottleneck, in the sense that the central component of each task cannot be carried out simultaneously. From these premises, one can predict the curve giving RTs for the first and second tasks (RT1 and RT2, respectively) as a function of delay, and how it changes with a manipulation of the P, M, and C components of either task. The sets of predictions and a sketch of the logic are presented in Figure 3. The aim here is thus to associate the experimental manipulations (notation, distance, and response complexity) to the different components in the PRP model (P, C, and M) by analysing the changes in mean RT with delay. Here and throughout the paper, we follow the convention that RTs to both tasks are reported from trial onset. Figure 3 Description of the Task and Sketch of the PRP Model and Its Predictions (A) Scheme of the main PRP effect. The vertical axis labels RT. The column on the left indicates the first task, and each coloured box within the column represents a different stage of processing: P component (dark green), C component (red), and M component (blue). The series of columns on the right indicate the processing time for task 2 at different delays (Δ), labelled on the x-axis. For each column, the three different boxes represent the three different stages of task 2: P component (green), C component (orange), and M component (cyan). As Δ progresses, the P component starts later. All components can be performed in parallel except for the C component, which establishes a bottleneck. This results in the following predictions: (1) response to the first task is independent of Δ, and (2) the RT2 (from onset of the trial) represented by the black line, is unchanged for small Δ while at sufficiently large Δ (noninterference regime) it increases linearly, with a slope of one, with Δ. (B) The predicted RT1 and RT2 (from trial onset) as a function of Δ is represented by the grey and black lines, respectively. (C) The model also establishes definite predictions for experiments in which one of the tasks is changed. The six different panels indicate all possible manipulations: first task changed (left column) or second task changed (right column) and whether the change affects the P component (first row), C component (middle row), or M component (bottom row). The changed component is labelled with a highlighted box and with an arrow. For simplicity, we assumed that the task manipulation always increases the duration of one component. RTs before the manipulation (which are the same across all panels) are represented with a solid line, grey for RT1 and black for RT2, and the RTs of the manipulated task are labelled with a dotted line with the same colour code. If the first task is changed (left column), different effects are observed depending on whether the change is in the M component or in the P–C components (which cannot be distinguished with this manipulation). If the M component is affected (bottom row), RT1 changes, but the response to the second task is unchanged. If the locus of the change is in either the P or the C component (middle and top rows), there is a larger delay until execution of task 2 and the following effect is observed: for small Δ (interference regime), RTs are increased and the regime of interference is increased, which is indicated by a shift of the kink to the right. If the second task is changed (right column), different effects are observed depending on whether the change is in the P component or in either the C or M component. If the change is in the P component (top row), for small Δ there is no net change in the response to the second task (because there was a wait at the end of the P component so extending it does not change total time execution), but there is less wait and thus the kink is shifted to the left. If the change is made in either the C or M component (middle and bottom rows) the result is a rigid shift, which is independent of Δ. By performing experiments in which the two tasks are presented in different orders, all task components can be differentiated. All task manipulations, according to the PRP model, should fall into one of the three categories, perceptual, central, or motor, each defined by its characteristic RT signature. We begin by describing the mean RT results when the number task (for which experimental parameters were varied) was performed first, and the tone task came second. The PRP model predicts that each of the manipulated variables of notation, distance, and response complexity should have a main effect on the first number-comparison task, but only some of those effects (those that affect P and C components of the first task) should propagate to the RT2, and should do so only at short interstimulus delays (Δ) (Figure 3). To evaluate these predictions, mean RTs were calculated within each condition and each subject, and submitted to ANOVAs with subjects as a random factor and delay and the variable of interest as within-subject factors. The detailed results of those ANOVAs are reported in Table 4. In the text, we merely draw attention to the main points. Table 4 Results of the ANOVAs of the Interference Experiments: Number Task Followed by Tone Task Each column corresponds to a different ANOVA. Each line represents a different effect: task manipulation, delay, and their interaction. Red indicates a significant effect. All 18 data cells follow the predictions of the PRP model DOI: 10.1371/journal.pbi0.0030037.t004 The ANOVAs on number-comparison RTs (RT1) revealed the expected main effects of number notation (74 ms, slower for verbal than for Arabic numbers), numerical distance (91 ms, slower for close digits than for far digits), and response complexity (175 ms, slower for two-tap responses than for one-tap responses). There was no main effect of Δ, and none of the task effects interacted with delay. These results suggest that, as requested, participants performed the number comparison as task 1 independently of the delay of presentation of the subsequent tone task. Similar ANOVAs on tone-decision RTs (RT2) revealed a main effect of delay, characteristic of the PRP phenomenon. As shown in Figure 4 (left column, black solid curves), RTs were independent of delay up to a certain value, then began to increase linearly with further increases in delay. Figure 4 Dissociating P, C, and M Components by Their Interference Patterns In the left column the number task is performed first and the tone task second. In the right column the tone task is performed first and the number task second. In both cases, the number task is manipulated by the three factors of notation, distance, and response complexity. In all panels the code is identical: RT1is coloured grey while RT2 is coloured black. The “easy” condition is represented by a solid line and the “difficult” condition by a dotted line. All the data can be explained in terms of the PRP model: notation (top row) affects the P component, distance (middle row) affects C, and response complexity (bottom row) affects M (see also Tables 4–6 for statistics, and note the agreement with the predicted RTs shown in Figure 3). Table 6 Results of the ANOVAs of the Interference Experiments: Tone Task Followed by Number Task Each column corresponds to a different ANOVA. Each line represents a different effect: task manipulation, delay, and their interaction. Red indicates a significant effect. All 18 data cells follow the predictions of the PRP model DOI: 10.1371/journal.pbi0.0030037.t006 Crucially, our three experimental factors had differential effects on those two segments of the RT curve. Notation and distance showed both a main effect and an interaction with delay (Table 4). As a further test, we analysed the data for short delays, within the interference regime (Δ ≤ 350 ms) and long delays (Δ ≥ 600 ms) (Table 5). For the notation and distance manipulation, when collapsing the data across all short delays, there was a significant effect of both factors (respectively 87 ms and 100 ms). For long delays RTs were no longer affected by those variables. These features are characteristic of effects that affect either the P or the C components of a task (see Figure 3). Table 5 t-Tests to Study the Effect of Each Manipulation on RT2 within the Regime of Interference (Short Delays) and within the Regime When the Two Tasks Are Performed Independently (Long Delays) Effect sizes are shown in milliseconds (in parentheses). Manipulations that differentially affect the short and long delays are responsible for the interactions reported in Tables 4 and 6. Red indicates a significant effect. All 12 data cells follow the predictions of the PRP model. Red indicates a significant effect DOI: 10.1371/journal.pbi0.0030037.t005 The situation was quite different for the response-complexity variable. The ANOVAs did not reveal either a main effect of response complexity or an interaction with delay on the RT2 (see Figure 4, bottom left, black curves; see also Table 4). Thus, none of the larger (175 ms) effect that was observed on the first task was propagated to the second task. This result is confirmed by the t-tests, where we did not observe a significant difference either in the short delays or in the long delays (see Table 5). This is characteristic of a variable that affects the motor stage of processing. We now describe the mean RT results when the tone task was performed first, and the number task (for which experimental parameters were varied) came second. In this case the PRP model predicts that there should be no effect of the manipulated variables of notation, distance, and response complexity on the first tone task; in addition, the RT2 should exhibit a constant increase (independent of delay) when the change affects the M and C components and should change only for large delays when the change affects the P component (see Figure 3). As described above, to evaluate those predictions, mean RTs were calculated within each condition and each subject, and submitted to ANOVAs with subjects as a random factor and delay and the variable of interest as within-subject factors (see Table 6). The ANOVAs on the tone task RTs (first task, RT1) revealed no effects on the task manipulation, as predicted by the PRP model because response to task 1 should be independent of the nature of task 2. The ANOVAs on the number-comparison RTs (second task, RT2) again revealed a very significant nonlinear effect of delay characteristic of the PRP effect. In addition, for the distance and response-complexity manipulations, we observed a task effect that did not interact with delay (see Table 6), typical of central and motor manipulations. For the notation manipulation, we observed a task effect that interacted with delay, typical of perceptual manipulations. These observations were consistent with the t-tests performed for short and long delays. When data were collapsed across all short delays for the distance and motor manipulation, there was a significant effect of both factors (respectively 86 ms and 255 ms). In contrast, there was no significant difference in RT for the number task for the different notations for small delays (see Table 5). For all comparisons there was a significant effect for long delays: notation (68 ms), distance (92 ms), and response complexity (215 ms). Thus, the notation effect behaves with the characteristics of a variable that affects the P component, and combining this analysis with the prior in which the number task came first, we observe that each manipulated variable affects a different component: notation affects P, distance affects C, and response complexity affects M (compare the predictions of each stage, Figure 3; and the data resulting from each manipulation, Figure 4). The dependence of RT on delay follows the prediction of the PRP model for all conditions, task manipulations, and task orders. However, we find a small departure from the model when we compare the mean RTs for both tasks when they were presented either first or second at the maximum delay (1,025 ms). In both cases we find that the response is slower when the task is presented first: number task, 756 ms when presented first and 678 ms when presented second; tone task, 720 ms when presented first and 518 ms when presented second. Thus there is a fixed component (independent of delay) of approximately 150 ms, which needs to be added to RT1 to fully explain the data. Detailed Analysis of the Distribution of RTs Effect of the different manipulations of the number task The shape of the RT distributions (for correct trials) was analysed for each task when it was presented first. For the number task we analysed six different cases corresponding to the three different manipulations (Digits 1 Tap, Words 1 Tap, and Digits 2 Taps), and two levels of numerical distance: close distances (≤12) and far distances (>12). For each of these distributions, the histograms of RTs and their cumulative distributions were calculated, and the latter were fitted to a simple model of RTs. The model was based on a fixed onset delay, t o, followed by a forced random walk to a threshold T with slope α and diffusion constant σ (Figure 5). The fixed delay (t o) corresponds to the sum of the P and the M components (see Figure 1). Figure 5 Dissociating Parallel and Serial Components by RT Distributions (A) RT histograms (when the number task was presented first) fitted by a simple random-walk model, separately for far distances (left column) and close distances (right column) and for the three different tasks: Digits 1 Tap (top row), Words 1 Tap (middle row), and Digits 2 Taps (bottom row). (B) Cumulative plots of the same data. The effect of both notation and response appears to be a shift of the distribution to the right while the distance effect is a change in the slope. Within each panel, we have overlapped the corresponding fit (blue line) and the fit to the easiest condition—Digits 1 Tap, Far Digits (red line)—to make the change between the different distributions apparent. (C) The two fitted values (fixed delay and integration time) as a function of numerical distance for the three different tasks. The integration time decreases with distance, but it is independent of the tasks. In contrast, the fixed delay does not change with distance but changes with the task. The summed delay plus integration time fit the mean reaction times for each distance (solid circles). (D) Statistics performed on the fit reveal that the fixed delay has a slope not significantly different from zero (i.e., it does not depend on distance), but it changes with task. In contrast, the integration time is significantly different from zero, but it does not change with task. The applicability of random-walk models to RT data has been widely studied in numerous tasks [18,19,20,21,22,23], including the number-comparison task [17]. While there are a large number of variants (see Discussion), allowing us to capture further details of the data at the expense of increases in theoretical complexity, our approach here is to remain with a model as simple as possible, whose sole purpose is to separate stochastic and invariant contributions to reaction times. The parameters were determined as follows. T can be set to one without loss of generality. For simplicity, we assumed that σ was the same for all six experimental conditions, while α and t o could vary (we verified that none of the results depended qualitatively on the particular choice of σ). The best-fitting values were determined by exhaustive search using a minimum-squares criterion. The value of 1/α characterizes the integration time (which explains all the variance), while t o captures fixed components that do not contribute to the variance. Thus, our purpose was to test the prediction of our model that the notation and response-complexity manipulations should affect the parameter t o while the distance manipulation should affect the parameter α. Figure 5 shows the fitted distributions of RTs corresponding to the three different tasks: Digits 1 Tap (Figure 5A and 5B, first row), Words 1 Tap (Figure 5A and 5B, second row), and Digits 2 Taps (Figure 5A and 5B, third row). For each of these tasks, we have separated the data corresponding to the close distances (right column) and the far distances (left column). The fit was accurate, with the exception that it was smoother than the real data and thus did not fully capture a fairly abrupt peak at the modal response. The shapes of the distributions appeared to change in two qualitatively different manners. For fixed distances (same column) but changing task, the distributions shifted in time. Conversely, for fixed task (same line) but changing the numerical distance, the distribution became wider. For a finer-grained analysis, and to test the significance of this phenomenon, we binned the data in 24 different bins based on their distance to the reference 45 used for numerical comparison. For each bin, we calculated the α and t o that provided the best fit. We found that the t o changes from task to task but does not depend on distance. In contrast, 1/α does not change across tasks but changes with numerical distance (Figure 5C). To test this, we performed a linear regression of both parameters as a function of distance, thus producing two estimates (the slope and the y-intercept at x = 0) (Figure 5D). For t o the value of the slope (for the three tasks) does not differ significantly from zero (p > 0.3) and the value of the intercept differs significantly across tasks (p < 0.001). In contrast, for 1/α the intercept is not significantly different across tasks (p > 0.5) while the slope is significantly different from zero (p < 0.001) Thus, response complexity and notation manipulation affect t o, while numerical distance affects 1/α. These results are consistent with the prior analysis, which showed that response complexity and notation manipulations did not significantly affect the interquartile range (another measure of dispersion) while the distance manipulation did significantly change the interquartile range. Prediction of the distribution of RT2s Here we try to explain the precise shape of RT2s, by combining, based on the PRP model, the distributions obtained for each task when presented first. If the two tasks were completely sequential, then the resulting distribution would be simply the convolution of the two original distributions. However, the PRP model states that only the C component is sequential, and, thus, because some operations can be done in parallel, the resulting RT2s are shorter than expected from a convolution. The operation performed is not completely trivial and is described step by step in Materials and Methods. The only essential point is that this calculation cannot be performed by simply knowing the RTs to each task, but also requires an estimate of the duration of the M component of the first task (M1) and the P component of the second task (P2). These durations are not directly accessible to measurement, but they can be estimated as a result of the fitting of the distribution of RT2. Thus, confronting the distributions of the first and second tasks provides access to the otherwise hidden durations of the postulated component stages, allowing further tests of our model. For each task (Digits 1 Tap, Words 1 Tap, and Digits 2 Taps) we tried to fit the 20 distributions of RT2 (ten for each value of the delay and the two possible orders of the tasks (tone–number or number–tone) from the distributions of RT1, with P2 and M1 as free parameters. We found that with these parameters alone, the data could not be fitted (there were no values of the parameters that gave mean square residuals less than 0.3 for all distributions, and the fitted curves were not similar at all to the real data). It seemed evident that the problem was that the predicted distributions were shifted in time with respect to the original distributions, and thus we decided to add one parameter, T d, a rigid shift in time of all distributions of RT2 (see Discussion for the rationale of this parameter). We then found good fits for the ensemble of distributions (Figure 6, mean square residuals < 0.015) with the following values of T d: Digits 1 Tap, 125 ms; Words 1 Tap, 125 ms; and Digits 2 Taps, 75 ms. Figure 6 Predicting the Distribution of RTs to the Second Task from the PRP Model Left: Cumulative plots of RTs to the number task when it is presented second (dots) and the predicted distribution based on the PRP model (solid lines). Each curve (coded in different colours) represents one of the ten possible values of Δ. Right: Same data for RTs to the tone task when it is presented second (dots) and the predicted distribution from the PRP model (solid lines). Each row corresponds to a different task: Digits 1 Tap (first row), Digits 2 Taps (second row), and Words 1 Tap (third row). Each panel was fit with three parameters: M1, P2, and a fixed delay. Each fit provides the parameters P2 and M1. When the number task was second, the parameters are P(Number) and M(Tone). When the tone task was the second, the fit parameters are P(Tone) and M(Number). The obtained values of the square residuals for different parameters were not sufficient to actually calculate precisely each parameter, since the fit was unstable in the P2 − M1 direction (i.e., it did not change much if both parameters were changed but their sum was kept constant), but they were sufficient to calculate their sum (Figure 7). In agreement with our previous observation, we found that the notation manipulation affects P(Number) + M(Tone) (Figure 7, left) but not P(Tone) + M(Number) (Figure 7, right). In contrast, and also consistent with our previous findings, the response-complexity manipulation affects P(Tone) + M(Number) (Figure 7, left) but not P(Number) + M(Tone) (Figure 7, right). Figure 7 Parameters Obtained from the PRP Fitting and Their Task Dependence The PRP fitting allowed us to estimate the values of P2 + M1. Depending on which task is presented first, we can calculate P(Number) + M(Tone) (left bars) or P(Tone) + M(Number) (right bars). P(Number) + M(Tone) changes with notation manipulation but not with response manipulation. Conversely, P(Tone) + M(Number) changes with response manipulation but not with the notation manipulation. Furthermore, the left bars are consistently higher than the right bars, suggesting that visual perception of digits and words takes approximately 150–220 ms longer than auditory perception of a single tone. Finally, the parameters obtained from the interference experiment may be compared to those of the previous fit, which was based on the shape of the distributions of RTs for the first task, and which yielded estimates of 1/α (the time of integration) and t o (a fixed delay). As expected from our model, across the different conditions summarized in Table 7, we observe that t o is always approximately equal to the sum of the durations of the P and M components, while 1/α is equal to the duration of the C component. This provides further evidence that the process of accumulation of evidence does indeed constitute the characteristic bottleneck (the C component) in dual-task experiments. Table 7 Fitted Parameters When the fit method is “RT model”, parameters were obtained by fitting the shape of the distribution of RTs when the number task is the first task; when the fit method is “PRP fit”, parameters were obtained following the PRP model of interference, from RTs measured when the number task is the second task. t o is the fixed delay and 1/α the integration time. The 1/α row also shows the percent of the total RT dedicated to the central integration process. The following parameters are estimated: P(Tone) + M(Number), P(Number) + M(Tone), and T d. The comparison of both methods indicates a good quantitative convergence: when summed, the noncentral P and M components of the PRP model account for the same amount of time as the fixed contribution t o in the RT distribution DOI: 10.1371/journal.pbi0.0030037.t007 Discussion We proposed a basic model that relates the organization of parallel and serial components and the process of accumulation of evidence to reach a decision. The model, although simple, results in a wide number of predictions that, as we have shown, hold over a vast variety of manipulations. We show that the perceptual transformation of sensory information into an abstract quantity representation can be carried out in parallel with another task and is a low-variability process (whose variability does not increase with the mean); that the accumulation of evidence establishes a bottleneck and is an intrinsically variable process; and that the execution of the response constitutes yet another parallel, low-variability process. Our data suggest that the integration of evidence in time to reach a decision constitutes the only central process in a simple cognitive task that links perceptions to actions. Validity of the PRP Model While dual-task experiments (in which two tasks are presented at variable delays) allow different interpretations, experiments in which one of the two tasks is parametrically manipulated provide a severe test of the PRP model [12,13,16]. Indeed, the simple hypothesis that the central module is the only serial stage results in concrete predictions about the dependence of mean RTs on delay [16]. In different cognitive tasks, the PRP model was successfully used previously to identify and dissect different processing components. For example, in a detection experiment where the brightness and the probability of target occurrence were manipulated, it was shown that brightness behaved as a perceptual component while frequency showed the characteristics of a C component [31]. PRP models have also been used to show that word selection involves C components while phoneme selection behaves as an M component [32,33]. Here we have tested, within the number-comparison task, three different manipulations, in the two possible orderings of the sequential tasks, thus providing an exhaustive test of the model. Our finding that all manipulations fall reliably within one of the PRP components provides strong evidence of the generality of this phenomenon. In addition, while it had been shown previously that the distribution of dual-task RTs was wider than that predicted by noninterfering processing of the two tasks [34], precisely adjusting this distribution based on the PRP model, to our knowledge, has not been done before. Our analysis of the distribution of RTs for the second task based on the distributions for each individual task when presented first implies that the model can explain not only the mean RTs, but also their entire distribution. Since the model is parametric, fitting it to the data yields absolute measurements of the duration of the central stage and of the sum of perceptual and motor stages (Table 7). Those measurements, which we obtained consistently by two different means (analysis of single-task RT distributions and of the PRP interference pattern), are consistent with previous experiments using the additive-factors method [29]. A striking result, however, is the duration of the C component, which even in a simple task represents about 70% of the total RT. Considering the simplest version of our task (comparison of Arabic numerals, one tap), our results indicate that 180 ms is taken by the sum of P and M components, while a full 550 ms is taken by the C component. Previous event-related potential experiments suggested that it takes approximately 190 ms to identify an Arabic digit and begin to access a quantity representation [29]. The present evidence indicates that this notation-dependent stage is absorbed during the PRP delay and thus belongs to the P component. Altogether, the evidence suggests that the central stage starts after digit identification and goes on all the way to the actual key press. While all the PRP predictions held, the only discrepancy with the model arose from an unexpected slowness of responses to the first task. As predicted, RT1 was independent of the delay. However the mean RT was larger than found previously when subjects performed only the number-comparison task [35]. Even within our experiment, it was larger than the time taken to perform the same task when it was presented second at a delay of 1 s (in the noninterference regime). This discrepancy also became evident in the convolution of the two distributions, where the fitting turned out to be impossible without a translation in time, but became very accurate once this translation was added to the fit. Previous PRP experiments have also observed a similar slowing down of the first task, independent of Δ [36]. Thus we believe that a correction needs to be made to the PRP model. There are at least two possible and not exclusive rationales for this correction. First, temporal attention could be involved. The presentation of the first task could act as a primer in time for the second task. Indeed, it has been shown that reaction times decrease when subjects know the precise timing of stimulus occurrence [37]. Second, executive attention might also have to be engaged before performing the first task, in order to prepare for the instructions of performing the two tasks in a specific order and with specific responses. Thus, two components, a structural central bottleneck and a central task-setting component, may contribute to the delay in the dual-task paradigm [14,38]. Here, as in other PRP experiments, we have designed the tasks in order to maximally separate the inputs and outputs to the system (different perceptual modalities and different response hands). Under these conditions, as described above, we still find a source of central interference. Moreover we find that the transformation from a word form to an abstract semantic representation does not participate in this central process, nor does the execution of two consecutive and repetitive motor actions. The generality of these findings, however, has obvious bounds. We do not state here that any motor manipulation should result in a change in a parallel component; more complex motor responses, however, might require central supervision and create a bottleneck. Similarly, while we claim that mapping a word form to an abstract number representation can be done in parallel, we do not mean that it would not interfere with any possible stimulus. Finally, under some situations that lead to high automaticity, either through extensive training [39,40] or very consistent stimulus–response mapping, the central bottleneck may be negligible [41,42,43]. Alternative RT Models There is a vast literature on the analysis of the shape of RT distributions as a source of knowledge about the human information-processing system, and many different models of these distributions have been proposed [20,21,23,26,44,45,46,47,48,49,50,51]. Here we did not intend to fully test the validity of the different models or to see which provided a better explanation of our data. We rather chose a simple model that contains the essence of a stochastic integrator and tried to use it to understand the effects of different manipulations. Our most important finding is that the distance manipulation, which is the only one to show interference, as revealed by the PRP experiment, is also the only one to change the stochastic integration time. Conversely, the manipulations that show no interference only affect the fixed delay. Thus there is a consistent parsing of the task from both methods. There are, however, several variations in the model that would be particularly interesting to test in this condition. First, as alternatives to the random-walk model with fixed mean and variance that we adopted here, one may propose a noise-free integration whose slope varies from trial to trial [52] or a diffusion model with variance in the drift [45]. Distinguishing these models may provide a way to measure the timing of the flow of information between perceptual stages and central stages. Do sensory systems provide only one vote to a noisy decision machinery, or do they rather provide a series of stochastic votes, which the decision machinery accumulates? And if the latter, what is the sampling time of communication between both systems? A second important type of alternative to our model concerns the nature of the central process. Instead of a unique integrator, there might be a network of interacting integrators with lateral connections, which collectively implement the decision-making process and whose interactions create a functional bottleneck [23]. The existence of rare but attested cases in which two response-time tasks can be performed in parallel without cost [41,42] might seem to favour the existence of multiple integrators, but it is also possible that highly trained sensorimotor tasks can eventually be triggered directly, without going through an accumulation-based decision stage [53,54]. Finally, for simplicity our model assumed a constant decision threshold T. In a more complicated model, the value of the threshold might be changeable. Such a feature might be needed to fit the results of experiments in which one varies the prior probability of a given response or its associated reward (variables that were fixed throughout our experiment). For example, in a go/no-go experiment involving digit comparison, in which the probability of a response was fixed at a controlled probability pgo, it has been shown that pgo affects T, but not the drift rate (α) [17]. Even in experiments with fixed response probabilities, subjects might continually adjust their threshold, lowering it after a successful response and increasing it after an error [55]. Such adjustments might capture another characteristic feature of RTs, which is their intrinsic autocorrelation structure and, in particular, their increase following errors [26,56]. While typically even simple tasks result in highly variable distributions of RTs, under some particular circumstances, including extensive practice, very precise (almost invariant) distributions of RTs can be obtained, e.g., in subjects trained to estimate a fixed duration [57]. It has also been shown that task modifications can lead to fixed delays, i.e., increases in RT that do not change the variance [57]. These findings support the idea that certain mental processes, as we propose here, can be carried out with negligible variance. They imply that variability in RT does not result merely from an intrinsically noisy neuronal machinery but rather from the computation underlying each process. Here, based on our results, we propose a hypothesis that needs further testing: that the central processes that involve integration of information represent the bulk of the variance while perceptual and motor processes are highly reliable. In particular, we predict that if a task can be performed in an invariable fashion it should also be automatic, in the sense of becoming immune to central interference. Cerebral Substrates of the Different Components While we characterized the different processing stages through behavioural observations, it is an essential issue to relate these findings to brain anatomy and physiology. At the single-task level, the neurophysiological bases of simple perceptual decision making have been widely studied in tactile- [58,59,60] and visual-discrimination tasks [53,54,61,62,63,64,65,66]. These studies have revealed direct physiological correlates of the accumulation process postulated in formal RT models. Some neurons appear to code for the current perceptual state. For instance, neurons in the middle temporal area (area MT) appear to encode the amount of evidence for motion in a certain direction [25,63]. Other neurons, distributed in multiple areas including posterior parietal, dorsolateral prefrontal, and frontal eye fields, appear to integrate this sensory information and thus show stochastically increasing firing rates in the course of decision making [25,61,67]. In agreement with the accumulation model of decision making, the rate of increase varies with the quality of sensory evidence [25,61,68], and the response is emitted when the firing exceeds a threshold [66]. Furthermore, accumulation of information about the upcoming response appears in the firing train after a latency of about 200 ms [69,70], which is relatively fixed for a given task and might thus index the duration of the initial perceptual stage. In humans, a similar indicator of accumulated evidence towards a motor decision is provided by scalp recordings of the lateralized readiness potential (LRP) [71]. The LRP is calculated as the difference in event-related potentials between electrodes overriding left and right motor cortices. In a bimanual task, this index shows a monotonously increasing deviation predictive of the side of the upcoming motor response, and whose intensity reflects the accumulated amount of evidence [72,73]. In numerical comparison, the LRP starts approximately at 200 ms [74], again compatible with a fixed perceptual delay. While the LRP component is localized to motor and premotor cortices, another event-related potential component more broadly distributed across the scalp, the P3 is also associated to postperceptual processes [71,72] and shows a continuous, accumulation-like increase as a function of numerical distance in a comparison task [75]. Thus, both LRP and P3 might reflect the accumulation of evidence observed in monkey electrophysiological studies in distributed parietal and frontal regions. Indeed, functional magnetic resonance imaging studies of the comparison task show that intraparietal and precentral cortices are systematically activated and that their activation correlates with the distance between the objects to be compared [76]. This bilateral parietal and frontal system has been identified as a shared response selection system across a diversity of input modalities and across different types of stimulus–response mappings [77]. There is a debate, however, concerning the universality of this system, because at least some studies have found variable sites of activation associated with response selection in different tasks [78]. For instance, in an auditory paradigm dissociating the amount of sensory evidence and the response accumulation process, the former was associated with superior temporal cortex and the latter with anterior insula and opercular frontal cortex [79]. What happens to those physiological decision processes during dual-task performance? At present, we know of no neurophysiological study and only a handful of human physiological studies of the PRP phenomenon. In event-related potentials, when C and P components were manipulated, perceptual manipulation led to a change in the P2 component (generally associated with perceptual processing), while the central manipulation affected the amplitude and the onset of the P3 component [31]. The LRP is also delayed during the PRP, in tight correlation with RT [80]. Finally, functional magnetic resonance imaging, a time-insensitive measure, showed that in the interference regime of the PRP there is no increase in activation relative to performing the two tasks independently, even when searching at a low threshold within regions of interest, which included the prefrontal cortex, the anterior cingulate, and supplementary motor area [36]. This result suggests that the PRP does not result from active executive monitoring processes, but rather from a passive queuing of the second task, as proposed in the present model. Altogether, neurophysiological and brain-imaging studies suggest that, beyond an initial perceptual delay of about 200 ms, there begins a process of accumulation of evidence, which involves the joint activation of a distributed network of areas, with partially changing topography as a function of the nature of the task, but with frequent coactivation of parietal and premotor regions. Our results suggest that this accumulation system is responsible for establishing the PRP bottleneck. This bottleneck might occur because the cerebral accumulation system is broadly distributed and largely shared across tasks, and thus must be entirely “mobilized”, at any given moment, by whichever task is currently performed (for a simulation of this process, see [81]). This neuronal implementation of our model leads to a precise electrophysiological prediction, which could be tested in further research: the accumulation neurons in the lateral intraparietal area and frontal eye field, in an animal trained to perform a pair of PRP tasks, should show two successive stages of accumulation staggered in time; in humans, this might be reflected in a rigid, nonoverlapping sequence of two LRP or P3 event-related components, whose respective durations should covary with the RTs to the two tasks. Materials and Methods Participants A total of 42 participants, all right-handed, were involved in this study (24 males). Sixteen participants (aged 25 y ± 5 y) performed the experiment in which the tone task was presented first, and the other 16 (aged 24 y ± 4 y) performed the experiment in which the number-comparison task was presented first. Ten participants (aged 22 y ± 2 y) performed the numeric task with the addition of the Words 2 Taps condition. Participants were all native French speakers and were remunerated for their participation. Procedure Participants were asked to perform two tasks, with the clear instruction that they had to respond accurately and as fast as possible to each of them. The delay in the onset of the two tasks changed randomly from trial to trial from 0 ms (simultaneous presentation) to 1,025 ms. Subjects responded to both tasks with key presses, with the right hand for the number-comparison task and with the left hand for the tone task. In the number-comparison task, a number was flashed in the centre of the screen for 150 ms, and subjects had to respond whether the number was larger or smaller than 45. The presented number ranged between 21 and 69, excluding 45. In different blocks, subjects performed three different versions of the number task. In the first version, the number was presented in Arabic digits and subjects were asked to respond by tapping once over the corresponding key (Digits 1 Tap). In the second version, the number was presented as a written word (in French), and subjects were also asked to respond with a single key press (Words 1 Tap; we refer to this as the “notation manipulation”). Finally, in the third version, the number was presented in Arabic digits, but subjects were asked to respond by tapping the corresponding key twice (Digits 2 Taps; we refer to this as the “response-complexity manipulation”). Within each block, both the numerical distance between the target and 45 and the delay between the presentation of the two stimuli varied randomly, and trials were presented with an intertrial interval that fluctuated between 2,600 and 3,000 ms. In each block, which lasted almost 2 min, subjects performed 40 trials. Before the beginning of each block, subjects saw instructions on the screen, which instructed them what the number task would be for this corresponding block. Subjects practiced one block of each task to get familiar with the task. After this brief training, they performed a total of 18 blocks (six for each version) in an approximately 45-min session. Stimuli Stimuli were shown on a black-and-white display on a 17-in. monitor with a refresh rate of 60 Hz. Subjects sat 1 m from the screen. Stimuli were always presented in the fovea, and their size was 1° for the Arabic digits and 2.5° for the words. Auditory stimuli were pure tones of 150-ms duration and 440- or 880-Hz frequency. Auditory stimulation was provided through headphones. Data analysis All the analyses described here were done only on correct responses (which comprised 83% of the trials). Since there were two tasks and each task had two possible responses, chance level for this experiment is at 25%. Errors (17%) included errors in either the first or second task and trials in which subjects failed to respond to either of the tasks, or both. One subject was discarded from the analysis because the data clearly revealed that he had not performed the task as required. His RT1 arrived systematically a few hundred milliseconds after the onset of the second task, indicating that he was waiting for both tasks to be presented in order to respond and not, as indicated, responding to both tasks as fast as possible. For similar reasons, for all analyses, trials in which the RTs to the first task were larger than 1,200 ms (<5% of the trials) were excluded. All the statistics were done using the R software package (http://www.r-project.org/, and in all ANOVAs subjects were treated as a random factor. Throughout the paper, RTs for both tasks are, per convention, measured from trial onset, i.e., the onset of the first stimulus. Distribution analysis RTs were fitted to a model based on a fixed delay onset (t o) followed by a forced random walk dV = α · dt + σ · dz and response emission as soon as V reaches a threshold b (see Figure 1). Thus the RT is defined by TR = inf[t ≥ 0, V(t) ≥ b], where b is the threshold. This problem (of the first hitting time to an absorbing barrier of a Brownian motion) has been widely studied and can be solved analytically using the Fokker–Planck equation. The probability of hitting threshold for the first time at time t is given by the following equation: Changing the onset by a fixed delay t o and setting the threshold to one simply shifts the distribution, which then becomes This is the equation we used to fit the RT distributions. All six distributions resulting from the different experimental manipulations corresponding to (Digits 1 Tap, Digits 2 Taps, Words 1 Tap) × (Distance Far, Distance Close) were fit to a fixed value of σ and to values of α and t o, which were allowed to vary across the different experimental conditions. The best parameters were obtained through exhaustive search using a minimum-squares criterion. For each value of σ, the best values α and t o were found for each experimental condition, and the mean square residuals were averaged across all distributions. It was found that the σ that minimized the mean squares deviation across all distributions was 0.018. The changes in the remaining parameters with different experimental conditions, which were of interest to this study, are reported in the Results sections. We repeated this fit for a broad range of σ and found that the obtained results did not depend on the choice of σ. Predicted distributions based on the PRP model Here we describe how RTs for task 2 can be predicted based on the distribution of RTs for both tasks when presented first. Because of the presence of the PRP wait (which depends on the value of the response to the first task), this operation is not strictly a convolution. Since the method is not trivial and, to our knowledge, it has not been performed elsewhere, we will describe it step by step: In a serial sequence of two processes (in which one needs to be finished before the next one starts), each with a probability distribution of RTs given respectively by R1 and R2, the probability of performing the sequence at time T is given by This formula is simply the convolution of the two original distributions. In a PRP experiment, however, the execution of the two tasks is not serial, since there are both serial (central) and parallel (noncentral) components. The first difference is that task 2 waits not for the complete execution of task 1 but rather for the completion of the P and C components of task 1 (see Figure 3). Hence, the first modification is that the first distribution needs to be shifted by M1 (to account for the real start-up time of the second distribution) R 1 (t) = R 1(t + M 1). The second modification, because of the nature of the PRP experiment, is that task 2 obviously cannot start until it is presented and thus the onset time is actually given by R 1 (t) = max[R 1 (t), Δ]. Thus the real distribution of onsets of task 2 is given by the accumulated probability of a shifted R 1 up to Δ (which results in a spike at Δ) followed by the tail of R 1 (t). The spike becomes more pronounced as the delay is larger, and thus the two tasks become independent. The last consideration has to do with the time it takes to respond to task 2. If Δ is sufficiently large (in the independent regime), the probability of executing task 2 at time t is given by R 2(t). However, within the interference regime (for small Δ), P2 (or part of it) has been executed by the time that the P and C components of the first task (which corresponds to t − M1) are finished (see Figure 3). The distribution coincides with R 2 at t = Δ, but as t increases, part of the P component of task 2 has been carried out and this saturates at t = Δ + P2. Thus the probability of executing task 2 at time t 2 given that task 1 has been executed at time t + M1 is given by R 2(t 2), where t 2 = min[(t − Δ), P2] + t. This formula is only valid for t > Δ, but this is not important because in any case R 1 (t) = 0 for t < Δ. The important issue, however, is that this transformation depends on t and thus the sum described in equation 3 is not strictly a convolution. We can still define R 2 (t,T) = R 2[T − t 2(t)] as the probability of completing task 2 in time T − t given that task 1 has been completed in t + M1. The final formula (adapting equation 3 after all the transformations) then becomes Since all these transformations depend on Δ, M1, and P2, this prediction is parametric. The data were fit by exhaustive search according to mean squares criteria. We fitted all the data (for each task and for all the different values, to obtain the values of M1 and P2). As described in Results, this model was not sufficient to fit the data (note that we are simultaneously fitting a family of 30 curves), so we included a third fixed delay parameter (T d) to the fit. With the inclusion of the parameter T d, the errors, measured as the mean square residual (i.e., the mean of the squares of the difference between the data and the fit across all the points of the ten distributions corresponding to all possible delays) were consistently below 0.015 (20 times smaller than could be found without the inclusion of this parameter), and we observed a parabolic type of distribution, with a clear minimum (reported in Figure 7) when plotting the error as a function of P2 + M1. When data were plotted in the orthogonal direction (P2 − M1), however, the fit was unstable with different local minima. We thank Sarah Addleman, Sarah Kouhou, and Jerome Sackur for helping us in data acquisition, and Christophe Pallier for useful suggestions on the statistical procedures. MS was supported by a Human Frontiers Science Program fellowship, and SD by a centennial fellowship of the McDonnell Foundation. Competing interests. The authors have declared that no competing interests exist. Author contributions. MS and SD conceived and designed the experiments. MS performed the experiments and analyzed the data. MS and SD wrote the paper. Citation: Sigman M, Dehaene S (2005) Parsing a cognitive task: A characterization of the mind's bottleneck. PLoS Biol 3(2): e37. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1571905710.1371/journal.pbio.0030041Research ArticleCell BiologyInfectious DiseasesMolecular Biology/Structural BiologyVirologyHIV/AIDSVirusesSIRT1 Regulates HIV Transcription via Tat Deacetylation SIRT1 Deacetylates TatPagans Sara 1 Pedal Angelika 1 North Brian J 1 Kaehlcke Katrin 1 Marshall Brett L 1 Dorr Alexander 2 Hetzer-Egger Claudia 2 Henklein Peter 3 Frye Roy 4 McBurney Michael W 5 Hruby Henning 6 Jung Manfred 6 Verdin Eric 1 Ott Melanie [email protected] 1 2 1Gladstone Institute of Virology and Immunology, University of CaliforniaSan Francisco, CaliforniaUnited States of America2Applied Tumorvirology, Deutsches KrebsforschungszentrumHeidelbergGermany3Institute of Biochemistry, Humboldt UniversityBerlinGermany4Department of Pathology, University of PittsburghPittsburgh, PennsylvaniaUnited States of America5Ottawa Regional Cancer CentreOttawaCanada6Department of Pharmaceutical Sciences, Albert-Ludwigs-UniversityFreiburgGermanyEmerman Michael Academic EditorFred Hutchinson Cancer Research CenterUnited States of America2 2005 8 2 2005 8 2 2005 3 2 e4122 7 2004 1 12 2004 Copyright: © 2005 Pagans et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation Novel Enzyme Shows Potential as an Anti-HIV Target The human immunodeficiency virus (HIV) Tat protein is acetylated by the transcriptional coactivator p300, a necessary step in Tat-mediated transactivation. We report here that Tat is deacetylated by human sirtuin 1 (SIRT1), a nicotinamide adenine dinucleotide-dependent class III protein deacetylase in vitro and in vivo. Tat and SIRT1 coimmunoprecipitate and synergistically activate the HIV promoter. Conversely, knockdown of SIRT1 via small interfering RNAs or treatment with a novel small molecule inhibitor of the SIRT1 deacetylase activity inhibit Tat-mediated transactivation of the HIV long terminal repeat. Tat transactivation is defective in SIRT1-null mouse embryonic fibroblasts and can be rescued by expression of SIRT1. These results support a model in which cycles of Tat acetylation and deacetylation regulate HIV transcription. SIRT1 recycles Tat to its unacetylated form and acts as a transcriptional coactivator during Tat transactivation. Cycles of Tat acetylation and deacetylation, mediated by human sirtuin 1 (SIRT1), regulate HIV transcription suggesting that SIRT1 could be a therapeutic target ==== Body Introduction The Tat protein of human immunodeficiency virus 1 (HIV-1) is essential for the transcriptional activation of the integrated HIV-1 provirus. Without Tat, HIV transcriptional elongation is inefficient and results in abortive transcripts that cannot support viral replication [1,2]. Tat is produced early after infection from rare full-length genomic transcripts generated despite the elongation defect. These transcripts lead to the synthesis of a few Tat molecules sufficient to stimulate HIV transcription elongation, leading to the production of additional Tat transcripts and protein. Tat activates HIV transcription through the trans-acting responsive element (TAR), an RNA stem-loop structure that forms at the 5′ end of all viral transcripts [3,4]. The TAR stem contains a three-nucleotide bulge structure recognized by the arginine-rich motif (ARM) in Tat (amino acids 49–57). In vivo, Tat binding to TAR requires cyclinT1, a cofactor that interacts cooperatively with both the N-terminal transactivation region of Tat (amino acid 1–48) and loop sequences at the top of the TAR stem-loop structure [5]. CyclinT1, a component of pTEFb (the positive transcription elongation factor b), recruits the cyclin-dependent kinase 9 (CDK9) to the HIV promoter. CDK9 hyperphosphorylates the C-terminal domain of RNA polymerase II, which potently enhances the processivity of the RNA polymerase II complex [6]. We and others have shown that Tat is acetylated at lysine 50 by the transcriptional coactivators p300 and human GCN5 (general control of amino acid synthesis 5) [7,8,9,10]. Tat acetylation is important for Tat activity and defines a critical cyclinT1-independent step in Tat transactivation [11]. Tat acetylated at lysine 50 cannot form a ternary complex with cyclinT1 and TAR RNA. It dissociates from TAR and binds instead to the p300/CREB-binding protein-associated factor (PCAF) via its bromodomain [12,13]. Our current working model is that Tat acetylation disrupts the Tat/TAR/cyclinT1 complex and leads to the transfer of Tat and PCAF to the elongating polymerase. According to this model, both forms of Tat, unacetylated and acetylated, play distinct roles in the HIV promoter transcriptional cycle and lead to the sequential recruitment of the cofactors cyclinT1 and PCAF. Because of the limiting amounts of Tat protein in the early stages of HIV infection and the critical role of unacetylated Tat for pTEFb recruitment to TAR, the question arises whether Tat acetylation can be reverted via a cellular Tat deacetylase. There are three distinct classes of human histone deacetylases (HDACs) based on their homology with yeast transcriptional repressors. Class I and II HDACs are homologous to the yeast proteins Rpd3p (reduced potassium dependency 3) and Hda1p (histone deacetylase A1), respectively [14,15]. The deacetylase activity of class I and II HDACs is efficiently inhibited by trichostatin A (TSA) and other related hydroxamate-based inhibitors. Class III HDACs, also named sirtuins (SIRTs), are homologous to the yeast transcriptional repressor silent information regulator 2p (Sir2p) [16]. Sir2p is a TSA-insensitive histone deacetylase that requires nicotinamide adenine dinucleotide (NAD+) as a cofactor [17,18,19]. Seven homologs of Sir2p have been identified in the human genome. Called SIRT1–7, they all contain a highly conserved catalytic domain [20]. Despite their enzymatic activity on histone substrates in vitro, recent experimental evidence suggests that SIRT proteins predominantly target nonhistone proteins for deacetylation, in both the nucleus and the cytoplasm. The nuclear SIRT1 protein deacetylates p53 [21,22,23], TAFI68 (Tata box-binding protein-associated factor I of 68 kDa) [24], PCAF and myoblast determination protein (MyoD) [25], p300 [26] and Forkhead transcription factors [26,27], the p65 subunit of nuclear factor kappa B (NF-κB) [28], and the Ku70 telomeric protein (also known as the thyroid autoantigen of 70 kDa or Ku antigen) [29]. The cytoplasmic SIRT2 protein is found associated with the microtubule network and deacetylates lysine 40 of α-tubulin [30]. SIRT3 is a mitochondrial matrix protein whose target has not been identified [31,32]. Here, we identify the class III HDAC SIRT1 as a specific Tat deacetylase and demonstrate that SIRT1 is a novel cofactor necessary for efficient Tat-mediated transactivation of the HIV promoter. Results To test the ability of SIRT1–7 to deacetylate Tat in vitro, we transfected HEK 293 cells with expression vectors for human SIRT1–7 and immunoprecipitated the FLAG-tagged proteins (Figure 1A). The immunoprecipitated material was incubated with a full-length synthetic Tat protein carrying an acetylated lysine at position 50 (AcTat). The extent of Tat deacetylation was determined by Western blot (WB) with antibodies specific for the acetylated ARM in Tat [11]. Incubation of AcTat with immunoprecipitated SIRT1, SIRT2, and SIRT3 resulted in deacetylation of Tat lysine 50 (Figure 1B). These enzymes also deacetylate histones as determined in a standard histone deacetylase assay (Figure 1B). All reactions contained equal amounts of AcTat as determined by immunoblotting with streptavidin-horseradish peroxidase conjugate (SA-HRP), which recognized the biotin label attached to the N terminus of AcTat (SA-HRP in Figure 1B). SIRT enzymes in the reactions were visualized by immunoblotting with FLAG antibodies (FLAG in Figure 1B). Figure 1 In Vitro Tat Deacetylation by Human SIRT Proteins (A) Scheme of Tat deacetylation assay with immunoprecipitated SIRT1–7 proteins. Expression vectors for FLAG-tagged SIRT proteins were transfected into HEK 293 cells, immunoprecipitated, and incubated with synthetic Tat (72 amino acids) carrying an N-terminal biotin label and an acetyl group at position 50 (AcTat) in the presence of NAD+. Immunoprecipitated material was also analyzed in a radioactive (3H) histone deacetylase assay using an H3 peptide as a substrate. (B) WB analysis of deacetylation reactions with antibodies specific for acetylated lysine 50 in Tat (α-AcTat), with SA-HRP, or with α-FLAG antibodies. (C) WB of Tat deacetylation by immunoprecipitated SIRT1 in the presence or absence of NAD+, TSA, or nicotinamide (Nic). SIRT2 and SIRT3 proteins are localized primarily in the cytoplasm and the mitochondria [30,31], and SIRT1 resides in the cell nucleus [23,33]. Since Tat is a predominantly nuclear protein, we focused our efforts on SIRT1. The SIRT1-mediated deacetylation of Tat was dependent on NAD+ and completely inhibited by nicotinamide, an inhibitor for class III HDACs [34,35]. TSA, a specific inhibitor of class I and II HDACs, had no effect (Figure 1C). These results demonstrate that the Tat deacetylase activity within immunoprecipitated SIRT1 material can be solely attributed to SIRT1 and not to a contaminating class I or II HDAC. To test whether Tat and SIRT1 interact, Tat/FLAG and SIRT1/influenza hemagglutinin (HA) were overexpressed in HEK 293 cells, and cellular lysates subjected to coimmunoprecipitation assays. Tat was detected with an α-FLAG antiserum in material immunoprecipitated with SIRT1 by the α-HA antibody in cells transfected with SIRT1- and Tat expression vectors, but no signal was obtained when SIRT1 or Tat alone was expressed (IP: α-HA in Figure 2A). Conversely, SIRT1 also specifically coimmunoprecipitated with Tat/FLAG (IP: α-FLAG in Figure 2A). The same was observed when Tat/T7 was coexpressed with SIRT1/FLAG and was immunoprecipitated with α-T7 antibodies (Figure 2B). No coimmunoprecipitation of Tat was observed with SIRT2 and SIRT6 (Figure 2B), two SIRT proteins that can also localize to the cell nucleus (BN and EV, personal communication), or any other SIRT protein (unpublished data). Furthermore, Tat coimmunoprecipitated with endogenous SIRT1 in Tat-expressing, but not in vector-transfected, HEK 293 cells (Figure 2C). No SIRT1- or Tat-specific signals were obtained after immunoprecipitations (IPs) in the absence of α-SIRT1 antibodies, excluding nonspecific binding of Tat to the Sepharose beads to which the antibodies were bound. Figure 2 Physical Interaction between Tat and SIRT1 (A) Immunoprecipitation (IP) and WB of FLAG-tagged Tat (Tat-FLAG) and HA-tagged SIRT1 (SIRT1-HA) after transfection of corresponding expression vectors (+) or empty vector controls (−) into HEK 293 cells. (B) The same experiments as in (A) performed with T7-tagged Tat and FLAG-tagged SIRT1, SIRT2, and SIRT6. (C) Coimmunoprecipitation of FLAG-tagged Tat with endogenous SIRT1 in HEK 293 cells transfected with the Tat expression vector or the empty vector control. IPs were performed with or without rabbit α-SIRT1 antibodies. (D) WB of recombinant SIRT1 protein after pulldown with synthetic biotinylated Tat or AcTat. Tat proteins were detected with antibodies specific for acetylated lysine 50 in the Tat ARM (α-AcTat) or SA-HRP. (E) Immunoprecipitation/WB of FLAG-tagged Tat or TatK50R and HA-tagged SIRT1. WT, wild type. To test whether Tat and SIRT1 interact directly, increasing amounts of biotinylated synthetic Tat (72 amino acids) were incubated with recombinant full-length SIRT1. After pulldown with streptavidin-conjugated agarose, SIRT1 coimmunoprecipitated with Tat in a dose-dependent manner (Figure 2D). Recombinant SIRT1 bound equally well to acetylated and unacetylated synthetic Tat, indicating that the interaction occurred independently of the acetylation state of Tat (Figure 2D). WB with AcTat antibodies showed that AcTat remained acetylated during incubation with the SIRT1 enzyme (Figure 2D). Re-blotting with SA-HRP detected both Tat proteins in equivalent amounts in the binding reactions (Figure 2D). We also tested the ability of a Tat mutant protein (termed TatK50R to indicate mutation of lysine to arginine at position 50 of the Tat protein) to interact with SIRT1. This mutation preserves the basic charge at position 50, but cannot be acetylated. After transfection into HEK 293 cells, TatK50R accumulated to lower concentrations than wild-type Tat, but was bound to SIRT1 efficiently in coimmunoprecipitation assays (Figure 2E). These results collectively indicate that Tat binds SIRT1 directly and independently of lysine 50. The effects of SIRT1 on Tat function were assessed after transfection into HeLa cells. SIRT1 modestly, but reproducibly, enhanced Tat-mediated transactivation of an HIV promoter luciferase construct (HIV LTR in Figure 3A). In contrast, expression of a catalytically inactive SIRT1 protein (termed SIRT1H363Y to indicate mutation of histidine to tyrosine at position 363 of the SIRT1 protein) suppressed Tat transactivation in a dominant-negative manner, indicating that the catalytic activity of SIRT1 is necessary for Tat transactivation. Similar results were obtained when an HIV promoter reporter construct containing mutant binding sites for the transcription factor NF-κB was used (HIV LTR ΔNF-κB in Figure 3A). This result indicates that the superinduction of Tat activity by wild-type SIRT1 and the suppression of Tat activity by catalytically inactive SIRT1 were dependent on the interaction between SIRT1 and Tat rather than on the interaction between SIRT1 and NF-κB/p65 [28]. Importantly, SIRT1 (both wild-type and SIRT1H363Y mutant) had no effect on the transcriptional activity of the Rous sarcoma virus (RSV) LTR, a promoter used to drive Tat expression in these cotransfection experiments. Figure 3 SIRT1 Is a Positive Cofactor for Tat Transactivation (A) Cotransfection of SIRT1 or the catalytically inactive SIRT1 mutant SIRT1H363Y with the HIV LTR luciferase construct and increasing amounts of a Tat expression vector (RSV-Tat: 0, 2, 20, and 200 ng), an HIV LTR luciferase construct containing mutated binding sites for the transcription factor NF-κB and RSV-Tat (20 ng), or with an RSV-luciferase construct (200 ng) in HeLa cells. The average of three experiments is shown (± standard error of the mean [SEM]). (B) WB analysis of HeLa cells 72 h after transfection of siRNAs directed against SIRT1 or GL3 control siRNAs. (C) Cotransfection of the HIV LTR luciferase construct with increasing amounts of CMV-Tat or CMV-TatK50R (0, 50, 100, 200, 400, and 800 ng) 48 h after transfection of double-stranded siRNAs directed against SIRT1 or GL3 control siRNAs in HeLa cells. Luciferase activity was measured 24 h after plasmid transfection and 72 h after siRNA transfection. Note that all luciferase reporter vectors used in this study are based on the pGL2 luciferase vector, which is not affected by GL3-specific siRNAs [36]. The average of three experiments is shown (± SEM). (D) The transcriptional activity of increasing amounts of the CMV-luciferase reporter (0, 50, 100, 200, 400, and 800 ng) was similar in SIRT1 knockdown or GL3-treated control cells. The average of two experiments performed in duplicate is shown (± SEM). (E) WB of endogenous SIRT1 or actin 72 h after transfection of siRNA directed against SIRT1 or mutated SIRT1 siRNA. (F) Cotransfection of the HIV LTR luciferase with increasing amounts of CMV-Tat (0, 2, 20, and 200 ng) in HeLa cells pretransfected with wild-type or mutant SIRT1 siRNA oligonucleotides as described in (C). WT, wild-type. The effect of SIRT1 on Tat transactivation was further examined using small interfering RNA (siRNA)-mediated knockdown of SIRT1. HeLa cells were transfected with double-stranded RNA oligonucleotides directed against SIRT1 or against firefly luciferase expressed from the pGL3 vector as a control. All luciferase reporter constructs described in this study are based on the pGL2 vector, which is not affected by GL3 siRNAs (siRNAs directed against firefly luciferase expressed from the pGL3 vector) [36]. Levels of endogenous SIRT1 were markedly reduced at 72 h after transfection of siRNAs specific for SIRT1 (Figure 3B). At that time, a significant decrease in Tat transactivation was noted in cells that had received the SIRT1 siRNA, but not the GL3 siRNA (Figure 3C). The SIRT1 siRNA slightly enhanced the basal HIV promoter activity without Tat, and had no effect on the transcriptional activity of TatK50R, the Tat mutant that cannot be acetylated (Figure 3C). Loss of SIRT1 had no effect on the transcriptional activity of the immediate early promoter of the cytomegalovirus (CMV) used to drive Tat expression in these experiments (Figure 3D). In addition, Tat levels in HeLa cells transfected with SIRT1 siRNAs were comparable to Tat levels detected in cells transfected with GL3 siRNAs as determined by WB (unpublished data). To confirm the specificity of the SIRT1 siRNA, mutant double-stranded siRNA oligonucleotides were generated which contained a two-nucleotide mismatch between the target mRNA for SIRT1 and the antisense strand of the siRNA. Transfection of mutant SIRT1 siRNA did not affect expression of endogenous SIRT1 protein in HeLa cells, indicating that the mutation abrogated SIRT1 mRNA cleavage (Figure 3E). SIRT1 siRNA, but not mutant siRNA, suppressed Tat transactivation of the HIV LTR luciferase construct, confirming that the observed suppression was dependent on the loss of SIRT1 protein (Figure 3F). Since SIRT1 only modestly enhanced Tat transactivation in HeLa cells, which already express significant levels of SIRT1, we examined the effect of SIRT1 on Tat transactivation in a SIRT1-negative background. We obtained mouse embryonic fibroblasts (MEFs) derived from SIRT1 knockout mice [37]. The HIV LTR luciferase reporter and the Tat expression vector were introduced into these cells by nuclear microinjections because of their low responsiveness to various transfection protocols. Because murine cyclinT1 does not support Tat transactivation [38,39], an expression vector for human cyclinT1 was included in the microinjections. A 120-fold increase in HIV promoter luciferase activity was detected in the presence of Tat and human cyclinT1 in SIRT1+/+ MEFs (Figure 4A). In contrast, Tat-mediated transactivation of the HIV LTR was reduced in SIRT1−/− MEFs (Figure 4A). Ectopic expression of increasing amounts of human SIRT1 resulted in a dose-dependent increase of Tat transactivation in SIRT1−/− MEFs (Figure 4B). In contrast, transactivation of the 5xUAS promoter by Gal4-VP16 was reduced in response to SIRT1 (Gal4-VP16 is a fusion between the binding domain of the DNA-binding transcription factor required for the activation of the GAL genes in response to galactose in Saccharomyces cerevisiae [termed Gal4], and the activator domain of the herpes simplex virus transactivator protein [designated VP16]) (Figure 4C). These results collectively demonstrate that SIRT1 represents a positive factor for Tat function. Figure 4 Impaired Tat Transcriptional Activity in Murine SIRT1−/− Cells (A) Nuclear microinjection of HIV LTR luciferase, RSV-Tat, and a human cyclinT1-expressing construct into MEFs derived from SIRT+/+ or SIRT−/− mice. In all experiments, a fixed amount of DNA was injected by adding the empty vector control. Cells were coinjected with CMV-GFP, and the luciferase activity per GFP-positive cell was calculated. An average of two injections is shown. (B) The HIV LTR luciferase construct together with RSV-Tat and the cyclinT1-expressing construct were coinjected into SIRT−/− MEFs in the presence of increasing amounts of a plasmid expressing human SIRT1. The average of three experiments is shown (± SEM). (C) Coinjection of the human SIRT1 expression vector together with the 5xUAS luciferase construct containing five Gal4 binding sites upstream of the thymidine kinase promoter and a Gal4-VP16 expression plasmid into SIRT1−/− MEFs. The average of three experiments is shown (± SEM). This model was further tested in nuclear microinjection experiments using synthetic full-length Tat and AcTat. Microinjection of increasing amounts of either Tat or AcTat proteins into HeLa cells caused a marked transactivation of the HIV LTR luciferase reporter in a dose-dependent manner (Wt TAR in Figure 5A). AcTat transactivated the HIV promoter approximately 1.5–3-fold better than Tat. Transactivation by Tat and AcTat was dependent on the bulge and loop regions of TAR, indicating that transactivation by both proteins required the formation of an intact Tat/TAR/cyclinT1 complex [4,5,40] (TAR ΔBulge and TAR ΔLoop in Figure 5A). In agreement with this conclusion, transactivation by both Tat proteins was inhibited in a dose-dependent manner by 5,6-dichlorobenzimidazole riboside (DRB), a CDK9 inhibitor known to block Tat function (Figure 5B) [6]. Figure 5 Transcriptional Activity of AcTat Depends on Deacetylation by SIRT1 (A) AcTat functions through TAR and cyclinT1 binding. Nuclear microinjection of increasing amounts of synthetic Tat or AcTat together with wild-type (wt TAR), TAR Δbulge, or TAR Δloop mutant HIV LTR luciferase constructs into HeLa cells. Cells were coinjected with CMV-GFP, and luciferase activity was calculated per GFP-positive cell. An average of three experiments is shown (± SEM). (B) AcTat transactivation requires CDK9. HeLa cells microinjected with Tat or AcTat (each 30 ng/μl) and the HIV LTR luciferase reporter were treated with increasing amounts of DRB, a known CDK9 inhibitor, for 4 h. (C) AcTat transcriptional activity is inhibited by nicotinamide, but not TSA. HeLa cells injected with HIV LTR luciferase and increasing amounts of AcTat were treated with TSA (400 nM) or nicotinamide (5 mM) for 4 h. The average of two experiments is shown. (D) SIRT1 is necessary for AcTat, but not Tat function. HeLa cells were transfected with siRNAs specific for SIRT1 or GL3 control siRNAs 48 h before microinjection of HIV LTR luciferase and Tat or AcTat (each 30 ng/μl). The average of three experiments is shown (± SEM). According to our working model, AcTat represents a second step in the transactivation cycle [11]. Since AcTat cannot form the trimolecular complex with cyclinT1 and TAR RNA in vitro, we hypothesized that AcTat becomes partially deacetylated by the Tat deacetylase upon microinjection. This would allow the initiation of the transactivation process by unacetylated Tat binding to TAR with cyclinT1 and CDK9. To further test this hypothesis, we treated cells with deacetylase inhibitors after microinjection of AcTat and the HIV promoter construct. Treatment with TSA, an inhibitor of class I and II HDACs, enhanced the transcriptional activity of AcTat as well as the basal HIV promoter activity (TSA in Figure 5C). In contrast, nicotinamide, an inhibitor of class III deacetylases, blocked transactivation of the HIV promoter by AcTat while stimulating basal HIV promoter activity (Nicotinamide in Figure 5C). Similarly, knockdown of SIRT1 using siRNA inhibited transcriptional activity of AcTat, while slightly enhancing Tat-mediated or basal transcriptional activity of the HIV promoter (Figure 5D). These results support the model that the transcriptional activity of AcTat depends on deacetylation by SIRT1 in cells. The identification of SIRT1 as an enzyme that catalyzes an important step in HIV transcription suggests that it could be targeted therapeutically. Splitomicin was identified as a small molecule inhibitor of the S. cerevisiae Sir2p protein [41]. While splitomicin did not inhibit human SIRT1, we identified a splitomicin derivative, called HR73, which is structurally related to a previously described inhibitor of Hst1, a homolog of Sir2p in yeast [42]. HR73 effectively inhibited the histone deacetylase activity of SIRT1 in vitro with an IC50 (concentration causing 50% inhibition) of less than 5 μM (Figure 6A and 6B). Treatment of HeLa cells with HR73 suppressed Tat-dependent HIV transcription in a dose-dependent manner (3-fold at approximately 1 μM) after transfection of the Tat vector and the HIV LTR luciferase construct (HIV LTR in Figure 6C). In separate experiments, HR73 induced hyperacetylation of another target of SIRT1, the tumor suppressor p53, at the same concentration (1 μM) (Wei Gu and EV, unpublished data). Importantly, HR73 (1 μM) did not suppress the activity of the RSV LTR, the promoter driving Tat expression in our experiments (RSV LTR in Figure 6C). Figure 6 Inhibition of HIV Gene Expression by a Small Molecule Inhibitor of SIRT1 (A) In vitro histone deacetylation assays including recombinant SIRT1, radioactively labeled histone H3 peptide, and indicated concentrations of splitomicin or HR73. The average (± SEM) of two experiments performed in duplicate is shown for each point. (B) Chemical structures of splitomicin and its derivative HR73. (C) Inhibition of Tat transactivation by HR73. RSV-Tat (0, 20, and 200 ng) and HIV LTR luciferase (200 ng) or RSV-luciferase (200 ng) vectors were transfected into HeLa cells. Transfected cells were treated with indicated concentrations of HR73 or DMSO for 8 h. (D) Inhibition of HIV gene expression by HR73. GFP expression in Jurkat T cells infected with HIVNL4–3 containing the GFP open reading frame in place of the viral nef gene or with an HIV-based lentiviral vector expressing GFP from the heterologous EF-1α promoter. Treatment with HR73 (1 μM in DMSO) or DMSO was performed for 36 h after overnight infection. The average (± SEM) of four experiments is shown. To examine the effect of HR73 on HIV infection, we generated infectious HIV particles using a molecular clone of HIVNL4–3 that contained the green fluorescent protein (GFP) open reading frame in place of the viral nef gene (HIVNL4–3 is a specific viral isolate) [43]. To restrict analysis to a single infection cycle, this clone also contained a frameshift mutation in the viral env gene. Viral particles were produced by cotransfection with a construct expressing the glycoprotein of the vesicular stomatitis virus (VSV-G). Jurkat T cells were incubated with viral supernatant for at least 18 h, washed to remove extracellular virus, and treated with HR73 (1 μM) or dimethyl sulfoxide (DMSO), as a control. We observed that HIV gene expression was reduced 5-fold in cells treated with HR73 as measured by GFP expression (HIV GFP in Figure 6D). In contrast, GFP expression in cells infected with an HIV-based lentiviral vector expressing GFP from the elongation factor 1α (EF-1α) promoter was not affected by HR73 treatment (HIV [EF-1α] in Figure 6D). These data confirm the selectivity of HR73 for HIV transcription and demonstrate that other steps in the viral life cycle, including reverse transcription, nuclear import, and integration, remain unaffected by HR73. These experiments collectively show that the SIRT1 deacetylase activity is required for HIV gene expression and establish SIRT1 as a potential drug target in the treatment of HIV-1 infection. Discussion Our previous work documented that Tat acetylation by p300 at lysine 50 is a necessary step in Tat-mediated transactivation and leads to the recruitment of PCAF to the elongating polymerase. We now report the identification of SIRT1, an NAD+-dependent class III protein deacetylase, as a Tat deacetylase and regulator of Tat activity. Tat and SIRT1 coimmunoprecipitate and synergize to activate the HIV promoter. Conversely, knockdown of SIRT1 via siRNA inhibits Tat-mediated transactivation of the HIV LTR. Tat transactivation is defective in SIRT1-null MEFs and can be rescued by expression of SIRT1. Recent observations indicate that SIRT1 can regulate transcription via histone-dependent and -independent mechanisms [20]. Since SIRT1 itself does not bind to DNA directly, targeted deacetylation of histones is thought to occur through its interaction with specific DNA binding factors, such as CTIP2 (chicken ovalbumin upstream promoter-transcription factor-interacting protein 2) [44], members of the hairy-related basic helix-loop-helix repressors (HES1 [hairy and Enhancer-of-split 1] and HEY2 [hairy and Enhancer-of-split related with YRPW motif 2]) [45], the MyoD/PCAF complex [25], and the N-CoR (nuclear receptor co-repressor) and SMRT (silencing mediator of retinoid and thyroid hormone receptor) co-repressors [46]. In addition, SIRT1 binds and deacetylates histone H1 and promotes the formation of facultative heterochromatin [33]. SIRT1 also deacetylates nonhistone factors, including the tumor suppressor p53 [21,22,23]. Detailed studies of acetylation of p53 and its regulation by SIRT1 have revealed a number of intriguing parallels to Tat regulation. p53 is acetylated by p300 at multiple lysine residues, including lysine 382, leading to an increased transcriptional activity of p53 [47]. Activation of p53 up-regulates the expression of genes whose products promote cell-cycle exit or apoptosis [22]. Therefore, p300 is a positive regulator of p53 activity, while SIRT1 negatively regulates p53 functions. SIRT1 deacetylation of p53 suppresses the induction of apoptosis and promotes cell survival in response to DNA damage [21,22]. Knockout mice for Sir2α show increased p53 acetylation after DNA damage leading to increased thymocyte apoptosis after ionizing radiation [48]. Interestingly, both SIRT1 and p53, and the Tat cofactor cyclinT1, are localized in promyelocytic leukemia protein bodies [23,49]. These unique nuclear substructures represent the natural accumulation sites of promyelocytic leukemia protein and are altered or disrupted in certain tumors and in response to different cellular stresses, including Tat expression (EV, unpublished data). Interestingly, HDAC1 (a class I HDAC) has also been reported to deacetylate p53 [50], but the relative contributions of both HDAC1 and SIRT1 to p53 deacetylation remain unclear. In a previous study, an enhancement of Tat-mediated transactivation was observed in response to TSA, an inhibitor of class I and II HDACs, and was interpreted as evidence that Tat acetylation is under the control of a class I or II HDAC [7]. However, a positive effect of TSA on Tat-mediated transactivation can occur for a number of reasons that are independent of Tat acetylation levels. First, the HIV promoter can be activated by TSA in the absence of Tat, a phenomenon likely to be mediated by transcription factors responsive to TSA that bind to the HIV LTR, such as Sp1 (stimulatory protein 1), YY1 (Yin Yang 1), Eed (embryonic ectoderm development protein), and NF-κB/p65 [51,52,53]. Tat-independent activation of the HIV LTR by TSA has been documented both in vitro using chromatinized templates, and in vivo using cell lines containing integrated HIV genomes defective for Tat-mediated transactivation [54,55,56]. Second, TSA activates many promoters that are used to drive Tat expression in transient transfection experiments, making interpretations of these experiments problematic. We have observed that Tat acetylation at lysine 50 is not specifically enhanced in response to TSA [11]. These results are supported by functional experiments with Tat mutants. In cotransfection experiments, enhanced transcriptional activity of Tat in response to TSA is preserved when lysine 50 is replaced by an alanine [7] or an arginine (MO, unpublished data). Although these experiments are complicated by the significant increase in Tat expression (due to the stimulatory role of TSA on Tat expression), they provide evidence that lysine 50 in fact might not be a target for a TSA-sensitive enzyme in HIV transcription. Another system that offers intriguing parallels to our observation is the role of the SIRT1/MyoD/PCAF complex in myogenesis [25]. In this complex, PCAF acetylates both itself and MyoD. SIRT1, in contrast, deacetylates both PCAF and MyoD, leading to a retardation of myogenesis. The negative effect of SIRT1 in the regulation of muscle gene expression plays at two distinct levels. First, SIRT1 induces deacetylation of histones in the promoter regions. Second, SIRT1 deacetylates MyoD, which leads to an inhibition of its transcriptional activity [57]. Our previous experiments documented the specific interaction of acetylated Tat and PCAF via the PCAF bromodomain and the ARM region of Tat and the role of this interaction in Tat transactivation [13]. The possibility that Tat bound to the elongating polymerase and to PCAF is regulated by a PCAF-SIRT1 complex is intriguing and will be further explored. In most identified sites of action, SIRT1 showed a negative activity on transcriptional regulatory mechanisms, resulting in an inhibition of target gene expression. In contrast, we have identified a positive regulatory activity of SIRT1 on Tat-mediated transactivation. According to our working model, Tat becomes acetylated at the level of the HIV promoter after binding to TAR. The acetylation of the Tat ARM can potentially affect other biological activities associated with this domain, including RNA binding, nuclear or nucleolar import, and protein stability. Tat acetylation could also be involved in the delivery of Tat to the transcription complex, and/or might moderate interactions with cyclinT1/CDK9 prior to transcription. However, we provided evidence that Tat acetylation leads to a dissociation of Tat from cyclinT1 and TAR and to its transfer to the elongating polymerase [11]. Acetylated Tat, bound to the elongating polymerase, recruits the transcriptional coactivator PCAF via its acetyl group and the bromodomain of PCAF [12,13]. An unresolved question raised by this model is the fate of acetylated Tat at the end of the transcription cycle. Since acetylated Tat cannot recruit cyclinT1 to the TAR element, a new transcription cycle would require either the synthesis of a new unacetylated Tat protein or the deacetylation of Tat. Tat concentrations are limiting early in the virus life cycle since Tat expression is under the control of the basal LTR. Tat recycling is therefore likely to represent a rate-limiting step in HIV transcription early during the replicative cycle of the virus. Experiments presented in this manuscript demonstrate that SIRT1 catalyzes this rate-limiting step in HIV transcription. These observations raise a number of additional questions. The demonstration that Tat transactivating activity is controlled by SIRT1 connects HIV transcription with the metabolic state of the cell. The HDAC activity of SIRT1 requires NAD+ as a cofactor. NAD+ and its reduced form NADH serve as cofactors in many metabolic and stress reactions involving oxidation and reduction. Changes in the cellular NAD+/NADH ratio may regulate SIRT1 enzymatic activity, Tat transcriptional activity, and HIV replication and pathogenesis. The site of Tat deacetylation is unknown at this point. We hypothesize that the deacetylation could be triggered by molecular events linked with the end of the transcriptional elongation process. Deacetylation of Tat by SIRT1 could lead to its dissociation from the elongating polymerase and from PCAF. The previous demonstration that PCAF and SIRT1 can be coimmunoprecipitated from cells supports this model [25]. The identification of SIRT1 as an enzyme that catalyzes a rate-limiting step in HIV transcription suggests that it could be targeted therapeutically. Since our preliminary experiments indicated that splitomicin, an inhibitor of the S. cerevisiae Sir2 enzymatic activity, did not inhibit the mammalian sirtuins, we screened several compounds structurally related to splitomicin for their ability to inhibit the mammalian sirtuins. This process led to the identification of HR73, an inhibitor that suppresses SIRT1 enzymatic activity in vitro at low micromolar concentrations. This inhibitor induced p53 hyperacetylation in vivo (unpublished data) and significantly decreased HIV transcription. These results validate SIRT1 as a novel therapeutic target for HIV infection. Future efforts will analyze in additional molecular detail the role of Tat acetylation and deacetylation in HIV transcription and replication. Materials and Methods Cells and plasmids HeLa, HEK 293, and Jurkat cells (obtained from the American Type Culture Collection, Manassas, Virginia, United States), and wild-type or Sir2α knockout MEFs [37] were maintained under standard cell culture conditions. The HIV LTR luciferase plasmid [58], the RSV LTR luciferase construct [11], the 5xUAS luciferase construct [59], the LTRΔNF-κB-luciferase, the full-length (101 amino acid) CMV-Tat/FLAG and CMV-Tat/T7 expression vectors [8], the full-length (101 amino acid) RSV-Tat expression vector [60], the full-length cyclinT1 expression vector [5], the Gal4-VP16 expression vector [13], the human SIRT1–7 expression vectors with a C-terminal FLAG tag [30], as well as wild-type and mutant human SIRT1 and SIRT1H363Y containing a C-terminal MYC (myelocytomatosis oncogene) tag [23] were previously described. The SIRT1 cDNA was subcloned to generate a C-terminal HA-tagged fusion in a derivative pcDNA 3.1 (+) backbone (HA vector) by standard PCR-based strategies. The mutant CMV-Tat/FLAG expression vector TatK50R was generated by site-directed mutagenesis with the following primers. Forward, 5′- CCTATGGCAGGAGGAAGCGGAGACAGCG-3′ and reverse, 5′- CGCTGTCTCCGCTTCCTCCTGCCATAGG-3′. The mutant HIV LTR luciferase constructs (nucleotides 1–791) were generated by site-directed mutagenesis with the following primers. TAR Δbulge (T223→A) forward, 5′- GGTTAGACCAGAACTGAGCCTGGGAGC-3′ and reverse, 5′- GCTCCCAGGCTCAGTTCTGGTCTAACC-3′. The TAR Δloop mutation (C230→A, T231→G, and G234→T) was generated with the following primers. Forward, 5′- GGTTAGACCAGATCTGAGCAGGGTAGCTCTCTGGCTAACTAGGG-3′ and reverse, 5′- CCCTAGTTAGCCAGAGAGCTACCCTGCTCAGATCTGGTCTAACC-3′. The CMV-luciferase construct was generated by cloning the firefly luciferase gene as a HindIII/BamHI fragment obtained from pGL2 Basic (Promega, Madison, Wisconsin, United States) into pcDNA3.1 (Invitrogen, Carlsbad, California, United States). The CMV-GFP expression plasmid is commercially available (Clontech, Palo Alto, California, United States). Synthesis of Tat and HR73 Synthesis of 72-amino acid Tat proteins was as described [11,13]. HR73 was synthesized starting from Phenylmeldrum's acid and 6-bromo-1-dimethylaminomethyl-2-naphthol as described [61]. Identity and purity were assured by mass, infrared, and NMR spectroscopy as well as by elemental analysis. In vitro Tat deacetylation assay Human SIRT1–7 FLAG-tagged plasmids were transfected in HEK 293 cells with Lipofectamine reagent (Invitrogen). Cells were lysed 24 h after transfection with lysis buffer (50 mM Tris-HCl [pH 7.5], 0.5 mM EDTA, 0.5% NP40, and 150 mM NaCl) in the presence of protease inhibitors (Roche Molecular Biochemicals, Indianapolis, Indiana, United States). Equal amounts of total proteins were immunoprecipitated with α-FLAG M2 agarose (Sigma, St Louis, Missouri, United States), for 2 h at 4 °C. Immunoprecipitated material was washed twice with IP buffer and one time with SIRT deacetylation buffer (50 mM Tris-HCl [pH 9], 4 mM MgCl2, and 0.2 mM DTT). The beads were resuspended in 100 μl of SIRT deacetylation buffer containing 1 μg synthetic Tat (72 amino acids) carrying an N-terminal biotin label and an acetyl group at position 50 [11]. Reactions containing TSA (400 nM; WACO Bioproducts, Richmond, Virginia, United States) or nicotinamide (5 mM; Sigma) were preincubated for 10 min at room temperature. After addition of NAD+ (1 mM), reactions were incubated for 2 h at room temperature with constant agitation. Reactions were stopped by the addition of SDS loading buffer, boiled, and after brief centrifugation, analyzed by WB with rabbit α-AcTat antibodies [11] or SA-HRP (Jackson Immunoresearch Laboratories, West Grove, Pennsylvania, United States). SIRT1–7 proteins were detected with polyclonal α-FLAG antibodies (Sigma). The histone deacetylation assay with recombinant SIRT1 (1–1.3 U/reaction; Biomol, Plymouth Meeting, Pennsylvania, United States) was performed as described previously for SIRT2 [30] in 100 μl of SIRT deacetylase buffer containing NAD+ (Sigma) and enzymatically [3H]-acetylated histone H3 peptide (amino acids 1–24) [62]. Splitomicin (a gift from Julian Simon, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States) and HR73 in DMSO were added to the reactions at the indicated concentrations with all components of the reactions in the absence of NAD+ for 10 min at room temperature prior to the initiation of the reaction by addition of NAD+ (1 mM). Coimmunoprecipitation experiments HEK 293 cells were cotransfected in duplicate with expression vectors for CMV-Tat/FLAG; CMV-Tat/T7 or CMV-TatK50R/FLAG; and the SIRT1/HA or SIRT1-, SIRT2-, and SIRT6-FLAG expression vectors or the respective empty vector controls using Lipofectamine reagent (Invitrogen). Cells were lysed after 24 h in 250 mM NaCl, 0.1% NP40, 20 mM NaH2PO4 (pH 7.5), 5 mM EDTA, 30 mM sodium pyrophosphate, 10 mM NaF, and protease inhibitors (Roche Molecular Biochemicals). Duplicates were pooled, and 1 mg of lysate was immunoprecipitated either with monoclonal α-HA (Roche Molecular Biochemicals) together with protein G-Sepharose (Amersham Biosciences, Piscataway, New Jersey, United States) with α-FLAG M2 agarose (Sigma) or α-T7-agarose (Amersham Biosciences) for 2 h at 4 °C. Beads were washed three times in lysis buffer, boiled in SDS loading buffer, and analyzed by WB with polyclonal α-FLAG (Sigma), monoclonal α-HA (Roche), or monoclonal α-T7 (Novagen, Madison, Wisconsin, United States) antibodies. For the IP of Tat with endogenous SIRT1, HEK 293 cells were transfected only with CMV-Tat/FLAG or the CMV-empty vector using Lipofectamine reagent. Cell lysates were immunoprecipitated with rabbit α-SIRT1 antibodies (generated against amino acids 506–747) together with protein G-Sepharose (Amersham Biosciences). Immunoprecipitated material was analyzed by WB with the M2 α-FLAG antibody (Sigma) or rabbit α-SIRT1 antibodies. For in vitro interactions, 10 U of recombinant SIRT1 (Biomol) was incubated with biotinylated synthetic Tat or acetylated Tat proteins (0, 0.25, 1, and 4 μg) together with streptavidin-Sepharose (Amersham Biosciences) in lysis buffer in the presence of 5 mM nicotinamide (Sigma) for 3 h at 4 °C. Pelleted beads were washed three times in lysis buffer, resuspended in SDS loading buffer, and analyzed by WB with polyclonal α-SIRT1 antibodies, rabbit α-AcARM, or SA-HRP (Jackson Immunoresearch Laboratories). RNAi and transfection experiments Double-stranded siRNAs directed against nucleotides 408–428 in the SIRT1 mRNA or control GL3 siRNAs (both Dharmacon Research, Lafayette, Colorado, United States) were transfected into HeLa cells plated in six-well plates with Oligofectamine reagent according to the manufacturer's guidelines (Invitrogen). The mutant SIRT1 siRNA was identical to SIRT1 siRNA except for a two-nucleotide mismatch between the target mRNA for SIRT1 and the antisense strand of siRNA at nucleotides 418 and 419. After 48 h, cells were retransfected with the HIV LTR luciferase construct (200 ng) together with increasing amounts of CMV-Tat expression vectors (0, 50, 100, 200, 400, and 800 ng in GL3/SIRT1 siRNA experiments; 0, 2, 20, and 200 ng in SIRT1/mutant SIRT1 siRNA experiments) and corresponding amounts of empty pcDNA3.1 vector (Invitrogen). In the control experiment, CMV-Tat was replaced by the CMV-luciferase construct, and HIV LTR luciferase was replaced by an HIV LTR promoter construct driving the expression of chloramphenicol acetyl transferase (CAT) [55]). Cells were harvested 24 h later and either processed for luciferase assays (Promega) or WB of total cell extracts with polyclonal α-SIRT1 or α-actin (MP Biochemicals, Aurora, Ohio, United States) antibodies. In cotransfection experiments, human CMV-SIRT1 or CMV-SIRT1H363Y (600 ng) was cotransfected into HeLa cells plated in six-well plates with the HIV LTR luciferase reporter (200 ng) or the LTRΔNF-κB-luciferase reporter (200 ng) and increasing amounts of RSV-Tat (0, 2, 20, and 200 ng) using the Lipofectamine reagent (Invitrogen). In the control experiment, RSV-Tat was replaced by RSV-luciferase (200 ng), and the HIV LTR luciferase construct was replaced by the HIV LTR CAT reporter. In transfections with HR73, HeLa cells were cotransfected with the HIV LTR luciferase reporter (200 ng) and RSV-Tat expression vectors (0, 20, and 200 ng) or the empty vector using Lipofectamine reagent. The RSV-luciferase construct was used as described above. After 4 h incubation with the DNA/Lipofectamine mix, the culture medium was changed and supplemented with indicated concentrations of HR73 dissolved in DMSO or DMSO alone. Cells were harvested 8 h later and processed for luciferase assays. Microinjection experiments Subconfluent MEFs (70%) were grown on Cellocate coverslips (Eppendorf, Westbury, New York, United States), and nuclear microinjections were performed at room temperature with an automated injection system (Eppendorf Micromanipulator 5171 together with Eppendorf Transjector 5246). Samples were prepared as a 20 μl injection mix containing the HIV LTR luciferase reporter or 5xUAS luciferase (each 100 ng/μl), RSV-Tat (10 ng/μl) or Gal4-VP16 (50 ng/μl), CMV-cyclinT1 (100 ng/μl), CMV-SIRT1 (100 or 300 ng/μl), together with CMV-GFP (50 ng/μl) in sterile water. At 6 h after microinjection, cells were examined under a Nikon Eclipse TE300 inverted fluorescent microscope (Nikon, Tokyo, Japan) to determine the number of GFP-positive cells, washed in cold phosphate buffer, and stored at −70 °C for luciferase assays (Promega). In HeLa cells, synthetic Tat or AcTat proteins (each 30 or 100 ng/μl) were coinjected with the wild-type or mutant HIV LTR luciferase reporters (each 100 ng/μl) together with CMV-GFP (50 ng/μl), and harvested 4 h after injection. Cells were treated immediately after injection with DRB (10 or 50 μM; Sigma), TSA (400 nM), or nicotinamide (5 mM). Microinjections in siRNA-treated cells were performed 48 h after siRNA transfection. Viral infection experiments The HIV molecular clone HIV-R7/E−/GFP containing the GFP open reading frame in place of the nef gene and a frameshift mutation in the env gene, as well as the method to generate pseudotyped viral particles with VSV-G, were previously described [43]. The number of infective particles per milliliter was established by infecting 3 × 105 Jurkat cells with different amounts of viral suspension. The titer of the viral stock was measured by flow cytometric analysis of GFP expression 48 h after infection. The pHR′-EF-1α/GFP construct is a minimal nonreplicative HIV-1 genome containing a heterologous promoter, EF-1α, driving GFP expression [63]. Viral particles were produced by cotransfection of the VSV-G-encoding pMD.G and the HIV-based packaging vector pCMVΔR8.91 as described [64]. All vectors for the production of HIV-based lentiviral vectors were provided by Didier Trono, University of Geneva, Switzerland. Jurkat T cells were incubated overnight with HIV-R7/E−/GFP or pHR′-EF-1α/GFP viral particles at a theoretical multiplicity of infection of 0.5 in 24-well plates. Cells were repeatedly washed and resuspended in fresh medium containing HR73 (1 μM) or DMSO alone. Viral infection was monitored 36 h later by flow cytometry analysis using a Calibur FACScan (Becton Dickinson, Palo Alto, California, United States). We thank K Jones, B Spiegelman, H Poepperl, T Kouzarides, D Trono, J Simon, J Denu, and members of the Verdin and Ott labs for sharing their reagents and expertise, and H zur Hausen and W Greene for helpful discussions. We thank J Carroll and C Goodfellow for graphics, G Howard and S Ordway for editorial advice, and S Sande for administrative assistance. SP is a recipient of a fellowship from the Ministerio de Educación, Cultura y Deporte, Spain. This work was supported by the Gladstone Institutes (MO and EV), and by NIH grants (to EV and MO). EV is a senior scholar of the Ellison Medical Foundation. Competing interests. The authors have declared that no competing interests exist. Author contributions. MO conceived and designed the experiments. SP, AP, KK, and BLM performed the experiments. SP, EV, and MO analyzed the data. BJN, AD, CHE, PH, RF, MWM, HH, MJ, and EV contributed reagents/materials/analysis tools. SP, EV, and MO wrote the paper. Citation: Pagans S, Pedal A, North BJ, Kaehlcke K, Marshall BL, et al. (2005) SIRT1 regulates HIV transcription via Tat deacetylation. PLoS Biol 3(2): e41. Abbreviations AcTatsynthetic Tat protein carrying an acetylated lysine at position 50 ARMarginine-rich motif CATchloramphenicol acetyl transferase CDK9cyclin-dependent kinase 9 CMVcytomegalovirus DMSOdimethyl sulfoxide DRB5,6-dichlorobenzimidazole riboside EF-1αelongation factor 1α GFPgreen fluorescent protein HAinfluenza hemagglutinin HDAChistone deacetylase HIVhuman immunodeficiency virus IPimmunoprecipitation LTRlong terminal repeat MEFmouse embryonic fibroblast MyoDmyoblast determination protein NAD+nicotinamide adenine dinucleotide NF-κBnuclear factor kappa B PCAFp300/CREB-binding protein-associated factor RSVRous sarcoma virus SA-HRPstreptavidin-horseradish peroxidase conjugate SEMstandard error of the mean Sir2psilent information regulator 2 protein SIRTsirtuin siRNAsmall interfering RNA TAR trans-acting responsive element TSAtrichostatin A UASupstream activating sequence VSV-Gglycoprotein of the vesicular stomatitis virus WBWestern blot ==== Refs References Kao SY Calman AF 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efficient gene delivery in vivo Nat Biotechnol 1997 15 871 875 9306402	15719057	PMC546329	CC BY	2021-01-05 08:21:20	no	PLoS Biol. 2005 Feb 8; 3(2):e41	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030041	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1571905810.1371/journal.pbio.0030044Research ArticleCell BiologyInfectious DiseasesMolecular Biology/Structural BiologyVirologyHIV/AIDSVirusesHIV-1 Tat Stimulates Transcription Complex Assembly through Recruitment of TBP in the Absence of TAFs Tat Promotes Transcription via a TAF-less TBPRaha Tamal 1 Cheng S. W. Grace 1 Green Michael R [email protected] 1 1Howard Hughes Medical Institute, Programs in Gene Function and Expression and Molecular MedicineUniversity of Massachusetts Medical School, Worcester, MassachusettsUnited States of AmericaKadonaga Jim Academic EditorUniversity of California, San DiegoUnited States of America2 2005 8 2 2005 8 2 2005 3 2 e449 6 2004 6 12 2004 Copyright: © 2005 Raha et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation An HIV Protein Plays a Surprising Role in Gene Activation The human immunodeficiency virus type I (HIV-1) transactivator protein Tat is an unusual transcriptional activator that is thought to act solely by promoting RNA polymerase II processivity. Here we study the mechanism of Tat action by analyzing transcription complex (TC) assembly in vivo using chromatin immunoprecipitation assays. We find, unexpectedly, that like typical activators Tat dramatically stimulates TC assembly. Surprisingly, however, the TC formed on the HIV-1 long terminal repeat is atypical and contains TATA-box-binding protein (TBP) but not TBP-associated factors (TAFs). Tat function involves direct interaction with the cellular cofactor positive transcription elongation factor b (P-TEFb). Artificial tethering of P-TEFb subunits to HIV-1 promoter DNA or nascent RNA indicates that P-TEFb is responsible for directing assembly of a TC containing TBP but not TAFs. On the basis of this finding, we identify P-TEFb-dependent cellular promoters that also recruit TBP in the absence of TAFs. Thus, in mammalian cells transcription of protein-coding genes involves alternative TCs that differ by the presence or absence of TAFs. The transcriptional activator, HIV-1 Tat, not only acts by promoting RNA polymerase processivity, but it is able to promote transcription complex assembly in the absence of TATA- box-binding protein-associated factors ==== Body Introduction In eukaryotes, gene regulation is largely controlled at the transcriptional level. Factors involved in the accurate transcription of eukaryotic structural genes by RNA polymerase II (class II genes) can be classified into two groups. First, general (or basic) transcription factors (GTFs) are necessary and can be sufficient for accurate transcription initiation in vitro (for review, see [1]). Such factors include RNA polymerase II itself and at least six GTFs: TFIID, TFIIA, TFIIB, TFIIE, TFIIF, and TFIIH. The GTFs assemble on the core promoter in an ordered fashion to form a transcription pre-initiation complex (PIC). The first step in PIC assembly is binding of the GTF TFIID to the TATA box. TFIID is a multi-subunit complex consisting of the TATA-box-binding protein (TBP) and a set of tightly bound TBP-associated factors (TAFs) [2,3,4]. Transcriptional activity is greatly stimulated by the second class of factors, promoter-specific activator proteins (activators). In general, cellular activators are sequence-specific DNA-binding proteins whose recognition sites are usually present in sequences upstream of the core promoter (reviewed in [5,6]). In addition to a sequence-specific DNA-binding domain, a typical activator also contains a separable activation domain. A variety of studies indicate that activators work, at least in part, by increasing PIC formation through a mechanism thought to involve direct interactions with one or more components of the transcription machinery [1,7,8,9]. Activators can also act through other mechanisms, such as increasing the rate of transcriptional elongation, promoting multiple rounds of transcription, and directing chromatin modifications (reviewed in [10]). The Tat protein of human immunodeficiency virus type I (HIV-1) is a potent activator of the viral long terminal repeat (LTR) and is required for viral replication (reviewed in [11,12,13]). Unlike typical activators, which bind to promoter DNA, Tat binds to nascent viral RNA through an RNA-binding site termed the TAR element. Tat function involves direct interaction with the cellular cofactor called positive transcription elongation factor b (P-TEFb), which is composed of two subunits, cyclin T1 (CycT1) and CDK9 (reviewed in [14,15]). Several lines of evidence have suggested that unlike typical activators, Tat stimulates transcriptional elongation rather than initiation. According to the current model (reviewed in [11,12,13]), in the absence of Tat, RNA polymerase II initiates from the HIV-1 LTR in a form unable to elongate efficiently and thus stalls (or pauses) near the transcription start site. When present, Tat binds to TAR and recruits P-TEFb, allowing CDK9 to phosphorylate the C-terminal domain of the RNA polymerase II large subunit, thereby increasing transcriptional processivity. The chromatin immunoprecipitation (ChIP) assay has provided a powerful approach to study in vivo the mechanism by which activators stimulate transcription and to delineate the composition of transcription complexes (TCs). In yeast, ChIP analysis has been used to show that activators function, at least in part, by stimulating TC assembly. These studies have also revealed that at certain promoters TBP is recruited in the absence of TAFs [16,17], indicating that, at least in yeast, some TCs contain TBP but not TFIID. Here we perform ChIP experiments to study the mechanism of transcription activation by Tat in vivo. Results Experimental Design To analyze transcription stimulation by Tat and other activators we performed ChIP experiments in transiently transfected mammalian tissue culture cells. Plasmids expressing a reporter construct, containing a core promoter and various combinations of activator-binding sites, were transiently transfected into HeLa or 293T cells. In some experiments, a second plasmid expressing an activator was co-transfected. Reporter activity or primer extension was used to quantitate transcription levels. TC assembly was analyzed by a ChIP assay using antibodies directed against components of four representative GTFs involved in distinct stages of TC assembly: TFIID (TBP and typically TAF1 and TAF5), TFIIB, mediator (CDK8 and hMed6), and RNA polymerase II (RBP1). In most experiments the analysis was performed using two sets of primers: one set encompassed the core promoter, transcription start site, and immediate downstream region, and a second set was located far downstream of the transcription start site, within the open reading frame (ORF). Diverse Activators Function by Increasing TC Assembly In yeast, it has been shown that activators stimulate TC assembly, which is evident at the earliest step of this process, interaction of TBP with the TATA box [18,19]. To confirm that this was also the case in mammalian cells, we used a ChIP assay to analyze TC assembly in three well-characterized model systems: transcription directed by Gal4-VP16, transcription directed by the SV40 enhancer, and transcription directed by the adenovirus (Ad) E1a protein. The artificial activator Gal4-VP16 contains an unusually potent acidic activation domain and can stimulate transcription in many species and cell types including mammalian tissue culture cells [5]. Figure 1A shows, consistent with previous studies, that on a synthetic promoter containing four Gal4-binding sites upstream of the TATA box, transcription was not detectable in the absence of Gal4-VP16, whereas addition of Gal4-VP16 led to a large transcriptional increase. The accompanying ChIP assay shows that in the absence of Gal4-VP16 there was no detectable association of GTFs with the promoter. However, addition of Gal4-VP16 led to a large increase in recruitment of all GTFs analyzed, explaining the transcriptional stimulation. Consistent with the transcription results, RNA polymerase II was associated with the ORF in the presence but not absence of Gal4-VP16. Also, as expected, the GTFs were associated with the core promoter and not the ORF. Figure 1 Diverse Activators Function by Increasing TC Assembly (A) Gal4-VP16. Left: a CAT reporter construct containing the E1b core promoter and four Gal4-binding sites (G4E1bCAT) was transiently trans-fected into 293T cells, together with a plasmid expressing Gal4-VP16. Middle: transcription levels were determined by quantitating CAT reporter activity. Right: TC assembly was analyzed by a ChIP assay using the indicated antibodies. The percentage of DNA immunoprecipitated relative to input is indicated. The location of the primers used in the ChIP assay to analyze the promoter (black) or ORF (gray) is schematically shown on the left. (B) SV40 enhancer. Shown on the left is a schematic diagram of a construct containing a minimal rabbit β-globin promoter and harboring or lacking the SV40 enhancer. Also shown is transcription analysis by CAT assay (middle) and TC assembly (right) in transiently transfected HeLa cells. (C) Ad E1a. Left: a CAT reporter construct containing the Ad E4 promoter and upstream ATF-binding sites (E4CAT) was transiently transfected into HeLa cells, together with a plasmid expressing Ad E1a. Middle: transcription analysis by CAT assay is shown. Right: TC assembly is shown. Transcription of certain genes requires a cis-acting enhancer element that is often located at a distance from the transcription start site (reviewed in [20]). The enhancer functions by providing binding sites for cellular activators. For example, a minimal β-globin promoter is inefficiently expressed, but addition of an appropriate enhancer element dramatically increases transcription [21]. Figure 1B shows, as expected, that a minimal rabbit β-globin promoter was virtually inactive, whereas transcriptional activity was greatly increased by addition of the SV40 enhancer. The ChIP analysis shows that in the absence of the SV40 enhancer there was no detectable association of GTFs with the β-globin promoter. Upon addition of the SV40 enhancer, there was a large increase in GTF recruitment that correlated with the increased transcriptional activity. Finally, we analyzed transcription directed by the Ad E1a protein. Efficient transcription from Ad early promoters requires the viral E1a protein as well as the participation of cellular transcription factors that also bind to the promoter (reviewed in [22]). For example, the Ad E4 promoter contains multiple binding sites for cellular ATF proteins, which are required for efficient transcription activation by E1a [23]. Figure 1C shows, as expected, that in the absence of E1a there was only a background level of Ad E4 transcription, and that E1a increased transcription dramatically. The accompanying ChIP assay shows that in the absence of E1a there was no significant association of GTFs with the promoter, whereas transfection of E1a resulted in recruitment of all GTFs analyzed in a manner that paralleled the increased transcriptional activity. In summary, the results of Figure 1 indicate, as in yeast, that in three well-studied higher eukaryotic examples, transcription activation involves promotion of TC assembly. Moreover, as in yeast, the stimulatory effect is evident at the earliest step of TC assembly, the TBP– TATA box interaction. The HIV-1 Tat Protein Stimulates TC Assembly through Recruitment of TBP in the Absence of TAFs We next investigated Tat-mediated transcription activation and TC assembly on the HIV-1 LTR promoter. Figure 2A shows, as expected, that in the absence of Tat there was no detectable transcription from the HIV-1 LTR, and that addition of Tat increased transcription dramatically. The accompanying ChIP experiment (Figure 2A, left) shows that in the absence of Tat, association of GTFs with the core promoter was virtually undetectable. Sp1, a constitutive cellular activator that binds upstream of the HIV-1 core promoter was, as expected, associated with the promoter in the absence of Tat and unaffected by Tat addition (Figure 2A, right). Significantly, in the absence of Tat there was no detectable association of RNA polymerase II near the transcription start site or within the ORF (Figure 2A, left). However, following addition of Tat, there was a large increase in association of TBP, TFIIB, mediator, and RNA polymerase II with the promoter, which paralleled the transcriptional increase. Also, as expected, there was a large increase in association of RNA polymerase II with the ORF. Following recruitment of P-TEFb to the HIV-1 LTR by Tat, P-TEFb associates with and phosphorylates RNA polymerase II [12,24]. Accordingly, the ChIP assay (Figure 2A, right) shows that in the presence of Tat the two P-TEFb subunits, CycT1 and CDK9, were present at both the promoter and the ORF. Unexpectedly, although TBP and the other GTFs were efficiently recruited to the promoter in the presence of Tat, there was no significant recruitment of the two TAFs analyzed, TAF1 or TAF5 (left panel). Figure 2 The HIV-1 Tat Protein Stimulates TC Assembly through Recruitment of TBP in the Absence of TAFs (A) A CAT reporter construct containing the TAR element and Sp1-binding sites ([−83]HIV LTRCAT) was transiently transfected into 293T cells, together with a plasmid expressing Tat. Left: transcription analysis by CAT assay is shown. Middle and right: TC assembly is shown. Bottom: a schematic diagram of the CAT reporter construct is shown. (B) TC assembly was monitored in a chronically HIV-1-infected cell line, 8E5/LAV, that harbors an integrated provirus that is constitutively transcribed. Virus production was confirmed by analysis of p24 levels and reverse transcriptase activity in the culture medium (data not shown). (C) TC assembly was monitored in the chronically infected cell line U1 on the integrated HIV-1 LTR in the presence or absence of PMA. We note that recruitment of Tat was not observed in the ChIP assay, which detects proteins bound either directly or indirectly to DNA (reviewed in [25]). Unlike all the other transcription factors, which associate with the TC through DNA–protein or protein–protein interactions, Tat is bound to nascent RNA [11,12,13]. To determine whether the lack of TAF recruitment was a general feature of Tat-directed transcription, we analyzed a chronically HIV-1-infected cell line, 8E5/LAV, which harbors an integrated provirus that is constitutively transcribed [26]. Figure 2B shows that TBP, TFIIB, mediator, Sp1, P-TEFb, and RNA polymerase II, but not TAF1 or TAF5, were associated with the integrated proviral promoter, consistent with the results of the transient transfection assay. We also analyzed a second chronically HIV-1-infected cell line, U1, which has been used as a model to study viral latency. U1 cells harbor an integrated viral genome, but, unlike 8E5/LAV cells, the constitutive level of viral expression is extremely low. Viral expression can be induced by phorbol esters, which stimulate transcription through upstream NF-κB-binding sites in the HIV-1 LTR, leading to Tat synthesis and a subsequent substantial Tat-mediated transcriptional increase [27,28]. Thus, U1 cells provide an experimental system to analyze TC assembly from an integrated HIV-1 LTR in the inactive (−PMA) or active (+PMA) state. Figure 2C shows that following activation of the HIV-1 LTR by PMA addition, there was a large increase in recruitment of TBP and RNA polymerase II, whereas TAF1 and TAF5 were once again not detected. To investigate further the composition of the TC formed on the HIV-1 LTR, we obtained a panel of antibodies directed against nine additional human TAFs (TAF2, TAF4, TAF6, TAF7, TAF8, TAF9, TAF11, TAF12, and TAF13), as well as antibodies against four subunits of the S TAGA complex (PAF65β, hSpt3, hGCN5, and TRRAP) and the TBP-interacting protein Mot1. The results (Figure 3A) show that when transcription was directed by Gal4-VP16, all 11 TAFs analyzed were bound to the promoter, as expected for recruitment of TFIID. By contrast, when transcription was directed by Tat, none of the 11 TAFs were recruited to the HIV-1 LTR. Gal4-VP16 also recruited all four S TAGA subunits analyzed, none of which were associated with the HIV-1 LTR in the presence of Tat. Finally, Mot1 was not recruited when transcription was directed by either Gal4-VP16 or Tat. On the basis of these data we conclude that the TC formed on the HIV-1 LTR lacks at least 11, and probably all, of the 14 TFIID TAFs. In the experiments presented below, TAF1 and TAF5 were analyzed as representative TAFs. Figure 3 TAFs Are Not Recruited to the HIV 1-LTR and Not Required by Tat for Transcription Activation (A) TC assembly was analyzed by a ChIP assay using antibodies directed against nine additional human TAFs, four subunits of the S TAGA complex, and Mot1. TAFs that are also present in S TAGA are indicated by asterisks. Bottom: schematic diagrams of the promoter constructs are shown. (B) Transcription analysis in ts13 cells, which harbor a temperature-sensitive mutation in TAF1. Ts13 cells grown at the permissive or non-permissive temperature were transiently transfected with a luciferase reporter plasmid and a plasmid expressing a transcriptional activator. Transcription was monitored by luciferase activity, and normalized relative to activity at the permissive temperature. (C) Left: immunoblot analysis is shown. 293A cells were transfected with a TAF5 or TAF12 shRNA expression vector or an empty vector (control) and analyzed by immunoblotting using the indicated antibodies. Right: transcription levels were monitored by luciferase reporter activity in shRNA-treated cells, and normalized relative to luciferase activity in non-shRNA-treated cells. TAFs Are Not Required for Tat-Mediated Transcription Activation The results of the ChIP experiments strongly suggested that transcription activation by Tat did not require TAFs. To confirm this supposition, we examined the ability of Tat to stimulate transcription following TAF inactivation. First, we examined the ability of Tat to activate transcription in ts13 cells, which harbor a temperature-sensitive mutation in TAF1 [29,30]. Figure 3B shows, as expected, that inactivation of TAF1 significantly diminished transcription activation by VP16 and E1a, whereas Tat-mediated transcription activation was unaffected. In a second approach, we used short hairpin RNAs (shRNAs) to knockdown expression of TAF5 or TAF12 in 293A cells. Immunoblot analysis confirmed that shRNA-mediated knockdown of TAF expression was efficient and specific (Figure 3C, left). Transcriptional analysis revealed that knockdown of TAF5 or TAF12 substantially decreased transcription directed by VP16, whereas Tat-mediated transcription activation was unaffected (Figure 3C, right). (Because 293A cells are transformed by and express E1a, activation by E1a could not be analyzed in this experiment.) Collectively, the results of Figure 3 demonstrate that TAFs are not involved in transcription activation directed by the HIV-1 Tat protein. Tat and P-TEFb Direct Recruitment of TBP in the Absence of TAFs The results described above indicate that on the HIV-1 LTR transcription activation involves assembly of an atypical TC that contains TBP but not TAFs. However, these experiments do not distinguish whether the critical determinant for recruitment of TBP and not TFIID is Tat or the promoter, the HIV-1 LTR. To address this issue we performed a series of artificial tethering experiments. Previous studies have shown that Tat can activate transcription when directed to the HIV-1 LTR through a heterologous DNA-binding domain [31,32,33]. We analyzed TC assembly using an HIV-1 LTR derivative that lacked the TAR element and contained upstream Gal4-binding sites. We first examined the TCs formed on this promoter by three Gal4 fusion proteins: Gal4-VP16, Gal4-E1a, and Gal4-Tat. Consistent with previous studies [32], Figure 4 shows that all three Gal4 fusion proteins activated transcription from this modified HIV-1 LTR derivative. The accompanying ChIP experiments show that Gal4-VP16 and Gal4-E1a supported assembly of a TC that contained all of the GTFs, including TAF1 and TAF5. By contrast, Gal4-Tat directed assembly of a TC in which the TAFs were present at a level significantly below that of TBP and other GTFs. These results indicate that the activator Tat, and not the HIV-1 LTR promoter, directs the selective recruitment of TBP in the absence of TAFs. Figure 4 Tat and P-TEFb Direct Recruitment of TBP in the Absence of TAFs When Tethered to DNA Transcription (left) and TC assembly (right) were examined using an HIV-1 LTR derivative that lacks the TAR element and contains upstream Gal4-binding sites (G6[−83]HIV LTRΔTARCAT; bottom) and a series of Gal4 fusion proteins, as indicated. As discussed above, Tat interacts directly with CycT1, a subunit of the transcription elongation factor P-TEFb. Thus, Tat is an adaptor whose function is to recruit P-TEFb. We therefore considered that P-TEFb was ultimately responsible for the selective recruitment of TBP in the absence of TAFs. To address this possibility, we analyzed transcription activation by the two P-TEFb subunits, CycT1 and CDK9. Consistent with previous results [34,35], both Gal4-CycT1 and Gal4-CDK9 activated transcription. The ChIP analysis indicates that both Gal4-CycT1 and Gal4-CDK9 stimulated assembly of a TC that contained all of the GTFs but lacked TAF1 and TAF5. These results confirm that P-TEFb is responsible for directing selective recruitment of TBP in the absence of TAFs. In the experiments shown in Figure 4, P-TEFb was tethered to DNA, whereas on the HIV-1 LTR, P-TEFb is normally bound through Tat to nascent RNA. We therefore sought to verify that P-TEFb, when bound to nascent RNA, would also selectively recruit TBP in the absence of TAFs. Previous studies have shown that recruitment of CycT1/P-TEFb to the HIV-1 LTR through a heterologous RNA-binding domain can activate transcription in the absence of Tat [36]. We therefore asked whether such an artificially recruited CycT1/P-TEFb protein would stimulate assembly of a TC that contained TBP but not TAFs. We used a previously characterized HIV-1 LTR derivative [36] in which the TAR element was replaced by a minimal binding site for the HIV-1 Rev protein, and examined the ability of a Rev-Tat or Rev-CycT1 fusion protein to stimulate TC assembly. Figure 5 shows that in the absence of a Rev fusion protein, both transcription and GTF recruitment were virtually undetectable. Addition of Rev-Tat or Rev-CycT1 resulted in a large transcriptional increase, as expected from previous studies. Significantly, addition of Rev-Tat or Rev-CycT1 also led to a large increase in recruitment of TBP, TFIIB, mediator, and RNA polymerase II, but not TAF1 or TAF5. Thus, in the absence of Tat, Rev-CycT1 stimulated assembly of a TC that contained TBP but not TAFs. Figure 5 Tat and P-TEFb Direct Recruitment of TBP in the Absence of TAFs When Tethered to RNA Transcription (left) and TC assembly (right) were examined using a Rev-Tat or Rev-CycT1 fusion protein and an HIV-1 LTR derivative in which the TAR element was replaced by stem loop IIB (SLIIB), a Rev-protein-binding site (HIV SLIIBCAT; bottom). Identification of Cellular Promoters That Recruit TBP in the Absence of TAFs P-TEFb is also a cofactor for several cellular activators, the best studied of which is CIITA, a transcription factor involved in the expression of major histocompatibility complex (MHC) class II genes (reviewed in [37]). In fact, overexpression of Tat can inhibit transcription of MHC class II genes by competing with CIITA for P-TEFb binding [38,39]. We therefore tested whether the promoters of MHC class II genes also recruited an atypical TC that contained TBP but not TAFs. We analyzed two MHC class II genes known to use CIITA, HLA-DM and HLA-DR, and, as a negative control, GAPDH. RT-PCR analysis confirmed, as expected, that both HLA-DM and HLA-DR were expressed in 293T cells (data not shown). The associated ChIP experiment (Figure 6) shows that the TCs formed on both HLA-DM and HLA-DR contained all of the GTFs but lacked significant levels of TAF1 and TAF5. By contrast, the TC formed on GAPDH contained all of the GTFs, including TAF1 and TAF5. These results indicate that TBP, and not TFIID, is also selectively recruited to specific cellular promoters and strongly support the conclusion that P-TEFb directs this recruitment. Figure 6 The Cellular Activator CIITA Directs Recruitment of TBP in the Absence of TAFs TC assembly was monitored on the promoters of HLA-DM and HLA-DR, two MHC class II genes known to use CIITA, and, as a negative control, GAPDH. Discussion A variety of studies have proposed that Tat promotes transcriptional elongation [11,12,13]. However, the effect of Tat on TC assembly in vivo has not been previously analyzed. In this report, we studied the mechanism of Tat action using ChIP assays and found, unexpectedly, that Tat dramatically stimulates TC assembly on the HIV-1 LTR. Perhaps most surprising, the HIV-1 LTR contains an atypical TC that lacks TFIID and that has not been previously described in mammalian cells. By contrast, transcription activation by Gal4-VP16, the SV40 enhancer, or the Ad E1a protein involves assembly of a TC that contains TFIID. Thus, our results reveal mechanistic similarities between apparently diverse classes of activators and unanticipated differences in the composition of mammalian TCs. In an HIV-1-infected cell, a low level of Tat-independent transcription can be elicited by cellular activators, such as Sp1, NF-κB, and NFAT, which are bound to upstream regions of the HIV-1 LTR [12]. Tat is recruited to the vicinity of the promoter by binding to this low level of nascent TAR RNA, where it facilitates subsequent rounds of TC assembly and transcription initiation. Thus, once a threshold level of transcription is achieved, Tat stimulates additional transcription, thereby increasing the number of Tat-binding sites (reviewed in [40]). This positive feedback mechanism would ensure that viral transcription rapidly increases or decreases, which may be relevant to the ability of HIV-1 to enter latency and, conversely, to be activated from the latent state. Tat Stimulates TC Assembly Previous studies have provided two principal lines of evidence that Tat functions by stimulating transcription elongation. First, Tat has been found to stimulate elongation in an in vivo nuclear run-off assay [41,42,43,44], and in the absence of Tat, apparent prematurely terminated transcripts have been detected [41,45]. However, these in vivo results are complicated by the presence of a second promoter, the initiator of short transcripts, adjacent to the HIV-1 LTR [46,47]. In our experiments RNA polymerase II was not detected either near or far downstream of the transcription start site in the absence of Tat and thus provided no evidence for a paused (or stalled) RNA polymerase II. Consistent with our ChIP data, nuclear run-off experiments have shown that Tat increases the density of RNA polymerase II 9- to 15-fold within the first 25 nucleotides downstream of the transcription start site [42,43], indicating that Tat also stimulates initiation. A second line of evidence has been derived from in vitro experiments in which Tat was found to enhance elongation but not initiation (see, for example, [48]). However, in vitro transcription experiments may fail to faithfully recapitulate in vivo regulation: for example, under in vitro conditions using naked DNA templates, PIC assembly and initiation may no longer be rate-limiting on the HIV-1 LTR. Consistent with this possibility, the HIV-1 LTR is a relatively strong promoter when analyzed as naked DNA in vitro in the absence of Tat (see, for example, [49]). Moreover, initiation effects would not be observed if the cell-free system did not support multiple rounds of transcription from a single DNA template (see, for example, [50]). It is important to emphasize that our ChIP assay only analyzed TC assembly. Thus, our results do not rule out the possibility that in addition to stimulating TC assembly, Tat also promotes transcription elongation. Based upon the results presented here and previous studies (reviewed in [11,12,13]), we believe that Tat stimulates TC assembly, thereby promoting initiation and elongation. This dual mechanism of action may explain why Tat is such a potent activator of transcription. Tat and P-TEFb Direct Recruitment of TBP in the Absence of TAFs We have shown that transcription from the HIV-1 LTR involves TBP but not TFIID. This result was particularly unexpected because in mammalian cells TBP is bound to TAFs extremely tightly and numerous biochemical studies have failed to find free TBP [2,3,4]. None of the 11 TAFs, four S TAGA subunits, or Mot1 was present on the HIV-1 LTR, strongly suggesting that free TBP is recruited. However, we cannot exclude the possibility that TBP may be associated with unknown proteins that remain to be identified. Several considerations rule out the possibility that TFIID is actually present in the TC directed by Tat/P-TEFb but that the TAFs are not detected in the ChIP assay. First, numerous studies have shown ChIP to be a remarkably general and robust assay that has successfully detected a wide variety of activators, general initiation and elongation components, chromatin remodeling factors, and histone variants. Second, in experiments not involving Tat or P-TEFb, TAFs were detected at levels comparable to that of TBP on five different promoters (G4E1bCAT, β-globin, E4CAT, G6(−83)HIV LTRΔTARCAT, and GAPDH). Third, when TC assembly was directed by Tat or P-TEFb the ChIP assay detected all GTFs analyzed, including TBP, but not TAFs on six different promoters ([−83]HIV LTRCAT, integrated HIV-1/LAV, HIV SLIIBCAT, G6[−83]HIV LTRΔTARCAT, HLA-DM, and HLA-DR]. Finally, and perhaps most persuasively, on the same promoter the presence or absence of TAFs as monitored by ChIP was dictated solely by the upstream bound Gal4 fusion protein (see Figure 4). Artificial tethering experiments clearly demonstrate that P-TEFb directs recruitment of TBP in the absence of TAFs. Strongly supporting this conclusion is our identification of cellular promoters for which P-TEFb is a cofactor and whose TC contains TBP but not TAFs. How P-TEFb selectively recruits TBP and not TFIID remains to be determined. It has been previously thought that P-TEFb is purely an elongation factor [14,15]. However, we have shown that when tethered to DNA or nascent RNA, P-TEFb can also stimulate TC assembly and dictate the composition of the TC. In yeast, promoters have been grouped into two extreme classes based on their requirement for TAFs [16,17]. TAF-dependent promoters require TAFs for transcription, and on these promoters TBP and TAFs are present at comparable levels. TAF-independent promoters do not require TAFs for activity, and on these promoters TAFs are either absent or present at levels far below that of TBP. These yeast results are strikingly similar to the findings reported here, and taken together these results indicate that in both yeast and mammalian cells, transcription of protein-coding genes involves alternative TCs that differ by the presence or absence of TAFs. Materials and Methods Plasmids To generate the G4E1bCAT construct, pDSG39, four Gal4-binding sites were inserted upstream of the E1b TATA box and the CAT ORF in the vector pSP72 (Promega, Madison, Wisconsin, United States). The β-globin constructs containing (pBS) or lacking (pB/E) the SV40 enhancer were provided by Walter Schaffner; pBS contains a 196-bp SV40 enhancer–containing fragment inserted into a plasmid derived from pB6 [51], which harbors the β-globin gene. The E4CAT construct, pE4CAT, was previously described [52]. The pFR-Luc plasmid, containing five Gal4-binding sites upstream of the E1b TATA box and the luciferase reporter gene, was obtained from Stratagene (La Jolla, California, United States). The HIV LTR-luciferase construct was previously described [53]. The (−83)HIV LTRCAT and G6(−83)HIV LTRΔTARCAT constructs, were previously described [54]. The HIV SLIIBCAT construct, pHIV/SLIIB/CAT [36], was provided by Bryan Cullen. Plasmids expressing the Gal4-VP16 fusion protein (pGal4-VP16; [55]), E1a (pSV-E1a; [52]), Tat (CMV-Tat; [56]), and the Gal4-E1a fusion protein (pGal4-E1a; [52]) were previously described. To generate pGal4-Tat, pGal4-CycT1, and pGal4-CDK9, the full-length Tat protein, the hCycT1-containing EcoR1 fragment from phCycT1-Rev [36], or the CDK9-containing EcoR1 fragment from pSG5-CDK9 (a gift from Claude Gazin), respectively, were individually cloned into plasmid pSG424 [55] as an in-frame fusion with the Gal4 DNA-binding domain. Rev fusion-protein plasmids pTAT-Rev and phCycT1-Rev [36] were obtained from Bryan Cullen. Antibodies The α-Gal4 mouse monoclonal (sc-510), α-CDK8 goat polyclonal (sc-1521), α-CDK9 goat polyclonal (sc-7331), α-CycT1 goat polyclonal (sc-8127), α-Med6 goat polyclonal (sc-9434), α-GCN5 goat polyclonal (sc-6303); α-TRRAP rabbit polyclonal (sc-11411), α-TAF1 mouse monoclonal (sc-735), α-TAF5 mouse monoclonal (sc-743), α-Sp1 rabbit polyclonal (sc-59X), and α-alpha tubulin mouse monoclonal (sc-5268) antibodies were obtained from Santa Cruz Biotechnology (Santa Cruz, California, United States). The α-RNA polymerase II mouse monoclonal antibody (8WG16) was obtained from BAbCO (Berkeley, California, United States). The α-Tat rabbit antiserum was obtained from the National Institutes of Health AIDS Research and Reference Reagent Program (catalog number 705; [57]). The α-TBP mouse monoclonal antibody (SL30–3-563) was provided by Nouria Hernandez; the α-TAF2, α-TAF4, α-TAF6, α-TAF7, α-TAF8, α-TAF9, α-TAF11, α-TAF12, α-TAF13, and α-hSpt3 rabbit polyclonal antibodies were provided by Robert Roeder; the α-Mot1 rabbit polyclonal antibody was provided by Franklin Pugh; and the α-PAF65β rabbit polyclonal antibody was provided by Yoshihiro Nakatani. Transcription Transcription was measured either by RT-PCR analysis from total RNA isolated from transfected cells or by chloramphenicol acetyl transferase (CAT) assay [58] using 14C chloramphenicol followed by thin layer chromatography. Signals were visualized by autoradiography and quantitated using National Institutes of Health Image 1.62 software. ChIP assay HeLa and 293T cells were transfected with 10 μg of effector plasmids and 5 μg of reporter plasmid using standard CaCl2 transfection methods [59]. Forty-eight hours after transfection, cells were cross-linked by adding formaldehyde to the medium (1% final concentration) and incubated for 10 min at room temperature. The cross-linking reaction was quenched by adding glycine to a final concentration of 0.125 M and incubating for an additional 10 min at room temperature. Cells were then washed with ice-cold PBS containing protease inhibitors, scraped in the same buffer, and washed in PBS. Cells were then lysed in SDS-lysis buffer (1% SDS, 50 mM Tris-HCl [pH 8.0], 10 mM EDTA, and protease inhibitors), and sonicated at least 6–8 times at output 8 for 10 s, with 2 min incubation on ice in between. Lysates were centrifuged at 15,000 rpm for 15 min at 4 °C; 5% of the lysates were kept as input. Lysates were diluted 10-fold with ChIP dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl [pH 8.0], 16.7 mM NaCl, and protease inhibitors) and pre-cleared with 80 μl of Protein A Agarose-50% Slurry (Upstate Biotechnology, Lake Placid, New York, United States) for 30 min at 4 °C with constant agitation. The agarose beads were pelleted, and the supernatant was incubated overnight with the primary antibody. To each tube, 50 μl of Salmon Sperm DNA/Protein A Agarose-50% Slurry (Upstate Biotechnology) was added, and the mixture was incubated for 1 h at 4 °C. The agarose beads were pelleted by gentle centrifugation, the supernatant was removed, and the pellet was washed twice with low-salt immune complex wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl [pH 8.0], and 150 mM NaCl), once with high-salt immune complex wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl [pH 8.0], and 500 mM NaCl), once with LiCl immune complex wash buffer (0.25 M LiCl, 1% NP40, 1% C24H39NaO4, 1 mM EDTA, and 10 mM Tris-HCl [pH 8.0]), and twice with TE (10 mM Tris-HCl [pH 8.0] and 1 mM EDTA). Chromatin was eluted with freshly prepared elution buffer (1% SDS and 0.1 M CHNaO3). After reversal of the cross-links, the samples were treated with proteinase K for 1 h at 45 °C, extracted by phenol/chloroform, and ethanol precipitated. The pellet was resuspended in TE, and PCR was performed. Autoradiograms were scanned and quantitated by the National Institutes of Health ImageJ program. IP DNA was quantitated and presented as the ratio of IP to input. Primer sequences are available upon request. TAF inactivation For TAF1 inactivation, ts13 cells [60] were initially cultured at the permissive temperature (33 °C), after which one set of 6-well plates was shifted to the non-permissive temperature (39.5 °C) while another set was kept at 33 °C. Sixteen hours later, cells were co-transfected with a plasmid expressing a transcriptional activator (Gal4-VP16, E1a, or Tat) and a luciferase reporter plasmid, and incubated further for 24 h prior to measuring transcription by luciferase assay. For shRNA-mediated inactivation of TAFs, 293A cells were transfected with either pcDNA3 (Invitrogen, Carlsbad, California, United States) or a TAF5 or TAF12 shRNA expression vector (Open Biosystems, Huntsville, Alabama, United States), and 18 h later were co-transfected with effector and reporter plasmids. Transcription activation was monitored by luciferase activity 24 h following transfection. Supporting Information Accession Numbers The LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/) accession numbers for the genes and gene products discussed in this paper are CDK8 (1024), CDK9 (1025), CIITA (4261), CycT1 (904), GAPDH (2597), hGCN5 (2648), HLA-DM (3109), HLA-DR (3122), hSpt3 (8464), MED6 (10001), Mot1 (9044), PAF65β (27097), RBP1 (5430), TAF1 (6872), TAF11 (6882), TAF12 (6883), TAF13 (6884), TAF2 (6873), TAF4 (6874), TAF5 (6877), TAF6 (6878), TAF7 (6979), TAF8 (129685), TAF9 (6880), Tat (155871), TBP (6908), TFIIB (2959), and TRRAP (8295). We thank Robert Roeder, Nouria Hernandez, Yoshihiro Nakatani, and Franklin Pugh for providing antibodies; Claude Gazin, Bryan Cullen, and Walter Schaffner for providing plasmids; Maria Zapp and Ellen Kittler for providing 8E5/LAV and U1 cells and advice; and Sara Evans for editorial assistance. This work was supported in part by an National Institutes of Health grant to MRG. MRG is an investigator of the Howard Hughes Medical Institute. Competing interests. The authors have declared that no competing interests exist. Author contributions. TR and MRG conceived and designed the experiments. TR and SWGC performed the experiments. TR, SWGC, and MRG analyzed the data. TR and SWGC contributed reagents/materials/analysis tools. TR and MRG wrote the paper. Citation: Raha T, Cheng SWG, Green MR (2005) HIV-1 Tat stimulates transcription complex assembly through recruitment of TBP in the absence of TAFs. PLoS Biol 3(2): e44. Abbreviations Adadenovirus ChIPchromatin immunoprecipitation CycT1cyclin T1 GTFgeneral transcription factor HIV-1human immunodeficiency virus type I LTRlong terminal repeat MHCmajor histocompatibility complex ORFopen reading frame PICpre-initiation complex P-TEFbpositive transcription elongation factor b shRNAshort hairpin RNA TAF TATA-box-binding protein associated factor TBP TATA-box-binding protein TCtranscription complex ==== Refs References Orphanides G Lagrange T Reinberg D The general transcription factors of RNA polymerase II Genes Dev 1996 10 2657 2683 8946909 Burley SK Roeder RG Biochemistry and structural biology of transcription factor IID (TFIID) Annu Rev Biochem 1996 65 769 799 8811195 Albright SR Tjian R TAFs revisited: More data reveal new twists and confirm old ideas Gene 2000 242 1 13 10721692 Green MR TBP-associated factors (TAFIIs): Multiple, selective transcriptional mediators in common complexes Trends Biochem Sci 2000 25 59 63 10664584 Ptashne M How eukaryotic 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1571906010.1371/journal.pbio.0030052Research ArticleMicrobiologyMolecular Biology/Structural BiologyBiochemistryEubacteriaOrganized Unidirectional Waves of ATP Hydrolysis within a RecA Filament ATP Hydrolytic Waves in RecA FilamentsCox Julia M 1 Tsodikov Oleg V 1 ¤Cox Michael M [email protected] 1 1Department of Biochemistry, University of WisconsinMadison, WisconsinUnited States of AmericaHaber James E. Academic EditorBrandeis UniversityUnited States of America2 2005 8 2 2005 8 2 2005 3 2 e522 3 2004 7 12 2004 Copyright: © 2005 Cox et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. DNA Recombination and Repair-A New Twist to RecA Function The RecA protein forms nucleoprotein filaments on DNA, and individual monomers within the filaments hydrolyze ATP. Assembly and disassembly of filaments are both unidirectional, occurring on opposite filament ends, with disassembly requiring ATP hydrolysis. When filaments form on duplex DNA, RecA protein exhibits a functional state comparable to the state observed during active DNA strand exchange. RecA filament state was monitored with a coupled spectrophotometric assay for ATP hydrolysis, with changes fit to a mathematical model for filament disassembly. At 37 °C, monomers within the RecA-double-stranded DNA (dsDNA) filaments hydrolyze ATP with an observed k cat of 20.8 ± 1.5 min−1. Under the same conditions, the rate of end-dependent filament disassembly (k off) is 123 ± 16 monomers per minute per filament end. This rate of disassembly requires a tight coupling of the ATP hydrolytic cycles of adjacent RecA monomers. The relationship of k cat to k off infers a filament state in which waves of ATP hydrolysis move unidirectionally through RecA filaments on dsDNA, with successive waves occurring at intervals of approximately six monomers. The waves move nearly synchronously, each one transiting from one monomer to the next every 0.5 s. The results reflect an organization of the ATPase activity that is unique in filamentous systems, and could be linked to a RecA motor function. The RecA filament plays a key role in bacterial DNA recombination and repair. Biochemical studies now reveal some unexpected properties of the filament ==== Body Introduction There are three prominent protein families that both form filaments and hydrolyze nucleoside triphosphates (NTPs). These include the tubulins [1,2,3], the actins [4,5,6], and RecA protein and its homologs [7,8,9,10,11]. Of these, the RecA family is unique in its formation of filaments on a DNA cofactor and in the steady-state hydrolysis of ATP by monomers within an assembled filament [10,11,12]. The function of RecA-mediated ATP hydrolysis has been a source of conjecture and debate for over two decades [10,11,13,14,15,16,17,18,19,20,21]. The bacterial RecA protein promotes the central steps of recombinational DNA repair [12,22]. In vitro, the RecA protein catalyzes a DNA strand exchange reaction that mimics the presumed function of RecA protein in vivo. The RecA protein first forms a filament on single-stranded DNA (ssDNA). The bound ssDNA is then aligned and paired with a homologous double-stranded DNA (dsDNA), forming a short (up to approximately 1 kilobasepair [kbp]) segment of paired DNA and initiating the DNA strand exchange [11,12]. The initial paired DNA can be extended in a reaction that generally requires ATP hydrolysis [10,11,12]. RecA filaments are assembled and disassembled in an end-dependent fashion. Filament assembly proceeds in steps, with a slow nucleation followed by a very rapid 5′ to 3′ extension phase to coat the available DNA [11,12,23,24,25]. The rapid extension phase does not limit overall filament assembly within the pH range normally used for RecA experiments [26,27]. Filament disassembly is also end-dependent and proceeds 5′ to 3′ such that monomers are subtracted from filaments at the end opposite to the end where monomers are added in the extension process (Figure 1) [25,28,29]. In studies carried out to date, we have not detected RecA monomer addition to the disassembling end or RecA monomer subtraction from the assembly (extension) end [25,27,28], although both processes presumably occur at some low rate. Figure 1 RecA Filament Assembly and Disassembly on ssDNA The reaction is limited by a slow nucleation step, followed by rapid extension in the 5′ to 3′ direction. Disassembly is also uniquely 5′ to 3′, proceeding from the end opposite to that where extension occurs. Dissociation of RecA monomers at the disassembling end requires ATP hydrolysis (see text). The RecA protein also binds to dsDNA, but nucleation onto dsDNA is much slower than onto ssDNA [26,30]. Nucleation directly onto dsDNA is pH-dependent, occurring more rapidly as the pH declines from 7.0 to 6.0 [30,31]. At any pH, filament extension on dsDNA is rapid, with complete filaments incorporating thousands of RecA monomers within a few minutes [25,32,33,34]. Upon binding dsDNA, RecA underwinds the DNA by approximately 40% and extends the helix to about 18 basepairs (bp) per turn relative to the B-form helix [35,36]. A RecA monomer binds three nucleotides (nt) of ssDNA or 3 bp of dsDNA, so one helical turn of the nucleoprotein filament includes approximately six RecA monomers. RecA protein within a filament hydrolyzes ATP in a reaction that is almost completely DNA-dependent under standard reaction conditions [31]. Under our normal reaction conditions, the intrinsic k cat values for ssDNA and dsDNA-dependent ATP hydrolysis rates are approximately 30 and 20 min−1, respectively, as determined in multiple trials over the past two decades [10,11,12]. ATPase rates are independent of pH in the range 6–9 [30,31]. ATP hydrolysis occurs uniformly throughout the filament of RecA-DNA complexes, and there is no detectable change or enhancement at filament ends [37]. In the presence of ATP analogs that are not hydrolyzed, RecA protein will form filaments on DNA and promote substantial DNA pairing and strand exchange [38,39,40]. However, RecA must hydrolyze ATP to bring about net filament disassembly [25,27,28,29,32,33,34], bypass of heterologous inserts during DNA strand exchange [41,42], DNA strand exchange with DNA substrates greater than 3 kbp in length [43], and DNA strand exchange between two duplex DNA molecules [42,44,45]. It is not clear how RecA protein-mediated ATP hydrolysis is coupled to these functions. Intriguingly, the core domain of the RecA protein (residues 34–269) is structurally homologous to several motor proteins, including hexameric helicases [46] and the mitochondrial F1-ATPase [47]. Models for a motor-like coupling of ATP hydrolysis to DNA strand exchange in certain DNA metabolic situations have been proposed [10]. There is now evidence for at least four different functional states of RecA protein, occurring at different reaction stages. These have recently been designated O, Ac, Ao, and P [10,48,49] (Figure 2). The O state is largely inactive and found in the absence of nucleotide cofactors or in the presence of ADP [50]. RecA in the O state can bind to DNA, creating a helical filament with a pitch of 76 Å [50]. Addition of ATP, ATPγS, or dATP results in a conformation change to an active form that is manifested by extended filaments on DNA with a pitch of 95 Å [50,51,52,53,54,55,56,57,58]. When RecA filaments form on ssDNA, they are in a structural and functional state designated A. Interconversion between the two different A states, which have somewhat different properties, is mediated largely by the Mg2+ concentration. However, all RecA filaments on ssDNA hydrolyze ATP with a k cat of approximately 30 min−1. Addition of a second DNA strand to a RecA filament, as in a filament bound to dsDNA or a filament promoting DNA strand exchange, converts the filament to the P state. The P state is characterized by 30% lower rates of ATP hydrolysis [59,60], higher rates of exchange of RecA monomers into and out of the filament [61,62], and a higher degree of cooperativity in the ATPase function [27,49,61] than the A conformations. Figure 2 Four Structural States of RecA Protein The O state is present in the absence of ATP or ATP analogs, whether the protein is free in solution or bound to DNA. The A state is found on ssDNA in the presence of ATP. Two versions of the A state, Ac and Ao, are found at different Mg2+ concentrations. Addition of a second DNA strand to the RecA filament converts it to the P state. How can RecA monomers be added to one end of a filament and deleted from the other end in the same test tube? The monomer-monomer interfaces are presumably the same at either end (and everywhere else) in the filament. As pointed out by Wegner [63], the dissociation constant, K D, for monomer addition to either filament end cannot be different unless an independent source of chemical energy is provided to affect the binding to one end or the other. As noted above, filament assembly does not require ATP hydrolysis, but filament disassembly does [25,27,28,29,32,33,34]. The hydrolysis of ATP by interior monomers does not generally result in dissociation, and under some conditions ATP hydrolysis can proceed with no evident dissociation of RecA monomers [25,61,64]. A simple model arises: ATP hydrolysis occurs everywhere, resulting in dissociation only for monomers at a disassembling end. Within a RecA filament, ATP hydrolysis could occur at random, or it could be highly organized as cooperative waves traveling through the filament. There is no convenient method for monitoring the effects of ATP hydrolysis in the middle of a RecA filament. However, important clues can be ascertained by examining one effect of ATP hydrolysis: the 5′ to 3′ end-dependent filament disassembly process. In effect, we cannot see waves of ATP hydrolysis within a filament, but we can monitor one of the waves—the one occurring at the disassembling filament end. The rate of disassembly can lead directly to an assessment of the degree of coupling of the ATP hydrolytic cycles of adjacent RecA monomers within the filament. For example, since RecA hydrolyzes ATP at a rate of approximately 30 min−1 on ssDNA, each RecA monomer is hydrolyzing one ATP molecule every 2 s. If the ATP hydrolytic cycles of adjacent monomers are not coupled in any way, this will be reflected in a predictable rate of filament disassembly. When the end monomer hydrolyzes ATP and dissociates, the next monomer in line could be at any point within its ATP hydrolytic cycle. However, in a large population of filaments, the next monomer will be on average halfway through the cycle, and it will hydrolyze ATP (and dissociate) one second later. Dissociation of one RecA monomer per second will lead to a measured disassembly rate of 60 monomers of RecA per minute per filament end. This is close to the situation observed for RecA monomers formed on ssDNA (functional state A), which disassemble with a rate of 60–70 monomers per minute per filament end [27]. Note that this is the slowest rate of disassembly compatible with the rate of ATP hydrolysis observed under the conditions of this experiment, and requires that every ATP hydrolytic event occurring in the RecA monomer at the disassembly end of the filament results in dissociation of that monomer. If the probability of dissociation upon ATP hydrolysis is less than 100%, the disassembly process would have to be slower than is observed. A coupling of the ATP hydrolytic cycles of adjacent monomers could in principle lead to greater rates of filament disassembly. We are particularly interested in the status of RecA filaments formed on dsDNA and those promoting DNA strand exchange. As indicated above, these filaments are in the P functional state and exhibit a higher degree of coupling involving the hydrolytic cycles of adjacent monomers than is seen for filaments formed on ssDNA. If ATP hydrolysis is organized into cooperative waves traveling through the filament, then the rate of disassembly from dsDNA will reveal the rate of movement of one of the waves and the interval, i, between successive waves within the entire filament. For example, consider monomers within RecA filaments on dsDNA that are hydrolyzing ATP at approximately 20 min−1 (or one ATP every 3 s). If ATP hydrolytic cycles of adjacent monomers are coupled within the filament so that ATP hydrolysis is organized in waves (Figure 3), then a new wave must reach a given monomer every 3 s to account for the observed k cat. If the waves are four monomers apart (i = 4 monomers, Figure 3A), a wave must move from one monomer to the next one in line every 0.75 s. At the disassembling end (the ultimate wave), one monomer would dissociate every 0.75 s, giving a disassembly rate of 80 monomers min−1 filament end−1. If instead the waves are organized at intervals of six monomers (Figure 3B), then to reach a given monomer every 3 s, a wave would have to move from one monomer to the next every 0.5 s. At the disassembling end, a monomer would dissociate every 0.5 s to yield a disassembly rate of 120 monomers min−1 filament end−1. We define the rate of end-dependent filament disassembly as k off. Note that the numerical relationship between k off and the k cat for ATP hydrolysis by individual monomers reveals the distance between waves within the filament, i, such that k off/k cat = i. For the examples above, 80/20 = 4, and 120/20 = 6. Figure 3 Coupled Waves of ATP Hydrolysis in RecA Filaments (A) The interval between hydrolyzing monomers (i) is set at four monomers. The dark monomers are those at the hydrolytic step of their ATP hydrolytic cycle. Each monomer within the filament (e.g., the one marked with an “X”) is hydrolyzing an ATP every 3 s, so that a new wave must reach it within that time span. If i = 4 monomers, the waves must move every 0.75 s. The last wave, at the disassembling end, results in dissociation. (B) The same considerations in (A) for i = 4 monomers are illustrated for i = 6 monomers. The k cat for ATP hydrolysis for RecA monomers on dsDNA is readily measured. An accurate determination of k off, as needed to determine i, is a more substantial challenge that is met in this report. Results Experimental Design A model system has been developed that allows the rate of end-dependent RecA filament disassembly from ssDNA to be determined quantitatively by monitoring the rate of ATP hydrolysis during the disassembly process [10,27]. The system is outlined in Figure 4A. RecA filaments are assembled on a linear ssDNA at a low pH, at which nucleation is fast enough that the DNA is saturated with RecA and there is little net disassembly (disassembling RecA monomers are rapidly replaced). The reaction mixture is then shifted to a higher pH, at which the rate of filament nucleation is slower and net filament disassembly can be observed. The rate of ATP hydrolysis will decline as RecA monomers dissociate from the DNA [27], reaching a nonzero steady-state endpoint (Figure 4B). The steady-state rate reflects the final balance between disassembly and renucleation of RecA protein and filament formation on the ssDNA vacated by the disassembling filament. Since filament extension is very fast relative to disassembly, any nucleation event will result in a filament that extends from the nucleation point to the disassembling end of the first filament, and in effect create a new point for disassembly (Figure 4A). On any of these DNA molecules, there is only one point where net disassembly occurs. Even when the second filament is not perfectly in phase with the first filament, such that some disassembly continues at the junction (Figure 4C), the RecA that dissociates from filament 1 at the junction is rapidly replaced by extension of filament 2 immediately behind it (filament extension is much faster than disassembly). Thus, there is no net change in bound RecA that would be reflected in the observed rates of ATP hydrolysis, except at the end of filament 2. To minimize the rebinding of RecA protein to vacated DNA, ssDNA-binding protein of Escherichia coli (SSB protein) is added at the time of the pH shift [27]. Bound SSB limits the nucleation of RecA filaments on ssDNA [25,65,66,67]. This in turn permits a measurable decline in ATP hydrolysis. Figure 4 Model for RecA Filament Disassembly from Linear ssDNA (A) In the model, end-dependent disassembly occurs, with SSB filling in the vacated DNA. At low frequency, additional RecA monomers nucleate filament formation on the vacated DNA, and the new filament (dark ovals) extends until it catches up with the original filament (open ovals). This creates a new disassembling end. (B) Kinetics of disassembly as monitored by DNA-dependent ATP hydrolysis. This curve is an approximation of curves reported in previous work [27]. The rate of ATP hydrolysis declines as RecA protein dissociates from the ssDNA, until a lower steady-state rate is reached. The steady state reflects a balance between disassembly and new filament formation. (C) If, upon rebinding, the new filament does match the old one in phase, the junction between the old and new filaments could be a site of RecA monomer exchange with the solution. There will be no net disassembly at this point and no resulting change in the rate of ATP hydrolysis, because any monomers that dissociate will be immediately replaced by extension of the trailing filament. The process in Figure 4 is modeled by equation 1, where k nuc is the rate of renucleation of RecA filaments to vacated DNA during the disassembly process, k off is the end-dependent disassembly rate as noted in the Introduction, n tot is the total number of RecA protein binding sites on the DNA molecule used as substrate, [D-ends] is the concentration of disassembling ends, and k cat is the turnover number for ATP hydrolysis by RecA monomers in the filament. The derivation of equation 1 is described in detail elsewhere [27]. The assumptions incorporated into the equation and the model of Figure 4 are also detailed elsewhere [27]. In brief, the assumptions are as follows. First, nucleation of filament formation is the rate-limiting step for RecA filament assembly. This assumption is documented in the Introduction of this paper and in previous reports [23,27]. Second, filament extension is faster than filament disassembly under all conditions. The simplest of the arguments [27] underpinning this assertion is that filaments would never form on DNA if RecA were subtracted from the disassembling end faster than it could be added to the extending end. Third, the model assumes that net disassembly is occurring at only one point in each filament. This is addressed in Figure 4C and follows from the fact that filament extension is faster than filament disassembly. There will be no net disassembly except at the 5′ proximal end of the RecA protein tracts on a given linear DNA. Any dissociation in the middle of a filament is rapidly replaced by the growth of trailing filament segments and thus cannot contribute to a net change in ATP hydrolysis. Finally, the assumption is made that the k cat for RecA-mediated ATP hydrolysis does not change in the range used for the pH shifts. This is documented elsewhere [26,27,30]. On dsDNA, the situation becomes more complicated because it is more difficult to define the orientation of the filaments. RecA protein binds to the two strands of a duplex DNA unequally. One strand is bound at the site normally occupied by ssDNA. This strand orients the filament, and it is protected from nuclease digestion better than the second strand of a duplex [33,68]. We will call this first strand the initiating strand [22]. In principle, either strand of a duplex can act as the initiating strand and organize the 5′ to 3′ assembly of RecA filaments. RecA filaments can thus form in two orientations on dsDNA (Figure 5A). If nucleation occurs in the middle of a linear duplex so that the filament extends to one end, and a second nucleation occurs on the same DNA but in the opposite orientation, there could be two filaments on the same DNA, both with disassembling ends (Figure 5A). This in turn would complicate the mathematical modeling of the disassembly reaction. This potential problem can be alleviated by supplying a good nucleation site in the form of a stretch of ssDNA. At pH 8 and above, RecA nucleation onto duplex DNA becomes very slow (measured in hours) [26,30]. If a 5′ single-strand extension is added to one end of the DNA, the nucleation occurs within a few minutes, and RecA readily extends from the single-stranded segment into the adjoining duplex [33]. A 3′ single-strand extension does not work [33], as would be predicted from the polarity of filament assembly from a nucleation site. This effect is seen in the experimental data presented in Figure 5B. RecA binds to a linear duplex of 3,162 bp only after a lag lasting tens of minutes, even at pH 6.6. A linear duplex of similar length, but this time with a 5′ single-strand extension of 30 nt, is bound without a measurable lag at the same pH. This indicates that the filaments are nucleating on the single-strand extension, and virtually all of the resulting filaments in the population will have a unique orientation over the entire length of the DNA that is dictated by the single-stranded tail. Figure 5 RecA Filament Formation on dsDNA (A) RecA filaments can nucleate on dsDNA so as to have two different orientations. The initiating strand guides the orientation of each filament, with its polarity labeled in each panel. If two nucleations occur on the same filament, there can be two independent disassembly points. (B) The effect of the 30-nt 5′ extensions on RecA protein filament formation. Reactions contained 4 μM RecA protein, 4 μM DNA, and no SSB and were carried out at pH 6.6. The tailed DNA substrate has 2,900 bp of duplex DNA with the 30-nt 5′ extension on one end. The completely duplex DNA is 3,162 nt in length. The enhanced nucleation afforded by these tails is evident in the rapid ATP hydrolysis seen when the tailed DNA is used as cofactor. To isolate the disassembly process so that it can be measured accurately, we must limit the rate of RecA rebinding to DNA that has been vacated as the RecA dissociates. Several parameters can be adjusted to accomplish this. The first one is pH. The rate of RecA protein disassembly reaches an apparent maximum at pH 8 and above [33]. The rate of disassembly should reach a maximum when every ATP hydrolytic event at the disassembling end results in dissociation of a RecA monomer. The rate of nucleation on dsDNA declines with increased pH such that working at high pH minimizes RecA rebinding. The second parameter is DNA length, which can affect the consequences of rebinding to duplex DNA. A nucleated filament is rapidly extended to the DNA end. A nucleation event on a long DNA will generate more bound RecA than a nucleation event on a short DNA. Thus, the use of shorter DNAs will limit the effects of each nucleation event. However, the DNA length cannot be reduced too much to suppress the levels of RecA protein rebinding, because a longer DNA (and filament) allows for a more gradual decline in the ATPase reaction that is easier to monitor. Trials using DNAs of several lengths indicated that a linear dsDNA with a length of approximately 3 kbp was optimal for this work [33,69]. These dsDNAs result in bound RecA filaments long enough (about 1,000 monomers) to disassemble over a time period of multiple minutes, while limiting the effects of RecA rebinding. As shown later, we also carried out measurements using dsDNAs of 2 kbp and 4 kbp. A third parameter that can be adjusted is SSB concentration. Adding SSB when the pH shift is carried out inhibits the rebinding of free RecA protein to the ssDNA extension. The concentrations of SSB used in these experiments were determined empirically to provide the maximum possible suppression of RecA renucleation. The effectiveness of this SSB-mediated suppression is illustrated in Figure 6. Figure 6 The Effect of SSB Protein in Suppressing Rebinding of RecA to the Single-Strand Tails (A) Normal RecA filament disassembly protocol. The RecA filaments were formed on the tailed DNA at pH 6.62. The pH was then shifted to 8.0 to allow disassembly to commence. Disassembly curves are shown in the presence and absence of SSB. Reactions contained (after the pH shift) 2 μM RecA protein, 2 μM DNA, and (where indicated) 0.05 μM SSB. (B) Direct binding of RecA protein to the tailed DNA at pH 8.0. Reactions contained 6 μM RecA protein, 6 μM DNA, and (where indicated) 0.15 μM SSB. When included, SSB was added prior to the RecA protein. Optimizing all of these considerations, the general protocol for these experiments is outlined in Figure 7A. A linear duplex of 2,900 bp, with a 30-nucleotide 5′ single-strand extension, is bound with RecA protein at pH 6.62. The DNA concentration is 12 μM, so that the concentration of RecA binding sites is 2 μM. The RecA protein is added in excess (12 μM) to facilitate saturation of the DNA. After binding is complete, the rate of ATP hydrolysis is measured. The pH is then shifted abruptly to 8 by diluting the solution 2-fold into a solution with another buffer. The RecA and DNA concentrations undergo a 2-fold decrease during the pH shift, but the concentration of ATP and ATP regeneration components are kept constant. The pH shift initiates a net disassembly process that leads to a decline in ATP hydrolysis. Rebinding of RecA protein to the vacated single-strand extension is suppressed by SSB added with the pH shift buffers. The production of ADP is monitored. The ATP regeneration system is set up so as not to limit the observed results. For example, results were the same if the phosphoenolpyruvate (PEP) concentration was reduced from 3 to 1.5 mM. The starting k cat for ATP hydrolysis is reported as the measured rate of ATP hydrolysis prior to the pH shift (in μM min−1), divided by the initial concentration of bound RecA protein (in 2 μM). Figure 7 Quantitative Measurement of RecA Filament Disassembly from dsDNA (A) The experimental protocol, as described in the text. (B) A typical disassembly curve. The black line is the measured ADP production curve. The orange line is the fitting of the data by equation 1. (C) The curve is identical to that shown in panel B for the 3-kbp DNA substrate, with the best fit line shown in orange. However, two additional curves (in solid black) are shown to illustrate the variation in fitting if the value of k off is constrained to values 10% above or 10% below the best-fit determination. With the filaments uniquely oriented on the tailed DNA and encompassing the entire length of the DNA, equation 1 can be applied to the measurement of disassembly from dsDNA in the same manner as it was used to analyze the disassembly from ssDNA. The rate of filament disassembly, k off, is obtained by fitting the equation to the ADP production curve. There is just one caveat. The renucleation onto the vacated DNA must be suppressed sufficiently so that there is, to a reasonable approximation, no more than one disassembling RecA filament end per bound DNA. If multiple filaments formed on the vacated dsDNA as a result of renucleation, this process would not be properly modeled by equation 1 (which was derived for a model in which all filaments had a single orientation [27]). As described below, the rates of renucleation are suppressed sufficiently in these experiments so that this condition is met. Measurement of Disassembly Rates from Duplex DNA A typical experiment is illustrated in Figure 7B. Equation 1 relates the production of ADP in terms of the two primary unknowns, k off and k nuc, as well as several known parameters. All concentrations are in μM, and all times are in minutes. The data from ATP hydrolysis assays were converted to [ADP], plotted as a function of time, and fit to equation 1 using the program SigmaPlot from SPSS (Chicago, Illinois, United States). The terms [D-ends], n tot, and [RecA] were known for each experiment and held constant for fitting. The terms k off and k nuc were the fitting parameters. The apparent k cat for ATP hydrolysis was determined independently as the rate of ATP hydrolysis before the pH shift and initiation of net filament disassembly divided by the concentration of bound RecA protein. However, k cat can be determined as a fitting parameter as well. Therefore, each dataset was fit to equation 1 twice: once with k cat held constant at the value measured prior to the pH shift, and once with k cat as a fitting parameter in addition to k nuc and k off. The fit parameters for both fittings were compared. Experiments were discarded in which the measured and fit k cat did not agree to within 40%, or the measured k cat was less than 18 min−1 (generally corresponding to a problem with one or more reagents or equipment on that day). For the 3 kbp substrate used in Figure 7, data were collected in 16 trials carried out over a period of 3 mo. During that time, 12 trials were utilized and four were discarded on the basis of these considerations. The plot in Figure 7B is nonlinear, reflecting the decline in the rate of ATP hydrolysis as the RecA filament disassembles from the DNA. The form of the curve is the same as that shown in Figure 4B, although the time scale is abbreviated. The rate of ATP hydrolysis settled to a slow steady-state rate in which disassembly and rebinding are balanced. The twelve trials that met the conditions elaborated above are detailed in Table 1. The result presented in Figure 7B is representative. The fitting performed with the k cat fixed at the average value measured prior to the pH shift, k cat = 19.5 ± 0.6 min−1, yields the best-fit values (averaged over 12 experiments) of k off = 120 ± 20 min−1 and k nuc = 4.6 ± 0.75 × 10−5 μM−1 min−1. When k cat was allowed to vary as a fitting parameter along with k off and k nuc, the parameters were again obtained from the best-fit curves and averaged. This yielded k cat = 21.6 ± 4.4 , k off = 133 ± 24 min−1, and k nuc = 5.1 ± 0.89 × 10−5 μM−1 min−1 for the 12 datasets. The reasonable agreement between the measured and fit k cat values in most trials helped provide confidence that the k cat measured prior to the pH shift was consistent with the observed reaction progress curve obtained after the pH shift. Table 1 Experimental Parameters Obtained in Individual RecA Filament Disassembly Trials a Units of k cat, min−1 b Units of k off, monomers per minute per filament end c Units of k nuc, μM−1 min−1 Ave, average; s, standard deviation The fitting process is quite sensitive to small changes in the fitting parameters. Figure 7C illustrates the result of constraining k off to values 10% above and 10% below the value obtained by fitting the data in Figure 7B for the 3 kbp DNA substrate. In both cases, the curves deviate substantially from the data and demonstrate the sensitivity of our model. As long as the stated constraints on the use of equation 1 are met, the results should be independent of DNA length. To confirm, disassembly rates were obtained using DNA substrates of 1,961 and 3,900 bp (referred to as our 2 kbp and 4 kbp substrates, respectively). Each of these had a 5′ extension, with the same length (30 nt) and sequence as the one used for the 3-kbp DNA substrate. In this series of experiments, there were 16 trials with each DNA substrate. Of these, 12 and 14 trials (for the 2- and 4-kbp substrates, respectively) met the conditions established above for the 3-kbp DNA substrate trials, and these are reported in Table 1. The total concentration of DNA base pairs was kept constant in these experiments, so that the concentrations of DNA molecules and thus disassembling ends either increased (2-kbp substrate) or decreased (4-kbp substrate). These new trials were carried out over the course of one month. The results agreed very well with those obtained with the 3-kbp DNA, with the k cat and k off values remaining constant (within experimental error) as a function of DNA length as summarized in Table 1. For the 2-kbp DNA, constraining k cat to the value measured before the pH shift (average 20.9 ± 0.6 min−1) yields the best-fit values of koff = 125 ± 9 min−1 and k nuc = 1.0 ± 0.075 × 10−4 μM−1 min−1. In the same manner, the 4-kbp DNA substrate yielded average values of k off = 124 ± 19 min−1 and k nuc = 2.5 ± 0.52 × 10−5 μM−1 min−1 with a measured k cat = 21.7 ± 1.8 min−1. When k cat was allowed to vary as a fitting parameter along with k off and k nuc, the best-fit values for the 2-kbp DNA substrate averaged k cat = 20.5 ± 2.4 min−1, k off = 121 ± 10 min−1, and k nuc = 9.9 ± 0.76 × 10−5 μM−1 min−1 for the 12 datasets. The 4-kbp DNA substrate yielded best-fit values of k cat = 23.6 ± 3.0 min−1, k off = 136 ± 14 min−1, and k nuc = 2.8 ± 0.26 × 10−5 μM−1 min−1 for the 14 datasets. All of this work is summarized in Table 1. If all of the data in Table 1 are averaged (38 total trials), the values obtained when constraining k cat to the value measured before the pH shift are k cat = 20.8 ± 1.5 min−1 and k off = 123 ± 16 min−1. When k cat was not constrained, the values were k cat = 22 ± 3.5 min−1 and k off = 130 ± 18 min−1. Unlike the other parameters, the nucleation of filament formation as expressed in k nuc should be affected by factors such as G:C content and sequence structure in the duplex DNA [26,30], and was not averaged over the three sets of trials. Within a single set of trials, k nuc was quite reproducible. Representative trials using the 2-, 3-, and 4-kbp DNA substrates are compared in Figure 8. The initial rates are essentially the same in each case, since the concentration of bound RecA is the same. If RecA dissociates from one end of the filament at a constant rate, disassembly of the longer filaments formed on longer DNAs should require correspondingly more time. The pattern seen here is consistent with that expectation. The final steady state rate of ATP hydrolysis reflects both k nuc and the length of the DNA, and thus varies somewhat. Figure 8 RecA Filament Disassembly Rates Are Independent of DNA Length Typical disassembly curves are shown for the 2-, 3-, and 4-kbp DNA substrates. The black lines are the data, and the orange lines represent the best fits to the data by equation 1. Note that the early, rapid phase of the curves are extended in concert with the increase in the length of the DNA substrates. The rates of disassembly derived from the curves are identical within experimental error, as summarized in Table 1 and the text. As already noted, equation 1 is useful only if there is no more than one disassembling end on a particular DNA molecule. The final steady state achieved after the major phase of filament disassembly is complete allows us to estimate the likelihood that multiple filaments with different orientations rebind to the same stretch of vacated DNA. We address this issue for the 3-kbp DNA substrate. Immediately following the pH shift, the concentration of RecA binding sites on the 6 μM DNA is 1 μM, and this represents the concentration of bound RecA at the outset. The average rate of ATP hydrolysis when disassembly was initiated was 19.5 ± 0.6 μM−1 min−1. The average final steady-state rate of ATP hydrolysis is 1.23 μM−1 min−1, or 6.3% of the rate observed prior to disassembly. This final rate corresponds to 6.3% occupancy of the available RecA binding sites, or 0.063 μM bound RecA protein. We estimate that the average filament at steady state occupies one-quarter of the available RecA binding sites in the DNA to which it is bound from the following considerations. When a new RecA filament nucleates onto the vacated DNA, the nucleation could occur anywhere along the length of the DNA. On average, the nucleation would occur at the center of the DNA, and the fast unidirectional filament extension phase would quickly coat the DNA from that point to one end, so that the typical new filament would occupy half of the available RecA binding sites on the DNA to which it was bound. At steady state, filaments would be disassembling at a rate that just balances the rate of renucleation of filaments to replace them (with the rate of disassembly much slower than filament extension). A given filament could be anywhere in the disassembly process, but the average filament bound at steady state will have lost half of its length. The average filament at steady state will thus occupy one-quarter of the RecA binding sites on a given DNA molecule. If 6.3% of the available DNA binding sites are occupied by RecA thus distributed in filaments that coat one-quarter of a DNA molecule, then we can estimate that 25.2% of the DNA molecules have some bound RecA protein. About 6% of the DNAs ([0.252]2, or approximately 0.06) will have two filaments bound. Half of these will have two filaments with the same orientation, and thus effectively have only one end where net disassembly will occur. The remaining 3% of DNAs with the potential for two bound filaments and two disassembling ends represent the maximum percentage of DNAs in this condition, and this maximum can only be approached late in the disassembly process after the DNA initially bound by oriented filaments has been vacated completely. Since the potential for multiple filaments is limited early in the dissociation curve, and 3% in any case is well within the error limits of our measurements, we have not adjusted our model or corrected our reported rates for this effect. To the extent that multiple disassembling ends contribute to the observed rates, they would lead to a very slight overestimate of the disassembly rate. The interval between waves of ATP hydrolysis, i, is k off /k cat. We calculate i to be 6.0 ± 0.5, 6.2 ± 1.1, and 5.8 ± 1.1 monomers for the 2-, 3-, and 4-kbp DNA substrates, respectively. As with the other parameters, these values agree well and are identical within experimental error. When averaged across all 38 trials, i is 6.0 ± 0.9 monomers if k cat is constrained and 6.0 ± 0.8 monomers if k cat is not constrained (see Table 1). As noted in the Introduction, the quantitative disassembly model predicts a different k off value for different values of i. To explore further the significance of this determination, we carried out an additional exercise. Each filament disassembly dataset was fit to equation 1 with k cat held constant to the value measured before net filament disassembly and k off held constant to the value predicted by the model for values of i from 4 to 9. When the quality of the data fit (as measured by R 2) is plotted against i, an optimum is seen at i = 6.4 monomers for the 3-kbp DNA substrate (Figure 9). The optimum is seen at i = 5.9 monomers for the 2-kbp DNA substrate, and at i = 5.8 monomers for the 4 kbp-DNA substrate (unpublished data). Figure 9 Quality of the Data Fitting, as Measured by R 2, Varies with Changes in i The calculation is illustrated for the 3-kbp DNA substrate. A maximum is seen at i = 6.4 monomers. For each of the 12 independent datasets used in this work, the measured k cat was multiplied by the indicated value of i to get a predicted k off. This value of k off was then used to fit the data to equation 1, constraining k off and k cat and allowing k nuc to vary. R 2 is then a measure of the quality of the resulting fit. The R 2 value in each case is averaged for 12 trials. Discussion We conclude that when RecA protein is bound to dsDNA, ATP hydrolysis within the filament occurs in highly organized and unidirectional waves. Successive waves occur at intervals of approximately six monomers and travel through the filament at a rate of approximately 120 monomers per minute. More precisely, the rate of RecA protein filament disassembly from dsDNA (which represents the ultimate wave) is 123 ± 16 RecA monomers per minute per filament end, under the conditions of these experiments. While this is occurring, ATP is being hydrolyzed by monomers within the filament with a measured k cat of 20.8 ± 1.5 min−1. The ratio k off/k cat gives i, the interval between the waves of hydrolysis within the filament. The k off/k cat = i = 6.0 ± 0.9 monomers. These results are taken from data in which the k cat for ATP hydrolysis was constrained to the value measured prior to the pH shift. The results are quite similar when k cat is not so constrained (Table 1). Given that there are six RecA monomers per helical turn of a RecA filament, this relationship reveals a striking pattern of ATP hydrolysis by RecA protein, one in which the ATP hydrolytic events at a given moment are lined up along one longitudinal face of the filament (Figure 10). These “stripes” of ATP hydrolysis proceed around the circumference of the filament in six steps, with successive steps occurring at 0.5-s intervals. The overall pattern corresponds well to a rotary motor model for the coupling of RecA-mediated ATP hydrolysis to DNA strand exchange, and fulfills the last of three major predictions of that model [10]. The motor function of RecA may play a role in certain aspects of the repair of stalled replication forks [10]. Figure 10 Coordination of ATP Hydrolytic Waves in a RecA Filament The dark monomers are those in the hydrolytic step of their hydrolytic cycle. The successive steps are separated by 0.5 s. The model shown assumes exactly six RecA monomers per helical filament turn and i = 6.0. As noted in the text, both the helical organization in the filament and the organization of ATP hydrolytic waves in RecA filaments may deviate slightly from this ideal. Note that there is no evidence that a RecA monomer in a filament is in contact with another RecA located six monomers away, so each of the gray balls shown in these illustrations are separated from their gray neighbors. An animated version of this model is available in Video S1. The rate of filament disassembly that we measure is consistent with a rough estimate of 2.4 monomers per second (144 min−1), obtained by Libchaber and colleagues [34]. This earlier estimate was based on single molecule experiments using very long DNAs, and ADP buildup that can contribute to aggregate filament dissociation [54,55,70] was not controlled. Importantly, the rate of RecA filament disassembly from dsDNA is nearly twice that from ssDNA [27], reflecting the change in functional state that is observed when a second strand of DNA is introduced into a RecA filament [10,48,49]. The observed rate of filament disassembly on dsDNA is three times that predicted for a similar filament with no ATP hydrolytic coupling between adjacent monomers. At a minimum, this must reflect an elaborate coordination of ATP hydrolytic cycles for adjacent RecA monomers in the filament. The functional state of RecA protein filaments on dsDNA is, in every experimental sense tested to date, identical to the functional state observed during active DNA strand exchange [10,12,49,62]. We therefore propose that the current results directly reflect the status of RecA filaments that are actively promoting DNA strand exchange. The extent of coupling between RecA monomers inferred from the current experiments can be correlated to information derived from EM observations of the nucleoprotein filament. The datasets fit best to equation 1 with a k off corresponding to ATP hydrolysis reflecting i = 5.8–6.4 RecA monomers. The directly measured average k off/k cat ratio for all datasets suggests that i = 6.0 ± 0.9 RecA monomers. Interestingly, previous EM reconstructions of the RecA-dsDNA complex indicate that there are 6.2 RecA monomers per helical turn of the nucleoprotein filament under at least some conditions [52]. This may imply an organization that is highly ordered, albeit not quite as ideal as the organization depicted in Figure 10. The RecA crystal structure does not reveal any contact between every sixth monomer in the helix [71,72]. This suggests that coordination within the filament is mediated through adjacent monomer-monomer contacts. With ATP hydrolytic waves spaced at six-monomer intervals, it is tempting to postulate a hydrolytic cycle with six distinct steps. In one turn of the helix, there could be six slightly different monomer conformations, corresponding to various stages of binding, hydrolyzing, and releasing nucleotide. Indeed, multiple conformations of RecA protein within the RecA nucleoprotein filaments have been observed [73]. Unlike a hexameric circle (such as the hexameric helicases to which RecA appears related), the synchronization between steps in the hydrolytic cycle could readily produce a noninteger separation of ATP hydrolytic waves (e.g., 6.2 monomers) in a filament. Although our average value of 6.0 for the parameter i is consistent with the idealized picture of Figure 10, the experimental error could readily accommodate such noninteger outcomes. The ATP hydrolytic cycle should now become the focus of more intensive investigation. We have little information about the steps in the cycle as they occur on RecA protein, nor do we know to which step dissociation of a RecA monomer might be coupled. For example, the dissociating form of RecA could be almost any species one could imagine as an intermediate in the ATP hydrolytic cycle, such as a RecA-ADP complex, a RecA-Pi complex, or any of a number of other possibilities. The model presented in Figures 3B and10 works regardless of the precise mechanism of ATP hydrolysis proposed. The patterns of ATP hydrolysis we observe in RecA filaments are quite distinct from those documented for other major filament types. The tubulins [1,2,3] and the actins [4,5,6] are filament-forming proteins that bind and hydrolyze GTP and ATP, respectively. The NTP hydrolysis affects the capacity of the filaments to assemble or dissociate. The proteins most readily assemble as the ATP- or GTP-bound forms. Very slow hydrolysis of the bound NTP results in a form that is more readily dissociated from the filament [4,5,6]. These filaments thus expand and contract in a pattern that is dictated by NTP hydrolysis, as well as by the activities of numerous regulatory proteins. The assembly and disassembly produces work at the filament ends. The ADP- or GDP-bound monomers in the filament interiors do not exchange nucleotides to rebind ATP or GTP, so there is no active nucleotide turnover within the filaments. In contrast, ATP hydrolysis occurs uniformly throughout a RecA protein filament formed on DNA. This ATP hydrolysis can result in filament disassembly at one end, as we continue to document here. However, the steady-state hydrolysis of ATP in the interior of RecA filaments does not result in dissociation and has the capacity to do a different kind of work. The rotary motor illustrated in Figure 10 can be coupled to DNA strand exchange. That coupling is inferred by the capacity of RecA to drive strand exchange through heterologous DNA inserts [41,42], promote unidirectional strand exchange [13,43], and promote strand exchange between two duplex DNAs [42,44,45]—only when it is hydrolyzing ATP. The coupling is also seen in the predictable relationship that exists between the rates of ATP hydrolysis and branch movement during strand exchange [59], and in a kind of indirect DNA helicase reaction that RecA promotes with certain branched DNA substrates [74]. At least one potential biological role for this motor function can be found in the regression of stalled replication forks that is sometimes required for their repair [10,12,75,76,77]. Fork regression is promoted by RecA protein, but again only if ATP is hydrolyzed [10,78,79]. If RecA protein is bound to a chromosome at a replication fork, and ATP is available, the surrounding DNA will not remain static. We do not expect the organization revealed here for ATP hydrolysis in RecA filaments to apply generally to the eukaryotic homologs of RecA. The eukaryotic Rad51 protein hydrolyzes ATP at rates 30- to 40-fold below those reported for bacterial RecA proteins. RecA protein generally requires ATP hydrolysis for extensive strand exchange [17,42,43], while Rad51 protein does not [80,81]. There are no reports that Rad51 can promote the kinds of reactions to which RecA protein couples ATP hydrolysis (four-strand exchange and strand exchange past a significant heterology in the DNA substrates) [82,83]. Also unlike RecA, the Rad51 protein promotes DNA strand exchange with no intrinsic polarity [82,84,85]. RecA-mediated ATP hydrolysis is thus likely to have a role unique to bacterial DNA metabolism or a role that is supplanted by other proteins in eukaryotes. Materials and Methods Proteins and biochemicals E. coli RecA protein was purified to homogeneity as described [86]. Two different preparations were used for analysis, with one preparation having the final fraction subjected to an additional step. The protein was loaded onto a PBE-94 column equilibrated with R buffer (20 mM Tris-HCl [80% cation, pH 7.5], 1 mM dithiothreitol, 0.1 mM EDTA, and 10% [w/v] glycerol), and the column was developed with a linear gradient from 0 to 1.0 M KCl. The RecA protein was eluted at approximately 600 mM KCl, dialyzed extensively against R buffer, and concentrated as described. Results with both RecA protein preparations were the same and were combined. E. coli SSB was purified as described [42,87]. The RecA protein and SSB concentrations were determined by absorbance at 280 nm, using extinction coefficients of ɛ280 = 0.59 A280 mg−1 ml [88] and ɛ280 = 1.5 A280 mg−1ml [89], respectively. RecA protein and SSB preparations were free of detectable endo- and exonuclease activities on dsDNA or ssDNA. Unless otherwise noted, all reagents were purchased from Fisher (Pittsburgh, Pennsylvania, United States). MES buffer, PEP, pyruvate kinase, lactate dehydrogenase, phosphocreatine, ATP, and NADH were purchased from Sigma (St. Louis, Missouri, United States). Creatine kinase and ATPγS were purchased from Roche Molecular Biochemicals (Indianapolis, Indiana, United States). Restriction enzymes were purchased from New England Biolabs (Beverly, Massachusetts, United States). T4 DNA polymerase was purchased from New England Biolabs, and T4 DNA ligase was purchased from Promega (Madison, Wisconsin, United States) and New England Biolabs. Dithiothreitol was purchased from Research Organics (Cleveland, Ohio, United States). Sephacryl S-500 and PBE-94 resins were purchased from Amersham Pharmacia Biotech (Piscataway, New Jersey, United States). DNA substrates Oligonucleotides were purchased from Integrated DNA Technologies. The concentrations of duplex DNA stock solutions were determined by absorbance at 260 nm using 50 μg ml−1 A260 −1 as a conversion factor. All DNA concentrations are given in terms of total nucleotides unless otherwise noted. The 3-kbp linear double-stranded DNA substrate with a 30-nt single-stranded 5′ tail was generated by first digesting pUC119 plasmid DNA [90] with two restriction enzymes, SapI and SmaI. After digestion, residual protein was removed by sequential 1:1 extractions with phenol/chloroform/isoamyl alcohol (25:24:1) and chloroform/isoamyl alcohol (24:1). The resulting 2,883- and 279-bp fragments were concentrated by precipitation in ethanol. The 2,883-bp fragment was separated from the 279-bp fragment using size-exclusion chromatography with Sephacryl S-500 resin and concentrated with an Amicon Microcon concentrator (Millipore, Billerica, Massachusetts, United States). Residual protein was removed by sequential 1:1 extractions with phenol/chloroform/isoamyl alcohol (25:24:1) and chloroform/isoamyl alcohol (24:1). The 2,883-bp fragment was concentrated by precipitation in ethanol. Two complementary oligonucleotides, 47 and 20 nt in length, were annealed and ligated in excess to the staggered end of the 2,883-bp fragment. Excess oligonucleotides were separated from substrate DNA using size-exclusion chromatography with Sephacryl S-500 resin. The substrate DNA was concentrated with an Amicon Microcon concentrator (Millipore). Residual protein was removed by sequential 1:1 extractions with phenol/chloroform/isoamyl alcohol (25:24:1) and chloroform/isoamyl alcohol (24:1). The substrate DNA was concentrated by precipitation in ethanol. The 2-kbp linear dsDNA substrate with a 30-nt single-stranded 5′ tail was generated by first digesting pUC119 with EcoRI and AatII. The larger fragment was isolated via agarose gel extraction. The DNA ends were filled in with T4 DNA polymerase and ligated with T4 DNA ligase to result in the 2,223-bp pEAW373 plasmid DNA. The 4-kbp linear dsDNA substrate with a 30-nt single-stranded 5′ tail was generated by first digesting pUC119 with BanII that cuts at two sites, and the 2,766-bp fragment was separated from the 396-bp fragment via agarose gel extraction. Next, pACYC184 plasmid DNA [91,92] was digested with HindIII and AvaI, with the resulting 1,396-bp fragment isolated via agarose gel extraction. The DNA ends of the 2,766- and 1,396-bp fragments were filled in with T4 DNA polymerase and ligated with T4 DNA ligase to result in the 4,162-bp pEAW374 plasmid DNA. Both pEAW373 and pEAW374 maintained unique SapI and SmaI sites; they were treated in the same manner as the 3-kbp DNA substrate to yield 2- and 4-kbp linear dsDNA substrates with single-stranded 5′ tails. The lengths and sequences of the 5′ tails were the same in each of 2-, 3-, and 4-kbp DNA substrates. To confirm the identity of the linear dsDNA substrates, a sample of each DNA substrate was digested with AflIII to yield a 133-bp fragment with a 4-nt 3′ tail and 30-nt 5′ tail. Digest of the duplex fragments without annealed oligonucleotides would have resulted in a 113-bp fragment with a 4-nt 3′ tail and 3-nt 5′ tail. Product separation in a 3.5% agarose gel confirmed the identity of the linear dsDNA substrate with a 30-nt 5′ tail and allowed quantitation of yield. In all DNA preparations used in these trials, the final yield of complete substrate DNA was greater than 97%. Reaction conditions All reactions were carried out at 37 °C in 25 mM buffer (detailed below), 10 mM magnesium acetate, 5% (v/v) glycerol, 1 mM dithiothreitol, 3 mM potassium glutamate, 3 mM ATP, an ATP regenerating system (10 units/ml pyruvate kinase and 3 mM or 2 mM PEP), and concentrations of DNA and RecA protein as described below and in figure legends. The coupled spectrophotometric assay also contained 10 units/ml lactate dehydrogenase and 3 mM NADH. Specific buffers used are described below and in the figure legends. DNA and protein concentrations are indicated for each experiment. Reactions were incubated for 10 min before ATP was added to start the reaction. ATP hydrolysis assays A coupled spectrophotometric assay was used to measure DNA-dependent ATP hydrolysis by the RecA protein [29,93]. The regeneration of ATP from ADP and PEP was coupled to the oxidation of NADH and monitored by the decrease in absorbance of NADH at 380 nm. The 380-nm wavelength was used instead of the absorption maximum at 340 nm so that the signal would remain within the linear range of the spectrophotometer for the duration of the experiment. The assay was carried out using a Varian Cary 300 (Varian, Palo Alto, California, United States) dual beam spectrophotometer equipped with a temperature controller and 12-position cell changer. The cell path length and band pass were 0.5 cm and 2 nm, respectively. The NADH extinction coefficient at 380 nm of 1.21 mM−1 cm−1 was used to calculate rates of ATP hydrolysis. Filament disassembly reactions ATP hydrolysis reactions were started as described above in 25 mM MES/NaOH (76% anion, pH 6.62) buffer. Reactions contained 12 μM RecA protein, 12 μM (total nucleotides) linear duplex DNA, and the ATP and ATP regeneration conditions indicated above. These were incubated for approximately 20 min to reach a stable steady-state rate of ATP hydrolysis. The rate was determined and reflected a virtually complete binding of all available DNA by RecA protein. Under these conditions, we routinely achieved rates reflecting an apparent k cat for bound RecA protein (one monomer per three available DNA base pairs) of 18–24 min−1. The few reactions displaying initial rates reflecting a k cat less than 18 min−1 generally reflected a problem with one or another reagent, and were discarded. After preincubation to achieve a DNA substrate saturated with RecA protein, the solution was subjected to a pH shift. The 200 μl reaction was diluted with gentle mixing into 200 μl of a solution (also preincubated at 37 °C), containing 25 mM Tris-acetate (30% cation, pH 8.50) buffer, 10 mM magnesium acetate, 5% (v/v) glycerol, 1 mM dithiothreitol, 3 mM potassium glutamate, 3 mM ATP, an ATP regenerating system (10 units/ml pyruvate kinase and 3 mM or 2 mM PEP), and 0.3 μM SSB. The final pH of the reaction after the pH shift was 8.00. The RecA and DNA concentrations were halved as a result of the pH shift, but the concentrations of ATP, Mg2+, and ATP regeneration system components were kept constant. Following the pH shift, the production of ADP was monitored spectrophotometrically as the RecA filaments disassembled. The rate of ATP hydrolysis declined, eventually settling to a slow steady-state rate of ATP hydrolysis that reflects the minimal amount of bound RecA protein for a given experiment (see Results). Supporting Information Video S1 Coordinated Waves of ATP Hydrolysis—The Movie This movie illustrates the model of Figure 10. The red RecA monomers are the ones hydrolyzing ATP at any given moment. The transitions from one set of monomers to the next occur every 0.5 s. In this animation, the 5′-proximal end of the initiating DNA strand (the strand that orders the orientation of the filament; see text) is at the upper left. The filament structure is based on the RecA structure of Story and Steitz [72], as cited in the text. (PDF not available). (148 KB MOV). Click here for additional data file. We thank James Keck and M. Thomas Record for helpful comments and discussions at various stages of this work. We also thank Elizabeth Anne Wood for preparation of the pEAW373 and pEAW374 DNA plasmids. The work was supported by grant GM32335 from the National Institutes of Health (to MMC). Competing interests. The authors have declared that no competing interests exist. Author contributions. JMC and MMC conceived and designed the experiments. JMC performed the experiments. JMC and OVT analyzed the data. OVT contributed reagents/materials/analysis tools. JMC and MMC wrote the paper. ¤Current address: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America Citation: Cox JM, Tsodikov OV, Cox MM (2005) Organized unidirectional waves of ATP hydrolysis within a RecA filament. PLoS Biol 3(2): e52. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030070SynopsisMicrobiologyMolecular Biology/Structural BiologyBiochemistryEubacteriaDNA Recombination and Repair—A New Twist to RecA Function Synopsis2 2005 8 2 2005 8 2 2005 3 2 e70Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Organized Unidirectional Waves of ATP Hydrolysis within a RecA Filament ==== Body Molecular motors harness the energy of ATP (or GTP, a related energy currency) and transform it into mechanical force. Well-known examples of motors include myosin and dynein, proteins that use ATP to ferry intracellular cargo along fibers made of actin or tubulin proteins. The ATP-dependent assembly of actin or tubulin fibers itself can work as a motor: for instance, the march of white blood cells toward pathogens is powered by the growth of actin filaments pushing against the cells' membranes. In all cases, coherent motion implies a coordinated and polarized use of energy. Now, Julia Cox, Oleg Tsodikov, and Michael Cox present evidence indicating that filaments of the bacterial RecA protein, long known for their role in homologous recombination and DNA repair, have properties reminiscent of a molecular motor as well. RecA filaments consist of DNA helices lined with RecA protein. RecA filaments invade a region of double-stranded DNA with similar nucleotide sequence, displacing one strand to pair with the other. Strand invasion can lead to a re-assortment—known as recombination—of DNA regions on either side of the shared sequence. It can also initiate the repair of DNA lesions during replication—the process by which a DNA molecule is copied to make two. RecA is also an ATPase, an enzyme capable of hydrolyzing (breaking down) ATP, when bound to DNA. RecA uses ATP to carry out strand exchange over long sequences and impose direction to the exchange, to bypass short sequence heterogeneities, and to stall replication so DNA lesions can be mended. But how RecA molecules within a filament coordinate and organize their activities to carry out these functions has remained obscure. Cox et al. addressed this problem in the test tube, by examining RecA filaments grown from mixing RecA protein with DNA. Previous experiments have shown that filament assembly spreads rapidly in the 5′-to-3′ direction once the first RecA molecule is loaded onto DNA. At the same time, ATP hydrolysis causes the release of RecA from DNA, but the exact rate of RecA dissociation is not known. Experiments suggest, however, that RecA molecules only dissociate from DNA when they are at the fiber's 5′ end, while ATP hydrolysis occurs all along its length. A RecA filament with every sixth molecule in red Under their experimental conditions, the authors found that a RecA molecule hydrolyzed 20 ATPs per minute—or one ATP every three seconds. If ATP hydrolysis occurred randomly in the fiber, one would expect a RecA molecule to dissociate about once every 1.5 seconds, or 40 RecA molecules to dissociate per minute. But this is not what the authors found—instead they estimated the dissociation to be at a rate of 120 RecA molecules per minute. Hence, six RecA molecules dissociate from a fiber in the time—three seconds—it takes for an individual RecA molecule to burn one ATP. This implies a filament organization in which every RecA molecule hydrolyzes ATP in synchrony with the sixth RecA molecule to its left and the sixth RecA molecule to its right. The authors note that there are approximately six RecA molecules per helical turn in a RecA filament. They propose that the RecA molecules hydrolyzing ATP at any given moment are aligned in a “stripe” that runs along the side of the filament. This stripe of ATP hydrolysis moves around the fiber in a repeating pattern of six steps. At the 5′ end of the fiber, ATP hydrolysis leads to RecA release. But in the middle of the fiber, it could work as a rotary motor, with the power to wind or unwind DNA and drive strand invasion through difficult passages of damaged DNA.	0	PMC546332	CC BY	2021-01-05 08:28:10	no	PLoS Biol. 2005 Feb 8; 3(2):e70	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030070	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030072SynopsisCell BiologyInfectious DiseasesMolecular Biology/Structural BiologyVirologyHIV/AIDSVirusesAn HIV Protein Plays a Surprising Role in Gene Activation Synopsis2 2005 8 2 2005 8 2 2005 3 2 e72Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. HIV-1 Tat Stimulates Transcription Complex Assembly through Recruitment of TBP in the Absence of TAFs Novel Enzyme Shows Potential as an Anti-HIV Target A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation ==== Body Retroviruses are expert manipulators when it comes to co-opting their host's cellular resources. A great deal of human complexity stems from the vast repertoire of proteins and mechanisms dedicated to the business of regulating gene expression, and retroviruses like HIV have evolved myriad ways of redirecting that machinery to their own benefit. Humans and other eukaryotes have three types of RNA polymerases, each charged with transcribing different types of genetic elements. RNA polymerase II transcribes protein-coding genes. RNA polymerases join with so-called general transcription factors to form a pre-initiation complex (PIC) on the gene's promoter, where it binds to region rich in thymine (T) and adenine (A) named the TATA box. The first transcription factor to associate with the TATA box is called TFIID, a large protein complex containing a protein that binds the TATA box (aptly named the TATA-box-binding protein, or TBP) and several cofactors called TBP-associated factors (TAFs). PIC assembly sometimes also requires activator proteins, which can enhance transcriptional activity by supporting proper elongation of nascent transcripts. Tat, an activator encoded in the HIV genome, is required for HIV gene activation and viral replication. It affects these processes, the current model holds, by stimulating transcript elongation and increasing RNA polymerase's processing efficiency. In a new study, Tamal Raha, Grace Cheng, and Michael Green work with human cell lines and find evidence that Tat can also stimulate PIC assembly. While most transcription factors bind to DNA, Tat binds to an area at the end of newly emerging viral RNA called the transactivation response element (TAR). Once bound, Tat recruits a cellular complex called P-TEFb (consisting of two subunits) to the HIV promoter, and enhances RNA polymerase's transcribing capacity. Previous studies in yeast had shown that activators appear to stimulate transcription complex assembly, leading the authors to ask whether Tat could play a similar role. To study this question in living human cells, Green and colleagues turned to chromatin immunoprecipitation, a technique that detects proteins bound (directly or indirectly) to DNA. Working with three well-known effectors of transcription—an activator (Gal4-VP16), a transcriptional enhancer, and another viral activator called E1a—the authors show that what's true for yeast also holds for mammals, or at least for the human cell lines investigated here. Each effector was required for PIC assembly, which was in turn required to activate transcription. An unexpected mechanism of HIV-1 Tat action The big surprise came in the next round of experiments, which explored Tat's influence on transcription and PIC assembly on the HIV promoter. As expected, transcription factors were “virtually undetectable” at the core promoter in the absence of Tat. Adding Tat recruited all the usual transcription factors to the promoter and increased transcription. But none of the TAFs that normally associate with TFIID were found. When the authors used the activator Gal4-VP16 to initiate HIV transcription, every one of the 11 TAFs studied appeared. None of them did so in the presence of Tat, suggesting that Tat-mediated HIV transcription doesn't rely on TAFs. Green and colleagues confirmed this hypothesis in experiments showing that Tat-driven transcription proceeded as usual in cells lacking TAFs. And they demonstrated that it is Tat—along with its cofactor P-TEFb, which is normally bound to RNA through Tat—that recruits the TAF-deficient TBP. Altogether, these results show a surprising new role for Tat in stimulating assembly of a transcription complex. What's more, the complex lacks the TAFs typically linked to TBP in mammalian cells. Because their experiments analyzed only transcription complex assembly, the authors are careful to note that Tat may well stimulate assembly in addition to promoting transcription elongation. And it may be this resourcefulness that makes Tat such a potent activator—and HIV so hard to control. (For more on Tat's role in HIV transcription, see “Novel Enzyme Shows Potential as an Anti-HIV Target” [DOI: 10.1371/journal.pbio.0030074] and “A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation” [DOI: 10.1371/journal.pbio.0030076].)	0	PMC546333	CC BY	2021-01-05 08:28:11	no	PLoS Biol. 2005 Feb 8; 3(2):e72	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030072	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030074SynopsisCell BiologyInfectious DiseasesMolecular Biology/Structural BiologyVirologyHIV/AIDSVirusesNovel Enzyme Shows Potential as an Anti-HIV Target Synopsis2 2005 8 2 2005 8 2 2005 3 2 e74Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. SIRT1 Regulates HIV Transcription via Tat Deacetylation An HIV Protein Plays a Surprising Role in Gene Activation A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation ==== Body At just 9.8 kilobases, the HIV genome pales in comparison to the 3.2 gigabases of its human and nonhuman primate targets. The compact retrovirus encodes just 14 proteins, which play different roles in promoting viral infection and virulence. As a retrovirus, HIV uses the host's cellular machinery—including RNA polymerases, which carry out transcription—to copy its RNA genome into DNA and infiltrate human chromosomal DNA. Once the virus is integrated (now called a provirus), its genes can be transcribed. Adept as HIV is in exploiting its host's molecular resources, the virus can't establish a foothold without the services of its skeleton crew. The HIV transcription factor Tat (“transactivator of transcription”), for example, is an essential regulator of HIV gene expression. Without Tat, HIV transcripts don't reach full length and can't effect viral replication. In a new study, Melanie Ott and colleagues identify an enzyme that regulates viral transcription by modifying Tat. The regulation of HIV genes depends on a complex interplay between proviral DNA, cellular proteins and transcription factors, and Tat. Unlike most transcription factors, Tat activates transcription by binding to RNA, specifically to a bulging “stem-loop” structure that forms at one end of all viral transcripts called the trans-acting responsive element (TAR). Tat binding to TAR requires recruiting the enzyme cyclin-dependent kinase 9 (CDK-9) to the HIV promoter (where transcription begins). CDK-9 chemically modifies the RNA polymerase and enhances its transcribing efficiency. The transcription process—including the labyrinthine protein–protein and protein–DNA (and in the case of Tat, protein–RNA) interactions—is highly regulated. One process that figures prominently in this regulation is acetylation, which adds an acetyl group (a molecule made of oxygen, hydrogen, and carbon) to a molecule or protein. Histone acetylation was long thought to influence transcription by regulating the structure and function of chromatin, which is an assembly of proteins (mostly histones) and DNA. Another, more widely accepted model proposes that histone acetylation controls transcription by recruiting cofactors required for transcription. Acetylation and deacetylation enzymes can also target other proteins. Of the three classes of deacetylases known to modify human histones, the sirtuins (SIRT1–7) appear to preferentially target a number of nonhistone proteins. Ott and colleagues first tested the ability of all seven SIRT proteins to deacetylate Tat by placing them in a test tube with Tat proteins. Though three SIRT enzymes caused Tat acetylation, only one, SIRT1, is a nuclear enzyme, like Tat, suggesting that SIRT1 might work similarly in living cells. Recycling of Tat through deacetylation by SIRT1 Ott and colleagues went on to show that transcription via Tat occurs in the presence of SIRT1, but not when SIRT1's catalytic center is removed. Experiments using cells taken from transgenic mice lacking SIRT1 demonstrated that introducing human SIRT1 enzymes increased Tat's transcriptional effects in a dose-dependent manner, while treating cells with the small molecule HR73, a derivative of a molecule that inhibits the yeast version of the SIRT1 protein, caused a 5-fold reduction in HIV transcription. The authors propose a cycle of transcriptional transactivation in which SIRT1 deacetylates Tat at the HIV promoter. Deacetylated Tat associates with CyclinT1 and TAR, and leads to transcription. Tat acetylation dissociates Tat from CyclinT1 and TAR, and transfers Tat to the elongating polymerase complex. Since acetylated Tat can't recruit CyclinT1 and CDK-9, the authors explain, a new round of transcription requires that new, unacetylated Tats are produced or existing Tats are deacetylated. Thus, efficient viral replication depends on adequate Tat supplies. And since HIV gene expression relies on SIRT1's enzymatic activity, inhibiting SIRT1 could prove to be a promising anti-HIV therapy. Future study will have to verify whether inhibiting SIRT1 can successfully put the brakes on HIV transcription and control the virus (See also “A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation” [DOI: 10.1371/journal.pbio.0030076]).	0	PMC546334	CC BY	2021-01-05 08:21:19	no	PLoS Biol. 2005 Feb 8; 3(2):e74	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030074	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030083SynopsisCell BiologyDevelopmentMolecular Biology/Structural BiologyDiabetes/Endocrinology/MetabolismBiochemistryCaenorhabditisOpposing Fat Metabolism Pathways Triggered by a Single Gene Synopsis2 2005 8 2 2005 8 2 2005 3 2 e83Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Nuclear Hormone Receptor NHR-49 Controls Fat Consumption and Fatty Acid Composition in C. elegans ==== Body Regulating metabolism of fat is an important challenge for any animal, from nematodes to humans. Central players in the regulatory network are the nuclear hormone receptors (NHRs), which are transcription factors that turn on or off a set of target genes when bound by specific lipid molecules. NHR genes number 48 in mammals, and a surprising 248 in nematodes. Despite the difference in quantity, there are some structural similarities between NHRs in these two groups, in particular, between the nematode gene nhr-49 and the mammalian HNF4. In this issue, Keith Yamamoto and colleagues show that nhr-49 controls two different aspects of fat metabolism, which interact to form a feedback system controlling the consumption and composition of fats in the nematode. Regulation of fat content and lifespan in C. elegans Using RNAi to suppress gene expression, the researchers discovered that when nhr-49 was absent, the lifespan of the nematode was reduced by more than 50%, and the animal displayed numerous gross abnormalities in the gut and gonad. This was accompanied by unusually high fat content in the larvae. By using quantitative PCR to measure output of fat and glucose metabolism genes, the researchers showed that deletion of nhr-49 changed expression of 13 of these genes, with the most dramatic effects occurring within two metabolic pathways: mitochondrial lipid oxidation and fatty acid desaturation. Oxidation degrades lipids to release energy, explaining the build-up of fat in nhr-49-suppressed larvae. One of nhr-49's normal functions is to increase expression of the mitochondrial acyl–Coenzyme A (CoA) synthetase gene acs-2. A principal role for mitochondrial acyl-CoA synthetases is to “activate” free fatty acids for transport into mitochondria, where they are oxidized. This process involves attaching a CoA group to a free fatty acid, and often serves as a rate-limiting step in lipid oxidation. Indeed, the authors found that suppression of acs-2 alone was sufficient to reproduce the highfat phenotype, while overexpression of acs-2 rescued the phenotype even in the absence of nhr-49. Fatty acid desaturation is the process of converting saturated fats into unsaturated ones, by forming one or more double bonds between adjacent carbons in the tail. This process is catalyzed by fatty acid desaturase enzymes. nhr-49 increases expression of several desaturases, most importantly fat-7, which converts stearic acid to oleic acid; deletion of nhr-49 more than doubled the proportion of stearic acid compared to oleic acid. RNAi interference of fat-7 alone produced two interesting results. First, it shortened the nematode life span, suggesting this was the primary pathway through which nhr-49 suppression exerted that same effect. Second, it produced some effects that were opposite those of nhr-49 suppression: specifically, it reduced rather than increased fat content, and it increased rather than reduced expression of acs-2. These results show that in its normal actions, nhr-49 sets in motion two opposing pathways: it increases acs-2, which leads to reduction of fat content, and it increases fat-7, which, by reducing acs-2, increases fat content. Surprisingly, this behavior links nhr-49 most closely not to HNF4, with which it shares the most structural similarity, but to another type of mammalian NHR, called peroxisome proliferator-activated receptors (PPARs). Further investigation of this link may lead to better understanding of the functions of PPARs, and provide opportunities for altering their function for treatment of fat metabolism disorders such as diabetes and obesity.	0	PMC546335	CC BY	2021-01-05 08:21:19	no	PLoS Biol. 2005 Feb 8; 3(2):e83	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030083	oa_comm
==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030084SynopsisPsychologyHomo (Human)The Bottleneck of Central Processing: Clues from Reaction Times Synopsis2 2005 8 2 2005 8 2 2005 3 2 e84Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Parsing a Cognitive Task: A Characterization of the Mind's Bottleneck ==== Body Between stimulus and response lies a black box—the mind—whose inner workings are largely unmapped. One of the essential questions about those inner workings concerns the serial versus parallel nature of their processing capabilities. Parallel processing allows multiple tasks to proceed at once, while serial processing creates a bottleneck through which multiple tasks must pass, one at a time. Any reasonably complex task is likely to involve both parallel and serial components, and parsing a task into its components is a central goal for researchers of cognitive processing. In a new study, Mariano Sigman and Stanislas Dehaene propose a model of cognitive processing for a set of simple tasks in which a bottleneck occurs between initial sensory processing and motor response. They predict that this bottleneck will contribute significantly to variations in response time as the cognitive challenge increases and verify this by testing people on a specific numerical evaluation task. A simple but highly effective technique for examining bottlenecks is to measure variations in response time for a task as the stimulus is varied in some small but cognitively challenging way, or when the stimulus is presented along with a stimulus for a competing task. The task in this study was to determine whether a presented number was greater than or less than 45. The complexity of the task was determined by three variables: notation, distance, and response complexity. Notation was varied by presenting the number either as a numeral or its spelled-out equivalent (for instance, “36” or “thirty-six”). Distance was varied by presenting numbers either closer to or further from 45 (for instance, 31 versus 36), and the required response was either one or two finger taps. In a separate series of experiments, the researchers challenged the subject with an interfering tone-recognition task at the same time as or slightly after the presentation of the numerical task. Comparing human reaction times The use of these two sets of experiments allowed the authors to deduce two different kinds of information about the processing involved in the number task. First, by varying the delay in presentation of the tone-interference task and measuring the delay in response time for the number task, they showed that both number perception and motor response can proceed in parallel with other, competing tasks, while the central component of the number task—determining distance—was processed serially, through a central bottleneck. Next, Sigman and Dehaene turned off the tone, and asked how variations in notation, distance, and response complexity altered the variance in the response time—that is, the spread of values for the same task by the same subject. For all three variables, the more challenging task (numbers presented as words, smaller distance, or two taps) had an increased response time. However, only the calculation of distance increased the spread of values obtained in multiple trials. This further suggests that only the central calculation step—what the authors refer to as stochastic integration—proceeds through a central bottleneck, while the other two components can be processed in parallel. Thus, in both experiments, the task was parsed by the brain into the same components, with the serial component being the one subject to the most variance. Sigman and Dehaene note that the ability of the perceptual and motor parts to be performed without central computation depends on the extreme simplicity of the tasks in this experiment. More complex motor challenges, for instance, undoubtedly would require some central input, and thus proceed through the bottleneck. Similarly, a high degree of training in the numerical distance task would likely increase the automaticity of the response, thus avoiding the central slowing seen in the task-naïve subjects studied here.	0	PMC546336	CC BY	2021-01-05 08:21:19	no	PLoS Biol. 2005 Feb 8; 3(2):e84	utf-8	PLoS Biol	2,005	10.1371/journal.pbio.0030084	oa_comm
==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-71567993610.1186/1743-0003-1-7ResearchKnowledge discovery in databases of biomechanical variables: application to the sit to stand motor task Vannozzi Giuseppe [email protected] Croce Ugo [email protected] Antonina [email protected] Francesco [email protected] Aurelio [email protected] Department of Human Movement and Sport Sciences, University Institute for Movement Science, Roma2 Department of Biomedical Sciences, University of Sassari, Sassari, Italy3 Department of Informatics, University of Pisa, Pisa, Italy4 Department of Rehabilitation, AUSL 11, San Miniato, Pisa, Italy2004 29 10 2004 1 7 7 30 8 2004 29 10 2004 Copyright © 2004 Vannozzi et al; licensee BioMed Central Ltd.2004Vannozzi et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The interpretation of data obtained in a movement analysis laboratory is a crucial issue in clinical contexts. Collection of such data in large databases might encourage the use of modern techniques of data mining to discover additional knowledge with automated methods. In order to maximise the size of the database, simple and low-cost experimental set-ups are preferable. The aim of this study was to extract knowledge inherent in the sit-to-stand task as performed by healthy adults, by searching relationships among measured and estimated biomechanical quantities. An automated method was applied to a large amount of data stored in a database. The sit-to-stand motor task was already shown to be adequate for determining the level of individual motor ability. Methods The technique of search for association rules was chosen to discover patterns as part of a Knowledge Discovery in Databases (KDD) process applied to a sit-to-stand motor task observed with a simple experimental set-up and analysed by means of a minimum measured input model. Selected parameters and variables of a database containing data from 110 healthy adults, of both genders and of a large range of age, performing the task were considered in the analysis. Results A set of rules and definitions were found characterising the patterns shared by the investigated subjects. Time events of the task turned out to be highly interdependent at least in their average values, showing a high level of repeatability of the timing of the performance of the task. Conclusions The distinctive patterns of the sit-to-stand task found in this study, associated to those that could be found in similar studies focusing on subjects with pathologies, could be used as a reference for the functional evaluation of specific subjects performing the sit-to-stand motor task. knowledge discoverydata miningassociation ruleshuman movementsit to stand ==== Body Background In the last decade quantitative movement analysis has been increasingly used in clinical contexts [1]. This analysis makes use of fairly complex instrumentation and of models of the musculo-skeletal system. It provides a great amount of information, such as space and time characteristics of the motor task analysed, joint and segment kinematics and kinetics and electromyographic patterns of muscular recruitment. An integrated analysis of measured and estimated biomechanical quantities allows for the description of the subject performance, for the discrimination among different motor strategies and, therefore, it supports the clinical decision-making process [2]. Modern complex instrumentation and models, such as stereophotogrammetric systems and multi-segment models of the human body, provide a thorough and faithful description of the subject's movement at a local level (e.g. joints kinematics), to be used at its best as a support to the functional assessment of subsystems of the locomotor apparatus (e.g. joint function) [3]. However, the large amount of measured information is not paralleled by the capability of such information of supporting the assessment of the overall subject's mobility [4]. Simpler experimental set-ups and models may be more appropriate to functionally assess a subject performing a specific motor task [5]. In recent years, clinical tests have been devised aimed at quantitatively assessing the level of a subject's activity limitation based on simple and encumbrance-free experimental set-ups associated with mechanical models of the musculo-skeletal system. These models are designed to be associated with both the subject and the specific task being performed [6]. In this context, Minimum Measured Input Models (MMIM) have been proposed and proven to offer effective insights into the motor task execution [7]. Simplified, and therefore low-cost, experimental set-ups facilitate the gathering of data both locally (a shorter examination time is needed) and in multi-centre contexts (more laboratories can afford the necessary experimental set-up), allowing the collection of a great quantity of data which may be sent to a single data repository. However, even simplified experimental setups and models may provide a large amount of biomechanical data that requires considerable human efforts to be interpreted [8]. In fact, traditional methods of data analysis for the extraction of knowledge rely on a direct analysis, which is usually demanding and time-consuming, and on the interpretation of an experienced analyst [9]. Such analysis becomes hardly applicable when dealing with data collected multi-centrically. The aim of this study was to extract knowledge regarding the execution of a specific motor task. The term "knowledge" refers here to any relationship among attributes associated with the phenomenon under analysis. These relationships can be intended as causal and, therefore, suitable for the interpretation endeavours, or at least as tools for evidencing the presence of a repeatable pattern of variables. The declared goal was pursued by searching relationships among large amounts of biomechanical quantities by using an automatic method. Some data mining techniques (data mining is a step of a process called Knowledge Discovery in Databases (KDD)) lend themselves to be effectively used in this context since they may reveal meaningful patterns and data structures from massive databases [10,11]. A specific data mining technique was applied to the data yielded by the analysis of sit-to-stand (STS) trials performed by healthy adults and carried out using the above-mentioned MMIM approach. The STS motor task was chosen because it has been shown to be adequate for determining the level of subject-specific motor ability [12]. In addition, the data provided by MMIMs were shown to be powerful overall descriptors of motor tasks. A group of unrestricted age and gender healthy adults was used with the goal of discovering knowledge inherent to the way healthy adults perform the selected motor task. In order to properly frame this study, a summary description of the MMIM approach and an overview of the KDD process are reported. Methods A MMIM applied to the STS task – The TIP model A MMIM is a model of a portion of the musculoskeletal system that includes the invariant aspects of both the modelled mechanical system and the motor task being performed. Therefore, a MMIM requires a minimum amount of measurements and provides a physiology-related description of the motor task [4]. In analysing the STS, only measurements from a single force platform are needed. The task is divided in two time phases: before- and after-seat-off (BSO and ASO). In each time phase a Telescopic Inverted Pendulum (TIP) model is applied. A TIP is characterised by a fixed base of support and by a massless link joining the base of support of the moving portion of the body to its centre of mass (CM). The link can elongate, controlled by a linear actuator (LA), and can rotate around its base of support, controlled by two actuators acting in the sagittal (SA) and frontal (FA) plane, respectively. The kinematics of, and the dynamic actions on, the CM of the modelled portion of the body involved in the movement are needed as model inputs. The outputs of the TIPs are the kinematic and kinetic variables associated with the actuators. During BSO the TIP is applied to the upper part of the body with its base of support positioned on the chair, while during ASO the TIP is applied to the whole body and its base of support is located at the ankles. In order to apply the TIP model in each phase, subject specific and experimental set-up parameters are set. A list of TIP parameters and TIP output variables may be [7] collected into a database. The KDD process The KDD process was introduced in order to provide a framework in which data-miners could work in a logical and sequential way, considering all the research aspects from the data acquisition to the information extraction [13,14]. An iterative five phase process may be adopted (Figure 1) [15]. Figure 1 A scheme of the KDD process. Input data are initially selected and target data are isolated. Pre-processing and transformation are performed to ensure the database reliability. Data mining is the core analysis. The knowledge discovery process ends with the interpretation of the results. Initially, the domain understanding, the parameter selection, and the goal definition need to be set. A subset of interest of the stored dataset can then be isolated. Pre-processing is performed to reduce noise and fill possible gaps in the target dataset. Elimination of outliers, corrections of wrong elements in the database and reduction of dimensionality are crucial transformations to reach an adequate level of suitability of the database. Data mining "is a well-defined procedure that takes data as input and produces output in the form of models or patterns" [16] and is the core of the KDD process. It is used with different aims such as Exploratory Data Analysis (EDA) [17], Descriptive Modelling [18], Predictive Modelling such as Classification and Regression [19], Retrieval by Content [20] and Discovering Patterns or Rules [21]. Innovative techniques for the data mining have been introduced to be used either in conjunction with or in alternative to traditional statistical methods for two main reasons. First, while classical statistics is applied to data collected according to a specific goal of the analyst, data mining methods are applied to data already collected and aim at finding unknown relationships among them. Secondly, data mining allows to infer general rules with adequate approximation, even if the amount of data available is not as large as that generally required by inferential statistics [16]. The selection of the data mining technique is based on the specific analysis. Prediction, clustering, classification and research of association rules are the most common tasks and each of them may be accomplished with various algorithms. Finally, data interpretation helps the user in managing and understanding the results: visualisations (clustering) or extraction of symbolic rules are common ways of evaluating the discovered knowledge. The search for association rules The technique of research of association rules, which aims at finding the most recurrent patterns in a database, was selected for the data mining. Given a database D of experimental trials T, each experimental trial is a record of D and is made of a set X of literals called items. An item represents a specific value of an attribute of a table of D, and a record can be represented as an attribute (i.e. an output variable or a model parameter) together with its value [21]. The problem may be defined as follows. Let I = {i1, i2,...., im} a set of items of D therefore, T can be seen as a group of items such that T I. An association rule can be defined as a logical implication: X Y where X I is the antecedent of the rule, Y I is the consequent and X ∩ Y = . A rule X Y, over a set of trials T, has a confidence c if c % of the trials in T containing X, also include Y. The same rule in the same context has a support s if s % of the trials in T contain X Y. The confidence of a rule X Y can be calculated from the support of the antecedent X and the support of the union of the antecedent X and the consequent Y: Confidence is an index of the validity of a rule. A high confidence means that there is a strong relationship between X and Y in the sense that the presence of a pattern X in a trial implies, with a high probability, the presence of Y in the same trial. Given a set of trials T, finding "interesting" association rules in T is the problem of generating all the rules whose both support and confidence are greater than a set threshold (minimum support and minimum confidence). The extracted rules were reported in the following format: A → B [c % ] where the first item was the antecedent of the rule, the item which followed was the consequent, while the value indicated in square brackets was the confidence. The implication was intended to be valid only one way, from the left to the right. In case of validity of both directions, "definitions" were obtained: A ←→ B [c-min %] composed by two rules having the two items both as antecedent and consequent whose confidence c-min was the lowest of the two c values associated with the one way rules. The search for association rules followed the path illustrated in Figure 1. Initially, data were read from the sources and displayed, allowing for a straightforward selection of the dataset of interest. Next, the subset of data were prepared to the analysis by eliminating possible outliers and filling possible gaps in the database due to incorrect applications of the model. Typically, data preparation is the most time consuming phase of the KDD, but is also highly crucial since the effectiveness of a data mining analysis relies on the consistency of the database. Since the theory of association rules was formulated to deal with qualitative attributes [22] characterised by a limited number of scores, the virtually infinite values of quantitative attributes were assigned to a limited amount of intervals identified by progressive numbers. Such discretisation process [23] for each attribute A, generated a variable number n of partitions (Ai_n; i = 1,.., n). The first partition A1_n included the lowest values of A and the last partition An_n included highest values of A. Items (i.e. the attribute associated with a relevant discretised value) similar to the qualitative items could thus be generated (Figure 2). Figure 2 Example of a discretisation process of a quantitative attribute. Grey areas represent the different partitions, i.e. the items. Vertical lines represent the values of the quantitative attribute. Self organising maps (SOM) were used to cluster the values of the attributes. SOMs are widely known as a powerful clustering tool [24] and could overcome the disadvantages related to other unsupervised approaches as the equal frequency intervals or the equal interval width techniques. [25]. The latter methods, imposing an equal number of points belonging to each interval or, similarly, each interval having a pre-determined length, may generate meaningless or even empty intervals. SOMs were chosen and purposely implemented to properly isolate residual outliers [26] from the distribution of values of an attribute, to correctly define the clouds of values and to automatically set the optimal number of intervals [27]. Values and/or intervals were mapped in a discrete domain as integer numbers and items were created. In this way, the database became a set of itemsets. The search of frequent itemsets was performed on the selected dataset. Itemsets with support greater than the minimum support were found. The set of itemsets that appeared frequently in the transactions of the database was then identified. This step of the process was the most demanding in terms of processing time and computer memory occupation. The search for association rules was accomplished by using the APRIORI algorithm [10] which was shown to perform better then other common algorithms such as AIS [21] and SETM [28]. The APRIORI algorithm iterated the two following steps: • building of a candidate set Ck of itemsets, counting their occurrences; • defining "large itemsets" Lk on the basis of support constraints. In figure 3, the main steps of the algorithm are illustrated. Figure 3 The Apriori algorithm applied to the database under analysis. The two phases of the Apriori algorithm are highlighted. The first, referred as "join step" phase, aimed at the generation of the candidate itemsets Ck built starting from Lk-1, the frequent itemset of the previous phase. In the second phase the Ck itemsets underwent to a "pruning" procedure that selected the frequent itemsets Lk on the base of the support check. Each frequent itemset generated a set of rules and each rule was scored by its confidence. Only rules whose confidence was higher than the minimum confidence reached the following phase. The selected association rules represented the knowledge extracted from the database expressed in a quasi natural language that the user could interpret. Efforts were made toward a clustered representation of the set of rules to increase readability and interpretability of information. A software project for the data mining phase was purposely designed and implemented as follows: the software received as input all data from the database and returned a text file containing a list of the discovered association rules and all the possible unified rules and definitions derived from the entire dataset. Support and confidence thresholds were set to 35% and 85%, respectively. Considering that in a rule more than one consequent can be found [29], a maximum number of consequent items had to be set. In this analysis such number was experimentally set to 4 to avoid the presence of meaningless items in the resulting rules. Materials Healthy adult volunteers (N = 110), both males and females between 22 and 87, participated in the study, executing a total of more than 1100 trials. They were initially asked to sit on a seat. The height of the seat was set at a value equal to the subject's tibial plateau height [30]. Subjects could choose the distance of their feet from the seat and had to keep them parallel at a distance equal to that measured between iliac anterior superior spines. Both footprints were then drawn on the floor, ensuring that the subject's feet were in the same position during all the trials. In addition, medio-lateral and antero-posterior coordinates of selected foot points were measured. Anthropometric parameters, such as the body mass and the length of the lower limb segments, were also obtained. Subjects were asked to rise from the seat at the preferred speed after an audio start signal and look at a frontal one metre distant fixed point at the height of 80% of their eyes' height, maintaining the orthostatic posture until the stop signal. Arms were kept crossed on the chest during the trial to avoid that arm swing could affect CM movements. Ground reaction forces were measured using a six component Bertec force platform (0.4 m*0.6 m), positioned under both the seat and the subject's feet. Data were collected at a sampling rate of 100 Hz and pre-processed with an internally developed Labview® software (National Instruments Inc.). First, force platform signals were digitally low-pass filtered (second order Butterworth filter 15 Hz cut-off frequency). Data were then fed into the TIP model, which yielded the kinematic and kinetic time functions (displacement, velocity, force/couple and power) of the LA and SA. FA variables were not analysed since their contribution to the motor strategy was considered negligible, given the sagittal symmetry of the STS motor task. From these functions a subset of kinematic and kinetic variables (KK-set) was extracted including time events of the task (normalised with respect to the duration of the whole task, see caption of Table 1 for the complete list of variables) and together with experimental set-up and subject specific parameters were stored in a Microsoft Access database, and loaded using a Windows ODBC interface [31]. The resulting database contained a total of more than 52,000 items. The number of analysed attributes was set to 47, as listed in Table 1. Table 1 The 47 attributes analysed. They included subject initial conditions (ankle and thigh angles) and experimental setup/anthropometric parameters (seat height, thigh length, foot length, TIP1 hinge and malleoli coordinates), KK-set variables and important time instants. The KK-set was made of displacements (Disp), velocities (Vel), forces or couples and powers referred to the two LA and SA actuators. So referred to seat-off. In addition, ML, AP and V referred to the medio-lateral, antero-posterior and vertical directions. Finally, the attributes labelled with an initial "T" represented the instant of occurrence of the corresponding quantity (e.g. the attribute MaxLAVelASO referred to the maximum value of LA velocity after the seat-off and the attribute TMaxLAVelASO represented the corresponding instant of occurrence). Anthropometric and Experimental set-up Attributes Kinematic and Kinetic Attributes Time-Attributes RightAnkleAngle MaxSADispBSO Duration LeftAnkleAngle MaxSAVelBSO APRightMalleolusCoord MaxSACoupleBSO TMaxSADispBSO APLeft MalleolusCoord MaxSAPowerBSO TMaxSAVelBSO APHingeCoord SADispSo TMaxSAForceBSO MLRightMalleolusCoord SAVelSo TMaxSAPowerBSO MLLeftMalleolusCoord SACoupleSo MLHinge Coord SAPowerSo tSo VRightMalleolusCoord MaxSADispASO VLeftMalleolusCoord MaxSAVelASO TMaxSADispASO VHingeCoord MaxSACoupleASO TMaxSAVelASO SeatHeight MaxSAPowerASO TMaxSAForceASO ThighLength MaxLADispASO TMaxSAPowerASO ShankLength MaxLAVelASO TMaxLADispASO FootLength MaxLAForceASO TMaxLAVelASO RightThighAngle MaxLAPowerASO TMaxLAForceASO LeftThighAngle TMaxLAPowerASO Results Various rules and definitions were found. Among them, some referred to obvious relationships such as those related to symmetry between right and left coordinates, others related a single item of a temporal parameter (TMaxSADispASO3_3) as a consequent of the following kinematic and kinetic items: a) MaxSADispBSO3_6 and MaxSAPowerBSO1_6, before seat-off; b) SAVelSo3_6, at seat-off; c) MaxLADispASO3,4_6, MaxLAForceASO4_5, MaxSADispASO3_6 and MaxSAVelASO2,3_5, after seat-off; and of the following time events: Duration1_6, TMaxSADispBSO3_6, TMaxSAVelBSO3_6, TMaxSACoupleBSO2_6, TMaxSAPowerBSO2_6, TMaxLADispASO5_6 and TMaxSAPowerASO3_5. The attribute TMaxSADispASO was the attribute with the lowest number of partitions (three partitions) and its last partition included about 90% of the observations. The discovered definitions and rules that could not be easily predicted are illustrated in Figure 4 using a cluster representation, which highlights inner and crossed relationships among items of each phase of the task; values of confidence are reported in the figure caption. Figure 4 Graphic cluster representation of both the rules and the definitions found in the study. The first ones, marked with a single-ended arrow, were found to have a confidence ranging from 86% to 96%. The second ones, marked with a double-ended arrow, both presented a confidence of 95%. Involved items are positioned according to the STS time subdivision (BSO and ASO phases and seat-off timing). The definitions related exclusively time instant items: • TMaxSADispBSO3_6 ←→ tso3_6 [95 %], • TMaxSAAngVelASO3_5 ←→ TMaxSAPowerASO3_5 [95 %], The first definitions related the 'average' time of occurrence of maximum sagittal displacement during BSO (partition 3 of 6) to 'average' values of tSo (partition 3 of 6). The second definition associated the time instant of maximum sagittal velocity to that of maximum power, both after the seat-off. Moreover, meaningful rules were found that involved as consequent the item MaxSAPowerBSO1_6. This item showed relationships, with a value of confidence varying between 86% and 96%, with the following kinematic and kinetic items: MaxSACoupleBSO2_6, MaxSADispBSO3_6, MaxLAForceASO4_5, and MaxSAVelASO2_5; and the following temporal items: TMaxSACoupleBSO2_6, TMaxSAPowerBSO1_6, TMaxSAPowerASO3_5 and SAVelSo3_6. The partitions corresponding to the attributes involved in both rules and definitions, their support and their range of variability expressed in the relevant units of measurement (UoM), are reported in Table 2. Table 2 Items involved in the discovered rules and definitions, their support and their range of values. Item Support (%) Range UoM Duration 1_6 45.0 1.01 1.61 s MaxSADispBSO3_6 41.3 29 36 deg MaxSACoupleBSO2_6 35.5 0.06 0.09 Nm kg-1m-1 MaxSAPowerBSO1_6 81.5 0.00 0.08 Wkg-1m-1 SAVelSo3_6 40.1 0.58 0.77 rad s-1 MaxSADispASO3_6 59.7 -3 14 deg MaxSAVelASO2,3_5 84.7 0.44 1.32 rad s-1 MaxLADispASO3,4_6 87.2 45.4 54.5 % of TIP2 final length MaxLAForceASO4_5 43.9 10.85 11.94 N kg-1 TMaxSADispBSO3_6 36.7 39.2 48.0 % of duration TMaxSAVelBSO3_6 37.9 34.6 42.1 % of duration TMaxSACoupleBSO2_6 47.5 10.3 15.1 % of duration TMaxSAPowerBSO1,2_6 92.1 20.5 26.3 % of duration tSo3_6 35.4 46.9 55.9 % of duration TMaxSADispASO3_3 89.2 87.1 99.9 % of duration TMaxSAVelASO3_5 44.6 42.0 54 % of duration TMaxSAPowerASO3_5 45.4 42.2 54.4 % of duration TMaxLADispASO5_6 36.4 90.8 96.3 % of duration The items reported in Table 2 belong to a subset of 18 attributes of the original 47. Only a limited number of items was involved in the discovered rules. Discussion The data mining analysis allowed for the discovery of both definitions and rules relating various items obtained from a MMIM analysis of the STS motor task. The most obvious and/or expected relationships, such as those related to the symmetry between right and left coordinates, also noticeable by a visual examination of the task as performed by the investigated subject, were included in the set of discovered rules and definitions. The finding of such relationships provided elements to confirm the validity of the data mining analysis. The set of rules found that related the temporal item TMaxSADispASO3_3 to various temporal, kinematic and kinetic items needs a further analysis to be interpreted. In fact, the attribute TMaxSADispASO was mapped in only three partitions and most of its observations were concentrated in the last partition. This circumstance rendered highly probable the presence of rules relating the item TMaxSADispASO3_3 to those items of the various attributes with support higher than 35%. Therefore, these rules were used to highlight items involved with a considerable support and therefore the usefulness of such rules was deemed limited. In general, when interpreting a rule/definition found, the analyst should be aware not only of both its confidence and the support of the items forming it, but also of the number of partitions in which the attributes involved in the rule/definition were divided. The fewer are the partitions used for a quantitative attribute, the higher is the probability of finding rules/definitions unsuitable for drawing specific patterns. This is particularly true when most of the observations fall in a single partition of the attribute. Conversely, some of the rules and definitions discovered by the data mining analysis highlighted relationships that could not be easily predicted otherwise. The two definitions reported in the results section, that related time instant items, indicated that specific 'average' timings (items belonging to central partitions of the corresponding attribute) of the sit-stand task were closely related. This finding is consistent with those present in the literature [32,33]. In particular referring to the second definition reported, since power is the product of moment and angular velocity, the definition that associates 'average' values of the instant of maximum sagittal velocity to those of maximum power after the seat-off could be predicted. Very interestingly, the relationships of the item MaxSAPowerBSO1_6 with several KK-set items showed the importance of the SA in the execution of the task. In fact, almost all rules relating KK-set items to the MaxSAPowerBSO1_6 regarded the sagittal actuator. When the maximum SA power during BSO occurred early in the task, its value was among the lowest (TMaxSAPowerBSO1_6 → MaxSAPowerBSO1_6). Low values of SA maximum power during BSO also occurred in combination with low-to-medium couple values (MaxSACoupleBSO2_6 → MaxSAPowerBSO1_6) and early in the phase (TMaxSACoupleBSO2_6 → MaxSAPowerBSO1_6). The latter rules showed that before seat-off kinetic variables of the main actuator are strongly related to each other and their timing. Given a value of one of them, a limited range of values is to be expected for the others. Moreover, 'average' SA velocity at seat-off was found to be present in combination with low maximum SA power at BSO (SAVelSo3_6 → MaxSAPowerBSO1_6) showing that relatively high speeds at seat-off could be reached even when the power exerted before seat-off was low. The presence of low value partitions in the rules may suggest that most healthy adults tend to use the least amount of energy necessary to complete the first phase of the task, showing an effective strategy of reduction of the energy expenditure [34]. A validation of this hypothesis could be obtained in a rehabilitative context, by studying databases containing data of samples of different populations (i.e. healthy subjects versus subjects with a specific motor functional limitation). The rules relating the low maximum SA power during BSO to variables occurring during ASO allowed for BSO-ASO crossed inferences. When medium-to-high maximum LA force during the elevation of the centre of mass toward the standing position was found, a low maximum SA power was generated by the SA before seat-off (MaxLAForceASO4_6 → MaxSAPowerBSO1_6). Moreover, consistent with the relationship to the SA velocity at seat-off, low maximum power of the SA during BSO occurred in combination with low-to-medium maximum velocity values during ASO (MaxSAVelASO2_6 → MaxSAPowerBSO1_6) showing that after seat-off a low-to-medium SA velocity can be reached and kept during the remaining part of the task, even when a low power is exerted before seat-off. Finally, average timing of occurrence of maximum SA power after seat-off implied a low maximum SA power before seat-off (TMaxSAPowerASO3_5 → MaxSAPowerBSO1_6) showing that, when the task is performed with an 'average' distribution of the time instants, the power exerted before seat-off is at its lowest values. The results' representation of Figure 4 could be used as the main outcome of the knowledge discovery process to be used by the analyst as a reference for the examined population. In the case of the present study, the patterns found are representative of the most common characteristics of the way healthy adults, of both genders and in a wide age range, perform the sit-to-stand task. Any deviation from these patterns found in a healthy adult could be considered as an uncommon characteristic. The patterns resulting from the analysis of a database containing a subgroup of the subjects examined in the present study (i.e. female subjects or subjects over the age of 65) could be considered as specific of the selected subgroup. Similarly, if the analysis is applied to a database of subjects affected by a specific pathology then the resulting patterns would characterise that population of subjects. The comparison of those patterns and the patterns found in the present study would highlight how differently the two groups perform the task. In perspective, from a rehabilitation standpoint, the output of data mining analyses applied to various groups of subjects performing various tasks could be used as a reference tool to evaluate the performance of subject under examination and, therefore, her/his level of mobility. Conclusions The study focused on finding the most frequent patterns of biomechanical variables and parameters obtained from dynamometric measurements of healthy subjects performing the sit-to-stand motor task. Data collected in a large database underwent a knowledge discovery process. The size of the database is strongly related to the simplicity of the data acquisition procedures. Simple and less expensive experimental set-ups allow the gathering of more data and in more locations than high-cost experimental set-ups and procedures. Data acquired from force platforms, processed with specific biomechanical models, represent a favourable condition to apply knowledge discovery processes effectively. In this study, data from volunteers in a large age range and of both genders were analysed in order to extract the most common patterns of healthy people performing the task. The results of the knowledge discovery process showed that sit-to-stand time events were strongly interdependent. Low maximum sagittal power values before seat-off were strongly related to numerous parameters both before and after seat-off, highlighting, among other characteristics, that most often a low power before seat-off is related to a regular occurrence of time instants and to low-to-medium sagittal speed from seat-off to the end of the task. The patterns found may be considered as typical rules of the sit-to-stand motor task and could constitute the basis for comparisons of patterns characteristic of different groups. The knowledge acquired in this study is the first step in the direction of developing a robust clinical tool to evaluate subject mobility. Acknowledgements Supported by CNR, project "Un sistema Web-Based per gli operatori della riabilitazione dell'apparato locomotore" and by MIUR, project "Valutazione dell'abilità posturale e locomotoria umana per scopi clinici". ==== Refs Blanc Y Macellari V, Giacomozzi C The role of movement analysis in clinics In Rapporti ISTISAN 00/24 2000 3 6 Gage JR Gait Analysis: An essential tool in the treatment of cerebral palsy Clin Orthop 1993 288 126 134 8458125 Frigo C Davis R Strengths and Weaknesses of Motion Analysis In ISEK Newsletter 1–1995 Cappozzo A Minimum measured-input models for the assessment of motor ability Journal of Biomechanics 2002 35 437 446 11934412 10.1016/S0021-9290(01)00186-5 Andriacchi TP Alexander EJ Studies of locomotion: past, presence and future Journal of Biomechanics 2000 33 1217 1224 10899330 10.1016/S0021-9290(00)00061-0 Loslever P Laassel EM Angue JC Combined statistical study of joint angles and ground reaction forces using component and multiple correspondence analysis IEEE Trans Biomed Eng 1994 41 1160 1167 7851917 10.1109/10.335864 Papa E Cappozzo A A telescopic inverted-pendulum model of the musculo-skeletal system and its use for the analysis of the sit-to-stand motor task Journal of Biomechanics 1999 32 1205 1212 10541071 10.1016/S0021-9290(99)00103-7 Cappozzo A Camomilla V Della Croce U Mazzà C Quagliarella L Vannozzi G Zok M Musculo-Skeletal System Modelling in the Evaluation of the Motor Disability Theoretical Issues in Ergonomics Science 2005 Lavrac N Keravnou E Zupan B Kent A Intelligent Data Analysis in Medicine In Encyclopedia of Computer Science and Technology, (New York) 2000 42 113 157 Agrawal R Imielinski T Swami A Database mining: a performance perspective IEEE Transactions on knowledge and data engineering 1993 5 914 925 10.1109/69.250074 Bonato P Mork PJ Sherrill DM Westgaard RH Data Mining of Motor Patterns Recorded with Wearable Technology IEEE Engineering in Medicine and Biology Magazine 2003 22 110 119 12845827 10.1109/MEMB.2003.1213634 Papa E Cappozzo A Sit-to-stand motor strategies investigated in able bodied young and elderly subjects Journal of Biomechanics 2000 33 1113 1122 10854884 10.1016/S0021-9290(00)00046-4 Reinartz TP Focusing Solutions for Data Mining: Analytical Studies and Experimental Results in Real-World Domains Lecture Notes in Computer Science 1999 1623 Springer Verlag Carbone PL Kerschberg L Intelligent Mediation in Active Knowledge Mining: Goals and General Description In Proceedings of AAAI workshop on KDD 1993 241 253 Fayyad UM Piatetsky-Shapiro G Smyth P Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R From Data Mining to Knowledge Discovery: An overview In Advances in Knowledge Discovery and Data Mining 1996 Menlo Park, CA: AAAI Press 1 34 Hand D Mannila H Smyth P Principles of Data Mining 2001 The MIT Press Tukey JW Exploratory Data Analysis 1977 Reading, MA: Addison-Wesley Everitt BS Dunn G Applied Multivariate Data Analysis 1991 New York: Halstead Press Duda RO Hart PE Stork DJ Pattern Recognition 2001 New York: Wiley Jones KS Willet P Sparck Jones K, Willet P Readings in Information Retrieval 1997 Morgan Kaufmann Publishers CA Agrawal R Imielinski T Swami A Mining association rules between sets of items in large databases In Proceeds of the ACM SIGMOD Conference on Management of Data 1993 New York. 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==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-111567993710.1186/1743-0003-1-11ResearchReaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis Viau Antonin [email protected] Anatol G [email protected] Bradford J [email protected] Mindy F [email protected] School of Rehabilitation, Faculty of Medicine, University of Montreal, Canada2 Center for Interdisciplinary Research in Rehabilitation (CRIR), 6300 Darlington, Montreal, Quebec, Canada3 Department of Physiology, University of Montreal, Canada4 Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), Department of Rehabilitation, Laval, Canada5 School of Physical and Occupational Therapy, McGill University, Canada2004 14 12 2004 1 11 11 29 11 2004 14 12 2004 Copyright © 2004 Viau et al; licensee BioMed Central Ltd.2004Viau et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Virtual reality (VR) is an innovative tool for sensorimotor rehabilitation increasingly being employed in clinical and community settings. Despite the growing interest in VR, few studies have determined the validity of movements made in VR environments with respect to real physical environments. The goal of this study was to compare movements done in physical and virtual environments in adults with motor deficits to those in healthy individuals. Methods The participants were 8 healthy adults and 7 adults with mild left hemiparesis due to stroke. Kinematics of functional arm movements involving reaching, grasping and releasing made in physical and virtual environments were analyzed in two phases: 1) reaching and grasping the ball and 2) ball transport and release. The virtual environment included interaction with an object on a 2D computer screen and haptic force feedback from a virtual ball. Temporal and spatial parameters of reaching and grasping were determined for each phase. Results Individuals in both groups were able to reach, grasp, transport, place and release the virtual and real ball using similar movement strategies. In healthy subjects, reaching and grasping movements in both environments were similar but these subjects used less wrist extension and more elbow extension to place the ball on the virtual vertical surface. Participants with hemiparesis made slower movements in both environments compared to healthy subjects and during transport and placing of the ball, trajectories were more curved and interjoint coordination was altered. Despite these differences, patients with hemiparesis also tended to use less wrist extension during the whole movement and more elbow extension at the end of the placing phase. Conclusion Differences in movements made by healthy subjects in the two environments may be explained by the use of a 2D instead of a 3D virtual environment and the absence of haptic feedback from the VR target. Despite these differences, our findings suggest that both healthy subjects and individuals with motor deficits used similar movement strategies when grasping and placing a ball in the two reality conditions. This suggests that training of arm movements in VR environments may be a valid approach to the rehabilitation of patients with motor disorders. arm reachingprehensionrehabilitationstroketherapeutic approachhemiplegia ==== Body Introduction Virtual reality (VR) is a computer-based, multisensory interactive simulation occurring at the same speed and time as events in the physical world. Different levels of immersion can be achieved ranging from complete 3D (cave, head-mounted display) to partial 2D (computer display, TV screen) with different hardware configurations. Interface devices (computer mouse, joystick, force sensor, cyberglove) allow the user to move in and interact with objects in the virtual environment. Of crucial relevance to rehabilitation is the potential for increasing the user's level of interaction with their real physical environment so as to maximize their return to community life [1]. The efficacy of using VR to retrain movement and the issue of whether training in a virtual environment will transfer to meaningful function in the real physical world has been explored in a number of studies with encouraging early results [2-4]. Neurophysiologists and rehabilitation specialists like physical and occupational therapists are beginning to be interested in VR as a tool to study motor control and to evaluate and treat motor deficits secondary to central nervous system lesions such as stroke [5]. The use of virtual computer-based interventions for telerehabilitation is also gaining in popularity because of the possibility of providing extended practice in the patient's own home or community environment [6,7]. The advantage of using VR in community, clinical and laboratory settings is that by virtue of its programmability, environments and the amount and type of feedback can be modified according to the user's motor capacities, motivation and therapeutic goals [5,8]. In addition, sensory parameters of the environment can be creatively adapted to evoke responses to a larger number of situations in a shorter amount of time than is available in physical set-ups. For example, in research studies, when determining the capacity to reach and grasp static targets, methodologies are often limited to one or two tasks because of the inability to easily adapt the experimental hardware. VR permits the use of more dynamic experimental set-ups in which object locations and orientations can be reliably and rapidly modified. This study focused on the possibility of using virtual environments for the retraining of arm motor function in individuals with hemiparesis due to stroke. Major barriers to arm motor recovery after stroke are coordination deficits and the use of maladaptive movement strategies for reaching and grasping. Patient motivation and movement repetition are key factors in motor recovery [9-12]. Current practice of rehabilitation of reaching deficits after stroke is based on movement repetition of targeted tasks. However, improvements in tasks practised in clinical settings have not been shown to have adequate carry over into real world activities of daily living [10]. One of the factors that may decrease the real world relevancy of practice in the clinical setting is the lack of attention to the retraining of varied goal-directed, effector-relevant whole arm movements. VR is an ideal medium in which to create such practise environments that have the advantage of providing additional motivation to patients to perform repetitive movement and can be available in the home or community following formal rehabilitation [13]. Indeed, some studies have reported that motor gains achieved by patients with stroke in VR environments may transfer to physical tasks and be measurable using common clinical scales [2,5,14]. Despite the growing interest in the use of VR for motor retraining, it is not known if movements involving reaching and grasping objects in VR environments are performed in a manner similar to those done in the physical world. Thus, the goal of this study was to validate VR as a tool for studying reaching and grasping in healthy subjects and in individuals with hemiparesis by comparing movement kinematics of identical tasks made in a physical and a virtual environment. Since reaching and grasping deficits have been well characterized in individuals with hemiparesis [15-17], the purpose of the study was not to compare movements between groups but to establish the validity of using a VR environment for the study of movement in each group. Preliminary results have appeared in abstract form [18]. Methods Eight healthy subjects (4 males and 4 females; 56.8 ± 17.1 years) and 7 adults with hemiparesis (3 males and 4 females; 48.9 ± 18.6 years) with no prior experience with VR participated in the study. Potential participants were identified from discharge lists of Montreal area rehabilitation centres. Out of 17 medical charts screened, 12 patients met eligibility requirements according to study inclusion and exclusion criteria. Patients were included if they were under 60 years old, had sustained a single, non-traumatic unilateral stroke in the territory of the left middle cerebral artery and had arm paresis (3/7 for the hand and 6/7 for the arm on the Chedoke-McMaster Stroke Assessment Scale [19] (Table 1). Patients were excluded if they had cerebellar or brain stem lesions, shoulder pain or other neurological/orthopaedic conditions affecting reaching ability, visual field deficits, uncorrected problems of visual acuity or severe perceptuo-cognitive deficits (heminegligence, ataxia, receptive aphasia) determined by standard clinical tests. Of these 12 individuals, 10 expressed willingness to participate. After obtaining informed consent approved of by the institutional Ethics Committee, they were assessed by a physical therapist and 3 individuals were excluded because of inability to perform the task. Healthy subjects were recruited from the community. They had no orthopaedic or neurological disease. All patients had been discharged from all in- or out-patient clinical services. Table 1 Demographic characteristics and clinical scores of participants with hemiparesis Subject Age (yrs)/sex Time since injury (months) Type of lesion CM: arm CM: hand 1 63/M 28 Temporo-parietal 6 6 2 42/F 34 Parietal 7 6 3 27/F 63 Parietal 7 3 4 51/F 51 Frontal 7 6 5 31/F 33 Temporo-parietal 6 6 6 47/M 64 Fronto-temporo-parietal 7 6 7 81/M 33 Temporo-parietal 7 6 Mean ± SD 48.9 ± 18.6 43.7 ± 15.3 6.7 ± 0.5 5.6 ± 1.1 M = male, F = female, CM = Chedoke-McMaster stroke assessment Subjects performed 6 trials each of two near identical tasks set in the physical world or in a virtual environment. In both tasks, seated subjects grasped a real or virtual ball of 7 cm diameter with their right hand, beginning from the edge of a real or virtual table, reached forward by leaning the trunk and then placed the ball within a 2 cm × 2 cm yellow square on a real or virtual target (Figure 1). Care was taken to set-up the physical task so that the initial position of the arm, ball, table and wall were identical to that of the virtual task. Thus, in both environments, the initial position of the arm was about 0° flexion, 30° abduction and 0° external rotation (shoulder), 80° flexion and 0° supination (elbow) with the wrist and hand in the neutral position. The fingers were slightly flexed. The initial position of the ball was 13 cm in front of the right shoulder, 7 cm above and 3 cm to the left of the subject's hand. The target was placed 31 cm in front of the shoulder, 12.5 cm above and 14 cm to the right of the initial position of the ball (Figure 1). Figure 1 Experimental set-up. Physical (a) and virtual reality condition (b). For the VR task, the ball appeared on a computer screen inside a cube that also displayed the position of the subject's hand. The VR target was the upper right back corner of the cube. Subjects had to grasp the virtual ball, transport it to the VR target and release it. The VR environment was displayed in 2 dimensions (2D) on a computer screen placed 75 cm in front of subject's manubrium (Figure 1). The virtual representation of the subject's hand was obtained using a 22 sensor fibre optic glove (Cyberglove, Immersion Corp.) and an electromagnetic sensor (Fastrak, Polhemus Corp.) that was used to orient the glove in the 2D environment. Data from these devices were synchronized in real time. To enable the subject to "feel" the virtual ball, a prehension force feedback device (Cybergrasp, Immersion Corp.) was fitted to the dorsal surface of the hand. The Cybergrasp delivered prehension force feedback in the form of extension forces to the distal phalanxes of the thumb and each finger. Forces applied to the fingers were calibrated for each subject while he/she was wearing the Cyberglove. These ranged from 6 to 8 N per finger and all subjects perceived that they were holding a spherical object in their hand. Prior to data collection, all participants practised the tasks in physical and virtual conditions (20 – 40 min). To better compare the participants' performance in the two environments, the glove and grasp devices were worn on the hand in both conditions (Figure 1). Kinematic data from the right arm were recorded with 6 infrared-emitting diodes (IREDs) placed on the distal phalanx of the index and thumb, the distal head of the first metacarpal, the radial styloid process, the lateral epicondyle of the humerus and the acromion (120 Hz, Optotrak Motion Analysis System, Northern Digital Corp.). Each trial was divided in two phases: 1) reaching and grasping the ball and 2) ball transport and release. For the first movement phase, 4 temporal and 4 spatial parameters of reaching and grasping were determined. Temporal parameters were movement time, time to peak wrist velocity (RPV), time to maximal hand aperture (RMGA), and the delay between them (RPV-RMGA). Spatial parameters were endpoint path curvature, maximal grip aperture, angular ranges of joint motion and elbow-shoulder interjoint coordination [16]. For the second movement phase, we determined one temporal (movement time) and 4 spatial (endpoint path curvature, trajectory length, angular ranges of joint motion and interjoint coordination) parameters. Movement onsets and offsets of each phase were defined as the times at which the tangential velocity of the IRED on the index finger surpassed and remained above or fell and remained below 10% of the maximal peak velocity respectively. The temporal parameters (time to peak wrist velocity, time to maximal grip aperture) were normalized to movement time and the delay between them was calculated. For the spatial parameters, the curvature of the trajectory of the IRED on the index finger was estimated as the ratio between the actual trajectory length and a straight line segment between the initial and final positions [20]. Joint angular excursions were expressed as the difference in degrees between the angle at the beginning and the end of movement, according to movement times defined above. For interjoint coordination, we determined the slopes between elbow extension and 1) shoulder flexion and 2) shoulder abduction. The slope of the angle-angle relationship describes the relative contribution of each joint throughout the movement where a slope of 1 indicates an equal contribution of each joint. Slopes greater than 1 indicated a larger contribution of elbow extension than shoulder movement and vice versa. The relationship between both angles was considered linear since all regression correlation coefficients were = 0.8. However, since a linear approximation was used, the slope provides only a general estimate of the contribution of each angle. Statistical analysis Both parametric and non-parametric statistics were used. For within-group comparisons between the two conditions of reality, Student t-tests were used. However, since variances were not homogeneous (Levene's test) for healthy subjects and participants with hemiparesis, non-parametric tests were used for between-group comparisons (Kruskal-Wallis ANOVA). A significance level of p < 0.05 was used, adjusted for multiple comparisons by type using the Bonferroni correction. Results All healthy subjects and participants with mild upper limb motor deficits were able to reach, grasp, transport, place and release the virtual ball using movement strategies that were similar to those used for the physical ball (Tables 2 and 3). Arm movement trajectories (Figure 2) were smooth and followed similar paths for movements made in both environments for both subject groups. Trajectory lengths were similar in both conditions for healthy subjects (289 ± 28 mm in real compared to 302 ± 55 mm in VR) and for participants with hemiparesis (251 ± 25 mm in real compared to 260 ± 30 mm in VR). Table 2 Comparisons between reality conditions for the first phase of movement: reaching and grasping the ball. Healthy Stroke Physical condition VR condition Physical condition VR condition Mean SD Mean SD Mean SD Mean SD Temporal parameters Movement time – onset to grasping (s) 0.68 0.17 0.95 0.35 1.23† 0.27 1.43† 0.41 RPV (%) 44.9 10.4 41.8 6.1 34.3 12.6 40.8 2.9 RMGA (%) 72.5 12.5 60.9 11.8 73.5 16.1 65.3 9.1 Delay between RPV and RMGA (%) 31.6 16.6 19.2 10.7 34.5 15.0 24.3 8.1 Spatial parameters Curvature index 1.39 0.16 1.62 0.44 1.76 0.62 1.97 0.86 Wrist extension at grasping (°) 1.4 9.1 -3.9 8.2 12.1 2.2 4.5 14.1 Slope elbow extension/shoulder flexion 0.70 0.34 0.60 0.26 0.53 0.33 0.47 0.27 Slope elbow extension/shoulder abduction 2.30 2.02 2.31 2.54 2.65 1.66 2.30 1.26 Maximal grip aperture (mm) 95.7 16.4 90.2 20.5 89.8 20.2 84.9 19.3 RPV = relative time to peak velocity of the wrist; RMGA = relative time to maximal grip aperture; VR = virtual reality † Significant difference between groups, Kruskal-Wallis, p < 0.05 Table 3 Comparisons between reality conditions for the second phase of movement: ball transport and release Healthy Stroke Physical condition VR condition Physical condition VR condition Mean SD Mean SD Mean SD Mean SD Temporal parameters Movement time – onset to placing (s) 0.84 0.29 1.18 0.31 1.49† 0.45 2.28† 0.82 Spatial parameters Curvature index 1.14 0.03 1.23 0.29 1.37† 0.27 1.42 0.57 Wrist extension at placing (°) 18.2 12.1 4.0* 8.1 20.3 8.2 6.4 16.0 Elbow extension (°) 25.6 6.9 38.4* 10.9 26.9 11.0 37.3 19.6 Shoulder flexion (°) 24.8 5.2 33.0 7.1 28.4 4.9 35.5 18.2 Shoulder abduction (°) 13.6 5.2 16.4 4.6 18.5 6.9 20.7 8.9 Slope elbow extension/shoulder flexion 1.08 0.08 1.21 0.16 1.01 0.26 1.18 0.15 Slope elbow extension/shoulder abduction 2.36 0.76 2.88 1.19 1.51† 0.69 1.48† 0.51 * Significant difference between reality conditions, Student t-tests with Bonferroni correction (p < 0.05/4 angles = 0.013); VR = virtual reality † Significant difference between groups, Kruskal-Wallis, p < 0.05 Figure 2 Mean endpoint (marker on the index finger) trajectories for the two phases of the movement task for one healthy subject in the two reality conditions. In healthy subjects, the temporal and spatial aspects of the two phases of the task were almost identical between the physical and virtual conditions (Tables 2, 3). However, there was a non-significant tendency to make movements more slowly and to use less wrist extension for grasping during the first phase of the movement (reaching and grasping the ball) in the virtual condition (Table 2). During the second phase, healthy subjects used significantly less wrist extension (paired t-test, p < 0.05) and more elbow extension (paired t-test, p < 0.05) to place the ball on the virtual vertical surface (Table 3, Figure 3). In these subjects, there were no other differences between any other temporal or spatial parameter for both movement phases (peak wrist velocity, relative time to peak wrist velocity, timing of maximal grip aperture, trajectory curvature or interjoint coordination). Figure 3 Interjoint coordination. Relationship between elbow extension and shoulder horizontal adduction (mean traces per condition) during the second phase of the movement (placing) for both conditions in two healthy subjects (A,B) and in two individuals with hemiparesis (C,D). In all examples, subjects used more elbow extension in the virtual reality condition. Movements made by individuals with hemiparesis in the physical environment differed from those made by healthy subjects in three ways. In both phases, movements were significantly slower and in the second phase, trajectories were more curved and interjoint coordination was altered (Tables 2 and 3). In particular, the slope of the relationship between elbow extension/shoulder abduction was lower than in healthy subjects during the second phase of the movement (p < 0.02, Figure 3). This decrease in slope was due to a more abducted position of the shoulder in the patient group. Despite these differences, patients showed tendencies similar to healthy subjects when reaching and grasping in the VR environment compared to the real environment (Tables 2, 3). They tended to decrease the speed of movements made in VR compared to the physical environment, to use less wrist extension in both movement phases and to use more elbow extension in the second phase of the movement. In addition, 5 out of 7 participants with hemiparesis significantly decreased the wrist extension while 4 increased elbow extension at the end of the second phase of the movement (at the time of placing the ball) in the VR condition. Discussion The similarity in movement kinematics between physical and virtual reaching and grasping suggests that virtual reality may be an effective environment for rehabilitation. Interest in training in virtual environments is increasing amongst rehabilitation professionals in light of recent evidence suggesting that neuronal recovery after stroke-related brain damage critically depends on the motivation of the individual and the intensity of training [10,11]. Virtual reality represents a novel training environment in which a wide variety of tasks can be easily practiced. It is also becoming increasingly accessible with the advent of home-based computers and telerehabilitation technology [7]. Thus, the demonstration that movements practiced in a virtual environment are kinematically similar to movements with physical objects is essential to ensure the transfer of training benefits to the real-life situation. Our results show that subjects tended to decrease wrist extension and increase elbow extension in the virtual compared to the physical condition. Two principal factors may explain those differences: the absence of depth perception in the VR condition and the absence of tactile feedback at the end of the reach. Binocular vision enables humans to perceive depth in a 3 dimensional (3D) environment. This faculty is called stereopsis (for a review see [21]). Since the VR condition in our experimental set-up was a 3D task presented on a 2D display, the results can be compared with those investigating reaching and grasping movements made under conditions of monocular vision in which depth perception is reduced. Indeed, such studies have shown that reaching and grasping movements are characterized by shorter movement time and shorter relative time to maximal grip aperture [22]. The difference in depth perception in the 2D virtual environment may also be responsible for the tendency to increase elbow extension in both groups. Previous studies have shown that fine motor corrections are produced by distal joints [23]. The 2D display resulted in the subject underestimating the real distance to the wall so that during the course of the second phase of the movement, the subject had to compensate by increasing the extension of the limb until the screen display indicated that the ball had reached the target distance. This caused a slight change in strategy for the second phase of the movement requiring an increase in the amount of elbow extension. Participants with hemiparesis showed the same tendencies as the healthy group but differences were not significant due to subject variability. Changes in motor patterns may be avoided by using 3D immersive environments, such as those visualized through a head-mounted display. The absence of depth perception cannot explain the decrease of wrist extension at the end of the second phase of the movement in VR compared to the physical condition. A more likely explanation involves the type of haptic feedback provided to the subject. In the physical condition, subjects had to extend the wrist so that the ball and not their fingers would make contact with the target. In the VR condition, wrist extension was not necessary because subjects only had to place the ball at the coordinates of the virtual wall without encountering a physical barrier. To avoid such differences between physical and VR environments, relevant haptic feedback is necessary to indicate contact of the hand with the object or target. Another way is to integrate physical objects into the VR environment such as the manipulation of a real paper envelope in real-time in a VR environment [6], or the mimicking of irregularities in a walkway with a multi-dimensional movement platform [24,25]. Overall, the finding that both healthy subjects and individuals with motor deficits used similar movement strategies in a physical and a limited virtual environment suggests that VR technology is a valuable tool for studying and retraining reaching, grasping and placing movements. Whether movement kinematics may be improved with the use of interfaces providing stereopsis and more relevant haptic feedback should be investigated by comparing movements made in immersive to those made in non-immersive virtual environments. Acknowledgements Source of support: Canadian Foundation for Innovation, Canadian Institutes of Health Research, AV received a summer research scholarship from the Université de Montréal. Some of this work was presented at the XVth Congress of ISEK, Boston, June 18–21, 2004 ==== Refs Stanton D Foreman N Wilson P Riva G, Wiederhold BK, Molinari E Uses of virtual reality in clinical training: developing the spatial skills of children with mobility impairments In Virtual Environments in Clinical Psychology and Neuroscience: Methods and Techniques in Advanced Patient-Therapist Interaction 1998 Amsterdam: IOS Press 219 232 Deutsch JE Merians AS Burdea GC Boian R Adamovich SV Poizner H Haptics and virtual reality used to increase strength and improve function in chronic individuals post-stroke: two case reports Neurol Rep 2002 26 72 86 Merians AS Jack D Boian R Tremaine M Burdea GC Adamovich SV Recce M Poizner H Virtual reality-augmented rehabilitation for patients following stroke Phys Ther 2002 82 898 915 12201804 Sveistrup H McComas J Thornton M Marshall S Finestone H McCormick A Babulic K Mayhew A Environmental studies of virtual reality-delivered compared to conventional exercise programs for rehabilitation Cyberpsychol Behav 2003 6 245 249 12855079 10.1089/109493103322011524 Holden MK Dyar T Virtual environment training: a new tool for neurorehabilitation Neurol Rep 2002 26 62 71 Piron L Tonin P Atzori A Trivello E Dam M A virtual-reality based motor tele-rehabilitation system [abstract] In Proceedings of the Second International Workshop on Virtual Rehab 2003 21 26 Riva G Gamberini L Virtual reality in telemedicine Telemed J E Health 2000 6 327 340 11110636 10.1089/153056200750040183 Rizzo A A SWOT analysis of the field of virtual rehabilitation [abstract] In Proceedings of the Second International Workshop on Virtual Rehab 2003 1 2 Bütefisch C Hummelsheim H Denzler P Mauritz KH Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand J Neurol Sci 1995 130 59 68 7650532 10.1016/0022-510X(95)00003-K Kwakkel G Wagenaar RC Twisk JW Lankhorst GJ Koetsier JC Intensity of leg and arm training after primary middle-cerebral-artery stroke: a randomized trial Lancet 1999 354 191 196 10421300 10.1016/S0140-6736(98)09477-X Nudo RJ Milliken GW Reorganization of movement representations in primary motor cortex following focal ischemic infarcts in adult squirrel monkeys J Neurophysiol 1996 75 2144 2149 8734610 Wu C Wong M Lin K Chen H Effects of task goal and personal preference on seated reaching kinematics after stroke Stroke 2001 32 70 76 11136917 Kizony R Raz L Katz N Weingarden H Weiss PL Using a video projected VR system for patients with spinal cord injury [abstract] In Proceedings of the Second International Workshop on Virtual Rehab 2003 82 88 Todorov E Shadmehr R Bizzi E Augmented feedback presented in a virtual environment accelerates learning of a difficult motor task J Motor Behav 1997 29 147 158 Cirstea M Levin MF Compensatory strategies for reaching in stroke Brain 2000 123 940 953 10775539 10.1093/brain/123.5.940 Levin MF Interjoint coordination during pointing movements is disrupted in spastic hemiparesis Brain 1996 119 281 293 8624689 Michaelsen SM Levin MF Short-term effects of practice with trunk restraint on reaching movements in patients with chronic stroke: a controlled trial Stroke 2004 35 1914 1919 15192250 10.1161/01.STR.0000132569.33572.75 Viau A Levin MF McFadyen BJ Feldman AG Reaching in reality and in virtual reality: A comparison of movement kinematics [abstract] ISEK, Boston 2004 Gowland C Stratford P Ward M Moreland J Torresin W Van Hullenaar S Sanford J Barreca S Vanspall B Plews N Measuring physical impairment and disability with the Chedoke-McMaster stroke assessment Stroke 1983 24 58 63 8418551 Archambault P Pigeon P Feldman AG Levin MF Recruitment and sequencing of different degrees of freedom during pointing movements involving the trunk in healthy and hemiparetic subjects Exp Brain Res 1999 126 55 67 10333007 10.1007/s002210050716 Cumming BG DeAngelis GC The physiology of stereopsis Annu Rev Neurosci 2001 24 203 238 11283310 10.1146/annurev.neuro.24.1.203 Bradshaw MF Elliott KM The role of binocular information in the 'on-line' control of prehension Spatial Vision 2003 16 295 309 12858953 10.1163/156856803322467545 Seidler R Stelmach GE Trunk-assisted prehension: specification of body segments with imposed temporal constraints J Mot Behav 2000 32 379 388 11114230 Bioan RF Kourtev H Deutsch JE Lewis JA Burdea GC Dual stewart-platform gait rehabilitation system for individuals post-stroke [abstract] In Proceedings of the Second International Workshop on Virtual Rehab 2003 93 Comeau F Chapdelaine S McFayden BJ Malouin F Lamontagne A Galiana L Laurendeau D Richards CL Fung J Development of increasingly complex virtual environments for locomotor training after stroke [abstract] In Proceedings of the Second International Workshop on Virtual Rehab 2003 90	15679937	PMC546398	CC BY	2021-01-04 16:37:38	no	J Neuroengineering Rehabil. 2004 Dec 14; 1:11	utf-8	J Neuroeng Rehabil	2,004	10.1186/1743-0003-1-11	oa_comm
==== Front Cerebrospinal Fluid ResCerebrospinal Fluid Research1743-8454BioMed Central London 1743-8454-1-41567993810.1186/1743-8454-1-4ResearchLifestyle in adults aged 35 years who were born with open spina bifida: prospective cohort study Hunt Gillian M [email protected] Pippa [email protected] Addenbrooke's Hospital, Cambridge CB2 2QQ, UK2 Department of Community Health Sciences, St George's Hospital Medical School, London SW17 ORE, UK2004 10 12 2004 1 4 4 21 10 2004 10 12 2004 Copyright © 2004 Hunt and Oakeshott; licensee BioMed Central Ltd.2004Hunt and Oakeshott; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background and Methods From 1963 to 1971, 117 babies with open spina bifida were treated non-selectively from birth. In 2002 we reviewed all the survivors by postal questionnaire and telephone call. The aims were to find out how many were living independently in the community or were in open employment or drove a car. In addition to these achievements we recorded health, medication and admissions to hospital and asked how much daily help they needed. Results Ascertainment was 100%. There had been 63 deaths, mainly of the most severely affected. The mean age of the 54 survivors was 35 years. The outcome in terms of disability ranged from apparent normality to total dependency. It reflected both the neurological deficit, which had been recorded in infancy in terms of sensory level, and events in the CSF shunt history. Overall about 2 in 5 of the survivors lived independently in the community, 2 in 5 drove a car, 1 in 5 was in competitive employment and 1 in 5 could walk 50 metres. Conclusion Although those who survived to age 35 years tended to be less disabled, 2 in 5 continued to need daily care. ==== Body Background Neurosurgical intervention in babies with open spina bifida had dramatic results in terms of survival. However, the disability and the complications of the survivors were often severe [1-5]. Many efforts were made to enable them to walk, to control their urinary incontinence while safeguarding renal function, and to overcome problems associated with the shunt treatment of hydrocephalus. Promising new methods of management, such as the psoas transplant, urinary diversion and artificial urinary sphincters, which seemed highly successful in the short term, lost favour after 10 or 15 years because of disappointing long-term results. In this unsteady course of progress it is helpful to have a long term follow up of a complete cohort of patients with open spina bifida as a realistic basis for helping parents facing the difficult decisions about termination of an affected pregnancy or treatment after birth. Methods Patients In 1963 the Regional Neurosurgical Unit at Addenbrooke's Hospital, Cambridge, England offered treatment to all cases of open spina bifida, without any attempt at selection. Between 1963 and 1971, after a detailed neurological examination, 117 babies (50 male, 67 female) had their open spinal defects closed within 48 hours of birth. A ventriculo-atrial cerebrospinal fluid (CSF) shunt was inserted for hydrocephalus when required. Data collection In 2002 all survivors were surveyed by confidential questionnaire and telephone interview. They were asked about health, disability and achievements in terms of living independently, driving a car and working in open employment. Causes of death for those who had died were obtained from medical records and from the Office of National Statistics. The study was approved by the Cambridge Local Research Ethics Committee. Statistical analysis When first surveyed at the mean age 4 years, the cohort had been classified into four groups according to sensory level to pin prick recorded in infancy [4]. Those with intact sensation right down to the knee (sensory level below L3) had a better short-term outcome than those with no sensation below the umbilicus (sensory level above T11). Mortality and measures of disability and achievement were compared in those with different sensory levels and CSF shunt histories using χ2. Results Ascertainment was 100%. Twenty (37%) of the 54 survivors responded to the questionnaire and all survivors or a carer or relative were interviewed by telephone. Mortality Figure 1 summarises the outcome for the complete cohort. Sixty-three cases had died, 25 before their first birthday and a further 15 before their fifth. Thereafter the death rate remained constant with an average of 1% of the remainder dying each year [6]. Figure 1 Outcome in open spina bifida at the mean age of 35 years Survivors Of the original 117 babies there were 54 survivors (46%) age range 31–38 years. Of these 24 were male and 29 female and there was one who had undergone a gender change from male to female. The disability of the survivors ranged from blindness with paraplegia and double incontinence to apparent normality. Table 1 shows that sensory level in infancy was a predictor of overall disability, the need for a CSF shunt, IQ, and need for a wheelchair or daily care at age 35 years. Table 1 Sensory level in infancy related to disability at the mean age of 35 years in 54 survivors with spina bifida Sensory level n = 54 (%) Below L3 n = 24 L3-T11 n = 15 Above T11 n = 12 Asymmetrical n = 3 χ2 for trend(1) Severe disability(2) n = 20 (37) 2 6 12 0 p < 0.0001 CSF shunt n = 46 (85) 17 15 11 3 p < 0.05 IQ < 80 n = 15 (28) 3 4 6 2 p = 0.05 Wheelchair n = 38 (70) 9 14 12 3 p < 0.0001 Daily care needed n = 20 (37) 5 6 9 0 p < 0.05 Lack of achievement(3) n = 27 (50) 7 9 10 2 p < 0.01 Notes: (1) Patients with lower sensory levels have less disability. Asymmetrical sensory level excluded from the analysis. (2) Severe disability defined as very poor mobility and incontinent with additional handicaps including low IQ, epilepsy, visual defects (2 blind), severe spinal deformity and pressure sores. (3) Lack of achievement in terms of living independently, driving a car or working in open employment. Mobility Only 16 (30%) remained community walkers defined as being able to walk ≥50 metres with or without aids. Ten of the 16 could walk at least a kilometre. Table 2 shows the deterioration in walking since childhood and its relationship to sensory level. By the age of 35 there was only one community walker with a sensory level as high as L3. But of those with a low sensory level of L5 and below, 88% (14/16) remained walkers. In terms of motor function, 30 had been recorded as having bilateral quadriceps activity in infancy, but only 53% of them (16/30) remained walkers at the age of 35 years. Table 2 Influence of sensory level and age on walking in 54 survivors with spina bifida Sensory level in infancy n = 54 Walkers1 at age 9 n = 31 (57%) Walkers at age 35 n = 16 (30%) Above T11 n = 12 0 0 T11-L3 n = 15 5 1 L4 n = 8 8 1 L5-S2 n = 6 6 5 No sensory loss n = 10 10 9 Asymmetrical loss n = 3 2 0 1 Walkers defined as able to walk ≥50 metres using aids if required. Survivors with lower sensory levels more likely to be walkers. (χ2 for trend p < 0.0001 for both age 9 and age 35. Asymmetrical sensory loss excluded from the analysis.) Cerebrospinal fluid (CSF) shunts Of the 54 survivors, eight (15%) never had a shunt, seven of whom had little or no disability and a sensory level below L3. The remaining 46 had had a ventriculo-atrial shunt inserted. In 16 the shunt had never been revised. The other 30 had had a total of 104 revisions: for shunt insufficiency (65), infection (15), detachment (14), extrusion or leaking wound of back (5), unknown (5). In 9 patients revisions were done only before the age of 2 (mean 1.3 revisions, range 1–3), and in 21 between the ages of 2 and 35 (mean 3.1 revisions, range 1–14). Elective revisions were not performed; and shunts were inserted or revised only in response to definite clinical need. Of those who had revisions, 75% had had symptoms of raised intracranial pressure. Health Nearly half of the survivors had been in hospital during the previous 5 years. The main reasons were urological (7 patients), neurosurgical (3), and sepsis (7). Pressure sores were responsible for four of the admissions for sepsis, and 12 patients were currently being treated at home for pressure sores. Only 11 patients (20%) were fully continent of bladder and bowel without the use of catheters or appliances. Two patients needed nocturnal respiratory support, two were totally blind following shunt dysfunction and four others had severe visual defects. Endocrine conditions were common: two patients had diabetes mellitus, one had adrenal hyperplasia, one had primary azoospermia and six had had precocious puberty. Twenty-four patients were on long-term therapy: antihypertensives (12), anticonvulsants (10), antibacterials (10) and antidepressants (4). Eight patients needed regular analgesics for musculo-skeletal pain, mainly backache. Parenthood Seven women and two men had become parents. One man had minimal disability and no detectable sensory loss; the other had undergone percutaneous epididymal sperm aspiration followed by intracytoplasmic sperm injection. None of the 13 children had visible spina bifida. Residence and dependency Twenty-two individuals (41%) lived independently in the community, 11 of them used wheelchairs. A further 12 (22%) were personally independent but had supervision and help when required. The remaining 20 (37%) needed help daily for dressing, shaving, toilet or nursing care (mainly pressure sores). Ten of these still lived with a parent now aged 52–77, two women were in the care of their partners, five were in residential establishments and three lived in the community with help from social services (Table 3). Table 3 Where are the 54 survivors living? Residence and Dependency Number of individuals Percentage Independent living 22 41 Sheltered environment with help available 12 22 Dependent on daily help 20 27 Car drivers Twenty-nine (54%) of the survivors had passed the driving test, but 9 had discontinued driving. Eleven others were unfit to drive on account of poor sight (3), epilepsy (3) or severe cognitive or perceptual defects (5). Employment Nine men and four women were in open employment. All had an IQ ≥80 and five used wheelchairs. Three did clerical work, three were teachers, two were unskilled manual workers and the remainder were a business executive, accountant, engineer, van driver and builder. Three were studying in addition to working full time. Three men and two women were in sheltered employment. Lifestyle and achievements Twenty-seven survivors (50%) had one or more achievements in terms of living completely independently in the community (22), driving a car (20) or working in open employment (13). Achievements were related to sensory level in infancy and to shunt history (Tables 1 and 4). All but one of the 8 patients without a shunt and 75% (12/16) of those in whom the shunt was never revised were classified as achievers. They lived independently or drove a car or worked in open employment compared with 40% (4/10) of those needing revision at age <2 and 20% (4/20) of those revised after age 2 (p < 0.01). Table 5 shows that late revisions of shunt after the age of 2 were also associated with a birth head circumference ≥90th centile relative to birthweight, a history of symptoms of raised intracranial pressure, visual defects and the need for daily care. Table 4 Lifestyle related to history of CSF shunt in 54 survivors at the mean age of 35 years Lifestyle No shunt n = 8 Shunt not revised1 n = 16 Shunt revised age <2 n = 10 Shunt revised age 2–35 n = 20 Living independently n = 22 (41%) 7 9 4 2*** Driving a car n = 20 (37%) 5 8 4 3* In open employment n = 13 (24%) 3 6 3 1* Any achievement(2) n = 27 (50%) 7 12 4 4** χ2 comparing those with shunt revisions aged 2–35 with those never revised or revised at age <2. p < 0.05 p < 0.01, **p < 0.001. Those with no shunts excluded from the analysis. (1) Shunts were only inserted or revised in response to definite clinical need such as symptoms or signs of raised intracranial pressure. (2)Any achievement: in terms of living independently, driving a car or working in open employment. Table 5 Features related to CSF shunt history in 54 patients with open spina bifida at the mean age of 35 years Features No shunt n = 8 Shunt not revised n = 16 Shunt revised age <2 n = 10 Shunt revised age ≥2 n = 20 χ2 for trend Birth head circumference ≥90 centile n = 10 0 1 1 8 p = 0.05 History of symptoms of raised intracranial pressure n = 23 0 1 5 17 p < 0.0001 Visual defects (mainly squint) n = 33 3 7 6 17 p < 0.05 Daily care needed n = 20 1 2 2 15 p < 0.001 Discussion By the mean age of 35 years, over half the cohort had died, mainly the most disabled. About 40% of the survivors lived independently, 20% needed some support and 40% needed daily care. Lifestyle and achievements depended on the degree of disability, which could have been forecast from sensory level recorded in infancy, indicating the extent and severity of the neural deficit. Data from this cohort show that babies with sensation below the knee (L3) are unlikely to be seriously disabled and could be achievers in adulthood. Babies who cry during a routine heel prick have a sensory level of S1 or below and are likely to remain community walkers in adulthood. Those with sensation to pin prick in the saddle area (S2, 3, 4) are likely to have bladder and bowel control. Events in the history of the CSF shunt also had a profound effect on outcome and achievement. Revisions of shunt were associated with poor achievement particularly when the revisions were needed after the age of 2. The cranial sutures have usually fused by the age of 2 after which the skull is less expansile rendering the brain more susceptible to pressure [7]. Table 5 shows that most of those who had revisions after the age of 2 had had symptoms or signs of raised intracranial pressure. By contrast an uneventful shunt history was sometimes associated with remarkable achievement despite severe disability. This may imply that it is the raised intracranial pressure which has the adverse long term effect on achievement and enterprise [8]. The main strength of the study is the community basis, which provides social as well as clinical data enabling the realities of adulthood to be seen against the optimistic forecasts of the early years [9]. Less than half of the survivors were still attending hospital. Thus a hospital-based study would have given an incomplete picture. As the patients grew older, the reduction in support, rehabilitation and encouragement from dedicated physiotherapists, parents and other carers revealed an outcome which was related to the patient's own motivation and enterprise as well as to the basic neurological deficit [8]. The main limitation of the study is that the very long follow up relates to some treatments, which have been superseded. Improvements in the diagnosis and management of renal and neurological problems have halved the mortality by the age of 5 [10,11], but have less influence on long term disability. Although outcome in childhood of early operated spina bifida has been widely reported [1-5,11], there are few studies of long term outcome. McLone has argued strongly that prognosis is improving due to advances in treatment [12]. Our results may not predict outcome using today's standards of care. However, a recent survey from McLone's group of a cohort of 118 adults aged 20–25 with 16% loss to follow up, found continuing deterioration and a formidable number of neurosurgical and spinal operations [13]. Ours is the only 35-year prospective study of open spina bifida with 100% ascertainment by the same independent observer. Conclusions These data may help health professionals counselling parents of a child with spina bifida. They show a range of possible outcomes in adulthood when parents may no longer be able or willing to look after their child [14-16]. Two out of 5 survivors continue to need daily care. Advances in treatment may have improved prognosis, but the most important predictor remains the basic neurological deficit. Those looking after patients with spina bifida need to know both their long term potential and the limitations of treatment in order to focus on realistic goals [17]. Competing interests The authors declare that they have no competing interests. Acknowledgements We thank the patients and their carers, Sally Kerry for statistical advice and The Association for Spina Bifida and Hydrocephalus (ASBAH) for funding. ==== Refs Lorber J Spina bifida cystica. Results of treatment of 270 consecutive cases with criteria for selection for the future Arch Dis Child 1972 47 854 873 4567074 Stark GD Drummond M Results of selective early operation in myelomeningocele Arch Dis Child 1973 48 676 683 4270149 Smith GK Smith ED Selection for treatment in spina bifida cystica Br Med J 1973 4 189 197 4586033 Hunt G Lewin W Gleave J Gairdner D Predictive factors in open myelomeningocele with special reference to sensory level Br Med J 1973 4 197 201 4586034 Laurence KM Effect of early surgery for spina bifida cystica on survival and quality of life Lancet 1974 1 301 304 4130481 10.1016/S0140-6736(74)92606-3 Hunt GM 'The median survival time in open spina bifida' Dev Med Child Neurol 1997 39 568 9295857 Miyan J Sobkowiak C Draper C Humanity lost: the cost of cortical maldevelopment. Is there light ahead? Eur J Pediatr Surg 2001 11 Suppl 1 S4 S9 11813125 10.1055/s-2001-19740 Hunt GM Oakeshott P Kerry S Link between the CSF shunt and achievement in adults with spina bifida J Neurol Neurosurg Psychiatry 1999 67 591 595 10519863 Thomas A Bax M Coombes K Goldson E Smyth D Whitmore K The health and social needs of physically handicapped young adults: are they being met by the statutory services? Dev Med Child Neurol Suppl 1985 50 1 20 3161771 Lapides J Diokno AC Silber SJ Lowe BS Clean, intermittent self-catheterization in the treatment of urinary tract disease J Urol 1972 107 458 461 5010715 Steinbok P Irvine B Cochrane DD Irwin BJ Long-term outcome and complications of children born with meningomyelocele Childs Nerv Syst 1992 8 92 96 1591753 10.1007/BF00298448 McLone DG Pediatric neurosurgery has the expertise and technology to cure many and insure independence for most children Pediatr Neurosurg 1997 27 167 9577968 Bowman RM McLone DG Grant JA Tomita T Ito JA Spina bifida outcome: a 25-year prospective Pediatr Neurosurg 2001 34 114 120 11359098 10.1159/000056005 Hunt GM Implications of the treatment of myelomeningocele for the child and his family Lancet 1973 2 1308 1310 4127652 10.1016/S0140-6736(73)92881-X Hunt GM Oakeshott P Outcome in people with open spina bifida at age 35: prospective community based cohort study BMJ 2003 326 1365 1366 12816823 10.1136/bmj.326.7403.1365 Hunt GM Open spina bifida: outcome for a complete cohort treated unselectively and followed into adulthood Dev Med Child Neurol 1990 32 108 118 2186948 Hunt GM Spina bifida: implications for 100 children at school Dev Med Child Neurol 1981 23 160 172 7011888	15679938	PMC546399	CC BY	2021-01-04 16:37:38	no	Cerebrospinal Fluid Res. 2004 Dec 10; 1:4	utf-8	Cerebrospinal Fluid Res	2,004	10.1186/1743-8454-1-4	oa_comm
==== Front Cerebrospinal Fluid ResCerebrospinal Fluid Research1743-8454BioMed Central London 1743-8454-1-51567993910.1186/1743-8454-1-5CommentaryEpidemiology of neural tube defects and folic acid Shurtleff David B [email protected] Department of Pediatrics, University of Washington, Seattle, WA 98105, USA2004 10 12 2004 1 5 5 29 9 2004 10 12 2004 Copyright © 2004 Shurtleff; licensee BioMed Central Ltd.2004Shurtleff; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This review article combines four disparate observations about Neural Tube Defects (NTDs). They are the worldwide decline in the birth incidence that began prior to prenatal diagnosis; family recurrence risks; the effect of prenatal diagnosis and termination of affected pregnancies; and the effect of folic acid. ==== Body Discussion Variations in birth incidence NTDs are due to many different causes [1]. The incidence varies and some etiologies are more common at different prenatal ages and birth [2-5]. Epidemiological data from the period before in-utero diagnosis and termination of affected pregnancies established two trends. The first illustrated epidemics of the incidence of NTDs that cycled over years, and the second established that there has been a general decline in the birth incidence of NTDs over several decades, which has been noted worldwide. The best example of epidemics of the occurrence of NTDs was reported from Birmingham, England [6]. The prevalence rose from 2.0 per 1000 births in 1936–7, to 2.8 in 1940, fell to 1.54 in 1948 and peaked again in 1956 at 2.8 per 1000. Elwood and Elwood [7] have documented both "epidemics" and the decline in incidence worldwide. Other "epidemics" have been described in Boston, Providence and New York in the USA, Canada, Berlin and in Western Germany, and in a number of other areas [7]. The general decline in birth incidence has been reported in most countries studied but it has occurred at different times. A variation within countries was also reported, with a Northeast to Southwest gradient from higher to lower in the UK and from East to West in Canada and the USA. For example, the highest occurrence risk for the general population on the North American continent is 2.5 – 3.5 per 1000 amongst citizens of Nova Scotia [8]. These data include findings of elective terminations, spontaneous miscarriages and births. The lowest general population occurrence is 0.21 – 0.03 per 1000 live births in the Pacific Northwest of the USA (Figure 1). These geographic variations may relate to migrations of ethnic groups. The highest birth incidences for NTDs are reported amongst descendants of the Celts in the UK, Canada and the USA. These racial data suggest the importance of a genetic component that has been borne out in family studies [7]. Figure 1 These data were obtained from the Birth Defects Clinic attendance records. Patients with myelomeningocele seen in this clinic represent 98% of individuals with myelomeningocele born in Washington State, USA, during the period that the state maintained a Birth Defects Registry. Preliminary data for the year 2003 suggest a decline in incidence to near the lowest level observed prior to 2002. Family recurrence risks Family studies suggest the recurrence risk for first-degree relatives of affected individuals is approximately 1 in 30. For second-degree relatives (the children of the mother's sisters and brothers) the risk is approximately 1 in 220 [9]. However, the authors of the same study state there is not yet agreement upon the accurate recurrence risk data for family members. Others report a recurrence risk among the mother's or an affected child's first-degree relatives is as high as 1 in 70 to 140 [10,11]. McManus [12] reports recurrence risks in first and second-degree relatives of 1 in 40 for sisters of the mother of an affected child, and 1 in 90 for the offspring of those sisters. She reported the mother's brothers and the father's sisters and brothers to have lower recurrence risks of 1 in 140 to 1 in 190. Arata et al [13] report that affected mothers appear to have only a 0.5 to 1% chance of having a child with an NTD. This last observation concurs with our finding that only one mother with myelomeningocele has an affected child. That child is one of 106 otherwise unaffected by an NTD. Prenatal diagnosis and termination The third aspect, prenatal diagnosis and termination of affected pregnancies, is one that should be discussed with all women in the reproductive age range and, more importantly, with patients who have a family history of an NTD. Folic acid taken orally on a daily basis is shown to lower the occurrence and recurrence of NTDs in their own offspring and in their relatives. The Medical Research Council [14] was the first to prove conclusively that when women who had had a previous child affected by an NTD took 4.0 mg of folic acid daily, beginning three months prior to conception, there was a 70% reduction in the recurrence in subsequent offspring. Wald et al [15] have recommended 5 mg daily. Because of the higher occurrence in first- and second-degree relatives, we recommend 4.0 mg daily, beginning three months prior to a planned conception. We suggest the effect in the United States may be nearer to a 40 – 50% reduction for two reasons. First, Berry et al [16] demonstrated a 79% reduction in occurrence in north China (an area of high incidence) but only 40% in south China (where there is a low incidence) when the women took 0.4 mg of folic acid periconceptually. The incidence of NTDs in the United States is closer to that seen in south China rather than that in north China. Secondly, recent data from Canada and Mexico are the first to indicate that lower incidence communities on the North American continent can achieve a 45 – 55% reduction in occurrence with a regimen of 5 mg of folic acid per week [17] and dietary fortification added to recommendations for supplementation periconceptually with 0.4 mg dose of folic acid daily [8,18]. Folic acid supplementation When recent trends in the birth incidence of NTDs are reported, they focus additionally on the effect that folic acid has on the early second trimester prevalence of affected fetuses. As for the epidemiological studies noted above, these reports include varying types of cases; some report only "spina bifida", others "spina bifida" and anencephaly, and still others mention these two types and encephalocele with or without hydrocephalus. Some studies report only deaths due to complications in these groups of patients as stillborns, or deaths in the neonatal time period; other reports study all affected newborns, and still others cover selective or spontaneously aborted fetuses. Those that include time intervals after the introduction of intrauterine diagnosis and selective termination do not take into consideration the variations in incidence at different gestational ages and at birth, whether stillborn or live [5]. Creasy and Alberman [2] reported that 3% of 1216 (30 per 1000) spontaneously aborted fetuses had central nervous system malformations. The majority of the malformations were NTDs. Forty percent also had chromosomal aberrations. The prevalence varied from about 21 per 1000 during each three-week period of gestational age between 8 and 19 weeks, to 105 per 1000 amongst fetuses greater than 27 weeks gestational age. The live born birth incidence at that time was 1.5 per 1000. Nishimura et al [4], reported 13 per 1000 spina bifida embryos amongst 3402 induced abortion fetuses for social reasons at a gestational age between 3 and 10 weeks old. The live born incidence at the time was 2 per 1000. Adams et al [19] reported 10 of 34 fertilized ova up to the age of 17 days were malformed (an incidence of 294 per 1000). Studies after the initiation of prenatal diagnosis and before folic acid supplementation and fortification clearly demonstrate a remarkable decrease in live born birth incidence ([1,20,21] and Figure 1). In Washington State, USA, prenatal diagnosis and termination of affected pregnancies began in 1980. Beginning in 1991, concerned specialists and the media advised supplementation with 400 mcg (0.4 mg) of folic acid daily for women in the childbearing age group. Fortification with an estimated 140 mcg (0.14 mg) per serving of flour-containing foods was added to the recommendations, beginning in 1996, and implemented over the next three years. The marked increase of 7 fold from 0.03 per 1000 in 2001 to 0.21 in 2002 coincided with the completion of fortification (Figure 1). Conclusions Our data and this review clearly demonstrate the effects of intrauterine diagnosis and selective termination prior to the recommendation for supplementation and fortification of foodstuffs with folic acid. Because the reason for termination of a pregnancy is not reportable in our state and the USA, we cannot determine the effect of folic acid on the prevalence of myelomeningocele and anencephaly in first and early second trimester fetuses. Studies of the effect of folic acid in reducing the birth incidence in communities with a low incidence, and active prenatal diagnosis associated with termination of affected fetuses, require longer-term studies than published to date. The differences in data discussed above need to be considered if one is to evaluate the effect of prenatal diagnosis and elective termination as well as the effects of fortification or supplementation with folic acid. We recommend that these variables be discussed with women of reproductive age, particularly if they are relatives of a patient with an NTD. Regardless of the uncertainties, we recommend supplementation of the diet of women, beginning three months prior to an anticipated pregnancy. We recommend all women of childbearing age take at least 400 mcg of folic acid daily when they begin sexual activity. Relatives of a patient with an NTD should take 4.0 mg daily, beginning three months prior to conception. Competing Interests The authors declares that he has no competing interests. Acknowledgements None ==== Refs Shurtleff DB Lemire RJ Epidemiology, etiologic factors, and prenatal diagnosis of open spinal dysraphism Neurosurg Clin N Am 1995 6 183 193 7620346 Creasy MR Alberman ED Congenital malformations of the central nervous system in spontaneous abortions J Med Genet 1976 13 9 16 775092 Luthy DA Wardinsky T Shurtleff DB Hollenbach KA Hickok DE Nyberg DA Benedetti TJ Cesarean section before the onset of labor and subsequent motor function in infants with meningomyelocele diagnosed antenatally N Engl J Med 1991 324 662 666 1994249 Nishimura H Takano K Tanimura T Yasuda M Uchida T High incidence of several malformations in the early human embryos as compared with infants Biol Neonat 1966 10 93 107 5919856 Roberts CJ Lowe CR Where have all the conceptions gone? The Lancet 1975 March 1 498 499 10.1016/S0140-6736(75)92837-8 Leck I Changes in the incidence of neural-tube defects Lancet 1966 2 791 793 4162336 10.1016/S0140-6736(66)90383-7 Elwood JM Elwood JH Epidemiology of anencephalus and spina bifida 1980 New York, Toronto, Oxford University Press 85 119 Persad VL Van Den Hof MC Dube JM Zimmer P Incidence of open neural tube defects in Nova Scotia after folic acid fortification CMAJ 2002 167 241 245 12186168 Toriello HV Higgins JV Occurrence of neural tube defects among first-, second-, and third-degree relatives of probands: results of a United States study Am J Med Genet 1983 15 601 606 6614048 Chatkupt S Skurnick JH Jaggi M Mitruka K Koenigsberger MR Johnson WG Study of genetics, epidemiology, and vitamin usage in familial spina bifida in the United States in the 1990s Neurology 1994 44 65 70 8290094 Timson J A study of the first degree relatives of the parents of spina bifida children Clin Genet 1972 3 99 102 4559910 McManus S Neural tube defects: identification of 'high risk' women Ir Med J 1987 80 166 168 3610577 Arata M Grover S Dunne K Bryan D Pregnancy outcome and complications in women with spina bifida J Reprod Med 2000 45 743 748 11027084 Group MRCVSR Prevention of neural tube defects: results of the Medical Research Council Vitamin Study. MRC Vitamin Study Research Group Lancet 1991 338 131 137 1677062 10.1016/0140-6736(91)90133-A Wald NJ Law MR Morris JK Wald DS Quantifying the effect of folic acid Lancet 2001 358 2069 2073 11755633 10.1016/S0140-6736(01)07104-5 Berry RJ Li Z Erickson JD Li S Moore CA Wang H Mulinare J Zhao P Wong LY Gindler J Hong SX Correa A Prevention of neural-tube defects with folic acid in China. China-U.S. Collaborative Project for Neural Tube Defect Prevention N Engl J Med 1999 341 1485 1490 10559448 10.1056/NEJM199911113412001 Martinez V Perez JZ Vazquez PA Herrera RH Campos MR Lopez RA Ramirez JL Sanchez JM Villarreal JJ Garza MT Limon A Lopez AG Barcenas M Garcia JR Dominguez AS Nunez RH Ayala JL Martinez JG Gonzalez MT Alvarez CG Castro RN Decline of neural tube defects cases after a folic acid campaign in Nuevo Leon, Mexico Teratology 2002 66 249 256 12397633 10.1002/tera.10094 Gucciardi E Pietrusiak MA Reynolds DL Rouleau J Incidence of neural tube defects in Ontario, 1986-1999 CMAJ 2002 167 237 240 12186167 ADAMS EC HERTIG AT ROCK J A description of 34 human ova within the first 17 days of development Am J Anat 1956 98 435 493 13362122 Laurence KM The apparently declining prevalence of neural tube defect in two counties in South Wales over three decades illustrating the need for continuing action and vigilance Z Kinderchir 1985 40 Suppl 1 58 60 2418600 Omran M Stone DH McLoone P The Chief Scientist reports.... Pattern of decline in prevalence of anencephaly and spina bifida in a high risk area Health Bull (Edinb ) 1992 50 407 413 1399588	15679939	PMC546400	CC BY	2021-01-04 16:37:38	no	Cerebrospinal Fluid Res. 2004 Dec 10; 1:5	utf-8	Cerebrospinal Fluid Res	2,004	10.1186/1743-8454-1-5	oa_comm
==== Front Cerebrospinal Fluid ResCerebrospinal Fluid Research1743-8454BioMed Central London 1743-8454-1-61567994010.1186/1743-8454-1-6ReviewA temperament for learning: The limbic system and myelomeningocele Vachha Behroze [email protected] Richard C [email protected] Pediatric Developmental Disabilities, Texas Scottish Rite Hospital for Children, Dallas, TX 75219, USA2 Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75219, USA2004 10 12 2004 1 6 6 5 10 2004 10 12 2004 Copyright © 2004 Vachha and Adams; licensee BioMed Central Ltd.2004Vachha and Adams; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article was the winner of the triennial Casey Holter Memorial Prize awarded by the Society for Research into Hydrocephalus and Spina Bifida, 2004. Abstract This essay explores the link between the limbic/hypothalamic systems within the complex conditions of hydrocephalus and myelomeningocele. Acknowledging the neuroanatomical and neuroendocrine risks inherent in the developing brains of these individuals, we focus on the converging components of temperament, cognition, and language. ==== Body Introduction Children with myelomeningocele (MM) do not easily fit into stereotypical profiles. Professionals, families, and inter-disciplinary teams, devoted to the support of these individuals and the reduction of secondary disabilities, see daily testaments to individual variations. Clinical commonalities among those with MM are frequently noted and perhaps too often – and too simplistically – considered inherent to the condition. Behavioral characteristics have long been one such category for easy generalities. As modern investigators study the underlying keys to cognitive and behavioral disabilities, theories related to cortical development and differentiation have offered insightful possibilities [1]. Much remains to be discovered in the realm of behavior, emotions, and personality profiles within this group and the biologic differences that may explain them. This essay is submitted with the suggestion that in our ongoing quest to understand the learning and behavioral characteristics in youth with MM, focus might reasonably be placed on cellular and neurobiological mechanisms specifically related to the limbic and hypothalamic systems. Given the developmental shifts in neuro-architecture in the fetus and infant with MM, we offer the limbic system with its cortical, and brain stem interconnections, and particularly its close association with the hypothalamic region, as an area wherein many of the phenotypic commonalities may arise. Similarities and variations in temperament profiles among children with MM present an opportunity to explore and enhance our understanding of this ontologically old but, as yet, not fully understood portion of the brain. Temperament patterns among this group, differing from other cohorts, may be reflective of altered integrity within this intricate neural system. The converging components of temperament, memory, and language in this group of children with developmental differences may help the researcher and the clinician to better address the so-called behavioral issues that impact academic learning. We will re-visit established and emerging information related to the limbic and hypothalamic systems. We will review reports from recent studies on language and on temperament among children with MM, and comparative work focused with different at-risk populations of children. These will be considered along with other clinically relevant phenotypic findings among children with MM that have clear links to the limbic and hypothalamic systems. Le Grand Lobe Limbique The limbic system consists of structures reminiscent of the old mammalian brain corresponding to that of the so-called higher mammals [2,3]. The various components of the limbic system influence a diversity of functions that are integral to the cognitive aspects of autonomic, affective, and sexual behavior [4]. Components of the system and their general activities that might be expected to impact memory and/or temperament include the following: Amygdala – little almond shaped structure near the temporal pole. It receives catecholamine and 5-HT containing projections from the brain stem. Its most prominent projection (stria terminalis) runs in the wall of the lateral ventricle, ultimately connecting to the hypothalamic center. Along with other behaviors (particularly sexual), it mediates the major affective activities describe as fear [3,5,6]. Anterior thalamic nucleus – associated with variations in emotional reactivity [3]. Cingulate gyrus – located on the medial side of brain near the corpus callosum. Implicated in encoding and retrieval of semantic and episodic memories, attention, drive and pain perception [7]. Hippocampus – formed from the inferior portion of the temporal lobe into the lateral ventricle. It is involved in memory, especially long-term memory. When both hippocampi are completely ablated, nothing can be retained in the memory. The intact hippocampus allows comparison of the conditions of a present challenge with similar previous experiences. This process is central to the ability to make choices [3,8]. This can be critical for survival in the wild – or perhaps survival in the classroom. Entorhinal, subicular cortices – are implicated in spatial memory storage, connect hippocampus to remainder of temporal cortex [2]. Septal nuclei – a subcortical target of hippocampus [2]. Mamillary complex – is the major limbic-hypothalamic area [2]. From a cellular and structural aspect, the proximity of limbic structures to the ventricles hints at the possibility of neuropathological changes in these structures either directly through mechanical compression effects of the dilated ventricles, or indirectly via alterations of metabolic pathways in children with MM [9-11]. Structural changes, and potential functional sequellae, may also arise through deficiencies in the developmental process when HC occurs in the fetus. Behaviorally, an emerging body of neuropsychological data suggests a critical relationship between these structures, their functions, and their relationship to emotion, memory, and learning. Tell-tale signs of limbic influence: Learning and Temperament Much has been written over the past decade describing phenotypic profiles related to executive functions among youth with MM. While the term executive function continues to be somewhat enigmatic, it has generally been used to encompass a set of abilities including: sustained focused attention and organizational skills, goal-directed behaviors, flexibility with novel situations, generation of unique plans, and working memory functions [12,13]. Long relied-upon anecdotal concepts (short attention spans, poor organization skills, and memory problems) have given way to more controlled studies [14-19]. Language skills evaluated in recent research described both language production and comprehension within theoretically grounded language subsystems [20-30]. From this, a modal profile for MM has been proposed: strengths in syntax and lexicon; and weaknesses in pragmatic communication, making inferences, and understanding nonliteral language [31]. Research supporting this modal profile for children with MM (especially as it relates to higher-order language skills) is based on their inclusion within the larger heterogeneous samples of children with hydrocephalus (HC) whose conditions were associated with multiple etiologies, both congenital and acquired (e.g. refs. [22,30]). Recent studies of a large sample of children with shunted HC and MM only, demonstrated that these children scored lower than age-matched controls in all areas of language skills – even in the more basic lexical semantic skills which, have long been considered "strengths" [26,29]. But two areas provided the children with particular difficulty: supralinguistic skills (making inferences and understanding nonliteral language) and pragmatic judgment. Cognitive skills of attention/executive function and memory are deeply embedded within acquisition and appropriate application of language [32,33]. The ability to make inferences and understand ambiguities (both supralinguistic tasks) involves the integration of previously learned world and linguistic knowledge with the new knowledge presented [34,35]. For a pragmatically competent communicator, successful language outcomes depend on adequately functioning systems of memory (encoding and retrieval of experiential and linguistic knowledge), attention (ability to focus on the presented communicative task), and problem solving (including the ability to judge and utilize social situations). The few studies that measured memory function in mixed samples of children with MM and HC have demonstrated memory deficits in encoding and retrieval both for verbal and nonverbal information [16,18]. Similarly, current research seems to suggest a prevalence of attention/executive deficits in children with MM [14,15,17,19,36,37]. A more than moderate interaction between the successful development and function of the limbic structures and successful learning experiences among children with MM seems likely. Structural or functional malformations of this system should impact the unique learning and cognitive profiles of these children. Prefrontal regions have been identified as critical in functions of memory, language and logical reasoning [38-40]. Animal models and clinical evidence suggest the integral role of the limbic system. The uni-modal and poly-modal inputs from neocortical association areas (including prefrontal), important for information processing, storage, encoding and retrieval, all rely on competent structures within the limbic system [8,41-46]. How might these facts relate to our children with MM? Shortly after the neural tube is formed, its caudal portion develops into the spinal cord and the rostral portion into the brain [3]. Of the three early brain vesicles, the forebrain gives rise to the telencephalon and diencephalon from which structures of the limbic system and the hypothalamus arise. The limbic system, arising from this phylogenetically ancient region of the brain, incorporates the hippocampus, the amygdala, and numerous receptor sites for adrenal and endocrine neuromodulators. MM, resulting from a developmental disruption of this normal neuroembryogenic process, is commonly characterized by its more visual and tangible manifestations: defects within the bony vertebra, associated impairment of the spinal cord, and the functional motor and urological deficits. The clinical manifestations of underlying neurodevelopmental aberrations centrally (the Arnold-Chiari II malformation, ventricular variations, callosal variations, and associated migrational anomalies) are frequently as disabling as the more obvious motor deficits. Structural and functional differences can result from developmental and/or mechanical disruptions in the formative stages – prenatally or thereafter. These central nervous system anomalies, often accompanying the MM, would be expected to influence behavior and learning in these children [e.g. [47,48]]. Developmental disruptions such as those related to MM can also provide "spin-off" developmental deficits related to anatomy and physiology arising from structures that form the limbic system and hypothalamus. Similarly, the potential exists for the developing HC, with its cerebrospinal fluid blockage within and around the brain, to impact the architecture of the emerging limbic system and its neuronal projections. For example, vacuolization and degeneration of neurons in the hippocampal formation have been observed both within hydrocephalic rabbits and humans [9]. Dendritic changes were observed in the hippocampus of neonatal rats with kaolin-induced HC [10], and HC exacerbated hypoglycemic injury in rat hippocampal cells [49]. Indeed in a rat model of kaolin-induced HC, the resultant impairment of glucose metabolism was first observed in the CA3 region of the hippocampus [50], suggesting this area is metabolically vulnerable to dysfunction. Further support of limbic involvement in HC comes from a recent study that reports damage to the fimbria/fornix of the hippocampus from autopsy-acquired brains of humans with chronic HC [51]. Clinical evidence from patients, and recent brain imaging studies suggest lesions of the right hippocampus result in spatial memory deficits while left lesions impact verbal memory [3,8,52]. Thus, resultant left and right CA3 dysfunction potentially contributes to the verbal and nonverbal memory deficits noted in children with MM and HC. Further, findings by Dolan and Fletcher [53] provide evidence that the left hippocampus is active in encoding of episodic memories by registering and processing novel verbal material, the end-product being the so-called memory trace or engram. The inability of children with MM and HC to respond efficiently to novel stimuli is well documented [54,55]. Anatomical and central molecular dynamics involved with these language and memory deficits remain critically important areas for future research. Equally intriguing are those – or other – molecular substances and pathways that mediate the response to the perceived frustrations associated with functional deficits, which come into play daily and hourly. What underlies and mediates the style of behavioral responses? In other words, how does the child's temperament phenotype become an integral part of the clinical management, and neurophysiological understanding of learning differences among children with MM? This task of describing individual differences among individuals within a society is ancient in its origin. Early Chinese tradition offers a view of human nature differences based on a dark, female, earthly force (yin) and the accompanying active, light, heavenly force (yang). The great 2nd century C.E. Roman physician, Galen, conceived a system of balanced equilibrium of four basic humors, which allowed the physician clues as to the nature of an illness by his awareness of personality traits. This system, based on yellow or black bile and phlegmatic or sanguine natures, represented a culmination of prior ideas (Hindu, Greek, Chinese, others) and provided the understanding of the relationship of temperament to health and function for more than a millennium [56]. More modern scientists have provided differing slants regarding temperament characteristics of children. The list includes Bates, Buss, Carey, Chess and Thomas, Goldsmith, Kagan, and Plomin [56-61]. The nature of temperamental categories, taking the various researchers' ideas into account, seems to embody four qualities described by Kagan: 1) variability among individuals, 2) relative stability over time and situation within the individual, 3) under some genetic influence, and 4) emergence early in life [56]. Temperament has been described in typically developing children as the "how " of behavior distinct from ability or "what" of behaving, and motivation or "why" of behavior [60]. One recurring theme among the conceptual descriptions of temperament remains the notion of multiple behaviors, along a continuum, which combine to interact either favorably or unfavorably with the surrounding environment. The nature of these behaviors is not at the level of psychopathology. Rather, the potential for pathologic development lies within the process of the interaction – the "goodness of fit" – between the environment and the individual's behavioral responses. Consonance between the child and his/her environment potentiates optimal development and supportable teachable moments [62]. Conversely dissonance between the child's capacities, and style of behavior and the environment demands result in maladaptive functioning. Such was the case with Annie. Annie's Story Annie is an attractive little girl, eight years old, and with abundant red-haired curls. Her diagnosis of MM and shunted HC requires part-time wheelchair use as she attends a public school and participates in recreation activities in the community. Intelligent and engaging, she has, nonetheless, met frustrations during her days at school. Variably described as shy, capable, anxious, and/or self-doubting, teachers and family members know of Annie's apparent emotional lability. Recent academic testing reconfirmed her cognitive strengths with a full scale IQ in the above-average range. Language evaluation showed "relative strengths" in lexicon and syntax, lesser scores in pragmatics, and significant problems with higher-order language, and cognitive tasks that required quick retrieval of stored information. Of note, during the initial testing, Annie became tearful quite quickly. Her negative responses related to the new tasks, new environments, and her persisting fear of "not being able to finish" (on any given activity). The examiner, familiar with children with MM, recognized the triggers and the responses before her. By careful explanations, negotiating cues to assure Annie of her successes in the testing, and agreements for "breaks" as needed, Annie calmed, warmed to the tasks at hand, and completed the testing successfully in the allotted time. The developmental pediatric team was recently notified by the family that Annie was experiencing episodes at school of skin flushing, sweating, sensations of nausea, and emotional stress. On one occasion, urinary incontinence accompanied the episode. These symptoms were eventually noted to be related to those (infrequent) occasions when the teacher felt it necessary to enter a note onto Annie's personal folder for parent review. Beginning hints of school avoidance prompted the parents' concern. Academic challenges and barriers as in the story of Annie are quite common among youth with MM. Layers of seemingly separate but simultaneous factors – cognitive, memory, language, temperament, physical – can elevate even daily and mundane tasks into sources of stress not experienced by the typical classmate. Recent studies of youth with MM and shunted HC, ages 5–12, demonstrated clusters of findings different from those classically described by Chess and Thomas ("easy", "difficult", or "slow to warm") or by Kagan ("inhibited" or "uninhibited"). Children with MM in these studies were reported to be significantly different from normative profiles in five areas: 1) adaptability – less; 2) distractibility – more; 3) approach – guarded; 4) persistence on task – less; and 5) predictability – less [55]. Given these temperament characteristics in a child with MM, perhaps related to the unique differences in both central molecular dynamics and neurodevelopmental anatomy, responses such as Annie's begin to be understood. While the focused study of the biological basis for temperament dates to mid-20th century, only recently have physiologic variables and temperament categorical descriptors been paired for critical comparisons. These investigations stem from relatively firm notions about the anatomy and physiology of the central nervous system. Researchers have tracked differing patterns of social response, and encoding of emotional memories to the limbic system, and particularly amygdala [63,64]. The amygdala has been implicated in social cognition, involving adequate recognition and judgment of facial expressions [3,65]. Preliminary studies within our institution reveal our patients with MM and shunted HC had difficulty understanding nonverbal facial cues and expressions of annoyance (explaining in part their poor pragmatic skills). Opioid-mediated inhibitory activities, or responses to the dozens of neurotransmitters or neuropeptides both have genetic implications and biochemical importance as they potentially affect excitability centrally within the amygdala or connecting structures [56]. Potential changes in functional outcomes might be anticipated. These limbic structures – and their vulnerability in the developing brain affected by MM – offer fertile ground for clinical exploration in the broader study of cognition, memory, and temperament. The segregation of emotion, temperament, language, and cognitive performance is difficult to accomplish in the typical child; it is decidedly more complex in the child with MM and HC who has co-existent ventriculomegaly from early gestation, mid-brain variations, and cerebellar differences. What then are the capacities and activities of these central nervous system components – the limbic and hypothalamic systems – that are typically relied upon for daily function, and which might have significant impact on those outward actions and responses which we categorize as temperament? Emotional triggers, goal direction for problem solving, assimilation of information from the various senses (vision, touch, auditory) – all of these relate to the anatomy and associated function of the limbic system (notably the amygdala), and the closely related hypothalamic region. Evidence from human and animal studies indicates that the amygdala intervenes between the regions concerned with the somatic expression of emotion (hypothalamus, brainstem) and the neocortical areas concerned with conscious feeling [3]. Similarly, the hypothalamus is a central player in the integration of information. For the specific areas throughout the neocortex to finalize and actualize those processes described as "executive functioning", regulation of stimuli from the environment and peripheral systems of the body is aided through hypothalamic function. Some actions might well be expected to impact behavior we characterize as components of temperament. The anatomy of the hypothalamus is important when considered along with the variations commonly noted in our individuals with ventriculomegaly and Chiari malformations. It lies on the ventral surface of the cortex. The third ventricle divides the hypothalamus and lies in close proximity to hypothalamic projections [66]. Disturbances or disruptions in these structures due to HC would not be unexpected. Generally, signs or symptoms associated with hypothalamic dysfunction might include: disorders of satiety, appetite and/or caloric homeostasis; sexual dysfunction including precocious puberty or hypogonadism; central autonomic disorders (sleep and consciousness regulation, gastrointestinal function, thermoregulatory control, sphincter disturbance); and affective variations [3,66]. Corticotrophin releasing factor, related to the paraventricular nucleus, is integrally involved in the management of autonomic centers within the brainstem and, via regulation of corticosteroids, with stress adaptation [3]. The effects of HC on neuronal pathology and function in these regions have been demonstrated in both human studies and animal models [10,49,50,67]. Such variations in anatomy and fetal development of the midbrain structures – areas of tremendous variations among individuals with MM – could play important roles in the ultimate behavioral phenotypes described by temperament. For example, in the temperament profile of the MM cohort described above, attention was a significant outlier. Mirsky [68] has reminded us of the multi-component nature of attention. The nature of the neuropsychological model of attention depends upon the data used to generate it. The network involved in the distribution of attention to extra-personal targets implicates cortico-limbic-reticular and hypothalamic circuits; damage to either impacts the attention process [68]. The ultimate functions completed within cortical regions are clearly important. But dysfunction in the limbic and hypothalamic regions seems intuitive within this population. They can be measured in some instances (particularly neuroendocrine), and they are consistent with the underlying biologic bases of temperament variation as posited by Kagan and others. Temperament as a Component for Assessment of Learning in Youth with MM Annie's tale is not unusual for our children with MM and shunted HC. Clinicians and researchers alike have experienced stories of lesser or more severe impairments. For the parents and teachers working daily with children such as Annie, an understanding of the normal process of neuro-development leads to clearer understanding and a better differentiation from the abnormal. The research and subsequent writings of T. Berry Brazelton have provided ready information to parents of typically developing children by blending the Gesellian milestones of development with the temperament concepts as outlined by Carey, Chess, and Thomas [69]. The interplay of cognition, temperament, language, executive function, given the underlying neuro-endocrine/central molecular dynamics that accompany MM, offers a greater challenge to clinicians, teachers, and parents. For the classroom, identification of stressors and methods to minimize autonomic over-response can be as critical to successful learning as the understanding of memory strengths, or language problems. As in our story, Annie demonstrated persistence to task, willingness to approach novel situations, positive mood, and the ability to adapt to the testing situation – all temperament domains with which she has struggled. The "goodness of fit" between Annie and the examiner was critical before any progress could be made in better understanding her relative academic and learning strengths. The autonomic dysfunction, manifested in her excessive sweating, flushing, and urinary incontinence in school, only served to heighten both the emotional and physical symptoms. The differences in temperament profiles among children with MM are unlike those in other diagnostic cohorts of children with special health care needs [70,71]. Hughes et al. [72] have described temperament patterns in preterm infants over the first year of life. Descriptors of temperament characteristics differed from that seen in typically developing full term infants and from that of our children with MM. Because temperament is believed to be influenced by heredity, biology, and experience, parenting must also be considered as a variable (moderator) in temperament development. This concept of "goodness-of-fit" plays an important role in the parents' development, their perception of the infant, and the ultimate quality of the parent-child interactions [72]. Learning, for the child with MM, certainly takes place beyond the confines of preschool and classroom. Negotiating physicians, curious strangers, medical emergencies, educational diagnosticians, environmental barriers, and other stressors is a continual process. Raising a happy, self-reliant, and resilient child is the dream of parents. Perrin [73] has suggested that "adjustment" might be conceptualized as 1] the presence of fewer behavioral problems, 2] inter-personal functioning or competence, and 3] development of a positive self-concept. Parental perceptions of their child's actions and reactions contribute greatly to the moderation of, or exacerbation of, affective differences. It is incumbent on the professional to accurately discriminate between temperament characteristics that are barriers to inter-personal optimal functioning and truly psychiatric disorders. Given the neuro-anatomical and neuro-endocrine risks associated with MM, heightened awareness and assessment of temperament in this discriminating process is important. Conclusion T.S. Eliot wrote, We shall not cease from exploration And the end of all our exploring Will be to arrive where we started And know the place for the first time. As we continue, on behalf of children and youth with MM, to explore and better understand the cognitive and learning differences within the group, the developmental variations related to underlying central nervous system structures may continue to hamper the human tendency to categorize. The modal profile may not be singular. As assessments of strengths and differences continue; as descriptors of quality of life are derived; as personality variances are accounted for; as curricula and "home programs" are constructed, the bio-psycho-social model increasingly becomes the clear route for professional and family partnership. For the researcher trying to join un-connected dots into a cohesive whole, we, like Eliot, may need to return to the early components long-standing in the evolution of the brain and its function. The ancient limbic and hypothalamic systems leave indelible tell-tale hints that should not be left behind in our quest for understanding and raising the resilient child. List of abbreviations MM, myelomeningocele; HC, hydrocephalus Competing interests The author(s) declare that they have no competing interests. Authors' contributions Both authors contributed equally to this work. All authors read and approved the final manuscript. Acknowledgments Our thanks to colleagues at home, and across the Atlantic for their support and continued interest in our research. ==== Refs Miyan J Sobkowiak C Draper C Humanity Lost: The cost of Cortical Maldevelopment. Is There Light Ahead? 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==== Front Aust New Zealand Health PolicyAustralia and New Zealand Health Policy1743-8462BioMed Central London 1743-8462-1-51567994110.1186/1743-8462-1-5ResearchThe Australian Health Care Agreements 2003–2008 Duckett Stephen J [email protected] Professor of Health Policy La Trobe University Australia2004 18 11 2004 1 5 5 11 8 2004 18 11 2004 Copyright © 2004 Duckett; licensee BioMed Central Ltd.2004Duckett; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Australian Health Care Agreements for the five years 1 July 2003 to 30 June 2008 were signed in August 2003 after vituperative debate and intransigence from the Commonwealth that vitiated the negotiation process. The new Agreements, which were not as generous as the Agreements they replaced, increase accountability on the States, requiring States to match increases in Commonwealth funding, and de-emphasise the prospects for further reform in Commonwealth-State relations during the course of the Agreements. This paper describes the new Australian Health Care Agreements and the process which led to them. ==== Body Introduction The most significant Australian health policy event for 2003 was the signing of the five-year Australian Health Care Agreements. The Agreements were preceded by an ultimatum to the States and Territories from the Commonwealth indicating that there would be no changes from the offer on the table. This led to bitter political recriminations, but the Agreements were eventually signed. In fact important preparations for Agreement renewal occurred in April 2002 with the Commonwealth and State Ministers, in a display of remarkable amity and accord, endorsing a new approach to the Agreements that: • Commonwealth/State relations in the health arena should focus on the provision of optimal care and health outcomes, regardless of jurisdictional boundaries. • It is in the best interests of all Australians for the Commonwealth, Stats and Territories to work co-operatively to improve the health and wellbeing of the community and the way in which health services are provided; • The 2003–08 Agreements should contain the principles, objectives and proposed health outcomes designed to achieve those objections. The Ministers also agreed to establish nine reference groups to address key issues in health reform which would feed into the Agreement "negotiation" process [1]. The reference groups addressed interaction between hospital funding and private health insurance; improving rural health; interface between aged and acute care; continuum between preventative, primary, chronic and acute models of care; improving indigenous health; improving mental health; information technology, research and e-health; quality and safety; and collaboration on workforce, training and education. The reference groups were co-chaired by senior Government officials and non-Government clinical experts involving participants from the bureaucracies, people who work in health agencies, and consumers. The reference groups created great expectations amongst the neophyte health policy contributors who believed the rhetoric of the Commonwealth about being prepared to consider wide ranging changes to the health sector. Seasoned commentators also called for reform [2-4]. Although extensive reports were produced by the Reference Groups and delivered to the Health Ministers the reports had no discernible impact on the 2003–2008 Agreements [5]. On 23 April 2003 the Commonwealth produced a non-negotiable offer with severe penalty clauses if States refused to sign by the Commonwealth's arbitrary deadline of 31 August 2003. An Australian Health Reform Alliance was formed to put pressure on the Commonwealth to respond to the reference group reports and to attempt to ensure that the 2003–2008 Agreements did not waste yet another opportunity to improve the efficiency, equity and quality of the health system. The Alliance's National Health Summit, which met at Old Parliament House, presented its final communiqué to non-Government politicians following a march up the hill to New Parliament House [6]. The Commonwealth deadlines remained and there was no change to the Agreement content. The Commonwealth's confrontationalist stance effectively destroyed relationships between the Minister for Health and Ageing, Senator Kay Patterson, and her State colleagues, and she was replaced as Health Minister by Tony Abbott MP in the Ministerial reshuffle of October 2003. The content of the Agreements There have been five Health Care Agreements since Medicare was introduced in 1984. The emphasis, orientation and priorities of these Agreements have changed over time (see Table 1). Table 1 Key elements of Commonwealth-state hospital funding agreements Agreement Political Objective Key Principles 1984–88 : Labor (Medicare Compensation Agreement) Introducing Medicare Compensation for cost increases and revenue losses 1988–93 : Labor (Medicare Agreement) Consolidating Medicare Growth and reform of public provision Incentives for system reform Penalties for lower public:private bed day shares and excess private medical service use 1993–98 : Labor (Medicare Agreement) Entrenching Medicare Expansion of public provision Reward for relatively higher levels of public provision and for increasing public provision relative to other states Post 1996, accountability for negotiated outcomes 1998–2003 : Coalition (Australian Health Care Agreement) Continuing with Medicare Increased Commonwealth funding with increased accountability for states Increased accountability on states for activity level changes Increased clarity of Commonwealth responsibility if health insurance levels change 2003–08 : Coalition (Australian Health Care Agreement) Continuing with Medicare Slowed Commonwealth funding growth Increased accountability for states Improved reporting, including of state spending Requirement on states at least to match Commonwealth funding increases Source: [11] The most significant elements of the 2003–08 Agreements are: • a base grant which is increased for weighted population increases, a further 1.7 per cent increase for utilisation drift, and indexation for wage movements • a withheld amount of 4 per cent of the grant paid on compliance with reporting schedules and funding growth matching requirements • a capital funding scheme to facilitate improvements in services involved in the transition from hospital to home ('Pathways Home Program') • funding for palliative care, mental health, and safety and quality initiatives. The most contentious difference between the 1998–2003 and 2003–2008 Agreements related to the indexation provisions (see Table 2 for significant areas of difference between the two Agreements). Table 2 Comparison of provisions of 1989–2003 and 2003–08 Australian Health Care Agreements Agreement Provision 1998–1998 Agreement 2003–2008 Agreement Indexation 2.1% above weighted population growth applied to 83% of the grant 1.7% above weighted population growth applied to 71% of the grant State matching Nil State "commits to increase its own source funding for public hospital services such that the cumulative rate of growth will at least match the cumulative rate of growth of Commonwealth funding" (Clause 11) Scope and level of services (State) "continues to provide services to public patients at an indicative public patient weighted separation rate of XX" (Clause 22) "The range of services available to public patients should be no less than was available on 1 July 1998" (Clause 7(a)) Reform The Commonwealth and Victoria recognise the need for service delivery reform and ongoing exploration of additional initiatives under a measure and share model. Victoria will work with the Commonwealth in evaluating the outcomes from the Co-ordinated Care Trials to provide information to guide future directions for the reform of health service delivery. The Commonwealth and Victoria will consider proposals which move funding for specific services between Commonwealth and State funded programs on the basis that each proposal meets the following criteria: • the proposal must be consistent with accepted evidence based best practice care models; • there should be a sound basis for believing that the reform will lead to improved patient outcomes and/or more cost effective care; • the impact of the proposal should be measurable in terms of change in services delivered and costs to the health system as a whole and to each party to this Agreement; • if the proposal is expected to lead to net savings, these should be shared equitably between the Commonwealth and Victoria; • the proposal should have potential to be replicated, be on a scale such that extension can be realistically tested and be evaluated in terms of such extension; and • the proposal must preserve eligible persons' current access to Medicare Benefits Schedule services or their equivalent. Reform proposals may result in the cashing out of State funded programs and/or Commonwealth funded programs, including the Medicare Benefits Schedule and Pharmaceutical Benefits Scheme. Victoria and the Commonwealth are committed to working with other States to progress the reform agenda agreed by Commonwealth and State Ministers for Health on 27 September 2002. The Commonwealth considers that for its part, such reform can taken place within existing funding parameters. In line with clause 18, the specific areas of national co-operation to deliver reform include: (a) improving the interface between hospitals and primary and aged care services; (b) achieving continuity between primary, community, acute, sub-acute, transition and aged care, whilst promoting consumer choice and improved responsiveness. Initial priorities for a stronger continuum of care approach will be cancer care and mental health services; and (c) exploring setting up a single national system for pharmaceuticals across all settings. Each of the predecessor Agreements provided indexation formulae to account for growth and ageing of the population. The 1998–2003 Agreements also recognised that there was further "utilisation drift", that is increases in utilisation were occurring in the hospital sector over and above that which can be explained by population growth and ageing. This utilisation drift was in part the result of new technologies that allowed for treatments for conditions for which there was previously no hospital treatment. Utilisation also increased because of shifts in treatment from general practitioners' rooms and other ambulatory settings to same day hospital admission. The 1998–2003 Agreements provided an escalation factor of 2.1% per annum over and above the growth caused by the increase in the rate of population for key elements of the grant. The 2003–08 Agreements reduced the utilisation drift factor to 1.7% and narrowed the applicable components of the grant, saving the Commonwealth Government about $1 billion from that provided for in the Forward Estimates. This reduction in growth provision was vociferously opposed by States and also by clinicians who were experiencing significant financial pressures on hospitals as a result of State Government funding constraints. The 2003–08 Agreements also addressed an ongoing concern of Commonwealth Governments (both Labor and Coalition): its perception that when the Commonwealth increased expenditure on hospital services, this often had no discernible impact on hospitals as State Governments withdrew funding concomitantly. As Deeble points out, the reality is more complex, but the evidence is that an increased Commonwealth share is associated with growth in spending [7]. The new Agreements provided that the States were required to increase their funding of hospitals at the same rate as the Commonwealth increases, otherwise the increases available to the State would not be paid. These stronger reporting frameworks built on the trend from the previous agreements and responded to a critical Auditor-General's report that concluded that the Commonwealth did not have all the performance information required to administer the Commonwealth funding allocated under the agreements [8]. The 'negotiation' processes Why were the processes so acrimonious and what shaped the Agreement outcome? To some extent the shape of the 2003–08 Agreement negotiations was inevitable. The political context, where all state and territory governments were of the opposite political colour from the Commonwealth, meant that harmonious negotiations were probably never seriously in contemplation. Commonwealth governments of both persuasions have tightened up accountability on states with successive Agreements and so tighter control was also probably inevitable. Two important political choices did exacerbate the tensions and inflamed the processes. First, the 2003–08 framework was more parsimonious than predecessor Agreements. As mentioned above, this represented a saving to the Commonwealth of about $1 billion on the Forward Estimates. A contemporary political issue was the decline in bulk billing. The Commonwealth's response to this involved an injection of around $1 billion. The link between the two policy debates within the Health portfolio is clear. Cabinet probably judged the political costs of finding a $1 billion saving from the states as low, as state premiers complaining about Commonwealth cuts and meanness is a regular part of the political landscape. Further, States would probably have criticised the Commonwealth position regardless of the offer made. The second choice that shaped the process was the Commonwealth's intransigence after the drafts were released. The Commonwealth's position here may have been based on a recognition that, eventually, all the states would have to sign the Agreements as they were politically committed to Medicare and free access at point of admission to hospitals, and that the states could not afford to suffer the cash flow consequences announced by the Commonwealth if the Agreements weren't signed by their deadline. The Prime Minister probably took a strong hand in this decision and left no room for his Health Minister to manoeuvre. The Minister's failure to attend meetings exacerbated an already difficult situation. A positive of the process was the extensive involvement of practitioners in the lead-up to the draft Agreements through the Reference Groups. Commonwealth-state negotiations had hitherto been an arcane process involving bureaucratic insiders. This widening of participation was welcomed by those involved and has set a precedent for future negotiations. Prospects for Reform The 2003–08 Agreements commit the Commonwealth and states to work towards reform in a number of areas including the interface between hospitals, primary care, and aged care; continuity of care particularly in cancer care and mental health services; and continued work on pharmaceuticals reform. A subtle shift from the predecessor Agreement model is the more sceptical and parsimonious approach to the potential for health care reform. Despite the aspirations implicit in establishing the nine reference groups, the language of the 2003–08 Agreements reflects a much more hard-nosed approach to reform with a strong emphasis on efficiency. This approach is most clearly articulated in Clause 18: "The Commonwealth considers that for its part such reforms can take place within existing funding parameters". Although predecessor Agreements also made provision for reform to Commonwealth/State relations, the progress in designing and implementing reform has not lived up to expectations. The most important shift that occurred during the course of the 1998–2003 Agreements was the rationalisation of hospital provision of outpatient pharmacy services, a long overdue response to a significant frictional issue in Commonwealth/States relations [9,10]. It is unclear whether the dynamic, facilitatory aspects of the 2003–08 Agreements will lead to any reform, especially given the acrimonious exchanges prior to signature of the Agreements. However it is important to note that, with a Federal election due at the end of 2004, there is a possibility that a Labor Government will be administering the remainder of the 2003–08 Australian Health Care Agreement. A new Government may be more committed to reforming and strengthening Medicare. However, despite the fact that a Labor would then hold political office throughout Australia, at all levels, this would not necessarily presage a more laissez faire attitude by a federal government to its state politically-allied counterparts. A Commonwealth Labor government would be just as keen as its Liberal predecessor to ensure that states are held accountable for maintaining spending and access. Competing interests The author(s) declare that they have no competing interests. ==== Refs A Report to the Australian Health Ministers' Conference from Australian Health Care Agreement Reference Groups 2002 Reid MA Reform of the Australian Health Care Agreements: Progress or political ploy? Medical Journal of Australia 2002 177 310 15 12225278 Paterson JP Australian Health Care Agreements 2003–2008: A new dawn? Medical Journal of Australia 2002 177 313 15 12225279 Duckett SJ The 2003–2008 Australian Health Care Agreement: An opportunity for reform Australian Health Review 2002 25 24 26 12536858 Australian Health Care Agreements Australian Health Care Summit Deeble J Funding the essentials: Australian Health Care Agreements, 2003–2008 Australian Health Review 2002 25 1 7 12536854 Auditor-General Performance information in the Australian Health Care Agreements 2002 Canberra, Australian National Audit Office Duckett SJ Hancock L 'Commonwealth/state relations in health' Health policy in the market state 1999 Melbourne, Allen & Unwin 71 86 Duckett SJ Mooney G, Plant A Rationalising roles in public hospital funding Daring to dream: The future of Australian health care 2001 Bentley W.A.: Black Swan Press 69 78 Duckett SJ The Australian Health Care System 2004 Melbourne: Oxford University Press	15679941	PMC546402	CC BY	2021-01-04 16:38:25	no	Aust New Zealand Health Policy. 2004 Nov 18; 1:5	utf-8	Aust New Zealand Health Policy	2,004	10.1186/1743-8462-1-5	oa_comm
==== Front Aust New Zealand Health PolicyAustralia and New Zealand Health Policy1743-8462BioMed Central London 1743-8462-1-81567994210.1186/1743-8462-1-8ResearchThe socioeconomic gradient and chronic illness and associated risk factors in Australia Glover John D [email protected] Diana MS [email protected] Sarah K [email protected] Public Health Information Development Unit (PHIDU), The University of Adelaide, South Australia, Australia 50052004 12 12 2004 1 8 8 7 9 2004 12 12 2004 Copyright © 2004 Glover et al; licensee BioMed Central Ltd.2004Glover et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective To examine the prevalence of major chronic diseases and their risk factors in different socioeconomic groups in the Australian population, in order to highlight the need for public policy initiatives to reduce socioeconomic inequalities in health. Methods Data were provided by the Australian Bureau of Statistics (ABS) from the 2001 National Health Survey (NHS) for selected chronic diseases and associated risk factors. Conditions selected were those, which form the National Health Priority Area (NHPA) conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers; and for which sufficient cases were available for analysis by socioeconomic status. Indirectly age-standardised prevalence rates were calculated by broad age group for Australia and for five groups of socioeconomic status; rate ratios were calculated to show variations in prevalence between these groups. Results Significant socioeconomic inequalities were evident for many of the major chronic diseases; the largest was for diabetes mellitus (at ages 25 to 64 years); and for many diseases, there was also a strong, continuous socioeconomic gradient in the rates. Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibited a strong differential in the 25 to 64 year age group. In the 65 years and over age group, the strongest inequalities were evident for mental and behavioural problems, diabetes (with a continuous socioeconomic gradient in rates) and respiratory system diseases. A number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight showed a striking association with socioeconomic status, in particular for people who were smokers and those who did not exercise. Conclusion This analysis shows that the prevalence of chronic disease varies across the socioeconomic gradient for a number of specific diseases, as well as for important disease risk factors. Therefore, any policy interventions to address the impact of chronic disease, at a population level, need to take into account these socioeconomic inequalities. ==== Body Background As in other developed countries, chronic diseases in Australia are major contributors to the extent of illness, disability and premature mortality in the population. They are estimated to make up the greatest proportion of the burden of disease, mental problems and injury for the population as a whole (about 80%), and for particular sub-population groups [1]. Chronic diseases are exemplified by having multifactorial aetiologies, including common disease risk factors and determinants; significant latency periods and protracted clinical courses; and are seldom cured completely [2,3]. Causal factors interact together at an individual and at a population level to determine the degree of disease burden and illness, and unhealthy risks can be passed on through families, communities, and populations following demographic gradients [4]. At different life stages, common risk factors and determinants include poor intra-uterine conditions; stress, violence and traumatic experiences; educational disadvantage; inadequate living environments that fail to promote healthy lifestyles; poor diet and lack of exercise; alcohol misuse and tobacco smoking [5,30]. Risk factors are also increasingly more prevalent in areas of low socioeconomic status and in communities characterised by low levels of educational attainment; high levels of unemployment; substantial levels of discrimination, interpersonal violence and exclusion; and poverty. There is a higher prevalence of such factors among Indigenous communities, and other socioeconomically disadvantaged Australians [5,6]. The inequalities in health observed across populations are many – some of them are inevitable and others, unnecessary and unfair. Those inequalities that are potentially avoidable are deemed 'inequitable' [7]. Despite significant medical advances and improved public health in recent decades, socioeconomically disadvantaged communities continue to suffer an unequal burden of illness, premature death and disability. Therefore, the study of socioeconomic inequalities in chronic diseases and conditions and in risk factors is important and necessary. This is particularly so, if we wish to develop more effective policy mechanisms for preventing and intervening earlier in the progression of chronic diseases and their associated risk factors across the diverse Australian population, and to reduce some of the existing health inequities. Our approach There have been a number of studies published in Australia on socioeconomic inequalities in mortality from various chronic diseases and conditions. The earlier ones were analysed using information on occupation recorded on the death certificate [8-10]. An alternative approach has been to examine variations in mortality rates by grouping residential locations according to socioeconomic criteria. A number of such studies have documented substantial variations in mortality for different age groups [11-17]. However, to date, there have been fewer studies that have examined socioeconomic inequalities in chronic disease prevalence in those still living (analyses of hospital admissions for chronic conditions have been published, but not of prevalence) [15]. One of the earliest was undertaken by Broadhead, who analysed data on morbidity and social status from the 1977–78 Australian Health Survey (ABS), and found that men in lower status occupations tended to suffer a higher age-standardised rate of self-reported chronic conditions and days of reduced activity; the picture for women appeared less clear [18]. Lee et al found that low income males were more likely to report mental health problems, chronic symptoms and acute symptoms than their high income counterparts [19]. Similar findings were reported in other studies, and risk factors associated with chronic diseases also were also associated with low income [15,19-21]. The work of Mathers is significant for its systematic documentation of health inequalities among working aged Australians (25 to 64 years) in the late 1980s. He examined mortality, disability, disease groups, specific diseases, self-perceived health, risk factors, health service use and use of preventive screening services, using data from the 1989–90 National Health Survey (NHS) [15]. Mathers found that there were no clear gradients of chronic, recent or minor illness with level of socioeconomic disadvantage of area, although there were some specific health status indicators (self-reported health, reduced activity, unhappiness) and certain risk factors (inactivity, smoking and alcohol use) that were reported more frequently by those in the more disadvantaged quintiles [15]. Current information on inequalities in health other than mortality is limited in Australia because of a dearth of suitable data collections. However, the release of data from the 2001 NHS provides an opportunity to examine the prevalence of self-reported chronic disease in Australia and the way in which this impacts on different socioeconomic groupings within the population. Results Information for a selection of chronic diseases is shown in Table 1. Diseases were included on the basis of either high prevalence or their contribution to the burden of disease. Table 1 Inequality in prevalence of selected chronic diseases1, 2001 Age group (years) and chronic disease Rate2 Rate ratio by quintile of socioeconomic disadvantage of area3 First Second Third Fourth Fifth 0–144 Mental and behavioural problems5 6 596 1.00 1.04 1.10 1.12 1.52** Respiratory system 21 807 1.00 1.07 1.05 1.11 0.99 Asthma 13 363 1.00 1.10 1.12 1.25* 1.12 15–24 Mental and behavioural problems5 10 284 1.00 1.02 0.97 1.08 1.28 Respiratory system 33 373 1.00 1.04 1.12 1.09 1.00 Asthma 16 263 1.00 0.82 1.14 1.02 1.00 Bronchitis/emphysema 1 701 1.006 1.326 1.666 1.946 1.976 Musculoskeletal system7 19 088 1.00 1.11 1.00 1.08 0.94 25–64 Diabetes mellitus 2 234 1.00 1.37 1.67* 1.72* 2.28*** Mental and behavioural problems5 11 093 1.00 1.05 1.20* 1.36* 1.67* Circulatory system 17 491 1.00 1.04 0.97 1.15* 1.28*** Hypertensive disease 9 751 1.00 1.12 1.01 1.24* 1.54*** Respiratory system 32 964 1.00 1.00 0.99 0.99 1.01 Asthma 10 393 1.00 1.10 0.99 1.19* 1.14 Bronchitis/emphysema 3 429 1.00 0.97 1.14 1.55 1.70* Digestive system 8 074 1.00 1.03 1.07 1.12 1.37*** Musculoskeletal system7 39 840 1.00 1.10* 1.16* 1.16* 1.22*** 65 & over Diabetes mellitus 8 981 1.00 1.13 1.14 1.52* 1.56* Mental and behavioural problems5 7 222 1.00 1.21 1.62* 1.67* 1.56* Circulatory system 56 592 1.00 1.09 1.06 1.10 1.19* Respiratory system 31 442 1.00 1.03 0.87 0.95 1.22* Musculoskeletal system7 63 669 1.00 1.02 1.06 1.03 1.08 1Survey respondents can report more than one disease. 2Rate is the number of persons per 100,000 population reporting the disease. 3The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00); rate ratios differing significantly from 1.0 are shown with * p < 0.05; p < 0.01; * p < 0.001. 4Information about these age groups were collected by proxy, using parental report. 5Information may be based on self-diagnosis, rather than diagnosis by a health practitioner. 6Indicates rate ratio based on estimates with a Relative Standard Error of between 25% and 50% and should be used with caution. 7Includes diseases of the connective tissue. Source: National Health Survey, ABS 2002 The main findings are: The largest differential between those in the most well off and those in the most disadvantaged areas was for diabetes mellitus at ages 25 to 64 years, with the prevalence in the most disadvantaged areas being just over two and a quarter times (a rate ratio of 2.28) the prevalence for the least disadvantaged; there is also a strong, continuous gradient in the rates, with the rate ratios in each of the third to fifth quintiles statistically significant. There was a statistically significant differential of 67% at ages 25 to 64 years, with a strong, continuous gradient, in the prevalence of self-reported mental and behavioural problems across the socioeconomic gradient; differentials (also statistically significant) in the 0 to 14 year and 65 years and over age groups were 52% and 56%, respectively. Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibit a strong differential in the 25 to 64 year age group (statistically significant differentials of 28% and 54%, respectively). In the 65 years and over age group, the strongest differentials were evident for mental and behavioural problems (a statistically significant 56%), diabetes (with a continuous gradient in rates, statistically significant in quintile3 four and five) and respiratory system disease (a statistically significant 22%). Asthma accounted for almost two thirds of the rate of reporting of respiratory system disease in the 0 to 14 year age group, almost half in the 15 to 24 year age group, and for about a third of the rate in the 25 to 64 year age group. The NHS also included data on a number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight. A number of these risk factors show a striking association with socioeconomic status, in particular for people who are smokers and those who did not exercise, with continuous gradients and highly elevated rates of statistical significance (Table 2). The differences in male and female rates are also of interest. It was only for underweight females, and for the risk factor of high-risk alcohol consumption by females, that the socioeconomic gradient was reversed. Table 2 Inequality in prevalence of selected health risk factors, 18–64 years, 20011 Health risk factors Rate2 Rate ratio by quintile of socioeconomic disadvantage of area3 First Second Third Fourth Fifth Current smokers - Male 30 582 1.00 1.40* 1.55* 1.71* 1.95* - Female 24 009 1.00 1.29* 1.34* 1.48* 2.00* - Persons 27 275 1.00 1.35* 1.45* 1.61* 1.96* Alcohol – High risk - Males 6 976 1.00 1.09 1.26* 1.26* 1.45*** - Females 2 127 1.00 0.59* 0.94 0.76 0.87 - Persons 4 537 1.00 0.93 1.16 1.12 1.22* Did not exercise - Males 28 772 1.00 1.20 1.36* 1.52* 1.68* - Females 28 220 1.00 1.19 1.29* 1.35* 1.65* - Persons 28 494 1.00 1.20* 1.32* 1.43* 1.66* Underweight females 12 675 1.00 0.89 0.83* 0.72*** 0.91 Overweight/obese - Males 54 701 1.00 1.09* 1.11* 1.04* 1.00 - Females 37 004 1.00 1.09 1.21* 1.16 1.17 - Persons 45 798 1.00 1.09 1.15* 1.09** 1.06 1Survey respondents can be shown under more than one type of risk factor. 2Rate is the number of persons per 100,000 population estimated to be at risk from the health risk factor. 3The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00); rate ratios differing significantly from 1.0 are shown with * p < 0.05; p < 0.01; * p < 0.001. Source: National Health Survey, ABS 2002 It is important to note that the inequalities reported above relate to the health of those people living in a geographic area and to the overall level of socioeconomic disadvantage of that area. Most areas will contain varying levels of individual socioeconomic disadvantage and, to the extent that the poorer health is associated with individual economic circumstances and living conditions rather than communal environment, the inequalities will understate the true differences in health status according to socioeconomic disadvantage [15]. Furthermore, there are limitations to the use of area-based measures of SES. Due to misclassification error (i.e. ascribing area-SES to individuals), estimates of difference across the quintiles will be smaller than if data on individual-level measures of SES were used [28]. Thus, chronic disease inequalities in the wider population by SES are likely to be larger than those reported in this study. In addition, the exclusion of the 'sparsely settled' areas of Australia in NHS data collection results in the omission of data from a high percentage of Indigenous people, who are the population group with the poorest health. Discussion Our analysis indicates that socioeconomic inequalities in the prevalence of chronic diseases and their concomitant risk factors are evident across the Australian population. However, the diseases with substantial disparities across the socioeconomic quintiles are different, for different stages in the life course. Although these results cannot be directly compared with those of previous studies, because of definitional and methodological differences, the recurring finding of inequalities for chronic disease morbidity and risk factor prevalence across the socioeconomic gradient remains a significant concern. The burden in the Australian population attributable to socioeconomic inequality is large, and has far-reaching implications in terms of unnecessary disability and suffering, the loss of potentially economically productive members of society, and increased costs for the health and social care systems [35]. Despite the expenditure of millions of dollars to prevent and reduce the prevalence of chronic diseases and their risk factors, these inequities have persisted. However, the situation in Australia is by no means unique, for inequalities in these diseases and their risk factors have been observed for most of the developed countries in which they have been studied [26]. What should we be doing differently? There is a growing body of knowledge that will help to provide direction for developing policies to reduce inequities across the population. The socioeconomic environment is a powerful and potentially modifiable factor, and public policy is a key instrument to improve this environment, particularly in areas such as housing, taxation and social security, work environments, urban design, pollution control, educational achievement, and early childhood development [34]. However, attention must be paid to the nature of any action that is taken, to ensure that social and economic inequalities are not increased. Some programs, by their very success, can increase inequality by widening the gap between groups in the population; for example, such programs may be more attractive to those who are already healthier, or not as effective for certain groups with poorer health, less education or more stressful lives. In one smoking cessation initiative, it was found that the prevalence of smoking decreased predominately in those adults with higher education, thus increasing the existing difference with those who were more disadvantaged [37]. While smoking prevalence in Australia has reduced considerably over the last 20 years, attributes such as lower education and occupational status, unemployment, rented housing, and living in disadvantaged areas reflect a higher probability of reporting tobacco expenditure [32]. As a result, the tax revenue from the sale of tobacco products is being disproportionately drawn from the poorest households and represents a greater proportion of their household budget [32]. It is also evident that the ways in which systems such as education and health are delivered and structured can increase existing inequality. For example, schooling can be a way of addressing inequality and also a way of reproducing it. It has been suggested that there are two goals for a social justice program in education: to work to eliminate the contribution that the education system makes to the production over time of social inequality in general; and to maximise the positive contributions that the education system makes to reducing social inequality [33]. Therefore, different approaches and mixes of policies and programs must be mounted to address inequalities. These approaches may include more precise targeting, but also greater attention to community-based dimensions of 'interdependence' between individual behaviours, key determinants, and community and institutional resources. Policy-makers who wish to address socioeconomic inequalities in health may favour one of the following approaches. Some view the impact of socioeconomic disadvantage on those groups with the poorest health in the population, such as Aboriginal people and Torres Strait Islanders, as the priority policy goal. Others identify the gap between the health of those groups at the outer ends of the socioeconomic hierarchy (those with the poorest health and those with best health), and see the narrowing of the gap as the goal. Others prefer to focus on the socioeconomic gradient in health that runs across the whole population [31]. Graham has identified that the last approach widens the policy debate in three ways [31]. Firstly, it looks for the causes of health inequality in the systematic differences in life chances and opportunities, living standards and lifestyles that are associated with people's unequal positions across the socioeconomic hierarchy, and for the pathways through which they influence health [31,36]. Secondly, as a result, addressing health inequalities becomes a population-wide goal that includes every citizen [31]. Thirdly, 'reducing health gradients' provides a more comprehensive policy approach: one that encompasses 'remedying disadvantages' and 'narrowing health gaps' within the broader goal of 'equalising health chances across all the socioeconomic groups' [31]. She also observes that, "improving the health of poor groups and improving their position relative to other groups are necessary elements in a strategy to reduce the socioeconomic gradient. However, neither is sufficient on its own. To reduce the socioeconomic gradient, health in other socioeconomic groups also needs to improve at a faster rate than in the highest socioeconomic group. Thus, policies to ameliorate health disadvantage, to close health gaps and to reduce health gradients need to be pursued together, and not at the expense of each other" [31]. There is also an urgent need to make health inequalities a research priority for each stage of the life course – not just to monitor the size and extent of the disparities but also to undertake research that will find preventive approaches and further policy interventions that will be effective in reducing them, and that are likely to be implemented by governments and communities. Conclusions Clearly, any moves to address the impact of chronic disease at a population level must take into account socioeconomic inequalities in prevalence. More research is needed to determine which approaches are effective and why others have failed to have the desired impact, particularly for those who are from socioeconomically disadvantaged areas. Finally, although rates are generally highest at the oldest ages, the development of risk factors for many chronic diseases occurs early in life, and thus, it is essential those health inequities are addressed right across the life course. Methods Data sources The ABS conducts the National Health Survey (NHS) on a regular basis, most recently in 2001 [22]. The NHS collects information from approximately 26,900 people from all States and Territories living in private dwellings, selected at random using a multi-stage area sample of private dwellings. The survey is undertaken across much of Australia, but excludes the 'sparsely settled' areas, which comprised less than 1% of the non-Indigenous population and 25% of the Indigenous population at the 2001 Census: a separate Australia-wide survey of the health of Indigenous people, also conducted in 2001, surveyed these sparsely settled areas. The survey includes self-reported details of health conditions (both acute and long term) and major risk factors, as well as demographic and socioeconomic information about the survey respondent. Respondents were asked if they had been told by a doctor or nurse that they had asthma, cancer, heart and circulatory conditions, and/or diabetes. These conditions, together with injuries and mental health, form the NHPAs [27]. However, for long term mental health problems, respondents were not asked whether they had been told by a doctor or nurse that they had any mental health problems; thus, the responses may be based on self-diagnosis, rather than diagnosis by a health practitioner [22]. Respondents were also asked a series of questions about other specific, non-NHPA, conditions, covering eye and sight problems, ear and hearing problems, and arthritis, rheumatism and gout. They were then shown a series of three prompt cards (two with conditions listed, while the third contained more general descriptions of condition types) and asked whether they had any of the conditions shown or conditions similar to those shown or described. In each of these cases, details were recorded for conditions reported as current at the time of the survey; respondents were also asked whether the condition had lasted, or was expected to last, for six months or more. Information was gathered directly from individuals aged 15 years and older. For children up to the age of 15 years, information was provided by proxy, from a parent or guardian. The particular conditions for which data were requested from the ABS for this analysis were: the NHPA conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers [15]; and for which sufficient cases were available for analysis by five groups of socioeconomic disadvantage of area (see below for details of the way these groups were constructed). The risk factors used by the ABS were those identified for the NHPA conditions [27]. The ABS has coded conditions reported by respondents to output disease categories based on ICD-10. Conditions described as 'chronic' in this article include those long-term conditions reported in the NHS, which are commonly recognised by health practitioners as chronic diseases [23]. The risk factor for 'high risk due to alcohol' reflects the National Health and Medical Research Council's risk levels for harm in the long term from alcohol consumption [24]. The risk factors for overweight and underweight were calculated from self-reported height and weight information and grouped to reflect World Health Organization (WHO) guidelines [25]. Given the policy importance of the NHPAs, the 2001 NHS questionnaire underwent significant revisions to more precisely capture information on several of the NHPAs. Consequently, while the quality of the information on NHPAs has been improved from the 1995 NHS, the degree of comparability with previous surveys has been somewhat compromised for many of the major health conditions. Some specific conditions (e.g., diabetes) appear to be comparable between the 1989–90 and 2001 surveys, however, for most groups of conditions based on ICD chapter headings (e.g., all circulatory) the ABS advise that the combined effect of major conceptual changes as well as major classification changes between the 1989–90 and 2001 surveys would make direct comparisons very difficult. This analysis is therefore restricted to the 2001 data. Measurement of socioeconomic status The socioeconomic status (SES) of the address of residence of each survey respondent is available at the Census Collection District (CD) level and was added to the NHS file, as was the quintile of socioeconomic disadvantage of area into which that CD fell at the Census. The measure used to allocate CDs to quintiles was the 1996 Census Index of Relative Socio-Economic Disadvantage (IRSD). The IRSD is one of five Socio-Economic Indexes for Areas produced by the ABS using Principal Components Analysis. It summarises information available from variables collected in the 1996 Population Census including those related to education, occupation, and income. The variables are expressed as percentages of the relevant population. The NHS records were aggregated to the quintiles derived from the Census data, where Quintile 1 comprises the CDs with the highest IRSD scores (highest socioeconomic status, or most advantaged, areas) and Quintile 5 comprises the CDs with the lowest IRSD scores (lowest socioeconomic status, or most disadvantaged areas). Each quintile comprises approximately 20% of CDs. The ABS produced the estimates of the number of people with chronic diseases and risk factors by quintile. The method used resulted in the production of quintiles of varying sizes, ranging from 17.4% of the population in Quintile 5 (most disadvantaged areas) to 22.8% in Quintile 2 and 21.1% in Quintile 1 (most advantaged areas). This is a differential of over five percentage points between Quintile 5 and Quintile 2, or 1,027,030 fewer people in the most disadvantaged areas when compared with Quintile 2 (and 708,980 fewer in Quintile 5 than in Quintile 1). The effect of this lack of precision on the results by quintile is not known. Although, in part, the difference arises as a result of the method used (that is, that the quintiles are based on the Census population, and applied to the NHS population which has a different profile), it may also reflect different response rates to the survey from different socioeconomic groups. The NHS includes other measures that could potentially be used to measure socioeconomic status. For example, income reporting in ABS surveys is known to be incomplete, in particular for low income groups and, in the 2001 NHS, income was only collected for the respondent and their spouse. Information on education is also available, but a single measure has yet to be agreed. Problems in using education include that it is typically completed early in adulthood; it captures neither differential on-the-job training and other career investments made by individuals with similar levels of formal schooling, nor the volatility in economic status during adulthood that has been shown to have adverse implications for health [29]. The age structure of the population may also influence the indicator: an older population generally has lower education levels than a younger population due to improved access to education over time. Analysis Chronic disease and risk factor rates are expressed as rates per 100,000 population, indirectly age-standardised. The rates were also calculated by age although, given the small sample size of the NHS, only broad age groups (0–14 years, 15–24 years, 25–64 years and 65 years and over) were available. The standard population and quintile populations are the weighted 2001 survey populations from the NHS. The extent of any differential between the quintiles is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00). Competing interests The author(s) declare that they have no competing interests. Authors' contributions JG conceived the study, and was responsible for its design and coordination. ST participated in the design of the study and performed the statistical analysis. DH participated in the drafting of the manuscript. All authors read and approved the final manuscript. Acknowledgements The Australian Government Department of Health and Ageing funded this research. 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==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-81567994310.1186/1743-0003-1-8EditorialVirtual reality and physical rehabilitation: a new toy or a new research and rehabilitation tool? Keshner Emily A [email protected] Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Room 1406, 345 East Superior Street, Chicago, IL 60611, USA2 Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Room 1406, 345 East Superior Street, Chicago, IL 60611, USA2004 3 12 2004 1 8 8 26 11 2004 3 12 2004 Copyright © 2004 Keshner; licensee BioMed Central Ltd.2004Keshner; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Virtual reality (VR) technology is rapidly becoming a popular application for physical rehabilitation and motor control research. But questions remain about whether this technology really extends our ability to influence the nervous system or whether moving within a virtual environment just motivates the individual to perform. I served as guest editor of this month's issue of the Journal of NeuroEngineering and Rehabilitation (JNER) for a group of papers on augmented and virtual reality in rehabilitation. These papers demonstrate a variety of approaches taken for applying VR technology to physical rehabilitation. The papers by Kenyon et al. and Sparto et al. address critical questions about how this technology can be applied to physical rehabilitation and research. The papers by Sveistrup and Viau et al. explore whether action within a virtual environment is equivalent to motor performance within the physical environment. Finally, papers by Riva et al. and Weiss et al. discuss the important characteristics of a virtual environment that will be most effective for obtaining changes in the motor system. ==== Body Prevalence of virtual reality technology Virtual reality (VR) technology has been used for several decades for a variety of psychosocial interventions. But since the early 1990's there has been an explosion of laboratories and clinics promoting the use of virtual technology for physical rehabilitation [1-4]. Presently, combining the words virtual reality and rehabilitation brings up 132 articles in PubMed. I served as guest editor of a group of six papers on augmented and virtual reality in rehabilitation that appear this month on the Journal of NeuroEngineering and Rehabilitation (JNER). These papers demonstrate a variety of approaches taken for applying VR technology to physical rehabilitation. VR describes a computer-generated scenario (a virtual world) with which the user can interact in 3 dimensions so that the user feels that he or she is part of the scene [6]. Currently, there are 4 forms of virtual environments: head mounted display, augmented, Fish Tank, and projection-based [see [5-7] for a review]. A totally immersive VR system is the head mounted display (HMD) where the subject sees only the computer-generated image and the rest of the physical world is blocked from view. With augmented VR systems both computer generated images and the physical world are visible to the subject. Hence, the computer world is overlaid on the physical world. With "Fish Tank" VR, the stereo images are produced on a monitor in front of the subject [8]. These systems have a limited field of view (FOV) and space in which one can interact with the scene. Consequently, the resulting FOV is smaller than that available with other VR systems but the accompanying pixel visual angle is also smaller and, therefore, better. With projection-based VR, the computer generated imagery is projected on a screen or wall in front of the user much like that in a theater [9]. Back-projection is often used instead of front-projection to insure that the projected scene is not obscured by the subject's body. These systems usually have a wide field of view and can be multi-walled and floor systems as with the CAVE™ technology. Among the papers published this month on JNER, Sparto et al. present studies using a monocular projection based virtual environment to determine if patients with vestibular disorders will tolerate wide FOV environments. Also, Kenyon et al. explore emerging VR technologies and the application of a stereo projection based VR system to research in a posture laboratory. Why use a virtual world for rehabilitation? Many people question why we don't just have subjects perform motor tasks in the real world. The answer to this question is that VR offers us the opportunity to bring the complexity of the physical world into the controlled environment of the laboratory. VR gives us the potential to move away from reductionism in science and towards the measurement of natural movement within natural complex environments. In general, VR allows us to create a synthetic environment with precise control over a large number of physical variables that influence behavior while recording physiological and kinematic responses [10]. To this topic relate the papers by Sveistrup and Viau et al. also published on JNER this month. Viau et al. compare the kinematic strategies of reach, grasp, and place movements performed with physical and virtual objects by healthy adults and those with hemiparesis. Sveistrup presents current work on motor rehabilitation using virtual environments and virtual reality and, where possible, compares outcomes with those achieved in controlled real-world applications. There are numerous strengths underlying the use of VR with rehabilitation [11,12]. Among these are that VR provides the opportunity for ecological validity, stimulus control and consistency, real-time performance feedback, independent practice, stimulus and response modifications that are contingent on a user's physical abilities, a safe testing and training environment, the opportunity for graduated exposure to stimuli, the ability to distract or augment the performer's attention, and perhaps most important to therapeutic intervention, motivation for the performer. In the group of papers that I guest-edited for JNER, the application of Fish Tank VR as a rehabilitation tool for patients with spinal cord injury is explored by Weiss et al. Another question that has arisen at meetings and in the review of the papers for JNER is under what circumstances a computer generated environment should be considered virtual reality? Factors that differ among many of the laboratories claiming to use virtual reality and that also emerge amongst this group of papers include field of view, the presence of stereo vision, and real-time feedback of head position so that the scene can be updated to reflect natural movement of the visual world. There is evidence demonstrating that a transfer of training from the virtual to the physical environment is greater if the learner is immersed in the training environment [13]. Perhaps then the most important and defining factor for VR is the sense of presence of the performer in the environment. Thus, the first paper by Riva et al. that appears on JNER this month focuses on the meaning of presence and its importance to the use of VR for rehabilitation. ==== Refs Greenleaf WJ Tovar MA Augmenting reality in rehabilitation medicine Artif Intell Med 1994 6 289 299 7812424 10.1016/0933-3657(94)90034-5 Kuhlen T Dohle C Virtual reality for physically disabled people Comput Biol Med 1995 25 205 211 7554838 10.1016/0010-4825(94)00039-S Rose FD Attree EA Johnson DA Virtual reality: an assistive technology in neurological rehabilitation Curr Opin Neurol 1996 9 461 467 9007406 Tarr MJ Warren WH Virtual reality in behavioral neuroscience and beyond Nat Neurosci 2002 5 1089 1092 12403993 10.1038/nn948 Keshner EA Kenyon RV Using immersive technology for postural research and rehabilitation Assist Technol 2004 16 54 62 15357148 Sherman W Craig A Understanding virtual reality: Interface, application, and design 2002 California: Morgan Kaufmann Stanney KM Handbook of Virtual Environments: Design, Implementation, and Applications 2002 New Jersey: Erlbaum Assoc Arthur K Booth KS Ware C Evaluating human performance for Fishtank Virtual Reality ACM Transactions on Information Systems 1993 11 239 265 10.1145/159161.155359 Cruz-Neira C Sandin DJ DeFanti TA Kenyon RV Hart JC The CAVE automatic virtual environment Communications 1992 38 64 72 Carrozzo M Lacquaniti F Virtual reality: a tutorial Electroencephalogr Clin Neurophysiol 1998 109 1 9 11003058 10.1016/S0924-980X(97)00086-6 Rizzo AA Kim G A SWOT analysis of the field of virtual rehabilitation and therapy Presence: Teleoperators and Virtual Environments Rizzo AA Schultheis MT Kerns K Mateer C Analysis of assets for virtual reality applications in neuropsychology Neuropsych Rehab 2004 14 207 239 10.1080/09602010343000183 Stanney K Salvendy G Deisinger J DiZio P Ellis S Ellison J Fogleman G Gallimore J Singer M Hettinger L Kennedy R Lackner J Lawson B Maida J Mead A Mon-Williams M Newman D Piantanida T Reeves L Riedel O Stoffregen T Wann J Welch R Wilson J Witmer B Aftereffects and sense of presence in virtual environments: formulation of a research and development agenda Int J Hum Comput Interact 1998 10 135 187 11542908	15679943	PMC546404	CC BY	2021-01-04 16:37:38	no	J Neuroengineering Rehabil. 2004 Dec 3; 1:8	utf-8	J Neuroeng Rehabil	2,004	10.1186/1743-0003-1-8	oa_comm
==== Front Cerebrospinal Fluid ResCerebrospinal Fluid Research1743-8454BioMed Central London 1743-8454-1-31567994410.1186/1743-8454-1-3ReviewHomeostatic capabilities of the choroid plexus epithelium in Alzheimer's disease Johanson Conrad [email protected] Paul [email protected] Rosemarie [email protected] Anthony [email protected] John [email protected] Gerald [email protected] Edward [email protected] Department of Clinical Neurosciences, Brown Medical School, Providence, RI 02903, USA2 Department of Pathology, Brown Medical School, Providence, RI 02903,USA2004 10 12 2004 1 3 3 21 11 2004 10 12 2004 Copyright © 2004 Johanson et al; licensee BioMed Central Ltd.2004Johanson et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. As the secretory source of vitamins, peptides and hormones for neurons, the choroid plexus (CP) epithelium critically provides substances for brain homeostasis. This distributive process of cerebrospinal fluid (CSF) volume transmission reaches many cellular targets in the CNS. In ageing and ageing-related dementias, the CP-CSF system is less able to regulate brain interstitial fluid. CP primarily generates CSF bulk flow, and so its malfunctioning exacerbates Alzheimers disease (AD). Considerable attention has been devoted to the blood-brain barrier in AD, but more insight is needed on regulatory systems at the human blood-CSF barrier in order to improve epithelial function in severe disease. Using autopsied CP specimens from AD patients, we immunocytochemically examined expression of heat shock proteins (HSP90 and GRP94), fibroblast growth factor receptors (FGFr) and a fluid-regulatory protein (NaK2Cl cotransporter isoform 1 or NKCC1). CP upregulated HSP90, FGFr and NKCC1, even in end-stage AD. These CP adjustments involve growth factors and neuropeptides that help to buffer perturbations in CNS water balance and metabolism. They shed light on CP-CSF system responses to ventriculomegaly and the altered intracranial pressure that occurs in AD and normal pressure hydrocephalus. The ability of injured CP to express key regulatory proteins even at Braak stage V/VI, points to plasticity and function that may be boosted by drug treatment to expedite CSF dynamics. The enhanced expression of human CP 'homeostatic proteins' in AD dementia is discussed in relation to brain deficits and pharmacology. ==== Body Review Choroid plexus impact on Alzheimer's disease Accumulating evidence supports the idea that continually decreasing choroid plexus (CP) function in advanced ageing exacerbates Alzheimer's disease (AD). As part of a new paradigm to explain brain interstitium deterioration in age-related dementias, increasingly more attention is being paid to the role of compromised blood-CSF [1] and blood-brain [2] barriers. Structural alterations and functional failures in CP as well as brain capillary transport systems adversely affect fluid dynamics and composition [3,4]. This review treats mainly CP dysfunction and the compensatory reactions that occur in this epithelium in AD. Efficient CSF turnover is essential for a healthy brain. It depends upon an exquisite balance between CSF formation and reabsorption [5]. Compromised secretory phenomena at the CP 'upstream' predispose the brain to AD-type problems. On the other hand, defective clearance of CSF at the arachnoid membrane 'downstream' leads to normal pressure hydrocephalus (NPH) [6]. The pivotal role of the CP in CSF homeostasis and brain viability becomes more evident when the system fails. More information is needed to evaluate how diminishing choroidal functions affect the surrounding brain in the face of AD and other dementias. Brain fluid homeostasis: The role of CSF volume transmission In serving the brain's metabolic needs by supplying 'biochemical goods', the CP uses CSF as a conduit for convecting substances [7]. Numerous solutes ranging from ions to large proteins are entrained in the CSF that percolates through the ventricular axis (Fig. 1). CSF intimately contacts the periventricular brain tissue with which it exchanges materials bidirectionally by diffusion and bulk flow. Continually undergoing chemical modification as it flows downstream, the CSF completes the volume transmission process [7] by draining into venous blood at distal arachnoidal sites (Fig. 1). Figure 1 Schema for CSF convection of water and solutes: Arterial blood perfusing the choroid plexus continually provides water, ions and organic substrates for the CP epithelial cells to form the CSF that underlies volume transmission. Manufactured by the CP epithelium as the result of numerous transport processes, the CSF is actively secreted into the ventricles. In transit through the ventricular cavities to the arachnoid drainage sites, the CSF exchanges anabolites and catabolites with brain interstitial fluid. As a result, trophic and signaling molecules are delivered (blue arrow) to the neurons and, concurrently, toxic waste products and unneeded proteins are removed (red arrow) by CSF 'sink action' on the brain. The permeable ependymal membrane allows bi-directional diffusion of beneficial and harmful molecules. Bulk flow or volume transmission of CSF is thus essential in effecting the homeostasis of fluid composition. Reduced formation of CSF and stagnated flow in ageing and AD [4] limits the delivery of substances to neurons. This debilitates brain function [3]. Numerous proteins are synthesized and secreted by CP into CSF. Other substances are transported from blood. Table 1 overviews molecules normally distributed by the CP-CSF nexus. Arginine vasopressin (AVP) is a neuropeptide synthesized by CP epithelium and secreted into CSF [8,9]; it regulates CSF formation and modulates hippocampal memory mechanisms [10,11]. Growth factors foster cell growth, blood supply and water balance [1]. Trace element distribution across CP is complex and involves many mechanisms. Iron transport into CSF, for example, is regulated by several proteins [1] alterations in which may predispose to amyloidogenesis [12]. Vitamins B and C are actively transported at the blood-CSF barrier [13]. This stabilizes their concentration in CSF, except in late-onset AD [14]. The net entry of nucleoside bases into CSF is also determined by active transporters in CP [15,16]. Cysteine protease inhibitors like cystatin C are synthesized in CP epithelium for transport into CSF [17]. Hormones such as leptin and prolactin are translocated from plasma to CSF by saturable choroidal receptors [18,19]. Overall it is striking that the CP-CSF interface engages in such prolific transport. Disease-associated interference with CP transporters and volume transmission limits the availability of CSF molecules for the brain [1,20]. Table 1 Substances distributed to brain by transport at the blood-CSF gateway Class or group Examples Functions References Neuropeptides Arginine vasopressin Regulation of CSF formation [8–11] Growth factors Basic fibroblast growth factor Integration of water balance [55, 104] Trace elements Iron Enzyme function in neurons/glia [1, 12] Vitamins Ascorbate & folate Antioxidants & co-factors for brain [13, 14] Nucleoside bases Thymidine Nucleic acids & drug transport [15, 16] Protease inhibitors Cystatin C Neuroprotection after ischemia [1, 17] Hormones Prolactin & leptin Modulation of hypothalamus [18, 19] Structural and functional damage to the choroid plexus in AD Structure intimately relates to function in various epithelia. Therefore it is useful to analyze histopathological damage to CP at various stages of AD in order to clarify the onset of functional losses at the blood-CSF barrier. Ageing and AD cause similar degeneration in CP. Structural changes in AD though are usually greater than in non-demented control counterparts [21]. Serot and colleagues have delineated modifications in the choroidal epithelium, stroma and vessels in AD subjects [22-24]. There are substantial alterations in all tissue compartments. Several features characterizing the CP in AD are highlighted in Table 2. Epithelial cells are typically truncated, with an average volume 70% of that at birth. Such epithelial atrophy likely affects cellular functions. Particularly prominent is an increase in the number of Biondi bodies in AD [25]. These bodies are fibrillar inclusions in the cytoplasm of very old epithelial cells. Lipofuchsin vacuoles also occur frequently in the cytoplasm. The basement membrane underlying the cell often thickens to 350 nm, compared to a much thinner membrane (ca. 100 nm) in neonates [21]. Another liability in AD is immunological deposition of C1q, IgG and IgM along the epithelial basement membrane. As fibrosis intensifies with age and disease, the stroma attains a thickness of a few tenths of a micron [21]. At the inner core of the choroidal villus, the blood vessel walls thicken. This coincides with the appearance of amyloid, hyaline bodies, psammomas and calcifications. Altogether, the histopathologic changes in CP compartments point to grossly-declining secretory functions in AD. Such structural abnormalities coincide with diminished CSF production in ageing [26] and AD patients [4]. Table 2 Pathological changes in choroid plexus in Alzheimer's diseasea Epithelial atrophy (↓ cell size by 1/3) ↑ Biondi bodies Lipofuchsin vacuoles Basement membrane thickening (3-fold ↑) ↑ Stromal fibrosis IgG and IgM depositions aDescribed by Serot and colleagues in refs. 22–24 Due to the functional nexus of the CSF with brain interstitial fluid, the neuronal microenvironment in AD is impacted by markedly altered transport and permeability in CP. When neurodegenerative diseases, ischemia [27,28] and elevated pressure [29,30] inflict damage on the choroidal epithelium, there are resultant adverse changes in CSF composition and volume. Because neurons are sensitive to instabilities in CSF dynamics and constituents, it is important to assess the nature and progression of disrupted CP function in chronic diseases. In view of the expanding number of AD victims, it is timely to consider patterns of expression of chaperone proteins, receptors and transporters in CP at various Braak stages. Such information may abet future attempts to stabilize choroid plexus functional integrity perturbed in early AD dementia. Choroid plexus defense against insults in ageing and degeneration The CP is multifunctional, performing a wide range of homeostatic functions for the CNS [5]. CSF homeostasis is mediated mainly by CP. It involves the activity of many protein transporters and receptors at the basolateral and apical surfaces of the epithelial cells [31]. In addition to providing organic solutes for nutritive and trophic support of the brain, the CP secretions into the ventricles adjust the pH, osmolality, [K+] and immune molecule content of the CNS extracellular fluid [7]. Accordingly, healthy neurons are fundamentally dependent upon transport at the blood-CSF and blood-brain interfaces. Moreover to provide steady neuroprotection by regulating the extracellular milieu, the CP must protect itself against various stressor agents that build up during ageing and disease. There have been few investigations of the homeostatic systems within CP that stabilize choroidal functions in the face of ageing and AD. We have explored some candidate systems for compensatory responses by CP. Cytoplasmic heat shock proteins chaperone and perform housekeeping to maintain a healthy steady-state intraepithelial milieu [1,32]. Growth factors critically minimize cell morbidity and mortality [27]. Fluid-regulating proteins correct ion and water imbalances that occur in neurodegenerative diseases. Consequently our hypothesis is that the upregulation of certain proteins in CP (such as those discussed above) thwarts certain untoward effects of ageing and disease progression. The protein expression aspect of the hypothesis was tested by analyzing immunostaining patterns in human CP specimens from patients with varying severity of AD. Analyses of human choroid plexus: Usefulness and challenges It is difficult to functionally assess the CP in vivo [30,33], particularly in man. Alternatively one can evaluate the status of human CP in disease by analyzing autopsied tissues for variable protein expression [34]. Such findings are compared to appropriate age-matched controls. Immumocytochemical and biochemical data gleaned from human CP highlight directions to pursue with living animal models: transgenic mice with an AD phenotype or aged rats with CSF pathophysiology. Because protein expression relates to disease progression, it is essential to standardize grading for the severity of AD in subjects. We use a modified Braak & Braak staging system [35]: stages I/II (mild; disease involves hippocampus and entorhinal cortex); stages III/IV (moderate; AD spreading to the rest of the limbic lobe, e.g., amygdala); and stages V/VI (severe; further AD spreading to the prefrontal neocortex). Information about functional proteins in human CP is scarce. However protein expression was recently analyzed in autopsied lateral ventricle CP from normal adult brains and in those with confirmed AD [36,37]. The ages investigated were generally between 65 and 90 yr for all subjects. Control individuals typically died from cardiac disease or tumors. AD specimens covered all Braak stages. Causes of the AD deaths were usually cardiac complications or pneumonia [37]. Fortunately the postmortem intervals (mainly between 2 and 18 hr) did not affect antibody staining [37]. For specimen quality it is desirable to procure choroidal tissues quickly after death. Human CP banks are needed to systematically catalog tissues from various stages of AD. Regulation of CP 'homeostatic proteins' in health and disease Ageing and AD dementia tax the CP and other CNS transport interfaces. In late life the deteriorating brain presents many potentially-destructive metabolites to the CSF for multi-site excretion into blood. The greater burden of macromolecule disposal in AD occurs when CP and arachnoid membrane, due to ageing debilities, are less able to transfer solutes. Nevertheless the CP seemingly attempts to maintain its 'epithelial soundness' when challenged to perform additional cleansing acts for the brain extracellular fluid (CSF). In healthly, young adults the CP epithelium sensitively acclimates to chemical and physical distortions in blood, CSF and parenchyma. Following cortical stabbing in rats, the CP upregulates TGFβ presumably to provide this CSF-borne growth factor for repairing the injury [38]. A similar phenomenon occurs with IGF-II [39], which is manufactured in the CNS mainly by CP. In diabetes there is enhanced expression of the NKCC1 cotransporter in CP for adjusting CSF dynamics and water distribution [40]. Such compensatory responses at the blood-CSF barrier in early adulthood raise the question about CP's ability in later life, when besieged by the deficits of aging and AD, to adequately respond by expressing certain 'homeostatic proteins'. Accordingly to assess pathophysiologic consequences of AD we investigated human CP's ability to upregulate certain functional proteins (as distinguished from structural ones) in advanced states of AD dementia. With advancing knowledge about intricate cell physiology (coordinated interactions among organelles, cytoplasm and membrane-bound proteins) it is relevant to evaluate components of cellular homeostasis. The term 'homeostatic proteins'refers to chaperones, receptors and transporters that stabilize the internal environment of the cell. CSF stability depends in large part upon CP epithelial homeostasis. Our group has analyzed several 'homeostatic proteins' involved in CP intracellular milieu stabilization: Heat shock proteins A wide spectrum of protection against neurodegeneration is provided by HSPs. The list of protective effects bestowed by HSP molecules is diverse and includes: accelerated degradation of misfolded proteins, maintenance of membrane lipid integrity, prevention of deleterious protein aggregation, and preclusion of damage to the translational apparatus [41,42]. HSPs are also known as 'stress proteins' due to their role in shepherding adaptive responses to stressors such as ischemia, trauma, fever, dehydration, hydrocephalus and other brain disorders. To evaluate human CP expression, we selected HSPs overexpressed in AD brains: GRP94 and HSP90. In contrast to previously found upregulation in brain, there was downregulated GRP94 in lateral ventricle CP. In aged controls there was abundant staining in the epithelial cytoplasm and stroma (Fig. 2, top left). However in AD there was a striking decrease in immunostaining of GRP94 choroidal tissues (Fig. 2, top right). GRP94 is an atypical HSP in responding specifically to glucose deprivation rather than to generalized intracellular oxidative stress. In AD the opposite responses in GRP94 expression by CP vs. brain are interesting but not unexpected because secretory epithelium has biochemical characteristics fundamentally different from neurons. GRP94 chaperones protein folding, especially in endoplasmic reticulum [43]. Underexpressed GRP94 in CP of AD subjects may render the reticulum vulnerable to unfolded proteins. Figure 2 Heat shock (stress) protein expression in human CP: Formalin-fixed, paraffin-embedded specimens (8–10 micrometers thick) were de-paraffinized and rehydrated. Sections were incubated overnight with antibodies against HSP90 (1/500; SPA830) and GRP94 (1/500;SPA850) and stained by the ABC technique (Vectastain Elite ABC peroxidase). Deposition of the brown chromogen (diaminobenzidine) reaction product was either substantially reduced by preabsorption blocking or virtually eliminated by omission of either primary or secondary antibodies. Slides were assessed blindly for staining intensity and distribution [37]. See text for description of localization and interpretation. For each HSP, images are representative of 10 AD specimens and 5 age-matched controls. In the case of HSP90, the reverse pattern of GRP94 was observed. There was faint staining of non-AD tissues (Fig. 2, bottom left) but strong expression in epithelial cytoplasm in AD CP (Fig. 2, bottom right). Multiple effects can be induced by a particular HSP. By complexing with several intracellular protein kinases, HSP90 could alter CSF secretion; and by inducing the heme-regulated e1F-2 alpha kinase, the overexpressed HSP90 may downregulate gene transcription [44]. Another possible effect of HSP90 is to beneficially accelerate clearance (reabsorption) of Aβ peptide by CP. Such facilitation occurs in the microglial handling of Aβ [45]. It would be worthwhile to pursue the role of CP HSPs in removing Aβ from CSF. FGF peptides and receptors The FGF superfamily of peptides and its multiple receptors in CP, ependyma, and brain, modulate many actions on neurons and non-neural cells [30]. FGF2, or basic FGF, is prototypic of the family. CP synthesizes and releases FGF2 into CSF. Choroidally-secreted FGF2 stimulates receptors (FGFr) nearby in the CP apical membrane [9] and at more distant sites in the brain parenchyma [46]. FGF/FGFr is apparently unique among growth factors in directly effecting balance in the brain fluids, including the formation of CSF. This is relevant to AD in which brain FGF is increased [47] and CSF turnover declines [1,4,6]. FGF and FGFr are also fundamentally important in fostering neuron generation from stem cells in the subventricular zone (SVZ). This requires coordination between the CP-CSF and periventricular regions [46,48]. Pharmacological manipulation of SVZ stem cells is potentially important at all stages of life. For pathological and therapeutic reasons, therefore, it is important to delineate FGFr expression patterns and their significance in i) ontogeny, ii) normal adult maintenance, and iii) neurodegeneration. i) Ontogeny Receptor plasticity in aging and AD is better seen in light of information on FGF/FGFr expression dynamics in early life. In the fetus the formation of CP, neuronal stem cells and brain is promoted by CP growth factor secretion and CSF distribution [46,49]. Intense activity of FGF/FGFr figures prominently in CNS viability and expansion. Expression of FGFr-2 and -3 in murine CP is maintained prenatally, whereas FGFr-1 and -4 are present during the 2nd but not 3rd gestational week [49]. FGF peptides released from CP use autocrine and paracrine mechanisms to stimulate various forms of FGFr expressed by CP epithelium. Specific functions of the four different receptor isoforms need elucidation. Despite limited data for CP FGFr expression patterns in aging, the genetic regulation of FGFr during embryonic life [49] suggests the potential to pharmacologically enhance FGFr expression in AD. The goal in filling these knowledge gaps about FGFr is to attain more efficacious treatment of injuries to the brain interior. FGF2 derived from CP is also conveyed by CSF bulk flow to the fetal germinal matrix where it acts on stem cell FGFr to promote neuronal maturation [46]. By this endocrine-like mechanism, the CSF-mediated distribution of FGF2 and other peptides plays a prominent role in 'spawning' new neurons in the periventricular regions. Distorted CSF volume and flow in hydrocephalus interferes with the CSF provision of FGF2 to FGFr on stem cells in the SVZ [46]. Brain malformation ensues. Clearly the orderly function of the CP-CSF system, e.g., the programmed secretion and distribution of growth factors, is essential to normal CNS development. ii) Adult maintenance and response to stressors The FGF/FGFr system also has a key role in adult CNS fluid homeostasis. CP helps the brain adapt to alterations in blood composition and flow. In otherwise healthy young adults, the imposition of dehydration or sudden ischemia upon the CNS elicits striking adaptive changes in CP epithelium. To endure insults by chemical or physical stressors [29], it is critical that CP viability be maintained so that the brain can continue to benefit from 'homeostatic adjustments' in transport phenomena at the blood-CSF barrier [50]. Growth factors are an integral part of these adaptive responses to stress. Dehydration and ischemia are common to aging and AD. Elucidation of CP-CSF growth factor responses to these disorders in normal adults should enhance our perspective on homeostatic capabilities in AD. Dehydration seriously threatens CNS functions. Adjustments to dehydration in the healthy adult brain feature ion and water redistribution among fluid compartments. These compensatory responses to plasma hyperosmolality stabilize neuronal and interstitial volumes. Brain 'barriers' or transport interfaces are sites for the fluid homeostatic mechanisms. A working model for the restoration of fluid balance is offered: Dehydration or hyperosmolality upregulates the FGF2 and AVP peptides in CP [51,52]. FGF2 released by CP binds in an autocrine manner to FGFr. Such FGFr stimulation likely promotes AVP release from CP epithelium [9]. The extruded AVP then binds V1 receptors in CP to regulate ion transport [53] and fluid production [10,54]. FGF2 works in concert with AVP to control fluid movement across the blood-CSF barrier [51,55]. Interestingly in AD there are upregulated receptors for FGF (Fig. 3) and AVP [56] in human CP, presumably in response to fluid imbalance. Cumulative evidence points to co-localized growth factors and neuropeptides jointly stabilizing brain fluids after perturbed osmolality and volume. Figure 3 FGF receptor expression in human CP: Lateral ventricle plexuses obtained from pathologically-confirmed AD subjects were immersion-fixed in paraformaldehyde, cryoprotected and stored at -70°C. CP segments were free-floated for 72–96 hr in a 1/500 polyclonal antibody against the FGFr, which recognizes all FGF receptor subtypes. Specific staining was established by antibody omission/preabsorption [36]. ABC peroxidase/diaminobenzidine technique was used. Arrowhead points to small punctated dots of immunoreactivity. Localization of FGFr is described in text. Images are typical of those obtained from 8 controls and 8 AD specimens, respectively. Ischemia is another disorder with neuropathological consequences that are mitigated by growth factor upregulation or administration [57]. Bilateral carotid artery occlusion in young adult rats for 6–10 min wreaks damage to tissues surrounding the lateral ventricles [58,59]. Hence severe transient forebrain ischemia (TFI) injures the lateral plexus as well as the hippocampus [60]. However peptides such as FGF2 and TGFβ defend the forebrain interior against ischemic and hypoxic insults [57,58,61]. Although TFI with hypotension (40 mmHg) destroys many choroidal epithelial cells [60], there is a role by growth factors to efficiently repair the breached blood-CSF barrier [60]. Restitution of the epithelial lining of the choroidal villi within several hours post-stroke [60] implies the importance of a functional CP for CNS viability. Upregulated secretion of FGF2, TGFβ [58] and other growth factors by CP [27] undoubtedly protects the blood-CSF barrier and periventricular brain against compromised blood flow. A time-course analysis of FGF2-FGFr expression in the aging vs. diseased CP will reveal how the blood-CSF interface responds to reduced blood flow in NPH and AD [62,63]. iii) Neurodegeneration FGF2 titers and FGFr receptor densities in degenerating CNS compartments provide insight on adaptive responses. FGF2 concentration is augmented in the AD brain [47]. Moreover with immunostaining and ELISA it was demonstrated that FGF2 levels in CP are sustained in AD [36]. It is thus probable that FGF2 and other factors secreted into CSF of aged adults are essential to forestalling harm to neurons in ischemia. A CSF feedback control system for the choroidal production of FGF2 has been suggested by FGFr identification in young adult rat CP [9]. An elevated level of FGF2 in AD brains [47] is interpreted as peptide sequestration from CSF [36], thereby lowering CSF concentration. Diminished CSF FGF2 could cause a compensatory increase in CP FGFr expression. Testing this postulate, we found enhanced staining for FGFr in AD CP epithelium (Fig. 3). This observation supports a role of the CP-CSF system in responding to increased demands by brain for FGF2. To build this model, information is needed for FGF2 and FGFr isoforms in various regions of the CNS and CSF at specific stages of dementia. FGF2 has an interesting relationship with amyloid. A worthwhile goal is to probe mechanisms of FGF2 interaction with amyloid in neuronal networks, extracellular matrix and CP-CSF. FGF2 co-localizes with several chemical forms of amyloid. Neuronal coexistence of FGF2 and amyloid precursor protein (APP) [64,65] intimates a functional relationship between FGF2 and APP, perhaps in post-injury regeneration. FGF2 also minimizes metabolic injury caused by Aβ peptides. It was initially observed that FGF2 applied to cultured neurons reduced neurotoxicity of aggregated Aβ [66]. More recent findings confirm FGF2's benefit in abolishing neurotoxicity produced by Aβ1-43 [67] and attenuation of oxidative stress in hippocampal neurons induced by Aβ peptides [68]. In the extracellular matrix FGF2 competes with Aβ and APP for binding sites on heparan sulfate proteoglycans [69,70]. This competitive binding by FGF2 may suppress interstitial amyloid plaque formation. Because the interstitium receives FGF2 from CSF, we predict that pharmacological boosting of CP secretion of FGF2 would relieve AD. FGF2-mediated protective regulation of CP transport phenomena potentially affects the course of neurodegeneration. Considerable evidence points to CP's ability to remove Aβ from CSF [71-73]. This implicates reabsorptive transport at the blood-CSF barrier to reduce CSF Aβ burden in advanced AD. In clearing Aβ from the CNS, the CP epithelium is exposed to substantial amounts of Aβ with the potential to curtail energized ion transport and fluid formation. CP Na-K-ATPase, a key enzyme in CSF production [74], also enables the transport of organic compounds [13]. Significantly, FGF2 lessens the toxicity of Aβ on Na-K-ATPase activity and mitochondrial function in cultured hippocampal neurons [68]. Toxic Aβ loads on CP transporters might impair secretion. The resultant decrease in CSF volume transmission and turnover would further destabilize the CNS. However treatment of AD with FGF analogs and IGF-1 [75] holds promise for countering Aβ toxicity [68] by creating a better CSF 'metabolic environment' for CP and brain. Fluid-regulating proteins The diminished ability of CP to form fluid in advanced ageing and AD begs the question of how epithelial ion transport proteins are altered by distorted neurochemistry in senescence. In very old laboratory mammals the Na-K-ATPase activity of CP and the CSF generated by it are cut in half [26,76]. Another ion-translocating protein coupled to CSF formation is the apical NaK2Cl cotransporter isoform1 (NKCC1). NKCC1 transports Na, K and Cl into and out of the choroidal epithelium [77], depending upon ion gradients and hormonal modulation. Versatile bidirectional transport via NKCC1 confers flexibility for regulating ion movements and concentrations in the CP-CSF. Fluid secretion at the blood-CSF interface is linked to ion fluxes mediated by the loop-diuretic sensitive NKCC1 [78,79]. NKCC1 information is plentiful for laboratory animal CP-CSF [78-84] but scarce for the human counterpart. The cation-Cl superfamily of cotransporters includes NKCC1 and consists of 7 isoforms that actively transport Na and/or K electroneutrally with Cl [85]. NKCC1 in CP has several functions, i.e., to regulate epithelial [Cl], stabilize CSF [K] and control fluid secretion [77]. The T4 antibody differentially stains the NKCC1 secretory isoform in the apical membrane but not the KCl isoform at the basolateral surface of CP. In brain fluid homeostasis the expression of NKCC1 in CP is likely sensitive to perturbations in CSF osmolality, choroidal epithelial cell volume/ ion concentrations, intracranial pressure and ventricular volume. To shed light on compensatory responses by the CP to disease, our group analyzed NKCC1 expression in congenital, high-pressure hydrocephalus; and in adult chronic, closer-to-normal pressure hydrocephalus in AD/NPH syndromes. The NKCC1 helps cells and organs adjust to disrupted fluid balance. One thus expects homeostatic upregulation of this CP cotransporter in AD with its altered CSF dynamics. For delineating NKCC1 expression we used the T4 antibody to immunostain CP at various stages of AD dementia. Robust staining of the lateral ventricle plexus (even at Braak stage V/VI) occurred in the apical membrane and cytoplasm (Fig. 4). In an earlier study of CP specimens from various mammalian species (unpublished data), we observed uniform and consistent staining of the apical membrane NKCC1. T4 staining of the cytoplasm was more variable. In the human CPs analyzed, however, there was consistent cytoplasmic staining in AD and age-matched controls (Fig. 4). This may represent cytoplasmic NKCC1 protein available for insertion into the apical membrane. Figure 4 NaK2Cl cotransporter expression in human CP: Lateral ventricle plexuses were incubated with T4 (not thyroxine) antibody, which stains the secretory isoform 1 of the NaK2Cl (NKCC1) cotransporter protein. The T4 antibody (mouse monoclonal; 1:100) was from the University of Iowa Developmental Studies Hybridoma Bank (Iowa City, IA); the biotinylated secondary was a rat-absorbed horse antibody. Diaminobenzidine was used to develop the brown reaction product. Controls (negative staining results; not shown) involved omission of secondary and/or primary antibody. AD tissues were from patients at Braak stage V/VI (top right) and III/IV (bottom right). Images are representative of 6 CPs analyzed for AD (mean age of 76 yr) and 6 for age-matched controls (mean age of 76 yr). On average, the staining intensity of AD specimens was 50% greater than controls. The text describes staining localization. All photographs are at the same magnification. AD choroidal tissues had greater T4 staining than controls (Fig. 4). The apical NKCC1 (Fig. 5) is strategically positioned to sense physical changes in CSF resulting from AD deterioration. Changes in pressure [86] or volume represent potential stimuli for inducing NKCC1 in CP. Ventriculomegaly and transient elevations in ICP in AD and NPH may elicit a compensatory response in CP to downregulate CSF formation by promoting ion reabsorption via the NKCC1 (Fig. 5). This scheme fits the enhanced expression of NKCC1 in CP of HTx congenital hydrocephalus rats with ventriculomegaly [87]. ANP, AVP, angiotensin II, serotonin and catecholamines all reduce CSF formation rate [99]. Table 3 recapitulates the stimulating effect of these same agents on NKCC1 transport activity in various tissues [88-94]. This prompts the hypothesis that CSF formation is decreased secondarily to enhanced reabsorptive uptake of Na, K and Cl by CP from CSF (Fig. 5). Information about protein levels and mRNA for NKCC1 should relate CP cotransporter expression with CSF dynamics, ICP and ventricular volume. Figure 5 A working model for hydrocephalus-induced alterations in NKCC1 cotransporter expression in CP: Ion transport across basolateral and apical surfaces is driven by transmembrane ion gradients [74, 83]. Normally the net transport of Na, K, Cl, and HCO3 from choroid cell into CSF is integral to CSF production [5, 7]. However, we postulate that when CSF formation is inhibited by various neurohumoral agents there is stimulated (+) inward NaK2Cl flux from CSF into the cell; this would increase cytoplasmic Na and Cl concentration [77] thus creating a less favorable ion gradient for basolateral uptake of Na and Cl from plasma. Consequently, there is inhibited (-) basolateral ion uptake, and sequentially, reduced apical extrusion of Na into CSF [40]. Net effect = decreased CSF formation. Consistent with this idea are observations of enhanced expression of CP NKCCl in congenital hydrocephalus [87] and AD (Fig. 4), both of which are generally associated with lower rates of CSF formation. Agents in Table 3 (e.g., Ang II) simultaneously stimulate the inward and outward arms of NKCC1, but the former three times the latter, resulting in net inward flux of ions [discussed in ref. 93]. The model thus vectorially emphasizes the inward arm of the NKCC1 (large arrowheads) as the one primarily stimulated by agents that suppress CSF formation. We hypothesize that in hydrocephalus, with increased intracranial pressure and/or ventriculomegaly, there is an associated attenuation of fluid output by CP. Table 3 Stimulation of NaK2Cl cotransport by hormones, neurotransmitters and peptides that inhibit choroid plexus-CSF formation Active agent Model Species Cotransport activitya Reference Atrial natriuretic peptide Neuroblastoma Human 82% 88 Angiotensin II Aortic endothelium Cow 38% 94 Arginine vasopressin Medullary TALb Mouse 66% 89 Serotonin agonistc Fibroblasts Cell line 49% 90 Adrenergic agoniste Parotid gland epithelium Rat R5 stainingd 91 Adrenergic agoniste Skeletal muscle (plantaris) Rat 1700% 92 Basic fibroblast growth factor Aortic endothelium Cow 40% 93 a Bumetanide-sensitive 86Rb uptake by cells b Thick ascending limb of kidney tubule c (-)-2,5-dimethoxy-4-bromoamphetamine d R5 antibody-detected phosphorylation of NKCC1 proportional to functional activity e Isoproterenol Other factors must be considered in interpreting the enhanced NKCC1 expression (Fig. 4). An alternative but not mutually exclusive explanation is that upregulated NKCC1 in AD helps to counter cell shrinkage [95] in CP (Table 2). Moreover, a rise in CSF [K] resulting from neuronal damage, would be buffered by the NKCC1 [77] and Na pump [96] in CP. Therefore it is possible that NKCC1 in CP is concurrently carrying out several physiologic responses to stresses imposed by aging and disease. The ventriculomegaly and reduced CSF formation rate observed in AD and NPH [4] are consistent with the upregulated NKCC1 in human CP. To corroborate the model, however, more data are needed for humans and animals to tightly link alterations in CP transport and fluid turnover with AD progression. Because HSP90 binds to NKCC1 and modulates its function [97], it is also of interest to explore how upregulated HSP90 in AD (Fig. 2) mechanistically relates to enhanced expression of NKCC1 (Fig. 4). The choroid plexus as a 'bioreactor' to brain diseases CP is highly equipped, homeostatically speaking, to help the brain adapt to the metabolic distortions of dementia. Injured neurons undergo compositional changes. These cellular perturbations are transmitted to the extracellular space and distort the interstitial fluid composition. Many catabolites in the interstitium eventually gain access to the ventricles where they contact the CPs. By accepting a host of CSF-borne molecules, for either transport or receptor stimulation, the CP mediates a wide scope of renal- and hepatic-like activities [13,98]. This epithelial interface also integrates many neurohumoral activities of the endocrine and immune systems [98,99]. Diseases afflicting the CNS generate injury metabolites and cytokines that are conveyed to the ependyma, pia-glia, arachnoid membrane and CP epithelium. These interfaces handle catabolites and peptide fragments [100] by reabsorbing them into the systemic circulation for clearance or by sequestering toxic substances in lysosomes for metabolic conversion. Harmful substances are thereby effectively removed from the brain-CSF system. Signaling molecules are also carried by CSF from diseased regions to the CP. There they bind to specific receptors in the apical membrane and consequently elicit a variety of bioreactive responses. Binding sites for a wide array of peptides, proteins and other organic substances abound in the mammalian CP [73]. Choroidal epithelial cells can thus be regarded as 'bioreactors' that respond to chemical changes in extracellular fluid. Their response includes the synthesis of peptides, growth factors and sundry molecules for homeostatically repairing injured neurons. Currently, little is known about the spectrum of responses by CP to the disrupted CNS homeostasis in AD. It would be informative to compare AD stages for expression abilities of CP vs. the ependyma and meninges. Differences as well as similarities are anticipated. The initial studies of gene expression in human CP reported herein reveal that in Braak stages V/VI there is still strong expression of HSP90 and NKCC1 (Figs. 2 &4). Therefore even though the CP in advanced AD shows extensive histopathology (Table 2) and has reduced enzymatic activities and fluid formation [4,6,26], it evidently retains the ability to react to biochemical perturbations by expressing housekeeping proteins. This stabilizing effect on the blood-CSF barrier epithelium enables regulatory phenomena that ultimately support AD-stressed neurons. Insight can be gained by investigating CSF neurochemical composition as a function of AD severity. Additionally it would be instructive to analyze how cultured CP epithelium reacts to 'pathological' CSF from patients at progressive Braak stages. The Z310 cultured cell line and primary cultures of CP are useful for such analyses [101,102]. Given the significance of CP in facilitating repair of CNS structures, it should also be fruitful to analyze in vivo gene expressions that reflect pathophysiological interactions between CP-CSF and brain. How can CP and the regions it nourishes be protected from AD? Therapeutic strategies to halt CNS deterioration should include ways to defend the CP epithelium against the oxidative ravages of ageing and AD. Prolonging CP viability and work efficiency may be important in maintaining the well being of geriatric patients. Even without a cure for AD, if deleterious changes in the brain interstitium were minimized in the elderly by stabilizing the CP-CSF (as well as the BBB), then it might be feasible to prevent the early manifestations of AD (Braak stages I/II) from intensifying into the debilitating pathology of V/VI. One class of agents with considerable potential for AD therapeutics is the growth factor group. A useful paradigm for CSF growth factors and neuroprotection has evolved from experiments on transient forebrain ischemia in rats [59,60]. The TFI experiments characterized the destructive effects of acute ischemia on the lateral ventricle CP and the nearby CA1 region of hippocampus [50]; and the time course of cell recovery (or death) in these adjacent regions protected (or not) by supplemental infusion of FGF2 via CSF prior to the ischemic insult [61]. Exogenous FGF2 administered before or after the induced ischemia lessened cell death in CA1, probably in part by stabilizing CP functions [28,50,61]. Moreover the CP substantially repaired its epithelial cell barrier by 24 hr post-TFI, even without supplemental FGF2 [60]. Collectively these findings manifest the impressive plasticity of CP to reconstitute itself after disruption; and reveal the potential therapeutic value of pharmacologically-administered growth factors to reduce harm to CP and hippocampus. Several other growth factors synthesized by CP and secreted into CSF should be explored in translational research dealing with interacting ischemia, hydrocephalus and AD [1,4,6,28,36,50,55,103,104]. Following TFI the CP upregulates TGFβ, another peptide that helps the CNS adjust to injury [58]. Consequently choroidally-manufactured growth factors in disease states can benefit the plexus locally (by autocrine and paracrine mechanisms) and the brain more globally (by endocrine-like bulk flow of CSF). Our ischemia findings for the CP-CSF-hippocampus compartments [27,58,60] relate to AD in that reduced blood flow to the ageing CNS exacerbates AD progression. Growth factor supplements (e.g., VEGF, NGF, IGF-II & HGF) could augment viability in AD-vulnerable regions by enhancing vascularization, preventing programmed cell death or promoting stem cell conversion in the subventricular zone (SVZ). Newly-formed neurons in the SVZ might then migrate to atrophic regions to replace destroyed cells. One pharmacologic approach to stall AD onset is to administer a combination of growth factors designed to increase neuroprotection while minimizing fibrosis [55]. We theorize that an optimal regimen of growth factors and neuropeptides would restore CP function or prevent further loss of homeostatic capabilities. A look towards pharmacologic manipulation of CP in AD dementia In searching for agents to modify CP epithelial protein expression, the route of delivery of the active drug is of primary importance. Unlike the brain with its impermeable microvessels, the CP readily takes up water-soluble drugs from the plasma due to the highly-permeable choroidal capillaries. Consequently water-soluble agents freely diffuse to receptors or binding sites at the basolateral surface of the epithelium. Access of blood-borne hydrophilic compounds to the CSF-side of CP however is problematic because the tight junctions and basolateral membrane impede diffusing molecules as small as mannitol [105]. Molecular sieving at the basolateral membrane restricts the permeation of hydrophilic molecules as small as urea (m.w. = 60) into the CP-CSF compartments [106]. To circumvent the blood-CSF barrier, therapeutic agents are delivered into CSF by lateral ventricle catheters in experimental animals [55] or hydrocephalic patients. Gene therapy offers the additional challenge of finding a viral vector that selectively targets the CP epithelium for transduction [107]. Timely, innovative strategies are in order to find specific ways to target and improve CP function in neurodegenerative states. A compelling aspect of CSF translational research is to identify agents that effectively regulate fluid formation by CP. Whereas the difficulty in congenital hydrocephalus is to downregulate CSF formation, the challenge in AD is to enhance CSF turnover perhaps by accelerating fluid production as well as outflow. Augmented flow of CSF enhances 'sink action' [108,109]. This would expedite clearance of toxic molecules like Aβ out of the brain interstitial fluid into the ventricles. New agents that stimulate CP to secrete CSF more rapidly should be tested in aged animals and those with AD-phenotypes. A consistent finding of decreased CNS burdens of Aβ in animals with increased CSF turnover could spur the development of drugs for brain 'cleansing' in AD. Another CP pathology meriting pharmacological attention is the massive fibrosis that progressively envelops the interstitium in old age. This interstitial fibrosis is even more extensive in AD [21]. Fibrosis undoubtedly impedes efficient movement of molecules between blood and CSF. It is pertinent to ascertain if therapeutic minimization of CP fibrosis in later life would permit a brisk turnover of CSF to be sustained. Attenuating the formation of Biondi bodies and other choroidal cellular inclusions (Table 2) would be a unique approach to prevent age- and disease-related curtailment of CSF production. Optimal vascular-interstitial-epithelial interactions in the CP are foundational for vigorous CSF dynamics. The longer the blood-CSF interface retains its epithelial secretory capabilities, the more successfully it can conduct homeostatic activities to ward off AD. Recapitulation and projections The CP has the main responsibility for CSF homeostasis. Therefore the functional status of the blood-CSF barrier is of great consequence to the CNS. Maintaining the CSF at a stable, specialized composition is of the utmost importance to neurons. CSF is prominent in regulating brain interstitial fluid with which it exchanges nutrients and waste products. Diseases markedly affect these molecular exchanges. Maintaining healthy bidirectional transport across the CP epithelium (CSF-blood) and ependyma (CSF-brain) is thus integral to a sound brain fluid environment. CSF macrocirculation through the ventriculo-subarachnoid system together with CSF microcirculation in the perivascular Virchow-Robin spaces [110,111] perform distributive as well as collective functions. By gathering waste products, the CSF is a quasi-lymphatic system with critical functions in excreting harmful peptides and proteins. In early life the upregulated secretions of CP play a central role in brain ontogeny by furnishing growth factors to the germinal matrix. At the end of life with disease onset or aging consequences, the CP transporters are upregulated again to rescue failing neurons by providing neurotrophic materials to CSF. Equally important, peptides in excess such as amyloid beta (Aβ) fragments in AD must be eliminated from CNS by perivascular pathways [112]. To facilitate clearance of Aβ, the continual production of CSF by CP sustains 'sink action' on the brain interstitium [109]. As the main generator of CSF, the CP has a pivotal role in helping the brain cope with the twin stressors of ageing and disease. More attention should be focused on the blood-CSF interface for pharmacologic opportunities to stave off CP dysfunction. Our findings on human CP expression of HSP90, FGFr and NKCC1 demonstrate that this epithelium in AD reacts to metabolic insults by upregulating certain proteins. This suggests that even diseased CP could respond to therapeutic agents, thus opening new vistas for treating CSF dysfunction in age-related dementias. Conclusions Investigation of CP in AD is an area that is opening up. Translational research can now intensely focus on molecular factors that disable the CP to the point of reducing its ability to preserve brain integrity. Systematic CSF analyses using mass spectrometry and other cutting-edge biotechnology should generate neurochemical data specific for disease stages. New imaging approaches are essential to provide much needed functional data for CP, CSF and periventricular regions in AD patients. This should expedite the modeling of CP-CSF malfunctions and their resolution. Deeper insight into the pathophysiology of the blood-CSF transport interface will help to realize the development of novel therapeutic regimens for the AD family of diseases. Abbreviations AD, Alzheimer's disease; APP, amyloid precursor protein; Aβ, beta amyloid; AVP, arginine vasopressin; BBB, blood-brain barrier; CP, choroid plexus; FGF2, basic fibroblast growth factor 2; FGFr, receptor for fibroblast growth factor; GRP94, glucose regulatory protein 94; HSP90, heat shock protein 90; NGF, nerve growth factor; NKCC1, Na-K-2Cl cotransporter secretory isoform 1; NPH, normal pressure hydrocephalus; TGFβ, transforming growth factor beta; SVZ, subventricular zone; TFI, transient forebrain ischemia; VEGF, vascular endothelial growth factor; IGF-II, insulin-like growth factor II; HGF, hepatocyte growth factor; Declaration of Competing Interests The author(s) declare that they have no competing interests. Authors contributions CJ had the primary responsibility of organizing and writing the review, and had NIH support (NS 27601) to do the NaK2Cl cotransporter experiments. PM carried out the NKCC1 immunostaining runs with the T4 antibody and provided interpretation of the regional stainings. RT conducted the experimental analyses of the heat shock proteins and generated figures. AS did the image processing analyses of the cotransporter expression and assisted with the literature analysis. JD contributed ideas for the altered CP-CSF dynamics in hydrocephalus and ventriculomegaly. GS has developed the model that CP-CSF malfunction exacerbates AD progression, and helped to revise the manuscript. ES was responsible for the FGFr experiments and interpreted the human CP data. All authors read and approved the final manuscript. Acknowledgments We thank H. Schipper and S. Anthony for valuable contributions to the stress protein investigation; V. Hovanesian for image processing; N. Johanson for editing; and V. Kuo-LeBlanc and C. Ayala for help with the immunohistochemistry. 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==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-101567994510.1186/1743-0003-1-10ReviewMotor rehabilitation using virtual reality Sveistrup Heidi [email protected] School of Rehabilitation Sciences, Faculty of Health Sciences, University of Ottawa, Canada2004 10 12 2004 1 10 10 26 11 2004 10 12 2004 Copyright © 2004 Sveistrup; licensee BioMed Central Ltd.2004Sveistrup; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Virtual Reality (VR) provides a unique medium suited to the achievement of several requirements for effective rehabilitation intervention. Specifically, therapy can be provided within a functional, purposeful and motivating context. Many VR applications present opportunities for individuals to participate in experiences, which are engaging and rewarding. In addition to the value of the rehabilitation experience for the user, both therapists and users benefit from the ability to readily grade and document the therapeutic intervention using various systems. In VR, advanced technologies are used to produce simulated, interactive and multi-dimensional environments. Visual interfaces including desktop monitors and head-mounted displays (HMDs), haptic interfaces, and real-time motion tracking devices are used to create environments allowing users to interact with images and virtual objects in real-time through multiple sensory modalities. Opportunities for object manipulation and body movement through virtual space provide frameworks that, in varying degrees, are perceived as comparable to similar opportunities in the real world. This paper reviews current work on motor rehabilitation using virtual environments and virtual reality and where possible, compares outcomes with those achieved in real-world applications. ==== Body Introduction One of the major goals of rehabilitation is to make quantitative and qualitative improvements in daily activities in order to improve the quality of independent living. Three determinants of motor recovery are early intervention, task-oriented training, and repetition intensity [1] while a major objective of rehabilitation is to identify the means to provide repeated opportunities for tasks that involve multimodal processes (different sensory modalities including vision, haptics, proprioception, audition) and that further enable increases in function. Carr and Shepherd [2] focus on motor relearning where relearned movements are structured to be task specific. They suggest that the practice of specific motor skills leads to the ability to perform the task and that motor tasks should be practiced in the appropriate environments where sensory inputs modulate their performance. The functional relevance of the specific environmental context has been specifically addressed by Keshner and colleagues [3-5] as it relates to posture control. These authors have shown that specific postural responses differ between paradigms where isolated individual control pathways are manipulated (i.e., visual, vestibular, somatosensory pathway) as opposed to within a functionally relevant context where information from multiple pathways is available. The successful integration of virtual reality into multiple aspects of medicine, psychology, and rehabilitation has demonstrated the potential for the technology to present opportunities to engage in behaviors in challenging but safe, ecologically valid environments while maintaining experimental control over stimulus delivery and measurement [for review see [6,7]]. Moreover, in VR, the user (patient, therapist) interacts with a multidimensional, multisensory computer generated environment, a virtual environment, which can be explored in real time [8]. Virtual reality also offers the capacity to individualize treatment needs while providing increased standardization of assessment and training protocols. In fact, preliminary evidence [9-11] indicates that VR provides a unique medium where therapy can be provided within a functional, purposeful and motivating context and can be readily graded and documented. Several features distinguish virtual environments from other forms of visual imaging such as video and television. A key feature of all VR applications is interaction. Virtual environments (VE) are created that allow the user to interact with not only the VE but also with virtual objects within the environment. In some systems, the interaction may be achieved via a pointer operated by a mouse or joystick button. In other systems, a representation of the user's hand (or other body part) may be generated within the environment where movement of the virtual hand is "slaved" to the user's hand allowing a more natural interaction with objects. Finally, while many applications of VR allow the user to control the viewpoint on the screen, third-person views or images of the users themselves that appear as players in the environment also provide the opportunity for interaction with the VE. A broad range of visual interfaces are used to create varying degrees of immersion in a VE ranging from conventional desktop monitors to head mounted displays. Increasingly complex, fully immersive VR systems, such as the Cave Automatic Virtual Environment (CAVE) developed at the University of Illinois at Chicago, provide the illusion of immersion by projecting stereo images on the walls and floor of a room-sized cube. Several persons wearing lightweight stereo glasses can enter and walk freely inside the CAVE. A head tracking system continuously adjusts the stereo projection to the current position of the leading viewer. In order to integrate the movement of the user with that of the VE and virtual objects, user position and motion must be tracked so that virtual images can be updated in real-time. Motion tracking approaches include color subtraction technology, video frame subtraction as well as magnetic and infrared tracking devices. Technical advances in the development of these interfaces have minimized the once lengthy lag times responsible for some of the earlier reports of cybersickness. To date, rehabilitation applications have primarily used visual and auditory sensory input while the addition of haptics is less developed. Haptic interface devices including gloves, pens, joysticks and exoskeletons provide users with a sense of touch and allow the user to feel a variety of textures as well as changes in texture. There is increasing evidence that haptic information is an effective addition towards the accomplishment of certain treatment objectives such as increasing joint range of motion and force [12]. Haptic information has also been identified as a significant signal for improving a subject's performance in more difficult tasks. For example, Shing and colleagues [13] report a specific benefit of adding haptic information to an upper extremity movement when the difficulty of the task, in this case a 3D pick and place task, was high. Integration of visual and haptic interfaces with motion tracking allows the user to become immersed in three dimensional virtual environments, including three dimensional sound, and virtual objects that can be picked up, manipulated, and even felt with the fingers and hands [14]. Another cardinal feature of virtual reality is the provision of a sense of actual presence in, and control over, the simulated environment [15]. The sense of presence has been defined as the feeling of being in an environment even if one is not physically present and resulting in behavior that is congruent with the subject's situation in the environment [16]. Early studies relied on questionnaires to characterize presence within a virtual environment [15] with more recent work suggesting that physiological measures including heart rate and galvanic skin response provide important information about user immersion [17]. Movement elicited and generated in virtual reality applications One important consideration with the application of virtual reality and movement in virtual environments is the behavior or movement characteristics of subjects in virtual environments [8]. Recent work by Feldman and colleagues [18] specifically compared movements made with or to virtual objects in a VE to movements made with or to real objects in real environments. Virtual representations of the hand were obtained by combining a fiber optic glove with a prehension force feedback device. Orientation of the hand in the VE was achieved using an electromagnetic tracker while kinematic data of the arm and trunk were recorded as the participant reached separately to real and virtual targets. Minimal movement differences in spatial and temporal kinematics of reaching in healthy adults were identified and included the amount of terminal wrist and elbow extension as well as timing of maximal grip aperture. There were no differences in movement characteristics between the real and virtual task in participants with hemiparesis. The authors suggest that VR is similar enough to reality to provide an effective training environment for rehabilitation. In contrast, we have demonstrated significant differences between functional lateral reach performances when performed in the real environment versus in a virtual environment delivered on a flatscreen [19]. The VR technology, VIVID Group's IREX system, provided participants with a third-person view of the users themselves in the virtual environments where they acted on virtual objects. Both young and old adults reached significantly further when virtual objects were presented in the VE compared to when reaches were made to real objects presented in the periphery. Lateral stability is crucial for performance of many weight-bearing tasks including turning, transferring, and stepping onto a stool while controlling a reach made as far as possible to the side requires regulation of the position of the center of mass within the limits of stability. We proposed that embedding the reaching task within a VR application may have resulted in shifting attention away from the potential for loss of balance, whereas focusing attention on balance, such as in the real-environment, may have resulted in increased fear of destabilization and underestimation of true ability. Improving the functional abilities of patients is commonly achieved by using tasks of increasing difficulty in combination with physical and/or verbal guidance of the patient's movements or actions. Thus, integrating the means to modulate the level of difficulty within a VR task is of crucial importance. A virtual reality system (VIVID GX) was used to provide independent leisure opportunities to adults with cerebral palsy and severe intellectual disabilities who were non-speaking and who used wheelchairs for mobility [15]. The participants demonstrated an exceptional degree of enthusiasm during the VR experiences reacting with appropriate, goal-oriented responses. However, a small number of participants clearly displayed involuntary movement synergies, increased reflexes and maladaptive postures, which were attributed to the level of task difficulty. The ability to change the virtual environment relatively easily, to grade task difficulty and to adapt it according to the patient's capabilities are important advantages of VR, since these features are essential to cognitive and motor remediation [20]. Does the technology work? Transfer of training Central to the issue of virtual environments as a training medium is the issue of transfer of training; does task improvement or learning transfer reliably from a VE to a real environment? Virtual environments and VR interventions should not only be used to augment current ability or to provide exposure to "other" therapeutic possibilities, but importantly to demonstrate distinct carryover to real-life functional tasks. One major challenge is identifying effective and motivating intervention tools that enable transfer of the skills and abilities achieved during rehabilitation to function in the "real" world. For example, recent studies stress that simple repetitive movements of an affected limb are not productive for the reorganization process but that it is action related to skill acquisition which contribute to the desired effect [21]. Rose and colleagues studied the transfer of training of a simple sensorimotor virtual task to performance on the "real world" equivalent [22]. The real-world equivalent consisted of a curved wire suspended between two vertical supports. With the non-preferred hand, the subject held a rod with a wire loop at the end and guided the loop along the wire without touching it. Contact between loop and wire, defined as an error, produced feedback. Errors and time to complete task were recorded. The group provided with no practice did significantly worse that the two practice groups, one practicing with the virtual task and one practicing with the real task, although with no difference between the type of practice performed. In other words, within the constraints of this task, final real-world performance benefited as much from real as virtual practice. Thus, it is not sufficient simply to demonstrate that training does transfer in a given situation. It is crucial to identify whether a specific skill or a general familiarity with the training context is being transferred. If specific skills are transferred, it is important to determine whether the transferred training lasts as long and as reliably as an equivalent amount of real world training [22]. In addition, the conditions such as degree of immersiveness, overlap between real and virtual tasks, must be understood if we are to optimize or facilitate transfer. Balance and Posture Several systems have been used in studies of balance including a combined HMD display system combined with a fixed bicycle, a flatscreen VR system providing primarily 2D visual information and more recently an immersive dynamic virtual environment combined with a posture platform. Kim et al [23] reported preliminary data from healthy adults using a bicycle linked to a virtual visual environment and suggested that this training system would be beneficial for postural balance control. They described decreases in cycling path deviation and increases in cycling velocity following a short training period and suggested that these variables, in conjunction with additional parameters, may be relevant for determining a training effect on balance rehabilitation. Several problems remain to be resolved including the limited integration of bicycle motion and auditory cues. A specific concern is that a fixed bike was used which could provide the degree of safety necessary for an individual with a significant amount of balance impairment. However, a fixed bike sets up incongruence between the expectation of lean/tilt of the bike when covering a curved path and the sensory information indicating no tilt. Multiple applications of flatscreen VR for balance training have been reported that have used video capture technology from VividGroup's GX or IREX systems [see for example, [9,10,24-26]]. The systems take a video image of the user and use color subtraction software to remove a monochrome background and insert the user into a virtual environment. Proprietary software is used to allow the user to interact with virtual objects within the VE. Applications that have been used in various studies include: 1) a juggling task where the participant is required to reach laterally to juggle virtual balls; 2) a conveyer belt task where the participant is required to turn sideways, pick up a virtual box from a virtual conveyer belt, turn and deposit the box on a second virtual conveyer belt; and 3) a snowboard task where the user is required to lean sideways to avoid trees, rocks and other virtual objects while boarding down a hill. The applications are modifiable allowing the task difficulty to be modified by increasing the number of virtual objects to contact, increasing the speed at which the objects or environment moves, or increasing and decreasing the height of the objects requiring users to reach to the ground or to step up onto a stool. One of the earliest reports of use of the technology in rehabilitation compared therapy delivered through VR to a conventional approach in a sample of frail, older adults [25]. Greater improvements in dynamic standing tolerance were reported for a small (n = 3 to 4) group of older adults following a VR therapy than for a small group (n = 3 to 4 per group) following a standard occupational therapy program. We have used a similar approach with a significantly larger study population of community-living individuals with traumatic brain injury [see [9,10,26] for preliminary data]. A six week, three sessions per week intervention trial compared an activity-based exercise program (ABE) with a VR-based exercise program (VRE). Both exercise programs resulted in clinically significant changes on the Community Balance and Mobility Scale (CB&M) [27], used to measure functional mobility and balance, with average improvements of 6 and 10 points recorded for the ABE and VRE groups, respectively. Although not all participants involved in the exercise programs improved on their balance measures, 10 out of 14 individuals in the VRE group and 4 out of 10 individuals in the ABE group had clinically significant improvements. Most recently, we have demonstrated significant improvements in balance and functional mobility in community-living older adults following a VR exercise program. The comparison group completed a biofeedback exercise program and also demonstrated significant balance improvement [24]. Although these two studies did not demonstrate significantly greater improvements in balance outcome with the VR exercise program relative to the comparison intervention, other benefits of VR were identified. Specifically, the participants in the VR programs indicated greater enthusiasm about the exercise programs and reported greater enjoyment and improved confidence. The implications of these psychosocial benefits for long-term exercise compliance and participation have yet to be determined. More recently, Keshner and colleagues [4] have united an immersive dynamic virtual environment projected onto a wall with a linear accelerator (sled) that is translated in the anterior-posterior direction. Study participants stand on the sled in front of a screen on which a virtual image is projected. Various combinations of inputs (i.e., translating the support surface, moving the virtual scene, or combining different motions) are used to determine responses elicited when conflicts of different magnitudes between visual and vestibular/somatosensory signals are delivered. The results of initial experiments clearly demonstrate the non-linear effect in the postural response from single versus different combinations of inputs. These findings suggest that using this or similar complex, multimodal environments for rehabilitation intervention would promote ongoing recalculation of sensory inputs that would result in appropriate updates of posture within realistic environmental contexts. Locomotion Patients with Parkinson's disease akinesia have little difficulty stepping over objects in their path even when they are totally unable to initiate a step on open ground [28]. A virtual display superimposed over a user's visual field, augmented reality, has been shown to initiate and sustain walking in akinetic Parkinson's patients. Reiss and colleagues [28] reported that a stable cue appearing about six inches in front of the toes was required to initiate the first step, while cues scrolling toward the feet, as if stable on the ground as the person moves, were needed to sustain walking. The effectiveness of the visual cue was dependent on the degree and type of akinesia with, as a general rule, more realistic cues needed as the severity of akinesia increases. A locomotor interface, GaitMaster2 (GM2), intended to provide the user with the sense of forward movement while his/her actual position in space is constant, has been tested with two individuals with hemiplegia following a stroke [29]. The user stands on two footpads that move individually with each user's foot providing a sense of movement over a virtual terrain. The footpads in the GM2 follow the trajectory of a healthy individual when walking. The user thus experiences a corrected foot trajectory for each step. Modifications in gait patterns of two hemiplegic patients following gait training with the GM2 included moderate improvements in gait speed, improvements in leg muscle activity, increased symmetry during gait and improvement in QOL. A VR-enhanced orthopedic appliance for use with individuals with spinal cord injuries has also been developed and links a gait-inducing exoskeleton to a HMD providing binocular visual displays [30]. Briefly, the exoskeleton consists of a semi-rigid sling that supports the bust and lower limbs of the user. The sling is equipped with small actuators that move the lower extremities in accordance with human gait. Preliminary results from two experimental sessions with the same patient, a 26-year old with complete paraplegia, showed improvements in self-confidence, higher levels of optimism and motivation as well as increased relaxation and activity scores. A novel VR application for locomotor rehabilitation couples a three dimensional visual scene with a self-paced treadmill [31]. Briefly, both treadmill speed and scene progression are based on real-time feedback of subject position and progression with the speed of walking adjusted easily by the individual user. Preliminary trials of the system provided subjects with varying levels of interaction with the scene surface and surrounding objects with a strong sense of presence reported by users. Ongoing work by the group includes development and evaluation of a training protocol for locomotor rehabilitation in individuals with stroke. Upper and Lower Extremity Function Several upper and lower extremity VR applications have been developed using different technologies. Preliminary data suggest potential benefits of various systems. For example, a report based on two case studies using the Vivid GX video capture technology demonstrates improvements in upper extremity function [32]. The first individual had a T9 complete spinal cord injury requiring use of wheelchair for all mobility activities. His primary rehabilitation goal was to improve sitting balance in order to enable him to perform functional activities such as reaching out for a book placed on a shelf. Analysis of videotaped records of performance revealed that initially he used only one hand at a time to interact with the virtual objects while leaving the other on his lap or on the wheelchair arm rest in order to maintain balance. As sessions with the VR system progressed, he began to use both hands during the tasks relying on weak trunk muscles to maintain balance. The second individual had a right hemispheric stroke and ambulated with a cane due to poor control of foot and poor standing balance. He had functional movement in the upper extremity, suffered from mild attention deficit and required some help when dressing the lower extremity. The application he used consisted of balls appearing in the VE from all sides requiring that he pay attention to the entire visual space. After 3 minutes of interaction, he asked to get up and continue with therapy while in a standing position (although therapist behind was necessary for safety). Both participants reported enjoyment and wanted to repeat experience if possible. Importantly, they acknowledged the relevance of the experience to their rehabilitation process. Holden and colleagues [33] developed a VE training system based on the principle of learning by imitation. Pre-recorded movements of a virtual 'teacher' are displayed as either movements of the limb's endpoint or as an entire arm. Patient movements are recorded using an electromagnetic tracking device for the arm and hand segment or a CyberGlove for hand kinematics. The "teacher" shows the patient the trajectory of the end-point (hand) path for the movement to be reproduced. Frequency of visual feedback, speed of motion, degree of movement synchronization and other aspects of the teacher-patient relationship can be modulated. Data from eight chronic post-stroke patients demonstrated variable improvements on clinical measures of upper extremity function including strength. Piron et al. [34] used a virtual reality task to assess functional motor progress of a group of 20 post-stroke patients undergoing conventional rehabilitation. The patients were required to move an envelope instrumented with a magnetic receiver to a virtual mailbox slot. The participant was provided with a view of the trajectory of the corresponding virtual envelope as it moved. Patients improved on reach velocity and reach duration with the changes related to improvements on a clinical measure of upper extremity voluntary movement. The authors suggest that the reach trajectory characteristics also improved although limited data were presented. Several questions however remain. Primarily, would similar changes in movement trajectories be observed if the subject did not "see" a virtual mailbox? Moreover, in this paradigm, the trajectory to the mailbox is only one aspect of the functional task while an equally, if not more important task component is the orientation of the envelope once it reaches the mailbox slot. This emphasizes the need to adequately characterize and represent the functional task to be practiced within the VE. The Rutgers ankle and hand systems, both incorporating the haptic sense, were developed as assessment and intervention tools although there are limited clinical data available at this time regarding efficacy [see [35,36]]. The two systems combine force feedback with a virtual environment that requires subjects to complete various tasks such as a virtual PegBoard task as well as reach-to-grasp (hand system) or piloting a virtual airplane through loops (ankle system). Preliminary data suggest that the systems may be useful to augment rehabilitation in patients in the chronic phase following stroke. A recent study using the hand system demonstrated transfer of skills acquired with the VR system to a functional clinical outcome measure as well as improvement on a variety of movement parameters with greatest benefit recorded in the least impaired patients [37]. Exercise and pain tolerance Chuang et al [38] compared physiological responses of the cardiovascular and respiratory systems during incremental exercise testing with and without VR in healthy older adults. A mechanically braked bicycle was linked to a visual virtual scene projected on a flatscreen display. The rate of subject movement on the bicycle matched the environmental flow on the screen and included a 5 km straight or curved road bordered by grass, trees, seashore background and street lamps. No differences were observed on submaximal and peak exercise responses but the cycling with the VR scenario resulted in longer mean values for cycling duration, distance and energy consumption. It is possible that performing the exercises while immersed in a comfortable environment resulted in an increased degree of relative tolerance. Positive outcomes of virtual reality as a distractive technique have also been reported for physiotherapy treatment sessions. Hoffman and colleagues [39] report decreased anxiety and reductions in self-report of pain from a single-pediatric patient undergoing post-operative physiotherapy. The child underwent single event multi-level surgery including femoral de-rotation osteotomy, quadriceps tendon translocation and release of the Achilles and hamstring tendons. Children experience high levels of post-operative pain association with physiotherapy treatments despite standardized pharmacological analgesia. Effective use of VR as a non-pharmacological analgesia for patients post-surgery may result in greater therapy gains. Assessment Although the majority of VR environments that have been developed for assessment to date focus on daily living skills such as meal preparation [40], spatial memory [8] and cognitive function [41], specific applications have been developed for assessment of upper and lower extremity motor function, balance and locomotion. For example, two separate assessment approaches using the PHANTOM haptic interface, a 6 degree of freedom measuring device for positional input that provides feedback force in translation and rotation have been developed. Broeren et al [42,43] used a relatively simple task requiring the user to reach for, grasp and move the visual representation of the device from a home position to nine separate locations in the visual field. Preliminary data suggest that this is a potential tool for identifying specific deficits of movement such as timing or accuracy that vary across patients. A more complex use of the technology, labyrinth navigation, has been used to isolate more subtle aspects of movement in patients with neurological disease including tremor amplitude and frequency, movement control, and speed of advancement through the labyrinth [44]. Assessments can be developed using VR technologies that will provide objective, repeatable and quantitative results. Standardized instructions, non-varying environmental cues, tasks and feedback can be achieved. In the extreme condition, interactions are limited to those between the patient and a virtual assessor. Since the devices are programmable, varying the complexity of assessment tasks is relatively trivial allowing for batteries of simple and more complex tasks to be developed. For example, an upper extremity assessment scale may include tasks requiring self-selected motion as well as responses to force perturbations permitting assessment of feedback limb control. Access to rehabilitation The degree of functional movement outcome achieved by therapy is often sub-optimal since intensive therapy is limited by resource allocation and access. For many individuals, such as traumatic brain injury survivors, access to therapy is terminated once a level of function is achieved even if residual deficits remain. For other individuals, even when therapy is available such as during in-patient neurological rehabilitation, low levels of interaction between the patient and environment have been reported [45,46]. For example, Tinson [46] reported that individuals post stroke typically spent only 20–60 minutes per day in formal therapy. Common problems influencing the degree of interaction include boredom, fatigue, lack of motivation and lack of cooperation in attending therapy [47]. Clinicians agree that such problems are undesirable and restrict progress in rehabilitation. Increasing interaction is seen as vital to effective rehabilitation, a fact borne out by experimental studies of recovery after brain damage [48]. Development and incorporation of virtual reality applications in rehabilitation may increase the possibility of stimulation and interaction with the world with potentially little or no increase on the demands of staff time. Virtual reality may provide interesting and engaging tasks that are more motivating than formal repetitive therapy. In fact, our recent experience comparing participant perceptions of exercise programs strongly suggest there is added benefit with VR compared to a conventional program (M Thornton et al, unpublished data). For example, the son of a TBI survivor participating in a VR balance retraining program noted We have tried in the past to have him involved in things but he seemed uninterested. With these exercises (referring to a VR-exercise balance retraining program) he was trying to explain what he was doing, he was interested in what he was doing, he was looking forward to going. Summary An exponentially increasing number of distinct VR applications are being developed for intervention and assessment of a broad range of motor rehabilitation needs including upper and lower extremity function, balance and locomotion. Although the initial VR rehabilitation applications that were developed, in particular applications using video capture technologies and most HMDs, were subjected to relatively prohibitive entry level costs associated with the technology, recent developments in technology have made the number of low-cost multisensory VR applications increasingly available. Significant decreases in the costs associated with HMDs and motion trackers, desktop computers and certain haptic devices, are facilitating the development of low cost off-the-shelf applications. The applications reviewed in this paper have demonstrated improvements of specific motor function with certain populations. It is clear that many of the applications that have been developed, for example gait trainers, will serve a specific rehabilitation niche. These devices have the potential to significantly extend our current understanding of movement and therapy and may substantially impact delivery of rehabilitation interventions. Critical for continued successful integration of virtual reality in motor rehabilitation is the need for the ongoing development and use of the technology to be based on clear understanding of the complexity of voluntary movement [49]. Sensorimotor integration, movement production, learning and transfer as well as psychosocial benefits are critical issues to address in ongoing and future studies. Of crucial importance is the fundamental question "Can the same objective be accomplished with a simpler approach". Prior to adoption of novel rehabilitation approaches including virtual reality based applications, users must assess whether the VR technology will provide any additional benefits to that of well trained and experienced therapists. Acknowledgments Preparation of this paper was supported by NSERC Canada, the Ontario Neurotrauma Foundation, and the Ontario Ministries of Health and Longterm Care and Economic Development and Trade. IREX Corp. , a division of Jestertek, Inc., supplied the hardware, software and technical development expertise for the experiments carried out in our laboratories. 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==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-141567994610.1186/1743-0003-1-14ResearchSimulator sickness when performing gaze shifts within a wide field of view optic flow environment: preliminary evidence for using virtual reality in vestibular rehabilitation Sparto Patrick J [email protected] Susan L [email protected] Larry F [email protected] Joseph M [email protected] Mark S [email protected] Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA2 Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA, USA3 Department of BioEngineering, University of Pittsburgh, Pittsburgh, PA, USA4 Department of Computer Science, University of North Carolina-Charlotte, Charlotte, NC, USA2004 23 12 2004 1 14 14 29 11 2004 23 12 2004 Copyright © 2004 Sparto et al; licensee BioMed Central Ltd.2004Sparto et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Wide field of view virtual environments offer some unique features that may be beneficial for use in vestibular rehabilitation. For one, optic flow information extracted from the periphery may be critical for recalibrating the sensory processes used by people with vestibular disorders. However, wide FOV devices also have been found to result in greater simulator sickness. Before a wide FOV device can be used in a clinical setting, its safety must be demonstrated. Methods Symptoms of simulator sickness were recorded by 9 healthy adult subjects after they performed gaze shifting tasks to locate targets superimposed on an optic flow background. Subjects performed 8 trials of gaze shifting on each of the six separate visits. Results The incidence of symptoms of simulator sickness while subjects performed gaze shifts in an optic flow environment was lower than the average reported incidence for flight simulators. The incidence was greater during the first visit compared with subsequent visits. Furthermore, the incidence showed an increasing trend over the 8 trials. Conclusion The performance of head unrestrained gaze shifts in a wide FOV optic flow environment is tolerated well by healthy subjects. This finding provides rationale for testing these environments in people with vestibular disorders, and supports the concept of using wide FOV virtual reality for vestibular rehabilitation. VRbalancephysical therapyvirtual environmentCAVE ==== Body Background One out of three elderly persons and more than one out of five working adults report dizziness[1,2]. There is a growing body of literature that suggests that persons with dizziness due to vestibular disorders fall, regardless of age [3]. Falls in persons with vestibular disorders have potentially catastrophic consequences[4]. Thus, development of rehabilitation methodologies that can improve balance could have a great impact on public health. The use of virtual reality (VR) has been explored in many areas of physical and mental rehabilitation [5-8]. Viirre [9,10] and Kramer et al. [11] were the first to discuss the use of VR with persons with vestibular disorders. The theoretical basis for using VR in the treatment of vestibular disorders is two-fold. First, persons with peripheral vestibular disorders have disequilibrium and complain of visual blurring [12]. These common symptoms may be caused by abnormalities in the vestibulo-ocular reflex (VOR) gain during head movements. Functional recovery of the VOR requires both visual inputs and movements of the head and body [13]. Retinal slip, i.e. movement of a visual image across the retina, is a powerful signal that can induce adaptation of vestibular responses [14]. If care is taken to minimize delays between head tracking devices and image updates, VR-induced retinal slip can be delivered in a controlled manner in order to cause adaptation. A randomized trial has demonstrated that persons with uncompensated peripheral vestibular disorders can improve with vestibular rehabilitation directed at inducing retinal slip [15]. People can also adapt to vestibular injuries through movement. Shepard et al. were able to reduce symptoms in 87% of patients who had chronic unilateral peripheral vestibular loss for at least 2 months [16]. Therefore, exposure to visual experiences and movement are key to the functional recovery of persons with vestibular disorders. Secondly, people with vestibular disorders complain of what has been called "space and motion discomfort" (SMD) and "visual vertigo" [17,18]. Situations that have been reported to precipitate SMD or visual vertigo include: walking in supermarket aisles or shopping malls, movement in cars and trains, long visual distances, or complex and confusing visual stimuli. Consequently, VR could allow persons with vestibular disorders to experience graded exposure to symptom-provoking situations in a controlled environment. Using VR for exposure therapy has a well-established foundation in the treatment of specific phobias (e.g. fear of heights) [8,19]. Wide field of view (FOV), screen-based projection devices (or spatially immersive displays) originated with the CAVE™ at the University of Illinois in Chicago [20]. Although the space required and cost make these systems impractical for clinical use, their wide FOV allow research laboratories to investigate how different motion cues affect balance and vestibular rehabilitation. One advantage of wide FOV devices is their ability to provide motion cues in the periphery, which can result in a greater sense of vection, or self-movement, compared with more limited FOV devices [21]. We believe that these factors may provide a substantial benefit compared with narrower FOV devices such as HMDs in the treatment of vestibular disorders. However, the wide FOV devices have also been associated with greater reports of simulator sickness [22]. Thus, while a wide FOV is desirable from a theoretical standpoint because a greater perception of motion occurs in the periphery, this same factor may elevate levels of simulator sickness and may be cause for discontinuing a treatment. The primary purpose of this paper is to present preliminary evidence for the ability of subjects to tolerate gaze shifting while situated in a wide FOV optic flow environment. We will demonstrate that healthy subjects were able to tolerate the environments without having a large incidence in simulator sickness. The incidence of simulator sickness depended strongly on how much experience the subjects had in the environment, and weakly on the duration of exposure within each visit. Methods Subjects Nine adults (22–75 years, mean ± S.D. 39 ± 19 yrs) with no history of vestibular system pathology participated after providing informed consent. Subjects had a visual acuity of 0.3 LogMAR units or better without using corrective lenses, and contrast sensitivity greater than 1.8 (Pelli-Robson Contrast Sensitivity). The protocol was approved by the University of Pittsburgh Institutional Review Board. Equipment The Balance NAVE Automatic Virtual Environment (BNAVE), a wide field of view projection-based immersive display system, was developed to investigate the multi-sensory interactions in postural control [23]. Three 2.4 m × 1.8 m (vertical × horizontal) back-projected screens are arranged as shown in Figure 1. The side screens make an included angle of 110° with the front screen. The front screen is 1.5 m from the user, and the opening of the BNAVE at the location of the subject is approximately 2.9 m. The images are displayed using Epson 810p PowerLite LCD monoscopic projectors, with a pixel resolution of 1024 × 768 for each screen. Each projector is connected to an NVIDIA GeForce4 graphics processing unit (64 MB texture memory) installed in a separate PC (Pentium, 2.2 GHz, 512 MB RAM) running Windows 2000. The movement of the images on the three PCs is synchronized and controlled by a server via a local area network. The update rate of the images is consistently at least 30 frames per second. Figure 1 Experimental set-up for Task H, Visit 1 (see Tables 1 and 2 for explanation). Subjects stood upright on force platform and performed gaze shifts while target moved on a solid background. The target moved every 3 to 6 seconds from positions 1 to 4, located 40 to 50 degrees from midline. Several environments can be used for vestibular rehabilitation. One environment produces optic flow through the use of moving geometric patterns such as stripes or squares of alternating colors. Scene characteristics such as the spatial frequency, contrast, direction and speed of movement can all be independently prescribed. Furthermore, targets can be inserted into the environment that subjects are requested to look at. Consequently, for vestibular rehabilitation, we can ask a patient to perform unrestrained head gaze shifts to acquire moving targets while a background of moving stripes is moving past the patient, simulating the functional task of looking for a product while moving down the aisle of a grocery store. A virtual grocery store has also been developed (Figure 2). This environment contains several aisles, each with a different product theme. The dimensions of the aisle (width and length) are adjustable. Scene complexity can be altered by increasing the number of items on the shelves. The objects within the environment have both software-generated and photographic texture maps. In both environments, the task difficulty can be modified by varying the scene characteristics, thus exposing the patient to symptom-producing situations in a controlled and graded manner. In each environment, three-dimensional models were created using 3D Studio Max. Although the projectors used were not stereoscopic, a strong illusion of depth was elicited based on monocular depth cues such as perspective projection. The location of the eyepoint used for the perspective projection was based upon a fixed stance location in the horizontal plane and the subject's eye height. In addition, although head-tracked perspective correction was not used in the current application, this capability is possible and fully functional using a Polhemus Fastrak position tracking system (Polhemus, Inc. Colchester, VT). Figure 2 Virtual Grocery Store developed for providing exposure therapy for patients with dizziness that is increased in similar environments. Aisle length, shelf height, the number of products on the shelves, and object textures can all be manipulated depending on the goal of the therapy session. Procedure The ability of subjects to perform gaze shifts in response to moving targets superimposed on both static and moving backgrounds was examined. Eight different gaze tasks are performed on each visit (Table 1). Each gaze task was performed for 90 s with alternating movements to the left and right every 3 to 6 seconds (except for tasks B, F, H). Each task was performed with the six different backgrounds (Table 2). Each background condition was performed on a different visit. Background conditions 1 and 2 did not have optic flow and thus served as a control conditions for the remaining 4 backgrounds, which were randomized over the next 4 visits. During the high contrast conditions, the luminance of the stripes was 1 and 170 cd/m2, respectively. During the low contrast conditions, the luminance of the stripes was 15 and 34 cd/m2. The low contrast condition was based on average measurements of luminance obtained from products sampled at a local grocery store, using a luminance meter (LS-100 Luminance Light Meter, Minolta Corp. Ramsey, NJ). The spatial frequencies were set according to common sizes of soup cans (high, 4.2 cycles/meter) and cereal boxes (low, 1.4 cycles/meter) found in the local grocery store. The simulated velocity of the optic flow was 0.5 m/s. The central 25° of the display was masked by a solid region with a luminance of 15 cd/m2 in order to avoid aliasing in the display as the stripes became smaller in the distance. Table 1 Gaze tasks performed on each of the six visits. On trials 3 to 8, the order of tasks D, E, F, G, H, and I are randomized on each visit. Trial Task 0 A) Initial reading 1 B) 10 deg head saccades to the right – calibration of head sensor 2 C) Control task – no head or eye movements 3 D) Head center, Eye saccades to ± 10 deg 4 E) Head left 50 deg, Eye saccades to ± 10 deg 5 F) Head right 50 deg, Eye saccades to ± 10 deg 6 G) Gaze stabilization during sinusoidal head movement at 0.25 Hz (VOR) 7 H) Gaze saccades to ± 40 and ± 50 deg from midline 8 I) Smooth pursuit to left between 10 – 60 deg, followed by smooth pursuit to right between 10 – 60 deg Table 2 Background conditions for each of the six visits. On visits 3 to 6, the order of conditions C, D, E, and F are randomized. Visit Condition Optic Flow Contrast Spatial Frequency 1 A No None None 2 B No High Low 3 C Yes Low Low 4 D Yes Low High 5 E Yes High Low 6 F Yes High High Rests of 3 minutes were provided after each task, during which Subjective Units of Discomfort (SUDS, 0–10 range) was rated and the Simulator Sickness Questionnaire (SSQ) was completed [24]. The SSQ contains 16 items on which subjects rate the degree of severity on a 4-item scale (0 = none, 1 = slight, 2 = moderate, 3 = severe). A total score is computed along with 3 subscales – nausea (general discomfort, increased salivation, sweating, nausea, difficulty concentrating, stomach awareness, burping), oculomotor stress (general discomfort, fatigue, headache, eyestrain, difficulty focusing, difficulty concentrating, blurred vision), and disorientation (difficulty focusing, nausea, head fullness, blurred vision, dizzy: eyes open, dizzy: eyes closed, vertigo). Furthermore, pulse and blood pressure was monitored after every trial using an automatic device. Initial recordings of each of the measures were also recorded prior to exposure. During each trial, postural sway was recorded using a force platform and 6 degrees of freedom electromagnetic trackers placed on the head and waist (Polhemus Fastrak, Colchester, VT). The accuracy of the trackers is 0.8 mm in translation and 0.15° in rotation, and the stated resolution of 0.2 mm in translation and 0.025° in orientation. The trackers have a latency of 4 ms and were digitized at 20 Hz. The horizontal and vertical eye movements were measured using video-oculography (VOG, Figure 3). The VOG device (Micromedical, Chatham, IL) was fastened using an adjustable helmet insert and contained see-through dichroic glass that reflected images of the eyes up to infrared cameras. The accuracy of the VOG is 0.3 deg and the images are captured at 60 Hz. Using the sampling rates of the tracker and VOG, the maximum delay between recording simultaneous movements of both the head and eye would be 33 ms. The head and eye movements were calibrated using targets placed in known locations. Eye-in-head position is combined with head-in-space position to yield continuous gaze position (eye-in-space). The timing and accuracy of the head gaze movements with respect to the targets will be the subject of a future report. Figure 3 Video-oculography (VOG) see-through goggles used to measure eye-in-head position in the vertical and horizontal planes. Subject also wore 6 degrees of freedom electromagnetic tracker on top of his head to measure head in space position. Both signals are combined to obtain gaze, or eye gaze-in-space position. Data Analysis Five dependent variables of interest were examined: SUDS, total SSQ, and 3 SSQ subscales. The three subscales of the SSQ were computed by summing the scores for the component items of each subscale, and multiplying by an appropriate weighting factor (9.54 for Nausea, 7.58 for Oculomotor, and 13.92 for Disorientation) [24]. The total SSQ score was equal to the sum of the 3 subscales, multiplied by 3.7. Histograms of each dependent variable were plotted according visit number (1 to 6) and trial number (0 to 8). After observing that the data were not normally distributed due to a large majority of 0 responses and the presence of long tails, we tabulated the frequency of non-zero responses for each dependent variable. The effect of visit number and trial number on the frequency of non-zero responses was evaluated using χ2 statistics. Because of the large number of comparisons (5 dependent variables × 2 main effects), the significance level was set at α = 0.05/10 = 0.005. Results Gaze shifts performed by one subject in response to targets moving at least 80° in the yaw plane (Task G) are shown in Figure 4. These gaze shifts are combinations of head and eye movements. The top plot demonstrates the target-in-space yaw position (T), head-in-space yaw position (H), and eye-in-head yaw position (E). The last two quantities are combined to produce the gaze position (G) shown in the bottom plot. In this example, it can be seen that the eye and head movements effectively combine to allow the person to look at the desired target position. Analytical techniques will allow us to quantify head and eye coordination strategies in persons with vestibular disorders, both in optic flow fields and more complex environments. Figure 4 Gaze shifts during Task H obtained from 1 subject. Top: target yaw position (T), head-in-space yaw position (H), and eye-in-head yaw position (E). Bottom: target yaw position (T), and eye gaze-in-space yaw position (G). The tolerance of the subjects to the gaze shifts was assessed using Subjective Units of Discomfort (SUDS) and the Simulator Sickness Questionnaire (SSQ). For each of the measures, the majority of the responses were zero. Therefore, each of the measures was converted into a binary scale consisting of responses equal to zero or greater than zero. The SUDS rating was greater than zero only 25% of the time. For each of the SSQ subscales, a score greater than 0 was given if any of the 7 component items for the subscale was rated greater than 0. The SSQ:Oculomotor subscale had the most non-zero responses, at 29%. The SSQ:Nausea and SSQ:Disorientation subscales had 12% and 5% non-zero responses, respectively. Overall, the SSQ-Total had 31% non-zero responses. The effect of visit number and trial number on the frequency of non-zero responses was examined using χ2 statistics. The effect of visit number was significant for SUDS, SSQ:Nausea, SSQ:Oculomotor, and SSQ:Total (Table 3). The most obvious finding was that the number of non-zero responses was significantly greater the first visit. The effect of trial number was not significant for all measures (Table 4). Table 3 Incidence of non-zero responses for the self-reported Subjective Units of Discomfort SUDS) and Simulator Sickness Questionnaire (SSQ) subscales and total score, as a function of visit number. Mean incidence, χ2 test of association, and p value are also provided. * indicates significant effect of visit number. Visit SUDS SSQ:Nausea SSQ:Oculomotor SSQ:Disorientation SSQ:Total 1 35 27 56 9 56 2 38 11 33 0 33 3 22 11 26 5 28 4 25 16 19 10 26 5 19 0 26 1 27 6 10 8 14 8 14 Mean 25 12 29 5 31 χ2 17.9 25.2 30.7 10.1 25.3 p-value .003* <.001* <.001* .072 <.001* Table 4 Incidence of non-zero responses for the self-reported Subjective Units of Discomfort (SUDS) and Simulator Sickness Questionnaire (SSQ) subscales and total score, as a function of trial number. Mean incidence, χ2 test of association, and p value are also provided. No significant effect of trial number was found. Trial SUDS SSQ:Nausea SSQ:Oculomotor SSQ:Disorientation SSQ:Total 0 (initial) 4 7 11 0 11 1 17 6 15 2 15 2 24 9 24 11 31 3 28 13 28 2 30 4 30 13 31 6 33 5 26 15 35 6 37 6 30 15 39 7 41 7 32 15 38 8 40 8 34 17 40 8 40 Mean 25 12 29 5 31 χ2 15.0 5.2 16.0 8.0 16.2 p-value .06 .73 .04 .43 .04 Discussion The ability to perform coordinated gaze movements within an optic flow environment may lead to the development of tools to improve outcomes in vestibular rehabilitation. The current research represents the first attempt to assess self-reported tolerance to these movements in a wide field of view environment. The ratings indicate that on a majority of the trials, this group of healthy subjects experienced no discomfort and simulator sickness while performing 8 different types of gaze movements under different optic flow conditions. On 75% of the trials, subjects reported no subjective discomfort. On 69% of the trials subjects reported no symptoms of simulator sickness. Although there is no data with which to compare the incidence of symptoms during performance of coordinated eye and head movements in an optic flow environment, we have chosen to review the data obtained from flight simulators and head mounted display units. The incidence of symptoms in the current study is at the lower end of the range relative to the previous flight simulator based studies. For example, the incidence of simulator sickness in this military pilots has ranged from 6–62%, depending on the type of simulator [25]. The incidence of simulator sickness after VR exposure in non-pilots is slightly greater; approximately 60 to 80% of subjects report symptoms of eyestrain, headache, nausea, and malaise after only 10–20 minutes [26,27]. The decreased incidence of problems may be related to several factors. For one, the within trial exposure time was short, approximately 90 seconds. Therefore the total exposure time was only about 12 minutes, which is on the lower end of reported exposures described in the literature [26,28]. Secondly, significant rest breaks were provided between trials. The incorporation of rest breaks to reduce simulator sickness has not been studied well. Thus, the type of display device, as well as the content and nature of the task may have an effect on the amount of sickness. Thus, although the current results are not directly comparable to the previous research, they will serve as a foundation for future work that examines the incidence of symptoms while performing coordinated eye and head movement tasks within a virtual grocery store, or using a head mounted display. There was a significant effect of visit number of the number of non-zero responses. Analysis of the data revealed that subjects appeared to have greater levels of discomfort and symptoms of simulator sickness on the first visit. It is possible that subjects had greater levels of discomfort due to their lack of prior exposure to the environment/experiment. Furthermore, our data is consistent with findings from other studies that subsequent exposures to environments result in lower simulator sickness [26,28]. Interpretation of this finding is clouded by the confounding effect of the background displayed during visit 1. During the first visit, the subjects always experienced movement of targets superimposed on a solid background without optic flow. The experiment was designed in this way because we assumed that this background would elicit the least amount of symptoms, and would serve as a suitable background for subjects to learn the movements. Consequently, the finding of decreased tolerance to the movements during the first visit was unexpected. Unfortunately, we are not able to distinguish if the increased discomfort and simulator sickness was due to the subject's inexperience with the environment or due to the type of background. We did not find a significant effect of trial number on the number of non-zero responses to SUDS and the SSQ. However, it was apparent that there was a trend for greater number of non-zero responses as trial number increased for the SUDS, SSQ:Oculomotor, and SSQ:Total Severity. In previous reports using flight simulators, the level of simulator sickness increased as the duration of exposure increased [28]. Moreover, symptoms tended to persist after the simulation was finished [25,29]. Addition of more subjects may reveal the trial effect to be significant. Nonetheless, the short duration of exposure within each trial (i.e. 90 seconds) and the amount of rest provided to the subjects between trials (i.e. 3 minutes) may have ameliorated the development of symptoms as the trials accumulated. The SSQ:Oculomotor subscale had the greatest number of non-zero responses. Usually, this subscale is elevated secondary to the effects of using a head-mounted display (HMD) device. HMD users frequently suffer from eyestrain, and blurred vision and short-term changes in binocular vision possibly due to alterations in the balance between the vergence and accommodation systems [30,31]. In our case, we attribute the scores to the video-oculography (VOG) device. Several subjects commented that the VOG caused eyestrain. Furthermore, some subjects reported that the dichroic lenses interfered with their viewing of the environment. We intend to examine if using electro-oculography (EOG), which measures eye movements via surface electrodes surrounding the eyes, will reduce the amount of oculomotor symptoms. The ability to move one's head and search for targets is a functional task that is often impaired in people with vestibular disorders. In vestibular rehabilitation, patients are encouraged to move their head during daily activities because movement is needed for adaptation and reweighting of the sensory signals. Using optic flow and/or virtual environments, this activity could be restored. Our preliminary analysis demonstrates that healthy people are able to coordinate their head and eye movements in the presence of stationary and moving backgrounds with few side effects. The next step is to perform similar experiments with people who have vestibular disorders. In addition, it is imperative to study if these movements can be tolerated while using narrower FOV HMDs. Head mounted displays will most likely be the desired display system of choice for vestibular rehabilitation because of the relatively modest cost and high portability. However, there are other characteristics of the display systems that need to be considered. For example, it is common for users of HMDs to complain of eyestrain, blurred vision, headache, and nausea [30,31]. In addition, DiZio and Lackner suggest that wearing an HMD effectively increases the mass and inertia of the head, thereby leading to a sensory rearrangement that may have some part in simulator sickness [32]. This theory is supported by the work of Howarth and Finch, who examined the amount of nausea generated while subjects wore an HMD under 2 conditions [33]. In one, subjects changed heading by using a handheld input device. In the other, subjects changed heading by rotating their head. Nausea was significantly greater when subjects navigated using their head. The lag between head movement and scene movement, and the variability in frame update rate has also been considered to play an important role in generating sickness with the use of HMDs [26,33]. However, as head tracking technology has improved, and update lags have been reduced, this factor is probably not as important as it once was. Thus, research on the use of HMDs in people with vestibular disorders is necessary to determine if they can be safely used in this population. Conclusion The performance of head unrestrained gaze shifts in a wide FOV optic flow environment is tolerated well by healthy subjects. This finding provides rationale for testing these environments in people with vestibular disorders, and supports the concept of using wide FOV virtual reality for vestibular rehabilitation. Acknowledgments This research was supported in part by funding from the National Institutes of Health (P30DC005205, R21DC005372, K23DC005384, and K25AG001049) and the Eye and Ear Foundation. The authors gratefully acknowledge assistance from Sabarish Babu, Jeffrey Jacobson, and Leigh Mahoney ==== Refs Grimby A Rosenhall U Health-related quality of life and dizziness in old age.[see comment] Gerontology 1995 41 286 298 8537013 Yardley L Owen N Nazareth I Luxon L Prevalence and presentation of dizziness in a general practice community sample of working age people.[see comment] Br J Gen Pract 1998 48 1131 1135 9667086 Whitney SL Hudak MT Marchetti GF The dynamic gait index relates to self-reported fall history in individuals with vestibular dysfunction J Vestib Res 2000 10 99 105 10939685 Tinetti ME Williams CS The effect of falls and fall injuries on functioning in community-dwelling older persons Journals of Gerontology Series A, Biological Sciences & Medical Sciences 1998 53 M112 119 Sveistrup H McComas J Thornton M Marshall S Finestone H McCormick A Babulic K Mayhew A Experimental studies of virtual reality-delivered compared to conventional exercise programs for rehabilitation Cyberpsychol Behav 2003 6 245 249 12855079 Merians AS Jack D Boian R Tremaine M Burdea GC Adamovich SV Recce M Poizner H Virtual reality-augmented rehabilitation for patients following stroke Phys Ther 2002 82 898 915 12201804 Girone M Burdea G Bouzit M Popescu V Deutsch JE Orthopedic rehabilitation using the "Rutgers ankle" interface Studies in Health Technology & Informatics 2000 70 89 95 10977590 Rothbaum BO Hodges LF Kooper R Opdyke D Williford JS North M Effectiveness of computer-generated (virtual reality) graded exposure in the treatment of acrophobia Am J Psychiatry 1995 152 626 628 7694917 Viirre E Vestibular telemedicine and rehabilitation. 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==== Front J Autoimmune DisJournal of Autoimmune Diseases1740-2557BioMed Central London 1740-2557-1-41567994710.1186/1740-2557-1-4ResearchAntibodies to soluble liver antigen and α-enolase in patients with autoimmune hepatitis Bogdanos Dimitrios-Petrou [email protected] Daniele [email protected] Ilaria [email protected] Simona [email protected] Ragai R [email protected] Yun [email protected] Giorgina [email protected] Diego [email protected] Institute of Liver Studies, King's College Hospital, Denmark Hill, London SE5 9RS, UK2 Faculté de Médecine et Pharmacie, U519 INSERM, University of Rouen, Rouen, France2004 19 11 2004 1 4 4 8 10 2004 19 11 2004 Copyright © 2004 Bogdanos et al; licensee BioMed Central Ltd.2004Bogdanos et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Antibodies to a cytosolic soluble liver antigen (SLA) are specifically detected in patients with autoimmune hepatitis (AIH). The target of anti-SLA has been identified as a ~50 kDa UGA serine tRNA-associated protein complex (tRNP(Ser)Sec), through the screening of cDNA libraries. A recent report questioned the identity of tRNP(Ser)Sec as the real SLA antigen. The latter study identified α-enolase as a major anti-SLA target, through proteomic analysis. Methods In an attempt to explain the observed discrepancy we have investigated reactivity of SLA positive sera against α-enolase and tRNP(Ser)Sec using rat and primate liver homogenate and the recombinant antigens. Thirty-three serum samples, 11 from SLA-positive patients and 22 from SLA negative controls were investigated. SLA antibodies were detected by an inhibition ELISA and confirmed by immunoblot using human liver homogenate. Autoantibody reactivity was further evaluated using preparations of primate and rat liver homogenates. Anti-α-enolase antibody reactivity has been tested by immunoblot using recombinant α-enolase. An affinity purified goat polyclonal anti-α-enolase IgG antibody was used as reference serum sample. Anti-tRNP(Ser)Sec antibody reactivity was detected by ELISA or dot blot using recombinant tRNP(Ser)Sec antigen. Results and Discussion The affinity purified IgG antibody directed to human α-enolase gave a band of approximately 48 kDa in both human and rat liver homogenates. A high titre anti-tRNP(Ser)Sec antibody serum gave a single band of ~50 kDa in both liver preparations. All but one anti-SLA antibody positive sera reacted with a ~50 kDa but none immunofixed a 48 kDa band. All anti-SLA antibody positive sera reacted strongly with the recombinant full length tRNP(Ser)Sec protein. None of the anti-SLA negative sera reacted with tRNP(Ser)Sec. Anti-SLA positive, and anti-SLA negative sera reacted equally against recombinant α-enolase by immunoblot. Pre-incubation of anti-SLA positive sera with tRNP(Ser)Sec completely abolished the 50 kDa band. The findings of the present study indicate that α-enolase and tRNP(Ser)Sec are both expressed in primate and rat liver and have a respective MW of 48 and 50 kDa. They also show that anti-tRNP(Ser)Sec – but not anti-α-enolase – correlates with anti-SLA antibody reactivity. Conclusion Our findings indicate that tRNP(Ser)Sec is the most likely target of anti-SLA. ==== Body Background Antibodies to a cytosolic soluble liver antigen (SLA), detected originally by an inhibition ELISA using cytosolic liver fractions in a sub-group of patients with autoimmune hepatitis (AIH) negative for other autoantibodies, have recently been also reported in adult patients with anti-nuclear and/or smooth muscle antibody (ANA/SMA) positive type 1 AIH and in seronegative patients with a form of cryptogenic hepatitis resembling type 1 AIH [1-6]. In pediatric patients, anti-SLA has been described not only in type 1 AIH but also in anti-liver kidney microsomal-1 antibody positive type 2 AIH and autoimmune sclerosing cholangitis [7-10]. Anti-SLA is specific for these autoimmune liver diseases, where it is associated with a more severe course and is virtually absent in non-hepatic autoimmune disorders [1-9]. The target of anti-SLA has been identified by several groups as a ~50 kDa UGA serine tRNA-associated protein complex (tRNP(Ser)Sec), through the screening of cDNA libraries [2-4,7]. Anti-tRNP(Ser)Sec antibodies have been detected in up to 90% of serum samples positive for SLA by the original inhibition ELISA [1-8]. Using anti-SLA positive sera against rat liver cytosolic fraction in one and two-dimensional immunoblotting analyses and through peptide mass fingerprint analysis, following MALDI-TOF mass spectrometry, Ballot et al. [11] identified four isoforms of α-enolase, – a cytosolic antigen of 48–50 kDa –, as the major target of anti-SLA positive sera. These findings challenge the notion that tRNP(Ser)Sec is the sole target of anti-SLA antibodies [2-8]. Critically, no absorption studies were performed with purified α-enolase to confirm this proposal [11]. Moreover, α-enolase has been described as an antigen in several autoimmune disorders totally unrelated to autoimmune hepatitis [12-18]. Using recombinant tRNP(Ser)Sec antigen as competitor in inhibition experiments it has been found removal of the 50 kDa band immunofixed by SLA positive sera from immunoblots of primate liver homogenate [19]. Though this finding indicates tRNP(Ser)Sec as a major component of SLA, a view apparently shared by Ballot et al, several questions still remain unanswered: 1. Are there any differences in α-enolase expression between rat – used by Ballot et al [11] – and primate liver homogenate – used by our study [19] – that could explain the discrepancy between these studies? 2. Is it true that failure of proteomic analysis to detect tRNP(Ser)Sec is due to its presence in trace amounts in the supernatant of liver homogenate [11]? 3. What is the reactivity of SLA positive and negative sera against recombinant α-enolase? 4. How do we explain the apparent paradox of SLA being identified as α-enolase by proteomic analysis and as tRNP(Ser)Sec by the screening of cDNA libraries? Do α-enolase and tRNP(Ser)Sec cross-react? In the present study, we have investigated reactivity of SLA positive sera against α-enolase and tRNP(Ser)Sec using rat and primate liver homogenate and the recombinant antigens. Methods Patients Thirty-three serum samples, 11 from SLA-positive patients and 22 from SLA negative controls were investigated. SLA-positive patients included 8 paediatric patients with AIH1 [7 female, median age 12, range 5–17 years, all ANA positive, median immunofluorescence (IFL) titre: 1/320, range 1/80–1/1280] and 3 adults with AIH/PBC overlap syndrome, (2 female, median age 56, range 47–65), all but one AMA positive (1/5120), and ANA positive (median tire 1/320, range 1/80–1/640). Eleven case-matched SLA negative patients were tested as pathological controls including 8 with AIH1 and 3 with AIH/PBC overlap syndrome. Eleven demographically matched healthy subjects including 8 children (7 female, median age 11, range 6–16) and 3 adults (2 female, median age 53, range 42–63) negative for SLA were also tested as controls. Antibody Detection All sera have been tested for conventional antibodies by indirect IFL using rodent liver, kidney, stomach tissues. SLA antibodies were detected by a modified inhibition ELISA [1] and confirmed by immunoblot using human liver homogenate [20]. Autoantibody reactivity was further evaluated using preparations of primate (Euroimmun, Lubeck, Germany) and rat (AID Autoimmun Diagnostika GmbH, Strassberg, Germany) liver homogenates, according to manufacturers' instructions. Anti-α-enolase antibody reactivity has been tested by immunoblot using recombinant α-enolase, as described previously [17]. Briefly, the complete complementary DNA (cDNA) encoding human α-enolase was isolated from a cDNA expression library derived from synoviocytes obtained from a patient with rheumatoid arthritis (Stratagene, La Jolla, CA) and immunoscreened with goat anti-enolase antibodies. This cDNA was subcloned in frame in the pSPUTK in vitro translation vector (Stratagene) using the Apa I and Bam HI restriction sites. The translation product was synthesized as a separated biotinylated polypeptide, purified by SoftLink Soft Release Avidin Resin (Promega, Madison, WI), migrated in SDS-PAGE, according to the method of Laemmli, and electrotransferred onto a nitrocellulose membrane [17]. The filters were then incubated with goat anti-enolase antibodies (see below), a monospecific anti-α-enolase antibody positive serum from a patient with rheumatoid arthritis or with individual serum samples, in Tris buffered saline, 0.05% Tween 20, 5% dry milk for 2 hours. After washing, the filters were incubated for 1 hour with 1:15,000-diluted peroxidase-conjugated goat anti-human IgG (Sigma-Aldrich) in 0.05% TBST-milk. The filters were washed and revealed by a chemiluminescence reaction (Supersignal; Pierce, Rockford, IL) [17]. An affinity purified goat polyclonal anti-α-enolase IgG antibody raised against a peptide mapping near the carboxyl-terminus of human α-enolase, which is common to α, β, and γ isoforms of mouse, rat and human enolase (200 μg/ml; Santa Cruz Biotechnology, Santa Cruz, California, USA) was used as reference serum sample at a dilution of 1:100, according to the manufacturer's instructions. Anti-tRNP(Ser)Sec antibody reactivity was detected by ELISA or dot blot using recombinant tRNP(Ser)Sec antigen (Euroimmun). A high-titre anti-tRNP(Ser)Sec antibody positive serum was used as a positive control. Inhibition Studies To investigate whether the 50 kDa band immunofixed by anti-SLA is tRNP(Ser)Sec, inhibition experiments were performed using 3 anti-SLA positive serum samples, diluted at 1/1000, and pre-incubated with solid phase recombinant tRNP(Ser)Sec (Euroimmun, UK), as previously described [19]. Results and Discussion The affinity purified IgG antibody directed to human α-enolase gave a band of approximately 48 kDa in both human and rat liver homogenates as shown in Figure 1. A high titre anti-tRNP(Ser)Sec antibody serum gave a single band of ~50 kDa in both liver preparations (Figure 1). All but one anti-SLA antibody positive sera reacted with a ~50 kDa band similar to that obtained by the high titre anti-tRNP(Ser)Sec antibody serum in rat liver preparations (Figure 2) but none immunofixed a 48 kDa band. All anti-SLA antibody positive sera reacted strongly with the recombinant full length tRNP(Ser)Sec protein both in ELISA (mean titre 87 ± 23 RU/ml, cut off: 20 RU/ml) and dot blot. None of the anti-SLA negative sera reacted with tRNP(Ser)Sec. Anti-SLA positive, and anti-SLA negative sera reacted equally against recombinant α-enolase by immunoblot with 5/9 cases in each group giving a strong band (Figure 3). Figure 1 Immunoblot patterns produced by anti-α-enolases and anti-tRNP(Ser)Sec antibodies on electrophoretically separated primate and rat liver homogenates and dot blot results with recombinant tRNP(Ser)Sec. In both rat and primate liver preparations, a band of ~48 kDa is immunofixed by a polyclonal goat IgG anti-α-enolase specific antibody; a band of ~50 kDa is immunofixed by a serum containing a high-titre anti-tRNP(Ser)Sec antibody. Anti-α-enolase antibody does not recognize tRNP(Ser)Sec by dot blot analysis. Figure 2 Immunoblot patterns produced on rat liver homogenate by (lane 1) a polyclonal goat IgG anti-α-enolase specific antibody; (lanes 2–5) four representative anti-soluble liver antigen (SLA) positive sera; (lane 6) a reference serum containing high-titre anti-tRNP(Ser)Sec antibody. Figure 3 Immunoblot patterns obtained using 9 SLA positive and 9 SLA negative serum samples against recombinant α-enolase. A polyclonal goat IgG anti-α-enolase specific antibody has been used as a reference positive serum. Ab, antibody; ag, antigen Pre-incubation of anti-SLA positive sera with tRNP(Ser)Sec completely abolished the 50 kDa band immunofixed in either rat or human liver preparation. In contrast, a parallel experiment where the anti-α-enolase antiserum was pre-incubated with recombinant tRNP(Ser)Sec left unaltered the reactivity to the 48 kDa band. The findings of the present study indicate that α-enolase and tRNP(Ser)Sec are both expressed in primate and rat liver and have a respective MW of 48 and 50 kDa. They also show that anti-tRNP(Ser)Sec – but not anti-α-enolase – correlates with anti-SLA antibody reactivity suggesting that the target of anti-SLA antibody is tRNP(Ser)Sec and not α-enolase [1-9,19]. However, Ballot et al [11] state that α-enolase is a major SLA antigen since rat α-enolases have MW of 47.4 to 47.5 and Pi values of 5.8 to 6.2. These characteristics do not match the MW (48.8) and Pi (8.6) of tRNP(Ser)Sec. Ballot et al [11], however, show a Coomassie stained 2D-gel over the Pi range 6 to 11 and an immunoblot of this gel with several bands of a Pi above 8, but have not investigated these bands by MALDI-TOF analysis being therefore unable to rule out their possible relation to tRNP(Ser)Sec. Conclusion All the above observations indicate that tRNP(Ser)Sec is the most likely target of anti-SLA. List of Abbreviations AIH, autoimmune hepatitis; SLA, soluble liver antigen; tRNP(Ser)Sec, tRNA-associated antigenic protein Competing Interests The author(s) declare that they have no competing interests. Authors' Contributions DPB designed the study, performed the ELISA and inhibition experiments and contributed in writing the report. DG performed the immunoblot testing of anti-enolase antibodies. IB, SL & YM performed the immunoblot experiments involving liver preparations. RRM helped with the artwork. GMV provided clinical material and contributed in writing the report. DV supervised the study and wrote the report. Acknowledgements We thank Professor Huub J.M. Op den Camp (Department of Microbiology, University of Nijmegen, The Netherlands) for helpful comments. DP Bogdanos and G Mieli-Vergani are supported by the Children's Liver Disease Foundation (Birmingham, U.K.). G Mieli-Vergani is also supported by WellChild (London, U.K.). Y Ma is a Dorothy Hodgkin Fellow of the Royal Society (London, U.K.). ==== Refs Manns M Gerken G Kyriatsoulis A Staritz M Meyer zum Buschenfelde KH Characterisation of a new subgroup of autoimmune chronic active hepatitis by autoantibodies against a soluble liver antigen Lancet 1987 1 292 294 2880112 10.1016/S0140-6736(87)92024-1 Wies I Brunner S Henninger J Herkel J Kanzler S Meyer zum Buschenfelde KH Lohse AW Identification of target antigen for SLA/LP autoantibodies in autoimmune hepatitis Lancet 2000 355 1510 1515 10801173 10.1016/S0140-6736(00)02166-8 Costa M Rodriguez-Sanchez JL Czaja AJ Gelpi C Isolation and characterization of cDNA encoding the antigenic protein of the human tRNP(Ser)Sec complex recognized by autoantibodies from patients withtype-1 autoimmune hepatitis Clin Exp Immunol 2000 121 364 374 10931155 10.1046/j.1365-2249.2000.01280.x Wen L Ma Y Bogdanos DP Wong FS Demaine A Mieli-Vergani G Vergani D Pediatric autoimmune liver diseases: the molecular basis of humoral and cellular immunity Curr Mol Med 2001 1 379 389 11899084 Volkmann M Martin L Baurle A Heid H Strassburg CP Trautwein C Fiehn W Manns MP Soluble liver antigen: isolation of a 35-kd recombinant protein (SLA-p35) specifically recognizing sera from patients with autoimmune hepatitis Hepatology 2001 33 591 596 11230739 10.1053/jhep.2001.22218 Ma Y Okamoto M Thomas MG Bogdanos DP Lopes AR Portmann B Underhill J Durr R Mieli-Vergani G Vergani D Antibodies to conformational epitopes of soluble liver antigen define a severe form of autoimmune liver disease Hepatology 2002 35 658 664 11870381 10.1053/jhep.2002.32092 Vitozzi S Djilali-Saiah I Lapierre P Alvarez F Anti-soluble liver antigen/liver-pancreas (SLA/LP) antibodies in pediatric patients with autoimmune hepatitis Autoimmunity 2002 35 485 492 12765473 10.1080/0891693021000056712 Czaja AJ Shums Z Norman GL Frequency and significance of antibodies to soluble liver antigen/liver pancreas in variant autoimmune hepatitis Autoimmunity 2002 35 475 483 12765472 10.1080/0891693021000054101 Baeres M Herkel J Czaja AJ Wies I Kanzler S Cancado EL Porta G Nishioka M Simon T Daehnrich C Schlumberger W Galle PR Lohse AW Establishment of standardised SLA/LP immunoassays: specificity for autoimmune hepatitis, worldwide occurrence, and clinical characteristics Gut 2002 51 259 264 12117891 10.1136/gut.51.2.259 Vergani D Choudhuri K Bogdanos DP Mieli-Vergani G Pathogenesis of autoimmune hepatitis Clin Liver Dis 2002 6 439 449 12362577 Ballot E Bruneel A Labas V Johanet C Identification of rat targets of anti-soluble liver antigen autoantibodies by serologic proteome analysis Clin Chem 2003 49 634 643 12651817 10.1373/49.4.634 Adamus G Amundson D Seigel GM Machnicki M Anti-enolase-alpha autoantibodies in cancer-associated retinopathy: epitope mapping and cytotoxicity on retinal cells J Autoimmun 1998 11 671 677 9878089 10.1006/jaut.1998.0239 Lee KH Chung HS Kim HS Oh SH Ha MK Baik JH Lee S Bang D Human alpha-enolase from endothelial cells as a target antigen of anti-endothelial cell antibody in Behcet's disease Arthritis Rheum 2003 48 2025 2035 12847697 10.1002/art.11074 Moodie FD Leaker B Cambridge G Totty NF Segal AW Alpha-enolase: a novel cytosolic autoantigen in ANCA positive vasculitis Kidney Int 1993 43 675 681 8455367 Pratesi F Moscato S Sabbatini A Chimenti D Bombardieri S Migliorini P Autoantibodies specific for alpha-enolase in systemic autoimmune disorders J Rheumatol 2000 27 109 115 10648026 Roozendaal C Zhao MH Horst G Lockwood CM Kleibeuker JH Limburg PC Nelis GF Kallenberg CG Catalase and alpha-enolase: two novel granulocyte autoantigens in inflammatory bowel disease (IBD) Clin Exp Immunol 1998 112 10 16 9566783 10.1046/j.1365-2249.1998.00528.x Saulot V Vittecoq O Charlionet R Fardellone P Lange C Marvin L Machour N Le Loet X Gilbert D Tron F Presence of autoantibodies to the glycolytic enzyme alpha-enolase in sera from patients with early rheumatoid arthritis Arthritis Rheum 2002 46 1196 1201 12115223 10.1002/art.10252 Tanaka S Tatsumi KI Takano T Murakami Y Takao T Yamakita N Tahara S Teramoto A Hashimoto K Kato Y Amino N Anti-alpha-enolase antibodies in pituitary disease Endocr J 2003 50 697 702 14709840 10.1507/endocrj.50.697 Bogdanos DP Bianchi I Ma Y Mitry RR Mieli-Vergani G Vergani D Targets of antibodies to soluble liver antigen in patients with autoimmune hepatitis Clin Chem 2004 50 682 3; 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==== Front Cerebrospinal Fluid ResCerebrospinal Fluid Research1743-8454BioMed Central London 1743-8454-1-21567994810.1186/1743-8454-1-2ResearchEvidence of connections between cerebrospinal fluid and nasal lymphatic vessels in humans, non-human primates and other mammalian species Johnston Miles [email protected] Andrei [email protected] Christina [email protected] Giselle [email protected] Dianna [email protected] Neuroscience Program, Department of Laboratory Medicine and Pathobiology, Sunnybrook and Women's College Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada2004 10 12 2004 1 2 2 27 9 2004 10 12 2004 Copyright © 2004 Johnston et al; licensee BioMed Central Ltd.2004Johnston et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The parenchyma of the brain does not contain lymphatics. Consequently, it has been assumed that arachnoid projections into the cranial venous system are responsible for cerebrospinal fluid (CSF) absorption. However, recent quantitative and qualitative evidence in sheep suggest that nasal lymphatics have the major role in CSF transport. Nonetheless, the applicability of this concept to other species, especially to humans has never been clarified. The purpose of this study was to compare the CSF and nasal lymph associations in human and non-human primates with those observed in other mammalian species. Methods Studies were performed in sheep, pigs, rabbits, rats, mice, monkeys and humans. Immediately after sacrifice (or up to 7 hours after death in humans), yellow Microfil was injected into the CSF compartment. The heads were cut in a sagittal plane. Results In the seven species examined, Microfil was observed primarily in the subarachnoid space around the olfactory bulbs and cribriform plate. The contrast agent followed the olfactory nerves and entered extensive lymphatic networks in the submucosa associated with the olfactory and respiratory epithelium. This is the first direct evidence of the association between the CSF and nasal lymph compartments in humans. Conclusions The fact that the pattern of Microfil distribution was similar in all species tested, suggested that CSF absorption into nasal lymphatics is a characteristic feature of all mammals including humans. It is tempting to speculate that some disorders of the CSF system (hydrocephalus and idiopathic intracranial hypertension for example) may relate either directly or indirectly to a lymphatic CSF absorption deficit. ==== Body Background The formation of CSF has been studied extensively [1] and the physical location at which it is formed within the ventricular system (the choroid plexus) has been well established. In contrast, while CSF absorption parameters have been investigated in many species, the mechanism and location of the transport sites is far from clear. It is assumed that projections of the arachnoid membrane into the cranial venous sinuses facilitate CSF removal however, there is very little convincing quantitative evidence to support a role for arachnoid villi and granulations in this process. Recent experiments suggest that the movement of CSF directly into the cranial venous system may only occur at high pressures suggesting that the arachnoid projections may have an accessory function rather than representing the primary locations at which CSF is absorbed [2,3]. The experimental evidence would seem to favour the view that a significant portion of CSF is removed from the subarachnoid space by nasal lymphatic vessels. This conclusion is based on experiments that span over 100 years of investigation [reviewed in [4]] with the most extensive analysis of CSF transport having been carried out more recently in sheep [2,3,5-17]. CSF convects through the cribriform plate into lymphatic vessels located in the submucosa associated with the olfactory and respiratory epithelium. The lymph within the ethmoidal lymphatics appears to be continuous with CSF in the perineurial spaces associated with the olfactory nerves. Multiple lymphatic ducts form a collar around the emerging nerve roots, which facilitates direct connection between CSF and lymph in the olfactory submucosa [18]. An extensive complex of lymphatic vessels in the ethmoid turbinal system conveys CSF through a variety of lymph nodes in the head and neck region to the cervical lymphatics that in turn, convey the CSF to the venous system [3]. There is also substantial data with various tracers and dyes that this lymphatic CSF transport pathway is a characteristic feature of many mammals [4]. However, there is still some uncertainty regarding the applicability of animal concepts to CSF transport in man. It has been argued that the larger brain, lack of sophisticated olfactory mechanisms and bipedal versus quadrapedal location make CSF transport in humans fundamentally different than that in animals. There is no easy way to address the issue of lymphatic CSF transport in humans but recent experience visualizing CSF-lymph connections with Microfil, suggested that it may be possible to examine lymphatic CSF transport pathways in any species post-mortem. Microfil is a coloured, liquid silicone rubber compound which, when combined with a catalytic curing agent, polymerizes within 20 minutes. This agent facilitates the generation of 3 dimensional images of the spaces into which it has been injected. In a previous report, we infused Microfil into the subarachnoid compartment of sheep and noted that it was carried through the cribriform plate into an extensive network of lymphatic vessels in the nasal submucosa of this species [2,3,17,18]. Drawing on this experience, the main objective of the studies reported here was to examine potential CSF-nasal lymph pathways in a variety of species including human and non-human primates. The results of this investigation support the view that lymphatics play an important role in CSF transport in all mammalian species. Methods Animals All animal experiments were approved by the ethics committee at Sunnybrook and Women's College Health Sciences Centre and conformed to the guidelines set by the Canadian Council on Animal Care and the Animals for Research Act of Ontario. Studies in the sheep, mouse, rat, rabbit and pig were performed at Sunnybrook and Women's College Health Sciences Centre. Studies on the Barbados green monkey were performed at the Barbados Primate Research Centre and Wildlife Reserve. Investigation of human specimens was approved by the Chief Coroner and the General Inspector of Anatomy for Ontario. These studies were conducted at the University of Toronto, Department of Surgery, Division of Anatomy. Experimental Objectives The objective of these studies was to infuse a CSF tracer, Microfil (Flowtech, Mass), into the cranial subarachnoid space post-mortem to outline the pathways of CSF outflow. To accomplish this, the specimen was placed in sternal recumbancy with the head fixed in position. A laminectomy was performed on the upper regions of the spinal cord (generally between C1-C2) and the cisterna magna was cannulated with an angiocatheter or a silastic tube of appropriate size. A ligature was placed around the spinal cord and tightened to compress the spinal cord and meninges [13]. This ligature served two purposes. First, as we were interested in cranial absorption pathways, the amount of Microfil infused was less since this agent was prevented from passing into the spinal subarachnoid compartment during injection. Second, our experience suggested that infusion pressures effective in filling the pathways of interest were more easily achieved with the spinal CSF space negated. With the exception of studies in the mice, all experiments were performed by infusing Microfil manually using a syringe. We did not routinely monitor infusion pressures post-mortem. However, in several of our initial studies in sheep, we measured infusion pressures between 200 and 300 cm H2O. Mice on the other hand appeared to be very fragile and injection by hand was problematic. In this species, we found that Microfil filling was more successful if this agent was introduced into the CSF compartment using a free-flow pressure system and reservoir as described below. The best results were obtained using a Microfil preparation that was more dilute than that recommended in the product literature. To achieve this, 3 ml of diluent was used for every 1 ml of yellow Microfil® (MV-122) and the material catalyzed with 10% (of total volume) of MV Curing Agent. In several sheep, blue Microfil (prepared as recommended in the instruction manual) was introduced into the vasculature at the same time that yellow Microfil was injected into the CSF compartment. The carotid arteries were catheterized and 20 ml of blue Microfil® (MV-120) was infused into both arteries simultaneously. More specific details pertaining to each species is outlined below. Mice Randomly bred, wild-type C57/Bl mice (n = 21, 16–19 gm; ~4–5 weeks old) were used for this investigation. They were fed lab rat chow (LabDiet 5001) until sacrifice. At the start of the preparation, the mice were euthanized. A laminectomy was performed at the C7 cervical-thoracic level of the vertebral column. Microfil was infused into the cranial subarachnoid space by one of two methods. In some preparations, Microfil was infused into the cranial subarachnoid space over a 5–10 minute interval through manual injection into an angiocatheter. In other experiments, the Microfil was administered using a free-flow pressure system with a reservoir filled with saline regulating the rate of flow into the cisterna magna. To achieve this, the three ports of a 3-way stopcock were attached to (a) a syringe containing Microfil, (b) a cannula attached to an angiocatheter inserted into the subarachnoid space, and (c) a cannula filled with saline hung vertically from a pole. The reservoir height was adjusted such that the saline solution exerted a pressure of approximately 100 cm H20. Microfil was injected via the syringe until the agent reached the hub of the angiocatheter. At this point, the stopcock was adjusted to allow the column of PBS to exert a pressure on the Microfil, which pushed the contrast agent into the subarachnoid space. Rats Randomly bred, Fischer 344 rats (n = 6, ~300 gm; 11–15 weeks old) were used for this investigation. They were fed lab rat chow (LabDiet 5001) until sacrifice. The rats were anaesthetized initially by placement in an induction chamber in which 5% halothane was administered for 2 minutes. Immediately after anesthesia, the rats were euthanized. A laminectomy was performed at the C7 cervical-thoracic level of the vertebral column. Microfil was infused into the cranial subarachnoid space over 5–10 minutes. Rabbits Randomly bred, New Zealand white rabbits (n = 2, 3 and 4 kg; ~18 weeks old) were used for this investigation. They were fed rabbit chow (LabDiet 5321) until sacrifice. The rabbits were initially anesthetized with an intramuscular injection of Ketamine (50 mg/kg) and Rompun (5 mg/kg). Prior to surgery, the rabbits were euthanized. A laminectomy was performed at the C7 cervical-thoracic level of the vertebral column. Microfil was infused into the cranial subarachnoid space over a 5–10 minute period. Sheep Randomly bred sheep (n = 29; 2.6–35 kg) including newborns (2–7 days old) and adult animals (6–8 months old) were used for this investigation. The neonatal lambs were bottle-fed formula (Lamb Replacement, Grober Inc. Cambridge, Ontario) until surgery. The lambs were anaesthetized initially by mask administration of incremental concentrations of halothane from 0.5 to 3%. The adult sheep were anesthetized initially by intravenous infusion of 2.5% sodium Pentothal solution. Subsequently, 1–3% halothane was delivered through an endotracheal tube via an A.D.S.1000 or Narkomed 2 respirator for surgical maintenance. In this species, a laminectomy was performed at the C1-C2 level under anesthesia followed by sacrifice. Microfil was infused into the cranial subarachnoid space over 10–15 minutes. In some animals, blue Microfil (prepared as recommended in the instruction manual) was introduced into the vasculature at the same time that yellow Microfil was injected into the CSF compartment. The carotid arteries were catheterized and 20 ml of blue Microfil® (MV-120) was infused into both arteries simultaneously. Pigs Randomly bred, Yorkshire pigs (n = 5, 20–25 kg; 8–9 weeks old) were used for this investigation. They were fed pellets (LabDiet Laboratory Animal Diet) until sacrifice. The pigs were anesthetized initially with an intramuscular injection of Ketamine (30 mg/kg) and Atropine (0.04 mg/kg). Surgical anesthesia was induced and maintained by 2–5% halothane delivered through an endotracheal tube via a Narkomed 2 respirator. Prior to surgery, the pigs were euthanized. A laminectomy was performed at the C1-C2 level and Microfil was infused into the cranial subarachnoid space over a 10–15 minute period. Monkeys Randomly bred, Barbados green monkeys (Cercopithecus aethiops sabeus) (n = 6, 4.0–5.6 kg; ~6 years old) were used for this investigation. They were fed twice daily various natural foods until sacrifice. At the start of the preparation, the monkeys were anaesthetized with Xylazine/Ketamine (100 mg/kg) injection. Subsequently, the monkeys were euthanized. A laminectomy between C7 and T1 was performed and Microfil was infused into the cranial subarachnoid space over a 5–10 minute period. Humans Three recently deceased cadaveric preparations were examined (3 females, 77, 80 and 90 yrs old). Access to the cadaveric material was permitted between 6 and 7 hours post mortem. A laminectomy between C7 and T1 was performed and Microfil was infused into the cranial subarachnoid space. Analysis of tissues Following injection with Microfil, the heads of mice, rats, rabbits, sheep and pigs were stored at 4°C overnight. The next day, the heads were skinned, sectioned in a sagittal orientation and fixed in 10% formalin. In the monkey studies, the Microfil was allowed to set for 2 hours after injection followed by sectioning and fixation with formalin (2–3 weeks) and 70% alcohol (immediately before shipment to Canada). After Microfil infusion in humans, the heads were placed in a freezer for several days (-18°C) after which they were sectioned and fixed with Kaiserling's solution for at least 5 days before dissection. Sectioning of the fixed tissues was made in several orientations including coronal cuts anterior and posterior to the cribriform plate and/or sagittal sections. The various tissues were dissected as appropriate under a dissecting microscope (Leica M651, Wild Leizt; EMZ-TR, Meiji Techno; Fisher Stereomaster) and images were captured on a Nikon digital camera (Coolpix 995). All images presented in this report are in the midsagittal plane from the cut surfaces of the heads. Results Technical Issues Filling of the subarachnoid compartment and extension of the Microfil into the lymphatic vessels of the olfactory and respiratory submucosa was dependent on several factors including the size of the species and the time of injection after death. Filling of the CSF space and attendant lymphatic vessels with Microfil was easiest in the larger animals and was increasingly challenging as the species declined in size. Additionally, we were able to achieve successful filling up to 6 hours after death but the best results were obtained when the agent was injected immediately after the animal was euthanized. For reasons unknown, in a few unsuccessful preparations the Microfil did not cure correctly. The number of human specimens was limited to 3. In one attempt, there was incomplete filling of the subarachnoid space due to rupture of the meninges and dura. In another preparation, a large metastatic tumor located in the frontal lobe with accompanying perifocal edema and petechial hemorrhages caused significant dislocation of the brain and prevented filling of the CSF spaces. In a third preparation, where the cause of death was not connected to brain disease (lung cancer), the infusion was successful. Table 1 summarizes the experimental details and the success rates of the Microfil infusions in the various species. Table 1 Details of animal studies Species Number of animals used Number of successful preparations Hours after death Injected Microfil volume (ml) mice 21 7 0–1 1–1.5 rats 6 4 0–1 2.5–3 rabbits 2 2 0–1 10 sheep 29 26 0–1 20–25 pigs 5 2* 2–72 20–25 monkeys 6 5 0–1 20 humans 3 1** 5–7 60–180 * Successful filling was achieved 2 and 5 hours after death ** In the one successful human preparation, 180 ml of Microfil was infused CSF Transport Pathways Microfil was observed throughout the subarachnoid compartment associated with the base of the brain and generally filled the basal cisterns in all species. Microfil was also observed commonly in the subarachnoid space over the convexities of the brain. In all species examined in this report (mouse and rat, Figure 1; rabbit and sheep, Figure 2; pig and monkey, Figure 3; and human, Figure 4), Microfil could be observed in a patchy distribution along the olfactory nerves external to the cranium and in lymphatics associated with the submucosa of the olfactory epithelium. More extensive lymphatic networks containing Microfil were observed in the submucosa of the ethmoid labyrinth and adjacent nasal septum. In some of the sheep preparations, blue Microfil was introduced into the vascular system and yellow Microfil into the subarachnoid space. Yellow lymphatic vessels were easily distinguished from blue arterioles (Figure 5A). Figure 1 Microfil distribution patterns in the head of mice and rats. All images are presented in sagittal plane with gradual magnification of the olfactory area adjacent to the cribriform plate. Reference scales are provided either as a ruler in the image (mm) or as a longitudinal bar (1 mm). A-C illustrates images of the mouse and D-F images of rat. The Microfil can be viewed in the perineurial space of the olfactory nerves external to the cranium and a network of lymphatic vessels containing yellow Microfil can be observed in the ethmoid turbinal systems of both species. In the example illustrated in 1C, the image was captured before fixation. The reddish spots are areas of hemorrhage in this particular animal. b – brain; cp – cribriform plate; et – ethmoid turbinates; ob – olfactory bulbs; on – olfactory nerves; ns – nasal septum. Figure 2 Microfil distribution patterns in the head of rabbits and sheep. All images are presented in sagittal plane with gradual magnification of the olfactory area adjacent to the cribriform plate. Reference scales are provided either as a ruler in the image (mm) or as a longitudinal bar (1 mm). A-C illustrates images of the rabbit and D-F images of sheep. In both species, the Microfil can be viewed in the perineurial spaces of the olfactory nerves external to the cranium, which merge into network of lymphatic vessels in the ethmoid turbinal systems. b – brain; cp – cribriform plate; et – ethmoid turbinates; on – olfactory nerves; ns – nasal septum; arrow in D – portion of cribriform plate removed for histology. Figure 3 Microfil distribution patterns in the head of pigs and Barbados green monkeys. All images are presented in sagittal plane with gradual magnification of the olfactory area adjacent to the cribriform plate. Reference scales are provided either as a ruler in the image (mm) or as a longitudinal bar (1 mm). A-C illustrates images of the pig and D-F images of the monkey. In both species, the Microfil can be viewed in the perineurial spaces of the olfactory nerves external to the cranium, which merge into network of lymphatic vessels in the ethmoid turbinal systems. In A, Microfil can be observed penetrating the dura and entering the cavernous sinus with partial filling of the retromandibular vein (arrow). b – brain; cp – cribriform plate; et – ethmoid turbinates; ob – olfactory bulbs; on – olfactory nerves. Figure 4 Microfil distribution patterns in the head of a human (A-F). All images are presented in sagittal plane with gradual magnification of the olfactory area adjacent to the cribriform plate. Reference scales are provided either as a ruler in the image (mm) or as a longitudinal bar (1 mm). As in the other species, Microfil introduced into the subarachnoid space was observed around the olfactory bulb (A), in the perineurial spaces of the olfactory nerves (B, C) and in the lymphatics of the nasal septum (D), ethmoid labyrinth (E) and superior turbinate (F). Due to tissue deterioration, some of the lymphatic vessels had ruptured and Microfil was noted in the interstitium of the submucosa of the nasal septum (D). In (E), Microfil is observed in the subarachnoid space and the perineurial space of olfactory nerves. The perineurial Microfil is continuous with that in lymphatic vessels (arrows). Intact lymphatic vessels containing Microfil are outlined with arrows (D, E, F). b – brain; fs – frontal sinus; cp – cribriform plate; et – ethmoid turbinates; ob – olfactory bulbs; on – olfactory nerves; ns – nasal septum; sas – subarachnoid space. Figure 5 Visualization of lymphatic vessels containing Microfil. Reference scales are provided as a longitudinal bar (1 mm). A – illustrates the ability to separate blood vessels (blue) from lymphatics (yellow) using differently coloured Microfil preparations (sheep). B – demonstrates lymphatic networks that have taken up Microfil that was injected into the subarachnoid compartment (pig). These networks ultimately connect with various lymph nodes (C – retropharyngeal node example in sheep; D – submandibular node example in mouse). The prenodal vessels can be visualized converging on the lymph nodes with the lymphatics congregating into a labyrinth of small ducts or foot processes on the node capsule. Blood vessels containing blue Microfil can be observed proximal to the node in C. The functional contractile unit of lymphatic vessels is the lymphangion, which is a segment of vessel between 2 valves (valves illustrated by arrows in C). n – lymph node; L – lymphangion. The structural properties of the ethmoid labyrinth determined the density of distribution of Microfil into lymphatics of the nasal cavity. The rat, mouse, rabbit, sheep, and pig have large olfactory bulbs and a broad, deep olfactory fosse in the cribriform plate. The structure of the ethmoid bone determines the broad distribution of olfactory nerves from the midline to the latero-posterior nose. In monkeys and humans, the ethmoid bone is more delicate and the olfactory nerves are concentrated along the midline. The exits of olfactory nerves from the skull base were filled with Microfil and in most successful preparations it was possible to see the trunks of the olfactory nerves with Microfil distributed in the perineurial space. We reported previously that Microfil was observed primarily within lymphatic vessels in the sheep with very little evidence of this agent within the interstitium of the submucosa [17,18]. While we did not investigate the tissues with histological techniques in this report, this also seemed to be the case in all of the species examined except for the human specimens. This was most likely due to the fact that we were able to inject the Microfil immediately or shortly after death in the animals. In humans however, there was a delay in getting access to the cadavers with the result that there was some degree of deterioration of the tissues. From previous studies in which Microfil was injected into animals at various times after sacrifice, we know that the CSF transport pathways into lymphatics degenerate rapidly after death and the filling of the vessels with Microfil is compromised. In any event, while many vessels had ruptured in the human tissues, Microfil was observed in enough intact lymphatic vessels to confirm the principle of CSF-nasal lymph connections in man. It has been well documented that CSF transports through the cribriform plate into nasal lymphatics which progress downstream to various lymph nodes including retropharyngeal, cervical, submandibular and preauricular lymph nodes. In some preparations in sheep and mice we were able to follow Microfil-containing lymphatic networks to the point at which they converged on these lymph nodes (examples are illustrated in Figure 5C,5D). Some Microfil was observed in various veins in the head and neck region external to the cranium. An example can be seen in Figure 3A in the pig. In this case the retromandibular vein was partially filled with Microfil. Additionally, in some cases, Microfil was observed to enter the venous sinuses within the cranium. In some of the preparations in all species, Microfil was observed lying freely in the nasal cavity. This material lay proximal to terminal branches of the olfactory nerves and supports the concept of physiological and post inflammatory CSF rhinorrhea. Discussion Nasal lymphatic vessels have been implicated in CSF transport for many years. However, the concept has not received mainstream acceptance for several reasons. First, the data supporting lymphatic function has been based entirely on animal studies especially the sheep. However, histological and radiological evidence indicate that CSF-lymph connections exist in other species including rats [8,19-24], mice [25], rabbits [26-28], cats [20,29,30], dogs [20,30], guinea pigs [31], and non-human primates [30,32]. In the latter case, injection of radioactive albumin into the CSF compartment of monkeys led to elevated concentrations of tracer in the cervical lymph nodes [32]. Circumstantial evidence also suggests that similar CSF-lymphatic connections exist in humans as well [22,33-36]. For example, Indian ink administered into the CSF in human autopsy material [35] was observed to fill the perineurial spaces around the olfactory nerve branches and was found in the nasal submucosal tissue. Similarly, in an individual with subarachnoid hemorrhage, red blood cells were observed around the olfactory nerves and within the nasal mucosa. However, until now, direct evidence that the CSF and lymph compartments were linked in human and non-human primates was lacking. The Microfil images presented in this report demonstrate that the passage of CSF along the olfactory nerves into lymphatics is a characteristic feature of several mammalian species. The Microfil distribution patterns from the mouse to the human were remarkably similar. It must be acknowledged that we had limited access to human material and that studies with the cadaveric material are ongoing. However, the fact that Microfil was observed external to the cranium in the nasal submucosa adjacent to the cribriform plate in both human and Barbados green monkey specimens supports the view that CSF absorption in primates is not significantly different (at least qualitatively) from that in other mammalian species. In a previous study, we injected yellow Microfil into the subarachnoid space and blue Microfil into the carotid arteries [18]. This protocol permitted the separation of the blood vessels from lymphatics without the use of promiscuous molecular markers. Indeed, the larger Microfil-filled lymphatic vessels demonstrated the classical 'lymphangion' structure with constrictions of the vessels where the valves were positioned (example in Fig 5C). The vessels could be traced in some instances to prenodal ducts that converged on various lymph nodes [18]. In the study reported here, we have included examples of this in sheep and mice (Figure 5C,5D). Studies with Microfil provide valuable information on 'potential' CSF transport routes but one must be cautious in applying post-mortem observations to physiological processes. However, in sheep there is considerable quantitative data that supports the concept that the cribriform-olfactory nerve-lymphatic pathway contributes significantly to volumetric CSF absorption [2,3,5-9,11,13-16]. Given that the Microfil distribution patterns in sheep, pigs, rabbits, rats, mice, Barbados green monkeys and humans appear remarkably similar, it would seem likely that this pathway is important for CSF clearance in each of these species. Another reason for the skepticism regarding the role of lymphatics in CSF transport is the generally held view that arachnoid projections function in this role. However, the quantitative evidence supporting a role for arachnoid projections is very limited and unconvincing [37-39]. It must be acknowledged that we observed Microfil within a variety of veins external to the cranium and in the dural sinuses. With regards to the former, Microfil entry was due (at least in part) to passage of the contrast agent through the dura at the base of the brain into the cavernous sinus. The Microfil appeared to push into this venous system and fill several veins. Whether this observation has any physiological relevance is unknown. The route by which Microfil gains entry into the dural venous sinuses is unknown but is currently under investigation. However, the concept of direct CSF transport into the superior sagittal sinus continues to be problematic. We used several experimental approaches to assess this issue in previous studies in sheep. The most informative technique was to employ the classical approach of Mann to look for enrichment of a radioactive CSF tracer in the superior sagittal sinus compared to a peripheral vein [40]. The Mann group observed that the concentration of soluble and particulate tracer was considerably greater in the sinus blood than in the peripheral circulation (inulin concentrations were up to 30 times greater). However, the peak CSF pressures achieved during bolus infusions were greater than 80 cm H2O, a level of pressure that is clearly pathological. Other groups have failed to observe enrichment of a CSF tracer in the superior sagittal sinus of the rabbit, cat and monkey when the intracranial pressures achieved in the experiments simulated physiological conditions more realistically [32,41,42]. In our investigations in adult sheep, at a CSF-superior sagittal sinus pressure gradient of around 10 cm H2O (normal gradient ~5 cm H2O) no enrichment was observed. When the gradient favouring absorption was greater than 20 cm H2O, a modest enrichment of tracer was noted [3]. A similar result was obtained with tracer enrichment experiments in newborn lambs. The latter result was surprising. Adult sheep have abundant arachnoid projections in the superior sagittal sinus which are visible when stained with Evans blue dye but, in the neonatal lamb, arachnoid projections are few or absent [2]. Therefore, tracer transport into the superior sagittal sinus could not be correlated with the presence or absence of arachnoid projections although some transport directly into the cranial venous system seemed to occur. In our studies the maximum enrichment of CSF tracer in the superior sagittal sinus was observed only within the first few minutes after increasing intracranial pressure [3]. Within a short time, the tracer concentrations in the sagittal sinus and peripheral venous blood were equal. This suggested the possibility that the tracer enrichment method outlined above has the potential to underestimate the role of arachnoid projections since vigorous tracer transport to peripheral veins by some other mechanism could cancel out any perceived tracer enrichment in the cranial venous sinuses. Projections of the arachnoid membrane into the veins at the base of the brain have been described but at this time, there is no evidence that these structures play a role in CSF transport. The most obvious contributor to the peripheral venous tracer would be the lymphatic circulation. To determine if the lack of CSF tracer enrichment in the superior sagittal sinus was due to lymphatic transport into peripheral veins, we created what we thought would be the conditions most favourable to measure clearance via the arachnoid granulations. We sealed the cribriform plate and prevented CSF transport into the spinal subarachnoid compartment thus negating much of the lymphatic transport capacity. Nonetheless, even under these conditions when the CSF-superior sagittal sinus pressure gradient was raised to 20 cm H2O (~4 fold higher than normal), no enrichment was observed. At pressure gradients greater than this level, the concentration of the tracer was significantly higher than in the peripheral circulation. Therefore, the lack of enrichment of the CSF tracer in the superior sagittal sinus at normal physiological intracranial pressures does not appear to be due to downstream lymphatic transport of the CSF tracer. Contrary to the 'classical' view, direct transport of CSF into cranial veins (whether this occurs through arachnoid projections we cannot determine unequivocally) may represent an auxiliary system that is recruited when intracranial pressures are high or when other transport pathways are compromised. In summary, we have never been able to obtain persuasive evidence that direct CSF transport into the venous system is a physiological reality. In contrast, the qualitative and quantitative support for nasal lymphatic role in CSF absorption is gaining momentum. Conclusions In this report, we present the first direct evidence for CSF-lymph connections in primates including monkeys and one human. The fact that the Microfil distribution patterns were very similar in the seven species tested suggested that the lymphatic transport of CSF represented an important pathway by which CSF is cleared from the subarachnoid space in mammalian species. A workable hypothesis can be developed around the concept that defects in lymphatic CSF transport may contribute to syndromes characterized by elevated intracranial pressure and/or hydrocephalus. Competing Interests The author(s) declare that they have no competing interests. Author's Contributions MJ: conceived of the study, and participated in its design and coordination. AZ: developed the application of the Microfil method to the analysis of CSF transport pathways, participated in all experiments, created an image catalogue and helped in the preparation of the manuscript. CP: aided in the experiments in human, sheep, rats and mice and helped in the preparation of the manuscript GS: performed the studies in rats and mice DA: in conjunction with AZ performed the studies in humans and monkeys and assisted in all experiments. All authors read and approved the final manuscript. Acknowledgements This research was funded by the Canadian Institutes of Health Research (CIHR) and a CIHR/Spina Bifida and Hydrocephalus Association of Canada Doctoral Research Award to CP. We wish to express thanks to Dr. M. Wiley, Professor, Department of Surgery and Division Chair of the Division of Anatomy, University of Toronto for his help in securing access to the cadaveric material. We are also grateful to B. Woods, J. Topham and T. Irving (Anatomy technicians, Department of Surgery) for assistance with the human specimens. We also wish to thank the staff of the Barbados Primate Research Center and Wildlife Reserve for their support of this project; especially J. Baulu (Director), C. Sutton (Operations Officer), and R. Holder (Laboratory Technician). We gratefully acknowledge the assistance of C. Purcell and D. Pantlin (animal health technicians) who organized access to some of the species utilized in this report and who provided technical expertise. We are grateful to J. 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==== Front J Neuroeng RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central 1743-0003-1-121567994910.1186/1743-0003-1-12ReviewVideo capture virtual reality as a flexible and effective rehabilitation tool Weiss Patrice L [email protected] Debbie [email protected] Noomi [email protected] Rachel [email protected] Dept. of Occupational Therapy, University of Haifa, Israel2 School of Occupational Therapy, Hadassah-Hebrew University, Israel3 Dept. of Occupational Therapy, Chaim Sheba Medical Center, Israel2004 20 12 2004 1 12 12 29 11 2004 20 12 2004 Copyright © 2004 Weiss et al; licensee BioMed Central Ltd.2004Weiss et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Video capture virtual reality (VR) uses a video camera and software to track movement in a single plane without the need to place markers on specific bodily locations. The user's image is thereby embedded within a simulated environment such that it is possible to interact with animated graphics in a completely natural manner. Although this technology first became available more than 25 years ago, it is only within the past five years that it has been applied in rehabilitation. The objective of this article is to describe the way this technology works, to review its assets relative to other VR platforms, and to provide an overview of some of the major studies that have evaluated the use of video capture technologies for rehabilitation. ==== Body Introduction Two major goals of rehabilitation are the enhancement of functional ability and the realization of greater participation in community life. These goals are achieved by intensive intervention aimed at improving sensory, motor, cognitive and higher level-cognitive functions on the one hand, and practice in everyday activities and occupations to increase participation on the other hand [1,2]. Intervention is based primarily on the performance of rote exercises and/or of different types of purposeful activities and occupations [3,4]. The client's cognitive and motor abilities are assessed throughout the intervention period so that therapy may be continually adjusted to the client's needs. For many injuries and disabilities, the rehabilitation process is long and arduous, and clinicians face the challenge of identifying a variety of appealing, meaningful and motivating intervention tasks that may be adapted and graded to facilitate this process. Clinicians also require outcomes that may be measured accurately. Virtual reality-based therapy, one of the most innovative and promising recent developments in rehabilitation technology, appears to provide an answer to this challenge. Indeed, it is anticipated that virtual reality (VR) will have a considerable impact on rehabilitation over the next ten years [5]. Virtual reality typically refers to the use of interactive simulations created with computer hardware and software to present users with opportunities to engage in environments that appear to be and feel similar to real world objects and events [6-8]. Users interact with displayed images, move and manipulate virtual objects, and perform other actions in a way that attempts to "immerse" them within the simulated environment thereby engendering a feeling of presence in the virtual world [9,10]. The objective of this article is to briefly describe the use of VR in rehabilitation, and then emphasize the unique attributes of the video capture VR to rehabilitation, including an overview of some of the major studies that have evaluated the use of this technology for rehabilitation. Virtual reality applied to rehabilitation Virtual reality has a number of well-known assets, which make it highly suitable as a rehabilitation intervention tool [11]. These assets include the opportunity for experiential, active learning and the ability to objectively measure behavior in challenging but safe and ecologically-valid environments while maintaining strict experimental control over stimulus delivery and measurement. VR also provides the capacity to individualize treatment needs, while gradually increasing the complexity of tasks and decreasing the support provided by the clinician [5,12]. During the mid to late 1990s, virtual reality technologies first began to be developed and studied as potential tools for rehabilitation assessment and treatment intervention [7]. The list of applications is long and diverse, and only several examples are provided here. VR has been used as a medium for the assessment and rehabilitation of cognitive and metacognitive processes, such as visual perception, attention, memory, sequencing and executive functioning [13]. Rizzo and colleagues [14,15] developed a Virtual Classroom for the assessment and training of attention in children with Attention Deficits Hyperactive Disorder. Piron, et al. [16] used a virtual environment to train reaching movements, Broeren, et al. [17] used a haptic device for the assessment and training of motor coordination, and Jack et al. [18] and Merians, et al. [19] have developed a force feedback glove to improve hand strength and a joint position glove to improve the range of motion and speed of hand movement. The studies cited above share a common goal of using virtual reality to construct a simulated environment that aimed to facilitate the client's motor, cognitive or metacognitive abilities in order to improve functional ability. In some cases, the applications take advantage of the ability to adapt virtual environment to simulate real life activities such as meal preparation [20] or crossing a street [21-25]. The ultimate goal of such applications is to enable clients to become able to participate in their own real environments in a more independent manner. Attempting to achieve similar results via conventional therapy when clinicians and clients must deal with real world settings (e.g., a visit to a real supermarket) is fraught with difficulty. In contrast, virtual environments may be adapted with relative ease to the needs and characteristics of the clients under care. Given the variety of VR platforms and the diverse clinical populations that may benefit from VR-based intervention, it is helpful to view the VR experience as a multidimensional model that appears to be influenced by many parameters. A conceptual model was developed within the context of terminology established by the International Classification of Functioning, Disability and Health (ICF) [2] and the rehabilitation process [25,26]. This model helps to identify the clinical rationale underlying the use of virtual reality as an intervention tool in rehabilitation as well as to design research to investigate its efficacy for achieving improved performance in the real world. The process of using VR in rehabilitation is modeled via three nested circles, the inner "Interaction Space", the intermediate "Transfer Phase" and the outer "Real World". The "Interaction Space" denotes the interaction that occurs when the client performs within the virtual environment, experiencing functional or game-like tasks of varying levels of difficulty, i.e., the activity component according to the ICF terminology. This interaction is influenced by user characteristics, which include personal factors (e.g. age, gender, cultural background), body functions (e.g. cognitive, sensory, motor abilities) and structures (e.g., the parts of the body activated during the task). It is also influenced by the characteristics of VR platform and its underlying technology (e.g. point of view, encumbrance) that presents the virtual environment and the nature and demands of the task to be performed within the virtual environment. It is during the interaction process that sensations and perceptions related to the virtual experience take place; here the user's sense of presence is established, and the process of assigning meaning to the virtual experience as well as the actual performance of virtual tasks or activities occurs. The sense of presence enables the client to focus on the virtual task, separating himself temporarily from the real world environment. This is an important requirement when motor and, especially, cognitive abilities and skills are trained or restored. The concept of meaning is also thought to be an essential factor that enhances task performance and skills in rehabilitation in general [1,3], and thus also in the VR-based rehabilitation [27]. Environmental factors within the virtual environment may contribute information about issues that facilitate or hinder the client's performance, and may serve as facilitators of performance in the virtual environment leading to improved performance in the real world. Two outer circles, the "Transfer Phase" and the "Real World" denote the goal of transferring skills and abilities acquired within the "Interaction Space" and eliminating environmental barriers in order to increase participation in the real world (i.e., participation in the natural environment according to the ICF terminology). The "Transfer Phase" may be very rapid and accomplished entirely by the client or may take time and need considerable guidance and mediation from the clinician. The entire process is facilitated by the clinician whose expertise helps to actualize the potential of VR as a rehabilitation tool. Virtual reality platforms Virtual environments are experienced with the aid of special hardware and software for input (transfer of information from the user to the system) and output (transfer of information from the system to the user). The selection of appropriate hardware is important since its characteristics may greatly influence what is taking place in the Interaction Space, i.e., the way users respond (e.g. sense of presence, performance) to a virtual environment [28]. The output to the user generates different levels of immersion, which may be enhanced by different modalities including visual, auditory, haptic, vestibular and olfactory stimuli, although, to date, most VR platforms deliver primarily visual and auditory feedback. Visual information is commonly displayed by head mounted displays (HMD), projection systems, or flat screen, desktop systems of varying size. Input to a virtual environment enables the user to navigate and manipulate objects within it. Input may be achieved via direct methods such as inertial orientation tracker or by video sensing which tracks user movement. Input may also be achieved via activation of computer keyboard keys, a mouse or a joystick or even virtual buttons appearing as part of the environment. In addition to specialized hardware, application software is also necessary. In recent years, off-the-shelf, ready-for-clinical-use VR software has become available for purchase. However, more frequently, special software development tools are required in order to design and code an interactive simulated environment that will achieve a desired rehabilitation goal. In many cases, innovative intervention ideas may entail customized programming to construct a virtual environment from scratch, using traditional programming languages. Video capture VR Video capture VR consists of a family of camera-based, motion capture platforms that differ substantially from the HMD and desktop platforms in wider use. When using a video-capture VR platform, users stand or sit in a demarcated area viewing a large video screen that displays one of a series of simulated environments. Users see themselves on the screen, in the virtual environment, and their own natural movements entirely direct the progression of the task, i.e., the user's movement is the input. The result is a complete engagement of the user in the simulated task. A single video camera converts the video signal of the user's movements wherein the participant's image is processed on the same plane as screen animation, text, graphics, and sound, which respond in real-time. This process is referred to as "video gesture", i.e., the initiation of changes in a virtual reality environment through video contact. The user's live, on-screen video image responds at exactly the same time to movements, lending an intensified degree of realism to the virtual reality experience. Video capture provides both visual and auditory feedback with the visual cues being most predominant. Myron Krueger [29] was the first to investigate the potential of video capture technology in the 1970s with his innovative Videoplace installation. This was one of the first platforms that enabled users to interact with graphic objects via movements of their limbs and body, and was used to explore a variety of virtual art forms. The quality of the video image in these applications was relatively primitive, consisting of silhouetted figures. Nevertheless, the immediate response of the virtual environment in real-time to the user's movements presented compelling evidence of the possibility of using this technique for interactive simulation. The next major development occurred with the release of VividGroup's Mandala Gesture Extreme (GX) platform in 1996, together with a suite of interactive, game-type environments. This platform makes use of a chroma key-based setup so that the existing background is subtracted and replaced by a simulated background. GX VR has enjoyed considerable success around the world in numerous entertainment and educational facilities including science museums and entertainment parks. During the past five years it has also begun to be adapted for use in rehabilitation and has generated great interest in clinical settings (see below). GX VR currently offers a wide variety of gaming applications including, Birds & Balls, wherein a user is required to touch balls of different colors; if the touch is "gentle", the balls turn into doves whereas an abrupt touch causes them to burst. In another application, a soccer game, the user sees himself as the goalkeeper whose task it is to prevent balls from entering the goal area (see Figure 1). Figure 1 Individual with a stroke performing within the Soccer environment using the VividGroup GX system. In the late 1990s two other commercial companies developed video-capture gaming platforms, Reality Fusion's GameCam and Intel's Me2Cam Virtual Game System [30]. Both of these platforms aimed for the low-cost, general market, relying on inexpensive web camera installations that did not entail the use of the chroma key technique. For reasons that are not clear, Reality Fusion and Intel discontinued their products within the past two years. Somewhat later, Sony developed its very popular EyeToy application designed to be used with the PlayStation II platform . This is an off-the-shelf, low-cost gaming application, which provides the opportunity to interact with virtual objects that can be displayed on a standard TV monitor [31]. As with the VividGroup's GX platform, the EyeToy displays real-time images of the user. However, it does not require a chroma key blue/green backdrop behind the user nor bright ambient lighting (see Figure 2). This makes for an easier setup of the platform in any location but, on the other hand, it means that the user sees himself manipulating virtual objects within a video image of his own physical surrounding rather than within different virtual environments. An additional difference between the cheaper EyeToy platform and the more expensive GX platform is that the former is capable of recognizing users or objects only when they are in motion. A user who remains stationery does not exist for EyeToy applications. In contrast, the GX VR is responsive to users whether they are in motion or not. Figure 2 Individual with a stroke performing the Wishy Washy application using the Sony EyeToy system. The EyeToy application includes many motivating and competitive environments which may be played by one user or more than one user sequentially in a tournament fashion. With GX VR, two users can compete together simultaneously (e.g., boxing, spinning plates) as well as combine their efforts to create different visual effects without a competitive component (e.g., painting a rainbow, mirror image distortions and popping bubbles). The potential of these platforms for rehabilitation was readily apparent despite the fact that they were originally developed for entertainment and gaming purposes. Indeed, VividGroups's GX platform was first applied without adaptations within a clinical setting by Cunningham and Krishack [32] who used it to treat elderly patients who were unstable and at high risk for falling. Unfortunately, the inability to grade these platforms to levels suited to patients with severe cognitive or motor impairments initially limited the application of these environments in clinical settings. In order to broaden the potential clinical applications of the platforms, our research group adapted the GX VR platform [33,34]. VividGroup developed, and now also markets, a version of the GX platform, known as IREX (Interactive Rehabilitation EXercise) platform which enables therapists to adapt levels of difficulty and record performance outcomes [35]. Characteristics of the Video-Capture Platforms Video-capture VR differs from other platforms in a number of ways that have great relevance for its use as a tool for rehabilitation evaluation and intervention. Some of these characteristics appear to be advantageous whereas others may limit the utility of video-capture VR. Point of View Video-capture VR provides users with a mirror image view of themselves actively participating within the environment. This contrasts with other VR platforms such as the HMD which provides users with a "first person" point of view, or many desktop platforms in which the user is represented by an avatar. The use of the user's own image has been suggested to add to the realism of the environment and to the sense of presence [10]. It also provides feedback about a client's body posture and quality of movement, comparable to the use of video feedback in conventional rehabilitation during the treatment of certain conditions such as unilateral spatial neglect [36]. Freedom from encumbrance The user in video-capture VR does not have to wear or support extraneous devices such as an HMD, glove or markers in order to achieve a substantial intensity of immersion within the virtual environment. This eliminates a source of encumbrance that would likely hinder the motor response of patients with neurological or orthopedic deficits. Although the newer HMDs and stereoscopic glasses are considerably less cumbersome than previous models, little information is available regarding their use by individuals undergoing cognitive or motor rehabilitation. Interaction and Control This characteristic relates to how the user controls objects within the virtual environment. As indicated above, rather than relying on a pointing device or tracker, interaction within video-capture based environments is accomplished in a completely intuitive manner via natural motion of the head, trunk and limbs. Not only is the control of movement more natural, but, in the case of the chroma key GX VR, a "red glove" option (or any object with a distinct color) may be used to restrict system response to one or more body parts as deemed suitable for the attainment of specified therapeutic goals. For example, when it is appropriate to have the intervention directed in a more precise manner, a client may be required to repel projected balls via a specific body part (e.g., by the hand when wearing a red glove or by the head when wearing a red hat). Or, when intervention is more global, the client will not use the red glove option and thus be able to respond with any part of the body. The ability to direct a client's motor response to be either specific or global makes it possible to train diverse motor abilities such as the range of motion of different limbs and whole body balance training. Feedback A limitation of currently available video capture platforms is the reliance on visual and auditory feedback and the absence of a haptic interface that would provide participants with real-time indications of contact with the virtual stimuli. Such feedback could serve as an important addition when used in therapy since the balls, for example, could be rendered to appear to have progressively greater mass, making the task more or less difficult. It would also add an additional element of realism to the gaming experience, and ensure that feedback to participants was more realistic. This could be accomplished to some degree via a quasi-haptic effect that might use vibration to simulate a true haptic interface (A.A. Rizzo, personal communication). For example, small buzzers may be affixed to the tips of the digits. Touching a virtual ball in the Vivid GX Birds & Balls application would generate a low amplitude, high frequency "buzz". In contrast, repelling a larger ball in the Soccer application would generate a high amplitude, low frequency "buzz". User position Video-capture VR may be implemented while users stand, sit, or even walk on a treadmill. For example, the same environment may thus be suitable for training standing balance of a patient who had a stroke, sitting balance of an individual with an incomplete quadriplegic spinal cord injury, and balance during treadmill locomotion of an individual with a paraplegic spinal cord injury. Multiple users Moreover, one or more users may participate within the same environment. In some applications, the ability to have two "rival" users interact simultaneously within the same game or task adds an element of competitiveness that may be motivating. Of greater importance is the ability of the therapist to support a client or use handling techniques in order to facilitate active movement while the client interacts with the virtual stimuli. The therapist can be concealed behind the client in order not to be seen in the VE, or can join the client within the virtual environment. Two-dimensional motion plane Another limitation of the currently available video capture VR platforms is that they may be operated with only one camera. This means that all tasks must be performed within a single plane. In the case of the typical coronal plane setup where the camera is positioned to face the user, any functional movement that takes place in the sagittal or transverse planes is disregarded. Virtual scenarios must therefore be carefully designed such that a meaningful task can be performed despite the restriction to uniplanar movement. Moreover, care must be taken when analyzing the kinematic trajectories since any out-of-plane motion will not be recorded. It is encouraging to note that three dimensional, functional environments will likely soon become available (I. Cohen and A.A. Rizzo, personal communication). Applications of video-capture VR in rehabilitation Although video-capture platforms have only begun to be used for rehabilitation applications within the last five years, there are already results from a number of research groups who have studied its utility with different patient populations. In this section we highlight the major studies that provide evidence that this technology appears to be suitable for use in rehabilitation. The evidence concerning participant sense of presence, enjoyment, usability and performance are summarized as reported by studies of single platforms and by studies that compared different VR platforms Side effects None of the studies carried out to date have reported any significant occurrence of cybersickness-type side effects when using video-capture VR. Rand et al. [28] explicitly examined the incidence of side effect of a group of 89 healthy participants who experienced the GX platform. The occurrence of the side effects was very low, and no participants requested to terminate their participation in the study. To date, evidence from a fewer number of patient subjects with spinal cord injury (SCI) or stroke indicates that they also are not disturbed by side effects when using video-capture VR [25,34]. Presence and enjoyment Several studies examined the influence of video capture platform of the user's sense of presence and level of enjoyment. Rand et al. [28] in their study of 40 healthy young adult participants, compared two different VR platforms, the GX-monitor and a combination of GX environments viewed via an HMD. They found that the participants' sense of presence was significantly higher when using the GX monitor platform than when using the GX-HMD. In a companion study, which compared the GX-monitor with an HMD with two age groups, 33 young adults and 16 elderly participants, the older group felt a significantly higher sense of presence and enjoyment than did the younger group using the HMD. Lott et al. [37] used the IREX video capture platform and an HMD and found that the levels of presence reported by the young adult participants did not differ significantly for the two virtual reality conditions. The results of these studies showed that a high sense of presence and level of enjoyment can be achieved in a video capture VR platform. They also demonstrate that user characteristics such as age influence the sense of presence. In another study, Rand et al. [38] compared the sense of presence, performance and perceived exertion experienced by 30 healthy young participants when they engaged in two games performed within video-projected virtual environments that differed in their level of structure and spontaneity. The non-structured application was applied using VividGroup's Gesture Xtreme (GX) VR platform, and the structured application was applied using the IREX platform, a rehabilitation-oriented application of GX, developed to train a specific movement (e.g., shoulder abduction) in order to increase range of motion or endurance. No main effect or interaction effect was found for the sense of presence (assessed using Witmer & Singer's [39] Presence Questionnaire (PQ) although significant differences were found for several of the PQ sub-scales. It was concluded that it is possible to provide users with a satisfactory level of presence and enjoyment using both structured and non-structured paradigms. Therefore, both movement options, structured and non-structured, enhance the therapist's repertoire of VR intervention tools in order to maximize rehabilitation. Rand at al. [40] reported the results of another study, in which two different video-capture platforms, GX and EyeToy, were compared to determine their effect on users' sense of presence, level of enjoyment, perceived exertion and side effects. In this study, 18 healthy young adults experienced two games in each platform (Birds & Balls and Soccer in GX and Kung-Foo and Wishy-Washy in EyeToy) in a counter-balanced order. There was no significant difference in the sense of presence between the two platforms. However, the EyeToy Kung-Foo game, which encourages participants to eliminate successive invading warriors by hitting at them, was found to be significantly more enjoyable than the other games. In a continuation of this study, Rand et al. [40] examined the feasibility of using the EyeToy with healthy elderly users. Ten healthy elderly participants, aged 59 to 80 years, found this platform easy to operate and enjoyable. The results for patients with stroke at a chronic stage (1–5 years post stroke) were similar to the healthy elderly. They thought that it could contribute to their rehabilitation process, and were able to operate the platform independently. The responses of a third group of users, patients with stroke at an acute stage (1–3 months post stroke), were somewhat different. They also reported that they enjoyed the experience; however, they became frustrated while performing the EyeToy games, even when played at the easiest levels. This latter observation highlights a major limitation of the closed architecture of the EyeToy; to date, Sony has been unwilling to adapt the games to include a greater range of levels of difficulty, nor to provide tools to external programmers to do so (R. Marks, personal communication). It also emphasized the effect that user characteristics, in this case, time post onset of stroke, have on the sense of presence. The GX VR platform has consistently generated high levels of presence and enjoyment across a wide range of clinical populations and ages including adults with paraplegic spinal cord injury [34], stroke [25,33], and young adults with cerebral palsy and intellectual impairment [41]. A pilot study using the GX platform to determine its suitability for leisure time activities among older stroke survivors was carried out. These participants enjoyed the experience, and perceived it to be therapeutic [42]. Performance outcomes and sensitivity of video capture VR The measures of performance used by video-capture VR studies to date include response times to presented virtual stimuli, percent success with which a given game is performed (e.g., how many balls are repelled by the user in the role of soccer game goal keeper), a subjective report on how much effort the user has felt while in the environment. The chroma key video capture platforms such as GX and IREX also provide a relatively gross measure of limb kinematics. Whether these data have sufficient precision and resolution to warrant their inclusion in a research study remains to be investigated (F. MacDougal, personal communication). Sveistrup, McComas and colleagues have used the IREX platform for balance retraining. Following six weeks of training at an intensity of three sessions per week, improvement was found for all 14 participants in both the VR and control groups [35]. However, the VR group reported more confidence in their ability to "not fall" and to "not shuffle while walking". The same research group has also demonstrated that an exercise program delivered via video capture VR can improve balance and mobility in adults with traumatic brain injury [43] and the elderly [44]. Kizony et al. [34] performed a feasibility study of the GX-VR platform to train balance of people who had a paraplegic SCI. The study included 13 adult participants who had paraplegia. Results from the patient group were compared to data from a parallel study of a group of 12 healthy adult participants who performed a similar protocol, while sitting on a chair with hands supported. The results showed that the participants with SCI who had better balance function performed higher within the virtual environments and the healthy participants performed significantly better than the participants with paraplegia. This platform appeared to be suitable for use with people who have paraplegia and it was able to differentiate between participants with different levels of balance function. In a second study Kizony et al. [25] examined the relationships between cognitive and motor ability and performance within the GX-virtual environments with people who have had a stroke. Thirteen older adult patients with stroke participated in the full study. Significant moderate positive correlations were found between VR performance and cognitive abilities suggesting that higher cognitive abilities relate to higher performance within the VR. In contrast, almost no positive correlations were found with the motor abilities. Indeed, as pointed out by these authors, perhaps motor performance demands and their characteristics should not be expected to be identical within the real and the virtual worlds. It may be that differences in presence, motivation, or other factors influence the movement patterns differently in virtual versus natural environments. This result is in accordance with Lott et al.'s [38] findings which showed significant differences between functional lateral reach performed in a real versus virtual environment. They reported that the participants reached significantly further when virtual objects were presented within the virtual environment using a video capture VR platform than when they were asked to touch a person hand standing on their side. They suggest that embedding the reaching task in a game shifts the person's attention from the possibility of losing his balance thereby enabling him to achieve greater function. Rand et al. [28] used a virtual office environment which was developed by Rizzo et al., [15] and was displayed both via an HMD and via the GX-monitor platform. In this case, participants stood in front of the GX monitor and visually scanned the Virtual Office. Performance by both age groups was significantly higher when using the GX-monitor platform than when using an HMD, whereas the younger group's visual scan ability was better than the elderly on both platforms. The results also demonstrated the effect that different user characteristics, such as age and gender, have on the VR experience and thus should be taken into consideration when considering which VR platform to use in rehabilitation. Weiss at al. [41], in a study of five young male adults with physical and intellectual disabilities, explored ways in which virtual reality could provide positive and enjoyable leisure experiences during physical interactions with different game-like virtual environments and potentially lead to increased self-esteem and a sense of self-empowerment. The results of this study showed that the GX-VR platform was feasible for use with this population. The participants were able to use the platform and expressed their considerable enjoyment from the virtual games. However, the authors raised several concerns, especially that some of the participants displayed involuntary movement synergies, increased reflexes and maladaptive postures due to the too difficult levels of the games that were used in study. Thus, a more controlled study with the same population is currently in progress in order to examine more thoroughly the potential of the platform as a mean for providing leisure opportunities to this population. Performance within two games (Kung-Foo and Wishy-Washy) was measured while three different groups, young adult participants, healthy senior participants and individuals who were several years post-stroke, used several of the EyeToy games [40]. Performance was scored for each game in terms of how much of a given activity (e.g., how many windows washed, how many warriers eliminated) was accomplished within a preset time limit. Higher scores were achieved when clients were able to perform these activities faster and/or more accurately. There were significant differences in performance between the young and stroke groups, with the young adults having greater success in both games than the stroke group. The older adult group performed as well as the younger group. The performance results described above highlight the interplay between the user and VR platform characteristics, and emphasize the importance of taking these characteristics into consideration while using VR in rehabilitation. Moreover, they demonstrate the sensitivity of the VR performance measures in their capacity to differentiate between levels of participant ability. Due to the motivating nature of the game-like environments, it is important to determine how much effort healthy subjects and those with disabilities expend while engaged in these tasks. In a study of healthy young adults, the participants using the GX platform perceived the highest level of exertion while playing Soccer, less for Birds & Balls and still less for a third game, Snowboard where only weight transfer was needed [28]. When differences between the age groups were assessed, the younger group perceived higher levels of exertion in comparison to the older group. There were also differences in the perceived level of exertion of the Birds & Balls game in GX as compared to comparable games in the EyeToy [40]. Overall, the level of perceived exertion was rated as "somewhat difficult" which is an ideal level to use in therapy. Initial comparisons of VR-based intervention to conventional therapy Using the IREX platform, Sveistrup et al. [35] performed two studies designed to compare VR-delivered therapy to conventional therapy. In their first study, patients suffering from frozen shoulder received exercise either via IREX applications or via conventional physiotherapy. In both cases, therapy was directed at improving the quality of three specific shoulder joint movements. In the second study, individuals who suffered from post-traumatic brain injury were assigned to either VR-based (applications such as the virtual soccer game were used where patients were encouraged to reach towards the virtual stimulus in addition to weight transfer) or conventional therapy (e.g., stepping, picking up objects, reaching) for balance training for a total of 24 sessions. In their report on preliminary data from 14 patients, the authors concluded that both exercise programs resulted in improvement of patients' balance. However, additional benefits were identified for the VR group, including greater enthusiasm for the VR-delivered therapy program, increased enjoyment while doing the exercises, improved confidence while walking and fewer incidents of falling. Cunningham & Krishack [32] presented VR as it was used in occupational therapy to improve balance and dynamic standing tolerance with geriatric patients. They reported greater improvement in dynamic standing tolerance in a small group of older adults following a VR therapy than in a small group following a standard occupational therapy. More recently, Bisson, et al. [44] demonstrated significant improvements in balance and functional mobility in community-living older adults following a VR exercise program delivered with the IREX platform. The comparison group completed a biofeedback exercise program and also demonstrated significant balance improvement. Analysis of conventional and video capture VR treatment for SCI by specialists in rehabilitation highlighted several key differences between the two methods of intervention [34]. First, control over delivery of the stimuli via the VR platform enabled the therapist to intervene more effectively, especially in terms of physical guidance and support. In addition, the VR platform allowed precise control over delivery of the number of stimuli simultaneously presented to the patient as well as their speed and direction. These features appeared to increase the number of times a desired balance-recovery movement was performed by patients. Finally, the ease with which this platform elicited dynamic equilibrium recovery responses, an essential component in balance training and encouraged weight transfer movements was remarkable. In contrast, the static presentation of stimuli during conventional therapy restricts intervention to focus almost exclusively on weight transfer. Towards functional video-capture environments One of the newest developments in video-capture VR is the simulation of more functional environments. Rand et al. [45] have created a Virtual Mall (VMall), using the GX platform. It has been designed to support intervention of patients following a stroke who have motor and/or executive functions deficits that restrict their everyday activities. This environment enables participants to engage in tasks based on typical daily activities such as shopping in a supermarket. In the initial application, shown in Figure 3, the user moves from aisle to aisle by activating icons located on a large monitor around thereby encouraging active movement, transfer of weight from side to side, and balance reactions. Virtual food items are manipulated (e.g., selected from a shelf and placed in a supermarket cart in accordance with a shopping list selected in advance. The performance of the task provides multiple opportunities to make decisions, plan strategies and multitask, all in a relatively intuitive manner. Output measures include a record how well the user accomplishes the task (e.g., how many correct items selected) will be recorded and saved thus giving an option to monitor improvement over time. Initial performance measures and user feedback has been recorded from six patients who had a stroke more than two years since onset and suffer from residual motor and cognitive deficits. The results suggest that the VMall provides a motivating task that requires active movement as well as the ability to plan and problem solve. Figure 3 Screen shots of the VMall showing clients with stroke selecting a shopping aisle (left panel), a food item (middle panel) and verifying the contents of the shopping cart (right panel). Sony's EyeToy Wishy Washy application involves the cleaning of successive dirty windows via wiping movements of the hand and arms. Most recently, VividGroup has developed a laundry application (V.J. Vincent, personal communication). These moves towards more functional applications are encouraging. Conclusions Evidence from the literature has demonstrated the feasibility, usability and flexibility of video-capture VR, and there is little doubt that this technology provides a useful tool for rehabilitation intervention. The results of presence questionnaires, reports of user satisfaction, and the sensitivity to differences in user ability as functions of age, gender and disability are all strong indicators of the suitability of this tool. A short video-clip, taken from a local news report of applications of video-capture VR for stroke, illustrates the extremely positive response of one user to the use of this technology (see Video 1). To date, as indicated by the studies reviewed above, video capture VR shows great promise for a variety of therapeutic goals including intervention for cognitive and motor rehabilitation, functional activities and leisure opportunities. The general assets of virtual reality summarized above combined with several assets that are unique to video-capture VR, are compelling arguments for the inclusion of this technology in the repertoire of tools available in clinical settings. Market demand, user interest and improvements in technology have led to the availability of a number of different video-capture platforms. There is no doubt that these platforms are valuable as intervention tools during the rehabilitation of patients with neurological and musculoskeletal disorders. Motivated patients would be encouraged to practice movements in a repetitive manner thereby improving their condition, an achievement that is not easy to attain via conventional therapy [46]. Currently, the two main contenders for the rehabilitation market are VividGroup's GX and IREX platforms and Sony's PlayStation II's EyeToy. Both use large monitors to display real-time images of users interacting with virtual objects in a simulated environment. The VividGroup platforms are considerably more expensive and require a more elaborate setup including a chroma key blue/green backdrop behind the user and bright, ambient lighting. Sony's EyeToy is an off-the-shelf, low-cost gaming application that may be run under almost any ambient conditions. Studies comparing these two platforms have shown that presence, enjoyment, usability and performance were equivalent under many conditions and for diverse users. Thus, despite the EyeToy's limitations, its low cost, user-friendly interface and simple setup requirements makes it highly attractive to therapists. It may be readily acquired for use in any clinical setting, and even be purchased for use at home to provide regular, intensive therapy after discharge from hospital. Nevertheless, it is clear that the EyeToy is not suited for use with the most severely impaired users. The currently available games seem to have a broad appeal for users of different ages but an open architecture that permits adaptations of existing applications and development of new environments appears to be a basic requirement to make this platform truly functional as a clinical tool. A system for generating an outcomes report comparable to the IREX platform would also be of great benefit for clinicians. Additional low-cost video-capture platforms are currently under development (M. Shahar, personal communication). Moreover, video-capture platforms that will provide three dimensional, functional environments will likely soon become available (Cohen and Rizzo, personal communication). In contrast to the EyeToy's closed architecture, VividGroup's IREX platform provides a user-friendly interface that a therapist may use to specify a much greater range of levels of difficulty. Their SDK (Software Development Kit) provides programmers with the ability to further adapt existing applications such as the standard set of games [33] and to design and implement novel applications such as the virtual mall described above [45]. The popular press has been generating a considerable amount of publicity in the EyeToy platform [31], and it is clear that low-cost video-capture systems such as these are poised to make VR available to a wide range of users. We anticipate that future developments in technology, such as low-cost virtual environments that are more functional will enable clinicians to take advantage of the considerable benefits that VR has for rehabilitation. Supplementary Material Additional File 1 Video 1: This video clip shows a patient who had a stroke using the VividGroup VR system for cognitive and motor rehabilitation. Click here for file Acknowledgements We gratefully acknowledge programming support by Meir Shahar and Yuval Naveh. 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10835434	15679949	PMC546410	CC BY	2021-01-04 16:53:59	no	J Neuroeng Rehabil. 2004 Dec 20; 1:12	utf-8	J Neuroeng Rehabil	2,004	10.1186/1743-0003-1-12	oa_comm
==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-91567995010.1186/1743-0003-1-9ReviewPresence and rehabilitation: toward second-generation virtual reality applications in neuropsychology Riva Giuseppe [email protected] Fabrizia [email protected] Andrea [email protected] Applied Technology for Neuro-Psychology Lab., Istituto Auxologico Italiano, Milan, Italy2 Department of Psychology, Catholic University of Milan, Milan, Italy3 Department of Epistemology and Hermeneutics of Education, University of Milan-Bicocca, Milan, Italy4 Psychology Lab., Department of Preclinic Sciences LITA VIALBA, State University of Milan, Milan, Italy2004 8 12 2004 1 9 9 26 11 2004 8 12 2004 Copyright © 2004 Riva et al; licensee BioMed Central Ltd.2004Riva et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Virtual Reality (VR) offers a blend of attractive attributes for rehabilitation. The most exploited is its ability to create a 3D simulation of reality that can be explored by patients under the supervision of a therapist. In fact, VR can be defined as an advanced communication interface based on interactive 3D visualization, able to collect and integrate different inputs and data sets in a single real-like experience. However, "treatment is not just fixing what is broken; it is nurturing what is best" (Seligman & Csikszentmihalyi). For rehabilitators, this statement supports the growing interest in the influence of positive psychological state on objective health care outcomes. This paper introduces a bio-cultural theory of presence linking the state of optimal experience defined as "flow" to a virtual reality experience. This suggests the possibility of using VR for a new breed of rehabilitative applications focused on a strategy defined as transformation of flow. In this view, VR can be used to trigger a broad empowerment process within the flow experience induced by a high sense of presence. The link between its experiential and simulative capabilities may transform VR into the ultimate rehabilitative device. Nevertheless, further research is required to explore more in depth the link between cognitive processes, motor activities, presence and flow. ==== Body Introduction What is virtual reality (VR)? For many health care professionals, VR is first of all a technology. Since 1986, when Jaron Lamier used the term for the first time, VR has been usually described as a collection of technological devices: a computer capable of interactive 3D visualization, a head-mounted display and data gloves equipped with one or more position trackers. But is this definition enough to describe the potential of VR for rehabilitation? If we look at its actual applications in rehabilitation, the answer is probably not [1]. VR can be considered the leading edge of a general evolution of present communication interfaces like television, computer and telephone [2]. The main characteristic of this evolution is the full immersion of the human sensorimotor channels into a vivid and global communication experience [3]. In fact, VR is used in rehabilitation as "an advanced form of human-computer interface that allows the user to interact with and become immersed in a computer-generated environment in a naturalistic fashion" [4]. Following this vision Rizzo et al. [5] identify twelve assets that are available with VR for neuropsychological applications: • The capacity to systematically deliver and control dynamic, interactive 3D stimuli within an immersive environment that would be difficult to present using other means. • The capacity to create more ecologically valid assessment and rehabilitation scenarios. • The delivery of immediate performance feedbacks in a variety of forms and sensory modalities. • The provision of "cueing" stimuli or visualization tactics designed to help guide successful performance to support an error-free learning approach. • The capacity for complete performance capture and the availability of a more naturalistic/intuitive performance record for review and analysis. • The capacity to pause assessment, treatment and training for discussion and/or integration of other methods. • The design of safe testing and training environments that minimize the risks due to errors. • The capacity to improve availability of assessment and rehabilitation by persons with sensorimotor impairments via the use of adapted interface devices and tailored sensory modality presentations built into VE scenario design. • The introduction of "gaming" features into VR rehabilitation scenarios as a means to enhance motivation. • The integration of virtual human representations (avatars) for systematic applications addressing social interaction. • The potential availability of low cost libraries of VEs that could be easily accessed by professionals. • The option for self-guided independent testing and training by clients when deemed appropriate. In summary, VR provides a new human-computer interaction paradigm in which users are no longer simply external observers of images on a computer screen but are active participants within a computer-generated three-dimensional virtual world [6-8]: in the virtual environment (VE) the patient has the possibility of learning to manage a problematic situation related to his/her disturbance in a functionally relevant, ecologically valid experience [9,10]. This outline better clarifies the possible role of VR in rehabilitation: a communication interface based on interactive 3D visualization, able to collect and integrate different inputs and data sets in a single real-like experience [2,11]. This is possible because the key characteristic of VR, differentiating it from other media or communication systems, is the sense of presence [12], usually defined as the "sense of being there" [13], or the "feeling of being in a world that exists outside the self" [14]. This feeling is theorized to contribute to the efficacy of VR as rehabilitation tool: the successful use of VR exposure therapy for phobias [15-18], postraumatic stress disorders [19-21], and the pain reduction obtained in burn patients during a VR session [22-25] underline the possible role that an high level of presence, elicited by the VR experience, may have in the rehabilitation process. Thanks to presence, not only knowledge acquisition is possible in VR, but also this acquired knowledge can be transferred in a real environment [26,27]. This, evidence, coming also from different neuropsychological studies [1], adds value on VR use in the rehabilitation of highly social disabling cognitive functions, highlighting how goals reached in controlled settings may be transferred on patients' everyday life. Now, the challenge within this area is the creation of new paradigms [28]. As clearly underlined by Morganti [1] "More than a playing tool supporting cognitive or motor performances VR simulation has to provide a powerful chance to build personal meaning, map and strategies interacting with it." Within this context we propose to investigate the impact of VR on rehabilitation and subjective experience from a theoretical perspective that stresses the active role of individuals in interacting with their natural and cultural environment [29,30]. In this process a key role is played by the concept of "presence" and its link with our optimal experiences. A bio-cultural theory of presence Presence as separation between "external" and "internal" What is presence? Answering to this question is not a simple task [12,31,32]. In fact, if we check the present status of presence research, we can find two different but coexisting visions. A first group of researchers describes the sense of presence as a function of our experience of a given medium [33-41]. The main result of this approach is the definition of presence as the perceptual illusion of non-mediation [37], produced by means of the disappearance of the medium or its transformation into a social entity. Following this vision the experience of presence is only related to our interaction with an external artifact. The main advantage of this approach is its predictive value: the level of presence is reduced by the experience of mediation during the interaction. This approach, however, does not address some broader questions: What is presence for? Is it a specific cognitive process? What is its role in our daily experience? To answer these questions a second group of researchers considers presence as a neuropsychological phenomenon, evolved from the interplay of our biological and cultural inheritance, whose goal is the control of agency [2,12,42-49]. Within this paper, we will support the second vision, trying to link it with the outcome of the first one [50]. Here, presence is delineated as an evolved bio-cultural internal selection mechanism that helps the self in organizing the streams of sensory data: the more it can differentiate the self from the external world, the more is our experience of presence. The main goal of this differentiation is the control of agency to improve the possibility of survival within the external environment. To fully understand the key ideas behind this vision, three points are critical: – Presence has a simple but relevant role in our everyday experience: the control of agency through the unconscious separation of "internal" and "external". The meaning of "internal" and "external" is not related only to the body but also to the social and cultural space (situation) in which the self is in; – The presence-as-process (the separation mechanism) produces, but it is different from the presence-as-feeling (the experienced level of presence); – The presence-as-feeling is experienced indirectly by the self through the characteristics of action and experience. In fact the self perceives directly only the variations in level of presence-as-feeling: breakdowns and optimal experiences. First, presence is described here as a defining feature of self and it is related to the evolution of a key feature of any central nervous system: the embedding of sensory-referred properties into an internal functional space. As noted by Waterworth and Waterworth [47], the appearance of the sense of presence allows the nervous system to solve a key problem for its survival: how to differentiate between internal and external states. Without the emergence of the sense of presence it is impossible for the nervous system to experience distal attribution – the referencing of our perception to an external space beyond our boundaries – and effectively control its agency. In this vision it is important to distinguish between presence-as-process and presence-as-feeling. The presence-as-process is the continuous activity of the brain, organized around the three functionally and phylogenetically different layers discussed in the next paragraph, in separating "internal" and "external" within different kinds of afferent and efferent signals. A critical point here is to explain why we need to introduce a new cognitive process – presence-as-process – to monitor our activity. The answer comes from a recent paper by de Vignemont and Fourneret [51]. These authors discuss the position of Wittgenstein [52] about agency. According to this author, agency involves a primitive notion of the self as subject, which does not rely on any prior perceptual identification and which is immune to error through misidentification. However, both the neuroscience of action and the neuropsychology of schizophrenia are countering his position [53]. For instance, the analysis of positive deficits underlying positive symptoms in schizophrenia has shown that is not possible to reduce the sense of agency to action control or action awareness. To overcome this problem, de Vignemont and Fourneret distinguish in agency between the sense of initiation and the sense of one's own movements. As they underline [51] "the double sense of agency depends on the same mechanisms of action control: it results from the unconscious comparison between different kinds of afferent and efferent signals. Therefore, these monitoring systems allow one to automatically distinguish one's own actions and those of the other" (p. 15). So, presence-as-process can be described as a sophisticated form of monitoring of action and experience, transparent to the self but critical for its existence. As clarified by Russell [54]: "Action-monitoring is a subpersonal process that enables the subjects to discriminate between self-determined and world-determined changes in input. It can give rise to a mode of experience (the experience of being the cause of altered inputs and the experience of being in control) but it is not itself a mode of experience." (p.263). For this reason, the presence-as-feeling (level of presence) is not separated from the experience of the subject but it is related to the quality of our actions. It corresponds to what Heidegger [55] defined "the interrupted moment of our habitual standard, comfortable being-in-the-world" In fact, a higher level of presence-as-feeling is experienced by the self as a better quality of action and experience [46,56]. However, the self becomes aware of the presence-as-feeling separated by our being-in-the-world when its level is modified. More in detail, the self perceives directly only the variations in the level of presence-as-feeling: breakdowns and optimal experiences. The process of adaptation to the natural environment provided humans with specific biological features, such as the upright position, the opposing thumb and the increase in brain mass that allowed survival and reproduction in any environmental niche. At the same time, by means of the differential investment of attention and psychic resources, the individual selects and organizes the information acquired from his/her context according to an emergent, autonomous criterion: the quality of experience [57,58]. In our view, another evolutionary goal of presence-as-process is to track the quality of experience identifying highs and lows. On one side we have optimal experiences. According to Csikszentmihalyi [59,60], individuals preferentially engage in opportunities for action associated with a positive, complex and rewarding state of consciousness, defined optimal experience or flow. Here we suggest that flow is the result of the link between the highest level of presence-as-feeling, with a positive emotional state. In fact, it is also possible to experience the highest level of presence together with negative emotional states: e.g. in the battlefield during an attack from the enemy. On the other side we have breakdowns. Winograd and Flores [61] refer to presence disruptions as breakdowns: a breakdown occurres when, during our activity, an object or an environment becomes part of our consciousness. If this happens, we shift our attention from action to the object/environment to cope with it: e.g., when a wall stops our movement. Why do we experience these breakdowns? Our hypothesis is that breakdowns are a sophisticated evolutionary tool used by the presence-as-process to control the quality of experience: the more the breakdown, the less is the level of presence-as-feeling, the less is the quality of experience, and the less is the possibility of surviving in the environment. The importance of breakdowns for understanding presence is well reflected by Slater's concept of "break in presence" (BIP) [62]: a break in presence is the moment of switch between responding to signals with source in environment X to those with source in environment Y. In a BIP the critical issue is how will the actor act? To which set of signals will the actor respond? The answers to these questions are related to another important point of our vision: the meaning of "internal" and "external". In our vision, the boundaries are not only physical and related to our body (being there), but also social and cultural (making sense there). As underlined by Slater [63], Presence "is the total response (italics in the original) to being in a place, and to being in a place with other people. The 'sense of being there' is just one of many signs of presence – and to use it as a definition or a starting point is a category error: somewhat like defining humor in terms of a smile" (p. 7). If, in relatively simple organisms, this separation involves only a correct coupling between perceptions and movements, in humans it also implies the relation of the subject with a social and cultural space [44,64]. In fact, individuals actively interact with the environment, selecting and differentially replicating throughout their lives a subset of biological and cultural information, in terms of activities, interests and values. This vision has two important corollaries: – it is also "external" to the subject what is not related to his/her activities, interests and values. – to be more "present" in the situation (social and cultural space) defined by a symbolic system, the user has to be aware of its meaning. Only "making sense there", the user really experiences a full sense of presence. In giving sense to a situation an important role is usually played by narratives [56,65]. To make these concepts clearer an example may help. I'm in a restaurant for a formal dinner with my boss and some colleagues, but I don't know how to use the many different strange forks I have around my dish. In this situation I'm physically there, but the lack of knowledge puts me outside, at least partially, from the social and cultural space of the "formal dinner". The result is a limitation in my agency: I don't use the forks to avoid mistakes. This example shows clearly how both physical boundaries (wall, obstacles, etc.) and social and cultural boundaries have a strong influence on the possibility of action and the quality of experience of the subject. At this point our conclusions are: • In the real world, the feeling of presence is not the same in all the situations but can be different in relation to the characteristics of the social and cultural space the subject is in. For instance, if I'm attending a lesson in university, my level of presence can be lower or higher in relation to the interest I have in the topic discussed. If the lesson is totally boring I can be "absent" (totally internal): in absence attention is mostly directed towards internally-generated scenarios (in imagination) which are not currently present in the world [43]. The role of "absence" is critical for the survival of the subject. Is in fact in absence that the subject defines plans and organizes future behaviors. • There are some exceptional situations in real life in which the activity of the subject is characterized by a higher level of presence. In these situations the subject experiences a full sense of control and immersion. When this experience is associated to a positive emotional state, it can create an optimal experience, usually defined "flow". An example of flow is the case where a professional athlete is playing exceptionally well (positive emotion) and achieves a state of mind where nothing else matters but the game (high level of presence). The layers of presence At this point we have defined what presence is and its role in the human experience. However, there is yet another open question: how can we achieve high level of presence-as-feeling? To answer this question we have to analyze the neuropsychological nature of presence-as-process. Even if presence is a unitary feeling, on the process side it can be divided in three layers/subprocesses (for a broader and more in-depth description see [50]). These layers are phylogenetically different, and strictly related to the three levels of self identified by Damasio [66]: • The proto self: a coherent collection of neural patterns that map, moment by moment, the physical state of the organism; • The core self: a transient entity which is continuously generated through encounters with objects • The extended self: a systematic record of the more invariant properties that the organism has discovered about itself. Each layer of presence solves a particular facet of the internal/external world separation and it is characterized by specific properties. In particular we can make conceptual distinctions between proto presence (self vs. non self), core presence (self vs. present external world), and extended presence (self relative to present external world). More precisely we can define proto presence as an embodied presence related to the level of perception-action coupling (self vs. non-self). The more the organism is able to couple correctly perceptions and movements, the more it differentiates itself from the external world, thus increasing its probability of surviving. Proto presence is based on proprioception and other ways of knowing bodily orientation in the world. In a virtual world this is sometimes known as "spatial presence" and requires the tracking of body parts and appropriate updating of displays. Core presence can be described as the activity of selective attention made by the self on perceptions (self vs. present external world): the more the organism is able to focus on its sensorial experience by leaving in the background the remaining neural processes, the more it is able to identify the present moment and its current tasks, increasing its probability of surviving. Core presence is based largely on vividness of perceptible displays. This is equivalent to "sensory presence" (e.g. in non-immersive VR) and requires good quality, preferably stereographic, graphics and other displays features. Finally, the role of extended presence is to verify the significance to the self of the events experienced in the external world (self relative to the present external world). The more the self is present in significant experiences, the more it will be able to reach its goals, increasing the possibility of surviving. Extended presence requires intellectually and/or emotionally significant content. In humans the sense of presence-as-feeling is a direct function of these three layers: the more they are able to separate between "internal" and "external", the more is the feeling of presence, the better is the quality of action and experience. VR, presence and flow A corollary of the proposed vision is critical for our goals: it is possible to design mediated situations that elicit exceptionally high presence [67-69]. In particular, here we argue that virtual reality is the medium able to support the highest level of presence because it can trigger at the same time all the three layers discussed before. To understand this point and in particular the difference between VR and other media, here are some examples [50]: • In reading an engrossing book while sitting in a comfortable, safe place, extended presence will be engaged by media (engagement) but the other layers will not be involved. • In looking at a movie, you can activate a high level of core presence (vividness), a high level of extended presence (engagement) but no proto presence (spatial presence). • In experiencing an interesting immersive VR experience, proto (spatial presence), core presence (vividness) and extended presence (engagement) will be activated by the medium. • In an immersive VR experience, if you are pre-occupied with personal worries and the mediated content is not very engaging, proto (spatial) and core presence (vividness) will be invoked by the medium, but not extended presence. The possibility of activating all the three layers at the same time reduces the occurrence of breakdowns. As suggested by Marsh and colleagues [41], one of the main goals of VR is to maintain users' attention in the content/illusion of a VR system. The final result is the perceptual illusion of non-mediation [37], produced by means of the disappearance of the medium, which activates the highest level of presence. To achieve an optimal experience, however, a next step is required: the highest level of presence has to be linked to a positive emotional experience. Csikszentmihalyi [60,70] defines "flow" as an optimal state of consciousness characterized by a state of concentration so focused that it amounts to absolute absorption in an activity. According to Csikszentmihalyi [71] when people are in a flow state " [they] shift into a common mode of experience when they become absorbed in their activity. This mode is characterized by a narrowing of the focus of awareness, so that irrelevant perceptions and thoughts are filtered out; by loss of self-consciousness; by a responsiveness to clear goals and unambiguous feedback; and by a sense of control over the environment it is this common flow experience that people adduce as the main reason for performing the activity" (p72). Starting from this definition, different authors tried to define flow in an operational way. For Ghani and Deshpande [72] the two main characteristics of flow are (a) the total concentration in an activity and (b) the enjoyment which one derives from the activity. Moreover, these authors identified two other factors affecting the experience of flow: a sense of control over one's environment and the level of challenge relative to a certain skill level. In this paper we suggest that VR is the preferred medium for the activation of the flow experience. A number of recent experimental results might be considered to foster this vision. A first support comes from the work of Hoffman and his group in the treatment of chronic pain [22-25]. Few experiences are more intense than the pain associated with severe burn injuries. In particular the daily wound care – the cleaning and removal of dead tissue to prevent infection – can be so painful that even the aggressive use of opioids (morphine-related analgesics) cannot control the pain. However it is well known that distraction – for example, by having the patient listen to music – can help to reduce pain for some people. So, Hoffman and colleagues verified in a controlled study the efficacy of VR as an advanced distraction by comparing it with a popular Nintendo video game. The result showed dramatic drops in pain ratings during VR compared to the video game [73]. Further, using functional magnetic resonance imaging (fMRI) scanner they measured pain-related brain activity for each participant during conditions of no virtual reality and during virtual reality (order randomized). The team studied five regions of the brain that are known to be associated with pain processing – the anterior cingulate cortex, primary and secondary somatosensory cortex, insula, and thalamus – and found that during VR the activity in all the regions showed significant reductions. In particular, the results showed direct modulation of human brain pain responses by VR distraction: the amount of reductions in pain-related brain activity ranged from 50 percent to 97 percent. A second set of results comes from the work of Gaggioli [29,30]. Gaggioli compared the experience reported by a user immersed in a virtual environment with the experience reported by the same individual during other daily situations. To assess the quality of experience the author used a procedure called Experience Sampling Method (ESM), which is based on repeated on-line assessments of the external situation and personal states of consciousness [74]. Results showed that VR experience was the activity associated with the highest level of optimal experience (22% of self-reports). Reading, TV viewing and the use of other media – both in the context of learning or leisure activities – obtained lower percentages (respectively 15%, 8% and 19% of self-reports) of optimal experiences. A final result, is the preference of phobic patients for VR vs. traditional treatments as showed by two studies from García-Palacios and colleagues [75,76]. In their last study, which surveyed 102 patients who met DSM-IV criteria for specific phobias or panic disorder with agoraphobia, 70% of the patients asked to choose between "in vivo" exposure vs. VR exposure therapy, chose VR exposure. Further, 23.5% of the sample refused in vivo exposure whereas only 3% refused VR treatment. Presence and optimal experience: towards second-generation VR applications in rehabilitation Authentic rehabilitation implies the active participation of the patient in the cultural context, their exposure to opportunities for action and development, their freedom to select opportunities they perceive as the most challenging and meaningful ones for the subject [29,77]. Following this vision, another important asset potentially offered by VR to the rehabilitation process is the possibility of triggering optimal experiences [78]. Optimal experiences promote individual development. As underlined by Massimini and Delle Fave, [58] "To replicate it, a person will search for increasingly complex challenges in the associated activities and will improve his or her skill, accordingly. This process has been defined as cultivation; it fosters the growth of complexity not only in the performance of flow activities but in individual behavior as a whole." (p. 28). This process can be activated also after a major trauma. As noted by Delle Fave [79], to cope with dramatic changes in the daily life and in the access to environmental opportunities for action, individuals may develop a strategy defined as transformation of flow. Where possible, they keep cultivating former flow activities. Otherwise, as often happens, they manage to identify new and unexpected sources of concentration and involvement, sometimes in areas very different from their previous interests. The vision behind the concept of transformation of flow is the one from "Positive Psychology" [80]. According to this vision, existing professional treatments should include therapeutic factors that are related to positive experiences. These include increasing clients' positive expectations and hope about change, general sense of optimism, self-efficacy, and coping strategies. Numerous studies of patients with life-threatening diseases suggest that those who remain optimistic show symptoms later and survive longer than patients who confront reality more objectively [81]. That is, rehabilitative treatments should also be evaluated in terms of their ability to make life more fulfilling for clients. However, it is very difficult within the traditional rehabilitative practices to cope with the sense of hopefulness and depression expressed by many patients. In this area VR may offer a critical advantage: the possibility for the patient to manage successfully in a VE a problematic situation related to his/her disturbance. Using VR in this way, the patient is more likely not only to gain an awareness of his/her need to do something to create change but also to experience a greater sense of personal efficacy [82]. This approach was recently tested in the support of cerebral palsy. More in detail, Miller and Reid [83] investigated the personal experiences of 19 children aged 8–11 with cerebral palsy involved in a virtual reality play intervention program. The results showed that children perceived engagement and flow in the virtual reality, and increased their self-competence and self-efficacy. Further, they experienced a sense of control and mastery over the virtual environment and perceived physical changes and increased social acceptance from both peers and family. In another case study, Riva [84] tested the possibility of using a VE experience – a stroll through a mountain path to reproduce the feeling of an excursion to the mountains – to support the rehabilitation of a person with spinal cord injury. The results revealed slightly improved levels of self-confidence, will, relaxation, and activity. The patient also declared subjective improvement in his sense of well-being, mood, and quality of sleep. Generally, these techniques can be used as triggers for a broader empowerment process within the flow experience induced by a high sense of presence. In psychological literature empowerment is considered a multi-faceted construct reflecting the different dimensions of being psychologically enabled, and is conceived of as a positive additive function of the following three dimensions [85]: – perceived control: includes beliefs about authority, decision-making skills, availability of resources, autonomy in the scheduling and performance of work, etc; – perceived competence: reflects role-mastery, which besides requiring the skillful accomplishment of one or more assigned tasks, also requires successful coping with non-routine role-related situations; – goal internalization: this dimension captures the energizing property of a worthy cause or exciting vision provided by the organizational leadership. Virtual reality may be considered the preferred environment for the empowerment process, since it is a special, sheltered setting where patients can start to explore and act without feeling threatened. In this sense the virtual experience can be described as an "empowering environment" that rehabilitation provides to patients: nothing the patient fears can "really" happen to them in VR. With such assurance, they can freely explore, experiment, feel, live, and experience feelings and/or thoughts. VR thus becomes a most useful intermediate step between the therapist's office and the real world. Within this frame, therapists are encouraged to explore whether and how VR induced optimal experiences may facilitate recovery [86]. The use of VR as an empowerment tool was recently tested in the support of HIV-AIDS patients [87]. The system implemented a VR walkthrough experience of a relaxing campfire in a forest. The scene contains four interactive avatars that relate narratives compiled from HIV/AIDS patients. These narratives cover the aspects of receiving an HIV+ diagnosis, intervention, and coping with living with HIV+ status. In terms of emotional impact, the participants found their experience with the system mostly encouraging, particularly the narratives relating to adjustment and coping. Challenges and Open Issues VR is an advanced communicative interface based on interactive 3D visualization. Its simulative capabilities allow for the precise presentation and control of dynamic multi-sensory 3D stimulus environments, as well as providing advanced methods for recording behavioral responses. However VR, differently from other media, can induce a high sense of presence [12], usually defined as the "sense of being there" [13], or the "feeling of being in a world that exists outside the self" [14]. Thanks to presence, not only knowledge acquisition is possible in VR, but also this acquired knowledge can be transferred in a real environment. This paper introduces a bio-cultural theory of presence linking the state of optimal experience, defined as "flow", to a virtual reality experience. The key ideas behind the proposed vision of presence are: – Presence has a simple but critical role in our everyday experience: the control of agency through the unconscious separation of "internal" and "external". The meaning of "internal" and "external" is not related only to the body but also to the social and cultural space (situation) in which the self is in. – The presence-as-process (the separation mechanism) produces, but is different from, the presence-as-feeling (the experienced level of presence); – The presence-as-feeling is experienced indirectly by the self through the characteristics of action and experience. In fact the self perceives directly only the variations in level of presence-as-feeling: breakdowns and optimal experiences; – The presence-as-process can be divided in three different layers/subprocesses. They are phylogenetically different, and strictly related to the three levels of self identified by Damasio: proto presence (self vs. non self), core presence (self vs. present external world), and extended presence (self relative to present external world). A corollary of the proposed vision is the possibility to design mediated situations that elicit exceptionally high presence. In particular, here we argued that virtual reality is the medium able to support the highest level of presence because it can activate at the same time all the three layers. To achieve an optimal experience (flow), however, a next step is required: the highest level of presence has to be linked to a positive emotional experience. The link between presence, flow and VR suggests the possibility of using it for a new breed of rehabilitative applications focused on a strategy defined as transformation of flow. In this view, VR can be used to trigger a broad empowerment process within the flow experience induced by a high sense of presence. Linking this possibility to its simulative capabilities may transform VR in the ultimate rehabilitative device. Applications of VR in rehabilitation include the following disturbances: memory disorders, planning and motor disabilities, executive functions and spatial knowledge disabilities. For a full review, see Morganti [1]. However, the road is still long. Even if the significant advances in computer and graphic technology drastically improved the characteristics of a typical VE, VR is still limited by the maturity of the systems available. Today, no off-the-shelf solutions are available. So, the set up of a VR system usually requires much patience for dealing with conflicting hardware or lacking drivers. Nearly every VR system requires a dedicated staff or at least a computer technician to keep the system running smoothly. Moreover, introduction of patients and clinicians to VEs raises particular safety and ethical issues. In fact, despite developments in VR technology, some users still experience health and safety problems associated with the use of immersive headsets. Generally, for a large proportion of VR users these effects are mild and subside quickly. Further, even if the clinical rationale behind the use of VR in rehabilitation is now clear, much of this research growth has been as feasibility studies and pilot trials. Hence, there is still limited convincing evidence coming from controlled studies. Why there are so few controlled trials in VR research? The possible answers are two. First, there is a lack of standardization in VR devices and software. To date, very few of the various VR systems available are interoperable. This makes difficult their use in contexts other than those in which they were developed. Second, there is a lack of standardized protocols that can be shared by the community of researchers. Clearly, building new and additional virtual environments is important so therapists will continue to investigate applying these tools in their day-to-day clinical practice. In fact, in most circumstances, the clinical skills of the rehabilitator remain the key factor in the successful use of VR systems. Future research should explore how to develop VR environments able to provide the degree of challenge required to elicit the optimal experience. Further, research should also deepen the analysis of the link between cognitive processes, motor activities, presence and flow. This will allow a new generation of VEs in which the added value of VR is not only simulation and control. Acknowledgments The present work was supported by the Italian MIUR FIRB programme (Project Neurotiv – ) and by the Commission of the European Communities (CEC), through its Future and Emerging Technologies IST programme (Projects I-LEARNING – Immersion/imagery enhanced learning, and EMMA – Engaging Media for Mental Health Applications; , ). 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==== Front J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-1-131567995110.1186/1743-0003-1-13ResearchConsiderations for the future development of virtual technology as a rehabilitation tool Kenyon Robert V [email protected] Jason [email protected] Emily A [email protected] Electronic Visualization Lab, Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA2 Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA3 Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA2004 23 12 2004 1 13 13 29 11 2004 23 12 2004 Copyright © 2004 Kenyon et al; licensee BioMed Central Ltd.2004Kenyon et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Virtual environments (VE) are a powerful tool for various forms of rehabilitation. Coupling VE with high-speed networking [Tele-Immersion] that approaches speeds of 100 Gb/sec can greatly expand its influence in rehabilitation. Accordingly, these new networks will permit various peripherals attached to computers on this network to be connected and to act as fast as if connected to a local PC. This innovation may soon allow the development of previously unheard of networked rehabilitation systems. Rapid advances in this technology need to be coupled with an understanding of how human behavior is affected when immersed in the VE. Methods This paper will discuss various forms of VE that are currently available for rehabilitation. The characteristic of these new networks and examine how such networks might be used for extending the rehabilitation clinic to remote areas will be explained. In addition, we will present data from an immersive dynamic virtual environment united with motion of a posture platform to record biomechanical and physiological responses to combined visual, vestibular, and proprioceptive inputs. A 6 degree-of-freedom force plate provides measurements of moments exerted on the base of support. Kinematic data from the head, trunk, and lower limb was collected using 3-D video motion analysis. Results Our data suggest that when there is a confluence of meaningful inputs, neither vision, vestibular, or proprioceptive inputs are suppressed in healthy adults; the postural response is modulated by all existing sensory signals in a non-additive fashion. Individual perception of the sensory structure appears to be a significant component of the response to these protocols and underlies much of the observed response variability. Conclusion The ability to provide new technology for rehabilitation services is emerging as an important option for clinicians and patients. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies. Quantification of individual perceptual styles in the VE will support development of individualized treatment programs. The virtual environment can be a valuable tool for therapeutic interventions that require adaptation to complex, multimodal environments. NetworkingRehabilitationVirtual RealityField of ViewComplex Behaviors ==== Body Background Visual imaging is one of the major technological advances of the last decade. Although its impact in medicine and research is most strongly observed in the explosion of PET and fMRI studies in recent years [1], there has been a steady emergence of studies using virtual imaging to measure and train human behavior. Virtual environments (VE) or virtual reality (VR) have taken a foot hold in rehabilitation with dramatic results in some cases. Some applications have the patient wearing VE systems to improve their ability to locomote [2]. Others bring the VE technology to the patient to improve much needed rehabilitation [3]. With either approach, there are at least two issues that need to be addressed by the clinical or basic scientist employing virtual technology to elicit natural human behaviors. One is the ability of the technology to present images in real-time. If the virtual stimulus has delays that exceed those expected by the central nervous system (CNS), then the stimulus will most likely be ignored or processed differently than inputs from the physical world. Once a response is elicited, it must be determined whether the variability observed across individuals is due to individual differences or inconsistencies between expectation and the presentation of the virtual image. Components of a virtual environment Let us first define what we consider a VE and consider the signals that need to be transmitted for such a system to operate remotely (TeleImmersion). VE is immersion of a person in a computer generated environment such that the person experiences stereovision, correct perspective for all objects regardless of their motion, and objects in the environment move in a natural fashion with subject motion. To achieve theses characteristics, certain technology must be utilized. To provide stereovision, slightly different images must be presented to the right and left eyes with little if any cross talk between the two images. In some systems this is provided by using field sequential stereo in combination with liquid crystal shutter glasses (StereoGraphics, Inc). In this system the right liquid crystal lens is clear while the left is opaque and the perspective scene generated on the screen is that for the right eye. Then the left eye lens is clear and the right is opaque and the left eye's view is displayed. This method of producing stereo has found its way into projection based systems [4,5] and desktop systems also known as "fish tank VR" [6]. In other systems the person wears a head mounted display (HMD) where the right and left eye each see a dedicated display so that the computer generates a left and right eye perspective image and each image is connected to the corresponding monitor. Such systems have used miniature CRTs, Liquid Crystal Displays, and Laser light directed into the eye to create the image on the retina [7]. In contrast to the above mentioned systems, an auto-stereographic system displays stereo images to the person without the aid of any visual apparatus worn by the person [8]. The person merely looks at the screen(s) and sees stereo images as one might in the natural world. Because of their ease of use by the subject and their versatility these new and experimental systems have the potential of becoming the ultimate VE display when large motions of the subject are not needed. Regardless of the system used, to keep all the stereo objects in the correct perspective and to keep them from being distorted when the person moves in the environment, it is necessary to track the movements of the person so that the computer can calculate a new perspective image given the reported location of the person's head/eyes. The tracking systems that are used to do this are varied. The most commonly used of these are the 6-degrees of freedom (DOF) magnetic tracking systems (Ascension, Inc and Polhemus, Inc.). With these systems a small sensor cube is placed on the subject and the location of the sensor within the magnetic field is detected. When the sensor is place on the head or glasses of the person the orientation of the head and therefore the location of the eyes can be presumed. Other non-magnetically based systems use a combination of acoustic location to delineate position and acceleration detection to obtain body coordinates in space. The combination results in 6 DOF for the location information (InterSense, Inc). Other systems use cameras to track the person and then transform this information to the 6-DOF needed to maintain a proper image in the VE (Motion Analysis, Inc). So far we have confined our discussion to visual objects and have not considered the use of haptic or other forms of information to be integrated into the VE system [9]. To provide a realistic haptic experience to the subject, objects must be rendered at 1000 times per second. While a local haptic system such as that produced by Sensable Inc. and others can provide such high speed communication, when such information is floated over the network the issues of bandwidth and latency of the network are paramount to consider. While experimental networks have significantly increased the bandwidth of the network, our ability to move information over these networks is currently fixed by the speed of light. Prediction and other methods can be employed to help reduce the effective latency (Handshake Technologies, Inc), but this characteristic will continue to pose a problem for many conditions that we would like to use in tele-rehabilitation. In networked VEs several types of data need to be transmitted between collaborating sites: 1. the main data-set itself (this often consists of 3D geometry); 2. the changes to the data-set (these occur when collaborating users modify the geometry in some way – perhaps by moving the object or deforming it); 3. the virtual representation of the remote collaborator (this often is referred to as an avatar); 4. the video and/or audio channel (that facilitates face-to-face conversation.) Video has limited use in stereoscopic projection-based VEs because the large shutter glasses that the viewer uses to resolve the stereo tends to hide the viewers face from the camera. Furthermore most stereoscopic projection systems operate in dimly lit rooms which are usually too dark for effective use of video. The common model for data sharing in networked VEs is to have most of the main data-set replicated across all the sites and transmit only incremental changes. Furthermore the main data-set is often cached locally at each of the collaborating sites to reduce the need for having to retransmit the entire data-set each time the application is started. Classically TCP (Transmission Control Protocol – the protocol that is widely used on the Internet for reliable data delivery) has been the default protocol used to distribute the data-sets. TCP works well in low-bandwidth (below 10 Mb/s) or short distance (local area) networks. However for high-bandwidth long-distance networks, TCP's conservative transmission policy thwarts an application's attempt to move data expediently, regardless of the amount of bandwidth available on the network. This problem is known as the Long Fat Network (LFN) problem [10]. There are a wide variety of solutions to this [11], however none of them have been universally adopted. Changes made to the 3D environment need to be propagated with absolute reliability and with minimal latency and jitter. Latency is the time it takes for a transmitted message to reach its destination. Jitter is the variation in the latency. Fully reliable protocols like TCP have too much latency and jitter because the protocol requires an acknowledgment to verify delivery. Park and Kenyon [12] have shown that jitter is far more offensive than latency. One can trade off some latency for jitter by creating a receiving buffer to smooth out the incoming data stream. UDP (User Datagram Protocol) on the other hand transmits data with low latency and jitter, but is unreliable. Forward Error Correct (FEC) is a protocol that uses UDP to attempt to correct for transmission errors without requiring the receiver to acknowledge the sender. FEC works by transmitting a number of redundant data packets so that if one is lost at the receiving end, the missing data can be reconstructed from the redundant packets [13]. FEC however is not completely reliable. Hence to achieve complete reliability (at the expense of an infrequent increase in jitter) FEC is often augmented with an acknowledgment mechanism that is only used when it is unable to reconstruct a missing packet. The virtual representation of a remote collaborator (avatar) is often captured as the position and orientation of the 3D tracking devices that are attached to the stereoscopic glasses and/or 3D input device (e.g. a wand). With simple inverse kinematics one is able to map this position and orientation information onto a 3D geometric puppet, creating lifelike movements [14]. The 3D tracking information is often transmitted using UDP to minimize latency and jitter – however since the data is mainly used to convey a user's gesture, absolute delivery of the data is not necessary. Furthermore since tracking data is transmitted as an un-ending stream, a lost packet is often followed soon after (usually within 1/30th of a second) by a more recent update. Audio and video data are similar in property to the avatar data in that they usually comprise an unending stream that is best transmitted via UDP to minimize latency and jitter. Often video and audio packets are time stamped so that they can be synchronized on the receiving end. When more than two sites are involved in collaboration it is more economical to send audio/video via multicast. In multicast the sender sends the data to a specific device or machine that then copies the data to the various people that are subscribers to the data. For example, a user send their data to a multicast address and the routers that receive the data send copies of the data to remote sites that are subscribed to the multicast address. One drawback of multicast is that it is often disabled on routers on the Internet as one can potentially inundate the entire Internet. An alternative approach is to use dedicated computers as "repeaters" that intercept packets and transmit copies only to receivers that are specifically registered with the repeater. This broadcast method tends to increase the latency and jitter of packets, especially as the number of collaborators increases. Quality of Service (QoS) QoS refers to a network's ability to provide bandwidth and/or latency guarantees. QoS is crucial for applications such as networked VE, especially those involving haptics or tele-surgery, which are highly intolerant of latency and jitter. Early attempts to provide QoS (such as Integrated Services and Differentiated Services) have been good research prototypes but have completely failed to deploy across the wider Internet because telecommunications companies are not motivated to abide by each others QoS policies. It has been argued that QoS is unnecessary because in the future all the networks will be over-provisioned so that congestion or data loss that result in latency and jitter, will never occur. This has been found to be untrue in practice. Even with the enormous increase in bandwidth accrued during the dot-com explosion, the networks are still as unpredictable as they were a decade ago. Ample evidence is available from the online gaming community which often remarks about problems with bandwidth, latency and jitter during game sessions [15]. These games are based on the same principles that govern the design of networked VEs and therefore serve as a good metric for the current Internet's ability to support tightly coupled collaborative work. Customer Owned Networks Frustrated by the lack of QoS on the Internet, there is growing interest in bypassing the traditional routed Internet by using the available dark fiber in the ground. Dark fiber is optical fiber that has not yet been lit. Currently it is estimated that only about 5–10% of the available fiber has been lit, and each fiber has several terabits/s of capacity. The dot-com implosion has made this dark fiber and wavelengths of light in the fiber, very affordable. The newly emerging model is to construct a separate customer-owned network by purchasing or leasing the fiber from a telecommunications company, and installing one's own networking equipment at the endpoints. A number of federally supported national and international initiatives have been underway for the last few years to create customer-controlled networks explicitly for the scientific community. These include the National Lambda rail [16], StarLight [17], and the Global Lambda Integrated Facility [18]. By creating dedicated fiber networks, applications will be able to schedule dedicated and secure light paths with tens of gigabits/s of unshared, uncongested bandwidth between collaborating sites. This is the best operating environment for tightly coupled networked, haptic VEs. Connection Characteristics for Rehabilitation The ability to use virtual technology for rehabilitation is a function of cost, availability, and the kind of applications that can best utilize the network and provide rehabilitation services. Thus far, tele-rehabilitation research has focused on the use of low speed and inexpensive communication networks. While this work is important, the potential of new high-speed networks has not gathered as much attention. Consequently, we have little but imagined scenarios of how such networks might be utilized. Let us consider the case where a high-speed network connects a rehabilitation center and a remote clinic. The question is what kind of services can be provided remotely. The scenario that we envision is one where patients are required to appear at a rehabilitation center to receive therapy. Our scenario could work in several conditions. For example, a therapist at one location may want an opinion about the patient from a colleague at another location or, perhaps, the therapist can only visit the remote location once per week and with virtual technology the daily therapy could still be monitored by the therapist remotely. In our imagined condition we have a therapist at a rehabilitation center with VE, haptic and video devices and software to help analyze the incoming data (i.e., data mining) feeding to a remote clinic with identical equipment connected together through a dedicated high speed network. As displayed in Fig. 1, the therapist station has several areas of information that connects him/her to the patient in the remote clinic. The VE (in this case Varrier) provides the therapist with a representation of the patient and the kind of trajectory that will be needed for this training session. Notice that the use of Varrier removes the need for HMD or shutter glasses to be worn by the patient or therapist. This may seem like a minor difference, but now the patient and the therapist can see each other eye to eye. The video connection allows more communication (non-verbal or bed side manner) to take place between the two linked users of this system. The haptic device serves two purposes (1) to feedback the forces from the patient's limb to the therapist and (2) to feed the forces that the therapist wishes the patient to experience. Furthermore, we could provide a task that uses the affected limb so that learning and coordination is encouraged. Other possibilities include having the robot apply forces to the patient appendage so that adaptation and recovery of function occurs [9]. In our scenario we could allow the patient to see both the virtual limb and their own limb if needed by the therapy. As can be seen from Fig. 1, the bandwidth and latency requirements change as a function of the kind of information that is being transmitted. Figure 1 Possible tele-rehabilitation scenario facilitated by high bandwidth networking. A system as described above is possible today although expensive. The network characteristics that would be needed for each information channel would be as follows. A high-bandwidth connection would be needed for video and audio streamed to the plasma displays at each location, in addition to the high bandwidth a low latency and jitter connection would be needed for the Varrier Display system (VE). For a force feedback haptic device communicating between the patient and the therapist, a low network bandwidth could be used but the latency and jitter need to be low. Response behaviors in the virtual environment After all possible consideration of how to best construct the virtual system, the next concern is how to associate the complex stimuli with the behavior of interest. The relative influence of particular scene characteristics, namely field of view (FOV), scene resolution, and scene content, are critical to our understanding of the effects of the VE on our response behaviors [19] and the effect of these characteristics on postural stability in an immersive environment has been examined [20]. Roll oscillations of the visual scene were presented at a low frequency – 0.05 Hz to 10 healthy adult subjects. The peak angular velocity of the scene was approximately 70°/sec. Three different scenes (600 dpi fountain scene, 600 dpi simple scene, and 256 dpi fountain scene) were presented at 6 different FOVs (+/-15°, 30°, 45°, 60°, 75°, 90° from the center of the visual field) counterbalanced across subjects. Subjects stood on a force platform, one foot in front of the other, with their arms crossed behind their backs. Data collected for each trial included stance break (yes, no), latency to stance break (10 sec maximum), subjective difficulty rating (difficulty in maintaining the Romberg stance, 1–10 scale), and dispersion of center-of-balance. Postural stability was found to vary as a function of display FOV, resolution, and scene content. Subjects exhibited more balance disturbance with increasing FOVs, higher resolutions and more complex scene contents. Thus, altered scene contents, levels of interactivity, and resolution in immersive environments will interact with the FOV in creating a postural disturbance. Expectation of the visual scene characteristics will also influence responses in a VE. When subjects had some knowledge of the characteristics of a forthcoming visual displacement most reduced their postural readjustments, even when they did not exert active control over the visual motion [21]. Thus we can hypothesize that visual stimuli present an optimal pathway for central control of postural orientation as there are many cues in the visual flow field that can identified for anticipatory processing. The important parameters of the visual field on posture can be extracted from several studies. Vestibular deficient individuals who were able to stabilize sway when fixating on a stationary light [22] became unstable when an optokinetic stimulus was introduced, implying that velocity information from peripheral vision was a cause of instability. Focusing upon distant visual objects in the environment increased postural stability [23,24]. We have observed in the VE [25,26] that small physical motions combined with large visual stimuli trigger a perception of large physical movements as occurs during flight simulations [27] and gaming. We have also observed measurable increases in the variability of head and trunk coordination and increased lateral head and trunk motion when standing quietly and walking within a dynamic visual environment [28]. The challenge is to determine whether the subject has become immersed in the environment, i.e., has established a sense of presence in the environment (see paper by Riva in this issue), and then to establish the correlation between the stimulus and response properties. The experience within the VE is multimodal, requiring participation of all sensory pathways as well as anticipatory processing and higher order decision making. Consequently, it is difficult to attribute resultant behaviors to any single event in the environment and responses across participants may be very variable. We have united an immersive dynamic virtual environment with motion of a posture platform [25] to record biomechanical and physiological responses to combined visual, vestibular, and proprioceptive inputs in order to determine the relative weighting of physical and visual stimuli on the postural responses. Methods In our laboratory, a linear accelerator (sled) that could be translated in the anterior-posterior direction was controlled by D/A outputs from an on-line PC. The sled was placed 40 cm in front of a screen on which a virtual image was projected via a stereo-capable projector (Electrohome Marquis 8500) mounted behind the back-projection screen. The wall in our system consisted of back projection material measuring 1.2 m × 1.6 m. An Electrohome Marquis 8500 projector throws a full-color stereo workstation field (1024 × 768 stereo) at 200 Hz [maximum] onto the screen. A dual Pentum IV PC with a nVidia 900 graphics card created the imagery projected onto the wall. The field sequential stereo images generated by the PC were separated into right and left eye images using liquid crystal stereo shutter glasses worn by the subject (Crystal Eyes, StereoGraphics Inc.). The shutter glasses limited the subject's horizontal FOV to 100° of binocular vision and 55° for the vertical direction. The correct perspective and stereo projections for the scene were computed using values for the current orientation of the head supplied by a position sensor (Flock of Birds, Ascension Inc.) attached to the stereo shutter glasses (head). Consequently, virtual objects retained their true perspective and position in space regardless of the subjects' movement. The total display system latency from the time a subject moved to the time the new stereo image was displayed in the environment was 20–35 ms. The stereo update rate of the scene (how quickly a new image is generated by the graphics computer in the frame buffer) was 60 stereo frames/sec. Flock of birds data was sampled at 120 Hz. Scene Characteristics The scene consisted of a room containing round columns with patterned rugs and painted ceiling (Fig. 2). The columns were 6.1 m apart and rose 6.1 m off the floor to the ceiling. The rug patterns were texture mapped on the floor and consisted of 10 different patterns. The interior of the room measured 30.5 m wide by 6.1 m high by 30.5 m deep. The subject was placed in the center of the room between two rows of columns. Since the sled was 64.8 cm above the laboratory floor the image of the virtual room was adjusted so that its height matched the sled height (i.e., the virtual floor and the top of the sled were coincident). Beyond the virtual room was a landscape consisting of mountains, meadows, sky and clouds. The floor was the distance from the subject's eyes to the virtual floor and the nearest column was 4.6 m away. The resolution of the image was 7.4 min of arc per pixel when the subject was 40 cm from the screen. The view from the subjects' position was that objects in the room were both in front of and behind the screen. When the scene moved in fore-aft, objects moved in and out of view depending on their position in the scene. Figure 2 An illustration of the virtual environment image in our laboratory. Procedures Subjects gave informed consent according to the guidelines of the Institutional Review Board of Northwestern University Medical School to participate in this study. Subjects had no history of central or peripheral neurological disorders or problems related to movements of the spinal column (e.g., significant arthritis or musculoskeletal abnormalities) and a minimum of 20/40 corrected vision. All subjects were naive to the VE. We have tested 7 healthy young adults (aged 25–38 yrs) standing on the force platform (sled) with their hands crossed over their chest and their feet together in front of a screen on which a virtual image was projected. Either the support surface translated ± 15.7 cm/sec (± 10 cm displacement) in the a-p direction at 0.25 Hz, or the scene moved ± 3.8 m/sec (± 6.1 m displacement) fore-aft at 0.1 Hz, or both were translated at the same time for 205 sec. Trials were randomized for order. In all trials, 20 sec of data was collected before scene or sled motion began (pre-perturbation period). When only the sled was translated, the visual scene was visible but stationary, thus providing appropriate visual feedback equivalent to a stationary environment. Data Collection and Analysis Three-dimensional kinematic data from the head, trunk, and lower limb were collected at 120 Hz using video motion analysis (Optotrak, Northern Digital Inc., Ontario, Canada). Infrared markers placed near the lower border of the left eye socket and the external auditory meatus of the ear (corresponding to the relative axis of rotation between the head and the upper part of the cervical spine) were used to define the Frankfort plane and to calculate head position. Other markers were placed on the back of the neck at the level of C7, the left greater trochanter, the left lateral femoral condyle, the left lateral malleolus, and on the translated surface. Markers placed at C7 and the greater trocanter were used to calculate trunk position, and shank position was the calculated from the markers on the lateral femoral condyle and the lateral malleolus. For trials where the sled moved, sled motion was subtracted from the linear motion of each segment prior to calculating segmental motion. Motion of the three segments was presented as relative segmental angles where motion of the trunk was removed from motion of the head to determine the motion of the head with respect to the trunk. Motion of the shank was removed from motion of the trunk to reveal motion of the trunk with respect to the shank. Motion of the shank was calculated with respect to the sled. Results The response to visual information was strongly potentiated by the presence of physical motion. Either stimulus alone produced marginal responses in most subjects. When combined, the response to visual stimulation was dramatically enhanced (Fig. 3), perhaps because the visual inputs were incongruent with those of the physical motion. Figure 3 Relative angles of the head to trunk (blue), trunk to shank (red) and shank to sled (green) are plotted for a 60 sec period of the trial during sled motion only, scene motion only, and combined sled and scene motion (the same data are plotted against both the sled and the scene). Using Principal Component Analysis we have determined the overall weighting of the input variables. In healthy young adults, some subjects consistently responded more robustly when receiving a single input, suggesting a proprioceptive (see S3 in Fig. 4) or visual (S1 in Fig. 4) dominance. With multiple inputs, most subjects produced fluctuating behaviors so that their response was divided between both inputs. The relative weighting of each input fluctuated across a trial. When the contribution of each body segment to the overall response strategy was calculated, movement was observed primarily in the trunk and shank. Figure 4 Overall weighting of the input variables derived from the PCA for 3 subjects. The first 3 bars (blue) represent a subsequent non-overlapping 40 sec time period to sled motion only. The next 3 bars (red) represent non-overlapping 40 sec time periods to scene motion only. The last 6 bars represent non-overlapping 40 sec time periods to both sled (blue) and scene (red) motion. The direction of each bar indicates the relative phase between the response and the input signal. Discussion Results from experiments in our laboratory using this sophisticated technology revealed a non-additive effect in the energy of the response with combined inputs. With single inputs, some subjects consistently selected a single segmental strategy. With multiple inputs, most produced fluctuating behaviors. Thus, individual perception of the sensory structure was a significant component of the postural response in the VE. By quantifying the relative sensory weighting of each individual's behavior in the VE, we should be better able to design individualized treatment plans to match their particular motor learning style. Developing treatment interventions in the virtual environment should carry over into the physical world so that functional independence will be increased for many individuals with physical limitations. In fact, there is evidence that the knowledge and skills acquired by disabled individuals in simulated environments can transfer to the real world [29-31]. The ability for us to use this technology outside the area of research labs and bring these systems to clinics is just starting. However, the cost is high and the applications that can best be applied to rehabilitation are limited. The cost of such systems might be mitigated if this technology allowed therapists and patients to interact more frequently and/or resulted in better patient outcomes. Such issues are under study now at several institutions. This brings us to the idea of tele-rehabilitation, which would allow therapy to transcend the physical boundaries of the clinic and go wherever the communication system and the technology would allow [5]. For example, at some location remote from the clinic a patient enters a VE suitable for rehabilitation protocols connected to the clinic and a therapist. While this idea is not new, the kind of therapies that could be applied under such a condition is limited by the communication connection and facilities at both ends of the communication cable. The ability to provide rehabilitation services to locations outside the clinic will be an important option for clinicians and patients in the near future. Effective therapy may best be supplied by the use of high technology systems such as VE and video, coupled to robots, and linked between locations by high-speed, low-latency, high-bandwidth networks. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies. Conclusions The ability to provide rehabilitation services to locations outside the clinic is emerging as an important option for clinicians and patients. Effective therapy may best be supplied by the use of high technology systems such as VE and video, coupled to robots, and linked between locations by high-speed, low-latency, high-bandwidth networks. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies. Although responses in the VE can vary significantly between individuals, these results can actually be used to benefit patients through the development of individualized treatments programs that will raise the level of successful rehabilitative outcomes. Further funding for research in this area will be needed to answer the questions that arise from the use of these technologies. Acknowledgements This work is supported by grants DC05235 from NIH-NIDCD and AG16359 from NIH-NIA, H133E020724 from NIDRR and NSF grant ANI-0225642. ==== Refs Cabeza R Kingstone A Handbook of Functional Neuroimaging of Cognition 2001 Cambridge: MIT Press Riess T Weghorst S Augmented reality in the treatment of Parkinson's disease Proceedings of Medicine Meets Virtual Reality III, San Diego 1995 Amsterdam: IOS Press Hoffman HG Patterson DR Carrougher GJ Sharar SR The effectiveness of virtual reality-based pain control with multiple treatments Clin J Pain 2001 17 229 235 11587113 Cruz-Neira C Sandin DJ DeFanti TA Kenyon RV Hart JC The CAVE: audio visual experience automatic virtual environment Communications ACM 1992 35 65 72 Rosen MJ Rosen MJ Telerehabilitation NeuroRehabilitation 12 (1) Special Topic Issue on Technology in Neurorehabilitation 1999 Ware C Arthur K Booth KS Ashlund S, Mullet K, Henderson A, Hollnagel E, White T Fish tank virtual reality INTERCHI '93 Conf Proceedings 1993 NY: ACM Press 37 42 Tidwell M Johnston RS Melville D Furness TA The virtual retinal display – a retinal scanning imaging system Proceedings of Virtual Reality World '95 1995 Heidelberg: Springer-Verlag 325 333 Sandin DJ Margolis T Dawe G Leigh J DeFanti TA The VarrierTM auto-stereographic display SPIE 2001 4297 WA: SPIE Patton J Dawe G Scharver C Mussa-Ivaldi F Kenyon RV Robotics and virtual reality: a perfect marriage for motor control research and rehabilitation J Assist Tech Stevens WR TCP/IP Illustrated 1994 1 Boston: Addison Wesley 344 350 He E Leigh J Yu O DeFanti TA Reliable blast UDP: predictable high performance bulk data transfer In Proceedings IEEE Cluster Computing, Sept, Chicago, Illinois 2002 NY: IEEE Press Park K Kenyon RV Effects of network characteristics on human performance in the collaborative virtual environment IEEE Virtual Reality '99 Conference, Houston, TX NY: IEEE Press March 14–17, 1999 Leigh J Yu O Schonfeld D Ansari R He E Nayak A Krishnaprasad N Park K Cho Y Hu L Fang R Verlo A Winkler L DeFanti T Adaptive networking for tele-immersion In Proceedings Immersive Projection Technology: Eurographics Virtual Environments Workshop, Stuttgart, Germany May 16–18, 2001 Park K Cho Y Krishnaprasad N Scharver C Lewis M Leigh J Johnson A CAVERNsoft G2: a toolkit for high performance tele-immersive collaboration In Proceedings of the ACM Symposium on Virtual Reality Software and Technology 2000 8 15 Oct 22–25, 2000 Ghost Recon Enter the world of squad-based battlefield combat IGN Entertainment Corp National LambdaRail Starlight, The University of Illinois at Chicago Kees Neggers GLIF: global lambda integrated facility International Task Force Session of the Spring 2004 Internet2 Member Meeting, Arlington, Virginia, USA April 19, 2004 Kenyon RV Kneller EW The effects of field of view size on the control of roll motion IEEE Trans Systems Man Cybern 1993 23 183 193 Duh HBL Lin JJW Kenyon RV Parker DE Furness TA Effects of characteristics of image quality in an immersive environment Presence Teleoper Virtual Environ 2002 11 324 332 12238514 Guerraz M Thilo KV Bronstein AM Gresty MA Influence of action and expectation on visual control of posture Cogn Brain Res 2001 11 259 266 Kotaka S Okubo J Watanabe I The influence of eye movements and tactile information on postural sway in patients with peripheral vestibular lesions Auris-Nasus-Larynx 1986 13 S153 3493763 Bronstein AM Buckwell D Automatic control of postural sway by visual motion parallax Exp Brain Res 1997 113 243 248 9063710 Crane BT Demer JL (1998) Gaze stabilization during dynamic posturography in normal and vestibulopathic humans Exp Brain Res 1998 122 235 246 9776522 Keshner EA Kenyon RV Using immersive technology for postural research and rehabilitation J Assist Tech Keshner EA Kenyon RV Langston J Postural responses increase complexity with visual-vestibular discordance J Vestib Res Young LR Vestibular reactions to spaceflight: human factors issues Aviat Space Envion Med 2000 71 A100 104 Keshner EA Kenyon RV The influence of an immersive virtual environment on the segmental organization of postural stabilizing responses J Vestib Res 2000 10 201 219 11354433 Kuhlen T Dohle C Virtual reality for physically disabled people Comput Biol Med 1995 25 205 211 7554838 Viirre E Vestibular telemedicine and rehabilitation. 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==== Front Front ZoolFrontiers in Zoology1742-9994BioMed Central London 1742-9994-2-11567995210.1186/1742-9994-2-1ReviewLinking biogeography to physiology: Evolutionary and acclimatory adjustments of thermal limits Somero George N [email protected] Department of Biological Sciences, Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950-3094 USA2005 17 1 2005 2 1 1 17 10 2004 17 1 2005 Copyright © 2005 Somero; licensee BioMed Central Ltd.2005Somero; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Temperature-adaptive physiological variation plays important roles in latitudinal biogeographic patterning and in setting vertical distributions along subtidal-to-intertidal gradients in coastal marine ecosystems. Comparisons of congeneric marine invertebrates reveal that the most warm-adapted species may live closer to their thermal tolerance limits and have lower abilities to increase heat tolerance through acclimation than more cold-adapted species. In crabs and snails, heart function may be of critical importance in establishing thermal tolerance limits. Temperature-mediated shifts in gene expression may be critical in thermal acclimation. Transcriptional changes, monitored using cDNA microarrays, have been shown to differ between steady-state thermal acclimation and diurnal temperature cycling in a eurythermal teleost fish (Austrofundulus limnaeus). In stenothermal Antarctic notothenioid fish, losses in capacity for temperature-mediated gene expression, including the absence of a heat-shock response, may reduce the abilities of these species to acclimate to increased temperatures. Differences among species in thermal tolerance limits and in the capacities to adjust these limits may determine how organisms are affected by climate change. ==== Body Review Introduction Understanding the roles played by physiological adaptation to temperature in governing the distribution patterns of species has taken on a new urgency because of the potential effects of global warming on both aquatic and terrestrial ecosystems. There is already compelling evidence for widespread changes in ecosystems due to climate change. Recent meta-analyses have shown that species' distribution patterns, the structures of ecosystems, and the timing of annual events (phenology) such as migration and reproduction have changed markedly during the past century, when average global temperatures rose by approximately 0.6°C [1-4]. If the consensus view that average global temperatures will rise by approximately 3°C during this century is correct [5], then even more extensive changes in the biosphere are certain to occur and, in view of this five-fold increase in warming rate, to occur rapidly. Suffice it to say that analyses that can assist in predicting – and, perhaps, even in ameliorating – these biological effects are strongly needed at this time. Because most previous analyses of the biological effects of climate change have been correlative, rather than focused on the underlying causal mechanisms behind the observed effects, there is clearly a need for physiologists to contribute to this important, on-going discussion. In this brief review, I discuss studies performed with a taxonomically varied group of aquatic ectotherms that shed light on several questions related to the roles played by physiological adaptations in setting species' distribution patterns. The more clearly we understand the mechanistic basis of biogeography, the better prepared we will be to predict the effects of climate change on distribution patterns and ecosystem structure. The questions I address include the following. First, how do thermal tolerance limits differ among species adapted to different temperatures? Which species are most likely to be threatened by increases in habitat temperatures, i.e., which species currently live closest to their thermal tolerance limits? Second, what are the "weak links" in physiological systems that appear most likely to set thermal tolerance limits? Can we identify physiological, biochemical, and molecular level effects that account for thermal tolerance ranges? Third, how do capacities for acclimation to changes in habitat temperature differ among species? Which species have the greatest – or the least – ability to acclimate to increases in temperature? Mechanistically speaking, what types of acclimatory changes are needed to adapt physiological systems to permit tolerance of new thermal conditions? Fourth, what types of changes in gene expression are needed to achieve thermal acclimation? How does gene expression during acclimation to rapid, diurnal change in temperature differ from the response to slower, seasonal time-scale change in temperature? How do stenothermal and eurythermal species differ in their capacities for altering gene expression in temperature-adaptive manners? Have extreme stenotherms like Antarctic animals that have evolved for many millions of years under highly stable, cold temperatures lost key abilities to acclimate to increasing temperatures? To address these questions, I focus primarily on studies that our laboratory has performed on aquatic ectothermic species that have been chosen for study because they seemed to fit well the August Krogh Principle, which can be paraphrased as follows: For any question a biologist asks, Nature can provide a most appropriate study organism. Through study of such "Kroghian" species, it has been possible to obtain at least initial answers to the several questions raised above. Thermal tolerance relationships of porcelain crabs (genus Petrolisthes) and turban snails (genus Tegula) One of the challenges facing a comparative or evolutionary physiologist wishing to elucidate adaptive differences among species is the need to separate effects due to phylogeny from true adaptive variation. One means of achieving this end is to study groups of closely related organisms from widely different habitat conditions, for which a comprehensive phylogeny exists. A group of species that is especially "Kroghian" from this standpoint are porcelain crabs (genus Petrolisthes) from the eastern Pacific Ocean [6]. Congeners of Petrolisthes are abundant (46 species occur in the eastern Pacific), found over wide ranges of latitude, and occur in subtidal and intertidal habitats. Among these congeners, body temperatures extend from slightly below 0°C to over 40°C; for some eurythermal species, the range of body temperatures across different seasons can be over 32°C [7-10]. The thermal tolerance limits of field-acclimatized temperate (California and Chile), subtropical (northern Gulf of California) and tropical (Panama) congeners of Petrolisthes from different heights along the subtidal to intertidal gradient are shown in Fig. 1. Data are presented as LT50 values, the temperatures at which fifty percent of an experimental population is killed by the heat treatment. Heating rates simulated measured rates of environmental temperature change found in the species' habitats [10]. The predicted correlation between adaptation temperature and LT50 is found (Fig. 1A): the most heat-tolerant intertidal species, those from subtropical and tropical habitats, had LT50s that were approximately 15°C higher than those of temperate sub-tidal species and 6–7°C higher than temperate intertidal species. However, the likelihood of heat death facing the different species under natural habitat conditions is not reflected by the LT50 per se, but rather the by proximity of this trait to current extremes of habitat temperature. As shown by the alignment of LT50 values vis à vis the line of unity (LT50 = maximal habitat temperature) in Fig. 1B, warm-adapted intertidal species are in much greater jeopardy from heat death than their more cold-adapted subtidal relatives. Moreover, the abilities of congeners of Petrolisthes to increase thermal tolerance through acclimation to increased temperature are less in intertidal species than in more cold-adapted subtidal congeners [10]. Thus, warm-adapted intertidal species face greater current – and, most likely, future – threats from high temperatures than less heat-tolerant, subtidal congeners. Figure 1 Heat tolerance of porcelain crabs. Upper thermal tolerance limits (LT50 (°C)) of congeneric porcelain crabs (genus Petrolisthes) native to eastern Pacific habitats in California, Chile, the northern Gulf of California, and Panama and occurring at different heights along the subtidal-to-intertidal gradient. Each symbol represents a different species of crab. B. Thermal tolerance versus maximal habitat temperature, with a line of unity given to show the proximity of current habitat temperatures to lethal temperatures. Hatched symbol represents P. cinctipes. The R2 value for the regression line for all species is given. Data are from [10]. One physiological system that is a "weak link" in the thermal tolerance of these species is heart function. As shown in Fig. 2A, sharp reductions in heart rate occur when a species-specific high temperature is reached. This is termed the "Arrhenius break temperature" (ABT) to denote that it is the temperature at which a sharp discontinuity in the slope of an Arrhenius plot (ln rate of heart beat versus reciprocal temperature (K)) occurs. Once the ABT is exceeded, heart function of porcelain crabs does not recover from heat stress. ABT values for the temperate congeners P. cinctipes (intertidal) and P. eriomerus (subtidal) are 31.5°C and 26.5°C, respectively. The maximal habitat temperatures for the two species are approximately 31°C and 16°C, respectively [9,10]. Thus, whereas the subtidal congener has an approximately 10°C range between its highest habitat temperature and the upper thermal limit of heart function, the intertidal congener's upper habitat temperature, LT50, and ABT for heart function are essentially the same, 31–32°C. It bears emphasizing that ABT values for other physiological processes, for example, mitochondrial respiration [11,12] and enzymatic activity [13], may be considerably higher than upper lethal temperatures of the whole organism. For some species of animals, then, heart function appears to be a "weak link" in the "chain" of physiological processes that govern thermal tolerance. Figure 2 The effects of temperature on cardiac activity in porcelain crabs. Upper panel. Arrhenius plots of ln heart rate (beats per minute) versus measurement temperature (1/K) for two congeners of porcelain crabs, Petrolisthes cinctipes and P. eriomerus, having maximal habitat temperatures of approximately 32°C and 16°C, respectively. Arrhenius break temperatures (ABTs) are the temperatures at which a sharp decrease in heart rate is noted (see Stillman & Somero [9] for computational methods). Lower panel. Acclimatory-induced change in upper and lower critical temperatures (CTmax and CTmin, respectively) of heart function for 4 congeners of Petrolisthes: P. cinctipes and P. eriomerus from temperate central California habitats, and P. gracilis and P. hirtipes from the northern Gulf of California. CTmax is the ABT, and CTmin is the temperature at which heart beat ceased as temperature was lowered. Decreasing temperatures did not cause a sharp break in Arrhenius plots, so ABTs could not be determined at low temperatures [8,9]. Acclimation temperatures were 8°C and 18°C for the California species and 15°C and 25°C for the Gulf of California species. The differences in CTmax and CTmin between the two acclimation groups of each species are shown. Each symbol represents a different species, whose maximal habitat temperature is given on the abscissa. Figure modified after [8]. The acclimatory plasticity of ABTs of heart function differs among species in a parallel fashion to acclimatory change in LT50 (Fig. 2B[8]). In this experiment, 4 congeners were studied: the two temperate species from California discussed above, P. cinctipes and P. eriomerus, and upper intertidal and mid-intertidal species from the northern Gulf of California, P. gracilis and P. hirtipes, respectively. Acclimation temperatures were 8°C and 18°C for the temperate species and 15°C and 25°C for the sub-tropical species. ABT (= CTmax) of P. eriomerus rose by 2.3°C when acclimation temperature was increased by 10°C, but the most warm-adapted species, P. gracilis, increased ABT (CTmax) by only 0.5°C. The eurythermal intertidal species did exhibit greater capacities than the subtidal species to acclimate to lower temperatures, however, as shown by their greater ability to lower the temperature at which heart beat ceased (CTmin) (Fig. 2B). Despite being eurythermal, the warm-adapted species are again seen to be in greatest jeopardy from further increases in maximal habitat temperature; their abilities to extend their tolerance of lower temperatures is not matched by a similar capacity for increasing tolerance of heat. The differences in thermal tolerance and capacities for warm acclimation noted with porcelain crabs were mirrored in studies done with another set of congeneric marine invertebrates found at different vertical positions along the subtidal to intertidal gradient, turban snails of the genus Tegula [14]. Intertidal and subtidal Tegula congeners exhibit significant differences in thermal limits of protein synthesis and onset temperatures for production of heat-shock proteins that reflect their vertical distributions [15,16]. In agreement with these data, field-acclimatized populations of three Tegula congeners had significantly different thermal limits of heart function: the low- to mid-intertidal congener, T. funebralis, had a higher ABT of heart function (31.0°C) than two subtidal congeners, T. brunnea and T. montereyi (ABTs of 25.0°C and 24.2°C, respectively) [17]. The ABT of T. funebralis approximated the highest body temperature recorded for this species in the field, 32°C [15]. Body temperatures for the subtidal species only rarely reach 20°C [15], so these species would seldom, if ever, experience heart failure in their habitats. Furthermore, in agreement with the studies of porcelain crabs [8], T. funebralis had a lower ability to increase ABT during warm acclimation than the subtidal species [17]. These interspecific differences in thermal tolerance limits for whole organism survival and maintenance of heart function provide important lessons concerning biogeographic and vertical patterning and the potential effects of climate change. First, the differences in thermal tolerance between subtidal and intertidal species indicate that the former species would be unable to persist at the upper habitat temperatures commonly found in the intertidal zone during daytime low tides in warm seasons. For example, the temperate subtidal species Petrolisthes eriomerus has an upper lethal temperature approximately 6°C below the upper limit of body temperature recorded for the intertidal species P. cinctipes. Interspecific variations in LT50 and ABT values also mirror the biogeographic patterning found across latitude. For instance, north temperate congeners of Petrolisthes would be unable to survive under the habitat conditions found in the northern Gulf of California or Panama. The close agreement among LT50, ABT, and maximal habitat temperatures for intertidal species suggests that further increases in habitat temperature could have strong impacts on the persistence of these species in their habitats, especially when the limits of acclimatory ability are reached. Because the greatest thermal stress occurs during emersion periods during the warmest time of the day, and because solar heating is more critical than ambient water temperature, the precise consequences of global warming are difficult to predict. Furthermore, because of latitudinal variation in the timing of tidal cycles, these and other intertidal species may face greater threats from climate change at mid-latitudes than at lower latitudes [18]. Despite these uncertainties, however, the seemingly paradoxical conclusion reached above, to the effect that many warm-adapted species are more threatened by increases in habitat temperature than cold-adapted congeners, appears valid and should be taken as a caveat that predicting the consequences of climate change is a complex challenge. Acclimatory changes in gene expression: eurytherms versus stenotherms The abilities of ectotherms to cope satisfactorily with increases in habitat temperature are based on several factors, including the proximity of habitat temperatures to the edges of the thermal tolerance range and the abilities to shift the tolerance range through acclimatization. In the case of porcelain crabs and turban snails, acclimatory ability was shown to vary among species. This finding that even closely related congeneric species differ in acclimatory ability raises questions about the mechanistic basis of phenotypic plasticity and the roles of evolutionary thermal history in establishing this plasticity. The ability to modify the phenotype in response to a change in body temperature is certain to depend strongly on a capacity for modulating gene expression in an adaptive manner. Although comprehensive surveys of temperature-induced shifts in gene expression have not been done for any marine organisms, studies of model species, defined here as species for which the genome has been sequenced and relatively well annotated, are beginning to reveal the pervasiveness of stress-induced shifts in gene expression. The studies of Gasch and colleagues [19] on yeast, for example, have shown that a variety of physical and chemical stressors, including temperature, hypoxia, reactive oxygen species and osmotic shock, triggered relatively similar changes in expression of approximately 900 genes, which represents approximately 14% of the yeast genome. They termed this gene regulatory response the "environmental stress response (ESR)". Because the proteins, lipids and nucleic acids that form the structural foundation of all physiological processes of cells will be affected in a qualitatively similar manner by changes of temperature in all organisms [20], it is reasonable to conjecture that an ESR generally similar to that found in yeast may characterize all species. For instance, induction of stress-induced chaperones (heat-shock proteins) for repair of damaged proteins, and shifts in synthesis of the various enzymes required for modifying the properties of lipid-based systems are likely to be ubiquitous events in the thermally induced ESRs of different species [20]. Despite the likely occurrence among species of generally similar requirements for temperature-adaptive shifts in gene expression, species that fall into different regions of the stenotherm to eurytherm spectrum may have distinctly different capacities for acclimatory regulation of transcription. In the context of effects of climate change, one of the most important interspecific differences relates to the ability of extreme stenotherms, which may have evolved for millions of years under stable thermal conditions, to alter gene expression in the face of temperature change. The Antarctic notothenioid fishes are a primary case in point. These species have evolved for 10–14 million years in a thermally stable "ice bath," in which annual temperature variation is usually less than 1–2°C [21]. McMurdo Sound populations of notothenioids are unable to acclimate to temperatures above approximately 4°C [22,23], making these species among the most stenothermal of organisms. Antarctic marine invertebrates, too, are extreme stenotherms, with heat death occurring at temperatures only a few degrees above 0°C [24]. In the case of the notothenioid fish Trematomus bernacchii, the lack of capacity to acclimate to elevated temperatures may stem in part from deficiencies in its ability to alter gene expression as its body temperature changes. Unlike virtually all other species, T. bernacchii is unable to increase the synthesis of any class of heat-shock protein following thermal stress [23]. Lacking this ability, T. bernacchii may be unable to effect adequate chaperone-mediated restoration of the native structures of heat-denatured proteins. A build-up of aggregates of denatured proteins is likely to be cytotoxic and lead to the eventual death of cells [25]. The absence of a heat-shock response in Antarctic notothenioids, but its presence in temperate New Zealand notothenioids [26], suggests that evolution under cold, stable thermal conditions has led to a depletion of the genetic resources of Antarctic fish. The inability of some notothenioid species to synthesize hemoglobins [27] or myoglobins [28] is another illustration of the depauperate genome of these cold-adapted stenotherms. Loss of oxygen transport pigments is viewed as a reflection of the lack of need for these pigments because of the combination of high oxygen solubility at low temperatures and the generally sluggish locomotory habits of the notothenioids [27,28]. In general, disappearance of these diverse genetic capacities in notothenioids is consistent with relaxed selection against the loss of genes that are no longer needed under conditions of stable low temperatures. Although future work on temperature-induced changes in the transcriptomes of Antarctic stenotherms will be needed before any broad generalizations can be made about the contents of their "genetic tool kits," it seems likely that the stenothermy of these species could be due in large measure to a loss of the type of acclimatory plasticity that is found in eurytherms. Lacking this phenotypic plasticity, Antarctic stenotherms seem uniquely vulnerable to the effects of global warming. Even among eurythermal ectotherms, there may be a variety of capacities for modulating gene expression to compensate for changes in body temperature. Although few data are currently available to test this point, acclimatory responses to rapid, diurnal variations in body temperature may require different shifts in gene expression from those that characterize slower, e.g., seasonal time-scale, responses to temperature change. A recent study [29] of temperature-induced changes in the transcriptome (the population of messenger RNA (mRNA) molecules in the cell) of liver tissue of a eurythermal teleost fish, Austrofundulus limnaeus, revealed wide-scale shifts in gene expression, as noted earlier for yeast [19]. A total of 540 mRNAs out of 4,992 examined changed by two-fold or more during acclimation. Many of the mRNAs changing in response to thermal acclimation were for proteins that are well known to be key elements of acclimatory response. For example, transcripts for heat-shock proteins and for enzymes involved in temperature-compensatory shifts in membrane lipid composition showed the predicted changes. However, for both classes of proteins, distinctly different expression profiles were found under the two acclimation regimes employed in this study: (i) steady-state acclimation at 20°C or 37°C for up to two weeks, and (ii) diurnally cycling temperatures that varied from a high of 37°C near mid-day to 20°C at night, a cycle that simulates the environmental conditions the species encounters in its shallow pond habitats in South America [29]. Under steady-state acclimation, transcripts for heat-shock proteins Hsp70 and Hsp90 showed the largest amount of change among molecular chaperones. Under cycling conditions, low-molecular-mass chaperones showed the greatest change. This finding suggests that protein damage and the repair costs it entails may differ under constant versus intermittent heat stress. Although both acclimation regimes led to changes in transcript abundance for proteins associated with alterations in lipid composition, differences between cycling and steady-state acclimation were noted in the types of lipids that appear to be produced. For steady-state acclimation, the expected changes in transcripts for enzymes involved in acyl chain double bond content (saturation) were observed, consistent with many previous studies of homeoviscous acclimation in membranes [20]. During cycling conditions, changes in transcripts for proteins involved in cholesterol biosynthesis and transport were more pronounced than shifts in mRNAs for enzymes of acyl chain biosynthesis. Although the significance of this difference in adaptive response is not known, it seems possible that insertion into or removal from the membrane of cholesterol could be achieved more rapidly than changes in the composition of phospholipids. In any event, differences in the time-frame of thermal stress may lead to differences in the types of temperature-compensatory strategies used to modulate the fluidity and phase of membranes. One of the most striking changes in transcript abundance observed in the study of Austrofundulus limnaeus was for the message encoding high mobility group b1 protein (HMGB1)(Fig. 3). High mobility group proteins are DNA-binding proteins that exert wide-ranging effects on transcriptional activity [30]. Rather than serving as transcriptional regulators for specific genes, HMGB1 proteins are general activators of transcription that exert their effects by influencing the transcriptional competence or "openness" of DNA structure. Increases in concentrations of HMGB1 favor open DNA structures and increased transcriptional activity of many genes. The changes in mRNA for HMGB1 found during thermal cycling (Fig. 3) and during steady-state acclimation [29] indicate an extremely tight control of this message. The increase in content of HMGB1 message with falling temperature (steady-state or cycling) is consistent with the role of this protein in maintaining DNA structure in an open, transcriptionally competent state. Thus, decreases in temperature will enhance the stability of the non-covalent bonds that stabilize higher order of DNA structure. To offset this increased stability of DNA structure, the cell may increase the concentrations of HMGB1 proteins, thereby allowing transcriptional activity to be temperature-independent. This is not to suggest that changes in HMGB1 protein will lead to genome-wide changes in transcriptional activity, of course. Rather, by increasing the openness of DNA structure, the promoter regions of genes will be susceptible to the effects of gene-specific transcription factors whose influences will lead to temperature-specific alterations in the transcriptome. Figure 3 Temperature effects on the transcriptome of the eurythermal fish Austrofundulus limnaeus. Changes in liver tissue in the level of the mRNA encoding high mobility group B1 protein (HMGB1), during thermal cycling between 37°C and 20°C. The Y-axis plots the logarithm of the ratio of expression in experimental (thermally cycled) versus control animals. Expression of the hmgb1 gene showed no circadian rhythm in fish held at constant temperature. Data from [29]. The changes in gene expression noted in Austrofundulus limnaeus and in a recent study of carp [31] represent an initial view of the complexity of transcriptional changes that ectothermic animals may experience during thermal acclimation, either to short-term or long-term changes in body temperature. Different changes to the transcriptome may be involved in different time courses of acclimation. It will be important to establish whether extreme stenotherms like Antarctic fish are capable of adjusting their transcriptomes in temperature-adaptive manners, when body temperature is increased. The importance of maintaining DNA structure in an open configuration that allows transcription factors to effectively modulate gene expression is a phenomenon that merits additional study. This capacity may be of pivotal significance in determining the effectiveness with which ectotherms can respond to changes in temperature; it may be foundational for most, if not all, of the temperature acclimation response. How this capacity differs between eurytherms and stenotherms may determine how these two groups cope with thermal fluctuations in their present habitats and what their potentials for coping with climate change are likely to be. Conclusions Ectothermic species differ widely in thermal tolerance limits and in their abilities to adjust these limits in temperature-adaptive manners. Comparisons of congeneric species from different latitudes and different positions along the subtidal to intertidal gradient have provided important insights into adaptive variation and the threats posed by increased habitat temperature. In what at first view may seem paradoxical, warm-adapted congeners may be more threatened by increased temperatures than their cold-adapted subtidal relatives. This difference stems from two factors: the proximity of current habitat temperatures to upper lethal temperatures (LT50s) and the more limited abilities to acclimate to increases in temperature noted for intertidal species. The physiological determinants of upper thermal limits are certain to be multifarious, but heart function stands out as a key contributor to these limits. In porcelain crabs and turban snails, upper lethal temperatures coincide closely with temperatures of heart failure. Such cardiac effects also could contribute to a shift from aerobic to anaerobic processes of ATP generation, which has been proposed as one important mechanism of death from high and low temperatures [32,33]. Acclimatory response to change in body temperature is likely to be an important determinant of the effects of global warming on species. Temperature-adaptive shifts in gene expression serve as a foundation for physiological acclimation. Changes in the transcriptome differ between long-term, steady-state acclimation and responses to diurnally cycling temperatures. The ability to modulate gene expression requires an openness of DNA structure, which appears to be closely regulated during steady-state and cycling thermal regimes. Being able to modulate the transcriptional competence of DNA in the face of changing temperature, to ensure that necessary shifts in gene expression can occur, may be a fundamental requirement for thermal acclimation. Species such as Antarctic notothenioid fishes that have had a long evolutionary history at constant temperatures may have lost many of the critical gene regulatory responses needed for thermal acclimation and, as a consequence, they may be uniquely vulnerable to global warming. Acknowledgements Portions of these studies were supported by National Science Foundation grant IBN-0133184 and by the David and Lucile Packard Foundation through the Partnership for Interdisciplinary Studies of Coast Oceans (PISCO). This is PISCO contribution #167. ==== Refs Hughes L Biological consequences of global warming: is the signal already apparent? 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==== Front Respir ResRespiratory Research1465-99211465-993XBioMed Central London 1465-9921-6-21563663510.1186/1465-9921-6-2ResearchModulation of epithelial sodium channel (ENaC) expression in mouse lung infected with Pseudomonas aeruginosa Dagenais André [email protected] Diane [email protected] Claudine [email protected] Danuta [email protected] Yves [email protected] Centre de recherche, Centre hospitalier de l'Université de Montréal/ Hôtel-Dieu, Département de médecine, Université de Montréal, Montreal, Quebec, Canada2 Present address: Fonds de solidarité FTQ, Montreal, Quebec, Canada3 Departments of Experimental Medicine and Human Genetics, McGill University, Montreal, Quebec, Canada2005 6 1 2005 6 1 2 2 21 11 2003 6 1 2005 Copyright © 2005 Dagenais et al; licensee BioMed Central Ltd.2005Dagenais et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The intratracheal instillation of Pseudomonas aeruginosa entrapped in agar beads in the mouse lung leads to chronic lung infection in susceptible mouse strains. As the infection generates a strong inflammatory response with some lung edema, we tested if it could modulate the expression of genes involved in lung liquid clearance, such as the α, β and γ subunits of the epithelial sodium channel (ENaC) and the catalytic subunit of Na+-K+-ATPase. Methods Pseudomonas aeruginosa entrapped in agar beads were instilled in the lung of resistant (BalB/c) and susceptible (DBA/2, C57BL/6 and A/J) mouse strains. The mRNA expression of ENaC and Na+-K+-ATPase subunits was tested in the lung by Northern blot following a 3 hours to 14 days infection. Results The infection of the different mouse strains evoked regulation of α and β ENaC mRNA. Following Pseudomonas instillation, the expression of αENaC mRNA decreased to a median of 43% on days 3 and 7 after infection and was still decreased to a median of 45% 14 days after infection (p < 0.05). The relative expression of βENaC mRNA was transiently increased to a median of 241%, 24 h post-infection before decreasing to a median of 43% and 54% of control on days 3 and 7 post-infection (p < 0.05). No significant modulation of γENaC mRNA was detected although the general pattern of expression of the subunit was similar to α and β subunits. No modulation of α1Na+-K+-ATPase mRNA, the catalytic subunit of the sodium pump, was recorded. The distinctive expression profiles of the three subunits were not different, between the susceptible and resistant mouse strains. Conclusions These results show that Pseudomonas infection, by modulating ENaC subunit expression, could influence edema formation and clearance in infected lungs. ==== Body Background The epithelial sodium channel (ENaC) is expressed in epithelial cells of several tissues involved in salt and water reabsorption. The channel is composed of three related subunits (α, β, γ) that are able to reconstitute a functional channel when expressed in Xenopus laevis oocytes [1,2]. ENaC is expressed in a wide range of tissues, including the kidney [1,3-5], distal colon [1,3,5], lung [6-8], ear epithelium [9,10], papilla of the tongue [11-13], eyes [14], chondrocytes [15] and differentiating epithelia [16]. ENaC synthesis and activity are highly regulated by hormones, such as aldosterone, vasopressin and catecholamines, by intracellular pH, feedback inhibition and extracellular proteases [17,18]. In the lung, vectorial Na+ transport from the alveoli to the interstitium is the main force that drives water out of the alveoli [19,20]. This transport mechanism plays a crucial role late in gestation and at birth when sodium transport is involved in lung liquid clearance [21]. Its importance at birth has been shown unambiguously in αENaC gene knockout mice, where the inability to clear lung water rapidly leads to hypoxemia and death [22]. Na+ transport is also important in adults for lung liquid clearance [19,23]. Increased ENaC expression has been detected in the lung and in alveolar epithelial cells in vitro, following stimulation with steroids, β-agonists, catecholamines, and agents that increase cAMP concentration [24-27]. αENaC expression in the lung is modulated at birth when considerable liquid clearance is required [3,6,27]. It is also upregulated during hyperoxia [28,29] and downregulated during hypoxia, which could explain high altitude lung edema (HALE) [30,31]. Several lines of evidence suggest that up-regulation or downregulation of ENaC activity in the lung could be associated with lung infection. In type I pseudohypoaldosteronism, a recessive genetic disease leading to a non-functional ENaC, susceptibility to lung infection has been reported [32-34]. Although ENaC is not the primary defect associated with cystic fibrosis (CF), airway cells from CF patients show a 2–3-fold increase in Na+ transport compared to normal cells [35,36]. This sodium hyperabsorption results from the inability of cystic fibrosis transmembrane regulator (CFTR) in CF cells to downregulate ENaC activity [37,38]. Pseudomonas aeruginosa is a bacterium occuring naturally in a wide range of environments such as in soil, fresh and seawater, plants and decomposing organic matter [39]. Although not usually pathogenic, this common bacterium can evoke opportunistic infections in immunodeficient persons, such as patients with severe burns [39]. Pseudomonas can promote nosocomial lung infection after artificial ventilation [40] and is also present in patients with bronchiectasis [39]. Chronic lung infections are the major cause of morbidity and mortality in CF patients [41] where Pseudomonas aeruginosa is the main source of chronic lung infection in CF patients [42]. Instillation of Pseudomonas aeruginosa in the lung of anaesthetised rabbits has been reported to promote acute pneumonia, resulting in alveolar epithelial injury, loss of epithelial barrier integrity, lung edema, pleural empyema and pleural effusions within 8 h of infection [43]. A more chronic pneumonia model has been developed in the mouse by the intratracheal instillation of P. aeruginosa entrapped in agar beads. In this model, the lungs of susceptible mouse strains develop severe lung infection with a strong inflammatory response and some lung edema [44,45]. Pseudomonas by itself has been shown to inhibit active sodium absorption in cultured airway epithelial cells [46]. Here, we studied its impact on the expression of genes involved in the modulation of liquid absorption in alveolar and airway epithelium, namely the three ENaC subunits and the catalytic subunit of Na+-K+-ATPase. Pseudomonas entrapped in agar beads was instilled in the lung of resistant (BalB/c) and susceptible (DBA/2, C57BL/6 and A/J) mouse strains, and the expression of α, β, γENaC and α1 Na+-K+-ATPase mRNA was studied by Northern blotting in lungs infected between 3 hours and 14 days. Methods Infection of mice with P. aeruginosa Clinical strain 508 of P. aeruginosa (provided by Dr. Jacqueline Lagacé, Université de Montréal, Montreal, Canada) was entrapped in agar beads, and 50-μl suspensions containing 2 × 105 to 1 × 106 CFU/ml were instilled intratracheally in male mice of resistant (BALB/c) or susceptible (DBA/2, C57BL/6 and A/J) strains as described previously [44,45]. Macrophage and polymorphonuclear (PMN) counts in bronchoalveolar lavage (BAL) BAL were performed as described elsewhere with a few modifications [44]. The infected mice were sacrificed by CO2 inhalation at different time points after P. aeruginosa instillation in the lungs. The trachea was cannulated, and the lungs were washed seven times with 1 ml PBS. Total cell counts were conducted in a hemacytometer. Differential cell counts were made by Diff-Quick staining (American Scientific Products) of Cytospin preparations. Number of animals: day 1, n = 6; day 4, n = 16; day 6, n = 6; day 14, n = 3. Northern blotting The lungs from infected mice were harvested between 3 h to 14 days after infection, homogenized in 5 ml of 4 M guanidine isothiocyanate, and centrifuged on a cesium chloride gradient [44]. Fifteen to 20 μg of total RNA purified from the lungs were electrophoresed on 1% agarose-formaldehyde gel and transferred to Nytran membranes (Schleicher & Schuell, Keene, NH, USA) by overnight blotting with 10 X SSC. Hybridization was performed, as reported previously [3], in Church buffer (0.5 M Na phosphate, pH 7.2, 7% SDS (w/v), 1 mM EDTA, pH 8) [47]. The nylon membranes were hybridized successively with different cDNA probes. (αENaC, βENaC and γENaC, α1Na+-K+-ATPase, glyceraldehyde-3-phosphate dehydrogenase (GADPH) or 18S rRNA). To detect αENaC mRNA, the blots were hybridized with 764-bp mouse αENaC cDNA (His-445 to stop codon) [3]. The probes for rat β and γENaC cDNA were gifts from Dr. B.C. Rossier (Institut de pharmacologie et de toxicologie de l'Université de Lausanne, Lausanne, Switzerland) and coded for the entire cDNA [2]. The α1Na+-K+-ATPase probe was a gift from Dr. J. Orlowski (Physiology Department, McGill University, Montreal, Quebec, Canada) and consisted of a NarI-StuI 332-bp fragment coding from nucleotide 89 to 421 (from the 5'UTR to Arg-61) of the rat kidney and brain α isoform [48]. For quantitative study, αENaC mRNA expression was normalized to murine GADPH with a 455 bp cDNA probe cloned between nucleotide 146 and 601 [44] or with 18S rRNA, using a 640-bp cDNA probe between nucleotidet 852 and 1492 of the rat 18S rRNA sequence [26]. The blots were exposed to Kodak Xar-film with an intensifying screen, or to a PhosphorImager (Molecular Dynamics, Sunnyvale, CA, USA) for densitometric analysis. Because different strains of mice were investigated in this study (BalB/c, DBA/2, C57BL/6 and A/J), the expression of the different mRNA was calculated at each time point as the % of expression relative to an untreated control from the same strain. The data from the different strains were pooled and subjected to statistical analysis. Between each round of hybridization, the membranes were stripped by treatment with 0.1 X SSC, 1% SDS and 2.5 mM EDTA at 95°C. The blots were allowed to cool gradually with agitation for 30 min at room temperature. The membranes were then rinsed with 5 X SSC and rehybridized. Number of animals: n = between 6 and 8 animals for each time point and each mRNA studied. Statistics For the BAL cell count, the data are presented as means ± SE (standard error). For ENaC and Na+, K+-ATPase mRNA expresion, the comparisons between groups were analyzed by Wilcoxon signed rank non-parametric test using Statsview software (SAS Institute, Inc., Cary, NC, USA). Probability p values < 0.05 were considered to be significant. Results Inflammation in mice infected with P. aeruginosa The inflammation process evoked by Pseudomonas instillation in the lung of C57BL/6 mice was monitored by studying the number of total cells in BAL at different times after infection. As shown in Figure 1, the inflammation process was more pronounced on days 1 and 4 post-infection. Significant PMN recruitment was noted on day 1 after infection since these cells constituted 90% of the cell population in BAL at that time (Fig. 1). The proportion of PMN decreased gradually over time. On days 6 and 14, there was a significant reduction of PMN in BAL (p < 0.05) compared to day 1. PMN still constituted 18% of the cells in BAL on day 14. The infection also led to modulation in the number of macrophages with a significant increase (p < 0.05) on day 4 post-infection (Fig. 1). Figure 1 Differential cell counting in C57BL/6 mice infected with 1–2 × 105 Pseudomonas aeruginosa embedded in agar beads. Pseudomonas infection leads to strong inflammation with recruitment of PMN and macrophages in bronchoalveolar lavage on days 1 and 4 post-infection. Day 1, n = 6; day 4, n = 16; day 6, n = 6; day 14, n = 3. Differential cell counting: PMN in light grey, macrophages in dark grey. Modulation of α, β and γENaC expression following lung infection with P. aeruginosa Pseudomonas embedded in agar beads was administered intratracheally in resistant (BALB/c) and susceptible strains of mice (DBA/2, C57BL/6, A/J) as described previously [44,49]. α, β and γENaC expression in infected lungs was measured by Northern blot hybridization (Fig. 2). Expression of the three subunits was highly modulated in time after lung infection, but showed a similar pattern between the four mouse strains tested. The BALB/c strain that is resistant to Pseudomonas infection [45], as well as the DBA/2, C57BL/6 and A/J susceptible strains, showed increased α, β and γENaC expression at 24 h, followed by a decrease on day 3 post-infection. The GADPH standard gene did not manifest any modulation of its expression. Densitometric quantitative analyses of the Northern blots were performed for the four mouse strains. The relative expression at each time point was determined relative to uninfected animals of the same strain and the data from the 4 strains were pooled for analysis (Fig. 3). αENaC mRNA expression presented a significant decline to a median of 43% on days 3 and 7 post-infection, and was still decreased to a median of 45% on day 14 post-infection compared to uninfected controls (p < 0.05, Fig. 3). βENaC mRNA expression was increased to a median of 241% of uninfected control values, 24 h post-infection (p < 0.05), and was followed by a decrease to medians of 42% and 54% on day 3 and 7 post-infection (p < 0.05) (Fig. 3). Although the expression of γ ENaC mRNA showed an expression pattern very similar to the α and βENaC subunits, with an increased expression at 24 h (median of 171%) followed by a decreased expression on day 3 (median of 53%) and 7 (median of 66%) of infection, these changes failed however to reach significance (Fig. 3). No modulation of α, β or γENaC mRNA was detected when the lungs were instilled with agarose beads only (data not shown). We also investigated the expression of α1 Na+-K+-ATPase mRNA coding for the catalytic domain of the sodium pump, but could not find any significant change during infection (Fig. 3). Figure 2 Expression of α, β and γENaC mRNA in the lung following infection with Pseudomonas aeruginosa. Representative Northern blot of α, β and γENaC mRNA expression following infection with Pseudomonas in resistant (BalB/c) and susceptible (DBA/2, C57BL/6 and A/J) strains of mice. There is a characteristic modulation of the three ENaC subunits that is not different between strains. Figure 3 Densitometric analysis of the modulation of αENaC, βENaC, γENaC and α1Na+-K+-ATPase mRNA following Pseudomonas infection. The modulation of αENaC, βENaC, γENaC and α1Na+-K+-ATPase mRNA by Northern blots hybridization was subjected to a densitometric analysis. Because different strains of mice were investigated in this study (BalB/c, DBA/2, C57BL/6 and A/J), the expression of the different mRNA was calculated at each time point as the % of expression relative to an untreated control coming from the same strain. The α and βENaC mRNA were modulated at some time point by Pseudomonas infection compared to uninfected animals. There was no modulation for γENaC or α1Na+-K+-ATPase mRNA. αENaC mRNA was downregulated compared to uninfected controls on days 3, 7 and 14 post-infection (, p < 0.05). βENaC mRNA was elevated at 24 h post-infection (, p < 0.05) compared to uninfected controls and was downregulated thereafter on day 3 and 7 post-infection (*, p < 0.05). Number of animals: n = between 6 and 8 animals for each time point and each mRNA studied. Discussion The instillation of Pseudomonas enmeshed in agarose beads in the lung is a good model to study lung inflammation [44,45] and lung injury [43] secondary to an infection. For this study, P. aeruginosa enmeshed in agarose beads was instilled into the mouse lung because the model allows the development of chronic lung infection in susceptible mouse strains [44,45]. The infection leads to cellular infiltration and alveolar edema that stand on day 3 post-infection and that can be still demonstrated on day 14 post-infection in Pseudomonas-susceptible mouse strains [45]. Because the lung inflammation associated with Pseudomonas infection is accompanied by lung injury [43], and because we have shown recently that ENaC expression can be modulated under conditions that promote lung injury [50], we tested here if Pseudomonas was affecting the mRNA expression level of the three ENaC subunits as well as the catalytic subunit of the Na+ pump since these elements are involved in lung liquid balance across the alveolar epithelium [19,23]. The results reported here indicate that Pseudomonas infection modulated the expression of the three ENaC mRNA with a characteristic pattern. There was no significant difference, however, in the expression profile of ENaC mRNA between the Pseudomonas-resistant (Balb/C) and -susceptible (DBA/2, C57BL/6, A/J) mouse strains. Modulation of ENaC expression is therefore most likely not a genetic marker linked to the susceptibility of mouse strains to establishment of a chronic infection with Pseudomonas. Pseudomonas infection affected ENaC mRNA with a pattern consisting of increased expression at 24 h, followed by a marked decrease on day 3 post-infection. The change in ENaC mRNA was related to bacterial infection, since agarose beads alone failed to evoke any modulation of these RNA. The three ENaC subunits were modulated with a similar profile, with some noticeable differences, however. Although αENaC mRNA expression tends to increase by day 1, the most noticeable feature brought by Pseudomonas infection to αENaC mRNA was the significant decreases after 3 days, 7 days and 14 days post-infection. To the best of our knowledge, this is the first report demonstrating that bacterial infection in vivo can lead to modulation of ENaC mRNA expression. Recently, αENaC mRNA expression was found to be downregulated in the mouse lung after 7 and 14 days of adenoviral infection [51]. Furthermore, there is some evidence that αENaC expression is also decreased in other models of lung injury. Folkesson et al. [52] reported a decline in ENaC expression following subacute lung injury, 10 days after intratracheal administration of bleomycin. More recently, we recorded a decrease in ENaC expression after ischemia-reperfusion lung injury [50]. All these results, and the results reported in the present report, suggest that the modulation of ENaC expression associated with lung infection could be a widespread mechanism, not specific to a given pathogen or injury process, but a general response of the lung to inflammation and injury. The β ENaC subunit was also modulated by Pseudomonas infection. There was a significant increase in the mRNA expression on day 1 post-infection, followed, as for αENaC, by a decreased expression on day 3 and day 7 post-infection. Different stoichiometries have been proposed for ENaC. One model suggests a 2α, β, γ ratio [53,54] whereas others postulate an octomeric [55] or nonameric structure [55,56]. Although the expression of the α subunit alone is sufficient to allow ENaC activity [1], the three subunits are needed to get a fully functional channel [2]. The expression of the three subunits in Xenopus laevis oocytes increases amiloride-sensitive Na+ current by 100% compared to αENaC alone [2]. The α, β and α, γ channels are 20 times less effective in driving amiloride-sensitive current than the native channels and show differences in their biophysical properties [57,58]. Gene inactivation or over-expression of the different ENaC subunits has revealed important differences in the role each subunit plays in lung liquid management. αENaC knockout mice develop respiratory distress and die within 40 h from birth because of their inability to clear lung liquid [22]. Lung liquid clearance at birth is also slower in γENaC knockout mice [59], but is not affected in βENaC knockouts [60]. Increased transgenic expression of βENaC targeted in the airway epithelia, but not α or γ subunits, showed an increase Na+ transport across the airway epithelium and a reduced height of the airway surface liquid [61]. For all these reasons, it is difficult to predict how the modulation of ENaC mRNA expression and its effect on the ratio of the three subunits, would have an impact on ENaC activity. In addition, ENaC mRNA content also does not necessarily reflect the amount of active channel at the membrane. One thing seems clear however, because of its prominence in the lung, the diminution of αENaC expression that we detected in the lung following Pseudomonas infection, could certainly influence amiloride-sensitive current and lung liquid clearance as in αENaC KO mice rescued by transgenic expression of αENaC that has a lower expression of ENaC in the lung [62,63]. In such model, there is a reduced ENaC current in tracheal cells [63], and a much slower lung liquid clearance following thiourea or hyperoxia-induced lung edema [64]. The general biphasic modulation of ENaC mRNA expression with an increase at 24 h followed by a decrease thereafter is an interesting finding that could explain some contradictory reports concerning ENaC expression in lung following Pseudomonas infection. Acute bacterial pneumonia in rats has been shown to increase alveolar epithelial fluid clearance [65,66] when in late pneumonia, there is a decrease in the lung liquid clearance ability of the lung [66]. These contradictory results could be well explained by the modulation of ENaC expression reported here. The long term ENaC downregulation by Pseudomonas infection could be of potential clinical significance to understand the slow improvement in some ARDS patients. In contrast to α and β ENaC, α1 Na+-K+-ATPase mRNA was unaffected in the course of lung infection. This is similar to what has been reported during adenovirus lung infection [51] where αENaC, aquaporin 1 (AQP1) and AQP5 mRNA show decreased expression, but not α1 Na+-K+-ATPase. In ischemia reperfusion injury, there was also no modulation of α1 Na+-K+-ATPase expression despite significant ENaC downregulation [50]. These results, as well as the data reported here, suggest that the inflammatory process seems to selectively affect, and not in a non-specific way, some elements of lung liquid clearance. It would be difficult at this time to speculate on the reasons for this modulation. Na+-K+-ATPase is an important element in lung liquid clearance, however, by being one of the key generator of membrane potential, the enzyme also affects other channels and ion transport process. It is possible that by modulating ENaC expression and not α1 Na+-K+-ATPase, Pseudomonas infection alters the Na+ transport system but does not change other important cell functions meditated by Na+-K+-ATPase. Furthermore, despite a similar mRNA expression level, there could be a fall in protein content or activity of the sodium pump. Additional experiments are necessary to answer this question. Several studies report that in lung epithelial cells, viral infection [67,68], mycoplasma [69], bacterial infection [70,71], and inflammatory cytokines such as tumor necrosis factor-α (TNF-α) [70,72], interleudin-1β (IL-1β) [73], or TGF-β [74] decrease the expression of water channels, such as AQP1 and AQP5 and reduce the short circuit current generated by cells. Adenoviral lung infection in mice results in pulmonary inflammation and lung edema with lowered expression of AQP1, AQP5 and αENaC [51]. All these data, including the results presented here, suggest that lung inflammation, by decreasing the expression of αENaC and water channels, could hamper the liquid clearance ability of the lungs and favour edema formation. Conclusions We have shown in this report that Pseudomonas infection modulates ENaC mRNA expression. This modulation is independent of mouse strain susceptibility to establishment of chronic infection with Pseudomonas. Although there is an elevation of ENaC expression after 24 h, the most important feature is probably the long-lasting decrease of αENaC transcripts on days 3 and 7 post-infection. The lung inflammation induced by Pseudomonas infection therefore seems to favour a reduction in the expression of an essential element involved in lung liquid clearance as well as the regulation of airway surface liquid volume. Authors' contributions AD performed the hybridization, the statistical analysis of the blots and wrote the manuscript. DG performed the Pseudomonas instillation, RNA extraction and Northern blotting of RNA sample. The BAL recovery as well as PMN and macrophage counting was performed by CG. YB and DR designed and co-ordinated the study. All authors read and approved the final manuscript. Acknowledgements Dr Yves Berthiaume and Dr. Danuta Radzioch are Chercheur-Nationaux from Fonds de la recherche en santé du Québec. This work was supported in part by the Canadian Cystic Fibrosis Foundation and the Canadian Institutes of Health Research. 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==== Front Ann Clin Microbiol AntimicrobAnnals of Clinical Microbiology and Antimicrobials1476-0711BioMed Central London 1476-0711-4-21563893410.1186/1476-0711-4-2ResearchIn vitro activity of vancomycin, quinupristin/dalfopristin, and linezolid against intact and disrupted biofilms of staphylococci El-Azizi Mohamed [email protected] Suma [email protected] Termkiat [email protected] Nancy [email protected] Division of Infectious Diseases, Southern Illinois University School of Medicine, Springfield, IL 62794, USA2005 7 1 2005 4 2 2 30 9 2004 7 1 2005 Copyright © 2005 El-Azizi et al; licensee BioMed Central Ltd.2005El-Azizi et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Shed cells or disrupted parts of the biofilm may enter the circulation causing serious and very hard to treat biofilm-associated infections. The activity of antimicrobial agents against the shed cells/disrupted biofilms is largely unknown. Methods We studied the in vitro susceptibility of intact and disrupted biofilms of thirty clinical isolates of methicillin-resistant and methicillin–susceptible Staphylococcus aureus (MRSA and MSSA) and Staphylococcus epidermidis to vancomycin, quinupristin/dalfopristin, and linezolid and compared it to that of the suspended (planktonic) cells. Results Bacteria in the disrupted biofilms were as resistant as those in the intact biofilms at the minimum inhibitory concentrations of the antibiotics. At higher concentrations, bacteria in the disrupted biofilms were significantly (P < 0.001) less resistant than those in the intact biofilms but more resistant than the planktonic cells. Quinupristin/dalfopristin showed the best activity against cells of the disrupted biofilms at concentrations above MICs and vancomycin, at 500 and 1,000 μg/ml, was significantly more active against the biofilms of MRSA and S. epidermidis Conclusion The difficulty of treating biofilm-associated infections may be attributed not only to the difficulty of eradicating the biofilm focus but also to the lack of susceptibility of cells disrupted from the biofilm to antimicrobial agents. Biofilmdisrupted biofilmvancomycinquinupristin/dalfopristinlinezolid. ==== Body Introduction Gram-positive infections have become a serious problem, especially in the nosocomialsetting, and treatment of these infections is complicated by the emergence of multidrug-resistant pathogens [1]. Infections caused by Staphylococcus aureus and Staphylococcus epidermidis are among the most frequent causes of both healthcare-associated and community-onset infections [2]. Staphylococci cause a large percentage of infections by forming biofilms on medical implants, damaged tissues, and most commonly on indwelling vascular catheters [3-7]. Biofilm-associated infections are becoming more common, and occur largely because of the increase in the use of indwelling medical devices. Central venous catheters (CVCs), for example, are inserted in more than 20 million hospitalized patients in the United States alone each year [8]. The mortality rate due to CVC-related bloodstream infections is estimated to be 12–25%, with additional healthcare costs in the order of $33,000 to $35,000 per event [9]. The predominant microorganisms associated with CVC-related infections are Staphylococcus epidermidis and Staphylococcus aureus where they are often found in biofilms upon removal of the devices [10-12]. Biofilm associated infections are difficult to treat due to the inherent antibiotic resistance of the sessile bacteria [4,6,13]. A number of factors contribute to this resistance such as a slow growth rate [14,15], failure of the agent to penetrate the biofilm [16,17], physiological changes and gene expression or repression due to the biofilm mode of growth [18,19]. Other factors such as age of the biofilms [20], production of extracellular polymeric substance (EPS) [21-23], and presence of biomaterials [24] also play a role in decreasing susceptibility of the bacteria within the biofilms to antimicrobial agents. Routinely, the diagnostic laboratories report the susceptibilities done on planktonic bacteria only. Although many studies have focused on the antimicrobial susceptibility of bacteria grown in biofilms [25-28], none of these studies included bacteria that disrupted from the biofilms. Disruption of the biofilm can occur during the removal of colonized catheters or during fluid infusion through them. The result is the entrance of bacteria or groups of bacteria shed from the biofilm into circulation causing bloodstream infections. These bacteria may not belong to either the planktonic or the biofilm phase and consequently may have a different pattern of antimicrobial susceptibility. For this reason, we studied the in vitro susceptibility of intact and disrupted biofilms of MRSA, MSSA and S. epidermidis to vancomycin, quinupristin/dalfopristin, and linezolid and compared it to the susceptibility patterns of the same bacteria in suspension. 1. Materials and Methods Unless otherwise indicated, all chemicals (analytical grade) were purchased from Sigma Chemical Co., St. Louis, Missouri, USA. Antibiotics Vancomycin (VAN) was purchased from Sigma Chemical Co., St. Louis, Missouri, USA. Quinupristin/dalfopristin (Q/D) was provided by Rhone-Poulenc Rorer, Collegeville, PA, USA and Linezolid (LNZ) was provided by Pharmacia & Upjohn, Kalamazoo, MI, USA. Microorganisms Ten isolates each of MRSA, MSSA, and S. epidermidis were used in this study. The microorganisms are clinical isolates from patients with blood stream infections which were provided by the microbiology laboratories at St. John's Hospital, Springfield, Illinois. These isolates were screened for biofilm formation on polystyrene microliter plates as previously described [27]. Antimicrobial susceptibility in suspension The minimum inhibitory concentrations (MICs) of the antibiotics were determined by using the broth microdilution technique as described by the National Committee for Clinical Laboratory Standards (NCCLS) [29]. The minimum bactericidal concentrations (MBCs) were determined by mixing the contents of each well at MIC and higher concentrations. Ten-microliter portions were then taken from each well and streaked onto the surface of blood agar. After 24 h incubation, the number of colony forming units per milliliter (CFU/ml) were counted and the MBCs, defined as the concentration at which 99.9% of bacteria was killed, were determined. The MIC90 and MBC90 obtained in susceptibility testing on planktonic bacteria were used in interpreting the results of the experiments with intact and disrupted biofilms. Biofilm formation and quantification To form biofilms, 100 μl portions of Tryptic Soy Broth (TSB) (Difco laboratories, Detroit, MI, USA) containing 1 × 106 CFU/ml of the microorganisms were delivered to flat bottom 96 polystyrene plates (Falcon No. 353072, Becton Dickinson and Company, Franklin Lakes, NJ, USA). After 24 h incubation at 37°C, the supernatants were aspirated and the remaining biofilms were washed twice with distilled water. TSB with or without the antibiotics at MIC values or at 50,500, or 1,000 μg/ml was added to the wells. Biofilms in the plates used for disrupted wells were then dislodged by using sterile wooden sticks and all plates were incubated again for 24 h. Plates with disrupted biofilms were centrifuged at 3,000 rpm for 15 min. to sediment the biofilm particles and all plates were then cautiously aspirated. The intact biofilms and the sediments of the disrupted biofilms were then determined by using a modified colorimetric assay previously described by Roslev & King [30]. On this assay, bacteria with an active electron transport system reduce the tetrazolium salt (redoxdye) to water soluble orange formazan product. Briefly, 100 μl lactate Ringers solution containing tetrazolium sodium 3'-{1- [(phenylamino)-carbonyl]-3,4-tetrazolium}-bis (4-methoxy-6-nitro) benzene sulfonic acid hydrate (XTT) (0.5 gm/L) and menadione (1 μM) was added to the intact biofilms and the disrupted biofilm sediments. The contents of the plates were mixed via plate shaker (Lab-Line Instruments Inc., Melrose Park, IL, USA) for 5 min followed by incubation for 1 hat 37°C in the dark. Plates with disrupted biofilm were first centrifuged for 15 min at 4°C, and the supernatants containing the soluble colored formazon were transferred to new plates. The intensity of the color was then measured via micro plate reader (Multiscan Plus, Thermosan Systems, Finland) at 490 nm and compared to that of drug-free wells. For plates with intact biofilms, the intensity of the color of the soluble formazan was measured directly and compared to drug-free wells. Confocal Scanning Laser Microscopy (CSLM) One-milliliter portions of TSB containing 1 × 106 CFU/ml of S. epidermidis isolate (SE6) were used to inoculate sterile plastic cover slips placed in a 4 well multidish (Nunc No. 176740, Roskilde, Denmark). After 24 h incubation at 37°C, the cover slips were moved to new plates and washed twice with distilled water. Fresh TSB (1 ml) with 500 μg/ml of vancomycin was added to the wells. Biofilms on cover slips designed to study the disrupted biofilms were then carefully dislodged. After incubation for another 24 h, the biofilms (intact and disrupted) were stained with LIVE/DEAD BacLight bacterial viability stain (Molecular Probes, Eugene, OR, USA) following manufacturer's instructions. The biofilms (intact and disrupted) were then examined by Olympus Fluoview CSLM (model IX 70, Olympus America Inc. NY, USA). In another set of experiments, the susceptibility of planktonic cells was examined by growing the bacteria in 1 ml portions of TSB. After 24 h, the bacterial suspensions were centrifuged at 10,000 rpm for 10 minutes, washed twice with sterile distilled water and finally dispersed in 1 ml portions of fresh TSB with the antibiotic. After another 24 h of incubation, the bacteria were stained and examined as previously mentioned. Statistical Analysis The mean and S.D. were calculated from the results of 10 isolates of each of the Staphylococcal species. One-way analysis of variance (ANOVA) was used to determine the differences between various antibiotic treatments. Tukey's pair comparison test was used at the chosen level of probability (P < 0.05) to determine significance difference between means. Results In suspensions, all isolates were susceptible to all antibiotics tested (Table 1). At MICs, the antibiotics showed very little effect on the viability of bacteria within the biofilms (intact or disrupted). At higher concentrations (50,500 and 1000 μg/ml), the biofilms of all isolates were significantly (P < 0.001) less susceptible to the antibiotics compared to disrupted biofilms (Figures 1, 2, 3). Linezolid was less active than quinupristin/dalfopristin and vancomycin in killing the bacteria, especially in the biofilms. Table 1 Susceptibility of the tested isolates to vancomycin, quinupristin/dalfopristin and linezolid in suspension. Microorganism a Antimicrobial agents (μg/ml) Vancomycin Quinupristin/dalfopristin Linezolid Methicillin-susceptible S. aureus (MSSA) MIC Range 0.50–1 0.125–0.25 1–2 MIC90 1 0.25 2 MBC Range 8–16 8 >64 MBC90 8 8 >64 Methicillin-resistant S. aureus (MRSA) MIC Range 0.5–1 0.25–50 1–2 MIC90 1 0.50 2 MBC Range 2–16 4–16 >64 MBC90 8 16 >64 S. epidermidis MIC Range 2–4 0.06–2 0.50–1 MIC90 2 0.50 1 MBC Range 2–16 0.25–16 >64 MBC90 8 8 >64 a The MIC defined as the minimum concentration of antibiotic at which growth was completely inhibited while the MBC defined as the minimum concentration at which 99.9% of bacteria was killed. Figure 1 Susceptibility of intact and disrupted biofilms of MSSA, MRSA, and S. epidermidis at different concentrations of vancomycin. Figure 2 Susceptibility of intact and disrupted biofilms of MSSA, MRSA, and S. epidermidis at different concentrations of quinupristin/dalfopristin. Figure 3 Susceptibility of intact and disrupted biofilms of MSSA, MRSA, and S. epidermidis at different concentrations of linezolid. Quinupristin/dalfopristin showed the best activity against cells of the disrupted biofilms at concentrations above MICs. It was also more active than vancomycin against biofilms of both S. aureus and S. epidermidis at 50 μg/ml. Vancomycin at 500 and 1,000 μg/ml, was significantly more active against the biofilms of MRSA and S. epidermidis but not MSSA. Killing of the bacterial cells in intact or disrupted biofilms by quinupristin/dalfopristin and linezolid was independent of antibiotic concentrations over the range of 50–1,000 μg/ml, but for vancomycin, this was observed at higher concentration range (500–1,000 μg/ml). The ratios of viability of disrupted biofilms to that of intact biofilms were calculated for the isolates with each antibiotic concentration (Figure 4). The ratio values were similar for the three antibiotics at MICs. At other concentrations, the highest viability ratio was observed with linezolid and the lowest with quinupristin/dalfopristin. Figure 4 Viability ratios of MSSA, MRSA, and S. epidermidis of disrupted to intact biofilms at different concentrations of vancomycin, quinupristin/dalfopristin and linezolid. CSLM (Figure 5) demonstrated resistance of the intact biofilms to vancomycin, indicated by large number of viable cells, and resistance of the disrupted biofilm compared to the planktonic cells. It is also clear that the disrupted biofilm consists of clumps of larger size compared to that of the planktonic cells. Figure 5 CSLM images of S. epidermidis (SE6) intact biofilms (A), disrupted biofilm (B) and planktonic cells (C) on plastic coverslips after incubation for 24 h with 500 μg/ml of vancomycin. The bacterial cells were stained with LIVE/DEAD BacLight bacterial viability stain to directly visualize the effects of the antibiotic. The green fluorescence reflects processing of the dye by metabolically active cells while the red fluorescence is characteristic of dead cells. Note that while the green fluorescence was considerably more prominent in the intact biofilm image, the disrupted biofilm does display more green fluorescence than the planktonic cells. Also, note that the disrupted biofilm consists of large clumps and aggregates compared to the typical clusters of planktonic cells. Discussion Coagulase-negative staphylococci and S. aureus (mostly methicillin-resistant) are among the leading causes of nosocomial blood stream infections in the USA [31] with a crude mortality of 21–25% respectively [32]. S. aureus accounted for up to 13% of isolates recovered from patients with nosocomial infections from 1979 through 1995, and the percentage has increased in recent years [33,34]. It has been estimated that 65% of nosocomial infections are biofilm associated [5,35]. S. epidermidis is a common cause of blood stream infections associated with indwelling medical devices. S. aureus, in addition to causing blood stream infections, is a significant cause of tissue infections such as pneumonia and osteomyelitis [5]. Three phases of bacteria were used in this study; planktonic, biofilms and disrupted biofilms. Planktonic cells were used for determination of MICs and MBCs. This phase of bacteria has routinely been used as gold standard for determination of susceptibility of bacteria and prediction of clinical efficacy of antimicrobial agents. Planktonic cells grown in in vitro batch cultures are usually in nutrient-rich medium. Bacteria grown in biofilms differ greatly from the same organisms grown in suspensions by having different growth characteristics and taking up nutrients and drugs differently [36,37]. When a biofilm is at steady state, cells are shed from it at a constant rate [38]. These cells may enter into circulation and cause blood stream infection. Biofilm associated infections are 10 to 1,000 times more resistant to the effects of antimicrobial agents [5,35,39]. Bacteria are shed through biofilm disruption, which may result in entrance of biofilm pieces into circulation causing systemic infections. The question that needed to be answered is whether these cells behave as planktonic cells or as biofilm in regards to susceptibility to antimicrobial agents. We used 24 h incubation for interaction with antimicrobial agents in keeping with the procedures followed in diagnostic laboratories. Although it is possible that during this long incubation period the disrupted biofilm could have re-adhered to the polystyrene, we believe this was not the case. This is based on finding in our and other laboratories that antimicrobial agents are able to prevent the adherence of bacteria when exposed to them prior to formation of the biofilm [25-27] All isolates in suspension were susceptible to the antibiotics as determined by NCCLS guidelines. Vancomycin and quinupristin/dalfopristin were capable of killing 99.9 % of the bacteria in suspension at concentrations up to 16 μg/ml, while linezolid, a bacteriostatic antibiotic, did not show such effect even at the maximum concentration used. At the MICs, the antibiotics exerted little effect on the viability of the intact and disrupted biofilms. This was expected because even in suspension the MIC90s were 4–8 times lower then the MBC90s for vancomycin and 16–32 times lower for quinupristin/dalfopristin. At higher concentrations, although the intact biofilms were significantly more resistant than the disrupted biofilms, the recalcitrance of the latter was clear. The antibiotic concentrations were 100–4,000 times the MIC90s for quinupristin/dalfopristin and 25–1,000 times the MIC90s for vancomycin and linezolid. None of these concentrations were able to kill 99.9 % of the microorganisms in the disrupted phase. This was further demonstrated by examining the viability of S. epidermidis cells in the three phases in the presence of vancomycin by using CSLM. The susceptibility of the disrupted biofilm lies between the highly resistant biofilm and the susceptible planktonic cells. This may be attributed to the fact that disrupted biofilms consist of fragments that may retain some features of the intact biofilms. It is obvious from the images that the disrupted biofilm consists of large clumps and aggregates compared to the typical clusters of planktonic cells. The semi quantitative assessment of viability by CSLM was similar to those obtained by the colorimetric assay. Killing of the bacterial cells in intact or disrupted biofilms by quinupristin/dalfopristin and linezolid was independent of antibiotic concentrations over the range of 50–1000 μg/ml and over a higher range of vancomycin concentrations (500–1,000 μg/ml). Hamilton-Miller & Shah [40] found that killing of S. epidermidis in the biofilm by quinupristin/dalfopristin was independent of antibiotic concentrations over the range of 20–200 times the MIC. This lack of dose response at high concentrations favors the hypothesis that the bacteria disrupted from the biofilm are more or less similar to those in the biofilm rather than the bacteria in suspension. For better comparison, the viability ratios of the disrupted to the intact biofilms were calculated at different concentrations of the antibiotics. Linezolid was less efficient in killing bacterial cells in intact or disrupted biofilms which explains its highest viability ratio. Quinupristin/dalfopristin, with the lowest viability ratio, was more active against the cells of the disrupted biofilms at concentrations above MICs for both S. aureus and S. epidermidis. It was also more active than vancomycin against biofilms of both S. aureus and S. epidermidis at 50 μg/ml. On the other hand, vancomycin at 500 and 1,000 μg/ml, was significantly more active against the biofilms of MRSA and S. epidermidis but not MSSA. It has been reported that vancomycin accumulates at high concentration in the biofilms of gram positive bacteria especially S. epidermidis compared to linezolid [41]. This may be attributed to the ability of glycopeptides to bind to exopolysaccharides produced by the bacteria. However, such high concentrations of vancomycin or quinupristin/dalfopristin are not achievable in clinical practice. In general, our data show that Quinupristin/dalfopristin is more active than vancomycin and linezolid against the disrupted biofilms and there was no difference between methicillin-susceptible and methicillin resistant staphylococci. We conclude that the difficulty in treating the infections related to indwelling medical devices may not be only due to lack of eradication of the cells in the biofilm phase, but also due to resistance of bacteria disrupted from the biofilm. Authors' contributions ME did the biofilm work and participated with NK in the design of the study, review and interpretation of the data and discussion. SR and TK participated in the determination of MICs and MBCs of the antibiotics. All authors read and approved the final manuscript. Acknowledgements The authors would like to gratefully acknowledge Lori A. Koch and Nancy M. 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==== Front Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-4-41564413810.1186/1475-925X-4-4ResearchA case study of type 2 diabetes self-management Wu Hsin-i [email protected] Department of Biomedical Engineering, Texas A&M University, College Station, Texas, 77843-3120 USA2005 11 1 2005 4 4 4 3 12 2004 11 1 2005 Copyright © 2005 Wu; licensee BioMed Central Ltd.2005Wu; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background It has been established that careful diabetes self-management is essential in avoiding chronic complications that compromise health. Disciplined diet control and regular exercise are the keys for the type 2 diabetes self-management. An ability to maintain one's blood glucose at a relatively flat level, not fluctuating wildly with meals and hypoglycemic medical intervention, would be the goal for self-management. Hemoglobin A1c (HbA1c or simply A1c) is a measure of a long-term blood plasma glucose average, a reliable index to reflect one's diabetic condition. A simple regimen that could reduce the elevated A1c levels without altering much of type 2 diabetic patients' daily routine denotes a successful self-management strategy. Methods A relatively simple model that relates the food impact on blood glucose excursions for type 2 diabetes was studied. Meal is treated as a bolus injection of glucose. Medical intervention of hypoglycaemic drug or injection, if any, is lumped with secreted insulin as a damping factor. Lunch was used for test meals. The recovery period of a blood glucose excursion returning to the pre-prandial level, the maximal reach, and the area under the excursion curve were used to characterize one's ability to regulate glucose metabolism. A case study is presented here to illustrate the possibility of devising an individual-based self-management regimen. Results Results of the lunch study for a type 2 diabetic subject indicate that the recovery time of the post-prandial blood glucose level can be adjusted to 4 hours, which is comparable to the typical time interval for non-diabetics: 3 to 4 hours. A moderate lifestyle adjustment of light supper coupled with morning swimming of 20 laps in a 25 m pool for 40 minutes enabled the subject to reduce his A1c level from 6.7 to 6.0 in six months and to maintain this level for the subsequent six months. Conclusions The preliminary result of this case study is encouraging. An individual life-style adjustment can be structured from the extracted characteristics of the post-prandial blood glucose excursions. Additional studies are certainly required to draw general applicable guidelines for lifestyle adjustments of type 2 diabetic patients. ==== Body Background It is well established that diabetes can lead to acute and chronic complications, compromising the health and quality of life. Results from various studies [1] have demonstrated that improved control of blood glucose in type 2 diabetes reduces related complications. Type 2 diabetes results from the metabolic problem that is related to certain tissue resistance to insulin action and to the inability of the pancreas to appropriately regulate the quantity of insulin for glucose metabolism. These metabolic abnormalities lead to the many complications of diabetes. Type 2 diabetes historically occurs predominantly in adults aged 40 and over. A recent trend, however, indicates that children and adolescents of minority ethnic groups, especially in African Americans and American Indians, are increasingly susceptible to type 2 diabetes [2]. With the prevalence of type 2 diabetes and its associated risk for serious complications, issues related to proactive self-management become an urgent concern. Dietary management is frequently referred as the cornerstone, or the initial step, in treating of type 2 diabetes mellitus. Foods containing carbohydrates play an important role in the diet. The glycemic Index (GI) ranks foods according to their post-prandial glycemic responses. The GI was introduced more than twenty years ago and has been widely adopted in diabetes management in Australia, New Zealand, Canada, the United Kingdoms, and France [3]. The World Health Organization states that it is important to consider the GI in constructing a healthful diet because low GI foods help control blood sugar levels by producing minimal fluctuations in blood glucose [4]. For diabetic patients, choosing low GI foods is particularly important because consumption of high GI foods often results in far more exaggerated glycemic responses, creating a need for drug or insulin therapy [3,5]. Most published GI lists are for single food items only. A GI is a numerical measure of how a carbohydrate would increase one's blood glucose level over a period of two (for normal) or three hours (for diabetic patients) after eating [6,7]. The area of elevated blood glucose level from the baseline (the pre-prandial measure) is expressed as a percent of the area for the same amount of a reference carbohydrate such as a pure glucose or a white bread (usually 50 g) [8,9]. To plan a complete meal using the weighted mean [6] for various food items is not only tedious, but also impractical. Diet exchange lists are usually recommended for diabetic patients to use in formulating a sensible meal plan. However, an exchange list is not always convenient to use. Moreover, there is a lack of ethnic diet exchange lists. For a member of an ethnic minority to follow a diet exchange list, he or she must prepare his or her own meal away from the rest of the family. Nutall and Chasuk [10] have stressed that dietary recommendations for type 2 diabetes should be flexible and highly individualized, yet most of the prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities. When diet alone cannot effectively control the type 2 diabetic conditions, medical interventions, such as insulin injections or dispensing hypoglycaemic pills, are usually the next step of managing type 2 diabetes mellitus. Medical interventions notoriously exacerbate the fluctuation of blood glucose excursions. Even with the smallest dosage of hypoglycaemic drug (5 mg glucotrol or glyburide) once in the morning, the subject of this study still experienced frequent acute hypoglycaemias. Besides, his A1c levels hovered around 6.5 levels for many years following his physician's advice of taking 5 mg glucotrol per day. It became obvious that a properly designed drug dispensing regimen was needed to avoid hypoglycaemic bouts and effectively reduce A1c levels. Fasting blood glucose measurements are not consistent indicators, fluctuating widely from a low of 70 mg/dL to a high of 200 mg/dL (with most frequent range lay between 90 to 150 mg/dL) that were experienced by this type 2 diabetic subject prior to the model-based lifestyle adjustment. Initially, the subject tried to adjust lifestyle based on fasting glucose measurements, but it was not successful. His A1c measurements crept from 6.3 to 6.7 in a year. As glucose binds irreversibly to haemoglobin molecules within red blood cells, the amount of glucose that is bound to haemoglobin is directly tied to the concentration of glucose in the blood. The average life span of erythrocytes is about 120 days [11], measuring the amount of glucose bound to haemoglobin – by the A1c measurement – can provide an estimate of average blood sugar level during the 3 to 4 months period. It is obvious that A1c is a more reliable indicator than fasting glucose measurements for an effective blood glucose control self-management. It has been established that exercise can effectively alleviate diabetic conditions. Although no rigorous investigation has been performed here, nor is the focus of this current study, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these, prior to a meal or 3 to 4 hours after a meal, can significantly depress the volunteer's post-prandial blood glucose levels. However, it is impractical to substitute hypoglycemic pills with a multiple daily exercise schedule. A sensible lifestyle adjustment is required to manage the diabetic conditions without altering much of daily routines. Post-prandial blood glucose excursions (time series) for type 2 diabetes vary widely depending on the variety and the amount of food consumed. It also depends on long and short term physical conditions (exercise routines and stress levels such as insomnia) to a lesser scale. The recovery periods of blood glucose excursions returning to the pre-prandial level (or baseline) for diabetics are generally longer than those for non-diabetics. Although a simple glucose-insulin interaction compartmental model exists [12], not all the model parameters are readily interpretable. In addition, no case study is given to illustrate its potential applications. Compartmental models can provide first-order approximations that may be sufficient for specific goals. Simple models may not duplicate real phenomena but may reveal enough clues for which alternative approaches or experimental designs may come to light. A biophysically-based model of impulse-force-generated heavily damped oscillatory system is used here to capture the post-prandial blood glucose characteristics of type 2 diabetes. The model follows the general approach of glucose-insulin interaction model (bolus injection of glucose) with a few modifications, for which parameters can readily be interpreted and a case study is presented for exploring its potential applications. Rather than using single food items for their published GI values, or its cumbersome weighted mean of multiple ingredients in a meal, normally consumed lunch for the subject was used for the test meal. Based on the preliminary results obtained from the model, a moderate lifestyle adjustment was devised for the subject: swimming 20 laps for 40 minutes in a 25 m pool in the morning and dispensing 1/4 of 5 mg glyburide 1/2 to 1 hour before lunch and dinner – that enables him to reduce 10% of his A1c level in six months and maintain the desirable lower level for the subsequent six months. Methods The subject is a mid-sixty healthy male of 180 lbs with 5'10" frame, leading a productive professional life. He has been diagnosed with type 2 diabetes for more than 30 years. Initially, he was on diet regimen for nearly twenty years and then was instructed by his physician to dispense 5 mg glucotrol once every morning. He experienced frequent acute hypoglycemia that led him to discuss a possible self-managed regimen with his family physician. Lunch was chosen as the test meal for having sufficient time to take post-prandial measurements. The test meals were 15 sets of lunches that consisted either (1) 10 to 12 oz of steamed rice, stir-fried vegetables with 4 oz canned tuna (or steamed cod), or (2) 10 to 12 oz spaghetti with 6 medium sized meat balls (from Sam's family package). Five sets of data each were collected from: (i) without taking hypoglycemic pills before test meals; (ii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially right before the meal and (iii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially an hour before the test meals. One pre- and 8 to 12 post-prandial blood glucose measurements were taken at 30-minute intervals starting at the beginning of a meal (meal is usually consumed in 15 minutes): (i) for 6 hours, (ii) for 5 hours, and (iii) for 4 hours. In addition, for case (iii) two reference measurements were taken with one right before dispensing the pill and one an hour after completion of the 8 post prandial measurements, i.e., at hour 5, for a total of 11 readings. The purpose of the first set of measurements was to establish the baseline for this diabetic subject: the recovery period of post-prandial blood glucose excursion without medication. The second and the third sets of the trials were designed to quantitatively measure the hypoglycemic drug effects and the most optimal time frame to administer the pills. Raw data were averaged and the corresponding standard deviations were also calculated for 5 replicates at given times. The averaged data were then used for modeling analysis. Model formulation The post-prandial blood glucose excursion can be considered as a hormone regulated resilient system. The food intake is treated as a bolus injection of glucose, and thus the impulse force f(t); effects of exercises and hypoglycemic medication are lumped as the damping factor, β. The differential equation of such an oscillatory system, that is used to describe post-prandial blood glucose excursions, can be found in many physics texts: where x represents blood glucose level over the baseline at time t, ω0 is the system natural frequency [12]. The pre-prandial blood glucose levels are generally fluctuating with relatively insignificant magnitudes thus can be approximated as a flat level. If the impulse force f(t) takes the form of the Dirac delta function, Fδ(t-0) with F being a food intake dependent parameter, the solution of Eq. (1) is where is the frequency of the system. Equation (2) is a three parameter model: F, ω and β. Implications of these three parameters not only could reveal distinctive characteristics between diabetic and non-diabetic individuals but also provide guidelines to adjust one's lifestyle. Parametric estimation For a given blood glucose excursion, data was taken every 30 minute interval from the time a meal was initially consumed, from which the excursion peak (MR), xmax, and the corresponding time τ to reach MR can both be estimated. Setting dx/dt = 0 in Eq. (2), the time τ can be expressed as: Substituting Eq. (3) into Eq.(2), we have The area under an excursion curve, AUC, can also be obtained: where T = 2π/ω is the period of oscillation. The reason for setting the upper integral limit to T/2 is because the damping factor β effectively depresses the glucose excursion levels x near zero for t >T/2, i.e., it ripples about pre-prandial level. The time T/2 is therefore defined as the recovery period (RP). For type 2 diabetic patients who are not in a properly structured regimen, the recovery periods are often longer than 5 hours, by which time the next meal arrives and induces another blood glucose upswing. Equations (3) – (5) can be used to estimate the three parameters, F, ω and β, from the measurable quantities of τ, xmax, and AUC. The procedure is briefly described below: 1. Assign T as twice the roughly estimated recovery period in hours, which can be obtained from the raw data and thus ω = 2π/T. 2. The damping factor β can be estimated from Eq. (3): , and thus . 3. The estimation of food intake-dependent impulse force F can be obtained from Eq. (4): . 4. Fine tune these three parameters by using MATLAB function fminsearch to minimize [AUCdata - AUC(F, β, ω)]2, where AUCdata is calculated from the averaged data points by the trapezoidal rule and AUC(F, β, ω) is calculated from Eq. (5). 5. These three parameters can further be fine-tuned by fminsearch (sum of squared errors between the averaged data points and the model predicted values). Two MATLAB user defined functions: GlucoseModel (for No pill and Pill at meal) and GlucoseModel1 (for Pill one hour prior) to estimate these model parameters and calculating the relevant diabetic characteristic measures: τ, xmax, AUC are listed in the Additional files 1 and 2, respectively. Results Table 1 lists the fine-tuned values of model parameters: F, ω, β, and those characteristic parameters: RP, τ, xmax, and AUC, the latter three are calculated from Eqs. (3) to (5). Also included in Table 1 are the fitting statistics R2 values that indicate how well model curves fit the data. Table 1 Model and characteristic parameters for the post-prandial blood glucose excursion Parameters No pill 1/4 pill at time 0 1/4 pill at time -1 F (mg/dL/hr) 47.1 73.8 59.3 ω (hr-1) 0.46 0.67 0.84 β (hr-1) 0.35 0.56 0.44 τ (hr) 2.60 1.76 1.56 RP (hr): π/ω 6.77 4.71 3.72 xmax (mg/dL) 59.8 62.5 49.4 AUC (mg-hr/dL) 248 179 118 R2 0.92 0.99 0.97 The parametric value of F is the result of food impact, or the rate of glucose being absorbed into the blood stream. The interpretation of F is rather difficult as the liver acts as a storage compartment for glucose [12]. Liver regulates blood plasma glucose levels; if it is too high, the excess will be stored in the liver, and the reverse process will take place if the plasma glucose is too low. Although all three model parameters: F, ω, and β are more or less influenced by the liver function, the impact on F deems more pronounced as it has a direct impact on the glucose levels in the blood stream. As the function of the liver is not included in the current model, the estimated F values can only be loosely inferred as a function of insulin level, F increases as hypoglycemic drug depresses the blood glucose levels that in turn increases the absorption rate of glucose into the blood stream as in the case of 1/4 pill taken right before the meal. When the drug is taken an hour before the meal, the liver may have sufficient time to regulate blood glucose levels that additional glucose absorption becomes less intensive. The increases of ω and β along with the intake of hypoglycemic drug are expected, which renders favorable characteristic parameters of τ, RP and AUC, all of these are decreasing with the moderate level of medication. The characteristic parameter xmax has significantly depressed for the 1/4 size glyburide taken one hour before the meal while in the other two cases xmax are roughly the same. This implies that the hypoglycemic drug has a net delay effect. Moderate hypoglycemic medication can enhance the liver function to regulate blood glucose levels, alleviating its fluctuation intensities. Interestingly, many ratios of characteristic parameters are roughly equal to constants for all three cases, which indicates that characteristic parameters are not mutually independent. Table 2 gives ratios of various combinations of characteristic parameters. The ratios of τ xmax/AUC and are extremely attractive as both τ and xmax can be estimated with fewer number of post-prandial measurements that one may use τ and xmax to estimate more interpretable characteristic parameters of AUC and PR. Table 2 Ratio of characteristic parameters for the post-prandial blood glucose excursion Characteristic ratio No pill 1/4 pill at time 0 1/4 pill at time -1 τ xmax/AUC 0.627 0.614 0.653 2.97 2.96 2.95 τ/RP 0.384 0.374 0.419 AUC/RP 36.6 38.0 31.7 AUC/(RP xmax) 0.612 0.608 0.642 No pill trial Parametric values for no-pill trial reveal that glucose absorption rate is generally slower (low F value) in comparison with the other two cases. The exceedingly long RP of nearly 7 hours is undesirable: as it implies that the next meal time arrives before the blood glucose level could return to the baseline, i.e., an elevated blood glucose level would be sustained for a prolonged period of time. The high RP and AUC are unmistakably the characteristics for type 2 diabetes. Figure 1 compares the model and the data with the corresponding standard deviation bars. Model curves are extended for an additional hour beyond the last data point (and in all the figures herewith) to denote the trend of blood glucose excursion. Figure 1 Post-prandial glucose excursion: no pill trial 1/4 of 5 mg glyburide taken right before the meal The blood glucose characteristics are significantly improved with a 1/4 size of 5 mg glyburide taken right before lunch. Increased ω and β values translate to significantly lower RP and AUC with virtually unchanged xmax. Although the mean RP is less than 5 hours, it is still a bit too long in comparison with the non-diabetics [12] (~ 4 hours). A higher F value than the one for no-pill trial may partly due to the liver intervention. Figure 2 compares the model and the data. From the figure one can tell that hypoglycemic drug has an effective delayed effect of about two hours as the rising portion of the model is almost identical to the one for no-pill trial with both xmax are about 60, which may be the result of liver function that with initial stimulation of hypoglycemic drug, liver may also release glucose. As the hypoglycemic drug effect persists, the liver ceases to interfere. Figure 2 Post-prandial glucose excursion: 1/4 pill right before the meal 1/4 of 5 mg glyburide taken an hour before the meal From the personal experience of the participating subject, the hypoglycemia usually occurs 3 to 4 hours after taking the pill. The trial described in the previous section also reveals that no significant hypoglycemic drug effect is detected in the initial two hours. In order to learn the drug impact on an empty stomach, an additional glucose measurement was made prior to taking the hypoglycemic pill at -1 hour. Another measurement was also taken an hour after the blood glucose excursion returned to the baseline (i.e., at hour 5). This is meant to check if the blood glucose would remain near the baseline level. The drop of blood glucose levels between -1 and 0 hours are roughly 10 mg/dL, which can be contributed to the mild liver intervention. No net hypoglycemic drug effect is taking place before the meal as evidenced from the initial rise of the blood excursion curve as shown in Fig. 3 (in comparison with Fig. 2), where only data between hour 0 and hour 4 were used to generate the model curve. Indeed, all parametric values are improved significantly: both PR and xmax are decreased by 20% and their combination that reflected in AUC dropped nearly 35% in comparison to those for pill taken at meal trial as shown in Table 1. The food impact parameter F decreased a little from the one for pill at meal trial, which may indicate an hour after dispensing the pill, a quasi-equilibrium state has been reached among the liver function, hypoglycemic drug effects, and the bolus injection of glucose. The system frequency ω increased for more than 25%, which gives a shorter RP that compares favorably with non-diabetics. The drop of damping factor β may be the result of low F, as both τ and xmax are already significantly reduced that further strengthening of β becomes unnecessary. The hour 5 measurements confirm that although the model curve shows a decreasing trend, upon returning to the base level the blood glucose excursions practically stabilizes. In addition, the volunteer patient did not experience any hypoglycemia even two to three hours after the final post-prandial measurement. Figure 3 Post-prandial glucose excursion: 1/4 pill an hour before the meal Discussion This simple impulse-forced model provides a means to shape a self-management regimen for the type 2 diabetic subject: a moderate meal coupled with minimal amount of medical intervention has effectively modulated the blood glucose excursion by reducing its recovery periods and fluctuation amplitudes. Based on the model, the type 2 diabetic subject was able to adjust a lifestyle that include (a) 40 minute swimming in a 25 m pool in the morning, (b) a fruit of mid-size apple or its equivalent and a cup of coffee with cream for breakfast without taking hypoglycaemic pill, (c) moderate lunch with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (d) moderate early dinner, 4 hours prior to bed time, with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (e) snack a mid-size banana, or a small bag (3.5 oz) of peanuts, or 6 crackers when needed in between meals. With this regimen, he was able to reduce his A1c level from 6.7 to 6.0 in 6 months and maintained at this level for the subsequent 6 months. Moreover, he has not had any hypoglycaemic bouts ever since he particitipated in this study more than two years ago. Elevated blood glucose excursions during the night would boost the A1c levels. To keep a low average fluctuation of blood glucose excursion amplitudes, the evening meal is crucial. In order to avoid hypoglycaemia during the sleep, an early dinner is advised. The subject has been able to keep post-prandial blood glucose levels within 200 mg/dL with the mean fasting reading of 90 ± 20 mg/dL. Occasionally he consumes a can of beer or sugar free deserts. Although no rigorous study has been performed, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these is roughly equivalent to the effect of 1/4 size of 5 mg glyburide. Nonetheless, it is impractical to exercise more than once a day, thus the subject takes 2.5 mg of hypoglycemic pill a day instead. His physician originally prescribed him to take one 5 mg hypoglycemic pill daily. That was more that 10 years ago. The regimen did not work very well as he experienced hypoglycaemic bouts often. This model-based regimen not only reduced A1c level but entirely eliminated hypoglycaemic symptoms. In addition, one fasting blood glucose measurement in the morning is sufficient for him to maintain a healthy daily routine of exercise, consuming meals/snacks and leading a productive life with mental and physical activities. Conclusions Lifestyle adjustments are the best regimens for many chronicle ailments such as diabetes, hypertension, high cholesterol levels, etc. Although this model-based self-management regimen for the type 2 diabetic subject is only a case study, it certainly provides a general guideline for an applicable life-style adjustment. Currently not all the model parameters are entirely clear, additional data are required to draw a meaningful general conclusion. A pilot project of testing this regimen on six type 2 diabetic patients in a regional nursing home is proposed for the next phase of study. If future studies support that the ratios of τ xmax/AUC and are approximately constants, the combination of τ and xmax can then be used to estimate AUC and PR with fewer number of post-prandial measurements. This would be much more convenient to characterize a type 2 diabetic subject than using AUC and PR. Although derived characteristic parameters: RP and AUC (to a lesser degree, τ and xmax), carry clear meaning that can be used to characterize type 2 diabetic subjects from non-diabetics, the implications of model parameters, F, ω and β are not as translucent. With additional data, one may be able to draw plausible conclusions about (a) how F is influenced by food intakes, drug (delaying) effects, and liver (regulatory) functions; and (b) how ω and β behave, whether they are independent of F and of each other, or all three somewhat mutually dependent. Better understanding of these parameters would definitely enhance the self-management for type 2 diabetes. This model-based lifestyle adjustment has another advantage: it can be used to manage each individual needs. Nutall and Chasuk [10] have stressed that dietary recommendation for type 2 diabetes should be flexible and highly individualized; most of prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities. Once we have a comprehensive understanding of these parameters, it is possible to tailor individual lifestyle adjustment accordingly. For those individuals who are interested in self-managing the type 2 diabetes, the general advice is: avoiding big meals, may snack moderately between meals, eat an early dinner – about 4 hours before bedtime, and exercise regularly. If one is interested in "normal" meal effects on one's post-prandial blood glucose excursion, taking a pre-prandial blood glucose measurement prior to a typical lunch and 8 to 10 post-prandial measurements at half-hour intervals for 5 or more replicates and follow the procedure described here to obtain these characteristic parameters RP, τ, xmax, and AUC. Applying a small dosage of medical intervention prior to a meal can keep the blood glucose at a relatively flat level and depress the overnight blood glucose excursion; however, this practice needs the approval from one's family physician and is not recommended here. Authors' contributions Sole authorship: data collection/analysis, model building, parameter estimation/interpretation, and the design of life-style adjustment regimen for the participating subject. Supplementary Material Additional File 1 MATLAB user defined function: GlucoseModel (for No pill and Pill at meal) to estimate model parameters: F, β, ω and to calculate the relevant diabetic characteristic measures: τ, xmax, AUC. Click here for file Additional File 2 MATLAB user defined function: GlucoseModel1 (for Pill one-hour prior) to estimate model parameters: F, β, ω and to calculate the relevant diabetic characteristic measures: τ, xmax, AUC. Click here for file Acknowledgements The author wishes to express his appreciation to Ms. Katherine Jakubik for her editing efforts, to Professor Jame B. Bassingthwaighte and two other anonymous reviewers for their critical comments to an earlier version of this manuscript. ==== Refs Ratner RE Type 2 diabetes mellitus: the grand overview Diabet Med 1998 15 S4 7 9868984 10.1002/(SICI)1096-9136(1998120)15:4+<S4::AID-DIA735>3.3.CO;2-T Jiwa F Diabetes in the 1990s – an overview Stat Bull Metrop Co 1997 78 2 8 Brand-Miller J The Glucose Revolution 1999 Marlowe & Company Linder L What's your number, sweetie? The Washington Post, May 1, HE08 2001 Franz M In defence of the American Diabetes Association's recommendations on the glycemic index Nutrition Today 1999 34 78 81 Wolever TMS Jenkins D Jenkins AL Josse RG The glycemic index: methodology and clinical implication American Journal of Clinical Nutrition 1991 54 846 854 1951155 Brand-Miller J Diets with a low glycemic index: From theory to practice Nutrition Today 1999 34 64 72 Gannon MC Nuttall FQ Factors Affecting Interpretation of Postprandial Glucose and Insulin Areas Diabetes Care 1987 10 759 763 3322731 Truswell AS Glycemic index of foods Eur J Clin Nutr 1992 46 S91 S101 1330533 Nuttall FQ Chasuk RM Nutrition and the management of type 2 diabetes Journal of Family Practice 1998 47 S45 53 9834755 Fournier RL Basic Transport Phenomena in Biomedical Engineering 1998 Taylor & Francis Fisher RJ Enderle J, Blanchard S, Bronzino J Compartmental analysis Introduction to Biomedical Engineering 2000 London: Academic Press 369 410	15644138	PMC546416	CC BY	2021-01-04 16:37:34	no	Biomed Eng Online. 2005 Jan 11; 4:4	utf-8	Biomed Eng Online	2,005	10.1186/1475-925X-4-4	oa_comm
==== Front Int J Equity HealthInternational Journal for Equity in Health1475-9276BioMed Central London 1475-9276-4-21565198710.1186/1475-9276-4-2ResearchThe burden of non communicable diseases in developing countries Boutayeb Abdesslam [email protected] Saber [email protected] Department of Mathematical Sciences, Brunel University, Uxbridge, Middx UB8 3PH, UK2 Department of Mathematics, Faculty of Sciences, University Mohamed Ier, Oujda, Morocco3 Service Oncologie Médicale, Institut National d'Oncologie, Rabat, Morocco2005 14 1 2005 4 2 2 27 7 2004 14 1 2005 Copyright © 2005 Boutayeb and Boutayeb; licensee BioMed Central Ltd.2005Boutayeb and Boutayeb; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background By the dawn of the third millennium, non communicable diseases are sweeping the entire globe, with an increasing trend in developing countries where, the transition imposes more constraints to deal with the double burden of infective and non-infective diseases in a poor environment characterised by ill-health systems. By 2020, it is predicted that these diseases will be causing seven out of every 10 deaths in developing countries. Many of the non communicable diseases can be prevented by tackling associated risk factors. Methods Data from national registries and international organisms are collected, compared and analyzed. The focus is made on the growing burden of non communicable diseases in developing countries. Results Among non communicable diseases, special attention is devoted to cardiovascular diseases, diabetes, cancer and chronic pulmonary diseases. Their burden is affecting countries worldwide but with a growing trend in developing countries. Preventive strategies must take into account the growing trend of risk factors correlated to these diseases. Conclusion Non communicable diseases are more and more prevalent in developing countries where they double the burden of infective diseases. If the present trend is maintained, the health systems in low-and middle-income countries will be unable to support the burden of disease. Prominent causes for heart disease, diabetes, cancer and pulmonary diseases can be prevented but urgent (preventive) actions are needed and efficient strategies should deal seriously with risk factors like smoking, alcohol, physical inactivity and western diet. ==== Body Background For centuries, communicable diseases were the main causes of death around the world. Life expectancy was often limited by uncontrolled epidemics. After the second World War, with medical research achievements in terms of vaccination, antibiotics and improvement of life conditions, non communicable diseases(NCDs) started causing major problems in industrialized countries. Heart diseases, cancer, diabetes, chronic pulmonary and mental diseases became a real burden for health systems in developed countries. For a while, these diseases were associated with economic development and so called diseases of the rich. Then, by the dawn of the third millennium, NCDs appeared sweeping the entire globe, with an increasing trend in developing countries (Table 1) where, the transition imposes more constraints to deal with the double burden of infective and non-infective diseases in a poor environment characterized by ill-health systems. In 1990 the leading causes of disease burden were pneumonia, diarrhoeal diseases and perinatal conditions. By 2020, it is predicted that NCDs will account for 80 percent of the global burden of disease, causing seven out of every 10 deaths in developing countries, compared with less than half today[1,2]. Table 1 Evolution of NCDs in developing countries (in million) [1,8,9] Non-Communicable Diseases Communicable Diseases + Maternal + Perinatal + Nutritional Injuries total 1990 18.7 (47%) 16.6 (42%) 4.2 (11%) 39.5 (100%) 2000 25.0 (56%) 14.6 (33%) 5.0 (11%) 45.0 (100%) 2020 36.6 (69%) 09.0 (17%) 7.4 (14%) 53.0 (100%) Efficient (preventive)strategies are needed and urgent measures should be taken to control risk factors like tobacco, alcohol, obesity, blood pressure diet and inactivity. Otherwise, developing countries will be unable to provide their people with standard health care. The costly and prolonged treatment of NCDs raises the equity problem between and within countries. As expressed by the WHO Director-General in his overview to the annual report[1], If a Japanese woman develop chronic diseases, excellent treatment and rehabilitation services will be available and she can expect to receive, on average, medications worth about US$ 550 per year and much more if needed. Meanwhile, a woman in Sierra Leone can expect, on average, medicines worth about US$ 3 per year and, if she survives middle age and develop chronic diseases then she will die prematurely as a consequence of inadequate treatment. The contrasts in opportunities of treatment exist also within developing countries; between poor and rich, cities and rural areas and also between men and women. In previous papers, the authors proposed mathematical models dealing with the burden of diabetes and its complications[3], Dynamics of a disabled population[4], the effect of physical exercise[5] and a model of dengue fever[6]. The present paper is devoted to the burden caused by NCDs in developing countries. In order to reverse the increasing trend of this burden (or at least to control it), the focus is made on the risk factors associated with these diseases. Different methods can be considered to quantify the burden of NCDs. In order to overcome the specific problems of each country, the most used method is the approach that measures the global burden of NCDs in terms of Disability Adjusted Life Years (DALYs) which is a combination of Years of Life Lost(YLL) through premature death, and Years Lived with Disability(YLD). Thus, DALY is thought of as one lost year of healthy life [7-9]]. For example, deaths from underweight every year rob the world's poorest children of an estimated total of 130 million years of healthy life[10]. According to this approach, the burden of adult NCDs account for 80% in developed countries and for 70% in middle-income countries. Even in the high-mortality regions of the world, almost 50% of the adult disease burden is attributable to NCDs. Methods Data from national registries and international sources are collected, compared and analyzed in order to show the trend of NCDs. Four diseases or cluster of diseases(Cardio-Vascular Diseases(CVDs), diabetes, cancers and chronic respiratory diseases) are considered to illustrate the growing burden of NCDs in developing countries. The main sources of data are the annual reports and regular publications released by the World Health Organization(WHO), World Heart Federation(WHF), Pan American Health Organization(PAHO), International Diabetes Federation (IDF), International Agency for Research on Cancer(IARC), Centre for Chronic Disease Prevention and Control(CCDPC), International Task Force for Prevention of Coronary Heart Disease and a multitude of websites and papers dealing with NCDs. The literature associated with these diseases in developed countries is abundant. However, despite the encouraging programmes and joint projects proposed by WHO and other organisms in the form of collaborative research agreements to developing countries, in order to support national registries, unreliable and insufficient data are still prevailing in most of these countries. Moreover, the release of health data is shadowed by the security vision in some countries. Extrapolations are needed in the case of missing or incomplete data. Consequently, more efforts are needed to convince health decision makers in low- and middle-income countries of the necessity to develop epidemiological studies that allow for preventive strategies making health policy at the centre of sustainable development. Results According the World Health Organization's statistics, chronic NCDs such CVDs, diabetes, cancers, obesity and respiratory diseases, account for about 60% of the 56.5 million deaths each year and almost half of the global burden of disease. In 1990, 47% of all mortality related to NCDs was in developing countries, as was 85% of the global burden of disease and 86% of the DALYs attributable to CVDs. An increasing burden will be born mostly by these countries in the next two decades. The socio-economic transition and the ageing trend of population in developing countries will induce further demands and exacerbate the burden of NCDs in these countries. If the present trend is maintained, it is predicted that, by 2020, NCDs will account for about 70 percent of the global burden of disease, causing seven out of every 10 deaths in developing countries, compared with less than half today. In 1990, approximately 1.3 billion DALYs were lost as a result of new cases of disease and injury, with the major part in developing countries. In 2002, these countries supported 80% of the global YLDs due to the double burden of communicable and non communicable diseases. Consequently, their people are not only facing higher risk of premature life(lower life expectancy) but also living a higher part of their life in poor health[1]. These remarks indicate that NCDs are exacerbating health inequities existing between developed and developing countries and also making the gap more profound between rich and poor within low and middle-income countries. CVDs in developing countries CVD is the name for the group of disorders of the heart and blood vessels and include hypertension (high blood pressure), coronary heart disease (heart attack), cerebrovascular disease (stroke), peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease and cardiomyopathies. These diseases constitute the major contributor among NCDs (Table 2). Table 2 Deaths caused wordwide by specific diseases (× 103) Deaths & % Disease 2002 [1] 1990 [8] Ischaemic heart disease 7000 (12.6%) 6260 (12.4%) Cerebrovascular disease 5400 (09.6%) 4380 (08.7%) Lower Respiratory Diseases 3700 (06.6%) 4300 (08.5%) COPD 2700 (04.8%) 2211 (04.4%) Cancer(all types) 7100 (12.6%) 6200 (11.2%) Diabetes 3200 (05.6%) 2400 (05.0%) Worldwide, an estimated 17 million people die of these diseases, particularly heart attacks and strokes, every year. Once associated with industrialized countries, CVDs are now emerging or rapidely increasing in developing countries. Indeed, in 1998, 86% of the DALYs caused by CVDs were attributed to developing countries and in 1999 CVDs contributed to a third of global deaths with 78% in low- and middle-income countries. The trend is increasing, indicating that by the year 2010 CVDs will be the leading cause of death in developing countries as a consequence of lifestyle changes brought about by industrialization and urbanization in developing countries engaged in the socio-economic transition. CVDs are promoted by risk factors like tobacco use, alcohol, physical inactivity and unhealthy diet. Unfortunately, the harm caused by these risk factors affects the rise of life expectancy in developing countries[1,11,12]. The costly and prolonged care of CVDs in low-and middle-income countries often divert the scarce family and societal resources to medical care. Consequently, the lower socio-economic groups have greater prevalence of risk factors, higher incidence of disease and higher mortality. Diabetes The recent statistics released by the World Health Organization and the International Diabetes Federation are alarming[1,12](Table 3). The number of diabetes in the world is expected to increase from 194 Million in 2003 to 330 in 2030 with three in four living in developing countries. Moreover, in developed countries most people with diabetes are above the age of retirement, whereas in developing countries those most frequently affected are aged between 35 and 64 which makes the burden in terms of DALYs and YLDs heavier in poorer countries. Indeed, in some countries of the Middle East, one in four deaths in adults aged between 35 and 64 years is attributable to diabetes. The burden is exacerbated by the complications such as blindness, amputations and kidney failure for which diabetes is the leading cause, and the interfering action of CVDs which are responsible for between 50 and 80% of deaths in people with diabetes. The burden of premature death from diabetes is similar to that of HIV/AIDS, yet the problem is largely unrecognised [13]. Table 3 Diabetes prevalence (× 106) [13] Country 2000 2030 India 31.7 India 79.4 China 20.8 China 42.3 United States 17.7 United States 30.3 Indonesia 8.4 Indonesia 21.3 Japan 6.7 Pakistan 14.9 Pakistan 5.2 Bangladesh 11.8 Russia 4.6 Brazil 11.3 Brazil 4.5 Japan 8.9 Italy 4.2 Italy 5.4 Bengladesh 3.2 Russia 5.3 Studies in different countries have shown that diabetes is a costly disease accounting for between 2.5 and 15% of the total healthcare expenditure[3]. For the age category 20–79, the world annual direct cost is estimated to be over $153 billion and expected to double in 2025. According to the National Institute of Diabetes and Digestive Kidney Disease(NIDDK) and the American Diabetes Association, diabetes was the sixth leading cause of death in 1999 with a direct cost of $44 billion and an indirect cost of $54 billion annually. In 2002, the direct and indirect cost totalled $132 billion. In France, an estimation of $5.7 billion was given for the direct cost of diabetes, whereas, an equivalent cost of 5.2 billion, representing approximately 9% of the annual NHS budget, was given for UK in 2000. The burden affects more and more developing countries as stressed by the different authors who attended the seventh congress of the Pan-African diabetes study group in 2001[14] and the Metabolic syndrome, type II diabetes, and atherosclerosis congress in 2004[15]. Cancer Cancer is now a major cause of mortality throughout the world (Table 4). In the developed world, it is generally exceeded only by CVDs but developing countries are responsible for the globally increasing trend. Over 10 million new cases and over 7 million deaths from cancer occurred worldwide in 2000[1,2,16-19]]. The contribution of developing countries was 53% for incidence and 56% for deaths (Table 5). From 1990 to 2000, the incidence and deaths increased by 2.4% per annum. Table 4 Cancer by types and numbers worldwide (× 103): Cancer 2000 [18] Incidence % 2000 deaths % 1990 Incidence % 1990 [19] deaths % Lung 1239 12.3 1103 17.8 1037 12.8 921 17.8 Breast 1050 10.4 373 6.0 796 9.8 314 6.1 Colorectal 945 9.4 492 8.0 783 9.7 437 8.4 Stomach 876 8.7 646 10.0 798 9.9 628 12.1 Liver 564 5.6 546 8.8 437 5.4 427 8.2 Prostate 543 5.4 204 3.3 396 4.9 165 3.2 Cervical 471 4.7 233 3.7 371 4.6 190 3.7 Oesophagus 413 4.1 337 5.4 316 3.9 286 5.5 Head&neck 390 3.9 207 3.3 306 3.8 162 3.1 Bladder 336 3.3 132 2.1 261 3.2 115 2.2 Other 3228 32.2 1934 31.0 2582 32.0 1537 30.0 Total 10055 100% 6209 53% 8083 100.0 5182 100.0 Table 5 Cancer in developing countries :incidence & deaths (× 103) in 2000 [18] Cancer Developing Incidence % Developing deaths % Lung 792 14.7 522 14.6 Breast 471 8.8 184 5.6 Colorectal 334 6.2 252 7.0 Stomach 543 10.1 417 11.7 Liver 457 8.5 443 12.4 Prostate 127 2.4 76 2.1 Cervical 379 7.0 194 5.4 Oesophagus 341 6.3 274 7.7 Head&neck 262 4.9 154 4.3 Bladder 124 2.3 65 1.8 Other 1546 71.2 992 27.8 Total 5376 100% 3563 57.4 Between 2000 and 2020, the total number of cases of cancer in the developed world is predicted to increase by 29% whereas, in developing countries an increase by 73% is expected (largely as a result of an increase in the number of old people and as a result of urbanization and change in dietary habits). The incidence of cancers of the lung, colon and rectum, breast and prostate generally increases in parallel with economic development, while the incidence of stomach cancer usually declines with development[2]. Lung cancer This is currently the most common cancer in the world. In developed countries, smoking causes over 80% of such cancers and generally, heavy smoking increases the risk by around 30-fold making lung cancer a major problem in developing countries where the consumption of tobacco is flourishing. Breast cancer According to the International Agency for Research on Cancer (IARC), there were over a million new cases in the world in the year 2000, making it the second most common in the world and the most common among women with 47% in developing countries. Although rates are five times higher in industrialized countries, the burden of disease is heavier in poorer countries because breast cancer is highly curable if detected early and, unfortunately, about 80% of the cases are detected at advanced stages in developing countries. Colorectal cancer Ranking at the third place, with incidence rates tenfold higher in developed than in developing countries, this type of cancer is assumed to be mainly related to dietary factors which account to up to 80% of the between-country differences in rates. Stomach cancer 20 years ago, this cancer used to be the most common in the world. At the moment, it is the fourth most common in the world but the second most common in developing countries. Substantial evidence suggests that risk is increased by high intakes of some traditionally preserved salted food and that risk is decreased by high intakes of fruit and vegetables. Liver cancer Approximately 75% of cases occur in developed countries, the rate vary over 20fold between countries. In developing countries, ingestion of contaminated food is an important risk factor together with active hepatitis virus infection whereas, alcohol consumption is the main diet-related risk factor in the world. Cervical cancer 80% of the new cases and deaths are occurring in developing countries where it constitutes a major health problem. In developed countries, screening programmes and early detection have led to a noticeable decline in cervical cancer incidence and mortality, whereas, the trend is stable or increasing in low- and middle-income countries owing to their limited health care resources but also to their ill-health systems generating inefficient (or no)strategies[20]. Oral cavity, pharynx and oesophagus In developed countries these types of cancer are mainly correlated to alcohol and tobacco(up to 75% of such cancers are attributable to these two lifestyle factors). In developing countries, around 60% of such cancers are thought to be a result of micronutrient deficiencies related to a restricted diet that is low in fruit and vegetables and animal products. There is also consistent evidence that consuming drinks and foods at a very high temperature increases the risk for these cancers[2]. Pancreatic, endometrial, prostate and kidney cancers These types of cancer are more common in industrialized countries. However, the fact that overweight/obesity is an established risk factor, their incidence is expected to increase in developing countries engaged in the socio-economic transition[2]. Chronic respiratory diseases Chronic respiratory diseases represent a major burden for the health systems worldwide. Most developing countries have no standard protocols for assessing and managing chronic non communicable respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and Asthma. In these countries, the population afflicted by poverty and illiteracy, having very little (or no) access to health services, will die before the age of 40 years. They comprise 15% of the population in Latin America, 34% in Arab world, 45% in Sub-Saharan Africa and south-east Asia[21,22]. Respiratory diseases cause 15% of the global burden of disease. Worldwide, it is estimated that 600 million people suffer from COPD and 2.5 million deaths were attributed to these diseases in 2000. By 2020, COPD is expected to become the third most common cause of mortality in the world. Discussion Risk factors: the enemies In the previous sections, we considered four classes of non communicable diseases, namely, CVDs, diabetes, cancer and chronic respiratory diseases. Despite some differences between these classes and into each class, they do have a common denominator which is the risk factors. Indeed, Tobacco, alcohol, high blood pressure, diet and physical inactivity were indicated, at different levels, as risk factors in the four classes of NCDs. Moreover, these risk factors are seen to affect people worldwide with an increasing tendency. (Table 6) Table 6 Burden of disease and risk factors worldwide:year 2002 [1] Risk factor Deaths (× 103) % of total death DALYs (× 103) % of total DALY Hypertension 7141 12.8 64270 04.5 Tobacco 4907 08.8 59081 04.1 High cholesterol 4415 07.9 40437 02.8 Low fruit & veg 2726 04.9 26662 01.9 Overweight 2591 04.6 33415 02.3 Alcohol 1804 03.2 58323 04.0 Phys. inactivity 1922 03.4 19092 01.3 Globally, many of the risk factors for heart disease, diabetes, cancer and pulmonary diseases are due to lifestyle and can be prevented. Physical inactivity, western diet and smoking are prominent causes[23]. Tobacco is the enemy number one. It is the most important established cause of cancer but also responsible in CVDs and chronic respiratory diseases. Tobacco and diet are the principal risk factors, responsible for more than 40% of cancer deaths and incidence. Obesity and dietary habits are the principal risk factors for diabetes of type 2. Tobacco[1,2,24] In the 20 the century, approximately 100 million people died worldwide from tobacco-associated diseases such as cancers, chronic lung disease, diabetes and CVDs. While tobacco consumption is falling in most developed countries, it is increasing in developing countries by about 3.4% per annum. Today, 80% of the 1.2 billion smokers in the world live in poorer countries where smoking prevalence among men is nearly 50% (48%) and 50% of the 5 million deaths attributed to smoking in 2000 occurred in developing countries, also responsible for the increase in deaths by more than one million during the last decade. Tobacco remains the most important avoidable risk for the four classes of NCDs. It increases the risk of dying from coronary heart disease and cerebrovascular disease 2–3 fold. It increases the risk of many types of cancer, for lung cancer the risk is increased by 20–30fold. According to studies conducted in Europe, Japan and North America, 83–90% of lung cancers in men and 57–80 in women, are imputable to tobacco. Between 80 and 90 % of oesophagus, larynx and oral cavity are caused by tobacco and alcohol [17]. In developing countries, an estimated one-third of all cancer deaths was attributable to smoking in 1995. Finally, tobacco exacerbates the conditions of people living with COPD and asthma. Lifestyle[2,25-27] Up to 80% of cases of coronary heart disease, and up to 90% of cases of types 2 diabetes, could potentially be avoided through changing lifestyle factors. One-third of cancers could be avoided by eating healthily, maintaining normal weight, and exercising throughout life. It was estimated that in high-risk populations, an optimum fish consumption of 40–60 grams per day would lead to approximately a 50% reduction in death from coronary heart disease. A recent study based on data from 36 countries, reported that fish consumption is associated with a reduced risk of death from all causes as well as CVD mortality. Unfortunately, the fish consumption is very low even in some countries known for their large fish stock like the north African region. Daily intake of fresh fruit and vegetables in adequate quantity (400–500 grams per day), is recommended to reduce the risk of coronary heart disease, stroke and high blood pressure. But, once more, this is thwarted by the western lifestyle invading developing countries. Overweight/Obesity[2,28] Overweight and Obesity lead to adverse metabolic changes such as insulin resistance, increasing blood pressure and cholesterol. Consequently, they promote CVDs, diabetes and many types of cancer. Worldwide, overweight affects 1.2 billion of which 300 million are clinically obese. In some developed countries like USA, the prevalence reaches 60% but developing countries like Kuwait have also a very high prevalence. More and more children are suffering from overweight and obesity. However, the most contrasting phenomenon is to find Overweight/Obesity and malnutrition side by side in low- and middle-income countries and hence contributing to the growing burden afflicting these countries. According to the International Obesity Task Force (IOTF) and the WHO World Health report 2002, about 60% of diabetes globally can be attributable to overweight and obesity. In other respects, it is estimated that 60% of world's population do not do enough physical activity. Alcohol[2] Alcohol consumption has also increased in the last decades, with the major part of this increase imputable to developing countries. In 2000, Alcohol was responsible for nearly 2 million deaths in the world, representing 4% of the global disease burden. Moreover, alcohol was estimated to cause 20 to 30 % of oesophagus cancer, liver disease, epilepsy, motor vehicle accidents and other hazards. Conclusions Non communicable diseases are more and more prevalent in developing countries. These diseases are highly correlated to risk factors like smoking, alcohol, obesity, diet and inactivity. The World Health Organization and many other organisms and associations are urging health decision makers to develop efficient preventive strategies to halt the growing trend of NCDs through the control of risk factors. However, although most of developed countries have reacted by pragmatic measures, the trend remain globally passive mainly because developing countries have been, so far, satisfied with adopting national conventions and adhering to international recommendations instead of pragmatic decisions such as prohibiting smoking in public areas, controlling alcohol abusers, encouraging physical activity, promoting healthy diet and improving primary health care for screening and early detection of chronic diseases. In these countries, 2.8 billion people live with less than 2 dollars, 1.2 billion live with less than one dollar and 1.3 billion live on fragile and often remote rural ecosystems[29]. So, the behaviour can be partly explained by lack of means and poor budget affected to health care but, in general, bad management and absence of goodwill assume a large part of responsibility. For instance, many developing countries have signed the Framework Convention on Tobacco Control(FCTC) and voted laws that prohibits smoking in public areas but the laws are not executed. Also, in the absence of early detection, many people are diagnosed at advanced stages of cancer, cardiovascular diseases and diabetes complications. Also, in these countries, until recently, it was widely believed that economic development was a necessary prerequisite for improving a population health status and the health was often classified as a non productive sector. Now, politicians and health policy makers are timidly recognizing that investing in people's health is a necessary condition for economic development but energetic decisions are needed for the adoption of urgent and consequent strategies. The need for such strategies is enhanced by the fact that risk factors like cholesterol, tobacco, blood pressure, and obesity are no more a specificity of industrialized countries, they are becoming more prevalent in developing nations, where they double the burden of infectious diseases that have always afflicted poorer countries. Moreover, multinational companies have been competing fiercely to expand their sales in developing countries and western lifestyle is invading middle-income countries [2,30]. Adhesion to the Framework Convention on Tobacco Control(FCTC) and other international strategies must be taken seriously by developing countries facing the pandemics of NCDS. Competing interests The author(s) declare that they have no competing interests. Authors' contributions AB contributed by the collection of data concerning CVDs and diabetes and to English writing SB contributed by the collection of data concerning cancer and chronic pulmonary diseases. The two authors contributed equally to the final version of the paper. Acknowledgements This paper is dedicated to Wiam Boutayeb for her sixteenth birthday of which fifteen years lived with diabetes. ==== Refs The World health report Today's challenges Geneva, World Health Organization World Health Organisation Diet, Nutrition and the prevention of Chronic Diseases Technical report Series 916 2003 Geneva, World Health Organization Boutayeb A Twizell EH Achouyab K Chetouani A A mathematical model for the burden of diabetes and its complications BioMedical Engineering Online 2004 3 20 15222886 10.1186/1475-925X-3-20 Derouich M Boutayeb A The effect of physical exercise on the dynamics of glucose and insulin Journal of Biomechanics 2002 35 911 917 12052393 10.1016/S0021-9290(02)00055-6 Boutayeb A Chetouani A Dynamics of a disabled population in Morocco BioMedical Engineering Online 2003 2 2 12625838 10.1186/1475-925X-2-2 Derouich M Boutayeb A Twizell EH A model of dengue fever BioMedical Engineering Online 2003 2 4 12657162 10.1186/1475-925X-2-4 Mathers CD Bernard C Iburg KM Inoue M Fat DM Shibuya K Stein C Tomijima N Xu H Global Burden of Disease in 2002 data sources, methods and results Paper 54 WHO Burden of Disease Unit The global burden of disease in 1990 Harvard University Press Hutubessy R Chisholm D Edejer TT Generalized cost-effectiveness analysis for national-level priority setting in health sector Cost Eff Resour Alloc 2003 1 8 14687420 10.1186/1478-7547-1-8 The World health report Reducing Risk: Promoting Health Life Geneva, World Health Organization Lenfant C Can we prevent cardiovasculazr diseases in low- and middle-income countries? 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==== Front Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-4-31564932510.1186/1476-4598-4-3ResearchEctopic expression of PTTG1/securin promotes tumorigenesis in human embryonic kidney cells Hamid Tariq [email protected] Mohammed T [email protected] Sham S [email protected] Department of Medicine, University of Louisville, Louisville, KY 40202, USA2 James Graham Brown Cancer Center, University of Louisville, KY 40202, USA3 Department of Biochemistry and Molecular Biology, University of Louisville, Louisville, KY 40202, USA2005 13 1 2005 4 3 3 18 11 2004 13 1 2005 Copyright © 2005 Hamid et al; licensee BioMed Central Ltd.2005Hamid et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Pituitary tumor transforming gene1 (PTTG1) is a novel oncogene that is expressed in most tumors. It encodes a protein that is primarily involved in the regulation of sister chromatid separation during cell division. The oncogenic potential of PTTG1 has been well characterized in the mouse, particularly mouse fibroblast (NIH3T3) cells, in which it induces cell proliferation, promotes tumor formation and angiogenesis. Human tumorigenesis is a complex and a multistep process often requiring concordant expression of a number of genes. Also due to differences between rodent and human cell biology it is difficult to extrapolate results from mouse models to humans. To determine if PTTG1 functions similarly as an oncogene in humans, we have characterized its effects on human embryonic kidney (HEK293) cells. Results We report that introduction of human PTTG1 into HEK293 cells through transfection with PTTG1 cDNA resulted in increased cell proliferation, anchorage-independent growth in soft agar, and formation of tumors after subcutaneous injection of nu/nu mice. Pathologic analysis revealed that these tumors were poorly differentiated. Both analysis of HEK293 cells transiently transfected with PTTG1 cDNA and analysis of tumors developed on injection of HEK293 cells that had been stably transfected with PTTG1 cDNA indicated significantly higher levels of secretion and expression of bFGF, VEGF and IL-8 compared to HEK293 cells transfected with pcDNA3.1 vector or uninvolved tissues collected from the mice. Mutation of the proline-rich motifs at the C-terminal of PTTG1 abolished its oncogenic properties. Mice injected with this mutated PTTG1 either did not form tumors or formed very small tumors. Taken together our results suggest that PTTG1 is a human oncogene that possesses the ability to promote tumorigenesis in human cells at least in part through the regulation of expression or secretion of bFGF, VEGF and IL-8. Conclusions Our results demonstrate that PTTG1 is a potent human oncogene and has the ability to induce cellular transformation of human cells. Overexpression of PTTG1 in HEK293 cells leads to an increase in the secretion and expression of bFGF, VEGF and IL-8. Mutation of C-terminal proline-rich motifs abrogates the oncogenic function of PTTG1. To our knowledge, this is the first study demonstrating the importance of PTTG1 in human tumorigenesis. ==== Body Background Pituitary tumor transforming gene 1 (PTTG1), a recently characterized oncogene, was initially identified on analysis of a rat pituitary tumor [1]; subsequently, a human homologue of PTTG1 was cloned by us and others [2-4]. Three members (PTTG1, PTTG2 and PTTG3) of the PTTG family, which exhibit differential expression in normal and tumor cells have been reported [5], although only PTTG1 has been studied in detail. PTTG1 is located on chromosome 5q35.1 [6], a locus associated with recurrent lung cancer and myelogenous leukemias [7]. Moreover, it has been shown to be expressed highly in various tumors, and cell lines derived from such tumors, including tumors of the pituitary, thyroid, colon, ovary, testicles, and breast [8-12]. In normal tissues, its expression is low or undetectable except in testis [4,1]. Recent studies have indicated that elevated expression of PTTG1 in some tumors may serve as a prognostic marker for tumor invasiveness and metastasis [13]. A clue to its function was gained from its structural similarity with the yeast securin, which led to its identification as a human securin [14] and suggested that it may play a role in regulation of sister chromatid separation. It appears, however, to have multiple effects in cells with enhanced expression being associated with an increase in the expression of the c-myc oncogene [15], an increase in the expression of p53 [16,17], an increase in the secretion and expression of basic growth factor (bFGF) [18] and an increase in the secretion and expression of vascular endothelial growth factor [VEGF) [18,19]. To date, the evidence for the oncogenic function of PTTG1 has been obtained by overexpression of PTTG1 in mouse fibroblast cells (NIH3T3) followed by assessment of its ability to induce cellular transformation in vitro (colony formation in soft agar) and tumor formation in nude mice [2,4]. Due to the biological differences between human and rodent cells, however, care must be taken in extrapolating results obtained using rodent cells to human cells. There are now a number of examples in which it has been demonstrated that although overexpression of an oncogene can induce transformation of primary rodent cells [20], it fails to induce transformation of the same cell type derived from humans. Usually this failure is attributable to the requirement for co-expression of another gene or oncogenic cooperation of another gene [21-26]. Similarly, much of the evidence concerning the mechanisms by which PTTG1 may affect the phenotype of the cell has been obtained using transfected NIH3T3 cells. It is known that the secretion of growth factors and cytokines by tumor cells, and the cells that infiltrate and surround the tumor mass play an essential role in the regulation of tumor growth and metastasis [27]. Both bFGF and VEGF have been implicated in tumorigenesis and the expression and secretion of these molecules has been demonstrated on transfection of NIH3T3 cells with PTTG1 cDNA [18,19], but this has not been confirmed on transfection of human cells. The effects of expression of PTTG1 on another cytokine that is known to play a key role in tumorigenesis, interleukin-8, (IL-8), have not yet been analyzed. The purposes of this study were, therefore, three-fold. Firstly, to determine whether PTTG1 can induce cellular transformation of normal human cells; secondly, to determine if PTTG1 is sufficient in itself to induce transformation; and, thirdly, to characterize the changes in secretion and expression of key metastatic, angiogenic and chemokine factors (bFGF, VEGF and IL-8) associated with PTTG1-mediated transformation in human cells. For these studies, we selected the human embryonic kidney (HEK293) cell line as our model. The transformation of these cells by human adenovirus type 5 prevents their senescence [28]. These cells have been reported to have a moderate tumorigenic potential [29] and have been used as a cellular model for normal human cells to study the oncogenic potential of a number of genes [29-31]. Mice xenografted with these cells do not develop tumors even after three months of injection [31]. Results Generation of HEK293 cells stably expressing PTTG1 and mPTTG1 HEK293 cells were transfected with pcDNA3.1-PTTG1, pcDNA3.1-mPTTG1 or pcDNA3.1 vector. After G418 selection, 10 clones from each of pcDNA3.1, pcDNA3.1-PTTG1 or pcDNA3.1-mPTTG1 transfected cells were picked, cultured and expanded. The PTTG1 protein expression of these transfectants was detected by western blot analysis using PTTG1 antiserum. Two representative clones from PTTG1 transfected (named HEKPTTG1-1 and HEKPTTG1-3) and mPTTG1 transfected (named HEKmPTTG1-2 and HEKmPTTG1-4), and one clone from pcDNA3.1 vector (named HEKpcDNA3.1) was selected for further studies. Selection of clones was based on the level of expression of PTTG1 protein. Fig. 1 shows the protein expression of these clones. Transfection of cells with the pcDNA3.1 vector resulted in expression of very low level of PTTG1 protein. The clones of the pcDNA3.1-PTTG1 and pcDNA3.1-mPTTG1 transfected cells that exhibited approximately equivalent levels of expression of PTTG1 and mPTTG1 proteins were processed to establish stable cell lines. Figure 1 Western blot analysis of HEK293 cells transfected with pcDNA3.1, pcDNA3.1-PTTG1 or pcDNA3.1-mPTTG1. a: HEKpcDNA3.1, b: HEKPTTG1-1, c: HEKPTTG1-3, d: HEKmPTTG1-2, and, e: HEKmPTTG1-4 cells. Stable transfection of PTTG1 induces cell proliferation and transformation of HEK293 cells overexpressing PTTG1 Previously, we have shown that over expression of PTTG1 in mouse fibroblast NIH3T3 cells results in an increase in cell proliferation [4]. To determine if over expression of PTTG1 in HEK293 cells produces similar effects, we estimated the proliferation at 24, 48, 72 and 96 hours after plating of stably transfected HEK293 cells expressing high levels of PTTG1 or mPTTG1 protein. Both clones of PTTG1-transfected cells (HEKPTTG1-1 and HEKPTTG1-3) exhibited significantly greater proliferation than the cells transfected with vector only at all time points tested, and the time course of proliferation was very similar in both clones, increasing by 20–30% after 24 hours, 30–40% after 48 hours. This level of increase in cell proliferation was retained at least up to 96 hours (Fig. 2). Somewhat surprisingly, the proliferation of the cells expressing the mutated PTTG1 was equivalent to that of the cells expressing the wild-type PTTG1 and was significantly higher than that of the cells transfected with vector only (Fig. 2). These experiments indicate that over expression of PTTG1 does induce a significant proliferative effect in HEK293 cells; however, at least under the conditions used, mutation of the proline-rich motifs of PTTG1 does not affect this response. Figure 2 Cell proliferation of HEK293 cells stably transfected with pcDNA3.1, PTTG1 or m-PTTG1. The 5 × 103 cells were plated/well. The results are expressed as % of control (HEK293 cells stably transfected with pcDNA3.1 control vector). Error bars represent ± SEM (n = 4) of three independent experiments. Overexpression of PTTG1 induces cellular transformation As anchorage-independent growth is considered to be an in-vitro test for angiogenesis we assayed the effects of transfection with PTTG1 on the ability of the HEK293 cells to form colonies in soft agar. As shown in Fig. 3, over expression of PTTG1 in HEK293 cells resulted in a higher incidence of colony formation than that observed on transfection with the vector only. The cells transfected with vector only formed few colonies and these were of small size during 14 days of culture, whereas both the cell lines expressing wild type PTTG1 formed a significantly higher number of colonies, which were of a large size. The incidence of colony formation was 2% for HEKpcDNA3.1 cell line but was 19% for the HEKPTTG1-1 cell line and 30% for the HEKPTTG1-3 cell line. In this case, mutation of the proline-rich motifs of PTTG1 resulted in a significant reduction in the number of colonies formed with the incidence of colony formation for the HEKmPTTG1-2 and HEKmPTTG1-4 cell lines being similar to the vector-only transfected cells (HEKpcDNA3.1). These results suggest that over expression of PTTG1 in HEK293 cells induces cellular transformation, and mutation of proline-rich motifs does not effect the cell proliferation but abrogates the cellular transformation ability of PTTG1. Figure 3 Colony formation of HEK293 cells stably transfected with pcDNA3.1, pcDNA3.1-PTTG1 or pcDNA3.1-m-PTTG1 a: HEKpcDNA3.1, b: HEKPTTG1-1, c: HEKPTTG1-3, d: HEKmPTTG1-2 and e: HEKmPTTG1-4. The data shown is representative of three independent experiments. PTTG1 induces tumor formation in nude mice injected with HEK293 cells stably expressing PTTG1 protein To determine whether PTTG1 promotes tumor formation in nude mice, we subcutaneously injected nude mice with HEK293 cells expressing PTTG1 or mPTTG1. Three out of four mice injected with the HEKPTTG1-1 or HEKPTTG1-3 cell lines developed large size tumors within four weeks of injection (Fig. 4). Pathologic analysis of the tumors revealed that they were poorly differentiated (Fig. 5). Mice injected with the HEKmPTTG1-2 cell line did develop tumors, but the tumors were of a small size. None of the mice injected with the other cell line-expressing mutant PTTG1 (HEKmPTTG1-4) or the vector-only cell line (HEKpcDNA3.1) developed tumors within the time frame of this experiment (Fig. 4). The tumor volumes, measured at the end of experiment (six weeks after injection of cells), were 150–1320 mm3 for HEKPTTG1-1, 72–1404 mm3 for HEKPTTG1-3 and 8.8–12.6 mm3 for HEKmPTTG1-2 (Table 1). These results clearly demonstrate that PTTG1 gene is a potent oncogene. Moreover, they demonstrate that PTTG1 possesses the ability to enhance the tumorigenic potential of immortal human cells and that it does not require the ectopic co-expression of other oncogene(s) to achieve its tumorigenic function. Figure 4 Tumor development in nu/nu mice on injection of HEK293 cells stably transfected with pcDNA3.1, pcDNA3.1PTTG or pcDNA3.1mPTTG1 cells. Each mouse was injected with 1 × 106 cells. After 6 weeks of injection, mice were photographed and sacrificed, tumors and other tissues were collected and tumor volume was measured a: Mouse injected with HEKpcDNA3.1 cells, b: mouse injected with HEKPTTG1-1 cells, c: mouse injected with HEKPTTG1-3 cells and d: mouse injected with HEKmPTTG1-2. Arrows indicate the tumors. Figure 5 Histolopathological analysis of the tumors excised from animals injected with HEK293 cells expressing PTTG1 or m-PTTG1. Tumors were fixed, sectioned and stained for H & E. A: Tumor from animal injected with PTTG1-1, B: Tumor from animal injected with HEKPTTG1-3. Table 1 Tumor formation induced by PTTG1 expressing HEK293 cells in nude mice Stable Cells Animals with tumor Tumor Volume HEKpcDNA3.1 0/4 NA HEKPTTG1-1 3/4 150–1320 mm3 HEKPTTG1-3 3/4 72–1404 mm3 HEKmPTTG1-4 2/4 8.8–12.6 mm3 HEKmPTTG1-2 0/4 NA PTTG1 stimulates expression and secretion of bFGF, VEGF and IL-8 Local invasive growth is a key feature of primary malignant tumors. A correlation between the levels of expression of PTTG1 with increased tumor invasiveness and with the degree of malignancy has been demonstrated in pituitary and colorectal tumors [9,35]. The specific mechanisms by which PTTG1 facilitates the invasive behaviors of tumor cells remain obscure, however. Recently, Ishikawa et al [18] and McCabe et al [19] have shown that transfection of NIH3T3 cells with PTTG1 cDNA results in an increase in secretion and expression of both bFGF and VEGF. A direct correlation between high IL-8 expression and tumor metastases has been shown in a number of cancers [36-38], and IL-8 also has been reported to possess mitogenic [39] and angiogenic effects [40]. We therefore measured the levels of bFGF, VEGF and IL-8 in HEK293 cells transiently transfected with pcDNA3.1 or pcDNA3.1-PTTG1 cDNA and in tumors developed on injection of nude mice with HEK293 cells that constitutively express PTTG1. As shown in Fig. 6A, the levels of bFGF, VEGF and IL-8 were comparatively higher in conditioned medium of cells transfected with pcDNA3.1-PTTG1 cDNA than from cells transfected with pcDNA3.1 vector only. Cells transfected with pcDNA3.1-PTTG1 showed a 2-fold increase in bFGF, a 3.5-fold increase in VEGF and a 2-fold increase in IL-8 levels compared to cells transfected with pcDNA3.1 vector only. Measurement of the mRNA levels of these factors by RT/PCR showed significantly higher levels in cells transfected with pcDNA3.1-PTTG1 cDNA compared to cells transfected with pcDNA3.1 vector (Fig. 6B). To determine, if over expression of PTTG1 results in increase in levels of bFGF, VEGF and IL-8 in vivo, we measured the levels of bFGF, VEGF and IL-8 proteins in lysates from tumors developed on injection of nude mice with HEK293 cells stably transfected with PTTG1. As shown in Fig. 7, the levels of bFGF, VEGF and IL-8 were significantly higher in three out of four tumors compared to normal tissues (kidney, liver, lung and heart) collected from the same animals. Since the size of the tumors that developed on injection of cells expressing mutated PTTG1 (HEKmPTTG1-2) were small, we were unable to analyze the bFGF, VEGF and IL-8 levels in these tumors. Measurement of mRNA for bFGF, VEGF and IL-8 revealed significantly higher levels of expression in tumors compared to normal tissues and tumors developed on injection of HEKmPTTG1-2 cells. As expected, levels of PTTG and mPTTG were higher in tumors as compared to normal tissues (Fig. 8). bFGF levels were found to be comparatively higher in heart which is consistent with other investigators [41] Taken together our results suggest that over expression of PTTG1 in HEK293 cells results in an increase in secretion and expression of bFGF, VEGF and IL-8 in vitro and in vivo, suggesting that increase in secretion and expression of bFGF, VEGF and IL-8 by PTTG1 may be one of the mechanisms by which PTTG1 achieves its oncogenic function and increases tumor angiogenesis. Figure 6 Introduction of PTTG in HEK293 cells induces secretion and expression of bFGF, VEGF and IL-8. HEK293 cells were transiently transfected with pcDNA3.1 or pcDNA3.1-PTTG1 cDNA. The culture medium was collected and lyophilized. bFGF, VEGF and IL-8 secreted in culture medium were measured by ELISA. A: Amount of bFGF, VEGF and IL-8 in culture medium. Vector: cells transfected with pcDNA3.1 vector DNA; PTTG1: Cells transfected with pcDNA3.1-PTTG1 cDNA. B: Expression of bFGF, VEGF and IL-8 mRNA in cells. Lane 1: pcDNA3.1 transfected cells and Lane 2: pcDNA3.1-PTTG1 transfected cells. GAPDH was used as an internal control. The data are representative of two independent experiments. Figure 7 Analysis of bFGF, VEGF and IL-8 expression in tumors and other tissues. Tumors and other tissues were excised from the animals injected with HEK293 cells stably transfected with PTTG1 (clone 1 and clone3) and homogenized. bFGF, VEGF and IL-8 in the homogenates were analyzed by ELISA. Each analysis was performed in triplicate tissue and was normalized to mg of protein. Error bars represent ± SEM of three independent experiments. Figure 8 Analysis of expression of PTTG bFGF, VEGF and IL-8 mRNAs from tumors and other tissues collected from mice subcutaneously injected with HEK293 cells stably expressing PTTG1. A: RT-PCR analysis and, B: Western blot analysis. T1: tumor 1, T2: tumor 2, M: HEKmPTTG1-2, H: heart, K: Kidney, Li: liver, Lu: lung. GAPDH and β-actin were used as control to examine equal loading. The gels are representative of two independent experiments. Discussion Oncogenic function of PTTG1 was established by its overexpression in mouse fibroblast cell line (NIH 3T3) followed by assessment of its ability to induce cellular transformation and tumor formation in nude mice [2,4]. However, the differences in biology between the rodent cells and human cells have brought the validity of this model into question. There are a number of instances in which an oncogene has been shown to induce transformation in rodent cells but has failed to induce transformation of same types of cells obtained from humans. To test the ability of PTTG1 to induce transformation in human cells, we selected the human embryonic kidney-293 (HEK293) cell line as our model. Our data clearly demonstrate that over expression of PTTG1 in HEK293 results in an increase in cell proliferation induces cellular transformation in-vitro (increase in anchorage-independent growth) and promotes tumor formation in nude mice. Cells transfected with pcDNA3.1 vector did not form colonies in soft agar or develop into tumors on implantation in nude mice, confirming that HEK293 cells do not possess a tumorigenic phenotype. Furthermore our data suggest that overexpression of PTTG1 in these cells results in a greater propensity for tumor development, a shortened latency period and an enhanced growth rate compared with pcDNA3.1-transfected control HEK293 cells. A second issue that we were able to address using the HEK293 cell model was the question of the ability of PTTG1 to induce transformation of normal human cells, i.e., whether it acts alone or in cooperation with another oncogene to achieve its tumorigenic function. It has been reported that a single oncogene may not be sufficient for induction of transformation but requires co-expression, or oncogenic cooperation, of another oncogene(s) to induce tumorigenesis in normal primary human cells [20,21,24,25,42,43]. Our results clearly show that PTTG possess the ability to induce cellular transformation in vitro and promotes tumor formation in nude mice. Since, HEK293 cells are transformed with adenovirus type 5 thus making them different from the normal primary cells, therefore it remains unclear if PTTG is sufficient by itself to initiate the tumorigenesis of primary human cells. However, the data clearly demonstrate that PTTG1 overexpression in these cells accelerates their tumorigenic capacity in comparison to that of unmodified cells. PTTG1 contains several-proline rich motifs (PXXP); two of these that are located in the C-terminal domain have been reported to be potential binding sites for SH3-domians [44]. In our study we confirm that mutation of these C-terminal proline-rich motifs abrogates the tumorigenicity of PTTG1 in human cells. Such loss of tumorigenicity on mutation of PTTG1 could be due to a loss of expression. Our western blot analysis of the stable cell lines (HEKmPTTG1-2 and HEKmPTTG1-4) that constitutively express mutated PTTG1 protein showed high levels of expression of mPTTG1 protein (Fig. 1), suggesting that the loss of tumorigenic function of mPTTG1 protein is not due to loss of expression but due to the loss of its ability to induce cellular transformation. These results are consistent with other investigators for rodent cells [2] and confirm the importance of C-terminal proline-rich motifs to mediate the oncogenic function of PTTG1. The molecular mechanisms by which PTTG1 achieves its tumorigenic function remain unclear. PTTG1 has been reported to induce expression of the c-myc oncogene [15], bFGF [18] and VEGF [19]. bFGF is a broad spectrum and pleiotropic mitogen for growth and differentiation affecting various mammalian cells and organ systems and a large number of cells lines [45,46]; besides stimulating wound healing, tissue repair and hematopoiesis [47], bFGF induces cell migration and proliferation [48] and acts as an agiogenic factor that induces migration, proliferation and differentiation of endothelial cells [49]. In addition, it has been reported to modulate the invasion of tumor cells through surrounding tissue to form new capillary cord structures by regulating the activities of extracellular molecules including collagenase, proteinases and integrins [49]. Regulation of secretion and expression of bFGF by PTTG1 in NIH 3T3 cells has been shown [18]. Consistent with these reports, our results demonstrate a significant increase in secretion and expression of bFGF in HEK293 cells on transient transfection with PTTG1 cDNA (Fig. 6) as well as in tumors developed by injection of stable cell lines that constitutively express PTTG1 both at protein and mRNAs levels (Figs. 6, 7, 8). VEGF is a potent stimulant of the vascularization of tumors and is one of the most specific markers of tumor vasculature observed to date [50,51]. VEGF is a multifunctional cytokine acting as a potent permeability agent, an endothelial cell chemotactic agent, an endothelial cell survival factor and an endothelial cell proliferation factor [52]. The expression and secretion of VEGF has been shown to be a crucial rate-limiting step during tumor progression [53]. Our results demonstrate a significant increase in secretion and expression of VEGF in HEK293 cells on transfection with PTTG1 and also from tumors excised from animals injected with HEK293 cells that stably express PTTG1 (Figs. 6, 7, 8). A direct correlation between high IL-8 expression and metastases in melanoma [36], ovarian cancer [37], prostrate cancer [38] and pancreatic cancer [51] has been reported. To determine if overexpression of PTTG1 induces change in secretion and expression of IL-8, we measured its levels in HEK293 cells on transfection with PTTG1 cDNA and in tumors developed on injection of HEK293 cells transfected with PTTG1. Our results demonstrate for the first time that overexpression of PTTG1 induces IL-8 expression in vitro and also in tumors in vivo (Figs. 6, 7, 8). Methods Generation of cell lines constitutively expressing PTTG1 The human embryonic kidney cell line (HEK293), which had been transfected by exposing these cells to sheared fragments of adenovirus type 5 DNA [28] was purchased from ATCC (American Type Culture Collection; Rockville, MD) and cultured according to the instructions provided. The cells were transfected with pcDNA3.1 vector, pcDNA3.1-PTTG1 or pcDNA3.1-mPTTG1 to generate stable clones that constitutively express human wild-type PTTG1 or mutated PTTG1 (mPTTG1) protein as described previously [4]. The mPTTG1, which carries a double amino acid change within the SH3 binding domain of PTTG1 (P163 to A163, P170 to A170 and P172 to A172, and P173 L173), was generated by site-directed mutagenesis using the Quick-change mutagenesis kit (Stratagene, La Jolla, CA) according to the manufacturer's instructions. Mutation of these amino acids has been reported to abrogate the tumorigenic function of PTTG1 and to block the secretion and expression of bFGF in mouse NIH3T3 cells [2]. The primers used for this site-directed mutagenesis were 5'-GATGCTCTCCGCACTCTGGGAATCCAATCTG-3' and 5'-TTCACAAGTTGAGGGGCGCCCAGCTGAAACAG-3'. The transfected cells were then selected in neomycin G418 (500 μg/ml) and the clones that expressed high levels of PTTG1 protein or mPTTG1 protein were selected. One clone from pcDNA3.1 transfected cells (HEKpcDNA3.1) two clones from pcDNA3.1-PTTG1 transfected cells (HEKPTTG1-1 and HEKPTTG1-3) and two clones from pcDNA3.1-mPTTG1 transfected cells (HEKmPTTG1-2 and HEKmPTTG1-4) were propagated into cell lines. Cell proliferation assay Cell proliferation was assayed using the CellTiter 96 non-radioactive cell proliferation assay kit (Promega, Madison, WI) according to the manufacturer's instructions and as described previously [4]. Briefly, cells growing in log phase were trypsinized and seeded in 96-well plates (5,000 cells/well in a final volume of 100 μl) in replicates of 4 and incubated at 37°C in 5% CO2 and 95% air. After incubation for 24 h, 48 h, 72 h or 96 h, 20 μl of dye solution from the kit was added to each well and incubated at 37°C for an additional 2 h. The quantity of formazon product was measured by its absorbance at 490 nm using a 96-well plate reader (Molecular Devices, Sunnyvale, CA). Each experiment was repeated at least three times. Soft agar colony formation (anchorage-independent cell growth) assay Anchorage-independent cell growth was determined by analyzing the formation of colonies in soft agar. Cells (104) from each cell line were suspended in 0.3% agar in DMEM containing 10% fetal bovine serum and plated on solidified agar (0.7%) in 35 mm dishes. After 14 days of culture, colonies formed were counted and photographed as described previously [4]. In vivo tumor growth assay Cells growing in log phase were harvested by trypsinization and washed twice with PBS. The cells were resuspended in PBS to a final concentration of 5 × 106/ml. The cells (1 × 106 cells in 200 μl PBS/site) were injected subcutaneously (s.c.) into both flanks of 5- to 6-week old female nu/nu mice (4 mice/group) (Charles River Laboratory, Wilmington, MA). All procedures were carried out following the protocol approved by The University of Louisville Institutional Animal Care and Use Committee. Four weeks after injection, the mice were scarified, and the tumors and other tissues harvested. The skin and connective tissues were dissected from the tumors, and the tumor volume was calculated from measurements of length × width × height. The tissues were divided into two parts, one part being fixed in 10% buffered formalin and the other stored in liquid nitrogen. For histopathologic analysis, 5 μm sections were cut from paraffin-embedded tissues, and mounted on slides. Sections were stained with H&E [52], and processed for histopathologic evaluations. Western blot analysis Cells growing in log phase were lysed in chilled lysis buffer [50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1% NP-40, 1 mM Na3VO4, and 1 mM NaF) supplemented with Complete Mini Protease Inhibitor tablets (Roche Molecular Biochemicals, Indianapolis, IN). Equal amounts of protein extracts (40 μg) were resolved on 12% SDS-PAGE gel, and transferred onto a nitrocellulose membrane (Amersham, Piscataway, NJ). Blots were probed with PTTG1 antiserum at a dilution of 1:1,500 as described previously [53]. Immunoreactive proteins were visualized using the Enhanced Chemiluminescent Detection System (Amersham) according to the instructions provided. ELISA analysis of bFGF, VEGF and IL-8 The levels of bFGF, VEGF and IL-8 in tissue culture supernatants and tissue homogenates were measured using commercially available ELISA kits from BD Biosciences (Minneapolis, MN). To measure bFGF, VEGF and IL-8 in the culture supernatants, HEK293 cells were transiently transfected with pcDNA3.1 or pcDNA3.1-PTTG1 cDNA using Fugene6 as the transfectant reagent as described previously [54]. After 24 h of transfection, the medium was replaced with serum free DMEM medium. Twenty-four later, the medium was collected and concentrated 5-fold (1.0 ml to 200 μl) using a speedVac system (Savant, Holbrook, NY). To measure bFGF, VEGF and IL-8 in tumor and other tissues, tissues were homogenized in 50 mM Tris (pH 7.4), 0.25% Triton X-100, 5 mM EDTA and 0.1% NP40 supplemented with Complete Mini Protease Inhibitor tablets (Roche Molecular Biochemicals, Indianapolis, IN) using a polytron homogenizer. Homogenates were centrifuged to remove particulate matter and then diluted with the diluent provided in the ELISA kit. The concentration of bFGF, VEGF and IL-8 in a sample was determined by interpolation from a standard curve. All measurements were normalized to protein concentration and performed in triplicate. Semiquantitative reverse transcriptase/polymerase chain reaction (RT/PCR) Total RNA from tumors and other tissues was purified using Trizol reagent (Invitrogen, Carlsbad, CA) following the manufacturer's instructions. The RNA pellets were resuspended in RNase-free water, and the contaminating DNA was removed from the preparations with DNaseI. The yield of total RNA was measured using a spectrophotometer, and the quality was assessed by electrophoresis through a 1% agarose gel. First strand cDNA was synthesized using the iScript™ cDNA synthesis kit (BioRad, Hercules, CA). PCR primers (Table 2) were designed, based on the human PTTG1, bFGF, VEGF and IL-8 cDNA sequences. The PCR conditions for each gene are listed in Table 1. GAPDH amplification was used as an internal control. Ten μl from a total of 50 μl PCR reaction mix was applied to a 2% agarose gel and after electrophoresis; the gel was stained with ethidium bromide to visualize PCR products. The densitometric values for the PCR-amplified products were quantified using BioRad software and normalized against the GAPDH values. Table 2 Primer sequences and PCR conditions for the amplification of PTTG1, bFGF, VEGF, IL-8, m-PTTG1 and GAPDH. Sense Primer Antisense Primer PCR Conditions PTTG ATGGCTACTCTGATCTAT AAAATCTATGTCACAGCAAAC 95°C 5 min, 95°C 30 s, 54°C 30 s, 72°C 30 s. 28 cycles. bFGF TTCTTCCTGCGCATCCACCC CTCTTAGCAGACATTGGAAG 95°C 5 min, 95°C 30 s, 56°C 30 s, 72°C 30 s. 26 cycles. VEGF GAATCATCACGAAGTGGTGA AACGCGAGTCTGTGTTTTTG 95°C 5 min, 95°C 30 s, 56°C 30 s, 72°C 30 s. 28 cycles. IL-8 ACCACCGGAAGGAACCATCT GAATTCTCAGCCCTCTTCAA 95°C 5 min, 95°C 30 s, 58°C 30 s, 72°C 30 s. 28 cycles. GAPDH TGATGACATCAAGAAGGTGGT TCCTTGGAGGCCATGTGGGCC 95°C 5 min, 95°C 30 s, 54°C 30 s, 72°C 30 s. 26 cycles. mPTTG GATGCTCTCCGCACTCTGGGAATCCAATCTG TTCACAAGTTGAGGGGCGCCCAGCTGAAACAG 95°C 5 min, 95°C 30 s, 54°C 30 s, 72°C 30 s. 35 cycles. Conclusion In summary, our results demonstrate that PTTG1 is a potent human oncogene and has the ability to induce cellular transformation of human cells. Overexpression of PTTG1 in HEK293 cells leads to an increase in the secretion and expression of bFGF, VEGF and IL-8. Mutation of C-terminal proline-rich motifs abrogates the oncogenic function of PTTG1. To our knowledge, this is the first study demonstrating the importance of PTTG1 in human tumorigenesis. Authors' contributions TH carried out the in-vivo studies, RT-PCR analysis, western blot analysis and drafted the manuscript. MTM generated stable clones for HEKmPTTG1-2 and 4, RT-PCR and ELISA analysis. 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==== Front Int J Behav Nutr Phys ActThe International Journal of Behavioral Nutrition and Physical Activity1479-5868BioMed Central London 1479-5868-1-171561756810.1186/1479-5868-1-17ResearchDiet and physical activity behavior among users of prescription weight loss medications Blanck Heidi Michels [email protected] Laura Kettel [email protected] Mary K [email protected] Division of Nutrition & Physical Activity, Centers for Disease Control and Prevention, Atlanta, GA, USA2004 23 12 2004 1 17 17 8 3 2004 23 12 2004 Copyright © 2004 Blanck et al; licensee BioMed Central Ltd.2004Blanck et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background There is limited population-based data on diet and physical activity behaviors and weight loss among users of prescription weight loss medications. Most findings are from clinical settings or from research that includes organized behavioral programs. Methods We analyzed data from the 1998 Behavioral Risk Factor Surveillance System, an annual telephone survey conducted in all fifty states, the District of Columbia and Puerto Rico. The sample consisted of 135,435 noninstitutionalized adults aged 18 years old and older. We determined the prevalence and odds of prescription weight loss medication use, odds of 10% weight loss, and among current weight loss medication users, the prevalence and odds for diet and physical activity behaviors. Results 10.2% of obese women and 3.1% of obese men reported using prescription weight loss medications in the past 2 years. Of users, 28.2% had lost at least 10% of their pretreatment body weight. The odds of losing at least this much weight were higher among women, those who usually consumed ≥ 5 fruits and vegetables daily and those who met physical activity recommendations. Among current prescription weight loss medication users, 26.7% reported both eating fewer calories and meeting recommended leisure-time physical activity levels (<40% of any group met both). Of those meeting both recommendations, almost half (47.2%) had lost 10% of their pretreatment body weight. Of current users, 9% reported using the medications for weight maintenance. Conclusions Only 26.7% of prescription weight loss medication users reported following recommended diet and physical activity behaviors. Further research is needed to assess whether behavioral changes are associated with greater weight loss and maintenance among prescription weight loss medication users. ==== Body Background Lifestyle modifications, including behavior therapy, diet, and physical activity, are the cornerstone of weight management [1]. Current weight management guidelines state that prescription weight loss medication only may be used as part of a comprehensive weight loss program including diet and physical activity modifications [1]. These modifications include increasing physical activity to 30 to 45 minutes on most days of the week and reducing daily caloric intake by 500 to 1000 calories. Clinical research also suggests that increasing consumption of whole grains, fruits, and vegetables in place of calorie-dense foods may increase satiety and decrease overall caloric intake aiding weight management [2]. The initial goal of weight loss therapy is to reduce body weight by approximately 10% over a 6-month period [1]. Even small losses in weight, such as 5% to 10%, have been found to reduce blood pressure, cholesterol and triglycerides levels, and reduce blood glucose levels among overweight and obese persons without diabetes [3,4]. Weight loss attributable to prescription weight loss drugs is modest, about 3% to 8% of body weight, and is based on a small number of patients assigned to drug treatment in clinical trials [5-8]. Lifestyle modification in combination with pharmacotherapy has been shown to improve weight loss more effectively than pharmacotherapy alone [9]. However, in the general population, data suggests that only one-fifth of individuals who were trying to lose weight reported using the recommended combination of eating fewer calories and meeting weekly physical activity recommendations [10]. Using population-based data we estimated that 4.6 million American adults used prescription weight loss medications between 1996 and 1998 [11]. During this period, obesity medications included phentermine, fenfluramine, dexfenfluramine, and sibutramine. Because of the paucity of population-based data on behaviors among persons using prescription weight loss medications, the purpose of our study was to further examine demographic and behavioral characteristics related to the reported use of prescription weight loss medications collected in the late 1990s as part of a national dataset. These data were collected the same year as issuance of guidelines for assessment and treatment of obesity and therefore provide baseline estimates. Specifically, we examined 1) prescription weight loss medication use by demographic and behavioral characteristics, 2) 10% weight loss among prescription weight loss medication users by demographic and behavioral characteristics, and 3) diet and physical activity behaviors among those who used prescription weight loss medications at the time of the survey. Methods We examined data from the 1998 Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS is a telephone survey of health practices that is conducted each year by all state health departments. Each state, the District of Columbia, and Puerto Rico select an independent probability sample of noninstitutionalized residents aged 18 years and older. In 1998, 149,806 persons responded to the BRFSS; several investigators have published detailed descriptions of survey methods and quality control indices [12]. The average 1998 state cooperation rate (completed interviews divided by completed, refused, and terminated interviews) was 73.4% (range, 45.4%–95.4%). To determine prescription weight loss medication use, respondents were asked, "In the past two years, have you taken any weight loss pills prescribed by a doctor? Do not include water pills or thyroid medications." Responses were coded as: 1) "Yes, I am currently taking them", 2) "Yes, I have taken them but I am not currently taking them", or 3) "No, I have not taken them". Respondents with a positive response were asked about their prepill weight, "How much did you weigh just before you started taking prescription weight loss pills for the first time?" At the end of the interview, respondents were asked to report their current height and weight without shoes. We used current weight and pretreatment weight to calculate current and pretreatment body mass index (BMI), respectively. BMI was calculated as weight (either current or pretreatment) in kilograms divided by current height in meters squared. BMI was categorized into 3 groups: normal weight (<25.0), overweight (25.0–29.9), and obese (≥ 30.0) [1]. Percent weight loss was defined as the difference between pretreatment body weight and current body weight [pretreatment body weight minus current body weight divided by pretreatment body weight times 100]. A 10% weight loss was used in the analyses as this is considered a reasonable individual initial weight loss goal by current clinical guidelines [1]. We used a 6-item question screener to assess the usual consumption of five or more fruits and vegetables per day [13]. Respondent were asked, 1) "How often do you drink fruit juices such as orange, grapefruit, or tomato? 2) Not counting juice, how often do you eat fruit? 3) How often do you eat green salad? 4) How often do you eat potatoes not including French fries, fried potatoes, or potato chips? 5) How often do you eat carrots?", And 6) "Not counting carrots, potatoes, or salad, how many servings of vegetables do you usually eat?" We used the following questions to determine weight control strategy, "Are you now trying to lose weight?" Those who responded "no" were then asked, "Are you now trying to maintain your current weight, that is to keep from gaining weight?" Additional questions on dietary practices and physical activity were asked of the subset of respondents (95,100) who responded "yes" to trying to lose or maintain their body weight. These respondents were asked "Are you eating either fewer calories or less fat to lose/maintain weight?" Their responses were either: 1) "Yes, fewer calories", 2) "Yes, less fat", 3) "Yes, fewer calories and less fat", or 4) "No". They were also asked "Are you using physical activity or exercise to lose/maintain weight? Their responses were either "yes" or "no". All individuals were also asked "In the past 12 months, has a doctor, nurse, or other health professional given you advice about your weight?" Responses were "Yes, lose weight", "Yes, gain weight", "Yes, maintain current weight", "No". In an earlier section individuals were asked, "About how long has it been since you last visited a doctor for a routine checkup? The latter question was used to distinguish those who had not received advice due to not seeing a doctor for a routine checkup in the past 12 months. For respondents who were currently using prescription weight loss medication and were trying to lose weight or maintain their current weight, we attempted to determine if they were meeting the minimal lifestyle recommendations. Respondents who stated they were eating fewer calories – either as fewer calories or as both fewer calories and less fat – were classified as meeting minimal dietary recommendations. Respondents were asked about the two physical activities or exercises they engage in most often and about the frequency, duration, and distance (as appropriate) of each activity [14]. Responses were then classified as one of 56 selected activities. Moderate activity was defined as any of the 56 selected activities, and vigorous activity was defined as aerobic physical activity classified as vigorous-intensity based on estimated metabolic expenditure (MET). To classify an activity as vigorous, it must be aerobic with an assigned MET value that was at least 60% of a person's maximal cardiorespiratory capacity. To have achieved recommended levels of physical activity, a person must have reported engaging in moderate-intensity physical activity ≥ 5 times per week for ≥ 30 minutes each time, vigorous-intensity physical activity ≥ 3 times per week for ≥ 20 minutes each time, or both during the preceding month. Persons reporting some activity during the preceding month but not enough to be classified as moderate or vigorous were classified as insufficient. Persons classified as inactive reported no physical activity outside of their occupation during the preceding month. In addition to these activity categories, we calculated minutes per week of leisure-time (non-occupational) physical activity levels. We also requested demographic and socioeconomic information such as age in years, sex, race/ethnicity, marital status, educational level, and household income. Of the 149,806 respondents, we excluded those who did not report information on prescription weight loss medication use (n = 1561), had a missing current weight (n = 5229) or height (n = 1369), were pregnant (n = 1780), were missing current weight goal (trying to lose weight or trying to maintain weight) (n = 749), or were missing information on sociodemographic variables, fruit and vegetable intake, or physical activity (n = 3595). We excluded an additional 88 respondents that had a questionable reported weight, height or BMI that was outside the sex-specific reference values from the Third National Health and Nutrition Examination Survey, 1989–1994 [15]. The final study sample included a total of 135,435 respondents. We used SAS and SUDAAN for the statistical analysis to account for the complex sampling design [16,17]. Student's t tests were used to test between-group differences for means and chi-square tests were used to test between-group differences for proportions. We set statistical significance at P < 0.05 for all comparisons. Multivariate logistic regression analyses were carried out to examine independent predictors of our outcome measures including pharmacotherapy use, 10% weight loss, and meeting diet and physical activity recommendations. Models of pharmacotherapy use were stratified by sex and adjusted for age, race/ethnicity, BMI, education level, household income, marital status, and geographic region. Models of 10% weight loss adjusted for sex, age, race/ethnicity, BMI, education level, household income, marital status, geographic region, smoking status, usual fruit and vegetable consumption, and past month leisure-time physical activity. Models of meeting both diet and physical activity recommendations adjusted for sex, age, race/ethnicity, BMI, education level, household income, marital status, geographic region, and smoking status. Results The results of our multivariate analyses show that the reported use of prescription weight loss medications in the past 2 years was higher among women (4.0%) than men (0.9%). For women, the odds of use was 19% lower among those aged 35–54 years and 75% lower among those 55 years old than among those aged <35 years. The odds of prescription weight loss medication use was 34% lower among non-Hispanic black women than among non-Hispanic white women; and 53% and 39% lower among those women residing in the Northeast and Midwest regions of the United States, respectively, than among women residing in the West region (Table 1). We also found the odds of prescription weight loss medication use was 62% higher among women with some college education than among those with less than a high school education. For men, the odds of use was 62% higher in Hispanics than among non-Hispanic whites. Table 1 Prevalence of Prescription Weight Loss Medication Use in the Previous 2 Years by Characteristics. Women Men (N = 78127) (N = 57308) n % (SE) OR* (95% CI) n % (SE) OR* (95% CI) Characteristic Age group (yrs) ≥ 55 26787 1.61 (0.14) 0.25 (0.20–0.32) 16484 0.87 (0.13) 0.92 (0.58–1.47) 35–54 31256 5.54 (0.22) 0.81 (0.71–0.93) 24320 1.06 (0.09) 1.03 (0.73–1.45) 18–34 20084 4.80 (0.23) 1.00 16504 0.64 (0.10) 1.00 Race/ethnicity Other 2613 2.80 (0.72) 0.69 (0.40–1.13) 2245 0.26 (0.10) 0.41 (0.20–0.86) Hispanic 6248 5.31 (0.48) 1.12 (0.91–1.38) 4447 1.26 (0.27) 1.62 (1.02–2.57) Non-Hispanic black 7086 3.90 (0.35) 0.66 (0.54–0.80) 4027 0.58 (0.15) 0.69 (0.40–1.19) Non-Hispanic white 62180 3.93 (0.12) 1.00 46589 0.88 (0.07) 1.00 Body mass index (kg/m2) ≥ 30 14296 10.2 (0.42) 2.27 (1.98–2.60) 10566 3.11 (0.26) 6.24 (4.43–8.79) 25.0–29.9 22005 4.88 (0.23) 1.00 26036 0.51 (0.08) 1.00 <25.0 41826 1.45 (0.10) 0.24 (0.20–0.28) 20706 0.20 (0.04) 0.41 (0.24–0.69) Education level College graduate 19203 3.50 (0.23) 1.13 (0.86–1.48) 17525 0.92 (0.11) 1.00 (0.61–1.64) Some college 22179 5.24 (0.25) 1.62 (1.28–2.06) 15151 0.93 (0.13) 1.04 (0.63–1.69) High school 26614 3.83 (0.18) 1.26 (1.00–1.59) 17840 0.75 (0.10) 0.85 (0.52–1.39) <High school 10026 3.00 (0.30) 1.00 6734 0.92 (0.19) 1.00 Household income Don't know/refused 11947 2.14 (0.21) 0.88 (0.69–1.13) 6402 0.55 (0.18) 0.90 (0.42–1.95) ≥ $75,000 6859 5.13 (0.41) 2.06 (1.60–2.65) 7523 1.41 (0.19) 2.24 (1.38–3.62) $50–74,999 9044 5.24 (0.38) 1.68 (1.34–2.10) 8690 1.03 (0.15) 1.51 (0.91–2.52) $35–49,999 12108 5.19 (0.35) 1.54 (1.24–1.92) 10975 0.88 (0.16) 1.28 (0.75–2.19) $20–34,999 19766 3.91 (0.22) 1.16 (0.97–1.39) 14929 0.68 (0.10) 0.99 (0.61–1.62) <$20,000 18403 3.52 (0.24) 1.00 8789 0.72 (0.16) 1.00 Marital status† Married 39608 4.28 (0.16) 0.90 (0.79–1.04) 33318 1.00 (0.08) 1.02 (0.74–1.42) Not married 38428 3.72 (0.1) 1.00 23923 0.65 (0.09) 1.00 Geographic region South 17887 5.11 (0.34) 0.90 (0.77–1.07) 14385 0.86 (0.17) 1.3 (0.83–2.04) Northeast 26747 4.70 (0.18) 0.47 (0.37–0.60) 18007 1.11 (0.11) 0.85 (0.50–1.44) Midwest 13508 2.32 (0.22) 0.61 (0.51–0.73) 10165 0.72 (0.12) 0.71 (0.44–1.15) West 18524 3.48 (0.21) 1.00 13839 0.62 (0.08) 1.00 Models are adjusted for age, race/ethnicity, body mass index, education level, household income, marital status, and geographic region. †Marital status: respondents were classified as "not married if they were divorced, widowed, separated, never been married, member of an unmarried couple. Women who were obese had more than two times the odds of prescription weight loss medication use compared to women who were overweight and men who were obese had more than six times the odds of use compared to men who were overweight. In addition, women and men who had an annual household income of at least $75,000 had more than two times the odds of prescription weight loss medication use compared to those respondents with an annual household income <$20,000. At the time of the survey, the average percentage of body weight that was reported lost by prescription weight loss medication users was 8.1% for women and 7.0% for men. About one fifth of users gained weight (women: 19.8%, men: 16.5%), one tenth lost no weight (women: 12.9%, men: 13.4%), one-tenth lost between 1% and 5% (women: 12.7%, men 21.0%), and one fifth lost between 5% and 9% of body weight (women: 19.6%, men: 24.1%), data not shown. Additionally among users, one-third of women (35.0%) and one quarter of men (25.0%) reported a 10% weight loss, Table 2. The odds of having a 10% weight loss was 37% less among men than women and 43% less among those 55 years old and older than among those 18–34 years. Those who were obese at pretreatment had more than twice the odds of having a 10% weight loss compared to those who were overweight. The odds of having a 10% weight loss was 39% higher among those who were current smokers compared to non-smokers. Those who met minimal recommendations for leisure-time physical activity had more than two twice the odds compared to those who were inactive. The odds of a 10% weight loss was 64% higher among those who usually consumed 5 or more fruits and vegetables daily than among those who usually consumed less than 3 fruits and vegetables a day. Inclusion of weekly minutes of physical activity instead of the meeting physical activity recommendations variable into the 10% weight loss final model resulted in significant odds ratios for individuals with 150–<320 minutes odds ratio (OR) 1.50 (95% confidence interval (C.I.) 1.09–2.17), 320–<420 minutes OR 2.10 (95% C.I. 1.33–3.31), and OR 1.61 (95% C.I. 0.98–2.64) compared to those individuals reporting less than 150 minutes. Table 2 Prevalence of 10% Weight Loss Among Past 2 Year Users of Prescription Weight Loss Medication. Characteristic n % (SE) OR 95% CI Sex Men 497 25.0 (2.8) 0.63 (0.44–0.92) Women 3012 35.0 (1.4) 1.00 Age group (yrs) ≥ 55 506 23.1 (2.9) 0.57 (0.38–0.85) 35–54 1927 34.0 (1.8) 0.95 (0.73–1.24) 18–34 1076 36.7 (2.3) 1.00 Race/ethnicity Hispanic/other 350 30.4 (7.4) 0.77 (0.53–1.14) Non-Hispanic black 258 35.2 (4.6) 0.76 (0.50–1.15) Non-Hispanic white 2404 35.6 (1.6) 1.00 Pretreatment body mass index (kg/m2) ≥ 30 1799 45.5 (2.0) 2.24 (1.17–2.94) 25.0–29.9 269 27.2 (2.3) 1.00 <25.0 292 7.1 (1.7) 0.22 (0.13–0.38) Education level College graduate 675 33.1 (2.9) 1.00 (0.62–1.61) Some college/tech 1106 37.2 (2.5) 1.16 (0.74–1.82) High school 993 35.9 (2.4) 1.35 (0.89–2.06) <High school 237 27.5 (4.4) 1.00 Household income Don't know/refused 231 32.8 (5.1) 0.80 (0.46–1.37) ≥ $75,000 343 33.0 (3.9) 0.66 (0.41–1.06) $50–74,999 467 33.4 (3.4) 0.65 (0.41–1.01) $35–49,999 625 32.2 (3.1) 0.66 (0.44–1.00) $20–34,999 800 36.5 (2.9) 0.82 (0.57–1.19) <$20,000 564 39.9 (3.6) 1.00 Marital status Married 1696 33.4 (1.8) 0.92 (0.70–1.21) Not married 1314 37.4 (2.4) 1.00 Geographic region South 1184 32.9 (1.8) 1.01 (0.74–1.39) Northeast 243 30.9 (4.8) 0.91 (0.70–1.21) Midwest 653 40.9 (3.2) 1.13 (0.79–1.64) West 884 34.9 (3.3) 1.00 Smoking status Current smoker 695 39.8 (3.2) 1.39 (1.04–1.86) Former smoker 709 30.1 (2.8) 0.87 (0.65–1.17) Never smoker 1608 34.7 (1.9) 1.00 Usual fruit and vegetable consumption ≥ 5 day 693 29.1 (3.1) 1.64 (1.19–2.27) 3–4 day 1205 35.4 (2.3) 1.16 (0.90–1.51) <3 day 1114 30.2 (4.4) 1.00 Past month leisure-time physical activity† Meeting recommendations 1331 42.9 (2.2) 2.19 (1.60–2.98) Insufficient 834 30.3 (2.5) 1.16 (0.83–1.60) Inactive 847 26.6 (2.7) 1.00 Models are adjusted for sex, age, race/ethnicity, body mass index, education level, household income, marital status, geographic region, smoking status, usual fruit and vegetable consumption, and leisure-time physical activity in the past month. †To have achieved recommended levels of physical activity, a person must have reported engaging in moderate-intensity physical activity ≥ 5 times per week for ≥ 30 minutes each time, vigorous-intensity physical activity ≥ 3 times per week for ≥ 20 minutes each time, or both during the preceding month [14]. We also assessed professional advice, diet, and physical activity among current prescription weight loss medication users by current weight intention (Table 3). Although most of the respondents who reported current medication use were trying to lose weight, 9% reported they were trying to maintain weight. Examination of past year health care professional advice about weight among those who had seen a doctor for a routine checkup showed that almost three quarters of those trying to lose weight had been advised to lose weight. However, almost one-quarter of current prescription weight loss medication users who were trying to lose weight were given no advice about their weight. Over half of users who were trying to maintain their weight were given no advice about their weight. Assessment of usual fruit and vegetable consumption showed that about one quarter of those trying to lose weight (28.3%) and one quarter of those trying to maintain their weight (27.6%) reported consuming 5 fruits and vegetables a day. Almost two thirds of current prescription weight loss medication users who reported they were trying to lose weight reported reducing caloric consumption (62.0%), 70.9% reported increasing physical activity, and 47.3% met physical activity recommendations. However, these behaviors were reported less often in current prescription weight loss medication users who reported they were trying to maintain their weight, 39.6%, 49.6%, and 29.9%, respectively. A similar proportion of weekly minute categories were observed among those trying to lose weight and those trying to maintain weight, with 8.0% and 9.7% respectively, reporting at least 420 minutes per week. Table 3 Prevalence of Professional Advice, Diet, and Physical Activity Behaviors Among Current Users of Prescription Weight Loss Medication. Current Weight Management Intention Lose Weight Maintain Weight Total (n = 563) (n = 59) (n = 622) % (SE) % (SE) % (SE) Health professional past year weight advice Lose weight 73.9 (3.6) 35.6 (10.7) 71.0 (3.4) Maintain weight 1.9 (0.6) 7.2 (5.2) 2.5 (0.7) Gain weight 0.1 (0.1) 0.0 (0.0) 0.3 (0.2) No advice 24.1 (3.5) 57.2 (10.6) 26.3 (3.3) Diet Fewer calories for weight control 62.0 (3.2) 39.6 (7.5) 60.2 (3.0) ≥ 5 fruits and vegetables daily 28.3 (3.4) 27.6 (7.7) 28.2 (3.1) Physical activity Increased physical activity for weight control 70.9 (3.4) 49.6 (9.3) 69.1 (3.2) Past month Inactive 25.1 (3.5) 47.9 (9.5) 27.0 (3.3) Insufficient 27.6 (3.3) 22.2 (7.1) 27.2 (3.1) Meeting recommendations** 47.3 (3.8) 29.9 (7.7) 45.8 (3.5) Past month (weekly minutes) <150 minutes 53.0 (4.1) 58.4 (11.1) 52.1 (3.9 150–<320 minutes 27.9 (3.8) 20.2 (8.0) 27.5 (3.5) 320–<420 minutes 11.1 (2.8) 11.7 (5.6) 11.1 (2.6) ≥ 420 minutes 8.0 (1.9) 9.7 (7.1) 9.4 (1.9) Among those who reported that had visited a doctor for a routine checkup in the past 12 months (n = 514). †To have achieved recommended levels of physical activity, a person must have reported engaging in moderate-intensity physical activity ≥ 5 times per week for ≥ 30 minutes each time, vigorous-intensity physical activity ≥ 3 times per week for ≥ 20 minutes each time, or both during the preceding month [14]. Only one-quarter (26.7%) of current prescription weight loss medication users met both lifestyle recommendations – eating fewer calories and attaining recommended physical activity levels. Our results indicate that among current users, the odds of meeting both recommendations was 62% lower among men than among women and 56% lower among other race/ethnic groups than among non-Hispanic whites (Table 4). We also found that the odds of meeting both recommendations was more than three time higher among those with a household income of at least $75,000 (OR 3.23) compared to those with a household income <$20,000 and those who usually consumed 5 or more fruits and vegetables a day had twice the odds compared to < 3 fruits and vegetables a day. In a sensitivity analysis, we found that adding to this model past year health professional advice about weight among those who had a routine checkup (n = 559) did not appreciably change the results and that advice to lose weight was not an independent predictor of meeting both recommendations (OR = 1.37, 95% C.I. 0.73–2.56). Table 4 Meeting diet (fewer calories) and Physical Activity Recommendations* Among Current Users of Prescription Weight Loss Medication. Characteristic n Total (SE) OR† 95% CI Sex Men 98 14.8 (4.4) 0.38 (0.17–0.84) Women 524 29.7 (3.3) 1.00 Age group (yr) ≥ 55 91 21.6 (6.9) 0.92 (0.34–2.46) 35–54 338 29.5 (4.1) 1.10 (0.58–2.11) 18–34 193 25.7 (4.1) 1.00 Race/ethnicity Other 154 16.9 (4.8) 0.44 (0.20–0.96) Non-Hispanic white 468 32.1 (3.4) 1.00 Pretreatment body mass index (kg/m2) ≥ 30.0 403 25.7 (3.1) 1.02 (0.54–1.91) <30.0 187 28.8 (6.0) 1.00 Education level College graduate 147 29.8 (6.4) 1.78 (0.76–4.15) Some college/tech 201 34.7 (5.1) 1.97 (1.00–3.90) <High school 274 19.7 (4.0) 1.00 Household income ≥ $75,000 91 39.0 (7.4) 3.23 (1.18–8.83) $50–74,999 80 29.0 (2.9) 2.49 (0.85–7.25) $35–49,999 140 32.4 (5.9) 2.45 (0.82–7.35) $20–34,999 157 27.1 (4.7) 2.14 (0.89–5.14) <$20,000‡ 154 14.9 (3.9) 1.00 Marital status Married 370 28.0 (4.9) 0.87 (0.47–1.59) Not married 252 24.6 (4.0) 1.00 Geographic region South 241 27.6 (4.0) 0.78 (0.34–1.78) Midwest/Northeast 189 26.1 (4.3) 1.03 (0.45–2.32) West 159 26.5 (6.3) 1.00 Usual fruit and vegetable consumption ≥ 5 day 158 34.7 (6.4) 2.30 (1.05–5.03) 3–4 day 227 26.3 (4.1) 1.30 (0.67–2.53) <3 day 237 21.3 (3.8) 1.00 Smoking Current smoker 149 31.5 (6.6) 1.78 (0.94–3.36) Former smoker 146 26.4 (5.5) 1.10 (0.54–2.26) Never smoker 327 24.3 (3.3) 1.00 *To have achieved recommended levels of physical activity, a person must have reported engaging in moderate-intensity physical activity ≥ 5 times per week for ≥ 30 minutes each time, vigorous-intensity physical activity ≥ 3 times per week for ≥ 20 minutes each time, or both during the preceding month [14]. †Models are adjusted for sex, age, race/ethnicity, body mass index, education level, household income, marital status, geographic region, smoking status, and usual fruit and vegetable consumption. ‡Includes "Don't know" and "Refused" annual household income responses. In a secondary analyses we assessed prevalence of a 10% weight loss among current prescription weight loss medication users who were trying to lose weight (not maintain) by whether or not recommendations were met for eating fewer calories and physical activity (n = 237). 39.6% (SE 5.3) of individuals who reported both eating fewer calories and who met physical activity recommendations had lost at least 10% of their pretreatment weight; 36.5% (SE 6.4) who reported eating fewer calories but who did not meet the physical activity recommendations had lost at least 10% of their pretreatment weight; 54.3% (SE 9.8) who reported not eating fewer calories but met physical activity recommendations had lost at least 10% of their pretreatment weight; and 25.5% (SE 5.9) who did not meet either lifestyle recommendation had lost at least 10% of their pretreatment weight, (chi-square p = 0.03). Discussion Although in 1998 clinical guidelines recommended that prescription medication for obesity be used in conjunction with lifestyle modifications [1], we found that during that same year only one quarter of current prescription weight loss medication users reported minimal diet (eating fewer calories) and physical activity behaviors recommended for weight loss. Our analyses indicated that meeting these recommendations was low across all sociodemographic groups (<40%) and, men were less likely to meet recommendations compared to women. Although patterns of prescription medication use differed by sex, for both men and women the odds of use was higher among those who were obese compared to those who were overweight although at different magnitudes. Our data show than more overweight women used prescription weight loss medications (4.88%) than did obese men (3.11%). It is possible that pharmacotherapy prescribing patterns differ by sex since more women than men attempt weight loss [10] or that for men, a higher BMI is needed before personal action regarding weight is taken. Eating fruits and vegetables and taking part in recommended levels of physical activity through leisure-time activities were more frequent behaviors among prescription weight loss medications users who had lost at least 10% of their body weight. Although we had no direct measurements of caloric intake, fruits and vegetables in their natural state are low in energy density. Thus, adding or substituting fruits and vegetables for energy-dense snacks and sweets can impact satiety and weight gain [2,18,19]. Our analyses also indicate that those respondents who met the minimal recommendations for leisure-time physical activity had twice the odds of having lost 10% of their body weight compared to those who were sedentary. We observed higher odds of 10% weight loss for those with at least 150 weekly minutes, but the magnitude did not appear to have a dose effect. We believe additional research is needed to determine whether physical activity modifications require greater duration, such as exercising 60 minutes or more daily to prevent weight regain as has been suggested by the Institute of Medicine [20], to improve the efficacy of currently approved obesity pharmacotherapy for weight management. About 40% of current prescription weight loss medications users who both reduced calories and met leisure-time physical activity recommendations had lost 10% of their body weight. Our data also show that respondents who consumed usually less than 5 fruits and vegetables per day were less likely to meet both diet and physical activity recommendations than did those who consumed 5 or more fruits and vegetables a day. Because fewer than half of physicians counsel obese patients about weight control [21,22] we believe the opportunity exists for physicians and other health care professionals to provide counseling that emphasizes appropriate weight control practices that include regular physical activity and a balanced low-calorie diet which includes increasing consumption of fruits and vegetables. The current study had several limitations. Although the participants self-reported pre-medication body weight and current weight, we did not ask for the date prescription weight loss medication use began and it could not be established whether weight loss was solely attributable to pharmacotherapy usage. We were also unable to determine if respondents had lost a substantial amount of weight but then regained the weight (weight history). Self-reported weights and weight history may also be a concern because individuals, especially those who are overweight, may underreport their weight [23]. We lacked dietary assessment measures to quantify the number of calories consumed. Thus, we could not determine whether individuals had actually reduced their caloric consumption to levels that would lead to substantial weight loss. Research suggests that some obese subjects underestimate their caloric intake and overestimate their physical activity [24]. In addition, we lacked data on whether respondents were counseled specifically about physical activity and/or nutrition for weight management or had undergone behavioral therapy. Although we found that about one fifth of participants had lost between 5% and 9% of their pretreatment body weight and one third of participants had lost at least 10% of their pretreatment body weight, the pill dosage and length of medication use were not collected. Thus, some users may have used pills for a short time, such as less than 6 months, and would not be expected to have lost 5% to 10% of their body weight. In current practice, physicians treating obesity can prescribe phentermine, sibutramine, and orlistat (available April 1999). Analysis of national data on patient visits found that phentermine was the most common antiobesity medication in 2001 and in early 2002 (based on January through March figures) [25], suggesting that our data, although collected in 1998, has relevance to current weight management. Most nonsurgical obesity treatments lead to weight loss for the first 6 months followed by regain [26]. Thus, some health care professionals suggest that prescription weight loss medication be prescribed either to enhance weight loss during the active weight-loss phase or to prevent later regain [26,27]. In our study, 9% of current medication users reported that they were currently trying to maintain their weight. We observed that the behaviors of these individuals differed from those who said they were currently trying to lose weight, e.g. fewer reported eating less calories, fewer met physical-activity recommendations, and fewer were advised by a health care professional to either lose or maintain weight. Additional research is needed on use of pharmacotherapy for weight maintenance. Conclusions We found that one in ten obese women and one in 33 obese men reported using a prescription weight loss medication in the past two years. Among medication users, one-third of women and one quarter of men reported a 10% weight loss. Only one quarter of current medication users reported the minimal diet and physical activity behaviors recommended for weight loss. Ongoing population-based surveillance is needed to assess the prevalence of prescription weight loss medication use and whether pharmacotherapy users are making appropriate lifestyle changes in order to lose or maintain weight. List of abbreviations BRFSS: Behavioral Risk Factor Surveillance System BMI: body mass index OR: odds ratio 95% C.I.: 95% confidence interval Competing interests The author(s) declare that they have no competing interests. The questions that were asked of all authors: Have you received reimbursements, fees, funding, or salary from an organization that may in any way gain or lose financially from the publication of this paper in the past five years? No. Have you held any stocks or shares in an organization that may in any way gain or lose financially from the publication of this paper? No. Do you have any other financial competing interests? No. Are there any non-financial competing interests you would like to declare in relation to this paper? No. Author contributions HB performed the statistical analysis and drafted the manuscript. LKK conceived the study questions, provided statistical input, and edited the manuscript. MKS conceived the study questions, provided statistical input, and edited the manuscript. All authors read and approved the final manuscript. 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==== Front BMC Med EthicsBMC Medical Ethics1472-6939BioMed Central London 1472-6939-6-110.1186/1472-6939-6-1Research ArticlePublic appraisal of government efforts and participation intent in medico-ethical policymaking in Japan: a large scale national survey concerning brain death and organ transplant Sato Hajime [email protected] Akira [email protected] Ichiro [email protected] Department of Public Health, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan2 Department of Biomedical Ethics, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan3 Department of Social Gerontology, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan2005 20 1 2005 6 1 1 12 10 2004 20 1 2005 Copyright © 2005 Sato et al; licensee BioMed Central Ltd.2005Sato et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Public satisfaction with policy process influences the legitimacy and acceptance of policies, and conditions the future political process, especially when contending ethical value judgments are involved. On the other hand, public involvement is required if effective policy is to be developed and accepted. Methods Using the data from a large-scale national opinion survey, this study evaluates public appraisal of past government efforts to legalize organ transplant from brain-dead bodies in Japan, and examines the public's intent to participate in future policy. Results A relatively large percentage of people became aware of the issue when government actions were initiated, and many increasingly formed their own opinions on the policy in question. However, a significant number (43.3%) remained unaware of any legislative efforts, and only 26.3% of those who were aware provided positive appraisals of the policymaking process. Furthermore, a majority of respondents (61.8%) indicated unwillingness to participate in future policy discussions of bioethical issues. Multivariate analysis revealed the following factors are associated with positive appraisals of policy development: greater age; earlier opinion formation; and familiarity with donor cards. Factors associated with likelihood of future participation in policy discussion include younger age, earlier attention to the issue, and knowledge of past government efforts. Those unwilling to participate cited as their reasons that experts are more knowledgeable and that the issues are too complex. Conclusions Results of an opinion survey in Japan were presented, and a set of factors statistically associated with them were discussed. Further efforts to improve policy making process on bioethical issues are desirable. ==== Body Background In Japan, it was not until 1997 that a law was finally enacted to legalize organ transplant from a brain-dead body. Since 1968, when the first heart transplantation from a person declared brain dead was performed, there have been long-standing struggles in Japan for and against this procedure. In addition to many non-governmental institutions and individuals, the Japanese government – both the legislature and administrative bodies – engaged in a variety of efforts for this enactment. A number of factors have been suggested for the prolonged lack of policy in this area: deep public mistrust of the medical profession caused by the 1968 heart transplant; the Japanese culture which still holds traditional Japanese view of death and the body; and the lack of the broad public consensus required as a precondition for a policy [1]. As elsewhere, public policy to resolve these social disputes was pursued [2]. Observers of the past policy process toward enactment offer contradictory evaluations: those for the speedy introduction of new medical technologies, for example, complain that possible organ recipients have suffered from the prolonged, impractical, and fruitless policy disputes, while those against hasty use of immature and controversial technologies have a good appraisal of the care exercised in past debates. Some argue that even now there are many unsettled issues. Previously, there has been no systematic study assessing past policy processes, factors that affect public appraisal of government efforts, and what future agenda items should include to ensure successful policy enactment. In the policy process, which entails the introduction and implementation of a policy, public opinion is generally considered an important factor affecting its fate. Without favorable public opinion, a policy cannot be introduced and implemented effectively [3]. Public opinion regarding the process of policymaking is another important indicator of how well the government functions. This public appraisal essentially measures the degree of congruence between the public's expectations of government actions and perceived fulfillment of these ideals. When contending ethical value judgments are involved, satisfaction with the process affects the legitimacy of political institutions and processes. This, in turn, could influence the fate of proposed policies and condition future policy making [4]. Public opinion thus constitutes valuable information for determining how government bodies can and should proceed. An important aspect in policy making is the degree of public participation, which is defined as a set of measures to consult with, involve, and inform the public to encourage participation in policy development [5]. Since experts, policymakers, and citizens all are limited in some aspects of their knowledge, public involvement is expected to improve the substantive quality of decisions, by incorporating public values, assumptions, and preferences. Public involvement also works toward educating the public, fostering trust in institutions, and reducing conflicts [6]. Citizen involvement legitimizes government efforts at policymaking, by lending credibility and thus increasing public trust in the political process. This study examines public appraisal of past efforts of the Japanese government to legalize organ transplant from brain-dead bodies. Other goals are to quantify the public's intent to participate in future policy discussions and to identify possible factors affecting both appraisals and intentions to participate. A brief chronology of Japan's efforts since the 1960's toward legalization of organ transplant upon brain death is also presented. Implications of the study findings are discussed, as are suggestions for future research. Methods Questionnaire and subjects A questionnaire includes sections on demographic characteristics (age, sex, education, occupation), health conditions (hospitalization in the past five years, current health condition), period of first issue attention (when did you first hear of the issue on brain death and organ transplant?), period of opinion formation (when did you arrive at the opinion you have now on this issue?), knowledge about donor card (are you familiar with the donor card, which indicates a personal directive to donate organs when determined to be brain-dead?), knowledge of past government efforts at informing the public and inviting their opinions (asking number of measures employed by the government), appraisal of past government efforts (How do you rate past government efforts around the issue of brain death and organ transplant?), and intention to participate in future (bioethics-related) policy discussions. Those who indicated unwillingness to participate were asked to provide reasons for that decision. The final questions concerned important agenda items for future policy processes. Questionnaires were sent to 3000 people, selected from 15 cities and towns nationwide by the stratified random sampling method. They were requested to answer the questions on the sheets, and send them back by mail in a postage prepaid envelope. The study was conducted in January 2002, and the overall response rate was 34.5%. Statistical analysis First, association of the questionnaire items both with a) appraisal of past government efforts and b) participation intent was examined using Mann-whitney tests (between two groups), Kruskal-Wallis tests (among multiple groups), and/or Spearman's rank correlations. Next, a multiple logistic regression model was applied to identify possible explanatory variables (entered and removed at the significance level of p = 0.05) to determine a set of variables that best predicts the dependent variables. Also, stepwise logistic regression analysis was conducted to determine factors affecting people's selection of important future agenda items. Additional materials Extant opinion polls regarding public perceptions of brain death and organ transplants from brain-dead patients were studied to identify possible trends. When more than one poll was conducted in a year, results were averaged to avoid possible biases deriving from different survey designs. The following national polls were used to plot the trends in opinions: Yomiuri Shimbun (1982, 1984–95, 1997–99); Asahi Shumbun (1985, 1988, 1992, 1996–98); Mainichi Shimbun (1985, 1990–91, 1997); Office of the Prime Minister (1987, 1991, 1998); Nippon Hoso Kyokai (1991–92, 1996); and Jiji Tsushin (1992, 1994) [7]. Results Descriptive statistics indicating basic attributes of study participants, as well as all other survey results, are shown in Table 1. A relatively large percentage of people responded that they became aware of the issue either at the point when the Ministry of Health and Welfare (MHW) drew up the diagnostic criteria for brain death (23.7%), or when the Organ Transplant Act was adopted (33.6%). Most people (97.2%) were aware of the issue. The majority of respondents (69.9%) crystallized their opinions either when the Act was first adopted or around the time when the first organ transplant was conducted from a brain-dead donor. Close to half of the respondents (43.3%) were completely unaware of any governmental efforts regarding the issue, while the remainder were divided in their appraisal of those efforts (26.3% positive, 30.6% negative ratings). A majority (61.8%) responded that they would not participate in future policy discussions on bioethical issues. Table 1 Description of subjects Attribute Category: number (frequency %); and/or average (sd) Age 20s: 81 (8.1), 30s: 130 (13.1), 40s: 186 (18.7), 50s: 231 (23.2), 60s: 218 (21.9), over 70: 149 (14.9) Sex male: 491 (49.7), female: 498 (50.4) Education junior high: 170 (17.2), senior high: 459 (46.4), vocational: 87 (8.8), vocational high: 10 (1.0), community college: 72 (7.3), college: 182 (18.4), grad school: 10 (1.0) Occupation private enterprise: 268 (27.3), civil service: 58 (5.9), self-employed: 153 (15.6), part-time: 100 (10.2), housekeeping: 240 (24.4), student: 18 (1.8), others: 147 (14.9) Hospitalization (past 5 years) none: 756 (76.0), once: 181 (18.2), repeated for a disease: 33 (3.3), several times for multiple reasons: 25 (2.5) CurrentHealthCondition healthy: 359 (36.2), relatively healthy: 545 (55.0), unhealthy: 87 (8.8) FirstAttentionPeriod Per1 : 230 (23.7), Per2: 114 (11.8), Per3: 138 (14.2), Per4: 326 (33.6), Per5: 135 (13.9), Per7: 27 (2.8) OpinionFormationPeriod Per2: 97 (11.0), Per3: 73 (8.3), Per4: 257 (29.2), Per5: 188 (21.4), Per7: 265 (30.1) KnowDonorCard yes: 894 (91.0), no: 89 (9.1) KnowGovtEfforts (score range: 0–7) 0: 92 (9.2), 1: 295 (29.5), 2: 233 (23.3), 3: 168 (16.8), 4: 129 (12.9), 5: 60 (6.0), 6:22 (2.2); average: 2.22 (1.48); Cronbach's alpha: 0.603 Appraisal of past governmental efforts (score range: 1–5) sufficient: 35 (3.6), relatively sufficient: 222 (22.7), relatively insufficient: 178 (18.2), insufficient: 121 (12.4), do not know any efforts: 424 (43.3) Average:3.69 (1.32) Participation intent(score range: 1–4) yes: 35 (3.5), relatively yes: 344 (34.7), relatively no: 531 (53.5), no: 82 (8.3) Note: Notation of Periods; Per1:1985 (MHW Brain death standard), Per2:1989 (Brain death ad hoc council set), Per3:1992 (Brain death ad hoc council rep), Per4:1997 (Law adopted), Per5:1999 (TPBD first conducted), Per6:2002 (15 cases done), Per7:(not yet accepted, not yet decided, not interested). KnowGovtEfforts: Score of the knowledge about the past government efforts, calculated as the number of items known. Items comprise Opinion polls & hearings, Expert councils, Public statements and announcements, Referendum, Town meeting & roundtables, Public comments, and Others. Figure 1 shows the general trends in public opinion concerning brain death and organ transplant from brain-dead bodies, the cumulative proportion of people who attended to the issue, and the cumulative proportion of people who initially formed their personal opinions at the point of study. Since the early 1980, Japanese acceptance of the concept of brain death steadily increased, while disapproval minimally declined. As public acceptance of organ transplants from brain-dead bodies increased, so did public opposition, meaning that more people were forming opinions than in the past. This is further indicated in the trend study: The proportion of people attending to the issue consistently increased over time, as did solidification of personal opinions, although on a smaller scale. Figure 1 Trend of public opinion on brain death and organ transplant in Japan Table 2 presents the correlation of factors associated with appraisal of past government efforts or personal intent to participate in future policy discussion. Sex, time period of first attention to the issue, period of opinion formation, knowledge of donor card, and knowledge of past government efforts were significantly associated with appraisal. On the other hand, age, education, period of first attention to the issue, period of opinion formation, and knowledge of past government efforts were associated with participation intent. For both appraisal and participation, there were similar tendencies observed. Respondents indicate more positive appraisals or greater participation intent when they are older, male, attended to or formed opinion of the issue earlier, are aware of the donor card, and more knowledgeable about past governmental efforts. Table 2 Determinants of public appraisal towards governmental efforts, and of public intention to participate in policy discussion (Bivariate analysis) Appraisal score [best = 1, worst = 5] Participation score [most = 1, least = 4] Category: average appraisal score + sd, and/or Spearman's correlation coefficient (rho) p-value Category: average appraisal score + sd, and/or Spearman's correlation coefficient (rho) p-value Age rho = -0.062 0.053 (3) rho = -0.085 0.009(3) male (3.58 + 0.06), female (3.80 + 0.06) 0.002 (1) male (2.64 + 0.71), female (2.71 + 0.03) 0.086 (1) Education junior high (3.73 + 1.39), senior high (3.76 + 1.37), vocational (3.1 + 1.20), vocational high (3.69 + 1.31), community college (3.73 + 1.28), college (3.83 + 1.47), grad school (3.17 + 1.47) 0.545 (2) junior high (2.73 + 0.75), senior high (2.73 + 0.61), vocational (2.60 + 0.67), vocational high (2.70 + 0.48), community college (2.69 + 0.75), college (2.49 + 0.73), grad school (2.36 + 0.67) 0.004 (2) Occupation 1 (3.74 + 1.35), 2 (3.80 + 1.27),3(3.79 + 1.36), 4 (3.92 + 1.30), 5 (3.79 + 1.41), 6 (3.42 + 1.30), 7 (3.60 + 1.38) 0.746 (2) 1(2.63 + 0.66), 2(2.60 + 0.72),3(2.64 + 0.67), 4(2.70 + 0.69), 5(2.70 + 0.66), 6(2.32 + 0.75), 7(2.71 + 0.69) 0.287 (2) Hospitalization 5 yrs rho = -0.021 0.510 (3) rho = 0.009 0.614 (2) 0.777 (3) CurrentHealthCondition rho = 0.013 0.689 (3) rho = 0.042 0.343 (2) 0.200(3) FirstAttentionPeriod Per1 (3.52 + 1.37), Per2 (3.75 + 1.33), Per3 (3.50 + 1.34), Per4 (3.73 + 1.37), Per5 (4.30 + 1.18), Per7 (4.31 + 1.46); rho = 0.210 0.000 (2) 0.000 (3) Per1 (3.52 + 1.37), Per2 (3.75 + 1.33), Per3 (3.50 + 1.34), Per4 (3.73 + 1.37), Per5 (4.30 + 1.18), Per7 (4.31 + 1.46); rho = 0.181 0.000(2) 0.000(3) OpinionFormationPeriod Per2 (3.40 + 1.30), Per3 (3.32 + 1.39), Per4 (3.45 + 1.34), Per5 (3.68 + 1.32), Per7 (4.27 + 1.24), others(3.98 + 1.38); rho = 0.269 0.000 (2) 0.000 (3) Per2 (3.40 + 1.30), Per3 (3.32 + 1.39), Per4 (3.45 + 1.34), Per5 (3.68 + 1.32), Per7 (4.27 + 1.24); rho = 0.204 0.000(2) 0.000 (3) KnowDonorCard yes (3.62 + 1.32), no (4.31 + 1.23) 0.000 (1) yes (2.66 + 0.67), no(2.80 + 0.70) 0.084(1) KnowGovtEfforts rho = -0.083 0.009 (2) rho = -0.175 0.000(3) Participation intent rho = 0.061 0.056 (3) Appraisal score rho = 0.061 0.056(3) Note: Significance in appraisal score across groups were tested by (1) Mann-whitney tests (between two groups), (2) Kruskal-Wallis tests (among multiple groups), and/or (3) Spearman's rank correlations. Moreover, when people are older, they are more likely to have recognized the issue and formed their opinions earlier, but know less about donor cards and past governmental efforts. Knowledge of donor cards and of government efforts were positively correlated, as were period of first attention to the issue and period of opinion formation. There was no significant correlation between appraisal and participation intent. Table 3 shows the results of multiple regression analyses, with the appraisal score and the participation intent score as dependent variables. A combination of age, period of opinion formation, and knowledge of the donor card best predicts public appraisal of the past governmental efforts, while a combination of age, period of first attention to the issue, period of opinion formation, and knowledge of governmental efforts best predicts individual intention to participate in future policy discussions. Individuals had more favorable appraisals of government actions when they were older, formed their opinions earlier, and knew about donor cards. On the other hand, individuals indicated greater intention to participate in future policy discussions when they were younger, paid attention to the issue earlier, formed their opinions earlier, and had more knowledge about past government efforts. Table 3 Predictors of appraisal/participation (stepwise multiple regression analysis) Selected independent variables (coeff + sd, p-value, 95% CI) Model adjusted-R2 (p-value) Appraisal score as a dependent variable [best = 1, worst = 5] Age (-0.059 + 0.030, 0.049, -0.117 - -0.000)Opinion Formation Period (0.196 + 0.027, 0.000, 0.144 - 0.248) Know Donor Card (0.583 + 0.168, 0.001, 0.253 - 0.913) 0.085 (0.000) Participation intent as a dependent variable [most = 1, least = 4] Age (0.043 + 0.016, 0.008, 0.011 - 0.075) First Attention Period (0.053 + 0.017, 0.002, 0.019 - 0.087) Opinion Formation Period (0.064 + 0.015, 0.000, 0.034 - 0.093) Know Govt Efforts (-0.048 + 0.016, 0.003, -0.080 - -0.017) 0.070 (0.000) Note: Age (20s = 1, 30s = 2, 40s = 3, 50s = 4, 60s = 5, over70 = 6), Know Donor Card (know = 0, do not know = 1), Notation of other variables explained in Table 1 (Note). As shown in Table 4, respondents (42.3–69.7%) indicated that the opinions of patients, experts, and citizens should be more respected in policymaking, and also that more information disclosure is desirable. About 20% of people thought that the time spent for policymaking was not appropriate, either too long or too short. Very few people believed that government opinion should be weighted more, or that the process timeline is just right (status quo). Bivariate analyses disclosed no significant relationship of the selection of particular future agenda items either with the appraisal of government efforts or with participation intent. Results of multiple logistic regression analysis indicated that knowledge of past government efforts is related to all the future agenda items, except for one (more government opinion). Among those items associated, all but status quo were more likely to be chosen as knowledge of past government efforts increased. Those who were unwilling to participate in future policy discussion cited several reasons for their decision: Experts know better (50.1%); Issue is difficult (44.9%); Participation is ineffective (21.6%); Too busy (14.1%); and Not interested (7.9%). Table 4 Important future agenda and their predictors Agenda Yes/No: numbers (%) Predictors of respondents who selected the items: selected independent variables (odds ratio + sd, p-value, 95% CI) Model peudo-R2 (p-value) More disclosure Yes: 695 (69.6) No: 304 (30.4) Education (1.178 + 0.060, 0.001, 1.065 - 1.302) KnowGovtEfforts (1.629 + 0.112, 0.000, 1.424 - 1.865) FirstAttentionPeriod (0.873 + 0.049, 0.016, 0.782 - 0.975) 0.104 (0.000) More citizen opinion Yes: 423 (42.3) No: 576 (57.7) KnowGovtEfforts (1.267 + 0.061, 0.000, 1.153 - 1.393) 0.021 (0.000) More patients' opinion Yes: 696 (69.7) No: 303 (30.3) Age (0.853 + 0.048, 0.005, 0.764 - 0.952) Sex (1.593 + 0.257, 0.004, 1.161 - 2.186) KnowGovtEfforts (1.443 + 0.088, 0.000, 1.280 - 1.627) 0.064 (0.000) More experts' opinion Yes: 565 (56.6) No: 434 (43.4) Age (1.122 + 0.056, 0.020, 1.018 - 1.236) KnowGovtEfforts (1.392 + 0.073, 0.000, 1.255 - 1.543) 0.038 (0.000) More gov't opinion Yes: 9 (0.9) No: 990 (99.1) Age (2.337 + 0.836, 0.018, 1.160 - 4.712) 0.098 (0.006) More time Yes: 211 (21.1) No: 788 (78.9) Age (1.214 + 0.077, 0.002, 1.071 - 1.375) Health condition (1.686 + 0.247, 0.000, 1.265 - 2.246) KnowGovtEfforts (1.255 + 0.073, 0.000, 1.119 - 1.407) 0.043 (0.000) More Speedy process Yes: 205 (20.5) No: 794 (79.5) Age (0.880 + 0.052, 0.030, 0.784 - 0.988) OpinionFormationPeriod (0.875 + 0.046, 0.011, 0.789 - 0.969) KnowGovtEfforts (1.243 + 0.071, 0.000, 1.112 - 1.391) 0.034 (0.000) Status quo Yes: 12 (1.2) No: 987 (98.8) KnowGovtEfforts (0.182 + 0.093, 0.001, 0.067 - 0.494) 0.199 (0.000) Possible explanatory (independent) variables for stepwise logistic regression analysis: KnowGovtEfforts, Age, Sex, Education, Occupation, Hospitalization, CurrentHealthCondition, FirstAttentionPeriod, OpinionFormationPeriod, KnowDonorCard, Appraisal score, and Participation intent. Discussion Using data obtained in an opinion survey, this study seeks to evaluate public appraisal of past government efforts to legalize organ transplant from brain-dead bodies in Japan. Even though public opinion is relatively unstable and sometimes an irrational response to surrounding symbols, it continues to be an important factor in policy advocacy. Public acceptance of a policy is considered important not only for the practical reason that adopted policy cannot be implemented effectively and efficiently without public consent, but also for the ideological democratic point of view that policy is to be based on the judgment of a rational, informed and willing public, or at least on conscious delegation of individual autonomy to expert authorities [8]. The degree of public approval also conditions the future course of events. In this context, efforts to keep relevant public informed are quite important, as are those toward the accomplishment of public policymaking. Policy advocates should generate and meet public expectations of responsible and reliable government actions, either by assuming a leadership role to generate public expectations or by taking a conforming posture to help meet those expectations. Chronology of the act on organ transplant in Japan The chronology of the major events leading to enactment of the Organ Transplant Act and the successful implementation of the first several operations in Japan has been described elsewhere in detail [9]. Here we present a succinct summary. An act which enabled cornea transplant operations with the consent of the family was passed in 1957. In the same time period, kidney (1956-) and liver (1964-) transplants also commenced. The first heart transplant in Japan was performed at Sapporo Medical College in 1968. Since brain death had not been officially established, there was concern about a possible conflict of interest, and some believed that the extraction of a heart was murder. Extended efforts have taken place since then, to develop criteria for death, specifically medical and biological definition and diagnosis, their social use, and how to determine a human/ individual death in relation to the criteria. In the medical field, a committee of the EEG Society published standards for diagnosing brain death in 1974. The donor card system was sanctioned by the government shortly thereafter in 1977, without defining the criteria for death. In the meantime, organ transplant was sporadically performed with the consent of families, but without any official regulation of the process. Consequently, the transplant of cadaveric organs spread only gradually, since harvesting organs from brain-dead bodies continued to invoke public dispute and sometimes resulted in lawsuits. In 1984, for example, when the first multiple transplant (a combined kidney/pancreas transplant) was performed from a brain-dead body at Tsukuba University, the Patients' Rights Conference (PRC) soon filed a charge of murder in the case. The medical community began to advocate legislation governing brain death and organ transplantation more openly. In 1985, the MHW announced its diagnostic criteria for brain death, though it stated that a patient's death couldn't be judged by brain death. [Period 1]. In 1986, the Japan Medical Association formed the Bioethics Discussion Group, a study group of interdisciplinary nature, and in 1988 issued its Final Report, which encouraged brain death legislation to facilitate organ transplantation. With the goal of shaping public opinion, the Japan Organ Transplantation Society sponsored a series of open symposia in 1989. [Period 2]. A group of politicians from the major party started investigating the current situation in other countries, considering possible legislation. Activities of patients' groups reportedly helped shift public attention away from the brain-dead potential donor to the seriously ill person who needs a transplant [10,11]. At the same time, opposition was increasing, especially from the PRC, the Japanese Society of Psychiatry and Neurology, and the Japan Federation of Bar Associations. These groups called for a (social) consensus, unitary and conclusive definition of death [12]. Due to these public debates and advocacy efforts, media coverage on brain death and organ transplant increased dramatically. Finally, in early 1990, more than 30 years after the first transplant, the office of the Prime Minister established a special commission, Provisional Commission for the Study of Brain Death and Organ Transplantation. To encourage public involvement, the Commission held a series of public hearings and town meetings, and issued newsletters. Its 1991 interim and 1992 final reports presented both a majority view and a minority view. The former stated that a social consensus on brain death had already been achieved, and the latter argued that such a consensus had not yet been achieved. Both groups approved organ transplant when the consent of the donor was definitely obtained. A number of scholars argued that it should be a personal decision whether or not one's death is to be determined by brain death criteria, making individual consent the basis of both brain death and organ donation [13]. [Period 3]. A bill to legalize the transplantation of organs was presented to the Diet first in 1994. After several years of discussion, the modified bill finally became law in October 1997. Organ transplantation was thereby legalized where the donor has given written consent both to transplant and to the determination of brain death. Brain death was accepted only in such a case to enable organ transplants. In practice, the patient's family can still override the prior consent decision. [Period 4]. In 1999, two years after the law was passed, the first heart transplantation was successfully conducted, at Osaka University Hospital [14]. That same year, the second and third cases were successfully operated at the National Cardiovascular Center. [Period 5]. Though several issues, such as privacy protection and information disclosure, coordination of donors and recipients, as well as medical expense coverage, were raised during this series of successful operations, strong opposition to the law was no longer voiced. By 2002, 15 organ transplants had been conducted from brain-dead bodies. [Period 6]. Issue attention and opinion formation As was indicated in Figure 1, though it is difficult to make qualitative judgments, the extended debates and struggles helped increase public awareness and knowledge of the issue, leading many to form policy preferences. More than anything else, the official diagnostic guidelines for brain death (1985) and the legislation (1997) increased public attention to the issue, and facilitated opinion formation (Proportion of people varied across time periods significantly at p = 0.05, by chi-squared tests). These official actions, accompanied by wide media coverage, mobilized a previously inattentive public through their social conspicuousness. Our results also indicate that as the number of people approving organ transplants from brain-dead bodies increased, the number of opponents increased in parallel, although in smaller numbers. This increase in political awareness or in political knowledge, as suggested elsewhere [15], led to increased polarization of attitude reports, resulting in the wider division between policy opinions. It should also be noted, however, that significant numbers of people had not formed their opinions until the first case of organ transplant was (successfully) achieved, and that about 30% of people remain undecided. The former appear to have waited to see what were the real consequences of the technology, i.e., its success or failure, as well as the social reaction. The latter are either watching for future developments or uninterested. As in other countries, the public debates on brain death and organ transplant, both in the private sphere and in the public sphere, were new attempts at governance of socio-technical innovation in the field of biomedicine. If social mobilization is fueled by the inability of the institutional system to respond adequately to public concerns, the issue status in Japan in the 1980s, when many individuals and institutions started to pay attention and get involved in the debate, might indicate insufficient mediation of the actors for conflict resolution (by the government) before and during that period. Generally in post-war Japan, a relatively small number of political and administrative elites have left the handling of many social conflicts to the workings of traditional social relations [16]. Similarly, the government, for a long time, largely left issue of brain death to medical communities and to a set of mobilized individuals and groups. The sporadic but recurrent implementation of transplant operations under no official rules made latent value conflicts manifest, randomly shaping the political landscape. Although lack of government action exacerbated social disputes, which in turn inhibited government intervention, it helped increase public awareness of the issue. Official actions were thus preceded by these social disputes. In the early 1990s when the Ad-hoc Council was set up, the Japanese government introduced a variety of measures to resolve social disputes by inviting the public into policy discussions. Our finding that many people recognized the issue and formed their opinions at times other than this period, however, suggests that these tactics were not very effective in terms of raising public awareness. In European countries, it was reported, public involvement measures served well as focusing devices, which helped attract attention and facilitate discussion among the various public [17]. The difference between Japan and European countries might be attributed to the difference in their participatory nature, as expected and instilled by these measures. At face value, the Japanese measures are designed to increase public involvement and active mobilization, but instead they function more as mechanisms for public consultation. A widespread norm of situational decision making, perceiving events and making judgments while experiencing them, could also induce people to reserve their judgments and opinion formation until implementation [18] Public appraisal of the past government efforts It is remarkable that more than 40% of respondents were unaware of any past government policy, despite longstanding struggles around the issue and much media coverage. Of those who were aware, about half of them had favorable opinions of government efforts, while a slightly larger percentage had negative views. The fact that only 30% of respondents reported satisfaction indicates that there is much room to improve public awareness, acceptance, and appraisal in the policy process on bioethical issues. Multiple regression analysis disclosed that age, period of opinion formation, and knowledge of donor cards are independent factors affecting public appraisal of past governmental efforts. Individuals tended to give higher appraisal points when they were older. Indeed, age seems to be a major factor in opinion formation on several policy issues [19]. Perhaps older people are more concerned with the issue of death and how it is defined because of its imminence and also because older people are more inclined to the traditional and community-oriented viewpoints. This in turn makes them more aware of and inclined to accept and praise a careful policy deliberation process. Younger people are, on the other hand, more free from traditional values and more concerned about individual rights and liberty, as was suggested by our finding that younger people were more likely to indicate that greater respect for patients' opinions and a speedier process are important items for future agendas. They could therefore have been frustrated by the time-consuming search for a social and unified definition of death. The finding that a higher appraisal was given by those people who made up their minds by the early debates and events, and by those who were aware of the donor card, might suggest that these people again value the freewheeling but deliberate process without any dictatorship. A national effort to incorporate ethical considerations into policy rests on an academic reservoir of technical experts, legal scholars and humanists, and on the public understanding of science and its social implications, as well [20]. The prolonged absence of direct leadership or clear policy provided society with ample time and opportunity for public debate, which is a collective learning process through a set of exchanges of viewpoints and/or social confrontations. After the early struggles which searched for unified value judgments, the policy discussions gradually shifted more to the social rules allowing adversarial opinions. Through these deliberations, many people have come to realize that organ transplant can be an acceptable and promising medical therapy so long as the donor's human rights are protected, and that the policy can protect the common good by tolerating divergent values and allowing individual choice of death criteria at the time of organ donation. This long social debate, which bore fruit in the enactment of a law, was considered an acceptable and even necessary step, by attentive members of the public, even though the debates were not necessarily strategically planned. According to Taylor and Fiske [21], people react critically to the arguments they encounter only to the extent that they are knowledgeable about political affairs. Hill [22] argues that ordinary citizens are rational only to a limited extent, but capable of good judgment when they have access to reliable facts and interpretations prepared by experts. In light of these arguments, the finding that the more attentive members of the public provided a better appraisal is promising, especially if those appraisals are more informed and rational than those rendered by the less attentive. If measures are taken to better inform the public of past policy discussions and of current policy as well, the public could be more content with government activities. In this context, the expert role of informing the public of past policy discussions would be critical to nurturing opinions [23]. In relation to this point, it should be noted that some changes in the policymaking process were regarded as important. A majority of respondents suggested that better information disclosure and more respect for both patients' and experts' opinions are desirable in the policy process. It follows that more effective involvement of the public, especially those stakeholders, in policymaking is warranted. Although an empirical assessment is not available, some procedures used in France and Germany might merit attention in modeling future policymaking for other countries. The National Consultative Bioethics Committee of France holds an annual public conference where, in addition to an activity report from the Committee, many ethical topics are discussed, with the participation of both experts and laypeople [24]. The German Reference Center for Ethics in the Life Sciences functions as a clearinghouse and library, open both to researchers, policymakers, and the public, while the German National Ethics Council holds conferences and issues newsletters both of which are open to the public [25]. Regular activities of this kind, targeting and involving the public, could be expected to increase the base of attentive, informed, and rational citizenry. Public involvement in the future In many countries, participation has gained momentum in a variety of policy domains [26]. In health care decisions, public participation, public involvement, or public consultation is considered desirable and even necessary by both policymakers and members of the public [27]. The participation process is used to obtain information from, and to provide information to, the community, which helps ensure fair, transparent and legitimate decision-making and garner support for the outcomes of the process [28,29]. In our study, a majority of people (61.8%) responded that they would not participate in future policy discussions, while the rest (38.2%) responded that they would. The absence of association between government appraisal and participation intent indicates that the latter is determined by factors other than the former. Multivariate analysis showed that younger age, earlier period of first attention, earlier period of opinion formation, and more knowledge of past governmental efforts are positively associated with the intent to participate in future policy discussions. This indicates that the more attentive members of the public, namely those long-term observers with their own opinions and knowledge of current policy, have more interest and consequently a greater intention to participate in policy discussions. This finding is consistent with past studies indicating that participation is facilitated by policy knowledge and/or political sophistication [30]. Positive association of knowledge of past governmental efforts with participation intent might, more specifically, suggest that a variety of measures newly employed by the government to incorporate public opinion, such as public hearings and comments, town meetings, and expert councils, were welcomed by the public, and somehow inspired their participation in the policymaking process. Reasons cited for being unwilling to participate in the process indicate that many feel unqualified or unknowledgeable but not necessarily too busy or uninterested. Further analysis revealed that older people are more likely to cite "Experts better" and "Ineffective", and are less likely to choose "Busy"; that females are more likely to cite "Difficult", and that people tend to cite "Ineffective" when they are more knowledgeable about past government efforts, while choose "Busy" or "Uninterested" when they are less knowledgeable. These findings suggest that despite their latent interest in the issue, people are unwilling to commit themselves to policy discussions because of their perception of inadequacy stemming both from their lack of knowledge and sense of inefficacy, as judged from past experiences. The absence of an association between appraisal and participation suggests that people might be uncertain about their own competence and efficacy in policymaking. It can be inferred that people hope for a means of understanding the issue, so as to formulate their opinions for themselves. Political participation is facilitated by having a personal stake and perceived self-efficacy in policymaking. Conversely, it could be hampered by both indifference to the issue and a sense of powerlessness [31]. More specifically, the factors influencing participation encompass the structural and social context of the population as predisposing factors (e.g., income and education), the institutional context for decision-making as an enabling factor (e.g., the activities of media, governments and other institutions), and the role of interests and interest groups as precipitating factors. Different degrees of public participation and different kinds of public involvement measures could be potent enabling factors affecting participation intent [32]. People are more willing to be involved in decision-making when there is a guarantee that their contributions will be heard and that decisions taken following consultation will be explained [33]. For the public to be effectively mobilized into policy debates, they must feel assured they are sufficiently informed and can assume an important role in policymaking [34]. Essentially, people will become involved if they believe they have the proper tools and their efforts will make a difference. In the context of Japan, it is important to remember that public involvement measures thus far used were not very effective in raising public awareness and that people consider some changes desirable in the policymaking process. It is possible that more people could be inspired to participate by changing the design of public involvement measures, from a consultation type of involvement to a partnership model [35]. Efforts to keep people informed, to help them understand the issues, to generate spheres for public deliberation, while at the same time creating mechanisms to ensure their voices will be heard in the policy process, could help mobilize the public and facilitate a discursive formation of opinion among them [36]. Both the public and the policymakers should acknowledge the important role citizens can play in policy discussions around biomedical ethics [37]. More innovative methods of public participation show promise and deserve consideration in improving policy process on medico-ethical issues and increasing public satisfaction with policy and politics. These include consensus conferences, citizens' juries, scenario workshops, deliberative opinion polls, among others [38]. Distinct from traditional opinion surveys, these methodologies seem intended to redress the deficiencies in citizen ability, such as limited expertise and attention to the issue among laypeople, and seek to elicit informed and rational choices. In some cases, debates at conferences are publicized through mass media, to raise public awareness of the issue and invite further social discussion [39,40]. Though overall satisfaction with and acceptance of these measures by the public has not been fully documented, the innovative methods reportedly had considerable success in increasing public awareness of an issue and facilitating logical and comprehensive discussion, which served as the basis for subsequent legislation [41]. Study limitations and future research agenda As is always the case with mass opinion surveys, this study cannot escape the possible bias introduced by the low response rates of polls [42]. A sampling with a disproportionately large number of the attentive public, omitting those with moderate positions, may result in opinion polarization, exaggerating the true conditions, while missing attentive part of the public can cause opinion neutralization, overlooking some important traits. These issues should be addressed in future research, hopefully also validating findings through the comparison of different studies. As was noted above, public participation in policy-making is a trend in many countries. The generalizability of our findings should be carefully tested by empirical studies. Among many topics to be considered for future research is the function of (mass) media vis-à-vis public opinion formation. The media should be examined critically as they influence both experts, policymakers, and the public. Mass media have a dual function in these processes: as a conduit of debates and negotiations as well as a source of influence [43]. Also on public side, the possibility of an active role for audiences in meaning creation should be explored. This study, without directly asking individual policy preferences, fell short of proposing or validating any theoretical model of opinion formation. The accumulation of knowledge by experimental and innovative public participation measures, such as deliberative polls, might answer some of these as yet unanswered questions. Conclusions Government decisions and their outcomes, namely the enactment and subsequent implementation of organ transplants, attracted public attention and helped formulate public opinion on the issue, more than did the processes leading to enactment. In the case of the concept of brain death and the legalization of organ transplant in Japan, many people still were unaware of past government efforts in policymaking, including the measures used for public involvement, despite past longstanding social debates. Only a small percentage of the public indicated satisfaction with the process. However, those who were attentive to the issue, knowledgeable of the past policy process as well as of the current policy, tended to rate the policy process more positively. Although people do not always manifest their intent to participate in future policy discussions, they might maintain sufficient interest in biomedical issues and have a latent wish to get involved in the policy process. Competing interests The author(s) declare that they have no competing interests. Authors' contributions All the authors (HS, AA, IK) fully participated in the planning, designing, and carrying out of the surveys for this study. HS performed the statistical analysis and drafted the article. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgments This study was conducted with a research grant to the project on "Strategies for Social Consensus Development concerning Advanced Medical Technologies (2001–2002)", from the Ministry of Education, Culture, Sports, Science and Technology. 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==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-101565924310.1186/1471-2105-6-10Methodology ArticleModelling the correlation between the activities of adjacent genes in drosophila Thygesen Helene H [email protected] Aeilko H [email protected] Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, Netherlands2005 19 1 2005 6 10 10 20 7 2004 19 1 2005 Copyright © 2005 Thygesen and Zwinderman; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Correlation between the expression levels of genes which are located close to each other on the genome has been found in various organisms, including yeast, drosophila and humans. Since such a correlation could be explained by several biochemical, evolutionary, genetic and technological factors, there is a need for statistical models that correspond to specific biological models for the correlation structure. Results We modelled the pairwise correlation between the expressions of the genes in a Drosophila microarray experiment as a normal mixture under Fisher's z-transform, and fitted the model to the correlations of expressions of adjacent as well as non-adjacent genes. We also analyzed simulated data for comparison. The model provided a good fit to the data. Further, correlation between the activities of two genes could, in most cases, be attributed to either of two factors: the two genes both being active in the same age group (adult or embryo), or the two genes being in proximity of each other on the chromosome. The interaction between these two factors was weak. Conclusions Correlation between the activities of adjacent genes is higher than between non-adjacent genes. In the data we analyzed, this appeared, for the most part, to be a constant effect that applied to all pairs of adjacent genes. ==== Body Background Several studies (Hamilton [1], Fukuoka[2]) have found stronger correlation between the expression levels of genes that are located close to each other on the genome than between those of distant genes: when gene expressions of many genes are measured for multiple tissue samples, for example using microarray technology, adjacent genes are sometimes found to be consistently up- or downregulated in a subset of the tissue samples. Gene expression is influenced by many factors (for a review, see Orphanides[3]), many of which could influence the correlation between the expression of two genes in general, and that between two adjacent genes in particular. Of particular interest are chromatin domains. DNA can exist in either one of two states: a condensed state, termed heterochromatin, which is broadly inaccessible to transcription (although there are exceptions (Orphanides[3])), and an active state, termed euchromatin. A chromatin domain (a segment of DNA which, in a given cell at a given moment, is either entirely euchromatin or entirely heterochromatin) typically spans several genes (Roy[4]). Therefore, one would expect the expressions of two adjacent genes to tend to be positively correlated, at least if it was possible to measure transcription in individual cells. If the chromatin state was completely random (Jackson[5]) suggested a dynamic equilibrium, where chromatin fluctuates, to some extent randomly, between the two states), the effect of chromatin domains would vanish when gene expression is measured in pools of many cells, as with microarray technology. However, there is ample evidence for non-randomness. For example, chromatin states tend to be preserved after cell division (Orphanides[3]). And Cho[6] demonstrated that the states of chromatin domains in yeast are related to the cell cycle. In addition to the chromatin theory, several other explanations have been suggested for the apparent correlation between the expressions of adjacent genes. Several authors (Cohen[7], Kruglyak[8]) have noted that divergent gene pairs show stronger correlation than tandem and convergent pairs, possibly because divergent pairs share an Upstream Activation Sequence. Lercher[9] found that many of the co-expressed adjacent genes in Caenorhabditis elegans are either operons or homologues (see also Llorente[10] and Rossfll]), and it has been suggested that evolution has arranged for functionally related genes to be located close to each other, either in order to promote consistent inheritance (Bleiweiss[12]), or in order to benefit from the correlation accounted for by the chromatin domains (Cohen[7]). Parisi[13] found a nonrandom distribution of the chromosomal location of genes with high expression level in testis and ovaria in Drosophila. Jackson[5] suggested that the location of a gene in the nucleus plays a role for its transcription, in relation to gradients of the concentration of transcription factors. Finally, since the action of a transcription factor on a promotor gets weaker with distance, genes belonging to the same pathway should show stronger correlation if they are located close to each other (Dorsett [14]). Due to this abundance of alternative theories, a study of gene-expression correlations should be designed in a way that makes it possible to distinguish correlation structures predicted by one model from those predicted by other models. The same applies to the statistical analysis techniques used. An important consequence of the evolution-based theories is that they predict a consistent coregulation structure. Suppose that two genes (in this case, two adjacent genes) are co-regulated because, for example, they participate in the same pathway. They would, then, show a strong correlation because they would be co-regulated in all tissue samples. This need not necessarily be the case with the chromatin domain model: the segments of euchromatin in one tissue sample may overlap with those in another tissue sample. This latter scenario, we call an inconsistent coregulation structure. With consistent coregulation, adjacent gene pairs will show either strong positive correlation, or they will be uncorrelated. With inconsistent coregulation, all adjacent gene pairs will show a modest positive correlation. In a microarray analysis of gene expressions in 35 pools of drosophila embryos and 54 adult drosophilae (Spellman and Rubin[15], reviewed by Oliver[16]), it was shown that adjacent genes with correlated expression levels tend to cluster. The method they used to demonstrate this was the following: let w be a fixed window size, e.g. 10. For each window of w adjacent genes, the average pairwise Pearson correlation coefficient within the window was computed. If that measure was found to be significant at, say, 1 - α = 0.999 (the p-value was estimated in a permutation experiment), all the genes in the sequence were tagged. Doing this for all windows (they were allowed to overlap), the total number of tagged genes was counted. Then the experiment was repeated with shuffled genes (i.e., as it would behave in the absence of positionally related correlation), and the number of tagged genes in the shuffled experiment was subtracted from the number of tagged genes in the original experiment. This difference (called "net genes") grows with window size and starts plateauing for a window size of approximately 10. Spellman and Rubin interpreted this as evidence for gene interaction within regions of approximately that size. One problem with the above method of analysis is that the increasing number of "net genes" would occur even without direct interactions between genes separated by up to ten positions. As shown in figure 1, the analysis gives similar results when applied to simulated data from a normal distribution, in which an autocorrelation of AC = 0.10 or 0.05 was imposed artificially. So we cannot, on the basis of the analysis described above, reject the hypothesis that the data arose from a simple first-order autocorrelation process, in which no clustering of correlated genes exists. It is true that gene-pairs with high correlation form clusters: the autocorrelation of Pearson's R for adjacent genes is 0.1, with a standard error 0.01. However, this can be explained by the fact that genes that tend to correlate strongly with other genes in general (for example because of low measurement noise) tend to correlate with both their neighbors. If one eliminates that confounder by looking at non-overlapping gene pairs only, the autocorrelation vanishes (0.01, standard error = 0.01). Another way of showing this is by means of cross-tables. We divided the adjacent gene-pairs into three groups: positively correlated pairs(R>0.7), negatively correlated (R< -0.7) and non-correlated. (The threshold of 0.7 was suggested by Cohen[7]). If the correlated gene-pairs were clustered, one would expect that a gene-pair belonged to the same group as the next gene-pair more often than would happen by chance. This is indeed the case when overlapping gene-pairs are considered: 627 gene pairs out of 12949 (4.8%) had an R > 0.7 while the next (overlapping) gene pair also had an R > 0.7. This is 2.22 times more than what we expected due to chance alone. However, the same was observed when only one of the two overlapping gene-pairs was was a neighbor pair and the other was a random pair (if the genes were labelled ABCD...Z, a strong correlation between A and B predicted a strong correlation between B and C but also between B and X, where X is a random gene). But when non-overlapping adjacent gene pairs were considered (say, AB versus CD), the contingency was 332 out of 12878(2.6%) which is only 1.18 times more than expected due to chance. So the apparent clustering of correlated gene pairs is mainly due to overlap rather than to adjacency. On the other hand, it is clear that there is some higher order correlation structure in Spellman and Rubin's data. This can be seen by computing the average correlation coefficient for subgroups of the gene pairs, based on their physical distance (table 1) – it decreases much slower with distance than would a first-order process. Hence, the question remains how the correlation structure should be modelled and analyzed. In this paper, we present a method to separate A) Correlation of gene expression that can be attributed to consistent coregulation, from B) The uniform correlation expected under an hypothesis of inconsistent corregulation. Results Data We used the Drosophila data published by Spellman and Rubin[15]. This data set consisted of normalized expression levels of 13090 genes in 89 flies (35 pools of embryos and 54 pools of adults), obtained with Affymetrix GeneChip microarrays. For the purpose of analysis based on physical distance between genes, the data set was linked to Flybase[17] on the basis of the CG-identifiers provided by Spellman and Rubin. 1871 genes had to be omitted from that analysis because of unmatched CG-identifiers. We checked that these omitted genes were not a biased sample with respect to correlation in expression with their neighbor genes. Non-adjacent gene pairs The distribution of the correlation coefficient between gene pairs in general, (i.e., genes that are not necessarily adjacent) was fitted with a three-component normal mixture, based on the Arcus Tangens Hyperbolic-transform (Fisher's z) of the correlation coefficients, which was validated with a Kolmogorov-Smirnov test (p = 0.6). As shown in table 2, the mean of the middle component, tanh(μ0), was 0.014 with a standard error of 0.002. (In all tables, μ has been transformed back with tanh so that it can be interpreted as a correlation). The fact that the standard deviation of the third component, σ+, is greater than that of the other components, suggests that the co-regulation of some gene pairs was stronger than that of others. Adjacent gene pairs Table 3 shows the fitted parameters in the same model, but for adjacent gene pairs. The results are very similar to those for the random gene pairs, although μ0 and μ+ are substantially higher. This suggests that the effect of adjacency is, for the most part, a mechanism that applies to adjacent-gene pairs in general, not just to specific adjacent-gene pairs. One way to illustrate this is by means of a qq-plot (figure 2), which shows that the correlation for random gene pairs and adjacent gene pairs have a similar structure, although the overall correlation is stronger in adjacent gene pairs. This contrasts with the qq-plot in figure 3, in which the gene-expression correlations in the subset of adult flies is compared to those in a mixed fly group. On average, the correlation is zero in both the adult group and the mixed group. However, the standard deviation of the correlation is much higher in the mixed group, presumably because genes that are expressed specifically in the adults (or specifically in the embryos) correlate strongly with each other in the mixed group. Table 4 shows the fitted values of the parameters μ0, μ+ and the size of the third component (that is, the fraction of the genes that are positively co-regulated), for random and adjacent gene pairs in four subsets of the flies: one containing all 54 adult pools, one containing all 35 embryo pools, and two random subsets of 35 and 54 pools, respectively. One can observe that μ0 (which can be attributed to corregulation of gene pairs in general, i.e., inconsistent co-regulation) is always high for adjacent gene pairs and low for random gene pairs. On the other hand μ+ and the "+"-fraction (which can be attributed to corregulation of specific gene pairs, i.e., consistent co-regulation) are always high in heterogenous fly groups and low in homogenous one. That difference between heterogenous and homogenous groups is what we expected: In a homogenous fly group, less gene pairs will come up as co-regulated, because some pathways may either be active in all flies, or inactive in all flies. That age groups does not explain all correlation, is hardly surprising: although the adult group and the embryo group are less heterogenous than the mixed group, neither is homogenous. Gene pairs of intermediate distance To get an impression of the size of the segments involved in co-regulation, we fitted the three-component mixture to the correlation coefficient between the expressions of more distant gene pairs. μ0 was 0.114 for the correlation between the expression of a gene and that of its direct neighbor, and 0.08 for the correlation with its second neighbor. From the fifth neighbor and until at least the tenth, a stable level of 0.03 is reached, which is still higher than for distant gene pairs. One possible interpretation of this is that two different co-regulation effects exist: a short-segment effect accounting for a correlation of approximately 0.114-0.03 = 0.081, and a long-segment effect accounting for a correlation of approximately 0.03-0.014 = 0.016. At first we had the suspicion that this stable level of 0.03 was a whole-chromosome effect, but that was not the case. We found no significant difference between the overall average correlation of random gene pairs from the same chromosome and random gene pairs from different chromosomes. In a first-order autocorrelation process, tanh(μ0) for the second neighbor would be the square of that for the first neighbor. However, the unexpectedly small difference between the two values could be due to the fact that some second neighbors are physically quite close. To confirm or reject the hypothesis of a first-order process, however, the analysis must be based on physical distance rather than simple adjacency. As shown in table 1, the decrease in average correlation as a function of physical distance is still too slow for a first-order process. Table 5 shows the fitted parameters for gene pairs within a physical distance of between 100 and 1000 bases. Unlike the fitted parameters for neighbor genes, μ+ is not significantly higher than for random gene pairs. Simulated data To validate our approach, we computed the correlation coefficients of adjacent and random gene pairs in simulated data, based on either consistent or inconsistent co-regulation. As expected, the simulated data with inconsistent co-regulation resulted in a correlation structure with only one component, whereas those with consistent coregulation showed two components. (Not three, since the segment effect (see Methods) was always positive in the simulated models). Discussion There are many theories that could explain correlation between the expressions of two genes in general, and that of two adjacent genes in particular. For the verification of the specific correlation structures predicted by each theory, Spelmann and Rubin's data turned out to be very useful. Their experimental design was based on two distinct classes of experimental conditions (in this case, embryos and adults), which makes a relatively simple correlation structure plausible. Also, the data from each of their microarrays could be fit nicely with a normal mixture, which makes it possible to analyze their data on the basis of the Pearson R. As expected, most of the consistent correlation was found to be related to age group and unrelated to adjacency. Maybe more surprisingly, most of the correlation that was related to adjacency could not be accounted for by consistent co-regulation. This suggest that, at the statistical level, co-regulation of adjacent genes should be understood in terms of the mechanics of the transcription process, rather than in terms of the evolutionary origin of specific gene groups. In other words, in this particular data set, consistent correlation was very widespread, applying to 40% of all gene pairs (table 2), half of this could be attributed to the two age groups (table 3), but this consistent correlation was not the most important contributor to the elevated correlation coefficients for adjacent gene pairs. Correlation between the expression of adjacent genes is evident, but it is possible, as suggested by Spellman and Rubin, that such a correlation between expression levels for two adjacent genes does not generally imply a relationship with respect to biological function. Spellman and Rubin concluded that the adjacent genes with correlated expressions were confined to certain domains, which spanned 20% of all genes. Our findings are different, in that we distinguished between two components of the correlation structure. We found a baseline correlation of 0.1 (the difference between μ0 in table 2 and table 3) applying to all adjacent gene pairs. In addition to that, we found that 8% of all adjacent gene pairs (the difference between the "+" -fractions in table 2 and table 3) were strongly positively correlated. Finally, unlike Spellman and Rubin, we found no evidence for clustering of the strongly correlated gene pairs. Since this data set contained data from whole animals, we cannot say anything about the role of chromatin domains (or other mechanisms causing correlation of the expression of adjacent genes) in tissue differentiation. Parisi et al. [13] found clustering of genes that were upregulated in drosophila germ cells, which suggests that one would find elevated consistent coregulation of adjacent genes when applying our model to data sets with different tissue classes. Even for random (non-adjacent) gene pairs, μ0 was slightly positive. It is hard to imagine a biological effect that could cause μ0 to be significantly greater than zero, but it could be an artifact of the normalization: if gene g is expressed at the same level in all tissue samples, and gene h as well, they should have a correlation coefficient of zero, but since they will both yield elevated measurements in flies in which the normalization bias is positive, the observed correlation will be positive. The fact that μ0 is almost zero suggests that normalization bias is not a major problem with this data set. The fact that μ0 for random gene pairs is not generally higher in mixed age groups than in pure adult or embryo groups (table 4) is consistent with the assumption that μ0 for random gene pairs does not have a biological interpretation. Although looking at adjacent genes is computationally convenient, it would probably be more correct to use physical distance, rather than adjacency, as a covariate. Actually, Fukuoka[2] found that physical distance is stronger related to correlation of expression levels than is adjacency. Table 5 shows that when a subgroup of the gene pairs is selected on the basis of physical distance, it becomes even more clear that the elevated correlation is a general property of all near-by gene pairs. This is not surprising since the group of adjacent gene pairs is heterogenous, containing gene pairs with vastly varying distances. Unfortunately, there are some problems related to the use of physical distance. First, it is not clear with respect to which anchor the physical distance should be defined. We have chosen to use the minimal distance (using the end nearest to the neighbor as an anchor), because it is not confounded by the length of the gene. However, depending on which co-regulation mechanism one has in mind, other distance measures might be more natural. This question becomes crucial if one wants to make subgroup analysis based on convergent, divergent and tandem gene pairs. Second, physical distance is a continuous variable, which means that the model must be augmented with an assumption regarding the relationship between distance and correlation. The question remains to what biological phenomena the baseline correlation between adjacent genes should be attributed. Several of the theories that have been proposed could account for it, and it is likely that several mechanisms play a role. In addition to the baseline correlation, we also found more consistent correlation between adjacent genes and inconsistent co-regulation, but this does not necessarily imply a combination of two mechanisms: one could imagine a kind of quasi-consistent mechanism, in which groups of adjacent, co-regulated genes could have boundaries everywhere, but with some boundary locations being more likely than others. Finally, it has been suggested that the correlation between the expressions of adjacent genes should be understood in terms of enhancer-promotor interaction, which weakens with distance(Dorsett[14]). It is possible that such a model would predict a pattern similar to what we have found. Conclusions It is possible to analyze correlation structures in gene expression data on the basis of simple, parametric models with known mathematical properties and parameters with biological interpretations, or at least some candidate interpretations. When applied to the data from Spellman and Rubin, it appeared that the expressions of two genes can show positive correlation for either of two largely distinct reasons: because they share some confounder unrelated to adjacency (this is often the age group, but could also be some other biological parameter, or a technical artefact), or because they are located close to each other on the chromosome. The underlying biological mechanisms remain unknown, but there appears to be a component of the correlation that depends on distance only and not of the biological function of the genes. If this is true, gene clustering algorithms might benefit from making distinction between the adjacency-related and not-adjacency-related co-regulation, for example by subtracting the effect of adjacency from the correlation of adjacent genes. Doing that would, according to our findings, lead to more reliable identifications of gene pairs with related biological functions. Methods Non-adjacent gene pairs First, we constructed a model for those co-regulation effects that were unrelated to the relative location of the two genes. Suppose that a gene can be active either in adults, in embryos, in both, or in neither. Let Ygi be the log-scale measured activity of gene g in fly i, fly i having development stage s (either "adult" or "embryo"). A natural model for Ygi would be a two-component normal mixture: Since the data were normalized so that the average logscale activity across all 89 flies was zero, the activity of gene g in the other development stage should be taken into account. We therefore assumed a three-component normal mixture: Actually, of the 89 flies, the gene expressions in 87 of them could be nicely fitted with a three-component, normal mixture, while in two of the adult flies we identified only two components (the component of genes that were passive in adults while active in embryos vanished in those two cases). If the three-component model is correct, Fisher's z-transform, which is the hyperbolic arcus tangens of the Pearson correlation coefficient Rgh for two genes, g and h, is We expected μ0 to be close to zero. This model was validated by implementing it on the neighbor correlations in a shuffled data set, that is, for each gene its Pearson R with a random gene was computed. This procedure was repeated with 10 shuffled data sets, each generated by shuffling the genes in the original data set and subsequently computing the Pearson R for each pair of adjacent genes. For each shuffled data set, the parameters in the model were estimated with the EM algorithm (Dempster et.al[18]). Adjacent genes Second, the distribution of the correlation coefficients of the pairs of adjacent genes in the unshuffied data set were compared to the same distribution for the shuffled data sets. Doing that, we could distinguish two different contributions to the correlation; A) co-regulation effects that are unrelated to the fact that two genes are adjacent, and B) co-regulation effects related to the fact that two genes are adjacent. We also analyzed each gene's correlation with its second, third etc. (up to the tenth) neighbor, in order to get an idea of the lengths of the chromatin domains in question. Third, we applied the above two procedures to two subsets of the data, namely the data for adult flies and the data for embryos. If our assumption, that all (or at least most) correlation could be attributed to the development stage, one would expect much less correlation in those subsets. However, those subsets differ from the entire data set by the mere fact that they are smaller. Therefore, we also applied the same procedure to two random subsets of 35 and 54 flies, respectively, stratified by development stage. Physical distance For those gene pairs where the start and the end of the open reading frame (ORF) was available, we defined the physical distance as the distance between the end of the ORF of the first gene and the start of the ORF of the second gene. We chose that definition because other distance definitions (defined on the basis of the direction of transcription, or on the basis of mid-points) would be confounded by the length of the ORFs. Simulated data Finally, in order to validate our method, we applied it to 6 simulated data sets, which we simulated on the basis of the two alternative hypothesis of consistent and inconsistent co-regulation. The simulated data sets contained 100 tissue samples and 1000 genes. The genes were randomly divided into either 67, 200 or 500 segments, corresponding to average segment sizes of 15, 5 and 2. This means that each gene belonged to exactly one segment. For each average segment size, we simulated one data set with consistent co-regulation, in which the same segments were used for all 100 tissue samples, and one data set with inconsistent co-regulation, in which a separate segmentation was sampled for each tissue sample. If gene g belongs to segment s in sample i, the expression Ygi was given by Ygi = xsi + εgi (4) where xsi + and εgi were both sampled from the standard normal distribution. Notice that this model does not account for different tissue classes, so it should be compared to the results from the adults-only and embryos-only subsets. The error term εgi should be interpreted as a combination of measurement errors and biological effects that are unrelated to the segmentation. Authors' contributions HT did the explorative data analysis and suggested the concept of consistent co-regulation and using three-components mixtures for the correlation structure. AZ suggested using Fisher's z-transform for the correlations. HT did the literature review. Both authors contributed to the manuscript. Acknowledgements Peter Sterk from the Microarray Department of the University of Amsterdam helped us linking the microarray data to Flybase annotations. We thank our colleagues Mark van Passel and Kimberley Boer, as well as the referees, for helpful suggestions. Figures and Tables Figure 1 Net genes for simulated data. Number of genes that contribute to a high moving average of Pearson R in simulated data, as a function of the size of the windows used for computing the moving average. The shapes of the curves are similar to the findings by Spellman and Rubin, although the convergence is somewhat faster. The simulated data are first-order Gaussian autocorrelation processes. Figure 2 Pearson R for adjacent genes in Spellman and Rubin's data set, compared to non-adjacent genes. The QQ-plot is shifted away from the diagonal over the whole correlation spectrum, suggesting that co-regulation applies to all pairs of adjacent genes. Figure 3 Pearson R for non-adjacent genes in the adult flies, compared to a mixed fly group of the same size (54 flies). The average correlation is the same in both fly groups, but strong positive and negative correlation occurs in the mixed group. This is consistent with the assumption that strong correlation of two genes can occur when both genes are upregulated (or downregulated) in one age group relative to the other age group. Table 1 Average correlation between gene pairs of different physical distances. The distances are minimal distances in bases. Distance Mean R SE(R) Overlapping 0.10 0.01 0–999 0.17 0.01 1000–1999 0.14 0.01 2000–2999 0.12 0.01 3000–3999 0.10 0.01 4000–4999 0.08 0.01 Table 2 Random gene pairs. Fitted parameters in the three-component mixture of ArcTanH(R) for random gene pairs. μ has been back-transformed so that it can be interpreted as a correlation. Component fraction SE (fraction) μ SE(μ) σ SE(σ) - .1687 .0007 -.660 .001 .332 .002 0 .5955 .0006 .014 .002 .330 .001 + .2352 .0008 .571 .002 .439 .002 Table 3 Adjacent genes. Fitted parameters in the three-component mixture of ArcTanH(R) for adjacent genes. μ has been back-transformed so that it can be interpreted as a correlation. Component fraction μ σ - .118 -.647 .312 0 .571 .114 .344 + .311 .655 .480 Table 4 Subsets of the flies. Parameters in the three-component mixture of ArcTanH(R) for different subsets of the flies. μ has been back-transformed so that it can be interpreted as a correlation. Flies Gene pairs μ0 μ+ "+" fraction 35 embryos Adjacents 0.09 0.44 0.21 35 embryos Random 0.03 0.30 0.16 35 random Adjacents 0.11 0.68 0.32 35 random Random 0.02 0.60 0.25 54 adults Adjacents 0.08 0.37 0.18 54 adults Random 0.01 0.25 0.15 54 random Adjacents 0.12 0.65 0.31 54 random Random 0.08 0.57 0.24 Table 5 Physically close gene pairs. Fitted parameters in the three-component mixture of ArcTanHyp(R) for gene pairs within a distance of between 100 and 1000 bases from each other. μ has been back-transformed so that it can be interpreted as a correlation. 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==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-151566379610.1186/1471-2105-6-15Methodology ArticleLarge scale hierarchical clustering of protein sequences Krause Antje [email protected] Jens [email protected] Martin [email protected] Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany2 Universität Bielefeld, Technische Fakultät, AG Genominformatik, Postfach 100131, 33501 Bielefeld, Germany3 TFH Wildau, Bahnhofstrasse 1, 15745 Wildau, Germany2005 22 1 2005 6 15 15 2 8 2004 22 1 2005 Copyright © 2005 Krause et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Searching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to. Results We report on our developments in grouping all known protein sequences hierarchically into superfamily and family clusters. Our graph-based algorithms take into account the topology of the sequence space induced by the data itself to construct a biologically meaningful partitioning. We have applied our clustering procedures to a non-redundant set of about 1,000,000 sequences resulting in a hierarchical clustering which is being made available for querying and browsing at . Conclusions Comparisons with other widely used clustering methods on various data sets show the abilities and strengths of our clustering methods in producing a biologically meaningful grouping of protein sequences. ==== Body Background With the overwhelming growth of biological sequence databases, handling of these amounts of data has increasingly become a problem. Protein sequences constitute one such data type. The number of unique entries in all protein sequence databases together exceeds now about a million. However, biological evolution lets proteins fall into so-called families, thus imposing a natural grouping. A protein family contains sequences that are evolutionarily related. Generally, this is reflected by sequence similarity. Therefore, one aims at organizing the set of all protein sequences into clusters based on their sequence similarity. Clustering a large set of sequences as opposed to dealing only with the individual sequences offers several advantages. A frequent problem is the identification of sequences that are similar to a new query sequence. This task can be executed much quicker when only one comparison to an entire cluster has to be performed rather than one comparison per database sequence. Another application lies in the possibility of analyzing evolutionary relationships among the sequences in a cluster and of the species they come from. Moreover, the presence or absence of sequences of a group of species can give useful information about their evolutionary relationship, if their complete set of protein sequences is known. The aim of clustering protein sequences is to get a biologically meaningful partitioning. One of the simplest well-studied and computationally cheap methods to construct a clustering of data points is single linkage clustering. Starting with the pair of data points of least distance, one incrementally merges single data points or already existing clusters. Such a hierarchical clustering can be viewed as a tree, called the single linkage tree. The leaves represent the individual data points, while the root of this tree corresponds to just one large cluster representing the whole data set. All other layers in between can be seen as cluster sets at different levels of similarity. However, it is not clear which layers give a meaningful partitioning of the data. They should be chosen so that they neither produce small trivial clusters nor form huge uninformative clusters. Several approaches already deal with the problem of partitioning a protein sequence database into protein families. Automatically generated cluster sets like ProtoMap [1], ProtoNet [2], or CluSTr [3] typically provide a hierarchical classification at several different levels of similarity. Others, like iProClass [4] or PIRSF [5] include further knowledge, e.g., from domain based classifications, or require manual interaction. Kawaji et al. [6] recently developed a graph-based clustering method for the detection of distantly related sequences of a protein family. TribeMCL [7] is a method for clustering proteins into 'protein families' using a Markov Clustering method. It is primarily used for comparing protein sequence sets of completely sequenced genomes. Reviews of currently available cluster sets can be found in: Heger et al. [8] and Liu et al. [9]. In our approach we first exploit the branching structure of the single linkage tree, which elucidates an unexpected structuring of the sequence space. Traversing the tree from a leaf towards the root we inspect the sizes of the merging subtrees. First one notices relatively small increases that correspond to very similar proteins. Later on, sequences merging in correspond to weakly related proteins. At one point, however, we observe an enormous increase in the size of the subtree, where a large part of the database merges in. All sequences below this point in the tree are assumed to belong to the same superfamily. Each superfamily typically covers several closely related protein families. They can be determined by revealing the connectivity of the sequences belonging to a superfamily. Since the single linkage tree is built using only the smallest distances connecting subtrees, information about the connectivity within these subtrees is lost in the hierarchy. For each superfamily, we construct a superfamily distance graph by including only those nodes labeled with sequences belonging to the respective superfamily. These graphs are then split at reasonable cut sites into highly connected subclusters. For historical reasons [10], we call our procedure as well as the resulting cluster set SYSTERS, which is short for SYSTEmatic Re-Searching. Up to that point, the hierarchy consists of superfamily and family clusters. However, protein sequences are built up of smaller entities, called domains. They again can be grouped independently of a certain order in a protein sequence. For this level we rely on one of the currently established domain databases, i.e., the Pfam database [11]. To allow the user to explore protein sequence space through the complete hierarchy, we present an interface to our cluster set on the Internet. It is possible to enter the hierarchy at each of the layers through various entry points and change to another layer whenever desired. Additional information like a multiple alignment or a phylogenetic tree is given for each of the family clusters. Here, we explain in more detail the SYSTERS algorithms developed to determine superfamily and family clusters. Each step is illustrated by an example. We report our results on clustering the non-redundant protein sequence space consisting of about 1,000,000 sequences. An overview of the availability and accessibility of the cluster set is given. Finally, we present a comparison of our clustering method with two other currently available and widely used clustering methods, namely single linkage clustering and TribeMCL. Results and discussion Clustering We have applied our algorithms as described in the Methods Section to a sequence set consisting of all known protein sequences from the Swiss-Prot Rel. 41 and TrEMBL Rel. 23 databases [12], and from several completely sequenced organisms [13-16]. The original set contains 1,168,542 sequences. Sequences which are too short to yield a result in the database search are removed from this set. Sequences which are identical (sub-)sequences of other sequences are sorted together and only the longest sequence is retained as the representative. In a pairwise comparison of all remaining 969,579 non-redundant sequences, this results in a triangular matrix sparsely filled with 775,133,144 E-values better than or equal to 0.05. Comparisons of a sequence to itself are not considered. By temporarily removing all those sequences which are at least 80% identical over at least 80% of their entire length to another sequence, this number decreases. These sequences are considered redundant, and are added to the cluster set again later in order to retain their annotation. By reducing the number of sequences to 546,538 non-redundant sequences, the remaining number of pairwise comparisons decreases significantly. Fortunately, the resulting triangular distance matrix turns out to be sparsely filled with only 52,618,818 values (0.035% of all possible pairs). Constructing the distance graph with these values, the data splits into 93,918 connected components with 76,347 components consisting of only one sequence. The resulting single linkage tree divides into 147,796 superfamilies with 110,308 of them consisting of only one sequence. The subclustering splits the data further into 158,153 family clusters with an overall number of 110,322 single sequence clusters. Access to the cluster set The SYSTERS cluster set [17] is available over the Internet at . There it is possible to explore the protein sequence space by navigating through the complete hierarchy consisting of superfamilies, family clusters, and domains. For the last layer in the hierarchy, the domain level, we rely on one of the currently established domain databases, namely the Pfam collection of protein domains. It is possible to enter the hierarchy at any layer, e.g., by searching for a keyword, choosing a species, or selecting a domain. For each family cluster a consensus sequence is generated. All consensus sequences together build a database searchable by BLAST. Thus, a clear assignment of a new protein or nucleotide sequence to a family and a superfamily is possible. Additional information like a multiple alignment or a phylogenetic tree is given for each of the family clusters. Whenever possible, links to external resources are provided to allow for further information, e.g., about structural properties or underlying genes. Validation For the validation of our clustering procedures we needed on one hand a "true" biologically verified cluster set and on the other hand results of other clustering procedures on this data set. Unfortunately, for large scale analyses such validated data is not available. Thus, we decided on performing our evaluations on two biologically meanigful data sets, namely well characterized sequences from Swiss-Prot and TrEMBL with (a) Pfam domain annotations and (b) ENZYME annotations. Clustering of such large data sets is not an every day routine. Normally the software to handle such data sets is not publicly available and only the results of their application are published. Although these results are mostly publicly available for browsing on the web the underlying primary data differs in all of these data sets. Additionally a systematic, unbiased and independent comparison would be intractable on a large scale by querying the web. One of the simplest well-studied and computationally cheap methods to construct a clustering is single linkage clustering. We implemented procedures to perform a single linkage clustering on the two data sets at various different cutoffs. This corresponds to performing single sequence searches with a certain E-value cut-off for all sequences in the data set with subsequent determination of the connected components of the results. Additionally we decided to compare our clustering procedure to one of the most widely used and publicly available methods for large scale protein sequence clustering, namely TribeMCL. We applied the single linkage clustering as well as the SYSTERS clustering to the Pfam data set and computed the Jaccard coeffcient, the sensitivity and the selectivity of the clustering results in comparison to the "true" cluster set as described in the Methods Section. All clusterings were performed on the non-redundant data set as described under Preprocessing in the Methods Section. After the clustering, redundant sequences were added again to the cluster sets to allow for a comparison with the "true" cluster sets. For the Pfam cluster set the best single linkage clustering with respect to the "true" cluster set can be achieved at an E-value cutoff of 1e-53 (cf. Table 1). The SYSTERS clustering results in a slightly higher Jaccard value. Note that the "best" single linkage clustering result can not be determined from the clustering itself, but was selected after comparison with the "true" cluster set, which is not available when clustering new sequence data. Thus, the SYSTERS clustering turns out to be superior to the single linkage clustering in the sense that it is able to determine the correct cluster granularity without manual interaction. In total we get only weak results for the Pfam data set. One of the reasons is the choice of the "true" cluster set. Figure 1 shows an example where sequences composed of the same domains and belonging to the same family of adenylate cyclases end up in different "true" clusters. The repetition of one domain and the presence/absence of another domain lets them fall into different "true" clusters. These sequences are in a biological sense correctly clustered by SYSTERS but cause a problem when comparing them to the "true" cluster set. In this case the "true" clusters build subsets of the SYSTERS subclusters. Another reason for the weak results in comparison with the Pfam data set are fusion proteins. They bring together sequences belonging to otherwise unrelated families. We applied the single linkage clustering, the SYSTERS clustering and the TribeMCL clustering to the ENZYME data sets and computed the Jaccard coeffcient, the sensitivity and the selectivity of the clustering results in comparison to the "true" cluster sets as described in the Methods Section. For this data set the SYSTERS clustering turns out to be superior to both the single linkage clustering and TribeMCL (cf. Table 1). In both ENZYME data sets the TribeMCL clustering shows the best ability to reject unrelated sequences but at the expense of finding distantly related sequences. As expected, the SYSTERS subclustering shows the best result on the lowest level of the ENZYME data set where individual enzymes are identified. In total all methods perform significantly better on the ENZYME data set. This data set is much smaller than the Pfam data set and contains well annotated enzymes. In contrast to the Pfam data set, the "true" cluster set was chosen on the basis of enzyme annotation, namely EC numbers, as described in the Methods section. Sequences belonging to the same "true" cluster thus may show the same domain composition but may also differ in this sense. Although this is a somehow weaker definition of a "true" cluster set it is more focussing on the functional properties of the proteins. Conclusions We have presented a hierarchical clustering of protein sequences into biologically meaningful superfamily and family clusters. A combination of an upward sweep with dynamic determination of superfamily cutoffs and a downward pass that divides superfamilies into families has been introduced. We determine a superfamily by detecting the largest increase in the size of the merging subtree traversing from a leaf in a single linkage tree to the root. We assume that at this point the twilight zone begins because suddenly a large number of supposedly unrelated sequences enters the cluster. Each of the superfamilies is further cut into family clusters by detecting weak connections in the underlying distance graph. It is interesting that both the superfamilies as well as the family clusters are generated solely from the structure of the single linkage tree (respectively the underlying distance graph), without any knowledge of the biological information represented. Such self-structuring properties have also been observed in other large data sets such as phone-call or web-link graphs [18]. An alternative approach for cluster determination is presented by Sharan et al. [19]. Their CLICK algorithm (Cluster Identification via Connectivity Kernels) uses graph-theoretic and statistical techniques to first identify tight groups of highly similar elements (kernels), which are likely to belong to the same cluster. Several heuristic procedures are then used to expand the kernels into the full clustering. In our much simpler approach, we produce a hierarchical clustering based on the partitioning into superfamilies, which already results in a biologically meaningful set of family clusters. Although the vast majority of cases we looked at are in agreement with biological knowledge, there exist some inconsistencies due to peculiarities in the data. Distinct protein families may end up erroneously in the same superfamily because of a fusion protein covering sequence information from both families. The same effect can be seen at multidomain protein families linked together by a single highly conserved common domain. Although the subclustering in most cases splits these superfamilies again into distinct families, we would prefer to take care of these cases already in the process of superfamily determination. Nevertheless, comparisons with other methods showed that our clustering methods are able to produce a biologically meaningful clustering. Thus far, our hierarchy consists of two layers representing protein superfamilies and families. For the third layer located at the domain level, we currently rely on well-established domain databases, but intend to follow our methodology also in the direction of deriving so far unknown domains. Future plans also include a regular update of the SYSTERS cluster set. Since the most time consuming part are the all-against-all sequence searches, new sequence similarities will be incrementally added instead of recalculating all similarity values. The clustering procedures themselves rely on the topology of the whole sequence space and can be run on the whole data set whenever the underlying sequence set changes. Other future developments will be in the direction of the so called tree of life. We plan to combine the evolutionary information given by each of the protein clusters to extend the knowledge about the relationship between different groups of species. Methods Clustering procedures Here we present the methods that we use to compute our clustering of protein sequences, i.e., selecting superfamilies and dividing them into reasonable family clusters. Figure 2 shows a schematic overview. Preprocessing The total number of entries in all protein sequence databases together now exceeds about a million. This number includes fragmental as well as identical (sub-)sequences from different resources. To reduce the amount of data without losing information we exclude redundant information in the form of identical and nearly identical (sub-)sequences from the data set prior to the clustering. We model the remaining protein space as a weighted undirected graph with pairwise distances attached to the edges. We decided on using E-values computed from pairwise local sequence alignments [20] as distances (all-against-all database searches were carried out on a Paracel GeneMatcher™ machine [21]). The E-value (short for Expectation value) is the number of alignments with similarity scores equivalent to or better than the score S that one expects to find in a database search by chance. Thus, the lower the E-value, the more significant is the score. Typically, matches with an E-value lower than 1e-20 are assumed to be relevant, while those sequence pairs with an E-value higher than 0.01 need further experimental evidence to be accepted as being distantly related. Values in between belong to the so called twilight zone, and a clear statement about relatedness cannot be made for them. All sequence pairs whose E-value was worse than 0.05 were assumed to be unrelated and their distance was set to infinity. We are aware that we may miss distantly related sequences with this E-value threshold in a single sequence search. However, by using each sequence in the data set as query in a database search and combining all results we hope to overcome this problem. The resulting symmetric distance matrix D contains all pairwise distances d(si, sj) for each pair of protein sequences si and sj, 1 ≤ i, j ≤ n, for which d(si, sj) < ∞. Single linkage tree The distance matrix D can be represented by an undirected weighted graph G, which we call the distance graph. G = (V, E) is defined as follows: V = {vi \| vi = {si}, i ∈ {1, ..., n}} and E = {(vi, vj) \| i, j, ∈ {1, ..., n}, i ≠ j, d(si, sj) < ∞}. The weight w(vi, vj) of an edge (vi, vj) ∈ E is given by w(vi, vj) = d(si, sj). The single linkage tree is built based on the distance graph G in an agglomerative manner. The algorithm starts with a forest (collection of trees) F where each sequence corresponds to a distinct tree. As long as there are edges in the graph G, the edge with the smallest weight is selected and the adjacent nodes in G are merged. Edges linking this newly created node to adjacent ones in the graph receive the weight of the smaller of the two original edges. The two corresponding trees in F are collected together in a new tree rooted by a parental node labeled with the connecting edge weight. Finally, to allow for a better handling of the data, the resulting unconnected trees are rooted by connecting their roots to an artificial overall root node with weight infinity. Superfamily determination Different protein superfamilies display a different degree of conservation. Therefore, for each superfamily, the twilight zone starts at a different cutoff. A crucial problem thus lies in the determination of an appropriate E-value threshold for each superfamily. To this end we have devised the following procedure. For an edge of the tree linking, say, a parent p and a child q, we compute the quantity J represents the ratio between the size of all the subtrees below p without the child q and the size of the subtree below q. Watching the development of J as one walks up the tree from a leaf towards the root, one can observe that J tends to increase dramatically as one leaves the superfamily to which the leaf belongs, and then decreases again. This intuition is captured by our algorithm. For each leaf, we determine the maximum J as one proceeds from the leaf to the root of the single linkage tree. This strategy is applied to all leaves in the tree, assigning a superfamily to each leaf. In the end, inclusions are resolved by keeping the largest superfamilies. We call the internal node induced by a superfamily the superfamily root. The E-value linked to this node is called the superfamily cutoff. Refer to Algorithm 1 in Figure 3 for more details. Figure 4 shows an example of the superfamily determination. Only a part of the complete single linkage tree consisting of 290,811 leaves and 186,176 internal nodes is shown. The superfamily procedure correctly determines the ephrin family of sequences. Ephrins are membrane-attached proteins involved in the development of the nervous system and can be further distinguished into type A and type B ephrins depending on their membrane binding mechanism. Subclustering Stepping down the hierarchy of the single linkage tree starting at a superfamily root usually splits off one sequence after another, but does not lead to a meaningful partitioning into families. Since the single linkage tree is built using only the best (lowest) E-values connecting subtrees, information about the connectivity within these subtrees is lost in the hierarchy. For each superfamily we construct a distance graph that includes only those nodes labeled with sequences belonging to the respective superfamily and those of the induced edges which are labeled with a distance better than or equal to the superfamily cutoff. Let SF be the set of sequences belonging to the superfamily sf and c the corresponding superfamily cutoff. We call the connected weighted graph G = (V, E) with V = {vi \| vi = {si}, si ∈ SF} and E = {(vi, vj) \| w(vi, vj) = d(si, sj), si, sj ∈ SF, i ≠ j, d(si, sj) ≤ c} the superfamily distance graph of sf. To split a superfamily distance graph into family clusters, we use an algorithm that can be seen as a weighted version of a method presented by Hartuv et al. [22]. First, we review some standard graph-theoretic definitions. The edge-connectivity k(G) of a graph G is the minimum number k of edges whose removal results in a disconnected graph. A cut in a graph is a set of edges C whose removal disconnects the graph into two disjoint components H1 and H2. A minimal cut is a cut with a minimum number of edges. The length p(u, v) of the shortest path between nodes u and v in G is the minimum length of a path from u to v, if such a path exists; otherwise p(u, v) = ∞. The diameter of a connected graph G is the maximum shortest path length p(u, v) over all pairs of nodes u and v in G. The key definition of the algorithm in [22] is the following: An undirected unweighted graph G with n > 1 nodes is called highly connected, if k(G) >. A highly connected subgraph (HCS) is an induced subgraph H ⊆ G, such that H is highly connected. In an unweighted graph this definition results in the following property: The diameter of every highly connected subgraph is at most two. Thus, these subgraphs are compact clusters which need not meet the constraint of being fully connected. The original HCS algorithm in [22] recursively splits a connected graph at a minimal cut site until a disjoint set of highly connected subclusters is reached. For our purposes we had to modify the algorithm to be able to handle a weighted graph. Precisely, in our weighted HCS algorithm, if the edge weights covered by the minimal cut are approximately the same as in the remaining graph, the graph is assumed to be already highly connected and is not further split into subclusters (see Algorithm 2 in Figure 3). The E-values in our data set range from 0 (corresponding to any E-value better than 1e-180) to 0.05. To be able to find a minimal cut in our graph, edge labels should be positive values with a low value representing a weak connection and a high value representing a strong connection. Instead of using the raw E-values we label the edges in our graph with the negative logarithm of the corresponding E-value each. Since the logarithm of 0 is not defined, we use an arbitrary value (e.g., 181) for these edges instead of the logarithm. The running time of both HCS algorithms is bounded by 2N * f(n, m), where N is the number of clusters found and f(n, m) is the time complexity of computing a minimum cut in a graph with n nodes and m edges. We use the implementation of the "mincut" algorithm given in the LEDA [23] distribution, which has a time complexity of (nm + n2 log n). To apply this algorithm to our data set we added a preprocessing as well as a postprocessing step as shown in Algorithm 3 in Figure 3. First, we describe the preprocessing. Cuts consisting of only one edge in the graph will be found first by the mincut algorithm, but are as time consuming to find as other cuts. Sequences being connected with the remaining graph by only one edge are either fragmental or are the so far sole representative of a protein family in the sequence database. The underlying data of our clustering is known to contain lots of fragmental sequences. Before applying the HCS algorithm to our graph, we repeatedly merge all nodes connected to the remaining graph with only one edge with their respective adjacent node. Nevertheless, the HCS algorithm may split off single sequences as subclusters. Thus, in a postprocessing step, sequences which ended up after the subclustering as a single sequence cluster are assigned to their closest neighboring cluster (singleton adoption), if there is no contradiction. When there are several minimum cuts in a graph, the original as well as our weighted HCS algorithm might choose a minimum cut which, from the clustering point of view, is not optimal. In many cases this process will break clusters into singletons. In the original algorithm in [22] iterations were introduced to handle these cases. Since we are working on a weighted graph these cases occur very rarely and mostly are compensated by the subsequent singleton adoption step. Figure 5 shows an example of splitting the superfamily distance graph of the ephrin superfamily (see Fig. 4) into two subclusters representing ephrin types A and B. Validation Pfam sequence set For our analyses we used all sequences from the Swiss-Prot and TrEMBL databases annotated with Pfam domains (Rel. 9). This data set consists of 5,724 single domain families assigned to 733,830 sequences. Since our aim is not to cluster single domains but full-length sequences, we define a "true" cluster consisting of all sequences having the same domain composition. Fragmental sequences will cause a problem in our analyses by showing a different domain composition than complete sequences. We restrict our analyses to sequences not annotated as being fragmental in the Swiss-Prot or TrEMBL databases. The resulting "true" cluster set thus consists of 442,872 sequences sorted into 16,990 distinct families. ENZYME sequence set The ENZYME database [24] stores data of a functional classification system based on function rather than sequence or structure. Each enzyme of known function is given an EC (Enzyme Commission) Number of the form A.B.C.D with A : type of reaction catalyzed (at present 6 classes) B : subclass, information about type of compound or group involved C : sub-subclass, further specifies the nature of the reaction D : serial number to identify individual enzyme within sub-subclass Although several distinct proteins may catalyze the same reaction, they are all ascribed the same EC number, since the naming system is based upon the reaction catalyzed. Thus, sequences given the same EC number do not necessarily show sequence similarity. For our analyses we used all sequences from the Swiss-Prot and TrEMBL databases annotated with a unique EC number. We define two different "true" cluster sets representing different levels of granularity as follow: (1) sequences having A, B, C, and D in common build a cluster, (2) sequences having A, B, and C in common build a cluster. The data set consists of 84,405 sequences. Clustering coeffcient Assuming we have a well defined cluster set, we can compare our cluster set with this "true" cluster set based on the following numbers: Number of sequence pairs clustered together in a: both cluster sets ("true positives"). b: the "true" cluster set, but not in our cluster set ("false negatives"). c: our cluster set, but not in the "true" cluster set ("false positives"). As similarity measure we decided on the Jaccard similarity [25] defined as follows: . A perfect clustering which is identical to the "true" cluster set would result in S = 1. Additionally we calculated the sensitivity (the ability to detect distantly related sequences: ) and the specificity (the ability to reject non-related sequences: ) for all cluster sets. Single linkage clustering We performed a single linkage clustering at various static E-values from 1e-02 to 1e-180. All resulting cluster sets have in common that when plotting the number of clusters against the cutoff E-value, one observes a continuous, smooth curve, indicating that there is no obvious (biologically given) choice of a cutoff (data not shown). TribeMCL TribeMCL [7] is a method for clustering proteins into 'protein families' using a Markov Clustering method. It is primarily used for comparing protein sequence sets of completely sequenced genomes. We performed TribeMCL clustering (Version 03–276) with different inflation value settings ranging from 1.1 to 5 for all data sets. The inflation parameter is part of the core MCL algorithm and influences the granularity (or size) of the output clusters. For very small or 'tight' protein families an inflation value setting of 4.0 or 5.0 is recommended. For larger (broader) protein families settings of 1.1, 2.0 and 3.0 can be used. For the Pfam data set we were not able to perform TribeMCL clustering due to memory allocation problems while executing the program. Authors' contributions MV had the initial ideas for SYSTERS. JS developed the superfamily determination. AK developed the subfamily determination, implemented the workflow from the raw sequence databases to a working web-server and made the validations. All authors read and approved the final manuscript. Acknowledgements We thank Hannes Luz and Thomas Meinel for fruitful discussions. This work is supported by BMBF (Bundesministerium für Bildung und Forschung) and HNB (Helmholtz Network for Bioinformatics). Figures and Tables Figure 1 Multi domain proteins Sequences with different domain compositions belong to the same family of Adenylate cyclases but form different "true" clusters (Pfam domains: RA: Ras association (RalGDS/AF-6) domain; LRR: Leucine Rich Repeats; PP2C: Protein phosphatase 2C; guanylate_cyc: Adenylate and Guanylate cyclase catalytic domain) Figure 2 Schematic overview of the clustering procedures We start with a single linkage tree constructed from pairwise distances. Each leaf in the tree corresponds to a protein sequence. Superfamilies are determined based on the internal structure of the tree. For each superfamily a distinct superfamily distance graph is built. This weighted graph is cut at weak connections into subclusters. Figure 3 The SYSTERS algorithms Figure 4 Excerpt from the single linkage tree The superfamily of sequence O93431 is determined as follows (traversing the tree along the branches depicted as bold lines). The first internal node connects this sequence with the four sequences P52794, P20827, P52793, and P97553 at an E-value of 1e-52. Thus, the ratio of the size of the merging subtree and the size of the current subtree at this point is 4/1. Stepping up the hierarchy, the next node (E-value 4e-38) connects these five sequences with a subtree consisting of 13 sequences, resulting in a ratio of 13/5 (= 2.6). Stepping further up the hierarchy, the following ratios are 1/18 (= 0.056 at E-value 6e-38), 2/19 (= 0.105 at E-value 2e-37), 15/21 (= 0.714 at E-value 2e-13), 1/36 (= 0.028 at E-value 5e-10), 211 975/37 (= 5729.054 at E-value 0.022), 259/212 012 (= 0.001 at E-value 0.023), etc. Taking the maximum of the ratios we find the superfamily root at E-value 5e-10 as the last node before the largest relative increase (depicted as a bullet in the tree). The superfamily of sequence O93431 hence consists of the 37 sequences belonging to the ephrin type A and type B families plus a few predicted proteins. Figure 5 The superfamily distance graph of the ephrin superfamily The graph contains only those edges which represent E-values of at least the superfamily cutoff 5e-10. The width of an edge is according to its E-value, here ranging from 5e-10 (thinnest edge) to 3e-149 (thickest edge). The subclustering procedure first splits off nodes from the bottom right of the graph as single sequence clusters. These sequences are predicted proteins which are not yet confirmed as functioning by any experiment. The last accepted split in the graph results in the partitioning into the two major groups of ephrin type A (left) and type B (right) sequences as shown by the dashed line. Single sequence clusters are added to the ephrin type B family in the subsequent singleton adoption step. Table 1 Comparison of SYSTERS, TribeMCL and single linkage clustering (SLC) on Pfam and ENZYME data sets (J: Jaccard Coeffcient, SE: Sensitivity, SP: Specificity). The best result in each row is shown in bold face. For the single linkage clustering only the results of the "best" clustering are shown together with the corresponding cutoff E-value. In the case of Sensitivity/Specificity these values were choosen according to the intercept point of the two curves when plotting the values for all possible E-value cutoffs. All clustering procedures were applied to the non-redundant data set and redundant sequences were added to the cluster sets again to compare to the "true" cluster sets: a33,963,365 pairwise values of 283,113 non-redundant sequences used for clustering and 442,872 redundant sequences used in comparison; b1,582,948 pairwise values of 38,176 non-redundant sequences used for clustering and 84,405 redundant sequences used in comparison. SLC SYSTERS TribeMCL at Inflation best at cutoff Superfam. Subclust. 1.1 2 3 4 5 Pfama J 0.19362 1e-53 0.15637 0.20815 --- --- --- --- --- SE 0.26886 1e-49 0.55272 0.48302 --- --- --- --- --- SP 0.26536 1e-49 0.17902 0.26781 --- --- --- --- --- ENZYMEb A.B.C.D J 0.88760 1e-21 0.77445 0.89670 0.60390 0.60074 0.59990 0.59942 0.59778 SE 0.92295 1e-08 0.92931 0.92297 0.61323 0.60328 0.60224 0.60164 0.59989 SP 0.93616 1e-08 0.82294 0.96924 0.97543 0.99304 0.99357 0.99388 0.99416 A.B.C.? J 0.71527 1e-15 0.65915 0.72410 0.48721 0.47900 0.47803 0.47746 0.47600 SE 0.74985 1e-03 0.75320 0.73727 0.49099 0.47996 0.47895 0.47836 0.47688 SP 0.80855 1e-03 0.84073 0.97592 0.98445 0.99586 0.99601 0.99608 0.99617 ==== Refs Yona G Linial N Linial M ProtoMap: automatic classification of protein sequences and hierarchy of protein families Nucleic Acids Research 2000 28 49 55 10592179 Sasson O Vaaknin A Fleischer H Portugaly E Bilu Y Linial N Linial M ProtoNet: hierarchical classification of the protein space Nucleic Acids Research 2003 31 348 352 12520020 Kriventseva E Servant F Apweiler R Improvements to CluSTr: the database of SWISS-PROT + TrEMBL protein clusters Nucleic Acids Research 2003 31 388 189 12520029 Wu CH Xiao C Hou Z Huang H Barker WC iProClass: an integrated, comprehensive and annotated protein classification database Nucleic Acids Research 2001 29 52 54 11125047 Wu C Nikolskaya A Huang H Yeh L Natale D Vinayaka C Hu Z Mazumder R Kumar S Kourtesis P Ledley R Suzek B 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==== Front BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-6-31565691310.1186/1471-2121-6-3Research ArticleHinderin, a five-domains protein including coiled-coil motifs that binds to SMC3 Patel Chirag A [email protected] Giancarlo [email protected] Department of Pathology and Cell Biology, Thomas Jefferson University, Philadelphia, 19107, USA2 Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, 19107, USA2005 18 1 2005 6 3 3 11 8 2004 18 1 2005 Copyright © 2005 Patel and Ghiselli; licensee BioMed Central Ltd.2005Patel and Ghiselli; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The structural maintenance of chromosome proteins SMC1 and SMC3 play an important role in the maintenance of chromosomal integrity by preventing the premature separation of the sister chromatids at the onset of anaphase. The two proteins are constitutive components of the multimeric complex cohesin and form dimers by interacting at their central globular regions. Results In order to identify proteins that by binding to SMC3 may interfere with the protein dimerization process, a human cDNA library was screened by the yeast two-hybrid system by using the hinge region of SMC3 as bait. This has lead to the identification of Hinderin, a novel five domains protein including two coiled-coil motifs and sharing a strikingly structural similarity to the SMC family of proteins. Hinderin is ubiquitously expressed in human tissues. Orthologue forms of the protein are present in other vertebrates but not in lower organisms. A mapping of the interaction sites revealed that the N- and C-terminal globular domains mediate the binding of Hinderin to SMC3. Hinderin/SMC3 complexes could be recovered by immunoprecipitation from cell lysates using an anti-SMC3 antibody, thus demonstrating that the two proteins interact in vivo. On the contrary, Hinderin did not interact with SMC1. In vivo the rate of SMC1/SMC3 interaction was decreased by the ectopic expression of Hinderin. Conclusions Hinderin is a novel binding partner of SMC3. Based on its ability to modulate SMC1/SMC3 interaction we postulate that Hinderin affects the availability of SMC3 to engage in the formation of multimeric protein complexes. ==== Body Background The structural maintenance of chromosome (SMC) proteins are involved in several aspects of chromosomal dynamic, in DNA recombination and in DNA repairs [1-3]. Two SMC proteins named SMC1 and SMC3 bind to and prevent the premature separation of sister chromatids at the end of mitosis [4,5]. SMC1 and SMC3 directly interact through their central globular binding domains by forming an heterodimer [6,7]. The protein complex encircles the sister chromatids and is stabilized though the interaction with two other cohesin proteins known as Scc1 and Scc3 in s. cereviasie [7,8]. At anaphase, the ring-shaped complex is broken down when separase, a cysteine protease, cleaves Scc1, thus freeing the sister chromatids to move in opposite directions [6,9]. Somatic cells with deranged separase activity or lacking Scc1 develop aneuploidy at increased rate. This suggests that the cohesin complex plays a major role in the maintenance of chromosomal stability [10-13]. The mechanism regulating the interaction between SMC1 and SMC3 is still poorly understood. It has however been established that a single point mutation of the central globular domain (known as hinge) of either one of these proteins strongly affects the dimerization rate and prevents the attachment of the cohesin complex to chromatid DNA [7]. Conceivably, proteins that interact with the hinge domain of SMC1 or SMC3 can act as modulator of the cohesin complex formation and may thus affect chromosomal stability. In this paper we report the identification of a new SMC3-interacting protein that specifically binds to SMC3's central globular domain. The sequence of the identified gene product matched that of a previously discovered hypothetical new protein with no known function. We have named the protein Hinderin. The gene is expressed in all the human tissues analyzed thus far. Orthologue forms of this protein are expressed in vertebrates but not in lower organisms. Hinderin is a five-domain proteins and its structure resembles that of SMC proteins with N- and C-terminal globular domains that are joined by a coiled coil region interrupted at the center by a third globular domain. However, unlike the canonical SMC proteins, Hinderin does not harbor ABC-like ATPase sequences. We have found that the protein interacts with the hinge region of SMC3 but not with SMC1. Hinderin acts as a binding competitor of SMC1 and, as such, qualifies as a putative modulator of the SMC3 function. Results Identification of Hinderin, an SMC3-interacting protein with five domain structure including coiled-coil motifs The region of SMC3 encompassing the protein hinge domain (Gln465 to Gln807) was used as bait in a yeast two-hybrid system to identify interacting proteins expressed by a human fetal brain Matchmaker two-hybrid cDNA library (Clontech). About 3 × 106 library clones were screened. Forty blue colonies reaching 2 mm in size after one week were collected and 21 of the isolated plasmids with inserts greater than 500 bp were sequenced. Three of the sequences matched the same region of the published cDNA Genbank clones AB037749 (coding for the hypothetical protein KIAA1328) and AL832625 (corresponding to the hypothetical protein DKFZp451C1618). The inserts of ~2 kb included part of the gene 3'-UTR. However the 5'-end of the coding region was not present in the retrieved clones. The issue was complicated by the fact that the sequences of AB037749 and AL832625 diverged at their 5'-end. 5'-RACE was thus employed to identify the transcriptional start site of the interacting gene by using mRNA derived from fetal kidney 293, hepatoma HepG2, and cervical HeLa human cells. All the cloned sequence coincided with that of the AL832625 clone and matched in full the putative coding sequence obtained by automated computational analysis of the human genome (Genbank XM029429). The conceptually translated sequence coded for a protein of 578 amino acids. The exam of the secondary structure of the protein revealed a remarkable similarity with the structural organization of SMC proteins, particular with regard to the five-domain structure typical of the SMC protein family. The protein features C- and N-terminal globular domains, joined by a coiled-coil sequence interrupted in the middle by a third globular domain. The size of the central globular domain is similar to that of SMC3 and SMC1 but the remaining domains are smaller in size. Based on its ability to interact with the hinge domain of SMC3 and thus to potentially affect SMC3/SMC1 dimerization, the new protein was named Hinderin. The Hinderin gene spans ~400 kb of the human genome and is located on chromosome 18p11-2. The coding sequence is organized in 10 exons (fig. 1A). Exon 10 is the largest and contains an extended 3'-UTR. Northern blot hybridization analysis performed on mRNA from HeLa and colon carcinoma HCT116 human cell lines, showed a single major transcriptional product of ~4.5 kb (fig. 1B). A correlation between the exonic organization and the protein structural motifs was apparent (fig. 1C). The N-terminal globular domain is encoded by exons 1–3. The first coiled-coil domain is encoded by exons 4–6. Exon 7 harbors the entire central globular domain, the central coiled-coil region, and part of the C-terminal globular domain. The polypeptide encompassing exons 3 to 6 displays a 36% sequence homology to the consensus sequence of SMC family of proteins. A bipartite nuclear localization signal (R441KERK) is located in exon 7 within a coiled-coil region. The Hinderin expression pattern in a panel of 16 human tissues was analyzed by semiquantitative RT-PCR (fig. 1D). The results indicate that the gene is expressed ubiquitously with the highest expression level detected in the lung, liver, placenta, kidney, and pancreas. The lowest expression level was detected in leukocytes and the prostate. Figure 1 Hinderin: Genomic organization, structural domains, and expression pattern in human tissues. A) The sequence of the Hinderin ORF was used to BLAST the human genome database. The numeration of the exon boundaries is relative to the transcriptional start site. The size of the intronic sequences is based on the numeration of the human contiguous sequences. B) Northern blot hybridization of Hinderin in HeLa and HCT116 cells. A single main transcript of ~ 4.5 kb is identifiable. C) Prediction of coiled-coil domains in Hinderin. The numbers on the abscissa corresponds to the amino acid residues. The probability of the polypeptide to assume a coiled coil conformation is plotted on the ordinate axis. The contribution of the different exons to the five-domain structural organization of Hinderin is also illustrated. The sequence encoded by exons 3 through 6, bears a 36% homology to the SMC protein consensus sequence. A bipartite nuclear localization signal is harbored in exon 7. D) Expression of Hinderin in different human tissues. PCR amplification was stopped after 30 cycles and the product analyzed on 2% agarose staining with ethidium bromide. The G3PDH transcript was amplified in 20 cycles and used to show uniformity of the source cDNA in the different specimens. Mapping of the Hinderin and SMC3 interaction domains In order to map the region of Hinderin interacting with SMC3, AH109 yeast was cotransformed with the SMC3-465/807 bait and with different Hinderin constructs (fig. 2). Yeast cotransformants of SMC3-465/807 and the Hinderin plasmid retrieved from the two-hybrid library (H-47/578) grew rapidly. This result was confirmed by switching the bait and prey vectors. However, the H-47/578 construct did not interact with the N- and C-terminal domains of SMC3 (SMC3-1/186 and SMC3-976/1217 respectively) or with the hinge region of SMC1 (SMC1-485/670). A Hinderin construct harboring only the protein central globular domain (H-177/360) furthermore did not interact with the SMC3 bait, thus pointing to a substantial difference in the binding properties of the hinge domain of the SMC proteins and the corresponding domain of Hinderin. On the contrary, both the N- and C-terminal domains of Hinderin (H-1/85 and H-360/578) interacted with the SMC3 hinge domain. The assay of the β-galactosidase activity expressed by the yeast as a result of the protein-protein interaction provided a quantitative measure of the rate of the process. The strongest interaction was detected between SMC3-465/807 or SMC3-465/716 and H-47/578. The Hinderin constructs H-1/85 and H-360/578 displayed a lower interaction activity suggesting that the protein N- and C-terminal domains synergically bind to SMC3. An Hinderin construct (H-64/360), consisting of the central globular domain in addition to part of the protein N-terminal domain, displayed binding activity similar to that of the N-terminal globular domain alone (H-1/85). The protein central globular domain therefore does not affect the binding rate of Hinderin to SMC3. Truncation mutants of SMC3-465/807 were generated to map the region of SMC3 responsible for the interaction with Hinderin. When tested only SMC3-465/716 gave origin to colonies growing at the same rate as observed with the SMC3 bait (fig. 2). The remaining constructs, in which the SMC3 hinge region had been partially or completely deleted, produced no colonies. The intact SMC3 hinge domain is thus required for the interaction with Hinderin. Similarly the interaction between SMC3-465/807 and the hinge region of SMC1 (SMC1-485/670) gave strong reactivity whereas truncation mutants of SMC3 lacking part of the cohesin protein central globular domain (SMC3-552/807 and SMC3-465/643) did not interact. This finding is consistent with previous reports showing that SMC3 and SMC1 dimerization occurs through interaction of the terminal regions of the hinge domains of the two proteins [7,14]. Figure 2 Yeast two-hybrid assay of the interaction of different regions of SMC3 and SMC1 with Hinderin. A) SMC3 and Hinderin constructs in pGBKT7 are schematized on the left-hand side. All numerals refer to the amino acid sequence. The SMC3-465/807 construct harbors the protein hinge domain and was utilized as bait for the screening of the Clontech Matchmaker human cDNA library. SMC1, SMC3 and Hinderin constructs cloned in pACT2 or pGADT7 are illustrated on the right-hand side of the panel. H-47/577 represents the pACT2 clone retrieved from the screening. We scored the strength of interaction based on the rate of appearance of Blue colonies and the intensity of the color developed. The null score (-) was assigned when no blue colonies were visible after ten days. To obtain a quantitative measure of the rate of protein interaction, yeast colonies were grown in selective media and after lysis the β-galactosidase activity released in the supernatant assayed using a chromogenic substrate as detailed in the Methods. The results shown are the mean ± SD of three independent determinations. Hinderin interacts with SMC3 in vivo and is a binding competitor for SMC1 In order to investigate whether Hinderin associates with SMC3 in vivo, 293 cells were transiently transfected with 1 μg/ml of Hinderin-V5 expression vector and incubated 48 h. The transfected cells displayed three-fold elevation of the Hinderin transcript level but SMC1 and SMC3 expression was not affected (fig. 3A, V5-IP input lanes and fig 3B, RT-PCR results). After addition of anti-SMC3 or alternatively anti-SMC1 antibody to the cell lysate, the immunocomplexes were analyzed on SDS-PAGE followed by Western-immunoblotting using anti-V5 antibody (fig. 3A). This revealed the presence of Hinderin-V5 in the SMC3 immunoprecipitate. On the contrary, Hinderin-V5 was not detected in the SMC1 immunoprecipitate, thus corroborating the results of the yeast two-hybrid system interaction experiments. Furthermore, SMC3 but not SMC1 could be recovered in Hinderin-V5 immunocomplexes. In order to examine whether the interaction between SMC1 and SMC3 is affected by Hinderin concentration in vivo, experiments were conducted in 293 cells expressing increasing levels of Hinderin and by assessing the rate of SMC1-SMC3 interaction using a mammalian two-hybrid system (Promega Checkmate). In this assay the interaction between the GAL4:SMC3 activating domain fusion protein and the VP16:SMC1-binding domain fusion protein activates the expression of the firefly luciferase encoded by a reporter vector (pGL5). The rate of interaction between SMC3 and SMC1 is thus directly proportional to the level of luciferase that is expressed. We found that transfection of 293 cells with increasing amount of the Hinderin-V5 expression vector caused a dose-dependent decrease of luciferase activity. In cells overexpressing Hinderin, the interaction between the cohesin proteins was gradually reduced down to ~34% (n = 3, p < 0.05) (fig. 3B). Ectopic expression of Hinderin had no effect on the expression of the bait and prey fusion proteins as determined by RT-PCR, using primers encompassing the coding sequence of fusion proteins. The results are consistent with the notion that Hinderin competes with SMC1 for binding to SMC3, thereby negatively affecting the interaction between the two cohesin proteins. Figure 3 Protein coimmunoprecipitation and competitive binding of Hinderin to SMC3. A) To monitor the Hinderin interaction, 293 cells were transfected with 1 μg/ml of Hinderin-V5 expression vector and incubated for 48 h. Mock transfected cells served as control. For SMC1 and SMC3 coimmunoprecipitation experiments, cell lysates (500 μl) were incubated with either 25 μg of anti-SMC1 or anti-SMC3 antibody for 2 h at RT. The immunocomplexes were then captured on agarose-protein G and analyzed by electrophoresis on 12% SDS-PAGE. After transblotting to a nitrocellulose membrane, Hinderin-V5 was identified using an anti-V5 monoclonal antibody and HRP-conjugated secondary antibody. The migration position of the Hinderin-V5 fusion protein present in the input cell lysate (200 μl) and in the immunoprecipitate is indicated by an arrow. Goat immunoglobulins are identified by an asterix. The presence of SMC1 and SMC3 in the Hinderin-V5 immunoprecipitate was assessed by incubation of cell lysates (500 μl) with 10 μg murine anti-V5 antibody. The immunocomplexes absorbed on agarose-protein G were analyzed on 8% SDS-PAGE and immunoblotted with either anti-SMC1 or anti-SMC3 antibody. SMC1 and SMC3 immunoblots of the input material (200 μl) are also illustrated. B) The effect of Hinderin on the interaction between the SMC3 and SMC1 hinge domains was investigated in 293 cells expressing different level of Hinderin by using a mammalian two-hybrid assay system. SMC3-474/702 and the SMC1-474/663 hinge domains in pBIND and pACT vectors respectively, were cotransfected in 293 cells together with Hinderin-V5 expression vector (0.3 or 1 μg/ml) or alternatively 1 μg/ml of the empty pcDNA3.1 vector (mock-transfected) and the reporter pG5/luc vector using Lipofectamine. Cells transfected with pBIND-Id and pACT-MyoD fusion protein expression vectors were used as control to assess the specificity of the Hinderin effect on protein-protein interaction. Luciferase activity was assayed after 48 h. The bars represent the mean ± SD of the values (n = 3). Semiquantitative RT-PCR was employed to assess the transcript level of GAL4:SMC3 and VP16:SMC1 fusion proteins and of G3PDH in cells ectopically expressing different levels of Hinderin. Discussion In eukaryotic cells, the disjunction of the sister chromatids at the onset of anaphase is required to maintain chromosomal stability and to prevent aneuploidy [12,15]. Human somatic cells with unrestrained separase activity display retarded sister separation and increased rate of chromosomal breakage [10]. On the other hand, the overexpression of Scc1 in mouse fibroblasts inhibits the cells proliferation, implicating this protein and its complex with the other cohesin components in the control of mitotic cell cycle progression [16]. We have previously reported that overexpression of SMC3 has cell transforming potential [17] and we have determined that SMC3 level is controlled in intestinal epithelial cells through the APC/β-catenin/TCF4 transactivation pathway [18], a signaling system that is almost invariably altered in colon carcinomas. These findings support the idea that alteration of the level of the components of the cohesin complex has important consequences that may trigger a tumorigenic cascade. A key event in the formation of the cohesin multimeric complex is the dimerization of SMC1 with SMC3 which occurs through the interaction between the two proteins central globular domains. The hindrance of this process is likely to have a significant effect on the formation and the function of the cohesin complex and on the maintenance of a stable chromosome population. Given this postulate, we have screened a human cDNA library to identify proteins interacting with the hinge region of SMC3. Clones coding for Hinderin accounted for 15% of those identified in this screening. Interaction between Hinderin and SMC3 has been further confirmed by mapping the site of binding, in co-immunoprecipitation experiments using cell lysates, and in a two-hybrid mammalian system. Furthermore we have determined that SMC1 and SMC3 interaction rate is inversely related to the level of Hinderin expressed by mammalian cells. Hinderin displays the same five-domain structural organization of the SMC family [19]. However, the central globular domain of the protein does not appear to be involved in the binding with SMC3. Protein-protein interaction studies with a set of truncation constructs are rather consistent with the conclusion that SMC3 specifically interacts with the Hinderin terminal globular domains. An interaction model that is plausible with the protein-protein interaction results is illustrated in fig 4. The model predicts the occupancy of the hinge region of SMC3 by the two terminal globular domains of Hinderin that come in close proximity by virtue of the flexibility of the protein central globular domain. In SMC1 and SMC3 the coiled-coil domains are in antiparallel orientation and by interacting they allow the N- and C-globular domains to join and form a functional ATPase head that interacts with Scc1 [19]. Given the similarity with the structural organization of the SMC proteins and the interaction modality with SMC3, the Hinderin coiled coil domains might contribute to the binding of the N- and C-terminal globular domains to SMC3. Figure 4 Proposed model of interaction of Hinderin with SMC3. A) The five-domain structure of Hinderin is compared to that of SMC3. The different structural domains are drawn in scale to allow a direct comparison of the size of the terminal globular regions, the two coiled-coil domains and the central globular domain in the two proteins. B) Mode of interaction of the SMC1-SMC3 dimer. The juxtaposition of the cohesins hinge domains interacting through sites located at the globular-coiled coil domain boundaries (ref. 6) is illustrated. C) Postulated mechanism for the competitive binding of Hinderin to the hinge domain of SMC3. The N- and C-terminal globular domains of Hinderin are shown to interact with binding sites located on the SMC3 hinge. The Hinderin central globular domain is not involved in the binding to SMC3 but may play a role by orienting the N- and C-terminal globular domains toward their targets. Conclusions In summary, we have identified a novel interacting partner of SMC3. The protein, named Hinderin, specifically interacts with the hinge domain of SMC3. The protein is ubiquitously expressed in human tissues. We speculate that when in a certain context, SMC3 association with Hinderin becomes favored compared to the association to SMC1, the availability of SMC3 to engage in the cohesin complex formation is reduced. The binding of SMC3 to proteins affecting its association to functional partners represents a new modality of regulation of SMC3 activity. Methods Screening of a human cDNA library by yeast two-hybrid system In order to identify proteins encoded by the human fetal brain cDNA library and interacting with the hinge domain of SMC3, a bait plasmid was generated by subcloning the SMC3-465/807 polypeptide coding region into the yeast two-hybrid system bait vector pGBKT7 (Clontech) (see fig. 2 for a diagram of the constructs used and their designation). The insert was generated by PCR using Pfu DNA polymerase and primers terminated with restriction sites that allowed the directional cloning of the products into the accepting vector (see Additional file 1 for a listing of the primers used in this study). Mouse full-lenght SMC3 cDNA was used as template. To identify the gene encoded by the interacting plasmid, the prey insert was sequenced by priming at the Gal4AD site (5'-AATACCACTACAATGGA-3'). The sequences were BLASTed against the nr and human dEST databases. DNA restriction digestions provided information on the size of the clones retrieved. 5' RACE and cloning of the complete Hinderin coding sequence The total RNA was isolated using TRI-reagent from 293, HepG2 and HeLa cells. The 5' RACE assay was performed using a RLM-RACE Ambion kit. The generated cDNA was used as template for nested PCR. The inner PCR product was cloned into pCRII-Topo vector (Invitrogen) and sequenced using a T7 primer. The sequence matched that of the DKFZp451C1618 clone (AL832625) and extended 5' to the published sequence of KIAA1328 (AB037749). Based on this information, the complete Hinderin coding region was generated by RT-PCR utilizing mRNA extracted from 293 cells, and the product cloned in frame with the tag sequence in pcDNA3.1/V5-His TOPO (Invitrogen). When transfected into 293 cells, the expressed product detected with an anti-V5 monoclonal antibody had size 69 KDa, as expected for a protein encoded by the Hinderin-V5 fusion gene (fig. 3A). Mapping of the protein interacting sites In order to map the SMC3 interacting site(s) a series of truncated constructs were produced in pGBKT7 vector. The inserts required for the SMC3-1/186, SMC3-976/1217, SMC3-552/807 and SMC3-711/807 constructs were produced by PCR. SMC3-465/550 and SMC3-465/716 were generated by introducing stop codons in the sequence of SMC3-465/807 using a QuikChange XL kit (Stratagene). The mutated duplex oligonucleotides used had the forward sequence: 5'-CTTTCTATACTTGTGT AAGTCACTGCTGGTAAC-3' and 5'-GACCAGTTGATGAACTAAATGCAGATAGAG-3', respectively. SMC3-465/643 and SMC3-643/807 were obtained by digesting SMC3-465/807 at the PstI or alternatively the NdeI restriction sites present in the vector multiple cloning site and at the SmaI site of the insert. The resulting linear constructs were blunt-ended and religated. pGADT7-SMC3-465/807 was generated by retriving the insert from the bait vector. SMC1-485/670 encoding the entire SMC1 hinge region, was generated by RT-PCR from 293 cells mRNA and cloned in pGADT7. Hinderin deletion constructs H-64/360 and H-360/578 were generated by restriction digestion of the pACT2-Hinderin-47/578 clone identified in the yeast two-hybrid system screening with NcoI/BglII and BglII/XhoI respectively, followed by religation of the blunt-ended vector. Hinderin in bait pGBKT7 vector was generated by retrieving the H-47/578 insert from the prey clone. H-177/360 and H-1/85 inserts were obtained respectively by PCR and by digestion of the Hinderin pcDNA3.1 expression vector with BamHI/EcoRI. Both were subsequently subcloned in pGADT7. To assess the strenght of interaction between different SMC3 and Hinderin domains, three colonies were randomly selected and grown overnight in 5 ml of selection media. After cell lysis by freeze thawing in 300 ml of 100 mM Na2HPO4 pH 7.0, 10 mM KCl, 1 mM MgSO4, buffer, β-galactosidase activity was assessed using ortho-nitrophenyl-β-D-galactopyranoside as substrate (1 mM) in the presence of 50 mM β-mercaptoethanol. After 2 h incubation at 37°C the color intensity was read at 420 nm. Northern blot hybridization and semiquantitative PCR Total mRNA was extracted with TRI-reagent from subconfluent HeLa and HCT116 cells and separated on 1% agarose. After transfection to a nitrocellulose filter, the Hinderin transcript was identified by hybridization to a 335 bp 32P-labeled cDNA probe annealing to the 5'-end region of the gene. In order to examine the expression of Hinderin in different human tissues, semiquantitative RT-PCR was performed by using 0.5 μg of Marathon-ready first strand cDNA (Clontech) from 16 tissues. The PCR reaction was monitored at 20 and 30 cycles and the product analyzed on 2% agarose. In order to normalize for possible differences in mRNA content, the expression of the housekeeping gene G3PDH was analyzed in each sample. Protein secondary structure prediction and identification of orthologue forms of Hinderin in other species The human polypeptide sequence was analyzed with the COILS program to predict globular and coiled-coil domains. The scanning window was set at 21. The homology with other known protein family was examined by querying the NCBI Conserved Domain protein database. We used PSORT to scan for nuclear and other localization signal consensus sequences. To identify orthologue forms of Hinderin in other species, the human protein sequence was BLASTed against the translated EST database of m. fascicularis, mouse, rat, cow, sheep, dog, zebrafish, c. elegans, drosophila, and s. cereviasie. When a homologous sequence had been identified in lower organisms, we ran the COILS program to assess whether it displayed the same secondary structure as that of the matching human sequence. Protein complex immunoprecipitation 293 cells were grown at ~80% confluence in 35 mm cm plates and transfected with 1 μg of Hinderin-V5 expression vector. After 48 h of incubation, the cells were washed in ice-cold phosphate-buffered saline and lysed in 1.2 ml of 50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% Nonidet P40, 0.5% Na-deoxycholate buffer containing 100 mM NaF, 2 mM Na3VO4, and a cocktail of protease inhibitors. The cell lysate was centrifuged at 12,000 g and the recovered supernatant preabsorbed on protein G-agarose. Aliquots (500 μl) of the cell lysate were then incubated overnight at 4°C with either 25 μg of goat anti-human SMC3 antibody or goat anti-human SMC1 antibody (Santa Cruz Biotech) or alternatively with 10 μg mouse anti-V5 antibody (Invitrogen). The immunocomplexes were captured on Protein G-agarose by incubating 1 h at 4°C. After washing in immunoprecipitation buffer containing 300 mM NaCl, the protein immunocomplex was analyzed by SDS-PAGE and the proteins transferred to nitrocellulose membranes by electroblotting. After saturation in 4% dry milk/0.1% Tween 20 in PBS, the filter was incubated 1 h at RT with primary antibody. The anti-SMC1 and anti-SMC3 immunoprecipitate filter blots were incubated with anti-V5 monoclonal antibody (200 ng/ml) whereas the V5 immunoblots were incubated with either anti SMC1 (100 ng/ml) or anti-SMC3 (100 ng/ml) antibodies. After incubation with species-specific anti IgG HRP-conjugated secondary antibody (1:10,000), the immunocomplexes were visualized by enhanced chemiluminescence reaction (ECL). Mammalian two-hybrid interaction assay Inserts corresponding to the SMC3-474/702 and SMC1-474/663 hinge domains were generated by PCR and ligated in the pBIND or the pACT vectors (Promega) respectively through ligation at the BamHI and SalI sites. The resulting bait and prey DNA constructs (0.25 μg/ml each) together with the Hinderin-V5 expression vector (either 0.3 or 1 μg/ml or alternatively 1 μg/ml of empty pcDNA3.1 vector) and the reporter plasmid pG5/luc encoding the firefly luciferase (0.1 μg/ml), were co-transfected into 293 cells using Lipofectamine. Control experiments were conducted using pACT-Id and pBIND-MyoD vectors (Promega) encoding respectively GAL4:Id and VP16:MyoD fusion proteins known to interact in vivo [20]. After 48 h incubation, cells were lysed and the expressed luciferase activity quantitated using a dual luciferase reporter assay kit (Promega) and a Lumat LB 9501 luminometer. Firefly luciferase values were corrected for the transfection efficiency using the values of the Renilla luciferase activity encoded by the pBIND vector under the control of a strong constitutive promoter. In order to assess the effect of Hinderin on the bait and prey expression, total RNA was extracted from a group of transfected cells and the specifc transcript levels quantitated by RT-PCR using primers annealing to the ends of the fusion protein coding sequence. Authors' contributions CAP carried out the two-hybrid system experiments including the immunoprecipitation studies, generated the necessary DNA constructs, and performed the gene expression analysis. GG conceived and coordinated the studies and drafted the manuscript. Supplementary Material Additional File 1 Primer and adapter sequences oligonucleotide sequence Click here for file Acknowledgments This work was supported by the NIH grants CA82290 to GG. ==== Refs Koshland DE Guacci V Sister chromatid cohesion: the beginning of a long and beautiful relationship Curr Opin Cell Biol 2000 12 297 301 10801457 10.1016/S0955-0674(00)00092-2 Swedlow JR Hirano T The making of the mitotic chromosome: modern insights into classical questions Mol Cell 2003 11 557 569 12667441 10.1016/S1097-2765(03)00103-5 Uhlmann F Chromosome cohesion and separation: from men and molecules Curr Biol 2003 13 R104 R114 12573239 10.1016/S0960-9822(03)00039-3 Uhlmann F Chromosome cohesion and segregation in mitosis and meiosis Curr Opin Cell Biol 2001 13 754 761 11698193 10.1016/S0955-0674(00)00279-9 Strunnikov AV Larionov VL Koshland D SMC1: an essential yeast gene encoding a putative head-rod-tail protein is required for nuclear division and defines a new ubiquitous protein family J Cell Biol 1993 123 1635 1648 8276886 10.1083/jcb.123.6.1635 Haering CH Nasmyth K Building and breaking bridges between sister chromatids Bioessays 2003 25 1178 1191 14635253 10.1002/bies.10361 Hirano M Hirano T Hinge-mediated dimerization of SMC protein is essential for its dynamic interaction with DNA EMBO J 2002 21 5733 5744 12411491 10.1093/emboj/cdf575 Losada A Yokochi T Kobayashi R Hirano T Identification and characterization of SA/Scc3p subunits in the Xenopus and human cohesin complexes J Cell Biol 2000 150 405 416 10931856 10.1083/jcb.150.3.405 Alexandru G Uhlmann F Mechtler K Poupart MA Nasmyth K Phosphorylation of the cohesin subunit Scc1 by Polo/Cdc5 kinase regulates sister chromatid separation in yeast Cell 2001 105 459 472 11371343 10.1016/S0092-8674(01)00362-2 Jallepalli PV Waizenegger IC Bunz F Langer S Speicher MR Peters JM Kinzler KW Vogelstein B Lengauer C Securin is required for chromosomal stability in human cells Cell 2001 105 445 457 11371342 10.1016/S0092-8674(01)00340-3 Hoque MT Ishikawa F Cohesin defects lead to premature sister chromatid separation, kinetochore dysfunction, and spindle-assembly checkpoint activation J Biol Chem 2002 277 42306 42314 12200439 10.1074/jbc.M206836200 Morrison C Vagnarelli P Sonoda E Takeda S Earnshaw WC Sister chromatid cohesion and genome stability in vertebrate cells Biochem Soc Trans 2003 31 263 265 12546698 Sonoda E Matsusaka T Morrison C Vagnarelli P Hoshi O Ushiki T Nojima K Fukagawa T Waizenegger IC Peters JM Earnshaw WC Takeda S Scc1/Rad21/Mcd1 is required for sister chromatid cohesion and kinetochore function in vertebrate cells Dev Cell 2001 1 759 770 11740938 10.1016/S1534-5807(01)00088-0 Hirano M Anderson DE Erickson HP Hirano T Bimodal activation of SMC ATPase by intra- and inter-molecular interactions EMBO J 2001 20 3238 3250 11406600 10.1093/emboj/20.12.3238 Yokomori K SMC protein complexes and the maintenance of chromosome integrity Curr Top Microbiol Immunol 2003 274 79 112 12596905 Darwiche N Freeman LA Strunnikov A Characterization of the components of the putative mammalian sister chromatid cohesion complex Gene 1999 233 39 47 10375619 10.1016/S0378-1119(99)00160-2 Ghiselli G Iozzo RV Overexpression of bamacan/SMC3 causes transformation J Biol Chem 2000 275 20235 20238 10801778 10.1074/jbc.C000213200 Ghiselli G Coffee N Munnery CE Koratkar R Siracusa LD The cohesin SMC3 is a target the for beta-catenin/TCF4 transactivation pathway J Biol Chem 2003 278 20259 20267 12651860 10.1074/jbc.M209511200 Melby TE Ciampaglio CN Briscoe G Erickson HP The symmetrical structure of structural maintenance of chromosomes (SMC) and MukB proteins: long, antiparallel coiled coils, folded at a flexible hinge J Cell Biol 1998 142 1595 1604 9744887 10.1083/jcb.142.6.1595 Finkel T Duc J Fearon ER Dang CV Tomaselli GF Detection and modulation in vivo of helix-loop-helix protein-protein interactions J Biol Chem 1993 268 5 8 8380166	15656913	PMC547899	CC BY	2021-01-04 16:39:11	no	BMC Cell Biol. 2005 Jan 18; 6:3	utf-8	BMC Cell Biol	2,005	10.1186/1471-2121-6-3	oa_comm
==== Front BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-661561932710.1186/1471-2458-4-66Research ArticleGlobal patterns of healthy life expectancy in the year 2002 Mathers Colin D [email protected] Kim Moesgaard [email protected] Joshua A [email protected] Ajay [email protected] Somnath [email protected]ün Bedirhan [email protected] Christopher JL [email protected] Evidence and Information for Policy, World Health Organization, Avenue Appia, Geneva, Switzerland2 European Office of the World Health Organization, Copenhagen, Denmark3 Harvard School of Public Health, Harvard University, Cambridge MA, USA2004 24 12 2004 4 66 66 13 7 2004 24 12 2004 Copyright © 2004 Mathers et al; licensee BioMed Central Ltd.2004Mathers et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Healthy life expectancy – sometimes called health-adjusted life expectancy (HALE) – is a form of health expectancy indicator that extends measures of life expectancy to account for the distribution of health states in the population. The World Health Organization reports on healthy life expectancy for 192 WHO Member States. This paper describes variation in average levels of population health across these countries and by sex for the year 2002. Methods Mortality was analysed for 192 countries and disability from 135 causes assessed for 17 regions of the world. Health surveys in 61 countries were analyzed using new methods to improve the comparability of self-report data. Results Healthy life expectancy at birth ranged from 40 years for males in Africa to over 70 years for females in developed countries in 2002. The equivalent "lost" healthy years ranged from 15% of total life expectancy at birth in Africa to 8–9% in developed countries. Conclusion People living in poor countries not only face lower life expectancies than those in richer countries but also live a higher proportion of their lives in poor health. ==== Body Background In the World Health Report 2000, the World Health Organization (WHO) reported for the first time on the average levels of population health for its 191 member countries using a summary measure that combines information on mortality and morbidity [1,2]. Because substantial resources are devoted to reducing the incidence of conditions that cause ill-health but not death and to reducing their impact on people's lives, it is important to capture both fatal and non-fatal health outcomes in any such measure of population health. Healthy life expectancy – sometimes called health-adjusted life expectancy (HALE) – is a form of health expectancy indicator that extends measures of life expectancy to represent the average health in a population in terms of equivalent years of full health, taking into account the distribution of health states [3]. HALE has been calculated previously for Canada and Australia using population survey data on disability [4-6]. The United States has adopted a public health policy goal to increase the expected years of healthy life in the population and has used a type of healthy life expectancy to measure progress towards this goal [7,8]. In calculating HALE for 191 WHO Member States for 1999, we carried out an analysis of 62 representative population health surveys which revealed substantial problems with comparability of self-report health status and disability data [2]. We used health state prevalence estimates from the Global Burden of Disease 2000 project to adjust for biases in self-report data; the independent information on levels of population health provided by the health surveys was thus quite limited. It has long been known that health expectancy estimates based on self-reported health status information are not comparable across countries due to differences in survey instruments and cultural differences in reporting of health [9,10]. The International Network on Health Expectancy (REVES) and international agencies have devoted substantial efforts to try to standardize questionnaire instruments and methods [11-13]. Though some cross-national surveys using a common self-report instrument have become available [14], standardized instruments alone do not solve comparability problems [15]. These relate more fundamentally to unmeasured differences in expectations and norms for health, so that the meaning different populations attach to the labels used for response categories in self-reported questions, such as mild, moderate or severe, can vary greatly. Given these problems, WHO undertook a Multi-Country Survey Study on Health and Responsiveness (MCSS) in 2000 and 2001 in collaboration with Member States using a standardized health status survey instrument together with new statistical methods for adjusting biases in self-reported health [16-19]. These new data, together with comprehensive analyses of epidemiological data for all regions of the world, and new life tables for all WHO Member States, have enabled us to calculate HALE for 192 countries for 2002 in a way that improves comparability across countries. These results are reported in the World Health Report 2004 [20]. A previous paper has examined variations in HALE among OECD countries [21]. This paper examines the implications of the results for our understanding of global patterns of health. Methods Calculation of HALE requires three inputs: life tables, prevalences of various health states, and valuations of time spent in these health states compared to full health. The WHO methods used to calculate HALE have been developed to maximise its comparability across populations. These methods are described in more detail elsewhere [22,23] and have been reviewed by an independent scientific peer review group [24]. A set of spreadsheet tools are also under development to provide full access to the inputs and calculations for country-specific HALE for 2002; these will enable users to modify inputs and to carry out sensitivity analyses for various factors. This section provides an overview of these methods and data sources and a more detailed description of adjustments for institutionalized populations. Life table methods Procedures used to estimate the 2002 life tables differed for Member States depending on the data availability to assess child and adult mortality. Complete or incomplete vital registration data together with sample registration systems cover 72% of global mortality. Survey data and indirect demographic techniques provide information on levels of child and adult mortality for the remaining 28% of estimated global mortality. Separate estimates were used for the numbers and distributions of deaths due to HIV/AIDS in countries with a substantial HIV epidemic [25]. A full overview of methods used to construct life tables is given elsewhere [20,26,27]. Following an initial scientific review [24], significant improvements were implemented in both data and methods used to calculate life expectancies for WHO Member States. Recent surveys and censuses provided substantially more information on levels of child and adult mortality in Member States lacking complete death registration. This has resulted in changes in point estimates of life expectancies and reductions in uncertainty ranges for some Member States compared to previously published estimates. Health state prevalence data Because comparable health state prevalence data are not yet available for all countries, two sources of information were used: the Global Burden of Disease (GBD) study and the MCSS. Data from the GBD study were used to estimate severity-adjusted prevalences by age and sex for all 192 countries [23]. Secondly, data from the MCSS were used to make independent estimates of severity-adjusted prevalences by age and sex for 55 countries. Finally, 'posterior' prevalences for all countries were calculated based on the GBD-based prevalences and the survey prevalences as described below. This process is summarized in Figure 1. Figure 1 Estimation of severity-adjusted health state prevalences for calculation of HALE. The GBD revisions draw on a wide range of data sources to develop internally consistent estimates of incidence, prevalence, duration and years lived with disability (YLD), for 135 major causes, for 14 sub-regions of the world [20]. Prevalence-based YLD rates were calculated, and adjusted for co-morbidity, giving direct estimates of the severity-weighted prevalence of health states attributable to each cause [23]. Tables 1 and 2 summarize the major causes of YLD for developed and developing countries in the year 2002 as published in the World Health Report 2004 [20]. Neuropsychiatric conditions accounted for 42% of YLD in developed countries and nearly 30% of YLD in developing countries. Unipolar depression is the leading contributor to this burden. Other major causes of YLD include vision and hearing loss (9% in developed countries and 13% in developing countries) and injuries (nearly 12% in developing countries and 7% in developed countries). More detailed estimates of YLD by age group and cause are available for 14 subregions of the WHO Regions, and for countries grouped into high, medium and low income categories, on the WHO website at . Table 1 Leading causes of disability, years lived with disability (YLD) by cause as percent of YLD from all causes, developed countriesa, 2002 Cause group % of total YLD Female to male ratio I. Communicable, maternal, perinatal and nutritional conditions 6.6 1.47 Infectious and parasitic diseases 2.4 0.94 Maternal conditions 0.9 - Perinatal conditions 0.8 0.95 Nutritional deficiencies 2.1 1.50 II. Noncommunicable diseases 86.2 1.12 Malignant neoplasms 2.4 1.54 Diabetes mellitus 2.3 1.10 Neuropsychiatric conditions 41.9 1.10 Unipolar depressive disorders 15.0 1.69 Bipolar disorder 2.2 0.99 Schizophrenia 2.3 0.94 Alcohol use disorders 6.8 0.24 Alzheimer and other dementias* 4.2 1.99 Drug use disorders 1.7 0.34 Other neuropsychiatric disorders 9.7 1.50 Sense organ diseases 8.6 1.16 Vision disordersb 3.0 1.44 Hearing loss, adult onset 5.7 1.04 Cardiovascular diseases 6.7 0.86 Respiratory diseases 6.9 0.96 Musculoskeletal diseases 7.6 1.53 Other non-communicable diseases 9.7 1.09 III. Injuries 7.2 0.45 Unintentional injuries 5.8 0.48 Intentional injuries 1.4 0.31 a Developed countries includes European countries, former Soviet countries, Canada, USA, Cuba, Japan, Australia, New Zealand, Brunei Darussalam, Singapore. b Vision disorders includes vision loss due to glaucoma, cataracts, macular degeneration and other age-related vision loss. Table 2 Leading causes of disability, years lived with disability (YLD) by cause as percent of YLD from all causes, developing countriesa, 2002 Cause group % of total YLD Female to male ratio I. Communicable, maternal, perinatal and nutritional conditions 23.4 1.36 Infectious and parasitic diseases 10.9 0.95 Maternal conditions 3.8 - Perinatal conditions 3.1 0.98 Nutritional deficiencies 4.4 1.08 II. Noncommunicable diseases 64.9 1.03 Malignant neoplasms 0.3 2.02 Diabetes mellitus 1.1 1.11 Neuropsychiatric conditions 29.4 1.09 Unipolar depressive disorders 11.1 1.47 Bipolar disorder 2.5 0.98 Schizophrenia 2.9 0.97 Alcohol use disorders 2.5 0.15 Alzheimer and other dementias* 1.0 1.40 Drug use disorders 0.8 0.28 Other neuropsychiatric disorders 8.6 1.37 Sense organ diseases 13.0 1.13 Vision disordersb 8.7 1.26 Hearing loss, adult onset 4.3 0.92 Cardiovascular diseases 3.3 0.82 Respiratory diseases 4.2 0.72 Musculoskeletal diseases 4.6 1.18 Other non-communicable diseases 8.9 0.90 III. Injuries 11.7 0.66 Unintentional injuries 9.7 0.75 Intentional injuries 2.0 0.32 a Developing countries includes all countries except for European countries, former Soviet countries, Canada, USA, Cuba, Japan, Australia, New Zealand, Brunei Darussalam, Singapore. b Vision disorders includes vision loss due to glaucoma, cataracts, macular degeneration and other age-related vision loss. Summation of prevalence YLD across all causes would result in overestimation of the total average severity-weighted health state prevalence because of comorbidity between conditions. In earlier calculations of HALE, adjustments were made for independent comorbidity assuming that the probability of having two (comorbid) conditions would equal the product of the probabilities for having each of the diseases. For the World Health Report 2003, further work was undertaken to take dependent comorbidity into account more rigorously [28]. For many diseases, the probability of having a pair of diseases is greater than the product of the probabilities for each disease, reflecting common causal pathways (for example common risk factors causing both diabetes and heart disease) and also that one disease may increase the risk of another. Data from five large national health surveys were analysed by age and sex to estimate "dependent comorbidity factors" for pairs of conditions. There was surprising consistency in these factors across the five surveys and the results were used for all Member States to adjust for dependent comorbidity in summation of prevalence YLD across all causes [28]. MCSS Survey estimates The MCSS was carried out in 2000–2001. A total of 71 surveys were completed in 61 countries using face-to-face, postal and telephone interviewing modes [16]. Thirty-five of the surveys were carried out in 31 Western and Eastern European countries, 27 surveys in 22 developing countries, and the remainder in Canada, USA, Australia and New Zealand. To overcome the problem of comparability of self-report health data, the WHO survey instrument used anchoring vignettes to calibrate self-reported health for the 6 core health domains (mobility, self care, pain, affect, work and household activities, cognition) and vision. Anchoring vignettes are short descriptions that mark fixed levels of ability (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) and allow us to adjust for individual variations in the use of response categories to describe the same health state [17,18,29]. We included in the analysis only those surveys that have met certain explicit criteria that reflect the quality of survey implementation with specific reference to the health vignettes. The USA postal survey was also excluded because respondents were presented the vignettes in order of severity rather than randomized as in the case of all other surveys. Sixty-two surveys met these criteria and were included in the model. Health state prevalences from the WHO Multi-country Household Survey Study were assumed to relate to calendar year 2000. Trends in prevalence YLD between 2000 and 2002 were calculated for each Member State using the GBD estimates. Aggregated across all causes, these trends were generally small. In calculating HALE for 2002, the 2000 survey results were adjusted for likely change over two years using these estimated trends. Health state valuations The health state valuations used in HALE calculations represent average population assessments of the overall health levels associated with different states. They range from 0 representing a state of good or ideal health to 1 representing states equivalent to being dead. These weights do not measure average levels of well-being or quality of life associated with health states, or imply any societal value of a person in a disability or health state. Rather they characterize health decrements on a continuum starting from the societal ideal of good health. Household surveys including a valuation module were conducted in fourteen countries: China, Colombia, Egypt, Georgia, India, Iran, Lebanon, Indonesia, Mexico, Nigeria, Singapore, Slovakia, Syria and Turkey. Data on nearly 500,000 health state valuations from over 46,000 respondents were used to develop a mapping function that captured the average relationships between levels on the six core health domains and overall health state valuations. This average global valuation function was then applied to the vignette-adjusted health domain levels for each survey respondent in order to estimate health state valuations for the calculation of HALE [30,31]. The MCSS survey samples did not include older people resident in nursing homes or other health institutions. Because these people will generally have worse health than those resident in households, adjustments were made to account for the older population who were resident in health institutions. Fifty-four national estimates of the proportion of the population aged 65 years and over who are resident in nursing homes were collected for 36 countries from national statistical publications and international statistical databases of OECD and the World Bank. These were used to estimate the percentage of the population aged 60+ years institutionalized in MCSS countries. This ranged from 3 to 5% in most OECD countries, was highest at around 7% in the Netherlands and Sweden, was substantially lower at around 0.3 to 1% in Eastern European countries, and was close to zero for developing countries. As data on the severity distribution of health states in institutionalized populations were not available, an average disability weight of 0.5 (corresponding to a health state with mobility and self-care limitations and where the person cannot carry out usual daily activities) was assumed for the institutionalized population. Sensitivity analyses showed that the resulting HALE estimates were not sensitive to the choice of this disability weight within a plausible range of variation. Calculation of posterior severity-weighted prevalences Because there is potential measurement error in severity-weighted health state prevalences derived from both household surveys and epidemiological estimates, posterior estimates of prevalence for the survey countries were calculated as weighted averages of the GBD-based prevalences and the survey prevalences [23]. The relationship between the GBD-based prevalences and the posterior prevalences was estimated for the survey countries using ordinary least squares regression and the results used to adjust the GBD 2000-based prevalences for the non-survey countries. This ensured that the use of the survey data did not introduce a prevalence differential between survey and non-survey countries, and allowed the survey evidence to be indirectly taken into account in making the best possible prevalence estimates for non-survey countries. Calculation of HALE and uncertainty intervals HALE was calculated using Sullivan's method [32] based on abridged country life tables and the posterior severity-weighted prevalences. Uncertainty ranges for HALE estimates were estimated using Monte Carlo simulation techniques to quantify the uncertainty in life expectancy projections, in GBD estimates for prevalence and disability severity, and in the survey-based prevalence estimates [23]. Apart from ongoing revisions to the GBD analyses of epidemiological information on diseases and injuries, the implementation of improved methods for dealing with comorbidity has resulted in a reduction in estimated proportion of healthy years of life lost at older ages compared to previous years. Improvements in methods used for the analysis of the MCSS survey data and in the adjustment of total YLD rates for dependent comorbidity have resulted in improved estimates of the severity-adjusted prevalence of health states and a reduction in the uncertainty associated with these estimates. For these reasons, HALE estimates for 2002 are not directly comparable with those for 2000 and 2001 published in the World Health Report 2002 [33]. Results The survey data identified considerable variability in the use of question response categories to describe the same level on a particular health domain and a systematic tendency for people in countries with higher income per capita to use more severe response categories in rating a given anchoring vignette. Thus the self-reported prevalence of problems for a health domain in a country may be quite different from the prevalence of problems after adjustment for these response category cut-point shifts. In France, for example, 70% of survey respondents gave self-report responses of mild or greater problems on the question for the affect domain (anxiety and depression), whereas the standardized (vignette-adjusted) prevalence at all levels higher than "none" was only 33% for France. In contrast, for some countries such as Egypt, the self-report and standardized prevalences were almost the same at around 38%. Figure 2 shows average HALE at birth for 192 countries, plotted against income per capita (Gross Domestic Product measured in international dollars using purchasing power parity conversion rates) on a logarithmic scale. The error bars show estimated 95% uncertainty ranges for HALE at birth. Country-specific estimates of male and female HALE and total life expectancy at birth and at age 60, together with 95% uncertainty ranges, are provided in the World Health Report 2004 [20]. Figure 2 Healthy life expectancy at birth versus Gross Domestic Product (GDP) per capita, 192 WHO Member States. Healthy life expectancy at birth in 2002, together with 95% uncertainty ranges, versus Gross Domestic Product (GDP) per capita for 2001 in international dollars (purchasing power parity conversion), 192 WHO Member States Japanese women led the world with an estimated average HALE at birth of 77.7 years in the year 2002, 7.5 years lower than total life expectancy at birth. HALE at birth for Japanese males was 72.3 years, 5.4 years lower than for females. This male-female gap in HALE was narrower than that for total life expectancy at birth (6.9 years). After Japan, in second to seventh places, were San Marino, Sweden, Switzerland, Monaco, Iceland and Italy, with HALE at birth (males and females combined) in the range 72.7 to 73.4 years, followed by Australia and a number of other industrialized countries of Western Europe. There was a considerable range of uncertainty in the ranks for countries other than Japan, with typical 95% uncertainty ranges for HALE of around 0.5 to 2 years for developed countries. Keeping these uncertainty intervals in mind, Canada was in 11th place (72.0 years) and the USA in 29th place (69.3 years). Other countries with reasonably high HALE in the Americas included Argentina (65.3 years), Chile (67.3 years), Costa Rica (67.2 years), Cuba (68.3 years), Mexico (65.4 years), Panama (66.2 years) and Uruguay (66.2 years). Brazil was split, with a high HALE in its southern half, and a lower one in the north. The total average was a relatively low 59.8 years, at 57.2 for males and 62.4 for females. Overall, global HALE at birth in 2002 for males and females combined was 57.7 years, 7.5 years lower than total life expectancy at birth (Figure 3). In other words, poor health resulted in a loss of nearly 8 years of healthy life, on average globally. Global HALE at birth for females was only 2.7 years greater than that for males. In comparison, female life expectancy at birth was 4.2 years higher than that for males. Global HALE at age 60 was 12.7 years and 14.7 years for males and females respectively; 4.3 years lower than total life expectancy at age 60 for males and 5.3 years lower for females. Figure 3 Life expectancy (LE), healthy life expectancy (HALE), and lost healthy years as per cent of total LE (LHE%), at birth and at age 60, by sex and region, 2002. HALE at birth ranged from a low of 40 years for African males to over 70 years for females in the low mortality regions of Western Europe, North America and the Pacific (Japan, Australia, New Zealand). This reflects an almost 2-fold difference in HALE between major regional populations of the world (Figure 3). The equivalent "lost" healthy years (total life expectancy minus HALE) ranged from 15% of total life expectancy at birth in Africa to 8–9% in the European region and the Western Pacific region. The sex gap was highest for Eastern Europe and lowest in North Africa and the Middle East. There was a similar almost two-fold variation in HALE at age 60 across the regions of the world, ranging from 19 years for women in low-mortality countries to around 10 years for men and women in sub-Saharan Africa. Males lost more healthy years of life at age 60 than females only in China and South East Asia, where life expectancy gaps at age 60 were also low compared with the more than 5-year gap in Japan (due to high female life expectancy) and in Eastern Europe (due to high adult male mortality rates). There was an enormous difference between the world's highest HALE at birth of 77.7 years (for females in Japan) and the lowest of 27.2 years (for males in Sierra Leone) in 2002. In Sierra Leone, people both lived shorter lives (male life expectancy at birth was estimated at 32.4 years), and had higher levels of disease and disability at all ages. The probability of a male child dying before his 5th birthday was 33% in Sierra Leone, compared with less than 0.5% in Japan. The low levels of HALE in sub-Saharan Africa reflect the additional impact of the HIV-AIDS epidemic, as well as war and conflict in some countries such as Sierra Leone. AIDS is now the leading cause of death in Sub-Saharan Africa, far surpassing the traditional deadly diseases of malaria, tuberculosis, pneumonia and diarrhoeal disease. AIDS killed 2.1 million Africans in 2002, versus 300,000 AIDS deaths in 1990 [20,34]. In Russia, HALE at birth was 64.1 for females, 4 years below the European average, but just 52.8 years for males, 9.4 years below the European average. This was one of the widest sex gaps in the world and reflects the sharp increase in adult male mortality in the 1990s. The most common explanation is the high incidence of male alcohol abuse, which led to high rates of accidents, violence and cardiovascular disease. From 1991 to 1994, the risk of premature death increased by 50% for Russian males [35]. Between 1994 and 1998, life expectancy improved for males, but has declined significantly again in the last 3 years [35-37]. Overall, HALE at birth for males in Russia and other former Soviet countries was 16 years lower than the average for males in Western Europe; the difference for females was lower at 9 years. Other Eastern European countries such as Ukraine and Belarus also had large gaps between male and female HALE at birth, as did Colombia, where male HALE at birth was nearly 9 years lower than that for females. At the other extreme there were 12 countries where female HALE at birth was lower than male, and an additional 20 countries where it was less than 1 year higher. These included African countries greatly affected by HIV/AIDS such as Botswana, Kenya, Tanzania and Zimbabwe, but also Eastern Mediterranean countries such as Bahrain, Kuwait, Qatar and the United Arab Emirates, and Asian countries such as Afghanistan, India, Pakistan and Bangladesh. Also included were Nigeria and Haiti. In most of these countries, female life expectancy was slightly higher than male; only in Qatar, Maldives and Bangladesh was female life expectancy at birth lower than that for males. However, higher levels of disability and poor health reduced HALE for females to a greater extent than for males in these countries. At a broader regional level, sub-Saharan Africa, the Eastern Mediterranean region, and the South East Asian region all had female HALE at birth less than three years higher than male, compared with a female-male gap of 6 to 7 years in developed countries. HALE at birth in Afghanistan was estimated at 35.5 years, the 11th lowest in the world in 2002. There was a large 95% uncertainty range around this estimate, of 27 to 44 years, reflecting the lack of population health information for that country. More health information was available for Iraq, where HALE at birth in 2002 was estimated at 50.1 years, with an uncertainty range of 47 to 54 years. China had a healthy life expectancy well above the global average, at 64.1 years, 65.2 years for women and 63.1 for men. Other countries in the Asian region generally had lower HALE. Improving health in Viet Nam has resulted in a HALE of 61.3 years, while Thailand has not improved significantly over the past decade, with a HALE of 60.1 years in 2002. HALE in Myanmar was just 51.7 years at birth, substantially behind its South East Asian neighbors. Figure 4 shows the expectation of lost healthy years at birth (LHE = LE – HALE) versus total life expectancy at birth for 192 countries. While lower life expectancies are generally associated with lower HALE, there were large variations in HALE and in LHE for any given level of life expectancy. For example, for countries with a life expectancy of 72, HALE varied from 61.1 to 64.6, a non-trivial variation. Figure 4 Lost healthy years at birth (LE – HALE) versus life expectancy at birth, by sex, 192 WHO Member States, 2002. Correspondingly, LHE varied from 7.3 to 11 years, or by up to 50%. If male and female HALE are considered separately, the range of variation increases to 59–66 at total life expectancy of 72 years. While LHE increases somewhat with increasing life expectancy up to around 70 years life expectancy, there is a flattening for males and a trend to decreasing LHE for females in the countries with longest life expectancies. Although there are higher prevalences of disabling conditions such as dementia and musculoskeletal disorders in countries with longest life expectancies, this is offset by lower levels of disability for diseases such as cardiovascular disease and chronic respiratory diseases where incidence and mortality rates are also lower. Discussion and conclusions As discussed elsewhere, the new methods used in the WHO Multi-Country Survey Study offer clear evidence that self-report data on health status are not comparable, and allow adjustments that improve the comparability of the resulting health status measures [18]. Building on the findings from the MCSS, WHO is now undertaking the World Health Survey in collaboration with Member States [38]. During 2003 and the first half of 2004, 73 Member States conducted the World Health Survey, and results have now all been received by WHO. The World Health Survey results will contribute to the analysis of healthy life expectancy in future years. Despite the fact that people live longer in the richer, more developed countries, and have greater opportunity to acquire non-fatal disabilities in older age, disability has a greater absolute (and relative) impact on healthy life expectancy in poorer countries. Separating life expectancy into equivalent years of good health and years lost to sub-optimal health thus widens rather than narrows the difference in health status between the rich and the poor countries. Richer countries should be much more active in seeking ways to improve the health of the world's poor. WHO has been a strong advocate for efforts to increase the resources available for this purpose, and the recent report of its Commission of Macro-economics and Health concluded that the bulk of the global disease burden is the result of a relatively small set of conditions, each with an existing set of effective interventions [39]. The main problems are the funding of these interventions and access of poor populations to these interventions. The Commission estimated that the essential interventions to target these problems could be provided for a cost of around $34 per person per year. The World Health Report 2002 included an analysis of mortality and morbidity attributable to the combined effects of 20 selected leading risk factors for 14 subregions of the world [33,40]. Globally 47% of premature mortality and 39% of total disease burden were attributable to the joint effects of the 20 selected risk factors. Removing these risk factors would lead to an estimated gain of 9.3 years (17%) in global HALE. The regional gains were between 4.4 years (6%) in the developed countries of the Western Pacific, to 16.0 years (43%) in parts of sub-Saharan Africa [41]. The World Health Report 2002 also analyzed the cost-effectiveness of a wide range of interventions to address these risks. For the first time ever, policy makers were provided not only with a summary measure of level of population health (HALE), but also with information on its determinants (diseases, injuries and major risk factors) and on the gains that could be achieved through specific intervention packages, along with an analysis of the potential improvements in healthy life expectancy for different regions of the world that could be achieved through reduction or elimination of exposure to 20 major global risk factors [33]. The regular assessment of levels of population health is a key input to the public policy process, and comparable measurement of population health levels creates possibilities of investigating broad determinants at national and cross-national level. Mortality indicators are not adequate for this purpose, given the considerable policy interest for many populations as to whether – and to what extent – gains in life expectancy have been accompanied by improvements in non-fatal health status [9,42-45]. The trend to flattening or decreasing LHE for countries with longest life expectancies, noted above, provides the first cross-population comparable evidence that compression of morbidity may be occurring in low mortality countries, although this evidence is cross-sectional rather than longitudinal. Comparability is fundamental to the use of survey results for development of evidence for health policy but has been under-emphasized to date in instrument development. We believe that the new methods used in the WHO Multi-country Household Survey Study and the World Health Survey have increased the comparability of self-report data across countries and provide the first steps towards the consistent and comparable measurement of population health across the world. Final confirmation that compression of morbidity is occuring in low mortality populations awaits longitudinal cross-population comparable data for specific populations: this is now achievable. Competing interests The author(s) declare that they have no competing interests. Authors' contributions CDM, CJLM and JAS developed the methods for calculation of HALE. CJLM, JAS and AT developed the methods and carried out the calculations for the measurement of health states and health state valuations in the MCSS, with contributions from the other authors. CJLM, JAS, SC and BU developed the MCSS survey instrument, and SC and BU coordinated the implementation and analysis of the surveys. KMI, CDM and JAS carried out analyses and calculations for the computation of HALE for the year 2002 for 192 WHO Member States, with contributions from CJLM and the other authors. CDM drafted the paper, with substantial contributions from other authors. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements The authors thank the many staff of the Global Program on Evidence for Health Policy who contributed to the development of life tables, burden of disease analysis and the development and conduct of the health surveys. In particular we thank Stephen Begg, Lydia Bendib, Christina Bernard, David Evans, Brodie Ferguson, Mie Inoue, Alan Lopez, Doris Ma Fat, Emre Ozaltin, Chalapati Rao, Ritu Sadana, Kenji Shibuya, Claudia Stein, Niels Tomijima, Cao Yang, Maria Villenueva and Hongyi Xu. 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77 181 185 10083720	15619327	PMC547900	CC BY	2021-01-04 16:28:47	no	BMC Public Health. 2004 Dec 24; 4:66	utf-8	BMC Public Health	2,004	10.1186/1471-2458-4-66	oa_comm
==== Front BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-5-31564931710.1186/1471-244X-5-3Research ArticleAcute weight gain, gender, and therapeutic response to antipsychotics in the treatment of patients with schizophrenia Ascher-Svanum Haya [email protected] Michael [email protected] Zhongyun [email protected] Bruce J [email protected] Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana2005 13 1 2005 5 3 3 10 8 2004 13 1 2005 Copyright © 2005 Ascher-Svanum et al; licensee BioMed Central Ltd.2005Ascher-Svanum et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Previous research indicated that women are more vulnerable than men to adverse psychological consequences of weight gain. Other research has suggested that weight gain experienced during antipsychotic therapy may also psychologically impact women more negatively. This study assessed the impact of acute treatment-emergent weight gain on clinical and functional outcomes of patients with schizophrenia by patient gender and antipsychotic treatment (olanzapine or haloperidol). Methods Data were drawn from the acute phase (first 6-weeks) of a double-blind randomized clinical trial of olanzapine versus haloperidol in the treatment of 1296 men and 700 women with schizophrenia-spectrum disorders. The associations between weight change and change in core schizophrenia symptoms, depressive symptoms, and functional status were examined post-hoc for men and women and for each medication group. Core schizophrenia symptoms (positive and negative) were measured with the Brief Psychiatric Rating Scale (BPRS), depressive symptoms with the BPRS Anxiety/Depression Scale and the Montgomery-Asberg Depression Rating Scale, and functional status with the mental and physical component scores on the Medical Outcome Survey-Short Form 36. Statistical analysis included methods that controlled for treatment duration. Results Weight gain during 6-week treatment with olanzapine and haloperidol was significantly associated with improvements in core schizophrenia symptoms, depressive symptoms, mental functioning, and physical functioning for men and women alike. The conditional probability of clinical response (20% reduction in core schizophrenia symptom), given a clinically significant weight gain (at least 7% of baseline weight), showed that about half of the patients who lost weight responded to treatment, whereas three-quarters of the patients who had a clinically significant weight gain responded to treatment. The positive associations between therapeutic response and weight gain were similar for the olanzapine and haloperidol treatment groups. Improved outcomes were, however, more pronounced for the olanzapine-treated patients, and more olanzapine-treated patients gained weight. Conclusions The findings of significant relationships between treatment-emergent weight gain and improvements in clinical and functional status at 6-weeks suggest that patients who have greater treatment-emergent weight gain are more likely to benefit from treatment with olanzapine or haloperidol regardless of gender. ==== Body Background Because antipsychotic drugs are considered the core treatment modality for schizophrenia [1] the differences among antipsychotics in terms of effectiveness, safety, and tolerability have expectedly become a topic of growing clinical and research interest [2]. The differences among antipsychotics in adverse events have garnered particular interest, with treatment-emergent weight gain becoming a focal point of attention and concern because weight gain can be associated with medical conditions such as type II diabetes, hypertension, and coronary artery disease [3]. Previous research has shown that there are variations with respect to the magnitude and the course of typical weight gain experienced during treatment with different antipsychotics [4]. Generally, the first generation antipsychotics, such as haloperidol, are associated with less weight gain than the second-generation antipsychotics. The newer atypical agents vary such that clozapine and olanzapine are associated with the greatest potential for weight gain, followed by risperidone, quetiapine, ziprasidone, [5] and aripiprazole. Although most of the literature on treatment-emergent weight gain tends to focus on this event as adverse, a growing body of research has demonstrated a significant link between beneficial therapeutic response and treatment-emergent weight gain. With the exception of a few studies that failed to find an association between weight gain and better clinical outcome [6-9], most studies, primarily on clozapine, suggest an association between weight gain and better clinical outcome [10-19]. This expanding body of evidence augments studies on first-generation antipsychotics predating the introduction of atypical antipsychotics by about 30 years, also suggesting a link between weight gain and improved therapeutic response [20-22]. One study [17] reported mixed findings, where the association between treatment-emergent weight gain and clinical outcome was found for patients treated with clozapine and olanzapine, but not with risperidone or haloperidol, suggesting that this phenomenon may be specific to particular antipsychotics. The study of gender differences in the relationship between treatment-emergent weight gain and therapeutic response has gained limited attention and provided conflicting results. A brief report on weight gain during clozapine therapy indicated that greater weight gain was associated with clinical improvement among women, but not among men [13]. In contrast, a more extensive analysis [16] demonstrated that clozapine-emergent weight gain predicted improvement in psychopathology among both men and women. It is unclear if there are gender differences in the association between treatment-emergent weight gain and therapeutic response, and if such gender differences exist, whether they are limited to a specific antipsychotic such as clozapine. Women in the general population appear to be vulnerable to the adverse emotional and psychosocial consequences of weight gain. For women, obesity has been linked to lower life satisfaction, increased social isolation [23], and lower levels of psychological and physical functioning [24-26]. Compared to men, women are more likely to perceive themselves as overweight [27], to diet [28], and to participate in weight loss programs [28]. Based on generalizations from studies on women in the general population, several authors have speculated that antipsychotic-emergent weight gain will be similarly accompanied by negative psychological consequences [29-31], which will negatively impact women's response to antipsychotic therapy. However, it is unclear if women who gain weight during treatment with antipsychotics tend to experience adverse emotional consequences similar to those noted among women who gain weight in the general population. It is also unclear whether the association between treatment-emergent weight gain and clinical response differs by patient gender and by type of antipsychotic. The primary objective of this study was to expand on prior research and investigate whether the relations between acute weight gain during antipsychotic therapy and treatment outcomes differ based on patient gender and the specific antipsychotic used in the treatment regimen, olanzapine or haloperidol. This study also aimed to broaden the definition of therapeutic response by extending beyond positive and negative symptoms of schizophrenia to depressive symptoms and levels of mental and physical functioning, because these domains tend to deteriorate with weight gain among women in the general population. Methods Subjects and study design We used data of 1296 men and 700 women who participated in a randomized, double-blind, multi-center, clinical trial comparing olanzapine to haloperidol [32]. Participants met DSM-III-R criteria for schizophrenia spectrum disorders (schizophrenia, schizoaffective disorder, or schizophreniform disorders), and were required to have a total score on the Brief Psychiatric Rating Scale (BPRS) [33] of ≥ 18 and/or intolerance to current antipsychotic therapy, excluding haloperidol. Following approval of institutional review boards, written informed consent was obtained from all participants. Participants were randomly assigned in 2:1 ratio (2 olanzapine subjects for each haloperidol subject). Although randomization was not stratified on gender or any other patient characteristics, it resulted in a 2:1 ratio for males (870/426) and for females (426/233). The olanzapine group (N = 1337) included 467 women and 870 men, and the haloperidol group (N = 659) was comprised of 233 women and 426 men. Participants were randomly assigned to olanzapine, 5 to 20 mg/day, or haloperidol, 5 to 20 mg/day. The type of antipsychotic medication used prior to enrollment was not assessed in the current study, but the likelihood of previous treatment with an atypical antipsychotic drug was very low, because the study was initiated in 1994 when only clozapine was available in some of the sites. Further, randomization worked for patient and illness characteristics [32], and there is no reason to expect that the randomization did not work for other characteristics such as type of prior antipsychotic medication. We used data from the acute phase, the first 6 weeks of the study, for several reasons. First and foremost, this study was a 6-week randomized double blind clinical trial with a 46-week "responder maintenance period", in which only patients who responded to the acute 6-week treatment per predetermined response criteria were eligible to continue. Consequently, the study design did not permit a longer-term analysis on the link between weight gain and improvement because only patients who improved during the first 6-weeks phase were followed-up for a longer time period. Second, the 6-week period represents a relevant time frame often used in clinical practice to determine treatment outcome and decide on treatment discontinuation [15]. For many clinicians the initial 6-weeks of antipsychotic therapy is a minimal time period in which to critically evaluate how patients are responding to a new course of therapy. Third, the rate of weight gain previously reported on clozapine was greatest in the first 6 weeks and slowed thereafter [16], such that the increase between 6 weeks and 6 months was equivalent in magnitude between baseline and 6 weeks. This observation further enhanced the relevance of studying this phenomenon during the first 6-weeks of treatment. And lastly, the short duration of the current study is comparable to most previous studies of antipsychotic-emergent weight gain and clinical improvement, thus enabling more direct comparisons between the present and the previous findings. During the 6-week acute phase, the mean modal dose was 13.2 mg/day (SD = 5.8) for olanzapine and 11.8 mg/day (SD = 5.6) for haloperidol. There were no discontinuations due to weight gain as an adverse event for any treatment group during the 6-week study period, and the rate of discontinuation for any cause was similar for women (62.7%) and men (60.8%), with a significantly smaller proportion of the patients in the olanzapine group (33.5%) than in the haloperidol group (53.2%, p < .001). In addition, the percentage of patients who discontinued treatment because of an adverse event or a lack of efficacy was significantly higher in the haloperidol group than in the olanzapine group. Further details on the parent study design and primary findings are available elsewhere [32]. Measures This investigation used measures of positive and negative symptoms ("core schizophrenia symptoms"), depressive symptoms, functional status, and body weight. Core symptoms of schizophrenia were assessed by the Positive Symptom and the Negative Symptom subscales on the BPRS (scored on a scale of 0–6) extracted from the Positive and Negative Syndrome Scale (PANSS) [34]. Levels of depressive symptoms were assessed by the Depression/Anxiety subscale in the BPRS and total score on the Montgomery-Asberg Depression Rating Scale (MADRS) [35]. The Physical and the Mental Component scores on The Medical Outcome Survey – Short Form 36 (SF-36) [36] assessed physical and mental functioning. The SF-36 provides scores on eight functional scales: physical functioning, role limitations due to physical functioning, bodily pain, general health, social functioning, role limitations due to emotional problems, vitality and mental health. The first four scales can be summarized into a Physical Component Score (PCS) and the latter four constitute the Mental Component Score (MCS). PCS and MCS are often used alone because they account for 85% of reliable variance of the eight SF-36 domains, without losing information. It is notable that unlike the symptom measures, which were clinician-rated scales, the SF-36 is a patient-reported measure that provides patients' subjective appraisal of current functional status independently of clinicians' perceptions. Weight change (in kilograms) was measured from baseline to 6 weeks, or to endpoint for patients who dropped out of the study prior to the 6-week visit. BMI was calculated as weight in kilograms divided by the square of height in meters (BMI = kg/m2). To enhance comparability of findings on different measures, the clinical measures were all standardized to z-scores. For the BPRS Core Symptoms, MADRS, and BPRS Anxiety and Depressive Subscale, this was done by subtracting the measure's overall mean and dividing by the measure's standard deviation at baseline. A single measure of depressive symptoms was calculated as the average of the standardized MADRS and BPRS Anxiety and Depressive Subscale. If a score was missing on either depression measure, the score on the available measure was used. The two depression measures were pooled because each is an independent and valid estimate of patients' level of depressive symptoms, and aggregating them should provide the best and most comprehensive estimate of depressive symptoms. Additionally, the pooling helped minimize loss of data, which are assumed not to be missing at random. The SF-36 Physical and Mental Component scores were converted from T-Scores to z-scores. Statistical analysis Baseline comparisons used independent samples t-tests for continuous variables and chi-square tests for categorical variables. Effects of treatment and gender on independent variables were assessed using ANCOVA, with the baseline score as well as the number of weeks in the study as covariates. The relationship between change in weight and change in each outcome variable was assessed using separate multiple linear regression analyses, each with corresponding clinical change score as a dependent variable, the corresponding baseline score and number of weeks in the study as covariates, and the following independent variables: weight change, treatment group assignment, and gender. In an additional analysis, the interactions of these three independent variables were added to the regression models. The analyses included measures from baseline and the 6-week visit. Missing data were handled by carrying forward the last observation for all patients with at least one post-baseline assessment. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 11.0. Results Baseline characteristics Relative to men, women were older, more likely to be Caucasians, were more likely to be overweight or obese, had less severe positive symptoms, lower levels of physical functioning, and had higher levels of depressive symptoms (Table 1). Women were also more likely to be diagnosed with a schizoaffective disorder and less likely to be diagnosed with schizophrenia. At baseline, the weight and BMI of the haloperidol-treated women and men, were significantly greater than the weight and BMI of olanzapine-treated women and men (Table 2). Further, men in either medication group weighed significantly more than women at baseline, on the average, and their mean baseline BMI was significantly lower than that of women. Table 1 Gender differences at baseline* All patients Women Men Characteristic N = 1996 N = 700 N = 1296 P Demographics Age 38.6 (11.4) 40.9 (12.8) 37.3 (10.4) <0.001 Race 0.013 Caucasian 1600 (80.2%) 587 (83.9%) 1013 (78.2%) 0.002 African descent 220 (11.0%) 67 (9.6%) 153 (11.8%) 0.128 Hispanic 83 (4.2%) 19 (2.7%) 64 (4.9%) 0.018 Other 93 (4.7%) 27 (3.9%) 66 (5.1%) 0.211 Diagnosis <0.001 Schizophrenia 1658 (83.1%) 526 (75.1%) 1132 (87.3%) <0.001 Schizoaffective disorder 300 (15.0%) 157 (22.4%) 143 (11.0%) <0.001 Schizophreniform disorder 38 (1.9%) 17 (2.4%) 21 (1.6%) 0.207 Core schizophrenia symptoms BPRS total 33.4 (10.7) 33.5 (11.2) 33.3 (10.5) 0.660 BPRS positive 10.3 (4.1) 10.0 (4.1) 10.5 (4.0) 0.017 BPRS negative 6.7 (3.3) 6.7 (3.4) 6.7 (3.3) 0.791 Depressive symptoms MADRS 16.6 (8.8) 17.3 (9.3) 16.3 (8.5) 0.031 BPRS anxiety and depression 7.5 (3.8) 8.0 (3.9) 7.3 (3.8) <0.001 Functional status SF-36 physical component 43.6 (13.0) 41.4 (14.0) 44.5 (12.5) 0.004 SF-36 mental component 34.6 (12.4) 34.6 (12.9) 34.6 (12.2) 0.958 Weight Weight, kg 76.8 (17.1) 70.2 (16.4) 80.4 (16.3) <0.001 BMI 26.0 (5.2) 26.5 (5.8) 25.8 (4.9) 0.007 BMI level <0.001 Underweight to average (BMI < 25) 930 (48.9%) 319 (47.6%) 611 (49.6%) 0.418 Overweight (BMI ≥ 25 and < 30) 609 (32.0%) 191 (28.5%) 418 (33.9%) 0.016 Obese (BMI ≥ 30) 364 (19.1%) 160 (23.9%) 204 (16.5%) <0.001 * Data are presented as Mean (SD) or N (%). P-values refer to differences between women and men. Table 2 Outcomes by gender and treatment group Women Men Olanzapine Haloperidol Olanzapine Haloperidol Outcome measure Baseline Endpoint Baseline Endpoint Baseline Endpoint Baseline Endpoint Core symptoms BPRS positive 10.1 (4.0) 6.5 (4.8) 9.9 (4.3) 6.9 (4.4) 10.3 (4.1) 7.0 (4.6) 10.7 (4.0) 8.0 (4.6) BPRS negative* 6.6 (3.3) 4.4 (3.1) 6.8 (3.4) 5.5 (3.3) 6.7 (3.2) 4.8 (2.9) 6.8 (3.5) 5.5 (3.2) Depressive symptoms MADRS* 17.8 (9.6) 11.0 (9.3) 16.1 (8.6) 13.5 (10.7) 16.0 (8.4) 10.5 (7.7) 17.0 (8.7) 13.7 (9.4) BPRS anxiety & depression* 8.1 (3.8) 5.0 (4.0) 7.8 (4.1) 6.0 (4.3) 7.1 (3.7) 4.5 (3.6) 7.7 (3.9) 5.7 (3.9) Functioning SF-36 mental component* 34.7 (13.1) 41.8 (12.1) 34.2 (12.4) 36.5 (13.0) 34.6 (12.4) 40.6 (12.0) 34.7 (11.7) 37.8 (12.3) SF-36 physical component* 41.3 (14.3) 45.0 (13.9) 41.9 (13.5) 42.1 (13.8) 44.7 (12.2) 48.7 (11.6) 43.9 (13.2) 45.4 (13.1) Weight Weight, kg* † 68.9 (15.5) 70.5 (15.8) 72.6 (17.8) 72.4 (17.6) 80.3 (15.9) 82.5 (16.2) 80.7 (17.1) 81.0 (17.4) BMI* † 26.1 (5.4) 26.7 (5.5) 27.2 (6.6) 27.1 (6.5) 25.8 (4.8) 26.5 (4.9) 25.7 (5.0) 25.8 (5.0) * Therapy effect (p < .05), reflecting significant differences between olanzapine and haloperidol-treated patients at baseline. † Gender effect (p < .05), reflecting significant differences between women and men on weight parameters within each treatment group at baseline, and between women and men when combined baseline values across treatment groups. Weight gain by treatment group and gender In order to illustrate the differences in weight gain by treatment group and gender, the patients were grouped into thirds based on their percentage of change in weight from baseline. Approximately one third of all patients (29.8%) lost weight (any decrease), one third (36.6%) had relatively stable weight (0% to <3% increase), and one-third (33.6%) gained weight (≥ 3% increase). The corresponding mean weight changes in kilograms were -2.1 kg, 0.9 kg, and 4.6 kg, for lost, stable, and increased weight groups, respectively. Figure 1 demonstrates that men and women had a similar weight gain pattern within each treatment group, and that 59% of olanzapine-treated patients and 82% of haloperidol-treated patients either lost weight or maintained stable weight. Further, 17.6% of the haloperidol treatment group and 41.4% of the olanzapine-treated patients gained at least 3% of their baseline body weight. Compared to the haloperidol-treated patients, the olanzapine treatment group had a greater increase in absolute weight (0.3 kg vs. 2.0 kg, F(1,1901) = 122.0, p < 0.001) and a significantly greater proportion of patients with a potentially clinically meaningful weight gain, defined as an increase of at least 7% from baseline body weight (3.0% vs. 13.6%, χ2(1, N = 1913) = 51.8, p < 0.001). Figure 1 Percentage of patients with different levels of weight change by gender and medication. Patients were placed in 3 equal groups based on their percent change in weight: "Lost" indicates any weight loss, "Stable" indicates ≥ 0% to <3% weight gain, and "Gained" indicates ≥ 3% weight gain. Olanzapine treatment group (N = 1337; 870 men, 467 women); Haloperidol treatment group (N = 659, 426 men, 233 women). Compared to women, men experienced greater increases in absolute weight (0.9 kg vs.1.5 kg F(1,1901) = 17.3, p < 0.001), were more likely to experience greater increases in BMI (0.35 vs. 0.48; F(1,1889) = 5.8, p = 0.016), and were more likely to have an increase of at least 7% from baseline body weight (8.1% vs.11.2%; χ2(1, N = 1913, p = 0.032). Within the olanzapine treatment group, but not the haloperidol treatment group, significantly more men than women experienced a potentially clinically meaningful weight gain (11.0% vs.15.0%; χ2(1, N = 1286) = 4.0, p = 0.045 for women and men in the olanzapine treatment group, and 2.3% vs. 3.5%; χ2(1, N = 627) = 0.7, p = 0.40, for women and men in the haloperidol treatment group). Outcomes by treatment group and gender Table 2 presents the outcome measures and BMI by gender and by treatment group at baseline and endpoints. As previously documented in the parent study [32], there were treatment effects on these outcome measures such that olanzapine-treated patients showed greater improvements than the haloperidol treatment group. There were no gender effects for any of the clinical (core symptoms of schizophrenia, depressive symptoms) and functional outcome measures (mental and physical functioning). Outcomes, weight change, and gender To assess the potential effects of gender on the relationship between outcomes and weight change we performed a set of regression analyses predicting change in each of the outcome variables (i.e., Core symptoms of schizophrenia, depressive symptoms, gender, and the interaction of these variables). The results indicated that gender was not a significant variable (i.e., the following components were not significant in any of the analyises: gender, gender by weight change, gender by treatment group, and gender by treatment group by weight change). Therefore, gender was dropped from subsequent analyses. Since men and women were not found to significantly differ on any of the clinical outcome measures and had a similar pattern of weight gain within each treatment group, we examined the association between weight change and change in treatment outcomes for all patients within each treatment group. Regression analyses demonstrated that for both olanzapine and haloperidol-treated patients, increases in weight were significantly associated with improvements in core schizophrenia symptoms, (B = -0.038, t(1899) = 5.6, p < 0.001), in depressive symptoms (B = -0.030, t(1899) = 5.3, p < 0.001), in mental functioning (B = 0.026, t(700) = 2.0, p = 0.047), and in physical functioning (B = 0.028, t(700) = 2.3, p = 0.021). Because level of depressive symptoms was based on two depression measures, the MADRS and the BPRS depression/anxiety subscale, we repeated the analysis using each of these measure separately. Results were unchanged. The regression coefficients (B's) indicated that every one- kilogram increase in weight at 6-weeks was associated with approximately 0.03 standard deviations improvement in each clinical outcome parameter, when controlling for the effects of treatment group, gender, treatment group-gender interaction, baseline weight, the corresponding baseline outcome measure, and the number of weeks in the study. In order to graphically illustrate the findings, the patients were grouped into thirds based on their percent change in weight as described above, resulting in lost, stable, and increased weight groups. Figure 2 demonstrates the similarity in the relationships between weight changes and changes in the four treatment outcome variables for the olanzapine and haloperidol treatment groups. Figure 2 Change in outcomes by change in weight and treatment group for all patients. Patients were grouped in thirds based on their percent change in weight: "Lost" indicates any weight loss, "Stable" indicates 0% to <3% weight gain, and "Gained" indicates weight gain of 3% or more. "Depression" as measured by the MADRS or BPRS anxiety and depression scale. "Schizophrenia Symptoms" as measured by the BPRS positive symptoms and BPRS negative symptoms scales. "Physical Functioning" as measured by the SF-36 physical component score. "Mental Functioning" as measured by the SF-36 mental component score. Error bars represent 95% confidence intervals. Consistent with a prior analytical approach by Czobor and colleagues [17], we also performed analyses (ANCOVA, controlling for baseline weight and weeks in the study) that specifically contrasted patients who demonstrated clinical improvement (reduction in BPRS Core Symptoms > 20%) with those who deteriorated by any amount. On core symptoms, improved olanzapine patients gained 2.49 kg, compared with 1.42 kg for those who deteriorated (F(1,1278) = 14.7, p < 0.001). Improved patients on haloperidol gained 0.08 kg while those who deteriorated lost 0.44 kg (F(1, 617) = 2.9, p = 0.087). In order to directly compare current findings with those previously reported by Czobor and associates, we repeated the analysis using their analytical variables (absolute weight change (kg) and PANSS total score), while covarying baseline bodyweight and baseline PANSS total score. This analysis demonstrated that improved patients on olanzapine gained 2.37 kg, compared with 0.59 kg for those who deteriorated (F(1,1278) = 53.1, p < 0.001). Improved patients on haloperidol gained 0.15 kg, while deteriorated patients on haloperidol lost 0.55 kg (F(1,618) = 6.7, p = 0.010). Results were similar when also controlling for weeks in the study. The partial correlations between weight gain (kg) and therapeutic response as measured by the PANSS total score (controlling for baseline weight, baseline PANSS total score, and weeks of treatment) were statistically significant for the olanzapine and haloperidol treatment groups (partial r = -0.15, N = 1279, p < 0.001 for olanzapine; partial r = -0.11, N = 618, p = 0.006 for haloperidol). When using the Czobor and associates method (controlling only for baseline weight and baseline PANSS total score), the partial correlations were more disparate across treatment groups (partial r = -0.24, N = 1280, p < 0.001 for olanzapine; partial r = -0.10, N = 619, p = 0.013 for haloperidol), highlighting the importance of controlling for weeks of treatment. These correlations were similar in direction but of smaller magnitude than the partial correlations reported by Czobor and associates (partial r = -0.57, df = 37, p < 0.001 for olanzapine; partial r = - 0.30, df = 35, p = 0.060 for haloperidol). Although weight gain was identified as a prognostic marker of therapeutic response for both treatment groups, it was unclear if this marker is stronger for the olanzapine than the haloperidol treatment group because the olanzapine-treated patients had greater weight gain and greater therapeutic improvements compared to the haloperidol treatment group. To address this question, we calculated the conditional probability of clinical response, defined as reduction in BPRS core symptoms > 20%, given that the patient experienced various amounts of weight gain on olanzapine and on haloperidol. Results in Table 3 demonstrate that weight gain was a similar prognostic indicator for each treatment group, as patients who gained more weight were significantly more likely to respond to treatment for both treatment groups. About half of the patients who lost weight responded to treatment, whereas three-quarters of patients who had a clinically significant weight gain (≥7%) responded to treatment. Table 3 Conditional probability of response given different amounts of weight gain for all patients and by medication Olanzapinea Haloperidolb N = 1283 N = 622 Weight change Did not respond Respondedc P(R\| W) Did not respond Respondedc P(R\| W) N % N % N % N % Lost weight (< 0%) 210 16.4% 187 14.6% .47 176 28.3% 166 26.7% .49 Gained 0 to < 3% 118 9.2% 234 18.3% .67 88 14.2% 83 13.3% .49 Gained 3 to < 7% 111 8.7% 248 19.3% .69 39 6.3% 51 8.2% .57 Gained ≥ 7% 43 3.4% 132 10.3% .75 5 0.8% 14 2.3% .74 Note. Response was defined as a greater than 20% decrease in BPRS Core Symptoms a Mantel-Haenszel test of linear by linear association, χ 2(1, N = 1283) = 52.3, p < 0.001. b Mantel-Haenszel test of linear by linear association, χ 2(1, N = 622) = 4.1, p = 0.044. cProbability of response given level of weight change. Discussion Like women in the general population, women with schizophrenia were more likely to be obese [25], depressed [37], and to physically function at a poorer level than men [26]. Despite these similarities, which would be expected to bode poorly for the effects of acute weight gain on women's treatment outcomes, women and men who gained weight during antipsychotic therapy demonstrated significant improvements on core schizophrenia symptoms, depressive symptom, and mental and physical level of functioning. Overall, weight gain was found to be linked to better clinical response among men and women treated with olanzapine or haloperidol. This link impacted olanzapine-treated patients more than those treated with haloperidol because improved clinical and functional outcomes were more pronounced for the olanzapine-treated patients, who were also more likely to experience weight gain than patients treated with haloperidol. The current study adds to the literature by demonstrating a positive association between treatment-emergent weight gain and better clinical outcomes that extends beyond positive and negative symptoms to depressive symptoms and functional status. Depressive symptoms in schizophrenia are known to be a distinctive clinical dimension of prognostic significance [38] that is associated with compromised quality of life [39], increased risk of psychotic relapse [40], suicidal tendencies, work impairment, lower activity, worse daily functioning, and poorer life satisfaction [41]. In this study, weight gain during antipsychotic therapy was linked to improvements in both core symptoms of schizophrenia and in depressive symptoms, two distinct and clinically meaningful dimensions of outcome in the treatment of schizophrenia. Current findings are consistent with previous research [10-19] and provide further support to the hypothesis [16] that a positive link between treatment-emergent weight gain and improved clinical response may be a generalized phenomenon across antipsychotic medications. Although a recent study [17] demonstrated this phenomenon for clozapine and olanzapine-treated patients but not in the haloperidol or risperidone treatment groups, examination of its findings revealed great similarity to the current results, with a moderate association between weight gain and therapeutic response for the haloperidol treatment group despite a small sample size. Interestingly, the size of the effects reported by Czobor and associates (partial correlations of -0.57 for olanzapine and -0.30 for haloperidol) were numerically larger than those found in the current study (partial correlations of -0.24 for olanzapine and -0.10 for haloperidol). Our study supports the findings of Czobor and associates but with sufficient statistical power to produce statistically significant results for both the olanzapine and the haloperidol treatment groups. Although the current results are consistent with those reported in a number of previous prospective studies, our findings are incongruent with two retrospective surveys. In the more recent study [42], self-administered surveys were distributed to schizophrenia patients through chapters of the National Alliance for the Mentally Ill to assess their perceptions about the negative impact of treatment-emergent weight gain on psychosocial functioning. The authors concluded that weight gain is directly associated with reduced quality of life. Several limitations of this study were previously noted [43], pointing particularly to a major confounding factor: most of the respondents started their antipsychotic medications several years before the survey. Because antipsychotics differ in the magnitude and in the trajectory of weight gain over time [44], the reported differences may reflect differences between a group of patients whose illness is well managed and thus are reporting a sense of relative psychological well being and a group of distressed patients who are adjusting to a new antipsychotic regimen [43]. The other survey [45] queried depressed psychotic patients who called a mental health crisis line about the impact of eight adverse events, including weight gain, on their emotional distress and satisfaction with treatment. Although weight gain was the adverse event reported least, it was viewed as the most distressing, particularly for women, and was linked to lower satisfaction with treatment. This survey, which was noted for its lack of rigorous design [2], did not report the treatment duration on the antipsychotic drugs. Resultantly, the respondents may have started the antipsychotic regimens years before the survey, obscuring the findings in a manner similar to that in the survey by Allison and colleagues [42]. In essence, the two retrospective self-reports appear to have assessed patients' treatment satisfaction and perceptions rather than objective parameters of clinical change and treatment progress. Studies that objectively measure weight change and clinical response in a prospective fashion are more desirable as they provide more objective information. This is especially important because retrospective self-reports may capture the social climate rather than objective changes in clinical outcomes. It is noteworthy, however, that there are four prospective studies reporting findings that are inconsistent with ours [6-9]. The reasons for the inconsistencies are not clear but may be due to small sample size. The sample size needed to detect a correlation of .20 with 80% power is 194, while sample sizes for these four studies ranged from 30 to 82. Although the current study found a link between treatment-emergent weight gain and better therapeutic response, its correlational nature does not allow for discerning the underlying causes. There are numerous factors and poorly understood mechanisms that may impact patients' weight gain during treatment, including environmental, behavioral, neurochemical, genetic, and clinical factors [17]. It was previously noted, for example, that the association between weight gain and therapeutic improvement may reflect for some patients the restoration of body weight lost during an acute episode because patients were previously found to restore their original body weight upon recovery, even prior to the introduction of antipsychotics [46]. While the association between treatment-emergent weight gain and therapeutic response may be due to specific pharmacological pathways, it is also possible that non-pharmacological pathways play an important role. The link between weight gain and therapeutic response may be an epiphenomenon that accompanies clinical and functional improvements by influencing patients' increased motivation, pleasures, and specific behaviors that enhance weight gain [16]. Future studies will be needed to evaluate the correlations reported here in order to better understand the underlying mechanisms of treatment-emergent weight gain, and help differentiate pharmacological from non-pharmacological pathways to weight gain in the treatment of schizophrenia with antipsychotics. A promising research strategy [47] may involve the use of placebo-controlled trials of antipsychotics to contrast weight change between patients who improved on placebo with those who deteriorated on placebo. It is important to note that regardless of the pathways to weight gain, the link between excess weight and greater morbidity and mortality calls for careful clinical attention during the treatment of patients with schizophrenia. Another important issue that needs addressing is the growing focus on the associations between obesity and poorer quality of life among schizophrenia patients [48]. Such research helps highlight the need to distinguish between treatment-emergent weight gain and obesity. Although these terms are not mutually exclusive, they are not synonymous either. Gaining weight during treatment with antipsychotics should not be equated with becoming obese. For example, thin individuals may gain a potentially clinically meaningful proportion of their baseline body weight (≥ 7%) and attain an average BMI, whereas obese individuals may gain the same proportion of their baseline weight but maintain their initial obese status per BMI categorization. There are numerous permutations to this phenomenon, suggesting the need to recognize its complexity and pursue further studies that may help clarify the causes and the consequences of treatment-emergent weight gain among individuals with schizophrenia who differ in their baseline body weight. The current study has its limitations. First, it examined weight change post-hoc and only during the acute phase of the illness, which was confined to the first 6 weeks of treatment, and the findings may not generalize to long-term treatment-emergent weight gain. It is noteworthy that this study assessed treatment-emergent weight gain at 6-weeks, although patients continue to accrue weight beyond the acute treatment phase. For olanzapine-treated patients, the mean weight gain observed at 6-weeks (2.0 kg) was about a third of 6.26 kg mean weight gain found at 39 weeks, when weight gain tends to plateau on olanzapine [49]. Similarly, the haloperidol-treated patients had a 0.3 kg mean weight gain at 6 weeks, which was less than half of 0.69 kg mean weight gain observed for these patients after 39 weeks of treatment. This observation highlights the need to assess the association between weight gain and treatment outcomes in longer-term studies. The choice of 6-weeks was not only driven by the design of the study, in which treatment responders in the acute phase were followed up in the 46-week maintenance period of the study, but also by the clinical relevance of the acute treatment phase. Clinicians often use the first 6-weeks of treatment to assess the tolerability and effectiveness of a new antipsychotic regimen and to decide whether to continue or discontinue that course of therapy [15]. Further, the study of the first 6 weeks of treatment enabled comparisons of the current findings with other studies, which were typically of short-term duration. Although weight gain appears to be greatest and most rapid during the first 6 weeks of treatment with clozapine [16], and during the first 12 weeks for olanzapine with a trend toward a plateau after approximately 39 weeks of treatment, [49] longer-term studies will be needed to determine the validity of the current findings in longer-term treatment. This may be, however, difficult to study. Patient attrition from studies is not random, with those experiencing poor treatment efficacy or poor tolerability being more likely to discontinue the study, leaving a relatively homogeneous group of study completers who are also treatment responders. Such reduction in the variability of treatment outcomes may diminish the likelihood of finding this phenomenon in long term randomized double blind studies. Further, if this phenomenon were to be investigated in long-term naturalistic observational studies, one would likely face another problem, namely the prevalent use of polypharmacy [50], and the dynamic nature of treatment for schizophrenia, [51] with frequent changes in antipsychotic regimens and in concomitant psychotropic medications. Such complexity may increase the difficult in identifying which treatment at what time was associated with which weight gain and treatment outcome. It is of interest to note, however, that despite rapid weight gain during the 6-week period in our study, when weight gain is more likely to be noticed by the patients and their clinicians and thus may elicit a negative emotional response, the weight gain in this study was not only linked to improved clinical and functional status but also to reduced emotional distress as measured by the depression scales. Another limitation of the study is its lack of assessment of patients' adherence with medication. It is possible that weight gain and improvement occurred together because improvements occur mostly in patients who are medication adherent. Although this was not assessed in the present study, this possibility was previously studied by Meltzer et al. [16], who found a significant association between clozapine-emergent weight gain and improved psychopathology. In their study, non-adherent patients were expected to have lower or absent plasma clozapine levels, but there was no relationship between plasma clozapine levels and weight gain or clinical response. Meltzer and colleagues also monitored adherence closely during weekly visits to determine white blood count and found no evidence of intermittent or poor adherence in their study patients. Additionally, the associations between weight gain and improved outcomes in our study were similarly found within the haloperidol and within the olanzapine treatment groups when controlling for treatment duration. Treatment duration is a proxy for time on the medication in randomized double-blind trials, where treatment discontinuation for any reason results in patient's discontinuation from the study. Thus, if patients were more adherent with one antipsychotic drug than with the other, and medication adherence influenced the associations between weight gain and outcomes, then one would expect to find the association between weight gain and improved outcomes to be present only in the more adherent treatment group, but not in both treatment groups, as found in the present study. Another study limitation is the correlational nature of the analyses, which precludes cause-effect relationship and allows for the possibility that the observed associations might be due to an unobserved variable or set of variables. Further, the relatively low correlations suggest that the association explains only a small proportion of the variance in treatment outcomes. Response to antipsychotic medications is a complex phenomenon that is associated by numerous relatively independent components [16] and weight gain is only one of them. Nonetheless, this link was demonstrated when using other statistical approaches, including contrasting of weight gains between responders and patients who did not respond, by identifying the degree of improvement associated with every 1-kg gained at 6 weeks, and by calculating the conditional probability of therapeutic response given various amounts of weight gain. These findings are important as they suggest that acute weight gain is a valuable prognostic marker in the treatment of schizophrenia. Next, because the study included patients with a moderately severe level of symptomatology, the current findings may not generalize to patients with milder or residual symptoms of schizophrenia. However, the relationships among the severity of patients' baseline symptomatology, treatment-emergent weight gain and therapeutic response is currently unclear. And lastly, this study used the SF-36, a self-report measure of functional status, which was not designed to assess the potential impact of weight gain on patients' functional status or quality of life. Preliminary information on the first measure designed to specifically capture the impact of antipsychotic-emergent weight gain on patients' psychosocial functioning was only recently published [52]. One would have expected, however, to detect a decline in patients' mental or physical levels of functioning if the experienced weight gain were to have adverse impact during the acute treatment phase. Conclusions Women (and men) with schizophrenia who gained weight during treatment with olanzapine or haloperidol did not experience worsening of clinical or functional status. To the contrary, they had significant improvements in core symptoms of schizophrenia, depressive symptoms, and mental and physical level of functioning. Although excessive weight gain, regardless of origin, is of concern due to its association with physical health problems, the current findings suggest that patients who have greater treatment-emergent weight gain are more likely to benefit from treatment with olanzapine or haloperidol. Findings highlight the complexity inherent in medication management of schizophrenia patients and the need to balance treatment risks and benefits for each patient. In addition, further prospective studies will be required to assess the effects of weight gain, in both psychiatric and medical terms, on individuals treated for schizophrenia with various antipsychotic medications. Competing interests The authors are employees of Eli Lilly and Company, Indianapolis, Indiana Authors' contributions • HAS conceived of the study, participated in its design, the analytical plan, the interpretation of the results, and drafted the manuscript • MS participated in the design of the study, the analytical plan, the interpretation of the results, and performed the statistical analysis • ZZ and BK participated in the design of the study, the interpretation of the results, and the drafting of the manuscript. 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==== Front BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-81566378410.1186/1471-2458-5-8Research ArticleTowards an understanding of barriers to condom use in rural Benin using the Health Belief Model: A cross sectional survey Hounton Sennen H [email protected] Hélène [email protected] Neil J [email protected] Department of HIV/AIDS and Reproductive Health, Centre MURAZ, Bobo-Dioulasso, Burkina Faso2 Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma, Oklahoma City, Oklahoma, USA3 Department of Health Promotion Sciences, College of Public Health, University of Oklahoma, Oklahoma City, Oklahoma, USA2005 21 1 2005 5 8 8 15 7 2004 21 1 2005 Copyright © 2005 Hounton et al; licensee BioMed Central Ltd.2005Hounton et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background HIV/AIDS is the most dramatic epidemic of the century that has claimed over two decade more than 3 million deaths. Sub Saharan Africa is heavily affected and accounts for nearly 70% of all cases. Despite awareness campaigns, prevention measures and more recently promotion of anti viral regimens, the prevalence of cases and deaths is still rising and the prevalence of systematic condom use remains low, especially in rural areas. This study identifies barriers to condom use based on the Health Belief Model (HBM) in Benin, West Africa. Methods The study was a cross-sectional survey conducted from June to July 2002. Two hundred fifty one (251) individuals were interviewed using a structured questionnaire adapted from a standardized WHO/GAP questionnaire. A logistic regression was used to identify factors associated with condom use. Results In spite of satisfactory knowledge on HIV/AIDS transmission, participants are still at high risk of contracting the infection. Sixty three (63) percents of the interviewees reported being able to recognize infected people, and condom use during the last occasional intercourse was declared by only 36.8% of males and 47.5% of females. Based on the HBM, failure to use condom was related to its perceived lack of efficacy [OR = 9.76 (3.71–30.0)] and perceived quality [OR = 3.61 (1.31–9.91)]. Conclusions This study identifies perceived efficacy (incomplete protective effect) and perceived utilization-related problem (any reported problem using condoms) as the main barriers to condom use. Hence, preventions strategies based on increasing perceived risk, perceived severity or adequate knowledge about HIV/AIDS may not be sufficient to induce condom use. These data will be useful in designing and improving HIV/AIDS prevention outreach programs in Sub Saharan Africa. ==== Body Background One of the current challenging tasks faced by health professionals and scientists worldwide is the prevention and control of HIV/AIDS. This disease claims yearly a huge toll of deaths, productivity and economic losses, especially in sub-Saharan Africa where the population is already weakened by poverty, malaria and tuberculosis [1,2]. Curtailing the HIV/AIDS pandemic requires a holistic approach [3]. In Benin, several programmes have been developed to target high-risk groups and to modify cultural risk factors for the transmission of the infection [4-7]. Nonetheless, the prevalence of HIV infection and the rate of other sexually transmissible Infections (STI), and the number of people living with HIV/AIDS (PLWHA) are still increasing [8,9]. In Benin (and in most of West African countries), limited accessibility to anti retroviral medication (ARV) has pushed public health authorities to focus on prevention measures. Nowadays, most of the efforts are being shifted towards access to ARV, care to PLWHA [10], and second generation surveillance. However, there is a scarcity of operational research designed to identify barriers and facilitators to behavioural change [11,12]. This study, which was conducted in rural Benin, identifies factors deterring condom use that could be targeted by HIV outreach programmes using the Health Belief Model (HBM) framework. The Health Belief Model postulates that an individual's actions are based on beliefs. It underlines main factors for decision making such as perceived vulnerability or susceptibility, perceived severity of the outcome or conditions, perceived efficacy or benefit of control measure and the perceived barriers to prevention. It has been extensively used in behavioural sciences to predict behaviours and to design behavioural prevention programs. [13-15] Methods Study location and participants Benin is a West-African country with a population of 6.2 million people. The male/female sex ratio is 0.96, 48.5% of the population is under age 15, the rate of literacy is nearly 30.0 % and farming as the main occupation [2]. The study was conducted in Toffo, a county with a population of 80,000 inhabitants, located 50 miles north of Cotonou, the economic capital of Benin. Individuals aged 15 to 55 years old living in Toffo between June 10 and July 23, 2002 were invited to participate in the study. Health professionals, individuals less than 15 years old and school teachers were excluded from the study. Authorization to conduct the study was obtained from the Ministry of Health and the National STI/HIV/AIDS programme in Benin. Sample size, sampling and data collection The sample size was calculated based on results from the Benin 2001 Demographic and Health Survey [2]. Using the proportion of condom use reported in that survey (14.6%) and to detect a difference of 10% with a power of 90% and an error of 5%, a sample size of 245 people was needed [16]. A stratified random sample was applied using the 10 villages of Toffo as strata, and an average of 25 individuals per stratum was invited to participate after a brief presentation of the study, its aims and its potential applications. Participation was voluntary and participants could withdraw anytime during the interview. 270 individuals in the catchments area were invited and 251 accepted to participate in the study. Sixteen completed forms were removed from the analysis because of incomplete data on demographic characteristics and key responses related to the Health Belief Model. The final sample size was 235. Data were collected using a questionnaire adapted from the surveillance questionnaire developed by the Global AIDS Program of the World Health Organization (see survey items in Annexes). A pretest of the questionnaire was carried out on a convenient sample of 20 people of both genders living in Toffo and interviewers (3 social workers, one female and 2 males) were trained by SHH using simulated interviewees. Data analysis Data were stored in a file using Epi info 2000 [3] and then imported in SPSS 11.0 [17] and in SAS 8.1 [18]. The analysis included descriptive statistics, cross-tabulation and logistic regression. The following variables were included in the analysis: socio-demographic characteristics, perceived vulnerability (participant feeling at risk or not), perceived severity of the disease (AIDS perceived as deadly or not), perceived efficacy (condom effective to prevent infection or not), perceived barrier (any reported problem with use of condoms) and condom use during the last occasional sexual intercourse Results Socio-demographic characteristic The age distribution of the study population was similar to that of the Benin population [2]. Table 1 shows that there were 1.9 times more males than females sampled and the former were older (difference between mean age = 3.2 years with 95%CI = 1.8–4.5). A proportion of 69% of the participants declared being married (monogamous or polygamous) and 28% single. Farming was the most common reported occupation (37.4%) followed by laborers and small business. Sixty-six percent of males compared to 30% of females declared not having had any school education. The most common reported religion was Christianity (52 %) albeit polytheism being a social fact in Benin. Nearly two-thirds of the participants (63%) declared "Fon" as their ethnic group. There was no significant difference in socio-demographic variables by gender. Table 1 Socio-demographic characteristics of 235 participants in a HIV/AIDS interview, Toffo county, Benin (June–July 2002) Characteristics Males N (%) Females N (%) Total N (%) All participants 155 (66.0) 80 (34.0) 235 Age groups 15–24 56 (36.1) 37 (46.3) 93 (39.6) 25–34 52 (33.5) 29 (36.3) 81 (34.4) 35–44 30 (19.4) 5 (6.3) 35 (14.9) 45 + 17 (11.0) 9 (11.3) 26 (11.1) Marital status Married, monogamous 87 (56.1) 27 (33.8) 114 (48.5) Married, polygamous 23 (14.8) 24 (30.0) 47 (20.0) Single 44 (28.4) 21 (26.3) 65 (27.7) Widow, Separated 1 (0.7) 8 (0.9) 09 (07.3) Occupation Farming 81 (52.3) 7 (8.8) 88 (37.4) Working class 48 (31.0) 7 (8.8) 55 (23.4) Small business 10 (6.5) 33 (41.3) 43 (18.3) Housewife NA 21 (26.3) 21 (08.9) High school student 4 (1.7) 11 (4.7) 15 (06.4) Other 12 (7.7) 1 (1.3) 13 (05.6) Educational level None 47 (30.3) 48 (60.0) 95 (40.4) Some 108 (69.7) 32 (40.0) 140 (59.6) Ethnicity Fon 104 (67.1) 46 (57.5) 149 (65.1) Aizo 26 (16.8) 24 (30.0) 49 (21.4) Other 25 (16.1) 10 (12.5) 37 (13.5) Religion Christian 42 (52.5) 80 (51.6) 122 (51.9) Muslim 3 (3.8) 2 (1.3) 5 (2.1) Traditional 21 (26.3) 54 (34.8) 75 (31.9) Other 14 (17.5) 19 (12.3) 33 (14.0) Knowledge Table 2 presents a summary of the knowledge of the participants on HIV/AIDS and crude odds ratio estimates comparing males to females. There was a high awareness of AIDS (99, 9%), and its perceived risk (97% of participants considered AIDS as a deadly disease) among participants. Females were more aware than males of the modes of transmission of HIV infection (87 % of females versus 50% reported knowing at least 2 modes of transmission). There was a difference in preventive measures for HIV/AIDS by gender: Females reported mainly fidelity and abstinence whilst males primarily reported condom use. In addition, 84% of females whereas 52% of males reported being able to identify an HIV-infected person. This indicates the need for improving knowledge of the disease in general for both genders. Education level and religion did not have a meaningful effect on knowledge across age groups. Table 2 Distribution of knowledge by gender of 235 participants in a HIV/AIDS interview, Toffo county, Benin (June–July 2002). Knowledge N (%) Difference* (%) Male Female [95% CI] Have you ever heard of HIV/AIDS? Yes 154 (99.4) 80 (100) - 0.6 [-1.8; 6.2] What is your source of information? Radio (± other sources) 146 (94.2) 58 (72.5) 21.7 [11.3; 32.2] Health professionals (only) 4 (2.6) 8 (10.0) - 7.4 [-14.4; -0.4] Friends (only) 5 (3.2) 14 (17.5) -14.3 [-23.1; -5.5] According to your knowledge, what is AIDS? Deadly disease 134 (86.5) 79 (99.8) -13.3 [-18.7; -7.8] Projected disease 17 (11.0) 1 (1.2) 9.8 [4.3; 15.3] Don't know 3 (1.9) 0 (0.0) 1.9 [-2.5; 4.0] Other 1 (0.6) 0 (0.0) 0.6 [-0.6; 1.8] Modes of transmission of HIV? Knows at least two modes of transmission 77 (49.0) 70 (87.0) -38 [-48.8; -27.2] Knows sexual transmission 68 (43.9) 6 (7.5) 36.4 [26.7; 46.1] Do not know any 9 (6.5) 4 (5.0) 1.5 [-4.6; 7.6] Prevention What are the prevention methods of getting HIV? Abstinence /fidelity 13 (8.4) 44 (55.0) -46.6 [-58.3; -34.8] Serological test. 0 (0.0) 8 (10.0) -10 [-16.6; -3.4] Condom 131 (84.5) 20 (25.0) 59.5 [48.4; 70.6] Don't know 4 (2.6) 0 (0.0) 2.6 [0.0; 5.1] Other 7 (4.5) 8 (10.0) -5.5 [-12.8;1.8] How can a HIV-infected person be identified? Symptoms 80 (51.6) 67 (83.8) -32.2 [-43.5; -21] Can not differentiate 23 (14.8) 2 (2.4) 12.4 [5.9; 19] Don't know 49 (31.6) 11 (13.8) 17.8 [7.3; 28.3] Other 3 (2.0) 0 (0.0) 2 [-0.2; 4.2] Difference* = Difference between males and females in the prevalence of knowledge with 95 % confidence interval (using a binomial distribution) Behavioural risk factors The overall condom use in this population was low (34%). Table 3 describes the distribution of frequency of not ever using condom, last occasional intercourse without condom use and median number of sexual partners during the past 12 months, according to age, gender, education and marital status. Single participants with some education declared using condom more frequently. The proportion of subjects who declared using condom decreased with age and with males being marginally more likely to declare using it than females. We did not find any significant difference about reports on the use of condom during the last occasional intercourse by age groups, gender, educational or marital status. The small number of individuals declaring condom use during the last occasional intercourse maybe the reason why no significant associations were found. Table 3 Distribution of selected behavioural risk factors of 235 participants in a HIV/AIDS interview by age groups, gender, education level and marital status in Toffo county, Benin (June–July 2002) Behavioural risk factors Number of participants who declared not using condom N (%) Last occasional intercourse without using condom N (%) Median number of sexual partners during the last 12 months N (%) Age group 15–24 56 (60.2) 54 (58.1) 1.0 25–34 50 (61.7) 49 (60.5) 1.0 35–45 26 (74.3) 23 (65.7) 1.0 45 + 22 (84.6) 14 (53.8) 1.0 Gender Male 96 (61.9) 98 (63.2) 2.0 Female 58 (72.5) 42 (52.5) 1.0 DIFF [95% CI] Female/Male 10.6 (-2.0; 23.0) - 10.7 (-24.0; 3.0) NA Education None 73 (76.8) 54 (56.8) 1.0 Some 81 (51.9) 86 (61.4) 2.0 DIFF [95% CI] None/Some 24.9 (13.0; 36.8) - 4.6 (-17; 8.0) NA Marital status Single 36 (55.4) 34 (52.3) 2.0 Other 118 (69.4) 106 (62.4) 1.0 DIFF [95%CI] Other/Single 14 (.07; 28) 10.1 (-4; 24) NA DIFF [95% CI] = Difference in Proportion with 95 % confidence interval NA: Not Applicable However, it is interesting to note that whilst females declared ever using condom less often than males, they declared having used the condom during the last occasional intercourse more often. In particular, even though 73% of females declared never using the condom, 47% declared having used it during the last occasional intercourse. None of these differences were significant but they clearly indicated contradictory tendencies and answers to the questionnaire. It may be that among females who ever used condom, they use it more frequently than males. Finally, the median number of sexual partners during the last 12 months varied by gender (2 for males versus 1 for females), by education (2 for some versus 1 for none) and by marital status (2 for single versus 1 for other) but not by age groups. Determinants of condom use behavioural change Overall, there was a high perceived risk of contracting HIV infection among interviewees: 94% considered themselves as vulnerable to HIV/AIDS. This proportion was higher in females compared to males. Similarly there was a high perceived severity of HIV/AIDS: 99% of females compared to 87% of males perceived HIV/AIDS as a severe and deadly disease. Conversely, there was a relatively low perceived efficacy of condom as a protective measure: only 37% of the interviewees perceived condom as an effective mean in protecting from getting HIV infection. We identified several socio-cultural barriers to behavioural change namely reported problems using condom (88% of the interviewees), the alleged capability to physically recognize an HIV infected person and the denial all together of the disease (only 19% participants believe HIV/AIDS exists). Also, cultural practices such as polygamy (20% of the study population), poverty, the belief that there is a cure for the disease (74%) and religion (9 % of non favorable reaction towards condom are among declared Christians) were all not favorable to HIV infection control. Logistic regression using the theoretical health belief model Table 4 describes the results of a logistic regression, fitted to assess the strength of association between perceived vulnerability (participant feeling at risk or not), perceived severity of the disease (AIDS perceived as deadly or not), perceived efficacy (condom effective to prevent infection or not), perceived barrier (problems with using condoms) and the lack of condom use. Perceiving condom as ineffective (OR = 9.8, 95%CI = 3.2–30.0) and having reported problems with using the condom (OR = 3.6, 95%CI = 1.3–9.9) were both associated with the lack of use of condom. However, perceiving oneself as vulnerable to HIV infection (OR = 6.9, 95%CI = 0.9 – 52.5) was not strictly statistically significant since most interviewees felt vulnerable, reducing the power of detecting a significant difference. This variable was also a weak confounder for the effect of perceiving the condom as ineffective. Not perceiving HIV/AIDS as a deadly disease (OR = 2.5, 95%CI = 0.3 – 19.7) was not associated with the lack of use of condoms. Table 4 Crude* and adjusted** odds ratio (OR) estimates with their 95% Confidence Interval (95% CI) of the effect of perceived efficacy of condom, barriers to condom use, vulnerability and severity on the lack of condom use for 235 participants in a HIV/AIDS interview, Toffo county, Benin (June–July 2002). Variables Crude OR1 (95% CI) Adjusted OR2 (95% CI) No perceived risk to HIV infection 6.9 (0.9 – 52.5) NA3 AIDS not perceived as a deadly disease 2.5 (0.3 – 19.7) NA3 Perceived incomplete protection using condoms 11.5 (3.8 – 34.7) 9.8 (3.2 – 30.0) Reporting any problem using condom 5.4 (2.1 – 13.7) 3.6 (1.3 – 9.9) 2 LL Chi-square: 32.2 P-value < 0.0001 Score Chi-square: 32.2 P-value < 0.0001 C-statistic = 0.81 1Crude OR: calculated from a univariate logistic regression 2 Adjusted OR: Calculated from a multivariate logistic regression including Perceived incomplete rotection using condoms and reporting any problem using condom. 3 NA: Not Applicable because these variables did not have an important effect on the use of condom. Discussion This study was the first ever to use the Health Belief Model (HBM) to assess cultural behaviour in rural Benin towards condom use and HIV/AIDS. The HBM was reported to be one of the most widely used behavioural frameworks for more than five decades but has been criticized for its inability to efficiently predict people's behaviour [14]. There is general agreement that the components of HBM should include self-efficacy and cues to action, and that susceptibility and severity should be conditional on action or inaction [13,14]. The lack of generally accepted model construct also makes comparisons difficult across studies. An effort was made in this study to address these concerns by clearly defining the model's construction. Our results showed there is a high awareness on AIDS in general and that women knew more about the modes of transmission of HIV/AIDS and its impacts than men. Conversely, women were more likely to feel that they could identify HIV-infected individuals from their symptoms. In addition, females were less likely to declare using condom in general even though a higher proportion declared having used condom during the last occasional sexual intercourse. This finding is disturbing and could be explained by the difference in perception of the question "do you use condoms?" It is difficult to judge what the true answer is but it is likely that rare events are better reported, and thus women may be more prone to recall the use of condom than men during occasional intercourse given that they declared on average fewer sexual partners. It is also possible that among women who do use condom, they will use it more regularly than men. Our measure of perceived vulnerability might not be sensitive enough to capture differences in perceived risks. In fact, all women and most men felt they were at risk of acquiring the infection, yet only a small proportion were using condoms. Another explanation may be that perceived risk is not a driving force in behavioural change in this subset of the population. This is an illustration of the complexity of modeling human behaviour and can thus make a case for further cultural-specific HIV-behavioural research. When only considering the percentage of condom use by gender, females appear to be at a higher risk of acquiring HIV even though they appeared to know more about transmission routes and prevention methods. This might be due to the well established difficulty facing women in negotiating the terms of sexual intercourse. In fact, gender inequality is associated with poverty, condom with distrust and sexual economic exchange is not perceived as prostitution [19]. All these factors make women vulnerable to acquire HIV infection, and therefore it is important to consider empowerment of women, gender inequality and poverty as key strategies of HIV/AIDS prevention programmes. Despite a relatively acceptable knowledge of modes of transmission and prevention methods, only a few of participants declared using condoms, which is an indication that a relatively good knowledge about HIV/AIDS, even though necessary, may not be a key factor in behavioural change in fighting HIV epidemic in the study population. These findings also indicate that programmes which aim only at increasing awareness and knowledge may not succeed. Using the HBM to analyze the determinants of behavioural change in our study population, we can conclude that there is a high-perceived vulnerability and perceived severity, and yet this does not encourage condom use. An important proportion of participants do not believe in the efficacy of condoms and there are barriers to the use of condoms. Our results are comparable to that found in a similar study in the USA [20] and in a review of published studies using HBM [14] where perceived barriers were found to be the single most powerful predictors of the HBM. Our findings are also consistent with results of studies conducted in Kenya [21] and in Ghana [22], in which perceived barriers were found as being the strongest predictors of condom use. However, these results can not be generalized across settings For example, in a study conducted among American university students, the HBM did not significantly explain condom use but rather condom use was associated with sexual practices [23]. Perceived benefit of avoidance of pregnancy was found as one of the strongest predictors of consistent condom use in New York female adolescents [24] and in Zimbabwe social support was found to be the most consistent factor associated with sexual risk reduction [25]. These observed differences in the strongest(s) predictor(s) of the HBM can be noticed through several other works [26-30]. Hence, it appears important to conduct operational behavioural researches in each local setting to identify factors that influence condom use. One limitation of our study was that for ethical reasons, subjects less than 15 years old were excluded even though some may have already been sexually active. Also, there was a potential selection bias by not having equal number of interviewers by gender, which resulted in an over-sampling of males. Our results would be biased if the reason for poor recruitment of women was linked to their behaviours, which is not likely to be the case. There were three males interviewers for one female (difficulties in recruiting female educated social worker in the area), and interviewer/participants must be from same gender. For the purposes of the analysis we assumed that reported knowledge and behavioural risk factors are independent. Finally there is no evidence for the validity or reliability for the original WHO questionnaire, however its use allows for comparability of results across settings. Conclusions Condom use, in our study population, depends on its perceived quality and perceived efficacy. There is an indication that behavioral communication change strategies based on increasing perceived risk or vulnerability of the population or based on fear factor by increasing perceived severity of HIV/AIDS are less likely to be deterrent towards condom use and require more researches. HIV outreach programs must target more barriers of condoms use. Condom outreach programmes should be defined at community level and must be defined in association with the community, using problem-solving techniques and selecting the most relevant targets, based on their importance and changeability [13]. Data from this study could be useful for the design and planning of health intervention programmes, resource allocation and evaluation of condom outreach activities in Benin. Competing interests The author(s) declare that they have no competing interests. Authors' contributions SHH conceived of the study, designed the protocol, carried out and supervised the field work and data collection, performed and interpreted the statistical analysis and wrote the manuscript. HC participated in analysis and interpretation of the data, and in the writing of the manuscript. NJH contributed in earlier analysis of the data and reviewed the manuscript. All authors read and approved the final manuscript. Table 5 Survey items, HIV/AIDS and condom use survey, Toffo county, Benin (June – July 2002). SURVEY ITEMS RESPONSE CATEGORIES General knowledge on HIV/AIDS 1. Have you ever heard about HIV/AIDS Yes/No 2. In your knowledge how severe is HIV/AIDS Deadly, don't know, imaginary, other 3. How could someone get infected by HIV? At least 2, one or no correct answer(s), 4. Who you think are at risk of getting HIV? Everyone/specific groups/Don't know 5. In your knowledge, what are the prevention methods of getting HIV? Abstinence, Fidelity, Condom, Other, Don't know 6. How could you recognize a HIV-infected person? Could not, Cachexia, Other symptoms, Don't know Beliefs on HIV/AIDS 7. Do you believe HIV really exists? Yes/No/Don't know 8. Do you think you are at risk of getting HIV? Yes/No/Don't know 9. If no to 8) why? Fidelity/Condom use/Other/Don't know 10. Where you believe HIV originates from? God/Bewitchment/Other/Don't know 11. Do you think one can completely cure from HIV/AIDS? Yes/No/Don't know 12. If yes to 11) how? Medicine/Herbs/Prayers/Other/Don't know 13. How would you rate the protective effect provided by condoms? Complete/Incomplete/Useless/Don't know 14. Does your religion believe HIV exists? Yes/No/Don't know 15. What is the position of your religion towards condom use? Favorable/Unfavorable/Indifferent/Don't know Behaviors and attitudes 16. Would you mind taking a HIV screening test if you were asked? Yes / No / Don't know 17. Do you use condoms? Yes / No / No answer 18. If No or No answer to 17) why? Don't like / Only God save / Other 19. How often do you use condoms? Always / Sometimes / Never 20. Did you use condom during the last occasional intercourse? Yes / No 21. Do you encounter any problem using condoms? Yes / No 22. If yes to 21) what type of problems Less lubrificated / Less pleasure / break easily / Other 23. Numbers of sexual partners during the last 12 months Socio demographic characteristics 24. Age Full years 25. Sex Male / Female 26. Marital status Married monogamous / Married polygamous/ Single / Divorced / Widowed / Separated 27. Education (Ability to read) Fluent / With difficulty / Not able to read at all 28. Occupation Student / Farmer / 29. Religion Christianity / Islam / Animist / Other 30. Place (please give the name of your village) Village name Adapted from the World Health Organization / Global AIDS Program's questionnaire Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgments The authors thank all the individuals who accepted to participate in this study. We also thank Dr Medegan-Fagla Valentine for her contribution during the design stage of the questionnaire, Drs Meda Nicolas, and Hassan Jacques for the review of an earlier version of the manuscript. We acknowledge the United States Agency for International Development (USAID), which provides funding through the ATLAS Fellowship. ==== Refs World Bank Group, les partenariats contre le SIDA l'expérience de l'Afrique australe dans le changement du comportement sexuel INSAE, ORC MACRO Enquête Démographique et de Santé au Bénin Cotonou 2002 ONUSIDA Rapport sur l'épidémie mondiale de VIH/SIDA Genève 2000 Centre de Coopération Internationale en Santé et Développement (CCISD) Programme National de Lutte contre le Sida Rapports Annuels Cotonou 2001 UNB, INE, CEFORP Enquête sur les connaissances, attitudes et pratiques relatives au SIDA, à la diarrhée, au paludisme et à la planification familiale Cotonou 2000 Fagla-Medegan V Prévalence de l'infection a VIH et autres infections sexuellement transmissibles et études des facteurs de risque associés chez les routiers au Bénin Cotonou 2001 Ministère de la Santé Annuaires Statistiques Cotonou 2000 ADJOVI CV Surveillance de l'infection par le VIH/SIDA/IST au Bénin Cotonou 2001 Brownson RC Remington PL Davis JR Chronic disease epidemiology and control American Public Health Association 2000 Brookmeyer R Gail MH AIDS Epidemiology. 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==== Front BMC Struct BiolBMC Structural Biology1472-6807BioMed Central London 1472-6807-5-11566378710.1186/1472-6807-5-1Research ArticleMolecular models of NS3 protease variants of the Hepatitis C virus da Silveira Nelson JF [email protected] Helen A [email protected] Carlos E [email protected] Souza Fátima P [email protected] Isabel MVGC [email protected] Paula [email protected] João RR [email protected] Azevedo Walter F [email protected] Department of Physics, IBILCE/UNESP, São José do Rio Preto, São Paulo, Brazil2 Department of Microbiology, Institute of Biomedical Science, USP, São Paulo, São Paulo, Brazil3 Adolfo Lutz Institute, São Paulo, São Paulo, Brazil4 Department of Biology, IBILCE/UNESP, São José do Rio Preto, São Paulo, Brazil5 Center for Applied Toxicology, Butantan Institute, São Paulo, São Paulo, Brazil2005 21 1 2005 5 1 1 13 8 2004 21 1 2005 Copyright © 2005 da Silveira et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Hepatitis C virus (HCV) currently infects approximately three percent of the world population. In view of the lack of vaccines against HCV, there is an urgent need for an efficient treatment of the disease by an effective antiviral drug. Rational drug design has not been the primary way for discovering major therapeutics. Nevertheless, there are reports of success in the development of inhibitor using a structure-based approach. One of the possible targets for drug development against HCV is the NS3 protease variants. Based on the three-dimensional structure of these variants we expect to identify new NS3 protease inhibitors. In order to speed up the modeling process all NS3 protease variant models were generated in a Beowulf cluster. The potential of the structural bioinformatics for development of new antiviral drugs is discussed. Results The atomic coordinates of crystallographic structure 1CU1 and 1DY9 were used as starting model for modeling of the NS3 protease variant structures. The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain. The protease domain exhibits the dual beta-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related beta-alpha-beta subdomains and a third subdomain of seven helices and three short beta strands. The latter domain is usually referred to as the helicase alpha-helical subdomain. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are presented for NS3 protease variant structures. Conclusions This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus. The structural models will be used to guide future efforts in the structure-based drug design of a new generation of NS3 protease variants inhibitors. All models in the database are publicly accessible via our interactive website, providing us with large amount of structural models for use in protein-ligand docking analysis. ==== Body Background After the development of serological tests for hepatitis A and B viruses in the 1970s it became clear that an additional agent accounted for approximately 90% of transfusion-associated hepatitis (non-A non-B hepatitis, NANBH) [1]. The novel agent, hence termed hepatitis C virus (HCV), currently infects approximately 3% of the world's population and it was classified within the Flavivirideae family. Diagnostic tests for anti-HCV antibodies developed thereafter proved that HCV was indeed the predominant cause of NANBH [2]. In view of the lack of vaccines against HCV, there is an urgent need for a treatment of the disease by an effective antiviral drug. This necessity has boosted research on the structural biology of HCV with the primary focus being to identify possible targets for pharmaceutical intervention [3]. Rational drug design has not been the primary way for discovering major therapeutics. However, recent successes in the area give reason to expect that drug discovery projects will increasingly be structure based. One of the possible targets for drug development against HCV is the NS3 protease variants. HCV RNA is translated into a polyprotein that during maturation is cleaved into functional components. One component, nonstructural protein 3 (NS3), is a 631-residue bifunctional enzyme with protease and helicase activities. The N-terminal portion of the NS3 protein was predicted to contain a serine protease domain as judged from conserved sequence patterns and by homology to Flavi- and Pestiviruses [4-6]. The NS3 serine protease processes the HCV polyprotein by both cis and trans mechanisms. The interative refinement and optimization of drug leads is an effective strategy for generating potent preclinical candidate [7,8]. Ongoing genome sequencing efforts have led to the identification of hundreds of potential therapeutic targets, many of which represent possible sources of crossover pharmacology. Homology or comparative modeling is a key feature of an integrated drug discovery effort because it allows this genomics information to be utilized early in the development of target ligands or in the engineering of ligand specificity [9]. Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms, including humans. We are now faced with assigning, understanding and modifying the functions of proteins encoded by these genomes. This task is generally facilitated by 3D structures [10], which are best determined by experimental methods such as X-ray crystallography and NMR spectroscopy. The theoretical approaches [11] can be divided into physical and empirical methods. The physical prediction methods are based on interactions between atoms and include molecular dynamics and energy minimization [12], whereas the empirical methods depend on the protein structures that have been already determined by experiment. They include combinatorial [13] and comparative modeling [14,15]. Comparative modeling uses experimentally determined protein structures to predict conformation of other proteins with similar amino acid sequences. For modeling of proteins was used restrained-based modeling implemented in the program MODELLER [16]. The models consist of coordinates for all non-hydrogen atoms in the modeled part of a protein. Models are generated entirely automatically in a four-step procedure [17]: (i) fold assignment, (ii) sequence-structure alignment, (iii) model building, and (iv) model evaluation. This procedure was applied to variants of NS3 protease using Perl-CGI, C and MPI programming. We modeled the structure of variants of NS3 protease variants available in the National Center for Biotchnology Information (Genbank), using structural bioinformatics tools. Knowledge of the three-dimensional structure variants will undoubtedly aid the design of useful inhibitors that may be used as a drug against hepatitis C virus. In order to speed up the modeling process all NS3 models were generated in a Beowulf cluster (BioComp, S.J. Rio Preto, Brazil). The potential of the structural bioinformatics for development of new antiviral drugs is discussed. Results and discussion Primary sequence comparasion The identity between the sequences of a bifunctional protease structure (PDB access codes:1CU1, 1DY9) [31,38] (templates) and NS3 protease variants (targets) is shown in Table 1. The secondary structural elements are indicated in the Figure 2 without inhibitor and in the Figure 3 with inhibitor. The sequence from crystallographic structure 1CU1 shows more than 79.1% identity with the sequences of NS3 protease variants, which provide high accuracy for the models (Table 1). Quality of the models The atomic coordinates of crystallographic structure 1CU1 solved to resolution of the 2.5 Å were used as starting model for modeling of the NS3 protease variant structures, and the structure of NS3 complexed with an inhibitor (PDB access code: 1DY9) was used to generate homology models for docking studies. Binding of an inhibitor to the active site of an enzyme is typically connected with local and possibly also global structural rearrangement of the enzyme (induced-fit mechanism). Therefore structure-based drug design preferentially relies on the crystal structures of enzyme-inhibitor complexes containing bound inhibitors of similar chemical structures to the compounds being designed. Such complexes offer more detailed and accurate picture of the inhibitor-enzyme interactions and structural complementarity between the inhibitor and the active site. The homology models of the variants of NS3 protease which used the NS3 complexed with an inhibitor are more adequate to docking simulations. The atomic coordinates of all water molecules were removed from the templates. The analysis of the Ramachandran diagram φ-ψ plots of the 1CU1 structure (template) were used to compare the overall stereochemical quality of the NS3 protease variants structures against template solved by biocrystallography (Table 1). They present over 94.0% of the residues in the most favorable regions. The same analysis for crystallographic structure (1CU1) present 88.9% of residues in the most favorable, 10.5% additional allowed regions, 0.6% generously allowed regions, and 0.0% disallowed regions, which strongly indicates that the molecular models present good overall stereochemical quality. Overall description The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain (Figure 2 and 3). The protease domain exhibits the dual β-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related β-α-β subdomains and a third subdomain of seven helices and three short β strands. The latter domain is usually referred to as the helicase α-helical subdomain. The 13-residue protease activation domain of NS4A contributes one strand to the N-terminal protease β-barrel and is considered to be the sixth subdomain [31]. Differences in subdomain structure in the NS3 protease variant molecule and in the structures of the isolated protease and helicase domains were assessed in several ways. Inspection of the molecule revealed that the subdomain folds are similar. Overall preservation of structure is also apparent when the subdomains from the various structures are superposed [31]. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are shown in Table 2 for NS3 protease variants structures. The same analysis for crystallographic structure (1CU1) present rmsd values of the 0.013Å bond lengths and 1.67°, the average G-fator values of the 0.14 torsion angles and 0.28 covalent geometry, and Verify 3D values of the 321.53 score total and 1.09S quality. Database design, access, and interface A MySQL database based on relational database management system (RDBMS) was developed to archive protein structure identified in infectious agents such as NS3 protease variants from hepatitis C virus. All supporting data related to the 3D structures modeling, such as protein codes, atomic coordinates in PDB format from modeled proteins, fasta sequence, links to others databases and various information about the protein were arranged in the MySQL [32] database under a master table. The aim this database is to provide access to a collection of annotated models generated by automated homology modelling of NS3 protease variants from hepatitis C virus. All models in the database are publicly accessible via our interactive website (Figure 1) [33]. The database user interface provides user friendly menus, so that all information can be printed in one step from any standard web browser. A small ribbon representation is included to obtain a first impression of the model structure (Figure 2 and 3). Atomic coordinates for the homology models can be downloaded in PDB format and their primary sequence in fasta format. The fields are defined with links to the target sequence, the template structure entries in PDB [34], structural information and analysis. There are two homology models for each sequence in the database, one obtained using 1CU1 as template and other using 1DY9 as template. The second model is adequade for docking simulation, since it was used as template a structure complexed with an inhibitor (PDB access code: 1DY9). Conclusions Large scale protein homology modeling, in which whole sequence databases or whole genomes are used as input into automated modeling algorithms, have been reported by several groups [35]. By utilizing powerful computer systems with multiple processors, these efforts have allowed the creation of large databases of homology models of proteins. This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus, bacteria and other organisms. Inhibition studies have shown that NS3 is only modestly inactivated by classic serine protease inhibitors such as chloromethylketones or phenylmethyl sylfonylfluoride [36]. The structural models will be used to guide future efforts in the structure based design of a new generation of NS3 protease variants inhibitors. This database is freelly available for all users on the Web, providing us with large amount of structural models for use in protein-ligand docking analysis. Methods Molecular modeling Molecular modeling is usually the method of choice when there is a clear relationship of homology between the sequence of a target protein and at least one known structure. This computational technique is based on the assumption that tertiary structures of two proteins will be similar if their sequences are related, and it is the approach most likely to give accurate results [18]. There are two main approaches to homology modeling: (1) fragment-based comparative modeling [14,19] and (2) restrained-based modeling [16]. For modeling of NS3 protease variants from hepatitis C virus we used the second approach. Model building of NS3 protease variants was carried out using the program MODELLER [16]. MODELLER is an implementation of an automated approach to comparative modeling by satisfaction of spatial restraints [20-22]. The modeling procedure begins with an alingment of the sequence to be modeled (target) with related known three-dimensional structure (templates). This alignment is usually the input to the program. The output is a three-dimensional model for the target sequence containing all main-chain and sidechain non-hydrogen atoms. Next, the spatial restraints and CHARMM energy terms enforcing proper stereochemistry [23] were combined into an objective function. Finally, the model is obtained by optimizing the objective function in Cartesian space. The optimization is carried out by the use of the variable target function method [24] employing methods of conjugate gradients and molecular dynamics with simulated anneling. Several slightly different models can be calculated by varying the initial structure. A total of 1000 models were generated for each enzyme and the final models were selected based on stereochemical quality. All optimization process was performed on a Beowulf cluster with 16 nodes (BioComp, AMD Athlon XP 2100+). Analysis of the models The overall stereochemical quality of the final models for each NS3 protease variants from hepatitis C virus was acessed by the program PROCHECK [25]. The root mean square deviations (rmsd) differences from ideal geometries for bond lengths and bond angles were calculated with X-PLOR [26,27]. G-factor value is essentially just log-odds score based on the observed distributions of the stereochemical parameters. It was computed for the following properties: torsion angles (the analyses provided the observed distributions of φ-δ, χ1-χ2, χ-1, χ-3, χ-4 and ω values for each of the 20 amino acid types) and covalent geometry (for the main-chain, bond lengths and bond angles) these average values were calculated using PROCHECK [25]. The Verify-3D measures the compatibility of a protein model with its sequence, these values were calculated using 3D profile [28-30]. Authors' contributions NJFS carried out the molecular modeling and participated in the building and design of the database tables, HAA and CEB carried of analysis of the models and created database interface, FPS selected the primary sequences of NS3 protease variants of the hepatitis C virus, IMVGCM participated of the study, PR conceived the study and suggestions in this manuscript, JRRP participated in the study and suggestions in this manuscript, WFA conceived the study, and participated in its analysis and coordination. All authors read and approved the final manuscript. Acknowledgements This work was supported by grants from FAPESP (SMOLBNet 01/07532-0, 02/04383-7, 04/00217-0), CNPq, CAPES and Instituto do Milênio (CNPq-MCT). WFA (CNPq, 300851/98-7) is researcher for the Brazilian Council for Scientific and Technological Development. Figures and Tables Figure 1 Database interface. The database user interface provides user friendly menus. In the menu of main page there are links to NS3 protein variant access codes of the Genbank, which lead to another page with the structural information and analysis about the generated model, such as a small ribbon representation, the atomic coordinates (model and template), and sequence in fasta format (model). The accuracy of the model can be viewed at link "NS3 protease variant table results" which shows results of values of the 3D Profile, rmsd, G-factor, and Ramachandran plot for each model. Figure 2 The strucuture of NS3 protease without inhibitor. The structure of NS3 protease variant, an engineered molecule that consists of 631 NS3 residues. It has six subdomains: two β barrels in the protease domain (down); two β-α-β subdomains (up on the left) and one α-helical subdomain in the helicase (up on the right). The figure was generated by Molmol [37]. Figure 3 The strucuture of NS3 protease complexed with 5,5-di-fluoro-2-keto-3-aminopentanoic acid. The NS3 protease-inhibitor complex shows the interaction between serine protease domain with 5,5-di-fluoro-2-keto-3-aminopentanoic acid. All models were generated using Parmodel [39]. Table 1 Analysis of the Ramachandran plot and identity between template and models of NS3 protease variants. The accuracy of comparative modeling is related to the percentage of sequence identity on which the model is based, correlating with the relationship between the structural and sequence similarities of two proteins. High accuracy comparative models are based on >50% sequence identity to their templates. They tend to have ~1 Å r.m.s. error for the main-chain atoms, which is comparable to the accuracy of a medium-resolution NMR structure or a low-relosution X-ray structure. All structure models in the database were generated using alignments with more than 79% sequence identity, which generating models with high accuracy. Genbank access code Residue Range Identity (%) Region of the Ramachandran plot (%) Most Favorable Additional Allowed Generously Allowed Disallowed AJ238800 1027 – 1657 95.0 94.9 5.1 0.0 0.0 AJ238799 1027 – 1657 95.7 95.1 4.6 0.2 0.2 AF139594 1028 – 1658 95.7 95.8 3.8 0.2 0.2 D17763 1033 – 1663 80.5 94.9 4.9 0.2 0.0 AF054247 1027 – 1657 94.0 95.0 4.8 0.2 0.0 AF054248 1027 – 1657 93.8 94.9 5.1 0.0 0.0 AF054249 1027 – 1657 93.8 95.2 4.8 0.0 0.0 D50409 1031 – 1661 80.3 95.1 4.9 0.0 0.0 D84262 1032 – 1662 84.2 95.0 5.0 0.0 0.0 D84263 1025 – 1665 83.1 95.2 4.8 0.0 0.0 D84264 1029 – 1659 83.9 95.2 4.8 0.0 0.0 D84265 1027 – 1657 82.5 94.9 5.1 0.0 0.0 D10749 1027 – 1657 90.5 94.9 5.1 0.0 0.0 D13558 1027 – 1657 94.1 94.8 5.2 0.0 0.0 D14853 1027 – 1657 88.8 95.3 4.7 0.0 0.0 D00944 1031 – 1661 79.8 95.1 4.9 0.0 0.0 D10988 1031 – 1661 79.7 95.1 4.9 0.0 0.0 AF046866 1033 – 1663 80.3 94.7 5.1 0.2 0.0 D49374 1035 – 1665 79.8 94.7 5.1 0.2 0.0 Y11604 1027 – 1657 84.7 95.4 4.6 0.0 0.0 Y13184 1028 – 1658 82.3 95.2 4.8 0.0 0.0 Y12083 1031 – 1661 81.9 94.6 5.2 0.0 0.2 U16362 1027 – 1657 92.6 95.2 4.6 0.2 0.0 U45476 1027 – 1657 94.7 94.9 5.1 0.0 0.0 AJ000009 1027 – 1657 95.2 95.1 4.9 0.0 0.0 M67463 1027 – 1657 89.6 94.9 4.9 0.2 0.0 D63822 1031 – 1661 79.2 95.2 4.8 0.0 0.0 D63821 1032 – 1662 79.1 94.7 5.1 0.2 0.0 D14484 1027 – 1657 94.3 95.8 4.2 0.0 0.0 D11168 1027 – 1657 94.9 95.0 5.0 0.0 0.0 D11355 1027 – 1657 94.6 95.0 5.0 0.0 0.0 D50480 1027 – 1657 93.5 95.0 5.0 0.0 0.0 D50481 1027 – 1657 94.6 95.4 4.6 0.0 0.0 D50482 1027 – 1657 94.7 95.5 4.3 0.2 0.0 D50483 1027 – 1657 93.6 95.2 4.8 0.0 0.0 D50485 1027 – 1657 94.3 95.6 4.4 0.0 0.0 D50484 1027 – 1657 94.1 95.3 4.7 0.0 0.0 D28917 1033 – 1663 80.5 95.2 4.6 0.2 0.0 D30613 1027 – 1657 93.8 95.4 4.6 0.0 0.0 D10934 1027 – 1657 95.2 95.3 4.7 0.0 0.0 AF207762 1027 – 1657 94.0 95.1 4.9 0.0 0.0 Table 2 Analysis of the rmsd from ideal geometry, 3D Profile, and G-factor. Genbank access code Residue Range rmsd 3D Profile* G-factor** Bond Lengths (Å) Bond Angles (°) Total Score Sideal Score Torsion Angles Covalent Geometry AJ238800 1027 – 1657 0.020 2.171 299.14 1.03S 0.01 -0.17 AJ238799 1027 – 1657 0.021 2.425 166.68 0.58S 0.07 -0.21 AF139594 1028 – 1658 0.021 2.193 152.89 0.53S 0.09 -0.20 D17763 1033 – 1663 0.019 2.143 290.96 1.00S 0.03 -0.14 AF054247 1027 – 1657 0.020 2.162 288.28 1.00S 0.07 -0.16 AF054248 1027 – 1657 0.020 2.194 292.48 1.01S 0.01 -0.19 AF054249 1027 – 1657 0.020 2.207 290.64 1.00S 0.05 -0.18 D50409 1031 – 1661 0.020 2.188 287.09 0.99S 0.02 -0.17 D84262 1032 – 1662 0.020 2.181 276.59 0.95S 0.06 -0.17 D84263 1025 – 1665 0.020 2.173 283.17 0.98S 0.01 -0.16 D84264 1029 – 1659 0.020 2.174 296.59 1.02S 0.03 -0.15 D84265 1027 – 1657 0.020 2.155 277.67 0.96S 0.02 -0.15 D10749 1027 – 1657 0.020 2.589 287.57 0.99S 0.02 -0.16 D13558 1027 – 1657 0.020 2.157 280.60 0.97S 0.04 -0.15 D14853 1027 – 1657 0.020 2.168 274.13 0.95S 0.02 -0.16 D00944 1031 – 1661 0.020 2.149 287.90 0.99S 0.40 -0.15 D10988 1031 – 1661 0.020 2.171 287.55 0.99S 0.02 -0.16 AF046866 1033 – 1663 0.020 2.957 281.07 0.97S 0.03 -0.16 D49374 1035 – 1665 0.020 2.163 292.23 1.01S 0.02 -0.15 Y11604 1027 – 1657 0.020 2.382 281.93 0.97S 0.02 -0.16 Y13184 1028 – 1658 0.020 2.166 296.66 1.02S 0.02 -0.15 Y12083 1031 – 1661 0.020 2.271 290.58 1.00S -0.01 -0.21 U16362 1027 – 1657 0.020 2.186 280.49 0.97S 0.01 -0.16 U45476 1027 – 1657 0.020 2.398 281.26 0.97S 0.03 -0.17 AJ000009 1027 – 1657 0.020 2.168 290.86 1.00S 0.03 -0.16 M67463 1027 – 1657 0.020 2.139 281.59 0.97S 0.03 -0.14 D63822 1031 – 1661 0.020 2.385 284.43 0.98S 0.03 -0.16 D63821 1032 – 1662 0.020 2.420 269.09 0.93S 0.03 -0.18 D14484 1027 – 1657 0.020 2.234 285.83 0.99S 0.00 -0.19 D11168 1027 – 1657 0.020 2.160 289.57 1.00S 0.04 -0.15 D11355 1027 – 1657 0.020 2.148 306.49 1.06 0.04 -0.14 D50480 1027 – 1657 0.020 2.123 291.79 1.01S 0.05 -0.13 D50481 1027 – 1657 0.020 2.151 277.60 0.96S 0.06 -0.14 D50482 1027 – 1657 0.020 2.172 284.69 0.98S 0.03 -0.16 D50483 1027 – 1657 0.020 2.149 291.04 1.00S 0.06 -0.15 D50485 1027 – 1657 0.020 2.138 293.16 1.01S 0.03 -0.14 D50484 1027 – 1657 0.020 2.204 289.47 1.00S 0.00 -0.17 D28917 1033 – 1663 0.020 2.221 295.32 1.02S 0.03 -0.19 D30613 1027 – 1657 0.020 2.248 282.94 0.98S 0.02 -0.20 D10934 1027 – 1657 0.020 2.434 289.87 1.00S 0.00 -0.18 AF207762 1027 – 1657 0.020 2.367 308.61 1.07S 0.04 -0.15 Total Score: is the sum of the 3D-1D scores (statistical preferences) of each residue present in protein. Ideal Score: Sideal = exp(-0.83+1.008xln(L)); where L is number of amino acids. Sideal Score: is compatibility of the sequence with their 3D structure. It is obtained Total Score / Ideal Score. Sideal Score above 0.45Sideal. *Ideally, scores should be above -0.5. 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