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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-391547154610.1186/1471-2180-4-39Research ArticleBranched-chain amino acid aminotransferase and methionine formation in Mycobacterium tuberculosis Venos Erik S [email protected] Marvin H [email protected] Cynthia L [email protected] Bradley J [email protected] Chemical & Biological Defence Section, Defence R&D Canada – Suffield, Box 4000 Station Main, Medicine Hat, Alberta, T1A 8K6, Canada2004 7 10 2004 4 39 39 9 6 2004 7 10 2004 Copyright © 2004 Venos 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
Tuberculosis remains a major world-wide health threat which demands the discovery and characterisation of new drug targets in order to develop future antimycobacterials. The regeneration of methionine consumed during polyamine biosynthesis is an important pathway present in many microorganisms. The final step of this pathway, the conversion of ketomethiobutyrate to methionine, can be performed by aspartate, tyrosine, or branched-chain amino acid aminotransferases depending on the particular species examined.
Results
The gene encoding for branched-chain amino acid aminotransferase in Mycobacterium tuberculosis H37Rv has been cloned, expressed, and characterised. The enzyme was found to be a member of the aminotransferase IIIa subfamily, and closely related to the corresponding aminotransferase in Bacillus subtilis, but not to that found in B. anthracis or B. cereus. The amino donor preference for the formation of methionine from ketomethiobutyrate was for isoleucine, leucine, valine, glutamate, and phenylalanine. The enzyme catalysed branched-chain amino acid and ketomethiobutyrate transamination with a Km of 1.77 – 7.44 mM and a Vmax of 2.17 – 5.70 μmol/min/mg protein, and transamination of ketoglutarate with a Km of 5.79 – 6.95 mM and a Vmax of 11.82 – 14.35 μmol/min/mg protein. Aminooxy compounds were examined as potential enzyme inhibitors, with O-benzylhydroxylamine, O-t-butylhydroxylamine, carboxymethoxylamine, and O-allylhydroxylamine yielding mixed-type inhibition with Ki values of 8.20 – 21.61 μM. These same compounds were examined as antimycobacterial agents against M. tuberculosis and a lower biohazard M. marinum model system, and were found to completely prevent cell growth. O-Allylhydroxylamine was the most effective growth inhibitor with an MIC of 78 μM against M. marinum and one of 156 μM against M. tuberculosis.
Conclusion
Methionine formation from ketomethiobutyrate is catalysed by a branched-chain amino acid aminotransferase in M. tuberculosis. This enzyme can be inhibited by selected aminooxy compounds, which also have effectiveness in preventing cell growth in culture. These compounds represent a starting point for the synthesis of branched-chain aminotransferase inhibitors with higher activity and lower toxicity.
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Background
Tuberculosis remains one of the leading causes of worldwide mortality and morbidity, infecting an estimated 8 million people annually with approximately 2 million deaths [1]. The situation regarding the control of tuberculosis has significantly worsened over the last decades, with the spread of multidrug resistant strains. In the absence of an effective vaccine for tuberculosis, there is an urgent need for the development of novel antimycobacterial agents. The study of mycobacterial biochemistry assists this development through the identification and characterization of cellular enzymes amenable to therapeutic inhibition.
Polyamine synthesis and its associated methionine (Met) regeneration pathway (Figure 1) are known to be potential drug targets in a variety of microorganisms [2-4]. The synthesis of polyamines is essential during periods of DNA replication, although the exact physiological role of these compounds remains unclear [3]. The production of spermidine from putrescine, or spermine from spermidine, consumes the amino acid Met in a 1:1 stoichiometry yielding methylthioadenosine (MTA) as a byproduct. As Met biosynthesis is energetically expensive, and many organisms lack the ability to synthesize the amino acid, a unique pathway exists which recycles Met from MTA. To date, the entire pathway has only been fully characterised in the Gram-negative bacterium Klebsiella pneumoniae [5-11] and the Gram-positive bacterium Bacillus subtilis [12-14] Selected individual enzymes active in the pathway have been studied in a wide variety of eukaryotic and prokaryotic organisms [7,15-20]. For Mycobacterium spp., only methionine adenosyltransferase has been cloned, expressed, and fully characterised [21].
The final step in Met regeneration is the transamination of ketomethiobutyrate (KMTB) by an aminotransferase. The specific aminotransferase responsible for the reaction has been identified and characterised in a number of microorganisms, including malaria, African trypanosomes, K. pneumoniae, B. subtilis, and B. anthracis [7,16,17]. In the lower eukaryotes Plasmodium falciparum, Trypanosoma brucei brucei, Giardia intestinalis, and Crithidia fasciculata, this reaction is catalysed by the subfamily Ia enzyme aspartate aminotransferase [17]. In K. pneumoniae, however, the reaction was performed by the close homologue tyrosine aminotransferase, which is also a member of subfamily Ia [7]. Gram-positive bacteria and archaea appear to lack any subfamily Ia homologues in their genomes, and B. subtilis, B. cereus, and B. anthracis were recently found to catalyse Met regeneration via a branched-chain amino acid aminotransferase (BCAT) [16]. This enzyme is a member of family III, along with D-amino acid aminotransferase (DAAT), and is unrelated structurally to family I enzymes [22]. Intriguingly, B. subtilis and B. cereus/B. anthracis utilised BCAT enzymes from separate subfamilies (IIIa vs. IIIb respectively). As Mycobacterium spp. also appear to have no subfamily Ia aminotransferase sequences ([16], and data not shown), it would be expected that M. tuberculosis also catalyses the conversion of KMTB to Met via a BCAT. In this paper, we report the identification, cloning, and functional expression of a single BCAT from M. tuberculosis. In addition, this enzyme has been demonstrated to actively catalyse Met formation and is subject to inhibition by a variety of aminooxy compounds.
Results
Branched-chain amino acid aminotransferase in M. tuberculosis
The complete, published genome of M. tuberculosis H37Rv was found to contain a single gene with a very high sequence homology to either B. subtilis YbgE or YwaA, which are both known to be subfamily IIIa BCATs [16,23]. In contrast, the tuberculosis genome did not contain a homologue to B. subtilis YheM, B. cereus BCAT, or B. anthracis BCAT, which are all subfamily IIIb aminotransferases [16]. This relationship can be clearly seen in Figure 2A, where selected family III aminotransferases have been aligned and an unrooted tree constructed. The putative M. tuberculosis BCAT gene, Rv2210c, has not been previously cloned, expressed, or characterised. It is interesting to note that the M. tuberculosis genome contains a single BCAT homologue and no obvious DAAT homologue.
Examination of complete and incomplete genome projects for Mycobacterium spp. uncovered a single gene in M. leprae, M. bovis, M. marinum, M. ulcerans, M. avium, and M. smegmatis with an extremely high identity to Rv2110c. Together, with other subfamily IIIa aminotransferases, the putative mycobacterial sequences were aligned and a cladogram constructed (Figure 2B). The M. tuberculosis and M. bovis sequences were identical, as were the M. marinum and M. ulcerans sequences. Aside from M. bovis, all the mycobacterial BCAT sequences were found to be 85 – 88% identical to the M. tuberculosis sequence. However, the tuberculosis sequence was 57% identical to the putative BCAT from Streptomyces coelicolor and 45% identical to B. subtilis YbgE. Figure 2B highlights the fact that the mycobacterial BCAT sequences are more closely related to eukaryotic enzymes than to most other bacterial homologues. There was little sequence conservation with enzymes found in subfamily IIIb, with only 27% identity to the E. coli BCAT, 18% to the B. anthracis BCAT, and 15% to B. subtilis YheM.
The low level of sequence conservation outside of the genus can be seen in the alignment of selected BCAT sequences shown in Figure 3. Only 19 residues are completely conserved across even this small sequence sampling. Interestingly, of the residues found by X-ray crystallography to be important in substrate binding to the E. coli BCAT [24], only K228(K159) and T339(T257) were conserved across the 13 sequences in Figure 3. The residues in parentheses represent the corresponding position in the E. coli BCAT. Of these two residues, K228(K159) is the PLP binding site and would be expected to be invariant. If one excludes the only DAAT in Figure 3, then Y91(Y31), F96(36), Y233(164), and A340(A258) can be added to this conserved list of residues important for substrate binding in the E. coli BCAT. Clearly, sequence conservation is very low across family III.
Expression and characterization of the branched-chain amino acid aminotransferase
The putative M. tuberculosis BCAT was cloned as a deca-histidine fusion protein for expression in E. coli. To prevent complete inclusion of the recombinant protein, it was necessary to induce expression with a relatively low concentration of IPTG (0.1 mM) at 20°C for 20 hr. Under these conditions, sufficient soluble material was produced and purified over Ni2+ affinity columns (Figure 4). Assay of the eluted material with 2 mM each of ADEFGHIKLNQRSTVWY and 1 mM KMTB resulted in appreciable Met production (data not shown), demonstrating that the enzyme was active and catalysed Met formation.
The purified enzyme was screened against 2 mM of each individual amino acid and 1 mM KMTB to determine the amino donor range for Met regeneration. Isoleucine, leucine, and valine were found to be the most effective substrates (Figure 5), while glutamate and phenylalanine were also active as amino donors. Tyrosine and tryptophan were found to have a much lesser ability to transaminate KMTB and all other amino acids were inactive. The five most active amino donors were more closely examined in order to determine their kinetic parameters (Table 1). The Km for Leu, Ile, and Val ranged from 1.77 – 2.85 mM, while that for Glu was 9.53 mM and Phe 7.44 mM. The Vmax for all five amino acids was similar at 2.17 – 5.70 μmol/min/mg protein. KMTB was found to have a Km of 4.20 mM. The enzyme was also examined for branched-chain amino acid and KG aminotransfer in order characterise the "classic" reactions associated with a BCAT (Table 1). The Km of the substrates was found to be similar, while the Vmax ranged from 11.82 – 14.35 μmol/min/mg protein. Therefore, the tuberculosis BCAT catalyses aminotransfer of KG about 3 times more readily than KMTB. This result is similar to that seen with the B. subtilis BCAT, which also transaminates KG at a higher rate than KMTB [16].
Inhibition studies
Thirteen aminooxy compounds were assayed for inhibitory effects on the tuberculosis BCAT. The enzyme was incubated with 2.0 mM leucine, 1.0 mM KMTB and 0.1 or 1.0 mM inhibitor to assay for the effect on Met regeneration (Figure 6). With the exception of O-trimethylsilylhydroxylamine, all of the compounds inhibited Met formation to some extent. The four most active compounds at 0.1 mM were O-allylhydroxylamine, carboxymethoxylamine, O-benzylhydroxylamine, and O-t-butylhydroxylamine, and these inhibitors were further examined in order to determine Ki values (Table 2). For all four compounds, the inhibition data was not consistent with a simple competitive or uncompetitive model, but fit very well with a model of mixed mode inhibition [25]. The competitive component of inhibition yielded a Kic of 8.20 – 21.61 μM, while the uncompetitive component gave a Kiu of 84.08 – 386 μM. Therefore, the inhibition of the tuberculosis BCAT by these four aminooxy compounds is primarily competitive.
These four inhibitors and canaline, an aminooxy analogue of ornithine that has been demonstrated to be an effective aminotransferase inhibitor in other systems [16,17,26-28], were screened against M. tuberculosis and M. marinum in vitro to determine potential antimicrobial activity. M. marinum is a close relative of M. tuberculosis that causes a similar disease in fish, grows faster than M. tuberculosis in culture, and does not cause serious infections in humans [29]. As such, it is an excellent surrogate for the initial screening of antimycobacterial agents, and we wished to validate its use for aminooxy compounds. All the inhibitors were found to have some degree of antimycobacterial activity (Table 3), with MIC values ranging from 78 μM – 10 mM and IC50 values of 8.49 μM – 467 μM. The best inhibitor was found to be O-allylhydroxylamine. While O-t-butylhydroxylamine and O-benzylhydroxylamine appeared to be the best enzyme inhibitors, they were significantly less effective than O-allylhydroxylamine as growth inhibitors. Unlike other organisms examined to date [27,30], canaline was not a particularly good inhibitor of both enzyme activity and cell growth. The inhibition results for M. tuberculosis and M. marinum were very similar, with MIC results being identical or within 1 dilution. In addition, M. tuberculosis was found to have an MIC of 2 μg/ml for streptomycin while M. marinum had one of 8 μg/ml.
Discussion
The specific aminotransferase involved in the formation of Met from KMTB has been examined in a number of eukaryotic and prokaryotic organisms [7,16,17]. However, within the low-GC content Gram-positive bacteria, only B. subtilis, B. cereus, and B. anthracis have been studied [16]. In all of these Bacillus spp., a BCAT has been found to be responsible for catalysing the reaction, with B. subtilis and B. cereus/B. anthracis utilising enzymes from different aminotransferase subfamilies. No member of the high-GC content Gram-positive bacteria has been previously examined. Like B. subtilis, M. tuberculosis has been found to catalyse Met regeneration using a subfamily IIIa aminotransferase. In fact, the kinetic parameters for the two aminotransferases were almost identical. The M. tuberculosis BCAT had Km values of 1.77 – 2.85 mM and Vmax values of 2.58 – 4.28 μmol/min/mg protein for branched-chain amino acids and KMTB, while the B. subtilis YbgE had the corresponding values of 2.36 – 3.20 mM and 1.84 – 2.03 μmol/min/mg protein [16]. For branched-chain amino acids and KG, the values were 5.79 – 6.16 mM and 11.82 – 14.35 μmol/min/mg protein for the M. tuberculosis BCAT, and 2.82 – 3.99 mM and 13.93 – 16.61 μmol/min/mg protein for B. subtilis YbgE [16]. Therefore, a 45% sequence identity between the two enzymes is sufficient to conserve both the substrate range and kinetic properties of the BCATs. Structural information is only available for the E. coli BCAT (IlvE) and the human mitochondrial BCAT [24,24,31], but the key residues involved in substrate specificity appear to be conserved in the M. tuberculosis BCAT. However, while the human mitochondrial BCAT is also a family IIIa aminotransferase, there are some clear differences when compared to the M. tuberculosis enzyme. The human enzyme will not accept aromatic amino acids, whereas the tuberculosis BCAT would use phenylalanine as an amino donor. In addition, the human enzyme contains the redox-active motif CXXC at positions 311–314 (positions 341–344 in Figure 3) which is essential for maintaining activity, while the tuberculosis BCAT lacks these residues. Structural analysis of the M. tuberculosis and/or B. subtilis enzymes would clarify these issues.
The M. tuberculosis BCAT was also screened with a variety of aminooxy compounds as potential inhibitors. These compounds are known aminotransferase inhibitors and act by forming a stable Schiff-base with the PLP cofactor [32]. Unlike previous studies [7,16,17,33], canaline was not found to be one of the better inhibitors of aminotransferase activity. Instead, O-benzylhydroxylamine, O-t-butylhydroxylamine, carboxymethoxylamine, and O-allylhydroxylamine were the most efficient inhibitors of Met formation from KMTB. In addition, these compounds demonstrated mixed type inhibition with a lower Ki for the competitive component. This result contrasts with that previously found for canaline with the Bacillus spp. enzymes, where inhibition was uncompetitive [16]. It may be possible that this difference may be due to the structure of the inhibitors, as canaline is a γ-substituted amino acid analogue, while the present inhibitors are α-substituted or non-amino acid analogues. Essentially, the inhibitors examined in this study do not present an α-amino group suitable for participation in the transamination reaction whereas canaline does.
Further screening of the inhibitors against M. tuberculosis and M. marinum in vitro demonstrated that the compounds can act as effective antimycobacterial agents. The close correspondence of the MIC values for M. tuberculosis and M. marinum validates the use of the latter organism as a more rapid and safe initial screen of the antimycobacterial properties of aminooxy compounds. The MIC values found for streptomycin against these two organisms was also found to be consistent with previously published values [34]. M. marinum can thus be used to quickly test a larger number of potential inhibitors, with M. tuberculosis used as a follow up for more promising candidates.
Interestingly, there was no direct correlation between the Ki of the compounds against recombinant M. tuberculosis BCAT and the MIC/IC50 against cell growth. It is possible that there may be differences in the uptake rate of the various compounds into viable cells. Alternatively, the most effective growth inhibitors act by inhibiting other PLP-dependent enzymes in addition to BCAT. In any case, O-allylhydroxylamine was the most effective antimycobacterial agent with an MIC of 78 μM against M. marinum and 156 μM against M. tuberculosis. Unfortunately, the compound is corrosive, and is thus unsuitable for further in vivo study. However, the structure of the compound might provide the basis for the design of less toxic, more active structural analogues. In future studies, it will be necessary to examine the effect of potential inhibitors on human BCAT, in order to better assess the potential for host toxicity. Any further development of aminooxy compounds as antimycobacterial agents will depend on discovering a selective inhibitor for the microbial enzyme.
Several older studies have been conducted on the antimicrobial effect of aminooxy compounds, with M. tuberculosis included amongst the organisms tested [35-39]. From these papers, the only compound in common with the present study was carboxymethoxylamine, which was found to have an MIC of 313 μM (present data), 910 μM [35], 170 – 686 μM [36], or 170 μM [37]. Given the variety of media used in these studies for determining the MIC value, the results are quite consistent. The variety of non-commercially available aminooxy compounds synthesized and tested in these older studies included aminooxy acids, aminooxy amides, aminooxy hydroxamic acids, aminooxy hydrazides, aminooxy alkanes, and aminooxy guanidines. Several of these compounds were very effective growth inhibitors in vitro, with MIC values as low as 0.30 μM against M. tuberculosis. One of the compounds has been administered to mice, with favourable, albeit sparsely detailed, results with regard to toxicity and in vivo antitubercular effect [38]. While it is unclear what effect these inhibitors would have against the M. tuberculosis BCAT, it would appear to be possible to design more effective, less toxic aminooxy compounds for use against M. tuberculosis.
Several interesting findings arose during the course of this investigation. First, while M. tuberculosis has only the one branched-chain aminotransferase, it does contain a coding sequence (Rv0858c) with a high similarity to the B. subtilis ykrV gene product. YkrV was found to be a subfamily If aminotransferase and could also catalyse the conversion of KMTB to Met using glutamine as the only effective amino donor [16]. Therefore, it is possible that the Rv0858c gene product might be capable of KMTB transamination. It should be stressed that while the recombinant B. subtilis YkrV could transaminate KMTB with glutamine, B. subtilis cell homogenates did not produce Met from KMTB when supplemented with glutamine [16]. Similarly, cell homogenates of M. smegmatis grown in Middlebrook 7H9 incomplete medium were only able to produce Met from KMTB when supplemented with valine, isoleucine, leucine, glutamate, or phenylalanine, as was seen for the recombinant M. tuberculosis BCAT in figure 5 (data not shown).
M. tuberculosis was found to contain no putative gene product with significant homology to a DAAT. In fact, the organism appeared to contain no subfamily IIIb aminotransferases. The physiological significance of a lack of a DAAT is unclear, but many organisms do not contain a homologue of this enzyme. With DAAT, there might be a diminished capacity to catabolise D-amino acids for energy, although the same reactions could be performed by a D-amino acid oxidase. M. tuberculosis is known to be reliant on carbohydrate catabolism during the active growth phase and lipid metabolism during the chronic, dormant phase [40]. Therefore, the lack of a DAAT might be reflective of a lifestyle where protein and peptide catabolism is relatively unimportant.
Similarly, M. tuberculosis was found to lack clearly identifiable homologues of several enzymes in the Met regeneration pathway. The most glaring omission is the lack of an S-adenosylmethionine decarboxylase (SAMdc) homologue (see Figure 1). M. tuberculosis contains the preceding enzyme, methionine adenosyltransferase [21], and has an easily identifiable homologue for the succeeding enzyme, spermidine synthase [23]. Therefore, M. tuberculosis must catalyse SAMdc activity via another enzyme in order to be able to synthesize polyamines. A previous study has demonstrated SAMdc activity in M. bovis homogenates, but has not identified the enzyme responsible [41]. Resolution of this issue is critical for a more complete understanding of polyamine biosynthesis in tuberculosis, and may yield a novel enzyme as an additional drug target. The M. tuberculosis genome also appears to be missing homologues of the enzymes converting methylthioribose to KMTB (see Figure 1). However, outside of K. pneumoniae and B. subtilis, these enzymes have not been well studied, and, between these two organisms, there are key differences in the enzymes catalyzing several steps [12,20]. In silico analyses have suggested that Pseduomonas aeruginosa, Xylella fastidiosa, Leptospira interrogans, and Thermoanaerobacter tengcongensis have readily identifiable, complete Met regeneration pathways [42]. However, the presence or absence of the pathway in a variety of prokaryotic and eukaryotic organisms remains to be determined by functional analysis. Therefore, there is much left to examine before concluding that M. tuberculosis contains neither homologues nor analogues to these Met recycling enzymes. However, even in the absence of a complete Met salvage pathway, M. tuberculosis, as an intracellular pathogen, might utilise exogenous KMTB as a Met source.
Conclusions
Branched-chain amino acid aminotransferase has been cloned and characterised from M. tuberculosis. This enzyme was found to be responsible for the formation of methionine from ketomethiobutyrate, and could be inhibited in vitro by a series of aminooxy compounds. Several of these compounds were found to be effective inhibitors of M. tuberculosis or M. marinum growth in culture, with MIC values as low as 156 μM and 78 μM respectively. These studies demonstrate the importance branched-chain amino acid and methionine metabolism to the survival of mycobacteria, and open up the potential for the development of more potent and less toxic aminooxy inhibitors of the branched-chain aminotransferase.
Methods
Cells and reagents
M. tuberculosis H37Rv and M. marinum Aronson (ATCC927) were cultured in liquid Middlebrook 7H9 complete medium or on Middlebrook 7H10 plates at 37°C for M. tuberculosis or 30°C for M. marinum. All substrates and inhibitors were obtained from Sigma-Aldrich (Oakville, ON, Canada).
Cloning and functional expression
Genomic DNA was isolated from M. tuberculosis by vortexing packed cells in a minimal volume of 50 mM Tris-HCl pH 8.0/10 mM EDTA/100 mM NaCl containing 500 μm acid washed glass beads (Sigma). After allowing the glass beads to settle, the supernatant was added to an equal volume of 10 mM Tris-HCl pH 8.0/100 mM NaCl/25 mM EDTA/0.5% w/v sodium dodecyl sulfate/0.1 mg/ml proteinase K and incubated for 1 hr at 37°C with occasional gentle mixing. The mixture was then subjected to extraction with phenol:chloroform:isoamyl alcohol (25:24:1), and the DNA ethanol precipitated.
The sequence of the putative M. tuberculosis BCAT gene was discovered by a BLAST search of the complete M. tuberculosis H37Rv genome using the B. subtilis YbgE, YwaA, or YheM gene products as the query proteins [16,23,43]. The single resulting putative BCAT gene was used to construct oligonucleotide primers for PCR amplification. The 5' primer was TCGAGGCGGCCGCAAATGACCAGCGGCTCCCTTCA and incorporated a NotI restriction site and an in-frame start codon. The 3' primer was ATCGAGCTCGAGTTACCCCAGCCGCGCCATCCAG and incorporated a XhoI restriction site and an in-frame stop codon. The BCAT gene was then amplified using a 5:1 mixture of Taq:Pfu polymerases (Promega; Madison, WI, USA) and the following program: 1 cycle of 95°C for 1.5 min; 30 cycles of 95°C for 1 min, 55°C for 1 min, and 72°C for 1 min; and 1 cycle of 72°C for 10 min. The resulting PCR product was excised from a 1% agarose gel and recovered using the Qiaex II kit (Qiagen; Mississauga, ON, Canada). The purified product was digested with NotI and XhoI and ligated into a similarly digested pET 19 m (a modification by us of pET19b (Novagen; Madison, WI, USA) to incorporate extra restriction sites in the multiple cloning site) using a Rapid Ligation kit (Fermentas; Burlington, ON, Canada). The recombinant plasmid was then transformed into Escherichia coli XL10 cells (Stratagene; La Jolla, CA, USA) and was subsequently recovered using the Qiaspin miniprep kit (Qiagen). Positive clones were determined by digesting the plasmid with NotI and XhoI to confirm the presence of the insert on a 1% agarose gel. The sequence of the insert was confirmed by using the Big-Dye cycle sequencing kit (ABI; Foster City, CA, USA) and an ABI Prism 310 genetic analyser.
The plasmid from positive clones was transformed into E. coli BL21(DE3) CodonPlus RIL cells (Stratagene) for functional expression. Cells were grown in liquid LB medium containing 50 μg/ml ampicillin and 50 μg/ml chloramphenicol at 37°C and 250 rpm until the culture reached an A600 nm of 0.6–0.8. The culture was then cooled to 20°C for 30 min at 250 rpm before the addition of 0.1 mM isopropylthiogalactopyranoside (IPTG) and an additional 20 hr of incubation at 20°C and 250 rpm.
The culture was centrifuged at 3500 × g for 20 min at 4°C, and the cell pellet resuspended in 50 mM HEPES (pH 7.4)/750 mM NaCl and frozen at -20°C. The resuspended cells were then thawed, sonicated on ice, centrifuged at 3000 × g for 20 min at 4°C, and the supernatant loaded onto a 1.6 × 9.5 Chelating-Sepharose-FF column (Amersham Biosciences; Baie d'Urfe, QC, Canada) charged with NiSO4. The column was washed with 50 mM HEPES (pH 7.4)/750 mM NaCl and 50 mM HEPES (pH 7.4)/750 mM NaCl/80 mM imidazole, before elution with 50 mM HEPES (pH 7.4)/750 mM NaCl/800 mM imidazole. Fractions containing the recombinant protein were pooled and concentrated to less than 3.0 ml using a 30 kDa molecular mass cut-off filter (Pall Filtron; Mississauga, ON, Canada). The concentrated enzyme was then dialysed against 50 mM HEPES (pH 7.4)/1 mM dithiothreitol/1 mM EDTA/trace pyridoxal-5-phosphate (PLP) overnight at 4°C. The concentrated enzymes were stored at 4°C for several days, or with 20% v/v glycerol at -20°C for several weeks, without appreciable loss of activity. Recombinant protein samples were examined by electrophoresis on 10% SDS polyacrylamide gels followed by Coomassie Brilliant Blue R250 staining. Protein concentration was measured using the Bio-Rad reagent (Bio-Rad; Mississauga, ON, Canada).
Enzyme assays and inhibition studies
Aminotransferase activities were assayed by an HPLC method [17]. 5 or 10 μl of recombinant enzyme was added to 100 μl of substrate mix (100 mM PO4 (pH 7.4)/50 μM PLP/various concentrations of amino acid/various concentrations of keto acid) and incubated for 30 min at 37°C. The samples were then stored at -20°C until analysis by HPLC. All samples were analysed by pre-column derivatisation and reverse-phase HPLC. 10 μl of sample was mixed with 50 μl of 400 mM borate pH 10.5 and then with 10 μl of 10 mg/ml o-phthalaldehyde/12 μl/ml mercaptopropionate/400 mM borate pH 10.5 prior to the injection of 7.0 μl onto a 2.1 × 200 mm ODS-AA column (Agilent; Mississauga, ON, Canada). The column was eluted using 2.72 mg/ml sodium acetate pH 7.2/0.018% v/v triethylamine/0.3% v/v tetrahydrofuran as Buffer A and 2.72 mg/ml sodium acetate pH 7.2/40% v/v methanol/40% v/v acetonitrile as Buffer B with a linear gradient of 0 – 17% B over 16 min followed by a linear gradient of 17–100% B over 1 min and 6.0 min at 100% B. The flow rate was 0.45 ml/min from 0 – 16 min and 0.80 ml/min from 17–30 min. The elution of derivatised amino acids was monitored at 338 nm and fluorometrically with an excitation of 338 nm and an emission of 450 nm. All separations were performed on an Agilent 1100 HPLC equipped with an autosampler, variable wavelength ultraviolet/visible spectrophotometric detector, fluorescence detector, and Chemstation operating system.
The amino donor range for Met regeneration was determined by incubating 2 mM of each individual amino acid and 1 mM KMTB, followed by HPLC for Met quantification. Amino acids which were effective amino donors were further studied at 0.1 – 10 mM amino acid and 10 mM KMTB to determine the kinetic constants. Similar assays were performed with 0.1 – 10 mM KMTB and 10 mM Leu. Replacement of KMTB with ketoglutarate (KG) in these experiments and subsequent HPLC analysis of Glu formation allowed for the determination of BCAT activity. The apparent Km and Vmax values for each substrate were assessed by non-linear curve fitting using the Scientist software programmed with the Michaelis-Menton equation (Micromath; Salt Lake City, UT, USA).
Initial inhibition studies screened 13 aminooxy compounds against M. tuberculosis BCAT using 2.0 mM Leu/1.0 mM KMTB/0.1 or 1.0 mM inhibitor in the enzyme incubation. Inhibitors which demonstrated better than 50% reduction of activity at the 0.1 mM concentration were further studied for the determination of Ki values. These reactions involved 0.5, 1.0, 2.0, or 3.0 mM Leu and 1.0 mM KMTB in the reaction mixture together with 0, 25, 50, 75, 100, 150, 200 μM of inhibitor. The Ki values were determined by non-linear curve fitting with the Scientist software programmed with competitive, uncompetitive, and mixed inhibition equations [25].
In vitro growth inhibition studies were performed on M. tuberculosis and M. marinum using the most effective enzyme inhibitors. Cultures at mid-log growth in Middlebrook 7H9 complete medium was diluted to 2 × 105 cfu/ml and 100 μl added to 96 well microtitre plates containing 100 μl of doubling dilutions of each inhibitor. The final inhibitor concentration ranged from 10 mM – 298 pM. Positive and negative controls consisted of 100 μl Middlebrook 7H9 medium replacing the inhibitor or cells respectively. The plates were incubated at 30°C for 8 days (M. marinum) or 37°C for 14 days (M. tuberculosis) with no agitation before measurement of growth at A650 nm using a Molecular Devices 96-well spectrophotometer (Sunnyvale, CA, USA). The MIC was determined as the lowest dilution that completely prevented microbial growth and the IC50 was determined by non-linear curve fitting with the Scientist software programmed with the Chou equation [44].
Phylogenetic analysis
Additional BCAT and DAAT sequences were obtained from GenBank [38]. Mycobacterium spp. BCAT sequences from preliminary genome projects were made available from The Institute for Genomic Research for M. smegmatis and M. avium, from The Sanger Centre for M. marinum, and from The Institut Pasteur for M. ulcerans. These sequences were aligned using the Clustal algorithm and the BLOSUM sequence substitution table in the ClustalX program [46]. Aligned sequences were viewed using the Bioedit program [47] and were then used with the ProtDist component of the PHYLIP [48] to construct a distance matrix that was the basis for tree construction using the neighbour-joining method [49]. All trees were visualised using Treeview [50].
Authors' contributions
ESV performed the cloning, expression, and characterisation of the enzyme, and assisted in writing the manuscript. CLR assisted in the cloning and expression experiments. MHK performed the M. marinum experiments. BJB conceived the study, performed the M. tuberculosis experiments, and wrote the manuscript.
Acknowledgements
This work was funded in part by a Defence R&D Canada Technology Innovation Fund award. The authors would like to acknowledge the assistance of the University of British Columbia Science Co-op Program.
Preliminary genome data was made available from The Institute for Genomic Research for Mycobacterium smegmatis (funded by NIAID) and Mycobacterium avium (funded by NIAID), The Sanger Centre for Mycobacterium marinum (funded by Beowulf Genomics), and The Institut Pasteur for Mycobacterium ulcerans (funded by the Association Raoul Follereau and WHO).
Figures and Tables
Figure 1 The formation of Met from KMTB. The pathway of polyamine synthesis and subsequent Met regeneration from MTA, as known from K. pneumoniae [11] and B. subtilis [12], is shown. Solid arrows represent steps that have been characterised in M. tuberculosis (present study and [21]). The conversion of KMTB to Met is shown at the top in bold. KIC = ketoisocaproate, KIV = ketoisovalerate, and KMV = ketomethylvalerate.
Figure 2 Relationship of M. tuberculosis BCAT to other family III aminotransferases. In (A) selected subfamily IIIa and IIIb aminotransferases were aligned and a tree constructed by the neighbor-joining method [49] in order to define the subfamily membership of the M. tuberculosis BCAT (in blue). In (B) subfamily IIIa BCATs were aligned and a cladogram constructed by the neighbor-joining method. The numbers represent the bootstrap values (in percentage) for each branch point.
Figure 3 Alignment of selected family III aminotransferases. The following sequences were aligned with the Clustal algorithm: Mt-BCAT, M. tuberculosis BCAT [23]; Mb-BCAT, M. bovis BCAT [51]; Mm-BCAT, M. marinum BCAT; Mu-BCAT, M. ulcerans BCAT; Ml-BCAT, M. leprae BCAT [52]; Ma-BCAT, M. avium BCAT; Ms- BCAT, M. smegmatis BCAT; Sc- BCAT, Streptomyces coelicolor BCAT [53]; Bs-BCAT, Bacillus subtilis BCAT YbgE [43]; Hs-BCAT1, human BCAT1 [54]; Ec-BCAT, Escherichia coli BCAT IlvE [55]; Ba-BCAT, B. anthracis BCAT2 [16]; Bs-DAAT, B. subtilis DAAT YheM [43]). Residues conserved by 80% of the sequences are shown in blue. The boxed residues represent the pyridoxal-5-phosphate binding site.
Figure 4 Purification of recombinant M. tuberculosis BCAT. E. coli BL21(DE3) CodonPlus-RIL cells were induced with IPTG and prepared as described in the Materials and Methods section. The cell lysate was separated by centrifugation into pellet (P) and supernatant (S) fractions. The supernatant was loaded onto an Ni2+-charged metal ion affinity column and flow through (F), 80 mM imidazole (W), and 800 mM imidazole (E) fractions were collected. Aliquots of each fraction were analysed on a 10% polyacrylamide gel under reducing conditions. Lane (M) contains molecular mass markers (units in kDa).
Figure 5 The amino donor range for Met formation. The enzyme was mixed with 1.0 mM KMTB, 2.0 mM of an individual amino acid, and PLP for 30 min at 37°C before HPLC analysis of Met production.
Figure 6 Inhibition of branched-chain aminotransferase by aminooxy compounds. Leucine, KMTB, PLP and 1 mM (black bars) or 0.1 mM (white bars) of inhibitor were incubated with Mt-BCAT as described in the Methods section. Percent activity is shown relative to a positive control which contained no inhibitor.
Table 1 Kinetic characterization of M. tuberculosis branched-chain aminotransferase. The enzyme was incubated with varying concentrations of substrate and 10 mM cosubstrate, as described in the Methods section.
Substrate Cosubstrate Apparent Km (mM) Apparent Vmax (μmol/min/mg protein)
Leu KMTB 2.50 ± 0.90 3.65 ± 0.43
Val KMTB 1.77 ± 0.86 2.58 ± 0.41
Ile KMTB 2.85 ± 0.56 4.28 ± 0.32
Glu KMTB 9.53 ± 3.43 5.70 ± 1.20
Phe KMTB 7.44 ± 1.40 2.17 ± 0.22
KMTB Leu 4.20 ± 1.79 4.22 ± 0.72
Leu KG 6.02 ± 0.94 13.44 ± 0.84
Val KG 5.79 ± 0.99 11.82 ± 0.80
Ile KG 6.16 ± 1.14 14.35 ± 1.08
KG Leu 6.95 ± 1.44 12.80 ± 1.12
Table 2 Ki determination for selected aminooxy inhibitors. The enzyme was incubated with variable amounts of leucine and inhibitor and fixed amounts of KMTB, as described in the Methods section. Kic and Kiu refer to the competitive and uncompetitive components of mixed-type inhibition [25].
Inhibitor Kic(μM) Kiu(μM)
O-(t-butyl)hydroxylamine 11.02 ± 2.76 85.60 ± 44.79
carboxymethoxylamine 20.97 ± 7.27 142.42 ± 69.76
O-allylhydroxylamine 21.61 ± 11.08 >200 (386)*
O-benzylhydroxylamine 8.20 ± 2.56 84.08 ± 31.91
*200 μM was the highest concentration of inhibitor tested in these experiments. The calculated Kiu value is shown in parentheses.
Table 3 In vitro growth inhibition of M tuberculosis and M. marinum by aminooxy compounds. One hundred μL of a mid-logarithmic culture of M. tuberculosis or M. marinum at a concentration of 2 × 105 cfu/ml was added to 100 μL of serial doubling dilutions of inhibitor in a 96-well microtitre plate. The drug plates were grown for 14 days at 37°C (M. tuberculosis) or 8 days at 30°C (M. marinum) with no agitation before checking for cell growth at A650 nm. The minimum inhibitory concentration (MIC) and inhibitory concentration 50% (IC50) were calculated as described in the Methods section.
Inhibitor M. marinum (n = 8) M. tuberculosis (n = 6)
MIC (mM) IC50 (μM) MIC (mM) IC50 (μM)
O-allylhydroxylamine 0.078 8.49 ± 1.96 0.156 39.22 ± 2.01
carboxymethoxylamine 0.313 89.32 ± 7.65 0.313 70.99 ± 7.83
O-benzylhydroxylamine 1.25 410.76 ± 67.10 1.25 467.54 ± 62.80
canaline 1.25 335.29 ± 13.64 1.25 273.18 ± 14.64
O-(t-butyl)hydroxylamine 10 43.18 ± 10.51 10 130.52 ± 23.10
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| 15471546 | PMC524486 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Oct 7; 4:39 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-39 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-401547391110.1186/1471-2180-4-40Research ArticleVariation suggestive of horizontal gene transfer at a lipopolysaccharide (lps) biosynthetic locus in Xanthomonas oryzae pv. oryzae, the bacterial leaf blight pathogen of rice Patil Prabhu B [email protected] Ramesh V [email protected] Centre for Cellular and Molecular Biology (CCMB), Uppal Road, Hyderabad-500007, India2004 9 10 2004 4 40 40 9 7 2004 9 10 2004 Copyright © 2004 Patil and Sonti; 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 animal pathogenic bacteria, horizontal gene transfer events (HGT) have been frequently observed in genomic regions that encode functions involved in biosynthesis of the outer membrane located lipopolysaccharide (LPS). As a result, different strains of the same pathogen can have substantially different lps biosynthetic gene clusters. Since LPS is highly antigenic, the variation at lps loci is attributed to be of advantage in evading the host immune system. Although LPS has been suggested as a potentiator of plant defense responses, interstrain variation at lps biosynthetic gene clusters has not been reported for any plant pathogenic bacterium.
Results
We report here the complete sequence of a 12.2 kb virulence locus of Xanthomonas oryzae pv. oryzae (Xoo) encoding six genes whose products are homologous to functions involved in LPS biosynthesis and transport. All six open reading frames (ORFs) have atypical G+C content and altered codon usage, which are the hallmarks of genomic islands that are acquired by horizontal gene transfer. The lps locus is flanked by highly conserved genes, metB and etfA, respectively encoding cystathionine gamma lyase and electron transport flavoprotein. Interestingly, two different sets of lps genes are present at this locus in the plant pathogens, Xanthomonas campestris pv. campestris (Xcc) and Xanthomonas axonopodis pv. citri (Xac). The genomic island is present in a number of Xoo strains from India and other Asian countries but is not present in two strains, one from India (BXO8) and another from Nepal (Nepal624) as well as the closely related rice pathogen, Xanthomonas oryzae pv. oryzicola (Xoor). TAIL-PCR analysis indicates that sequences related to Xac are present at the lps locus in both BXO8 and Nepal624. The Xoor strain has a hybrid lps gene cluster, with sequences at the metB and etfA ends, being most closely related to sequences from Xac and the tomato pathogen, Pseudomonas syringae pv. tomato respectively.
Conclusion
This is the first report of hypervariation at an lps locus between different strains of a plant pathogenic bacterium. Our results indicate that multiple HGT events have occurred at this locus in the xanthomonad group of plant pathogens.
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Background
LPS is an important constituent of the outer membrane of gram-negative bacteria. Variation in LPS composition can have profound consequences for these cells by potentially providing resistance against bacteriophages and antimicrobial compounds as well as facilitating evasion of the host immune system in animal pathogens. Extreme variation at LPS gene clusters has been reported in animal pathogenic bacteria. Recently, eleven highly divergent gene clusters were reported to occupy an LPSspecific locus in Pseudomonas aeruginosa, an opportunistic human pathogen [1]. The acquisition by horizontal gene transfer of a new LPS biosynthetic gene cluster in Vibrio cholerae is considered as a major cause for the cholera epidemic that originated in India in 1992 [2]. In plant pathogenic bacteria, LPS is an important virulence factor and mutations in the genes involved in LPS production result in severe virulence deficiency [3-8]. LPS has been shown to induce resistance in plants against pathogens [9,10] and in some recent studies, LPS is found to induce expression of plant defense genes [11,12] as well as an oxidative burst reaction in cell cultures [13]. Since LPS recognition appears to be an important aspect of plant defense responses, variation in lps gene repertoire is to be expected within different strains of plant pathogenic bacteria.
The genus Xathomonas includes a number of plant pathogenic bacteria. Two related members of this genus, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoor) cause diseases of rice [14]. They exhibit different tissue specificities with Xoo growing in the xylem vessels while Xoor grows within the intercellular spaces of the parenchymatous tissue. Xoo causes bacterial leaf blight, the most serious bacterial disease of rice. This disease is prevalent in many rice growing countries in Asia, extending from the Indian subcontinent to Japan and Korea. DNA fingerprinting studies using multi-locus RFLP and PCR probes have indicated that there is extensive genetic diversity within Xoo strains isolated from various countries [15-19]. In India, multi-locus RFLP profiling has indicated that one lineage of Xoo (called the BXO1 lineage, based on the type strain for this group) is widely distributed within the country. Strains within the BXO1 lineage cluster together at about the 90 % similarity level in a dendrogram. A second group of strains is quite diverse, both at the haplotypic and pathotypic level, and clusters with the BXO1 group at about the 55% similarity level [19].
In previous research, we have reported a 5.5 kb region in the genome of Xoo strain BXO1 and demonstrated that it encodes three genes that are involved in biosynthesis of LPS and extracellular polysaccharide (EPS) as well as in virulence [8]. All the three genes have atypical G+C content, as compared to the rest of the Xoo genome. In this study, we have completed the entire sequence of this 12.2 kb genomic locus and indicate that it encodes three additional genes, wxoD, wzt and wzm, that are postulated to be involved in LPS biosynthesis and transport. These newly described genes also have atypical G+C content and all the six genes at this locus exhibit altered codon usage pattern, as compared to other Xoo genes. We present evidence that this locus is present in many, but not all, Xoo strains and that it is absent in Xoor. Our results indicate that there is substantial variation at this locus among various xanthomonads. The possible significance of these results is discussed.
Results
Genetic organization of a Xoo lps locus
In an earlier study, a novel Xoo locus was reported to be required for LPS and extracellular polysaccharide (EPS) production as well as virulence. A 35 kb cosmid, pSD5, that complements mutations in this region was isolated [8]. Partial sequence (5.5 kb) of this locus indicated that the region has atypical G+C content and contains three genes which encode a predicted sugar nucleotide epimerase and two predicted glycosyl transferases. We report here the complete 12.2 kb sequence and genomic organization of this locus in Xoo strain BXO1 (Fig. 1). The insert in the pSD5 cosmid includes 7 EcoRI fragments (0.6, 2.2, 3.5, 4.0, 6.0, 9.0 and 10 kb). We subcloned all the fragments into pBlueScript. Based on the end sequences of the inserts in the subclones and pSD5, the lps locus was mapped to four of these EcoRI fragments (0.6, 4, 3.5 and 9 kb). The previously obtained sequence was found to include all of the 3.5 kb and part of the 4 kb fragment and the remaining sequence of this region was obtained by sequencing the 0.6 kb and the 9 kb fragment (Please refer Methods). A total sequence of 13.18 kb was constituted by joining 6.14 kb of previously obtained sequence [8] and 7.04 kb of new sequence. The 13.18 kb sequence includes 12.2 kb of the lps locus and some flanking regions. The additional sequence of the lps locus encodes three putative genes which encode a predicted O-antigen acetylase, a predicted ABC transporter permease and a predicted ATP-binding protein and three insertion sequence (IS) elements.
All of the putative genes have been named as per Bacterial Polysaccharide Genes Nomenclature (BPGN) [20]. The first three genes, wxoA (encodes a predicted epimerase), wxoB and wxoC (both encode predicted glycosyl transferases) have been described earlier. The fourth gene is wxoD and encodes a predicted 327 amino acids long protein. A BLAST [21] search reveals strong homology to acetyltransferases that are involved in LPS modification and the best match is with an acetyltransferase from Mesorhizobium loti (MAFF303099; 34% identity and 46% similarity at amino acid level). Interestingly, no homologs of this gene have been reported in any other xanthomonad. The fifth gene, wzt, encodes a predicted 436 amino acid long protein. A BLAST search reveals homology to functions involved in LPS transport. The best match is with the ATPase component of an ABC-type polysaccharide transport system from Burkholderia fungorum (ZP_00033174.1; 47% identity and 65% similarity at amino acid level). The sixth gene, wzm, encodes a predicted 437 amino acid long protein which is homologous to integral membrane protein components of ABC transporter systems that are involved in LPS transport. The best match is with a permease component of the ABC-type polysaccharide export system from Pseudomonas fluorescens PfO-1 (ZP_00085342.1; 50% identity and 65% similarity at amino acid level). The start codon of wzt overlaps with the stop codon of wzm. Homologs of wzt and wzm are typically present in many lps gene clusters. Interestingly, two complete Insertion Sequence (IS) elements (ISXo8 and IS1113) and one truncated IS element (IS1114) interrupt this cluster between the genes, wxoD and wzt. ISXo8 is a novel 1320 bp long insertion sequence and a BLAST search shows homology to transposase of ISRSO17 encoded by Ralstonia solanacearum (CAD17626; 51% identity and 63% similarity at amino acid level). A complete copy of the IS1113 element (AF482989) and a truncated copy of the IS1114 element (AF232058) are also present as indicated in Fig. 1. The presence of IS elements is a marked feature of many lps loci [22]. Transcriptional orientation suggests the possibility that ORFs wxoA, wxoB, wxoC and wxoD might constitute one operon and that ORFs wzm and wzt might be transcribed together. The overlap between the start codon of wzt and the stop codon of wzm also suggests that these two genes are co-transcribed.
The lps locus is flanked by metB, which encodes a predicted cystathionine gamma lyase, and etfA which encodes a predicted electron transport flavoprotein. The genome sequences of Xanthomonas campestris pv. campestris (Xcc; infects crucifer plants like cabbage, cauliflower, mustard, etc.) and Xanthomonas axonopodis pv. citri (Xac; infects citrus plants) have been obtained [23]. The Xoo metB gene (a partial sequence of 642 bp is available) exhibits within the sequenced region, 91% and 88% nucleotide identity to metB genes of Xac (AE012010.1) and Xcc (AE012157.1), respectively. The Xoo etfA gene (a partial sequence of 328 bp is available) exhibits within the sequenced region, 93% and 91% nucleotide sequence identity, respectively, with etfA genes in Xac (AE012009.1) and Xcc (AE012159.1). Interestingly, the lps biosynthetic gene cluster of Xcc, which comprises fifteen genes, is also located between the metB and etfA genes [24]. In Xac, this gene cluster is missing at this locus and is replaced by a set of fourteen genes, several of which are homologous to functions involved in LPS synthesis and transport. The gene clusters present at this locus in Xcc, Xac and Xoo have distinct nucleotide sequences, gene numbers (15 genes in Xcc, 14 genes in Xac, 6 genes in Xoo) and gene organization.
The Xoo lps cluster is a genomic island
a) Atypical G+C content
The average G+C content of Xoo and other Xanthomonads is estimated to be around 65% [25], while the average G+C content of the lps locus is 50.46% (excluding the IS elements) [Fig. 1]. The variation is much more marked among the genes, from as low as 45.0% (wxoD) to 56.3% (wxoC). Atypical G+C content is a characteristic feature of "genomic islands" that are believed to be acquired by horizontal gene transfer. The transposase genes encoded by ISXo8 and IS1113 have a G+C content that is >61%, a value which is typical for the genomes of Xoo and other xanthomonads. The G+C content of metB and etfA genes that flank the genomic island have G+C content of 64.3% and 61% respectively (within the partial sequences that have been obtained) which is typical of the Xoo genome.
b) Altered codon usage
An additional hallmark of a genomic island is the altered codon usage. Here we present a simple and graphical way of calculating and representing the codon usage differences and refer to it as Codon Usage Pattern or CUP (Please refer Methods). Eight aminoacids, i.e., Glycine, Valine, Threonine, Leucine, Arginine, Serine, Proline and Alanine, were selected to study CUP because they have atleast four synonymous codons. The percentage of synonymous codons that end with G or C was calculated for each aminoacid and gene. This analysis was conducted for six genes of the lps island and six genes from elsewhere in the Xoo genome (please refer Methods). We show that CUP of the genes present in the genomic island is dramatically different from the typical Xoo genes (Fig. 2). The %G+C at third codon position of synonymous codons for amino acid Glycine is only 52.5 % for genes present in the lps locus, while it is 78 % in case of Xoo genes that are located elsewhere in the genome. Similarly, for amino acids Valine, Alanine, Threonine, Serine, Arginine, Leucine and Proline the values are 46.6, 47, 59, 52, 53, 57 and 34.6 % respectively for genes at the lps locus, while the values are 84, 77.5, 89.5, 79.5, 75.6, 90.3 and 86.16 % for the respective aminoacids in case of the typical Xoo genes. Altered codon usage is a characteristic feature of horizontally acquired genes and CUP clearly indicates that the Xoo lps cluster is a genomic island (Fig. 2).
The lps locus is present in the genomes of many, but not all, Xoo strains
The presence of the genomic island in different Xoo strains was assessed by PCR using gene specific primers, for all the six lps genes, as described in the Methods. The list of strains used in the study is given in the Table 1 and the list of gene specific primers is given in Table 2. In order to confirm that the genomic island is present at the same genomic location in all strains, PCR was also performed using two primer pairs that are designed to amplify fragments from metB to wxoA and wzm to etfA, respectively. The analysis included nine Indian Xoo strains representing different geographic locations and the BXO1 and non BXO1 groups. The list also includes twelve Xoo strains from different Asian countries and a Xoor strain, BXOR1, from India. Our study revealed that the genomic island is present in the majority (7/8) of Xoo strains that we have examined from India (Fig. 1). Four BXO1 group strains (BXO4, BXO7, BXO13 and BXO479) and three of the non-BXO1 strains (BXO5, BXO6 and BXO20) have the genomic island. The genomic island is also present in two strains each from China, Malaysia, Indonesia, Philippines, Korea and one strain from Nepal (Fig. 1, Table 1). The lps locus is present, in all these strains, between the metB and etfA genes. Interestingly, we find that the genomic island is not present (as judged by PCR [Fig. 3A] and Southern hybridisation [Fig. 3B]; see Methods) in the genomes of Xoo strains BXO8 and Nepal624, as well as the Xoor strain, BXORI. The results obtained with the probes directed against the wxoA gene are presented but similar results were obtained using probes that are specific for the other five genes. The blots used above were reprobed as a positive control with a metB specific probe and the results gave an expected size band in BXO1 (lane 1), and different sized bands in BXO8 (lane 3), Nepal624 (lane 4) and BXOR1 (lane 2) indicating that the metB gene is present but located in different EcoRI fragments (Fig. 3C).
BXO8 and Nepal 624 have sequences related to Xac at the lps locus
What are the sequences present at this genomic location in the Xoo strains that lack the lps locus? Thermal Asymmetric Interlaced (TAIL) PCR is an efficient technique for isolation of target DNA segments adjacent to known sequences [26]. TAIL-PCR and sequencing using primers directed against the conserved flanking metB and etfA genes suggests that sequences which are significantly similar to the Xac lps gene cluster are present at this genomic location in both of these strains. Next to metB, a wzm homolog is present in BXO8 (a partial sequence of 398 bp is available) and Xac with 69.2% identity at nucleotide level within the sequenced region. Next to etfA, a putative integral membrane protein encoding gene is present in both BXO8 (a partial sequence of 405 bp is available) and Xac with 91.3% identity at nucleotide level within the sequenced region. The BXO8 and Nepal624 strains exhibit 100% nucleotide sequence identity within the sequenced region. TAIL- PCR analysis of the Xoor strain indicates that it has a hybrid lps gene cluster. Next to metB, a unique wzm gene is located (a partial sequence of 548 bp is available) which exhibits 62.8% nucleotide identity to wzm gene of Pseudomonas syringae pv. tomato strain DC3000 (AE016859.1). Next to etfA, a putative inner membrane protein encoding gene is located (a partial sequence of 402 bp is available) which exhibits 97% and 92% nucleotide sequence identity, respectively, with similarly located genes in BXO8 and Xac. Because the BXO8 and Nepal624 strains have different sequences at the lps locus, as compared to other Xoo strains, we inoculated these strains along with appropriate controls onto leaves of the susceptible rice cultivar Taichung Native-1. We find that BXO8 and Nepal624 strains are able to cause typical bacterial leaf blight disease symptoms that are indistinguishable from those elicited by other Xoo strains (data are not shown).
Presence of inverse repeats at the 3' ends of metB and etfA genes that flank the lps locus
We have performed an alignment using BLAST2 [27] of the nucleotide sequences derived from the metB and etfA genes in BXO1 and BXO8. The homology breakpoints appear to localise to the 3' regions of metB and etfA genes, exactly 18 bp upstream of their respective stop codons. Upto the break points, within the sequenced region at either end of the lps locus, the nucleotide sequence is identical in BXO1, BXO8 and Nepal624. The DNA sequence immediately preceding the break points was examined manually for presence of direct or inverse repeats. Interestingly, we could find three inverted repeats (I, II and III) within the 3' regions of metB and etfA near the homology breakpoints between BXO1 and BXO8 (Fig. 4). The first repeat is the smallest one (5 bp) and the third repeat is the largest (11 bp). The second repeat is 6 bp long and is 7 bp from the first repeat on the metB side and 9 bp from the first repeat on the etfA side. The distance between the second and third repeats is 4 bp in metB and etfA. We also found similarly located inverse repeats in the metB and etfA genes of Xac, Xcc and Xoor. A consensus sequence of the repeats was derived (Fig. 4) by scoring a nucleotide if it is present in a majority of repeats.
Relationship between BXO8 and Nepal 624 strains
The TAIL PCR results indicate that the BXO8 and Nepal624 strains have identical sequences in place of the BXO1 lps locus. As both the strains are from the Indian subcontinent, there is the possibility that these are identical/nearly identical to each other. We therefore performed DNA fingerprinting analysis of the BXO8 and Nepal624 strains using the IS1112 insertion element as a probe. This probe is highly informative and can clearly differentiate the BXO1 and non BXO1 group of strains in India [19]. The following strains were also included in the analysis: BXO1, three non BXO1 group strains (BXO5, BXO6, BXO20) and BXORI. The hybridisation pattern revealed that BXO8 and Nepal624 are quite distinct from each other (Fig. 5). We could score 42 unique bands and the data generated were used to calculate pairwise similarity coefficients and cluster analysis was performed to generate a dendrogram using UPGMA (please refer Methods). The similarity coefficient between BXO8 and Nepal624 is only 56%. The dendrogram (Fig. 6) indicates that BXO8 clusters with BXO#s 5, 6 and 20 at about the 58% similarity level while Nepal624 clusters with all these four strains at about the 53% similarity level. All of the Xoo strains cluster with each other at about the 51% similarity level. Although the bootstrap values for these clusters are low, it is clear that the BXO8 and Nepal624 strains are not closely related to each other. As expected for an outgroup strain, BXOR1 clusters with Xoo strains at the 29% similarity level and the bootstrap value for this cluster is a high 96.8%.
Discussion
We report here the complete sequence and genomic organization of the lps locus in the BXO1 strain of Xoo. Three of the genes in this locus i.e., wxoA, wxoB and wxoC were shown in an earlier study to be required for lipopolysaccharide production and virulence [8]. The predicted proteins encoded by the three new genes i.e., wxoD, wzt and wzm described in the present study are homologous to functions involved in lipopolysaccharide modification and transport. The wxoD gene encodes a predicted O-antigen acetylase which is homologous to similar functions encoded in phage genomes and other bacteria. O-antigen is the most variable part of LPS. Acetylation of O-antigen is shown to confer resistance to anitimicrobial peptides in Proteus mirabilis [28] and determines serotype in many bacterial pathogens [29-31]. The other two genes, wzm and wzt, are typically present in most lps gene clusters [including those of Xac and Xcc][23] as tandem genes and encode functions involved in LPS transport. The wzm and wzt genes of BXO1 have overlapping ORFs, an arrangement that is also seen in wzm and wzt genes of the lps loci in other bacteria including Xac. IS elements are frequently found interrupting many lps loci [22] and in BXO1, three IS elements interrupt the gene cluster between wxoD and wzt genes.
The complete genome sequences of more than 150 bacteria are now available [32] and studies have revealed the presence of DNA segments with G+C content and codon usage different from the rest of the genome. These regions are referred to as genomic islands and are believed to be acquired by horizontal gene transfer [33,34]. Another feature of genomic islands is their absence from the genomes of closely related strains. Our study clearly indicates that the lps locus of Xoo strain BXO1 fulfils all of the above criterion and constitutes a genomic island. The G+C content of this lps locus, excluding the IS elements, is 50%. The transposases encoded by ISXo8 and IS1113 have a G+C content that is >61%. This value, which is typical for the genomes of Xoo and other xanthomonads [25], suggests the possibility that these elements have transposed into the lps locus after it's transfer into the Xoo genome. The presence of this genomic island in Xoo strains that are distributed across a vast segment of the Asian continent suggests that it was introduced into the Xoo genome early in the evolution of this pathogen.
The BXO8 and Nepal624 strains do not have the lps locus that is present in the other Xoo strains. The related xanthomonad, Xoor, also has an lps locus that is different from the BXO1 lps locus. Also, different gene clusters are present at this locus in Xac and Xcc (Fig. 7). This indicates that multiple HGT events have occurred at this locus among xanthomonads. One HGT event occurred early in (or possibly at the time of) the evolution of the Xoo pathogen. This led to the introduction of the genomic island described in Figure 1. Two separate HGT events are likely to have occurred in the lineages that gave rise to BXO8 and Nepal624 Xoo strains. This is inferred from the observation that BXO8 and Nepal624 are quite unrelated in their genomic background. Another HGT can be inferred to have occurred in the Xoor strain wherein sequences that are most closely related to Pseudomonas syringae pv. tomato have been introduced at one end of the lps cluster. At least one more HGT has occurred to differentiate the lps gene clusters in Xcc and Xac.
The presence of invert repeats in the regions that flank the lps locus is likely to be significant. The presence of these repeats in the metB and etfA genes is especially striking as both genes encode completely different functions. The location of the repeats flanking the Xoo lps locus suggests that they might be involved in promoting recombination during HGT and/or gene regulation. A short inverted repeat sequence (GGCCAATCGA) flanking the lipopolysaccharide gene cluster has been reported in Mycobacterium avium subsp. paratuberculosis [35]. Another conserved sequence, called JUMPstart has been found located in intergenic regions upstream of polysaccharide biosynthetic gene clusters in several animal pathogenic bacteria like Escherichia coli strain K5, Vibrio cholera, etc. This sequence was implicated to be involved in gene regulation and has also been suggested to have a role in recombination [22,36].
As LPS is highly immunogenic, lps loci of animal pathogenic bacteria are under intense host selection and extreme variation is reported in lps specific gene clusters [22]. The observation that the two Xoo strains have different lps gene clusters suggests that the plant pathogenic bacteria are also under selection to vary their LPS. Alterations in LPS composition might result in resistance against predators like bacteriophages [4,10] or reduced susceptibility to certain anti-microbial compounds [7] in the host/environment. Most importantly, it might help in evasion of the host defense response.
Conclusions
These results provide, for the first time, evidence for substantial variation in lps biosynthetic gene clusters within different strains of a plant pathogenic bacterium. The results also indicate that multiple HGT events have occurred at this locus in various xanthomonads and provide a new parallel in the mechanisms that plant and animal pathogenic bacteria can employ to generate variability in cell surface molecules.
Methods
Complete sequencing of the lps locus in the BXO1 strain of Xoo
The lps locus was cloned as part of a 35 kb cosmid clone, pSD5. The insert includes 0.6, 2.2, 3.5, 4.0, 6.0, 9.0 and 10 kb fragments upon EcoRI (New England Biolabs [NEB], Beverly, MA) digestion and all the fragments were subcloned in to pBlueScript (Stratagene, La Jolla, CA). Most of the sequence obtained in this study was generated by sequencing the 9 kb subclone, pBP4, using a modified shotgun sequencing procedure. Here, pBP4 was digested with EcoRI and the 9 kb fragment was gel eluted. Then the fragment was partially digested (1.5–2.5 kb) using a blunt-end cutter, HaeIII (NEB) and cloned into pMOS (Amersham Pharmacia Biotech, Buckinghamshire, England). The inserts were amplified from random clones by colony PCR using vector primers and were sequenced using an ABI Prism 3700 automated DNA sequencer (Applied Biosystems, Foster City, CA). After editing, the assembly of the sequence data was done using GeneTools (BioTools, Alberta, Canada) and Blast2 [27]. Multiple single strand sequences (3–8 X coverage) were generated for each region in the sequence. Contig assembly was confirmed by restriction fragment analysis of a 12.5 kb PCR amplified product containing the lps locus that was obtained using long range PCR (Triple Master™, Eppendorf, Hamburg, Germany) with BXO1 genomic DNA as template. The sizes of the fragments corresponded to the sizes that are predicted by in silico analysis of the sequence (data are not shown). The ORF's were assigned using ORF finder [37] and genes were named as per Bacterial Polysaccharide Genes Nomenclature [20]. Two primers, Pbp1 and Pbp2 (Table 2), were used to derive the sequence of the 0.6 kb EcoRI fragment which is also a part of the lps locus. The Pbp1 primer binds just after the wxoC ORF (which forms part of the 3.5 kb EcoRI fragment) and Pbp2 binds within the wxoD ORF (which forms part of the 9.0 kb EcoRI fragment). A 0.67 kb PCR amplified fragment is obtained from BXO1 genomic DNA using Pbp1 and Pbp2. The band was gel eluted and was sequenced using Pbp1 and Pbp2. The sequence was found to include the 0.6 kb EcoRI fragment. In addition, the sequences of all six ORFs were confirmed by sequencing of PCR amplified fragments from genomic DNA using specific sets of gene specific primers (see the list of primers in Table 2).
Codon Usage Pattern
For each gene the frequency of codon usage for different aminoacids was calculated using a web based program [38]. Further, eight aminoacids i.e., Glycine, Valine, Threonine, Leucine, Arginine, Serine, Proline and Alanine that have atleast four synonymous codons were selected and the percentage of synonymous codons that end with G or C was calculated for each aminoacid and gene. The pattern was calculated for a group of genes by plotting mean values ± SD corresponding to a particular aminoacid. The first group was chosen to include genes that encode proteins which participate in diverse functions and are present at different locations in the Xoo genome outside the lps locus. These genes encode: a putative siderophore receptor (AF325732), Xanthomonas adhesin like protein (AF288222), a putative phytase (AY151260), rpfF (AF411962), shikimate dehydrogenase (AF258797) and secreted xylanase (AF331922). The second group comprised the six genes (excluding transposases) encoded in the Xoo lps gene cluster (AF337647).
Screening of Xoo strains and Xoor for the presence of the genomic island
Specific oligonucleotide primer pairs were designed and used to amplify gene specific fragments for each of the ORFs encoded in the BXO1 genomic island (see the list of primers given in Table 2). DNA sequencing was used to confirm the authenticity of the PCR product obtained with each primer pair using BXO1 genomic DNA as template. Southern hybridizations were performed using these gene specific PCR products as probes. Genomic DNA was isolated from Xoo and Xoor strains according to the procedure described by Leach et. al. [16]. The DNA was then digested with EcoRI (NEB) according to supplier's instructions. Digested genomic DNA was separated on a 0.8% agarose gel and vacuum transferred to a Hybond N+ filter (Amersham) using 0.4% NaOH as described by Sambrook et al. [39]. Probes were labelled with α-32P dATP using random primer labelling kit (Board of Radiation Technology, Mumbai, India). Prehybridization, hybridisation and washings were done at 68°C as described by Yashitola et al [19]. Membranes were then exposed to phoshoimager plates and images captured using a Fuji FLA-3000 phosphoimager system (Fuji, Japan).
To screen for the presence of the genomic island in different strains, a procedure for colony PCR was standardized. A portion of a single colony (or 10λ of a saturated culture that has approximately 1 × 109 colony forming units/ml) was lysed in 100λ of 0.01 N NaOH by boiling for 10 minutes. After spinning at 13 K for 1 min., 2λ of supernatant was used as template for PCR using the gene specific primers described above. The products were separated by electrophoresis on 1.5% agarose gels and visualized by ethidium bromide staining.
TAIL-PCR and sequence analysis
Specific primers were designed against the conserved metB and etfA gene sequences (Table 2) and the protocol for TAIL-PCR was as originally described by Liu and Whittier [26]. Sequencing of TAIL-PCR products was done using either the cglL3 or etfL3 primer. Homology searches were done using BLAST [21] through NCBI [40] and FASTA [41] through EMBL-EBI [42]. BLAST2 [27] was used to identify the homology break points in the genomic regions that flank the lps locus of BXO1 and BXO8. The sequences that were present upstream of the break points were manually examined and three repeat sequences were identified in the 3' coding regions of metB and etfA genes. Similar repeat sequences were identified in the corresponding regions of BXO8, Nepal624, BXORI, Xac and Xcc. A consensus was derived by aligning these repeat sequences and a particular nucleotide was scored if it is present in a majority of repeats.
DNA fingerprinting and data analysis
The Xoo IS element, IS1112 [16], was used as the hybridisation probe. This probe has been previously used to detect genetic variability in Xoo strains from different countries [15-19]. DNA isolation and Southern hybridisation was done as described in the section on screening of Xoo and Xoor strains for the presence of genomic island. The presence or absence of particular bands was scored as 1 or 0, respectively. The data were analysed using the Dice coefficient option in the program WINDIST [43] to generate distance matrices. The data were used to construct a dendrogram using the NEIGHBOR program in PHYLIP (phylogeny inference software package; University of Washington, Seattle) using the UPGMA (unweighted pair group method of averages) option. To test the robustness of the dendrogram, bootstrap analysis was carried out using the WinBoot program [43] with 2,000 iterations.
GenBank submissions
The nucleotide sequences obtained in this study have been deposited in GenBank with the following Accession numbers: Sequence of lps locus from BXO1 (AF337647); Sequences of TAIL-PCR products from metB end of BXO8 (AY319936), Nepal624 (AY319938) and BXORI (AY319940); Sequences of TAIL-PCR products from etfA end of BXO8 (AY319937), Nepal624 (AY319939) and BXORI (AY319941).
Authors' contributions
PBP carried out all the aspects of the work and drafted the manuscript. RVS conceived the study, and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
We thank Jan Leach and Marietta Ryba-White for providing Xoo strains. PBP was supported by a Senior Research Fellowship from the University Grants Commission (UGC), Government of India and currently has a Senior Research Fellowship from the Council of Scientific and Industrial Research. Meher Sultana and N. Nagesh are thanked for their help in oligosynthesis and sequencing.
Figures and Tables
Figure 1 Genetic organization of a locus encoding LPS biosynthetic genes in Xoo strain BXO1. a. Overall G+C content of the locus and the flanking regions. The G+C content of the genomic island was calculated without including the sequences of IS elements. The overall G+C content of the genome is ~65%. b. Organization and G+C content of individual genes and transposases of IS elements. IS1114 encodes a truncated ORF. Arrows indicate transcriptional orientation. c. and d. Presence (+) and absence (-) of genes/PCR products in particular strains. § Indicates PCR products obtained using primer pairs directed against either metB and wxoA or etfA and wzm. # similar results were obtained with all Xoo strains tested excepting BXO8 and Nepal 624. * similar results were obtained with the Nepal 624 strain.
Figure 2 Genes encoded in the lps locus exhibit altered Codon Usage Pattern (CUP). Eight amino acids, each of which has atleast four synonymous codons, are represented on the X-axis. The % of codons ending with G/C for each of these amino acids is represented on the Y-axis as mean ± SD. The lower line represents CUP for eight aminoacids of the six genes (excluding transposase ORFs) encoded in the lps locus. The upper line represents CUP of six Xoo genes from elsewhere in the genome (Please refer Methods).
Figure 3 The lps locus is absent from the genomes of Xoo strains BXO8, Nepal624 and Xoor strain BXORI. (A) PCR analysis using primers that are specific to wxoA gene. M is the λ HindIII Marker lane. An expected band of 1 kb (indicated by arrow) is present in the Xoo strains, BXO1 (lane 1), BXO5 (lane 3), BXO6 (lane 4) and BXO20 (lane 6) but absent in BXO8 (lane 5), Nepal624 (lane 7) and BXORI (lane 2). (B) Southern hybridization analysis of EcoRI digested genomic DNA using α-32-P labeled wxoA specific probe (see Methods). A 4 kb band can be seen (indicated by arrow) in BXO1 (lane 1) but not in BXORI (lane 2), BXO8 (lane 3) and Nepal624 (lane 4). Similar results were obtained for wxoB, wxoC, wxoD, wzm and wzt genes. (C) The blot from (B) was deprobed and was hybridized with α-32P labeled probe specific to the metB gene. A specific band can be seen in all the lanes. Note the sizes of the bands indicating that metB is present in different EcoRI fragments in BXO1, BXORI and BXO8/Nepal624.
Figure 4 Invert repeat sequences flanking the lps gene cluster in the BXO1 strain of Xoo. The horizontal arrows represent the ORFs of metB and etfA. I, II, III represent three different invert repeats in the 3' regions of metB and etfA genes. The vertical arrows represent the breakpoints of homology between BXO1 and BXO8. The distances of the break points from the stop codons of the metB and etfA genes are indicated. Dashed lines indicate the remainder of the metB and etfA genes. The sequence of the corresponding inverse repeats in Xac, Xcc and Xoor are also indicated along with the derived consensus sequence for the repeats. The numbers in brackets indicate distances in bp between individual repeats.
Figure 5 Restriction fragment length polymorphism analysis of Xoo strains. Southern analysis of EcoRI-digested genomic DNA was performed using α-32P labeled IS1112 as a probe (see Methods). Lanes: 1; BXO1, 2; BXO5, 3; BXO6, 4; BXO8, 5; BXO20, 6; Nepal624, 7; Xoor strain BXORI. M; indicates the size of molecular weight markers in kb.
Figure 6 Cluster analysis of Xoo strains. The dendrogram was constructed using the UPGMA option of PHYLIP on the basis of restriction fragment length polymorphism data obtained with IS1112 probe. Numbers or symbols at the internal branches indicate bootstrap values for clusters. The BXORI (Xoor) strain constitutes the outgroup.
Figure 7 Variation in lps gene clusters within the xanthomonads. The genes that are adjacent to metB and etfA in different xanthomonads are indicated. Dashed lines represent the remainder of the lps cluster. Empty and filled boxes represent sequences specific to Xoo and Xcc respectively. Boxes with dots indicate that the sequences are either from or related to Xac genes. Box with stripes represents sequences that are related to Pseudomonas syringae pv. tomato. Arrows indicate transcriptional orientation. The wzm gene encodes a predicted ABC transporter permease protein, wxoA encodes a predicted epimerase, wxcA encodes a glycosyl transferase and wxcH encodes a hypothetical protein.
Table 1 List of strains used in the study
Xanthomonas oryzae pv. oryzae (Xoo) strains used in the present study
From India
Strain Name Location Source
1) BXO1 Chinsuria, West Bengal Lab collection
2) BXO4 Kapurthala, Punjab "
3) BXO5 Ferozpur, Punjab "
4) BXO6 Patiala, Punjab "
5) BXO7 Titabar, Assam "
6) BXO8 Nellore, Andhra Pradesh "
7) BXO13 Marutheru, Andhra Pradesh "
8) BXO20 Pantnagar, Uttar Pradesh "
9) BXO479 Nawgam, Gujarat "
From other countries – (gift from Dr. Jan Leach)
1) China Xoo NX2
2) China Xoo #B21
3) Korea Xoo #197
4) Korea Xoo #220
5) Nepal Xoo #537
6) Nepal Xoo #624
7) Malaysia Xoo #90
8) Malaysia Xoo #101
9) Indonesia Xoo #16
10)Indonesia Xoo #40
11)Philippines PXO86
12)Philippines PXO99A9
Xanthomonas oryzae pv. oryzicola (Xoor) strain used in the present study
1) BXOR1 Rajendranagar, Andhra Pradesh lab collection
Table 2 Plasmids and primers used in this study
Plasmids Relevant characteristics Reference or source
pUFR034 IncW Nmr Mob+ mob (P) lacZ alpha Par+ cos (8.7 kb) [44]
Pbluescript Apr Stratagene
pSD5 pUFR034 + a 35 kb insert from the BXO1 genome [8]
pBP1 pBluescript + a 2.2 kb EcoRI fragment from pSR1 This study
pBP2 pBluescript + a 3.5 kb EcoRI fragment from pSR1 This study
pBP3 pBluescript + a 4.0 kb EcoRI fragment from pSR1 This study
pBP4 pBluescript + a 9.0 kb EcoRI fragment from pSR1 This study
pBP5 pBluescript + a 10 kb EcoRI fragment from pSR1 This study
Primers specific to ORFs present in the genomic island of BXO1
WxoA Forward primer CCAAGCGACCAGAGGTGCTTGACG
Reverse primer GAGGAGCACCATCCGCTACCGCCC
WxoB Forward primer GTTTTTGTTGGTACTGGGTGCGAG
Reverse primer GTACGCCACGGTCAGATCGGCTGC
WxoC Forward primer CTACTGATGTTGTTCGCAAGGTGG
Reverse primer GGCGACTCACCTGCATATCGAGCC
WxoD Forward primer GTGCTGGTGAGCCATCATTTTG
Reverse primer TTACTCACCGGCCATAATCCTTTT
Wzt Forward primer GACATCGCTATCGAAGTAAAAGGT
Reverse primer TCAGGTGCTGTTTGAAGTAGCGGAC
Wzm Forward primer CATCGGCAAACCCCTTTCGGGT
Reverse primer CGCAGCTTACTGATGGAACCCT
Primers used for TAIL PCR
designed against metB gene sequence of BXO1
CglL1 CTTCGACGCAGCCAAGCGTTTC
CglL2 CTGCGAGAAGACCGAGCTGTTCAC
CglL3 CGAATCGCTCGGTGGTGTTGAA
designed against etfA gene sequence of BXO1
EtfL1 TCGGCCAGACCGGCAAGATCAT
EtfL2 AGCTGTACATGGCCATCGGCAT
EtfL3 AGCATCTGACCGGCATCAAGGA
Other primers described in the paper
Pbp1 AGCGTGCTGGTGAGCCATCA
Pbp2 GCAGCAAAAATGCTGTCATAACCA
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| 15473911 | PMC524487 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Oct 9; 4:40 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-40 | oa_comm |
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BMC Blood DisordBMC Blood Disorders1471-2326BioMed Central London 1471-2326-4-41549150210.1186/1471-2326-4-4Case ReportEffect of splenectomy on type-1/type-2 cytokine gene expression in a patient with adult idiopathic thrombocytopenic purpura (ITP) Panitsas Fotios P [email protected] Athanasia [email protected] Hematology & Transfusion Medicine, Medical School, University of Patras, Patras, Greece2004 18 10 2004 4 4 4 23 6 2004 18 10 2004 Copyright © 2004 Panitsas and Mouzaki; licensee BioMed Central Ltd.2004Panitsas and Mouzaki; 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 view of clinical observations and laboratory results that support a central role of the spleen in idiopathic thrombocytopenic purpura (ITP) pathophysiology, we studied the effect of splenectomy on type-1 and type-2 cytokine gene expression in an adult ITP case, refractory to conservative treatment.
Case presentation
The patient was subjected to splenectomy 9 months after the diagnosis with complete response, attaining platelet counts over 150 × 106/L within 10 days after the operation. Two consecutive blood samples were obtained from the patient, 3 and 7 months after the splenectomy for the purposes of this study. A control group consisted of 11 healthy adults. Peripheral blood mononuclear cells were prepared from each blood sample and cultured in vitro for 8 h with the addition of the mitogens phorbol myristate acetate and ionomycin. Total cellular RNA extracted from 106 cells was submitted to semiquantitave reverse transcriptase-polymerase chain reaction (RT-PCR) for the amplification of IL-2, IFN-γ, IL-4, IL-5, and IL-10 metagraphs. The PCR products were run on ethidium-stained agarose gels, photographed and quantified by densitometry.
A steep decrease of type-1 cytokine expression (IL-2, IFN-γ) and their calculated sum expressing Th1 activity was observed at 7 months post-splenectomy compared to 3 months post-splenectomy, in parallel with a rise of platelet count from 190 × 106/L to 265 × 106/L. The change of type-2 cytokine expression (IL-4, IL-5, IL-10) was slight and the Th2 activity (IL-4+IL-5) remained largely unchanged. The Th1/Th2 ratio, that reflects the pathogenic disease-specific T-cell immune deviation, was accordingly reduced 7 months post-splenectomy (Th1/Th2 = 1.3) compared to 3 months (Th1/Th2 = 3.5).
Conclusions
The reduction of the Th1/Th2 cytokine ratio that was observed over time after splenectomy was accompanied by full clinical remission. Nevertheless, the persistence of a type-1 polarization, even after several months following spleen removal, is suggestive of a more basic abnormality of the immune function in these patients.
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Background
Adult autoimmune thrombocytopenic purpura (ITP) is a chronic acquired organ-specific autoimmune thrombocytopenic syndrome [1]. The low peripheral platelet concentration observed in ITP is the result of reduced platelet life span because of their early removal from the peripheral blood by the activated reticuloendothelial system of the spleen, liver or bone marrow, after their sensitization by autoantibodies that recognize their surface glycoprotein antigens [2]. Apart from phagocytosis, destruction mechanisms include complement activation [3] and direct cellular attack by T lymphocytes [4]. Ineffective thrombocytopoiesis because of autoimmune attack of megakaryocytes in the bone marrow contributes to the thrombocytopenia with varying degrees among cases [2]. The production of platelet autoantibodies by B-cells is driven by activated platelet-specific autoreactive T-cells [5]. The phenotype of the disease-specific T helper cells has been shown to be skewed towards type 1 cytokine production [6-8].
The spleen is considered to be the primary site of the autoimmune response where initiation, maintenance and regulation of the autoimmune attack take place. The spleen is the site of autoreactive T- and B-cell interaction and activation, and autoreactive anti-platelet antibody production [9]. Platelet destruction is also sited mainly in the spleen in most patients [2,10,11].
Splenectomy is followed by reduction of autoantibody peripheral blood titre [12,13]. Spleen cells isolated from ITP patients produce antiplatelet immunoglobulin in in vitro cultures [14]. The percentage of T- and B-cells with activated phenotype is far greater in the spleen than in the peripheral blood of ITP patients [9]. The number of circulating autoreactive anti-platelet T- and B-cells declines after splenectomy that leads to clinical remission, whereas the peripheral blood concentration of CD3+CD4+, CD3+CD8+, CD3+HLADR+, and CD3+CD25+ cells increases significantly in ITP patients refractory to splenectomy [15]. Changes in the histology of the spleen have been observed in ITP patients and include follicular hyperplasia, foam macrophages, and extramedullary hematopoiesis, among others [16].
Splenectomy is the most clinically effective therapeutic intervention in ITP patients, resulting in complete remission in two thirds of the patients with more than 60% maintaining the therapeutic effect in the long term [17,18]. Irrespective of clinical response, splenectomy seems to affect the natural history of the disease and to enhance the response of ITP patients to other treatments that follow [19].
Given the important immunoregulatory role of the spleen, we looked at the effects of splenectomy on immune activation and immune deviation indices in the peripheral blood of an ITP patient after splenectomy in association with peripheral platelet counts.
Case presentation
A 42 year old woman presented in October 1999 in Patras University Hospital (PUH) with lower limb purpura and low platelet count (7 × 106/L). Following clinical exclusion of causes of secondary thrombocytopenia [20] the diagnosis of ITP was reached.
The patient initially received glucocorticoid treatment to which she showed a temporary response until 6 months later when she relapsed. She was subsequently started on danazol without any clinical benefit. Intravenous immune globulin administration also proved ineffective after two 5-day cycles. As a result, the patient was subjected to splenectomy 9 months after the diagnosis with complete response, attaining platelet counts over 150 × 106/L within 10 days after the operation. Five years later, she remains in clinical remission.
Two consecutive blood samples were obtained from the patient, 3 and 7 months after splenectomy for the purposes of this study. A control group consisted of 11 adult healthy volunteers (6 women and 5 men, median age 40 years, range 18–65 years). Informed consent was obtained from the patient. PUH abides by the Helsinki declaration on ethical principles for medical research involving human subjects.
Peripheral blood mononuclear cells (PBMC) were prepared from each blood sample by centrifugation over a Ficoll-Paque gradient (Pharmacia, Sweden). The cells were cultured in vitro for 8 h with the addition of 20 ng/ml phorbol myristate acetate (PMA) and 1 μM ionomycin (Sigma, St-Louis, MI).
Total cellular RNA extracted from 106 cells was submitted to semiquantitative RT-PCR for the amplification of IL-2, IFN-γ, IL-4, IL-5, and IL-10 metagraphs [8]. Primers and conditions for the RT-PCR are summarized in Table 1. The PCR products were run on ethidium-stained agarose gels, photographed and quantified [8].
Table 1 Primers and conditions for the RT-PCR experiments performed in this study.
Gene Sequence (5'→3') T (°C) Product (bp)
IL-2 GCAACTCCTGTCTTGCATTG
AATGTGAGCATCCTGGTGAG 59 173
IFN-γ AGCTCTGCATCGTTTTGGGTTC
CAAATATTGCAGGCAGGACAACC 64 492
IL-4 CTGTGCTCCGGCAGTTCTAC
ACGTACTCTGGTTGGCTTCC 58 176
IL-5 GCTTCTGCATTTGAGTTTGCTAGCT
TGGCCGTCAATGTATTTCTTTATTAAG 59 291
IL-10 ACCCAGTCTGAGAACAGCTGC
GTTCACATGCGCCTTGATGTCT 61 260
β2m CCCCCACTGAAAAAGATGAG
TCACTCAATCCAAATGCGGC 56 150
A sharp decrease in the expression of the type-1 cytokines IL-2 and IFN-γ and their calculated sum expressing Th1 activity was observed at 7 months after splenectomy compared to 3 months after splenectomy (Figure 1); this was accompanied by a parallel rise of platelet count from 190 × 106/L to 265 × 106/L. Regarding type-2 cytokine gene expression, IL-4 increased, IL-5 decreased, and IL-10 remained unchanged, whereas the change in Th2 activity (IL-4 units plus IL-5 units) was slight (Figure 1). The Th1/Th2 ratio {(IL-2+IFNγ)/(IL-4+IL-5)}, that reflects immune deviation, was accordingly greatly reduced 7 months post-splenectomy (Th1/Th2 = 1.3) compared to 3 months (Th1/Th2 = 3.5) (Figure 2). Mean Th1/Th2 ratio of the controls was 0.5 with 95% confidence intervals of the mean (0.15–0.85). The Th1/Th2 values at 3 months and at 7 months post-splenectomy lie at 6.25 and 1.6 standard deviations above the mean of the controls, respectively.
Figure 1 Gene expression levels of individual cytokines and of calculated Th1 and Th2 activities at 3 and 7 months after splenectomy. Th1 equals with IL-2 units plus IFN-γ units and Th2 equals with IL-4 units plus IL-5 units.
Figure 2 Th1 activity (IL-2+IFNγ) versus Th2 activity (IL-4+IL-5) scatter gram. Asterisks denote the patient's case values at 3 and at 7 months after splenectomy. Diamonds denote the control values. Solid line is the regression line for the controls (r2 = 0.55, p = 0.014) and the two broken lines show the 95% confidence intervals.
The above results show that in this patient type-1 polarization persists after removal of the spleen and attainment of clinical remission. This may mean that the spleen is not exclusively responsible for the coordination or the maintenance of the pathological immune response in this patient, provided that no accessory splenic tissue exists. Other disease centres may control the autoimmune reaction as well, such as the liver or the bone marrow. Alternatively, it is possible that ITP is the manifestation of a general immune system malfunction that pre-existed before the development of thrombocytopenia and persists after removal of what seems to be the effector of a manifestation of an autoimmune proclivity.
Unfortunately, a pre-splenectomy sample was not available for analysis. As a result, no conclusions can be drawn about the effect of splenectomy on the direction of change of Th1 activity. Based on phenotypic studies showing increased presence of T lymphocytes with activated phenotype after splenectomy in ITP patients [15], it is plausible that peripheral Th1 activity may have increased after splenectomy.
The clinical remission may be due to the removal of a major platelet destruction site, although the underlying immune activity that drives the destruction may remain unaffected. Complete remission does not mean that increased platelet destruction has stopped after splenectomy. Platelet life span may still be shortened in this patient and/or her normal platelet count may be even higher than what was achieved after splenectomy.
The pathological immune activity seems to decrease over time after splenectomy, as reflected by the lower Th1/Th2 ratio that is indicative of the degree of immune polarization. This may be explained by reduced stimulation of the immune system by activated spleen reticuloendothelial cells that present platelet antigens to T helper lymphocytes. In this way, it may be hypothesized that removal of one vital component of the self-attacking immune process can break the vicious circle that culminates in even greater immune activation, polarization, and platelet destruction. Removal of a major site of autoimmune activity may have abrogated recruitment of naïve T-cells. As a result, overall autoimmune activity wears off, as existing activated Th1 effector cells perish leaving behind a much smaller population of peripheral memory cells that retain the initial Th phenotype.
Another consideration that stems from the results of this case study is that immune polarization and immune deviation of the pathological response depend more on upregulation of type-1 mediators rather than on suppression of type-2 cytokines, or that type-2 response is inadequate to control excess type-1 response in active disease.
Conclusions
Clinical improvement after splenectomy is associated with reduced but not normalized immune activation and polarization in the patient studied. However, the spleen seems not to be absolutely necessary for the maintenance of the autoimmune reactivity.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
FPP prepared the case report, performed the experiments and drafted the manuscript. AM conceived of the study, participated in its design and coordination and co-wrote the manuscript. Both authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Dr. Alexandra Kouraklis, PUH, for her help in this study.
Written consent was obtained from the patient or their relative for publication of the patient's details.
This work was supported in part by a grant from Novartis (Basel, Switzerland).
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| 15491502 | PMC524488 | CC BY | 2021-01-04 16:31:41 | no | BMC Blood Disord. 2004 Oct 18; 4:4 | utf-8 | BMC Blood Disord | 2,004 | 10.1186/1471-2326-4-4 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-391546182210.1186/1471-2334-4-39Research ArticleVaccine candidates derived from a novel infectious cDNA clone of an American genotype dengue virus type 2 Blaney Joseph E [email protected] Christopher T [email protected] Kathryn A [email protected] Brian R [email protected] Stephen S [email protected] Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, 20892 USA2004 4 10 2004 4 39 39 30 7 2004 4 10 2004 Copyright © 2004 Blaney 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
A dengue virus type 2 (DEN-2 Tonga/74) isolated from a 1974 epidemic was characterized by mild illness and belongs to the American genotype of DEN-2 viruses. To prepare a vaccine candidate, a previously described 30 nucleotide deletion (Δ30) in the 3' untranslated region of DEN-4 has been engineered into the DEN-2 isolate.
Methods
A full-length cDNA clone was generated from the DEN-2 virus and used to produce recombinant DEN-2 (rDEN-2) and rDEN2Δ30. Viruses were evaluated for replication in SCID mice transplanted with human hepatoma cells (SCID-HuH-7 mice), in mosquitoes, and in rhesus monkeys. Neutralizing antibody induction and protective efficacy were also assessed in rhesus monkeys.
Results
The rDEN2Δ30 virus was ten-fold reduced in replication in SCID-HuH-7 mice when compared to the parent virus. The rDEN-2 viruses were not infectious for Aedes mosquitoes, but both readily infected Toxorynchites mosquitoes. In rhesus monkeys, rDEN2Δ30 appeared to be slightly attenuated when compared to the parent virus as measured by duration and peak of viremia and neutralizing antibody induction. A derivative of rDEN2Δ30, designated rDEN2Δ30-4995, was generated by incorporation of a point mutation previously identified in the NS3 gene of DEN-4 and was found to be more attenuated than rDEN2Δ30 in SCID-HuH-7 mice.
Conclusions
The rDEN2Δ30 and rDEN2Δ30-4995 viruses can be considered for evaluation in humans and for inclusion in a tetravalent dengue vaccine.
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Background
The increased prevalence of disease caused by the mosquito-borne dengue (DEN) viruses (four serotypes; DEN-1 – DEN-4) has intensified the effort to generate a vaccine that would both confer protection and be economically feasible for use in countries with limited resources for healthcare [1]. Dengue fever and dengue hemorrhagic fever and shock (DHF/DSS) are a severe disease burden for tropical and semitropical countries inhabited by more than 2.5 billion people [2]. Risk factors for the more severe disease, DHF/DSS, include the strain of virus, age and genetic background of the host, and secondary infection by a DEN serotype different from that which caused the primary infection [2]. Increased risk associated with secondary infection by a different DEN serotype is believed to be caused both by increased virus replication resulting from antibody-dependent enhancement and by augmented immune activation induced by the secondary infection [3,4]. Typically, regions with DHF/DSS have all four DEN serotypes circulating simultaneously, and an effective DEN vaccine must contain a tetravalent formulation that confers protection against each of the four DEN serotypes.
Immunity to the DEN viruses is primarily mediated by neutralizing antibodies directed against the envelope (E) glycoprotein, and most vaccine strategies aim to induce antibody against this major protective antigen. Live attenuated tetravalent vaccines appear to be the best vaccine candidates since they are economical to manufacture and they induce long-term immunity with the live attenuated yellow fever virus vaccine serving as a successful model flavivirus vaccine [5]. Several strategies to produce live attenuated tetravalent vaccines are being pursued including attenuation of viruses by conventional passage in tissue culture or introduction of defined attenuating mutations into recombinant DEN viruses [6-9]. In addition, chimeric dengue viruses are being evaluated that contain the E protein of a DEN virus on a background of either an attenuated DEN virus from a different serotype or a more distantly related, but attenuated, flavivirus [10-12].
We have previously described attenuated and immunogenic monovalent vaccine candidates for DEN-1, DEN-2, DEN-3, and DEN-4 that were generated by two distinct recombinant methodologies. Using the first methodology, nucleotides 10478–10507 were deleted from the 3' UTR (Δ30) of a wild type cDNA clone for DEN-4 to generate a vaccine candidate, rDEN4Δ30, which is safe, attenuated, and immunogenic in rhesus monkeys and humans [13]. Incorporation of the Δ30 mutation into an infectious cDNA clone of DEN-1 wild type virus at a site homologous to that in DEN-4 attenuated DEN-1 for rhesus monkeys and is currently being evaluated in humans [14]. The Δ30 mutation did not confer attenuation upon DEN-3 for reasons that have not been defined [15]. Thus, this approach has yielded live attenuated virus vaccine candidates for both DEN-1 and DEN-4. Using a second methodology, antigenic chimeric viruses have been generated by replacing the membrane protein (M) and E structural genes of rDEN4Δ30 with those from DEN-2 or DEN-3 [12,15]. These antigenic chimeric viruses were attenuated and immunogenic in rhesus monkeys and represent vaccine candidates for DEN-2 and DEN-3. We have also described a set of point mutations that can attenuate wild type rDEN-4 for SCID mice transplanted with human liver cells (SCID-HuH-7) or for rhesus monkeys [16,17]. Such mutations identified in rDEN-4 could be introduced into conserved sites of cDNA clones for other DEN serotypes to fine-tune the level of attenuation of vaccine candidates.
We have found it prudent to pursue several strategies to develop a live attenuated virus vaccine for each dengue serotype recognizing that it has been a challenge to achieve a satisfactory balance between attenuation and immunogenicity [15,18-20]. Thus, in addition to the antigenic chimeric DEN-2 vaccine candidate described above, a second approach was pursued in the present study that involved the construction of an infectious cDNA clone of a wild type DEN-2 virus isolated in Tonga [21], and the generation of DEN-2 vaccine candidates by the sequential introduction of defined attenuating mutations into the recombinant version of the DEN-2 Tonga/74 wild type virus. The rDEN2Δ30 vaccine candidate was evaluated for replication in SCID-HuH-7 mice, mosquitoes, and rhesus monkeys. In addition, an attenuating point mutation, previously described in DEN-4, was introduced into the rDEN2Δ30 virus, and this rDEN2Δ30 derivative was characterized in SCID-HuH-7 mice.
Methods
Cells and viruses
Vero cells (African green monkey kidney) were propagated in OptiPro SFM (Invitrogen, Grand Island, NY) supplemented with 4 mM L-glutamine (Invitrogen). HuH-7 cells (human hepatoma) were maintained in D-MEM/F-12 (Invitrogen) supplemented with 10% fetal bovine serum (FBS), 1 mM L-glutamine and 0.05 mg/ml gentamicin (Invitrogen). C6/36 cells (Aedes albopictus mosquito cells) were maintained at 32°C in Minimal Essential Medium (MEM) containing Earle's salts and 25 mM HEPES buffer (Invitrogen) and supplemented with 10% FBS, 2 mM L-glutamine, and 0.1 mM non-essential amino acids (Invitrogen).
A dengue virus type 2 isolate, Tonga/74, was provided by Dr. Duane Gubler (CDC, Fort Collins, CO). The virus was isolated during a 1974 dengue outbreak in the South Pacific island of Tonga [21]. The virus was isolated by inoculation of patient sera into Aedes albopictus mosquitoes, and subsequent passage in C6/36 cells before determination of genomic sequence.
Sequence analysis
Viral RNA was isolated from DEN-2 Tonga/74 wild type virus using the QIAamp Viral RNA mini kit (Qiagen, Valencia, CA). Reverse transcription was performed using random hexamer primers and the SuperScript First-Strand Synthesis System for RT-PCR (Invitrogen). Overlapping PCR fragments of approximately 2000 base pairs were generated using DEN-2 specific primers and Advantage cDNA polymerase (ClonTech, Palo Alto, CA). Both strands of the resulting PCR fragments were sequenced directly on a 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA) using DEN-2 specific primers in BigDye terminator cycle sequencing reactions (Applied Biosystems) and the results were assembled into a consensus sequence. To determine the nucleotide sequence of the genomic 5' and 3' regions, the 5' cap nucleoside of the viral genome was removed with tobacco acid pyrophosphatase (Epicentre Technologies, Madison, WI), followed by circularization of the genome using RNA ligase (Epicentre Technologies). An RT-PCR fragment spanning the ligation junction was generated and sequenced using DEN-2 primers. For the DEN-2 Tonga/74 consensus sequence, GenBank accession number AY744147 was assigned.
Genetic construction of rDEN-2 Tonga/74 cDNA clone
cDNA fragments of DEN-2 Tonga/74 were generated by reverse-transcription of the genome as indicated in Figure 1. Each fragment was subcloned into a plasmid vector and sequenced to verify that it matched the consensus sequence as determined for the virus. This yielded seven cloned cDNA fragments spanning the genome. Cloned fragments were modified as follows: Fragment X, representing the 5' end of the genome was abutted to the SP6 promoter; Fragment L was modified to contain a SpeI restriction site at genomic nucleotide 2353; Fragment R was modified to contain a SpeI restriction site also at genomic nucleotide 2353, and, to stabilize the eventual full-length clone, two additional mutations at nucleotides 2362 – 2364 and 2397 were created to ensure that translation stop codons were present in all reading frames other than that used to synthesize the virus polyprotein; Fragment A was modified at nucleotide 3582 to ablate a naturally occurring SpeI restriction site and at nucleotide 4497 to ablate a naturally occurring KpnI restriction site; Fragment C was modified at nucleotide 9374 to ablate a naturally occurring KpnI restriction site; and Fragment Y, representing the 3' end of the genome was abutted to a KpnI restriction site. All mutations introduced into the cloned cDNA fragments were translationally-silent, thereby preserving the wild-type polyprotein sequence. Each fragment was added incrementally between the AscI and KpnI restriction sites of DEN-4 cDNA clone p4 (GenBank accession number: AY648301) to generate a full-length DEN-2 cDNA clone (p2) with the same vector background successfully used to generate rDEN-4 and rDEN4Δ30 virus [13]. cDNA clone p2 was sequenced to confirm that the virus genome region matched the DEN-2 Tonga/74 consensus amino acid and nucleotide sequence, with the exception of the translationally-silent modifications noted above. The Δ30 mutation which removes nucleotides 10541–10570 was introduced into Fragment Y to generate Fragment YΔ30. To create p2Δ30, the Fragment Y region of p2 was replaced with Fragment YΔ30 (Figure 1). The genomic region of each full-length cDNA was sequenced as described above and GenBank accessions were assigned as follows (cDNA clone: accession numbers): p2: AY744148, p2Δ30: AY744149.
Using site-directed mutagenesis, an attenuating amino acid change characterized in the NS3 gene of DEN-4 (nt 4995–7; a.a. 158, Ser→Leu) was introduced into the p2Δ30 cDNA clone [17]. A mutagenic oligonucleotide was designed to change DEN-2 NS3 amino acid 158 from Ser (AGT) to Leu (CTA) and used to construct the cDNA clone, p2Δ30-4995 (accession number: AY744150), which was sequenced for confirmation of nucleotide changes.
Recovery of rDEN-2 viruses
cDNA clones were linearized with Acc65I (isoschizomer of KpnI which cleaves leaving only a single 3' nucleotide) and were transcribed in vitro using the AmpliCap SP6 Message Maker kit (Epicentre Technologies, Madison, WI). Purified transcripts were then transfected into Vero or C6/36 cells. Viruses recovered in C6/36 cells were passaged 3 times in Vero cells, and all viruses were biologically cloned by terminal dilution in Vero cells. The genomes of recombinant viruses used to infect rhesus monkeys were completely sequenced as described above to identify adventitious mutations that had accumulated during transfection and biological cloning.
Replication in SCID-HuH-7 mice
Four to six week-old SCID mice (Tac:Icr:Ha(ICR)-Prkdcscid) (Taconic, Germantown, NY) were injected intraperitoneally with 107 HuH-7 cells suspended in 0.2 ml phosphate-buffered saline. Tumors were detected in the peritoneum, and mice were infected by direct inoculation of the tumor with 104 PFU of virus in 0.05 ml Opti-MEM (Invitrogen). On day 7 post-infection, serum was obtained from cardiac blood and stored at -70°C. Virus titer in serum samples was determined by plaque assay in Vero cells.
Replication, immunogenicity, and protection in rhesus monkeys
The DEN-2 viruses were evaluated in rhesus macaques using established methods [13]. DEN virus sero-negative monkeys were injected subcutaneously with 105 PFU virus diluted in L-15 medium (Invitrogen) or with a mock inoculum. Serum was collected on days 0–6, 8, 10, 12 and 28 after inoculation and stored at -70°C. Virus titer was determined for each serum sample by plaque assay in Vero cells and serum neutralizing antibody titer was determined for serum from days 0 and 28 by plaque reduction neutralization test. On day 28, monkeys were challenged with 105 PFU of DEN-2 Tonga/74, and serum was collected on days 29–34, 36, and 56. Virus titer was determined for serum from days 28–34 and 36 and serum neutralizing antibody titer was determined for serum from day 56.
Virus replication in mosquitoes
Replication in Aedes aegypti and Toxorynchites amboinensis mosquitoes was evaluated as previously described [22]. Briefly, A. aegypti were fed blood meals containing serial 10-fold dilutions of virus. After 21 days, viral antigen was detected in head and midgut preparations by immunoflourescence assay using DEN-2-specific hyperimmune mouse ascitic fluid and fluorescein isothyocyanate conjugated goat anti-mouse IgG (KPL, Gaithersburg, MD), and the mosquito infectious dose-50% (MID50) was determined. T. amboinensis were inoculated intrathoracically with a 0.2 ul dose containing serial ten-fold dilutions of virus and incubated for 14 days. Head preparations were made and antigen visualized as described above.
Results
Generation and sequence analysis of recombinant DEN-2 Tonga/74 viruses
A full-length cDNA clone, p2, was constructed that matched the genomic consensus sequence of the American genotype DEN-2 isolate, Tonga/74, with the exception of translationally-silent modifications made to facilitate cloning (Figure 1). The previously described Δ30 deletion mutation was incorporated into the p2 cDNA clone to form p2Δ30 [13]. The rDEN-2 virus was recovered in C6/36 and Vero cells, but the presence of the Δ30 mutation limited recovery to only C6/36 cells. After passage in Vero cells, adaptation mutations were identified by sequence analysis as had been described for other DEN viruses [23]. Both rDEN-2 and rDEN2Δ30 viruses accumulated a single nucleotide change in NS4B at nt 7169 encoding a Val→Ala change at amino acid position 115 as has been observed for rDEN-3 (Table 1) [15]. The same nucleotide change was previously reported to occur at the homologous site following passage of DEN-4 in Vero cells resulting in a Leu→Ser change (Table 1) [23]. Inclusion of the 7169 mutation into the p2Δ30 cDNA permitted recovery in both C6/36 and Vero cells (data not shown). The rDEN2Δ30 virus reached a virus titer of 6.6 log10PFU/ml in Vero cells.
Replication of rDEN-2 viruses in SCID-HuH-7 mice
As an initial evaluation of replication of the DEN-2 Tonga/74 virus and the rDEN-2 viruses, replication in SCID mice transplanted with HuH-7 human hepatoma cells (SCID-HuH-7 mice) was tested. Wild-type viruses from each DEN serotype have been shown to replicate to approximately 6.0 log10PFU/ml serum in SCID-HuH-7 mice, and an att phenotype in SCID-HuH-7 mice has been shown to be a predictor of reduced replication in rhesus monkeys [12,14,15,17]. The parent DEN-2 Tonga/74 virus replicated efficiently in SCID-HuH-7 mice and reached a mean titer in serum of 5.9 log10PFU/ml (Table 2) similar to that previously observed with the DEN-2 New Guinea C (NGC) prototype strain [12]. The rDEN-2 virus replicated to the same level as the wild-type isolate, while rDEN2Δ30 was 10-fold restricted in replication. This reduction was statistically significant (Tukey-Kramer post-hoc test; P < 0.05), and was similar to that observed for the well-characterized rDEN4Δ30 virus [17].
Replication of rDEN-2 viruses in mosquitoes
The DEN-2 viruses were evaluated for infectivity of Aedes aegypti fed on an infectious bloodmeal (oral infectivity only) and for Toxorynchites amboinensis inoculated intrathoracically (Table 3). At the doses tested neither DEN-2, rDEN-2, or rDEN2Δ30 were detected in the midgut or head of A. aegypti mosquitoes which had fed on an infectious bloodmeal 21 days earlier. The inability to infect the midgut led to a lack of infection in the head tissue. This indicates that the DEN-2 Tonga/74 viruses are poorly infectious for A. aegypti mosquitoes by oral infectivity, as has been demonstrated for multiple DEN-2 viruses of the American genotype [24,25]. In contrast the DEN-2 NGC prototype strain, an Asian genotype member, was highly infectious in A. aegypti mosquitoes when tested previously but it was not included here as a concurrent control [12].
The defect in rDEN-2 infectivity for A. aegypti was further investigated by directly inoculating the same virus stocks intrathoracically into T. amboinensis and measuring the ability of the viruses to infect the head tissues. Both rDEN-2 and rDEN2Δ30 were highly infectious by intrathoracic inoculation (Table 3). The Δ30 mutation did not alter the infectivity of rDEN-2 following intrathoracic inoculation, a property also previously observed for DEN-1, -3 and -4 [14,15,22]. These results indicate that the lack of infectivity for A. aegypti was likely caused by the inability of the DEN-2 Tonga/74 viruses to establish a midgut infection and that the viruses retained the ability to infect head tissues.
Replication, immunogenicity, and protective efficacy in rhesus monkeys
The replication (viremia), immunogenicity, and protective efficacy of the DEN-2 viruses in monkeys were studied. Monkeys inoculated with the DEN-2 Tonga/74 wild-type isolate were viremic for an average of 4.5 days with a mean peak titer of 2.1 log10PFU/ml (Table 4). Inoculation with rDEN-2 resulted in detectable viremia for 4.0 days with a mean peak titer of 1.9 log10PFU/ml. While the levels of rDEN2Δ30 replication (2.8 days viremia; mean peak titer of 1.7 log10PFU/ml) were lower than DEN-2 and rDEN-2, the differences were not as dramatic as had been observed for rDEN1Δ30 and rDEN4Δ30 when compared to their parent viruses [13,14]. The level of neutralizing antibodies induced by the rDEN2Δ30 virus was also less than that induced by the wild-type DEN-2 viruses, a finding consistent with the decreased replication exhibited by this vaccine candidate. Therefore, by three quantitative measures, duration and peak titer of viremia and the level of neutralizing antibodies induced, rDEN2Δ30 appeared to be attenuated when compared to DEN-2 Tonga/74. When vaccinated monkeys were challenged with DEN-2 Tonga/74, all monkeys were protected, as indicated by the lack of viremia (Table 4).
The 4995 mutation further attenuates rDEN2Δ30 in SCID-HuH-7 mice
Based on the limited attenuation conferred upon rDEN-2 by the Δ30 mutation in rhesus monkeys, we sought to construct a further attenuated derivative of rDEN2Δ30. To further attenuate rDEN2Δ30, an att mutation that has been characterized in another DEN serotype was imported into a homologous region in DEN-2. One such mutation, the 4995 mutation in DEN-4 NS3 at amino acid 158 (Ser→Leu), was previously incorporated into the DEN-4 vaccine candidate, rDEN4Δ30, and found to further attenuate the virus for SCID-HuH-7 mice and rhesus monkeys [17]. Site directed mutagenesis was used to introduce a Ser→Leu mutation at amino acid 158 of NS3 in rDEN2Δ30-7169, and the rDEN2Δ30-4995 virus was recovered in C6/36 cells and propagated in Vero cells reaching a virus titer of 6.2 log10PFU/ml (Table 1). Importantly, the resulting Leu codon would require two nucleotide changes to revert to one of the six odons encoding a Ser residue.
Replication in the SCID-HuH-7 mouse model was used as an initial assessment of the rDEN2Δ30-4995 virus phenotype. Table 5 includes results from three separate experiments (including those from Table 1) and confirms the approximate 10-fold reduction in replication conferred by the Δ30 mutation upon rDEN-2 replication in SCID-HuH-7 mice. The rDEN2Δ30-4995 virus had a mean peak virus titer of 4.6 log10PFU/ml which was only a modest reduction from that of rDEN2Δ30, 5.2 log10PFU/ml. However, comparison of a large number of samples indicated that the reduction in virus titer conferred by the NS3 4995 mutation upon rDEN2Δ30 was statistically significant (rDEN2Δ30-4995 versus rDEN2Δ30; Tukey-Kramer post-hoc test; P < 0.05). The virus titer of rDEN2Δ30-4995 virus in SCID-HuH-7 mice was over 60-fold reduced from that of the rDEN-2 parent virus.
Discussion
Development of a live-attenuated tetravalent dengue vaccine has been complicated by two major factors. First, monovalent vaccine candidates that exhibit a satisfactory balance between attenuation and immunogenicity have been difficult to identify [15,18-20]. Second, satisfactorily attenuated tetravalent vaccine formulations that induce a broad neutralizing antibody response against each of the four DEN serotypes have been difficult to develop [6,10,20,26]. For these reasons, we have sought to develop multiple vaccine candidates for each DEN serotype to increase the likelihood that a vaccine with a satisfactory balance between attenuation and immunogenicity will be identified. To produce a live-attenuated DEN-2 vaccine candidate, we previously generated an antigenic chimeric virus, rDEN2/4Δ30, expressing the M and E structural genes of the DEN-2 NGC strain on the attenuated rDEN4Δ30 background [12]. The vaccine candidates described in the present study, rDEN2Δ30 and rDEN2Δ30-4995, could serve as alternates to this antigenic chimeric virus if evaluation of the rDEN2/4Δ30 virus in humans, either as a monovalent vaccine or as a component of a tetravalent vaccine, indicates that it lacks a balance between attenuation and immunogenicity.
It was hoped that each of the four components of a tetravalent vaccine, consisting of DEN-1, -2, -3, and -4 wild type viruses, each with the common 30 nucleotide deletion mutation in the 3' UTR, would exhibit a similar level of attenuation in animal models [13-15]. Unfortunately, the level of attenuation conferred by the Δ30 mutation upon each of the four serotypes has proven to be variable. In rhesus monkeys, the rDEN2Δ30 virus appears to have an intermediate attenuation phenotype in between that of the attenuated rDEN1Δ30 and rDEN4Δ30 and the non-attenuated rDEN3Δ30 [13-15]. Although rDEN2Δ30 was slightly attenuated compared to its DEN-2 parent virus in rhesus monkeys, the reduction in replication was less than that of rDEN1Δ30 and rDEN4Δ30. While the latter two viruses had detectable viremia in only 50% of monkeys, a mean number of viremic days of less than one day, and a mean peak viremia of less than 1.0 log10PFU/ml [13,14], the rDEN2Δ30 virus infected 100% of the rhesus monkeys and reached a peak virus titer of 1.7 log10PFU/ml. However, the 10-fold reduction of replication of rDEN4Δ30 and rDEN2Δ30 in SCID-HuH-7 mice, compared to that of their respective wild type parents, was similar. To date, rDEN4Δ30 is the only Δ30 vaccine candidate that has been tested in humans, and it was found to be both safe and immunogenic [13]. If the level of attenuation in SCID-HuH-7 mice serves as a better guide to attenuation in humans, rDEN2Δ30 might be satisfactorily attenuated in humans since its level of attenuation for SCID-HuH-7 mice and that of the rDEN4Δ30 vaccine candidate are comparable.
To construct a further attenuated derivative of rDEN2Δ30, the 4995 mutation present in the NS3 gene of DEN-4 at amino acid 158 (Ser→Leu) was introduced into the homologous region of the NS3 protein of rDEN2Δ30 [17]. Although the 4995 mutation results in a single amino acid change and thus may be susceptible to reversion, the mutant leucine codon selected for insertion into rDEN2Δ30-4995 would require two nucleotide changes to revert to a serine codon. Introduction of the 4995 mutation into rDEN4Δ30 resulted in a 100-fold greater reduction of replication in SCID-HuH-7 mice [17]. In rDEN2Δ30, its introduction resulted in nearly a 10-fold reduction in virus titer, a smaller but still statistically significant reduction. These results provide a second example of the difficulty in predicting the precise level of attenuation following import of an attenuating mutation into a different DEN serotype. Nevertheless, the rDEN2Δ30-4995 vaccine candidate is more attenuated than its rDEN2Δ30 parent and warrants evaluation in rhesus monkeys and humans.
Epidemiologic and molecular pathogenesis studies of DEN-2 strains support the concept that the DEN-2 Tonga/74 virus, from which the vaccine candidates were derived, may naturally have a lower level of virulence than other DEN-2 viruses. If the DEN-2 Tonga/74 parent virus is naturally attenuated to some degree, only a small incremental increase in attenuation might be required to satisfactorily attenuate it for humans. Gubler et al. investigated the 1974 outbreak of DEN-2 infection in the Pacific island of Tonga [21]. In comparison to a subsequent DEN-1 outbreak, the 1974 DEN-2 outbreak was distinguished by mild disease with few hemorrhagic sequelae, low viremia, and an overall slow spread of virus infection [21]. The weak DEN-2 outbreak was proposed to be a result of the circulation of a strain with an inherently low level of virulence [21].
Since the Tonga/74 outbreak, additional evidence has emerged that supports the suggestion that there are at least two circulating lineages of DEN-2 viruses that differ in virulence [27-29]. The DEN-2 Tonga/74 virus is a member of the DEN-2 American genotype, which as a group appear to possess lower virulence than that of the Asian genotype of DEN-2 viruses [28,29]. Despite the presence of the American DEN-2 genotype viruses and limited co-circulation of DEN-1 and DEN-3 viruses in the Americas in the 1960s and 1970s, the first major epidemic of DHF/DSS in the Americas occurred only after the introduction of a DEN-2 Asian genotype virus in 1981 [27-30]. It was thought that genetic differences might have contributed to this difference in virulence and evidence to this effect has been forthcoming. Rico-Hesse and colleagues have defined genetic elements within the genome of DEN-2 American genotype viruses which distinguish them from members of the Asian genotype [31]. In addition, using chimeric rDEN-2 American/Asian viruses, introduction of three genetic elements (a point mutation in the E gene, the 5' UTR, and the 3' UTR) of the American genotype was found to confer reduced virus replication in dendritic cells and monocytes upon an Asian genotype rDEN-2 [32]. The Tonga/74 virus shares each of these three attenuating genetic determinants specific to the American genotype [31,32], which provides a possible explanation for its lower virulence in humans. The rDEN2Δ30 vaccine candidate, whose parent is the DEN-2 Tonga/74 American genotype virus, thus contains naturally occurring and experimentally introduced attenuating mutations. Thus, the small incremental increase in attenuation provided by the Δ30, with or without the 4995 mutation, might prove to satisfactorily attenuate the DEN-2 Tonga/74 for humans.
The American genotype DEN-2 viruses exhibit decreased infectivity for Aedes mosquitoes in comparison to Asian DEN-2 viruses [24,25]. Consistent with these observations, the wild type New Guinea C Asian DEN2 virus was highly infectious for Aedes mosquitoes in our laboratory [12] whereas the Tonga/74 American genotype virus was poorly infectious by the oral route (present study). In fact, the increased prevalence of DEN-2 viruses of the Asian genotype in the Americas has been suggested to be a result of their enhanced transmission [28]. However, the active circulation of American genotype viruses over many decades indicates that mosquito transmission does occur and large epidemics have been associated with viruses of this genotype [21,27]. At the doses tested, neither the DEN-2 Tonga/74 isolate nor the recombinant viruses were found to infect the midgut or head of Aedes aegypti mosquitoes fed an infectious blood meal. Since rDEN-2 and rDEN2Δ30 viruses were infectious by intrathoracic inoculation of Toxorynchites mosquitoes, the lack of infectivity for A. aegypti was likely caused solely by the inability of the DEN-2 Tonga/74 viruses to establish a midgut infection. Decreased infectivity for Aedes mosquitoes could serve to help limit transmission of the vaccine virus.
Conclusions
The live-attenuated DEN-2 virus candidates described here, rDEN2Δ30 and rDEN2Δ30-4995, have several properties desired in a live attenuated virus vaccine for humans. First, both viruses reached a titer over 6.0 log10PFU/ml in Vero cells that would permit economical manufacture. Second, the viruses are derived from the DEN-2 Tonga/74 strain, a member of the American genotype, which has been associated with decreased virulence. Third, rDEN2Δ30 was attenuated for replication in SCID-HuH-7 mice and slightly attenuated for rhesus monkeys while inducing a protective neutralizing antibody response. Fourth, rDEN2Δ30-4995 was more attenuated in SCID-HuH-7 mice than rDEN2Δ30. Fifth, the DEN-2 Tonga/74 strain, like other members of the American genotype, is poorly infectious for Aedes aegypti mosquitoes which would help to limit uncontrolled transmission of the vaccine virus.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
J.B. recovered viruses, conducted animal studies, and drafted the manuscript. C.H. and S.W. constructed the DEN-2 cDNA clone and C.H. performed sequencing. K.H. performed mosquito studies. B.M. and S.W. supervised the study and participated in planning and design. All authors read and approved the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Molecular construction of the DEN-2 full-length cDNA plasmids p2 and p2Δ30. A. Diagram of the complete full-length DEN-2 Tonga/74 cDNA plasmid p2 is shown annotated with the restriction enzyme and corresponding cleavage site locations used to assemble the subcloned RT-PCR fragments. Restriction enzyme cleavage sites are numbered relative to nucleotide position in the virus genome. The corresponding genomic regions encoded by each subcloned RT-PCR fragment, X, L, R, A, B, C, and Y, are shown above the plasmid diagram. Relative positions of the SP6 promoter and tetracycline resistance gene (Tetr) are indicated. B. To generate plasmid p2Δ30, 30 nucleotides are removed from the 3'-UTR (Fragment Y). The nucleotide sequence encompassing the Δ30 region is shown for the p2 parent cDNA and the resulting p2Δ30 cDNA. Nucleotide positions in the virus genome are indicated for p2.
Table 1 Missense and UTR mutations which arose spontaneously in rDEN-2 and rDEN2Δ30 viruses during passage in Vero cells.
Virus Gene Nucleotide position Nucleotide substitution Amino acid positiona Amino acid change
rDEN-2 NS4B 7169b U > C 115 Val > Ala
NS5 9248 A > C 560 Glu > Ala
rDEN2Δ30 NS4B 7169b U > C 115 Val > Ala
rDEN2Δ30-4995 NS3 4949 A > G 143 Lys > Arg
NS4B 7169b U > C 115 Val > Ala
3' UTR 10322 G > A -- --
a Amino acid position in the individual DEN-2 protein.
b Nucleotide position 7169 in DEN-2 corresponds to nt 7162 in rDEN-4, and the wild-type amino acid is Val and Leu, respectively [23].
Table 2 Addition of the Δ30 mutation to rDEN-2 decreases replication in SCID-HuH-7 mice by 10-fold.
Replication in SCID-HuH-7 micea
Virus No. of mice Mean virus titer (log10PFU/ml ± SE)
DEN-2 Tonga/74 6 5.9 ± 0.3
rDEN-2 7 5.9 ± 0.2
rDEN2Δ30 9 4.9 ± 0.2
a Groups of HuH-7-SCID mice were inoculated directly into the tumor with 104 PFU virus. Serum was collected on day 7 and tittered in Vero cells.
Table 3 DEN-2 Tonga/74 is poorly infectious for Aedes aegypti fed an infectious bloodmeal, and the Δ30 mutation does not decrease infectivity for Toxorynchites amboinensis.
A. aegypti (oral infection) T. amboinensis (intrathoracic inoculation)
Virus Dose (log10PFU)a No. tested % Infectedb Dose (log10PFU)c No. tested % Infectedd
Midgut Head
DEN-2 Tonga/74 2.8 20 0 0 -- -- --
rDEN-2 3.5 18 0 0 2.9 5 100
1.9 12 83
0.9 8 63
-0.1 7 14
-1.1 7 0
MID50 = 0.9
rDEN2Δ30 3.5 22 0 0 2.7 8 100
1.7 7 100
0.7 6 83
-0.3 7 43
-1.3 4 50
-2.3 5 0
MID50 = -0.2
a Amount of virus ingested, assuming a 2 ul bloodmeal.
b Percentage of mosquitoes with antigen detectable by IFA in midgut or head tissue prepared 21 days after oral infection.
c Amount of virus present in 0.22 ul inoculum.
d Percentage of mosquitoes with antigen detectable by IFA in head tissue prepared 14 days post-inoculation.
Table 4 Replication and immunogenicity of DEN-2 viruses in rhesus monkeys
Virusa % with viremia Mean no. of viremic days per monkeyb Mean peak virus titer (log10PFU/ml ± SE) Geometric mean serum neutralizing antibody titer on day 28 (reciprocal dilution)c Virus replication after challenged
% with viremia Mean peak virus titer (log10PFU/ml ± SE)
Mock 0 0 <0.7 <10 100 2.1 ± 0.1
DEN-2 Tonga/74 100 4.5 2.1 ± 0.3 311 0 <0.7
rDEN-2 100 4.0 1.9 ± 0.1 173 0 <0.7
rDEN2Δ30 100 2.8 1.7 ± 0.2 91 0 <0.7
a Groups of rhesus monkeys (mock: n = 2; viruses: n = 4) were inoculated subcutaneously with 105 PFU of the indicated virus in a 1 ml dose. Serum was collected daily for day 0 to 6; day 8, 10, 12 and 28
b Virus titer in serum was determined by plaque assay in Vero cells. The lower limit of detection was 0.7 log10PFU/ml. Viremia was not detected in any monkey after day 6.
c Plaque reduction (60%) neutralizing antibody titers were determined using DEN-2 Tonga/74. Limit of detection was < 1:10. All monkeys inoculated with virus had a four-fold or greater increase in neutralizing antibody titer.
d Vaccinated rhesus monkeys were inoculated subcutaneously with 105 PFU of DEN-2 Tonga/74 virus in a 1 ml dose. Serum was collected daily for day 0 to 8.
Table 5 Addition of the 4995 mutation further attenuates rDEN2Δ30 in SCID-HuH-7 mice.
Replication in SCID-HuH-7 micea
Virus No. of mice Mean virus titer (log10PFU/ml ± SE)
DEN-2 Tonga/74 6 5.9 ± 0.3
rDEN-2 15 6.4 ± 0.2
rDEN2Δ30 22 5.2 ± 0.1
rDEN2Δ30-4995 28 4.6 ± 0.2
a Groups of HuH-7-SCID mice were inoculated directly into the tumor with 104 PFU virus. Serum was collected on day 7 and titered in Vero cells. Data is included from three separate experiments.
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| 15461822 | PMC524489 | CC BY | 2021-01-04 16:03:31 | no | BMC Infect Dis. 2004 Oct 4; 4:39 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-39 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-401547154910.1186/1471-2334-4-40Research ArticlePatterns of Schistosoma haematobium infection, impact of praziquantel treatment and re-infection after treatment in a cohort of schoolchildren from rural KwaZulu-Natal/South Africa Saathoff Elmar [email protected] Annette [email protected] Pascal [email protected] Jane D [email protected] Wilhelm [email protected] Chris C [email protected] Harvard School of Public Health, Department of Nutrition, 665 Huntington Avenue, Boston, MA 02115 USA2 Danish Bilharziasis Laboratory, Jaegersborg Allé 1D, DK-2920 Charlottenlund, Denmark3 Child, Youth and Family Development, Human Sciences Research Council, Private Bag X07, Dalbridge, 4014, South Africa4 Zoologisches Institut der Universität Hamburg, Martin-Luther-King-Platz 3, 20146 Hamburg, Germany5 School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban 4041, South Africa2004 7 10 2004 4 40 40 9 6 2004 7 10 2004 Copyright © 2004 Saathoff 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
Schistosomiasis is one of the major health problems in tropical and sub-tropical countries, with school age children usually being the most affected group. In 1998 the Department of Health of the province of KwaZulu-Natal established a pilot programme for helminth control that aimed at regularly treating primary school children for schistosome and intestinal helminth infections. This article describes the baseline situation and the impact of treatment on S. haematobium infection in a cohort of schoolchildren attending grade 3 in a rural part of the province.
Methods
Primary schoolchildren from Maputaland in northern KwaZulu-Natal were examined for Schistosoma haematobium infection, treated with praziquantel and re-examined four times over one year after treatment in order to assess the impact of treatment and patterns of infection and re-infection.
Results
Praziquantel treatment was highly efficacious at three weeks after treatment when judged by egg reduction rate (95.3%) and cure rate of heavy infections (94.1%). The apparent overall cure rate three weeks after treatment (57.9%) was much lower but improved to 80.7% at 41 weeks after treatment. Re-infection with S. haematobium was low and appeared to be limited to the hot and rainy summer. Analysis of only one urine specimen per child considerably underestimated prevalence when compared to the analysis of two specimens, but both approaches provided similar estimates of the proportion of heavy infections and of average infection intensity in the population.
Conclusion
According to WHO guidelines the high prevalence and intensity of S. haematobium infection necessitate regular treatment of schoolchildren in the area. The seasonal transmission pattern together with the slow pace of re-infection suggest that one treatment per year, applied after the end of summer, is sufficient to keep S. haematobium infection in the area at low levels.
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Background
Schistosomiasis is one of the major health problems in tropical and sub-tropical countries [1]. The schistosomiasis endemic area in South Africa is situated in the north-east and covers roughly one quarter of the country, with Schistosoma haematobium being the most common species [2]. In 1995 it was estimated that more than four million South Africans were infected with schistosomes [3].
Possible consequences of S. haematobium infection include haematuria, dysuria, nutritional deficiencies, lesions of the bladder, kidney failure, an elevated risk of bladder cancer and – in children – growth retardation. Accordingly the estimates for morbidity and mortality in affected populations are high [4-7]]. School age children usually present with the highest prevalence and intensity of S. haematobium infection [8]. However, negative health consequences are not limited to this group since high intensity infections can cause serious chronic disease long after initial infection [9]. Some studies also suggest that schistosomiasis may play a role as a risk factor for HIV infection and that helminth infections in general negatively affect the immune system of HIV infected persons [10,11].
In 1998 the Department of Health of the province of KwaZulu-Natal (KZN) in co-operation with the Department of Education established a pilot programme for helminth control that aimed at regularly treating primary school children for schistosome infections and intestinal helminth infections [12]. All children in participating schools were treated without prior screening of infection status. The rationale behind this and similar programmes in other countries is not to eliminate infection in a given area, but to keep infection intensities low in this vulnerable age group in order to prevent serious morbidity [13,14].
Our objectives were to describe the pattern of schistosome infection at baseline, to monitor the impact of treatment in our study population and to assess re-infection after treatment in order to develop recommendations for future control activities.
Methods
Study area, population and treatment
The study was conducted in central Ingwavuma district in northern KwaZulu-Natal (Figure 1). The study area covers approximately 28 × 16 km on both sides of the perennial Pongola River (Figure 2). Climate is tropical to subtropical (Figure 3) with a hot and wet summer (November – February) and a cooler and dry winter (June – August).
The study population (Table 1) was recruited from all ten primary schools in the area. It was limited to children who attended grade 3 at the start of the study in order to keep disturbances of the school routine to a minimum and because this grade should represent the infection situation in a primary school relatively well [15]. All grade 3 pupils were eligible for participation with one exception: during the baseline survey two out of five and one out of four grade 3 classes in two large schools had to be excluded due to logistic constraints. These classes, however, were included during treatment and successive surveys.
All pupils who provided a urine specimen during the pre-treatment survey were included in the analysis of infection patterns at baseline but only children who had been treated with praziquantel were included in the analysis of the post-treatment surveys. Of these, another 4.6% who reported having received additional treatment for schistosome infection while our study was ongoing were excluded from analysis. Otherwise only children who refused to participate or who were absent or unable to produce a specimen during each of our repeated visits were not included in the analysis of the respective surveys. They were included, however, for those surveys where they participated in order not to increase bias due to the likely difference in disease status between absentees and pupils who attended school [16].
Treatment in all primary schools in the entire district was carried out in April and May 1998 by school nursing teams from the two local hospitals as part of the first treatment campaign of a provincial helminth control programme. The study team assisted the nurses with treatment and also recorded those of the study population who were treated and those who were not. All consenting children from all grades were treated for schistosome infection with a single dose of 40 mg/kg praziquantel (Biltricide®, Bayer) without regard to infection status. In order to facilitate administration of the drug, the nurses had been provided with a dosing sheet that showed the correct dosage for different bodyweights. The weight of the children was determined using an ordinary bathroom scale. Because one Biltricide® tablet containing 600 mg of praziquantel can be subdivided into four segments of 150 mg the required dose can be administered relatively accurately. The drug was administered with a glass of water after the children had eaten a peanut butter sandwich which was provided by the treatment team. Children were asked to swallow the tablets with some water in front of one of the team in order to monitor adherence. Because praziquantel is considered a safe drug and has been used extensively since its introduction in the early 80ies possible side effects of treatment were not monitored systematically [17].
Children were also treated for intestinal helminth infection with 400 mg albendazole (Zentel®, SmithKline Beecham) and albendazole treatment was repeated in October 1998 [18]. After the end of the study the participants were included in the normal treatment routine of the control programme.
Ethical considerations
Ethical clearance was obtained from the Ethics Committee of the Faculty of Medicine of the University of Natal/Durban and the study was also approved by the Central Medical Ethics Committee in Denmark. Before the onset of the study, information meetings were held with the staff and parents of the schools in the study. At these meetings informed consent was obtained from the parents. Informed consent from the children was obtained directly before the first specimen collection.
Specimen collection and processing
An initial survey in March 1998 to assess S. haematobium infection in the study population was followed by treatment for schistosome and intestinal helminth infections. Follow-up surveys to monitor loss of infection and re-infection were conducted at 3, 16, 41 and 53 weeks after treatment (Table 2).
Urine specimens were collected between 10:00 and 13:00 hours [13]. On our visits to the schools, pupils were provided with labelled 500 ml specimen containers and asked to provide a urine specimen. Each school was visited at least three times during each survey in order to include children who were absent or unable to deliver a specimen on the first occasion. Apart from the pre-treatment survey, where only one specimen was collected, an effort was made to obtain two urine specimens from each pupil. However, the results reported in this article are calculated using only the first specimen obtained. Otherwise the lower sensitivity of the pre-treatment survey would have invalidated comparisons with the follow-up surveys. The results of both obtained specimens are used only in Table 2 where we directly compare them to those of only one specimen.
Filled specimen containers were brought to the laboratory where filtration of a sub-sample of 10 ml was carried out on the same day [19]. Specimens of less than 10 ml were measured before filtration and the number of eggs per 10 ml calculated. Before microscopy, eggs were stained using 50% Lugol's iodine saline solution [20] and then counted by 3 microscopists in order to obtain an indirect measurement of infection intensity. These counts did not differentiate between viable and non-viable eggs. Repeat counts by different microscopists were done on a sub sample of about 5% of the slides for quality control purposes. These counts revealed no bigger discrepancies. Infection intensities are expressed as eggs per centilitre (EPC, 1 centilitre = 10 ml).
Statistics
Data were double entered, the duplicates compared and corrected for data entry errors. Statistical analysis was carried out in Stata 7 for Windows [21].
In order to reduce the influence of extreme outliers, geometric means were preferred to arithmetic means to summarise population infection intensity (Table 1 and Table 2). Uninfected children were included by adding 1 to all egg counts before log transformation and subtracting it again after re-transformation. However, when comparing intensity, non-parametric statistics were used because even the log transformed egg counts were still far from being normally distributed. Cure rates (CR) and egg reduction rates (ERR) were calculated using the formulae below [15]:
Results
Infection patterns at baseline
In the pre-treatment survey 68% of the study population were found infected with S. haematobium and 38% (= 56% of the infected children) had egg counts of 50 or more EPC, the WHO [4] threshold for heavy intensity infections (Figure 4). As shown in Table 1, differences between sexes regarding prevalence and intensity of S. haematobium infection were moderate.
Figure 5 demonstrates that age patterns of infection at baseline differed considerably between sexes. S. haematobium prevalence of boys slowly increased from 60% in the youngest to 79% in the oldest age group whereas the youngest girls had a much lower prevalence (37%) but the increase with age was steeper.
Treatment
Of the 1109 children who participated in the baseline survey only 852 (76.8%) were treated with praziquantel. 228 children (20.6%) did not receive treatment either because they refused to be treated (7 children) or because they were absent on treatment day (221) and for 29 children (2.6%) it is unclear whether or not they were treated. Differences between the treated and untreated groups (excluding children of unclear treatment status) regarding sex, age, infection status and infection intensity were small and not statistically significant.
S. haematobium infection status, CR and ERR over 53 weeks after treatment are summarised in Table 2. Infection intensity measured as the geometric mean EPC had decreased by more than 95% as soon as three weeks after treatment and over the following months a further small decrease is documented until 41 weeks after treatment. The pattern for prevalence and CR of heavy intensity infections is very similar to this. Total prevalence shows the same trend over time, but in contrast exhibits considerably smaller decreases than the other two measures.
In Table 2 we also report the results that were obtained when using both specimens that were collected in the post-treatment surveys. When comparing the different approaches it is obvious that – as expected – the use of only one sample considerably underestimates the total prevalence in this population, but that estimates for prevalence of heavy infections and geometric mean intensity are quite similar.
Re-infection
No discernible re-infection took place between 3 and 41 weeks after treatment (Table 2 and Figure 4) whereas the increase between 41 and 53 weeks after treatment was substantial (two-sided p < 0.001 for both prevalence and intensity using the sign test for equality of paired observations [22]).
The group at risk of re-infection (Figure 5) was defined as those children who were found uninfected in at least one of the three surveys at 3, 16 or 41 weeks after treatment (n = 796). Fifty-three weeks after treatment 16.8% of these children were found to be re-infected with S. haematobium. The geometric mean EPC including uninfected children of 0.44 was still far below the corresponding figure before treatment (16.08) and this was also true for the geometric mean EPC when excluding uninfected children (58.2 before treatment and 6.6 at 53 weeks after treatment).
Figure 5 shows that the age pattern of re-infection in the study population also differed between sexes: it continuously decreased with age for boys but not for girls where re-infection reached a peak in the group that had been 11 to 12 years old at the time of the baseline survey and that was about 12 to 13 years old at the end of the study.
Discussion
Infection patterns at baseline
The results of our baseline survey are in agreement with a survey conducted in the area about 20 years earlier. Schutte et al. [23] found prevalences of between 55% and 92% in the four surveyed schools that were situated in our study area. The high total prevalence and the large proportion of high intensity infections that we found indicate that according to WHO criteria regular treatment of schoolchildren in the area is indeed necessary [4]. The different age patterns for prevalence of S. haematobium infection in girls and boys might hint at different water contact patterns.
Treatment
The proportion of children treated in our study can not be regarded as representative for the control programme in general, because study participants were more informed about it than their schoolmates. However, the fact that only about three quarters of the children were treated is very disappointing. Although only 3 children did not consent to participate in our study, and only 7 of those who consented openly refused to be treated absenteeism was unusually high during the first round of treatment. This improved greatly in the second treatment half a year later, when – according to the opinion of school staff – pupils and their parents had realised that treatment was beneficial and had only mild and transient side effects. Unfortunately this second round of treatment did only include albendazole but not praziquantel because the schedule of the provincial treatment programme only provided one treatment for schistosome infection per year.
Praziquantel treatment resulted in drastic reductions of infection intensity and prevalence of heavy infections as soon as three weeks after treatment, which is in accordance with the literature [24]. The reduction in overall prevalence of less than 60% at three weeks after treatment is however unsatisfactory, even though it improved to about 80% at 41 weeks after treatment. The explanations that the treatment did not work or that its effect was delayed can be excluded because of the high ERR. It seems more likely that the relatively high post-treatment prevalences are not an indication of a high proportion of active infections after treatment, but that they are caused by "old" and mostly non-infective eggs, laid before treatment, that were trapped somewhere in the tissue and are slowly finding their way to the lumen of the bladder [25,26]. Unfortunately our laboratory examination did not differentiate between viable and non-viable eggs, but the data presented in Figure 4 are consistent with the above explanation because very little change is visible between 3 and 41 weeks after treatment with regard to infections of more than 50 EPC. During this period prevalence decreased almost exclusively in the low intensity range. If many active infections (= egg laying schistosomes) had been lost, this should also have had an impact on infections of higher intensity.
The high variability of repeated S. haematobium egg counts [27] renders single egg counts a less than optimal tool for estimating total prevalence and for identifying the infection status of individuals. Our results however show that estimates of the proportion of heavy infections and of population infection intensity are similar to those obtained when examining two specimens. The examination of three or more specimens per child would most certainly have led to even higher estimates of total prevalence but we doubt that it would have changed the other two estimates considerably.
All this indicates that the reporting of measures of infection intensity is not only important because they are a better indicator of population morbidity than prevalence [8,15], but that intensity is also a more reliable marker of treatment success defined as the removal of egg-laying worms. This is especially important when relying on single egg counts to assess the effectiveness of the intervention which is usually the case in treatment programmes and larger field studies [28].
Re-infection
Because of the slow decrease in total prevalence after treatment it did not seem appropriate to restrict analysis of re-infection to those children who were egg-negative three weeks after treatment. According to our above reasoning this would have excluded a number of children who had been treated successfully but were still excreting old eggs. On the other hand we did not want to restrict the analysis to those children who were found egg-negative at 41 weeks after treatment. Even though this was the survey where we found the lowest prevalence, we might have excluded children who had been treated successfully, but had become re-infected again before this survey. Therefore we included all children who were found egg-negative at either 3, 16 or 41 weeks after treatment into the group at risk of re-infection. We are, however, aware that this definition is likely to include some uncured children who were still harbouring low level infections.
Our data indicate that S. haematobium transmission occurred mainly during the hot and humid summer. According to the literature, S. haematobium has a pre-patent period (infection to egg-excretion by the host) of about eight to ten weeks [29,30]. Thus the surveys at 41 weeks and at 53 weeks after treatment should approximately reflect transmission from early May (treatment) to early December and from early December to late February respectively. The former period covers the South-African, winter, spring and only a short part of the summer whereas the latter covers most of the hottest and wettest part of the year (Figure 3), conditions which favour S. haematobium transmission and the development of their Bulinus globosus snail hosts [31,32]. Moreover, children go swimming more frequently because of the hot weather and the long summer holidays in December and January give them ample time to do so.
Seasonality of S. haematobium transmission is well documented for the highveld region of Zimbabwe[33] with patterns rather similar to the ones found here, which seem to be caused by seasonal variation in snail populations as well as human water contact patterns [34]. A study in southern Natal found that recreational activities accounted for most of the water contact and – unlike household related water contacts – showed strong seasonal variation [35].
Conclusions
Our study shows that according to WHO guidelines [4] the high prevalence and intensity of S. haematobium infection in the area indeed necessitate regular treatment of schoolchildren, that praziquantel treatment is highly efficacious in reducing the proportion of moderate and heavy infections and that one treatment per year after the end of summer is sufficient to keep infection intensities at low levels. Because levels of S. haematobium infection in the study area seem to be among the highest in the country [23] the latter would probably also apply to other parts of South Africa. The slow pace of re-infection might suggest that it could even be sufficient to only treat every two years, but this would need to be verified in a separate study that follows treated children over this period.
Because we would not want to discourage future attempts to control schistosomiasis in the region and elsewhere we would finally like to stress that the above described money and time consuming intensive examinations are only required when doing research but that for a control programme a minimum of surveillance is sufficient [4].
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
ES conceived of the study and designed it together with AO, PM, JDK and CCA. ES conducted the field work with contributions from AO, JDK, CCA and WB, did the statistical analysis and drafted the manuscript. All authors contributed to the final version of the manuscript and read and approved it.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank the pupils, staff and parents of the participating schools and the school nursing teams of Manguzi and Mosvold hospitals. The research was funded by the Danish Bilharziasis Laboratory. The KwaZulu-Natal Department of Health provided treatment, laboratory space and general assistance and the MRC National Malaria Research Programme in Durban provided logistic support. ES received a PhD scholarship from Evangelisches Studienwerk Villigst/Germany.
Figures and Tables
Figure 1 Location of the study area in northern KwaZulu-Natal.
Figure 2 Map of the study area.
Figure 3 Long-term monthly averages of rainfall and temperature in the area. Data from 1966 to 1990 for Makatini research station, about 30 km south of the study area [36].
Figure 4 Schistosoma haematobium infection at baseline and at various periods after treatment. Prevalence of infections >= any intensity threshold of interest can be read from the percentage scale. The intersection of each graph with the y-axis corresponds to the total prevalence. For the number of participants in each survey see Table 2.
Figure 5 Prevalence of Schistosoma haematobium infection before treatment (baseline) and after 53 weeks of re-infection. Re-infection data only include children who were found egg-negative in at least one of the surveys at 3, 16 or 41 weeks after treatment. The table below the graph shows the number of participants in each age/sex stratum.
Table 1 Age and sex of the study population and prevalence and intensity of Schistosoma haematobium infection at baseline
n = Males 510 Females 599 Male/Female Ratio 95%CI or P-value
Median age (inter-quartile range) 11.3 (9.8–12.2) 10.7 (9.7–11.5) - -
Prevalence (%) 65.9 70.3 0.937 0.864–1.017
Age adjusted prevalence ratio* - - 0.896 0.828–0.969
Prevalence of infections >= 50EPC in % 36.7 39.6 0.927 0.797–1.078
Geometric mean EPC incl. uninfected 14.3 17.8 0.805 p = 0.151†
*Mantel-Haenszel age adjusted male/female prevalence ratio
†Two-sided P-value of two samples Wilcoxon ranksum test with ties
Table 2 Schistosoma haematobium prevalence and infection intensity, cure rates and egg reduction rates at various periods after treatment with 40 mg/kg praziquantel
Weeks since treatment Pre-treatment 3 16 41 53
Time of survey Mar '98 May/Jun Aug Feb '99 Apr/May
n = 1109 977 922 848 825
First specimen only*
Prevalence (%) 68.3 28.8 18.7 13.2 20.1
Cure rate (%) - 57.9 72.7 80.7 70.5
Prevalence of infections >= 50 EPC (%) 38.2 2.3 2.1 1.9 4.1
Cure rate of infections >= 50 EPC (%) - 94.1 94.6 95.1 89.2
Geometric mean EPC incl. uninfected 16.1 0.8 0.4 0.3 0.6
Egg reduction rate (%) - 95.3 97.5 97.9 96.0
Both specimens†
Prevalence (%) - 39.9 29.3 17.6 28.7
Prevalence of infections >= 50 EPC (%) - 2.5 2.1 1.9 5.0
Geometric mean EPC incl. uninfected - 0.9 0.5 0.3 0.6
*Results obtained when utilising only the first of two specimens that were collected
†Results obtained when utilising both specimens that were collected
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-4-141546181710.1186/1471-2377-4-14Research ArticleMechanisms underlying fatigue: a voxel-based morphometric study of chronic fatigue syndrome Okada Tomohisa [email protected] Masaaki [email protected] Hirohiko [email protected] Yasuyoshi [email protected] Norihiro [email protected] National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, Japan2 Department of Physiology, Osaka City University Graduate School of Medicine, Osaka 545-8585, Japan3 Department of Health Science, Faculty of Health Science for Welfare, Kansai University of Welfare Sciences, Kashihara, Osaka 582-0026, Japan4 JST/RISTEX, 2-5-1 Atago, Minato-ku, Tokyo 105-6218, Japan2004 4 10 2004 4 14 14 21 8 2004 4 10 2004 Copyright © 2004 Okada et al; licensee BioMed Central Ltd.2004Okada 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
Fatigue is a crucial sensation that triggers rest, yet its underlying neuronal mechanisms remain unclear. Intense long-term fatigue is a symptom of chronic fatigue syndrome, which is used as a model to study the mechanisms underlying fatigue.
Methods
Using magnetic resonance imaging, we conducted voxel-based morphometry of 16 patients and 49 age-matched healthy control subjects.
Results
We found that patients with chronic fatigue syndrome had reduced gray-matter volume in the bilateral prefrontal cortex. Within these areas, the volume reduction in the right prefrontal cortex paralleled the severity of the fatigue of the subjects.
Conclusion
These results are consistent with previous reports of an abnormal distribution of acetyl-L-carnitine uptake, which is one of the biochemical markers of chronic fatigue syndrome, in the prefrontal cortex. Thus, the prefrontal cortex might be an important element of the neural system that regulates sensations of fatigue.
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Background
Chronic fatigue is common and is reported in more than 20% of people seen in primary care [1]. However, the neural substrates of chronic fatigue are not well understood. For clinical use, central fatigue is defined as difficulty in the initiation of, or the ability to sustain, voluntary activities [2]. Central fatigue, in contrast with neuromuscular or peripheral fatigue, represents a failure to complete physical and mental tasks that require self-motivation and internal cues, in the absence of demonstrable cognitive failure or motor weakness [3]. Based on this definition, Chaudhuri and Behan [2] proposed a conceptual model for central fatigue. The work output of voluntary activity depends on the applied voluntary effort, which is controlled by motivational input and perceived effort via feedback from motor, sensory and cognitive systems. Hence, any dissociation between the level of internal input (motivational and limbic) and that of the perceived effort from applied voluntary effort results in the sense of fatigue. Assuming that pathological fatigue is an amplified sense of the normal (physiological) fatigue induced by changes in the variables regulating work output, clinical studies of fatigue disorders can provide clues regarding the neural substrates of fatigue. Symptoms of lesions in the pathways of arousal and attention, such as the reticular and limbic systems, and the basal ganglia, generally include pathological fatigue [2]. Fatigue can also be the primary symptom of a disease itself – this is the case in chronic fatigue syndrome (CFS), which might therefore prove to be a good model for studying the mechanisms underlying fatigue sensation.
CFS is a clinically defined condition characterized by severe disabling fatigue and a combination of symptoms, the prominent features being self-reported impairments in concentration and short-term memory, sleep disturbances and musculoskeletal pain [4]. The diagnosis of CFS can be made only after alternative medical and psychiatric causes of chronic fatigue have been excluded [4]. Recent studies found biochemical and genetic characteristics in CFS patients, such as a decreased concentration of serum acetyl-L-carnitine [5], a serotonin-transporter gene-promoter polymorphism [6], and autoantibodies against the muscarinic cholinergic receptor [7]. Among these, administration of L-carnitine, which is the precursor of acetyl-L-carnitine, is known to improve the clinical status of CFS patients [8]. In the brain, the acetyl moiety of acetyl-L-carnitine is utilized mainly for the biosynthesis of L-glutamate [9]. In CSF patients, a significant decrease in the uptake of acetyl-L-carnitine was found in several regions of the brain, including the prefrontal (Brodmann's area (BA) 9/46d), temporal (BA21 and 41), and anterior cingulate (BA24 and 33) cortices and cerebellum [9]. However, whether such focal cortical hypofunction is due to an anatomical abnormality has not yet been investigated. We hypothesize that there might be regions with explicit anatomical abnormalities that correlate with the severity of fatigue. To measure the reduction in gray-matter volume, we conducted voxel-based morphometry with high-resolution magnetic resonance imaging (MRI) [10,11].
Methods
Sixteen CFS patients (aged 24–46 years; average age 34.0 years; 10 men and 6 women) and 49 age-matched healthy control subjects (aged 21–47 years; average age 34.4 years; 27 men and 22 women) were enrolled in the study. They were recruited from the outpatient fatigue clinic in Osaka University Hospital (HK's special clinic) where more than 430 CFS patients, who met the diagnostic criteria of CFS [4], are being followed. The protocol was approved by the ethical committee of the National Institute for Physiological Sciences, Japan, and all subjects gave their written informed consent for the study. The periods of CFS lasted between 10 and 244 months, and the mean duration was 69.8 months (Table 1). All CFS patients were unable to carry out normal activities or actively work for several days a week because of severe general fatigue at the time of diagnosis. The severity of fatigue was measured using self-reported ratings based on daily activities (performance status; Table 2). A detailed neurological examination, the time course of the patients' signs and symptoms, and additional MRI (for 7 out of 16 patients) made the diagnosis of multiple sclerosis (MS) unlikely. The characteristics of the patients are shown in Table 1. To compare brain volumes, high-resolution anatomical images were acquired using a 3 Tesla MR scanner (Allegra, Siemens, Erlangen, Germany). A three-dimensional structural MRI was acquired for each subject using a T1-weighted magnetization-prepared rapid-gradient echo sequence (repetition time, 1970 ms; echo time, 4.3 ms; inversion time, 990 ms; number of excitation, 1; flip angle, 8°; matrix size, 256 × 256; field of view, 210 × 210 mm) yielding 160 sagittal slices with a slice thickness of 1.2 mm and an in-plane resolution of 0.82 mm.
Table 1 Patient Characteristics
Patient number Age (years) Duration (months) PS Difficulty in thinking Inability to concentrate
1 39 132 8 2 2
2 33 56 8 2 1
3 26 10 4 2 2
4 31 37 2 1 1
5 30 36 8 2 2
6 27 42 5 1 2
7 27 100 7 1 1–2
8 27 153 8 2 2
9 37 17 4 1 1
10 46 244 2 2 2
11 24 10 4 1 1
12 34 10 7 2 2
13 36 131 7 2 2
14 35 14 6 1 1
15 46 56 8 2 2
16 45 69 7 2 2
Level of fatigue, difficulty in thinking, inability to concentrate and depression are rated as follows: 2, severe; 1, mild; 0, none. PS, performance status at MRI examination.
Table 2 Performance-status scores for evaluating the severity of fatigue in CFS patients.
Scores Condition
0 No complaints; able to carry on normal activity without fatigue.
1 Able to carry on normal activity, but sometimes feels fatigue.
2 Able to carry on normal activity or to do active work with effort; requires occasional rest.
3 Several days a month, unable to carry on normal activity or to do active work; requires rest at home without work.
4 Several days a week, unable to carry on normal activity or to do active work; requires rest at home without work.
5 Unable to carry on normal activity or to do active work at all, although able to perform light tasks; requires rest at home without work for several days a week.
6 Requires rest without work at home for over one-half of a week; able to do light tasks in good health.
7 Unable to carry on normal activity or to do light tasks at all; able to care for self without assistance.
8 Remains in bed for more than one-half of each day; able to care for self to some extent, but requires frequent assistance.
9 Unable to care for self; must remain in bed with day-long assistance.
Voxel-based morphometry (VBM) [12] was performed using SPM2 for image processing and was analyzed with SnPM99 [13] implemented in MATLAB 6.1 (MathWorks, Natick, MA, USA). VBM is a fully-automated whole-brain morphometric technique that detects regional structural differences between groups on a voxel-by-voxel basis. Briefly, images were segmented into gray matter, white matter, cerebrospinal fluid and skull/scalp compartments, then normalized to standard space and re-segmented. Any volume changes induced by normalization were adjusted [10,11]. The spatially normalized segments of gray and white matter were smoothed using a 12-mm full-width half-maximum isotropic Gaussian kernel. Statistical analysis of regional differences between groups was performed using a permutation test for decreased probability of a particular voxel containing gray or white matter. Potential confounding effects of age, sex and whole segment (gray or white matter) volume differences were modeled, and the variances attributable to them were excluded from the analysis [11,14,15]. The significance levels for statistics estimated by 500 nonparametric randomization and permutation tests were set at P = 0.05, corrected for multiple comparisons. Within the areas showing a significant volume reduction in patients, linear correlates between volume reduction and the degree of fatigue were examined under the threshold of P < 0.005.
Results
We observed a significant reduction in gray-matter volume in the bilateral prefrontal areas of CSF patients (Figure 1). The affected areas extended from BA8 to 9 in the right cerebral hemisphere, and from BA9 to 11 in the left. In comparison with healthy controls, there was an average of 11.8% volume reduction in CSF patients. Within these areas, there was a significant negative correlation between the gray-matter volume of the right prefrontal cortex and the performance status of the CFS group (r2 = 0.46, P = 0.004; Figure 2). This relationship was confirmed using Spearman's rank-correlation coefficient (P = 0.004). In this area, the gray-matter volume was reduced by 16.9% for patients compared with controls. No significant atrophy was observed in the white matter of CFS patients.
Figure 1 Regional differences between CFS patients and controls. Areas with significantly reduced gray-matter densities in the CFS patients were located at bilateral prefrontal areas, which were surface rendered onto the high-resolution MRI. The colored bar indicates the t-values.
Figure 2 (Left) Correlations between volume and the performance status of CFS patients in the right prefrontal cortex (BA9; Talairach's coordinates: x = 48, y = 32 and z = 41). The colored bar indicates the t-values. (Right) Gray-matter volumes of CFS patients at the voxels of maximum correlation (r = 0.71) plotted against the performance status. The linear-regression line is plotted in blue. a.u., arbitrary units; Rt PFC, right prefrontal cortex.
Discussion
The present study provides the first report of focal gray-matter atrophy in the prefrontal cortex of CFS patients. Previous MRI studies of CFS revealed non-specific abnormalities: hyperintense small punctuated subcortical white-matter foci were observed predominantly in the frontal lobes [16] and their prevalence did not differ from an age-matched control group [17,18]. Ventricular enlargement was also reported [19]. Usually, MRI abnormalities in CSF patients cause the physician to conclude that the symptoms might be secondary to another medical condition [20].
Prefrontal pathology has been reported in MS with pathological fatigue [21]. Roelcke and colleagues [21] reported that MS patients with fatigue had a reduction of the cerebral metabolic rate of glucose (CMRGlu) in the bilateral prefrontal areas compared with MS patients without fatigue. Moreover, scores on the fatigue-severity scale were inversely related to CMRGlu levels in the right prefrontal cortex, suggesting that fatigue in MS is associated with prefrontal dysfunction due to the demyelination of frontal white matter [21]. Although the Talairach's coordinates reported by Roelcke and colleagues (x = 18, y = 42 and z = 20) were more medial and ventral than those observed here (x = 48, y = 32 and z = 41), both results suggest that prefrontal hypofunction might underlie pathological fatigue. Although MS should be excluded in the diagnosis of CFS, as in the present study, the similar clinical manifestations of the illnesses suggest that a common pathogenesis underlies the symptoms of fatigue in both disorders. This speculation is supported by the fact that the administration of L-carnitine, which improves fatigue in CFS patients, was effective for treating fatigue in MS patients [22].
In the present study, right dorsolateral prefrontal-cortex atrophy was significantly correlated with the severity of fatigue, as measured by the performance-status scores. As the performance status rates the daily activities that trigger or aggravate fatigue, this correlated volume reduction might reflect a functional deficiency that makes patients susceptible to fatigue.
A single site in the dorsolateral prefrontal cortex revealed the parallel between volume reduction and fatigue severity. This does not necessarily mean that it is fatigue-specific; instead, this area might be the part of the network that, when functioning sub-normally, results in pathological fatigue. Fatigue is also a symptom of diseases that affect the basal ganglia, and that interrupt the connection between the prefrontal cortex and thalamus [3]. Hence, frontal-subcortical circuits might be important contributors to the sense of fatigue.
The dorsolateral prefrontal cortex has dense widespread subcortical and cortical connections [23]. A series of parallel frontal-subcortical circuits have been described that link specific regions of the frontal cortex to the striatum, globus pallidus and thalamus [24]. These originate in the prefrontal cortex, project into the striatum (caudate, putamen and ventral striatum), connect to the globus pallidus and substantia nigra, and from there connect to the thalamus. There is then a final link back to the frontal cortex in each circuit, forming a closed loop [25]. Corticostriatal and thalamocortical connections use excitatory glutamatergic projections [25]. Frontal-subcortical circuits serve as organizational axes, integrating related information from widespread areas of the brain and mediating diverse behaviors. The three principal behaviorally-relevant circuits originate in the dorsolateral prefrontal, orbitofrontal and anterior cingulate cortices [26]. The marker behaviors specific to each circuit are executive dysfunction (dorsolateral prefrontal-subcortical circuit), disinhibition (orbitofrontal-subcortical circuit) and apathy (medial frontal-subcortical circuit), respectively [26]. Hence, these circuits are capable of concurrent participation in separate functions, including motor, cognitive and limbic processing [3].
The dorsolateral prefrontal cortex also has widespread reciprocal corticocortical connections with posterior temporal, parietal and occipital association areas [23]. Furthermore, at the level of the frontal lobes, the orbitofrontal, anterior cingulate and dorsolateral prefrontal cortices are linked to each other without cross connections at subcortical levels [26]. Therefore, the dorsolateral prefrontal cortex is poised to serve as a principal site for the integration of information.
These anatomical and functional characteristics of the frontal-subcortical circuits suggest that the large decrease in acetyl-L-carnitine uptake in the dorsolateral prefrontal, anterior cingulate and temporal cortices [9] represents hypofunction of the frontal-subcortical circuits. Furthermore, this decrease might be due to the remote effects of the pathology in the dorsolateral prefrontal cortex [27]. Recently, Fillippi et al. [28] underwent fMRI with MS patients with fatigue using simple motor task. They found inverse correlation between fatigue severity score and the task-related activity of the thalamus, concluding that fatigue could be secondary to dysfunction of corticosubcortical circuits. Thus, according to the model by Chaudhuri and Behan [3], hypofunction of the dorsolateral prefrontal cortex might interrupt the associated striato-thalamo-cortical loop, resulting in enhanced fatigability.
Conclusions
The results of the present study suggest that the dorsolateral prefrontal cortex might be an important component of the neural substrates that regulate the sensation of fatigue.
List of abbreviations
BA, Brodmann's area; CMRGlu, cerebral metabolic rate of glucose; CFS, chronic fatigue syndrome; MRI, magnetic resonance imaging; VBM, voxel-based morphometry
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
TO carried out the MRI scanning, data analysis and drafted the manuscript. MT conducted MRI scanning and participants' coordination. HK conducted the medical evaluation of the participants. YW and NS participated in the study design. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by a Grant-in-Aid for Scientific Research C (number 16615007; T.O.) and a Grant-in-Aid for Scientific Research B (number 14380380; N.S.) from the Japan Society for the Promotion of Science, and by Special Coordination Funds for Promoting Science and Technology from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government.
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| 15461817 | PMC524491 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Oct 4; 4:14 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-14 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-701545651910.1186/1471-2407-4-70Research ArticleCytotoxicity of psammaplin A from a two-sponge association may correlate with the inhibition of DNA replication Jiang Yahong [email protected] Eun-Young [email protected] Seung Hee [email protected] Dong-Kyoo [email protected] Jang-Su [email protected] Hyun Joo [email protected] Song [email protected] Burm-Jong [email protected] Dong Seok [email protected] Jee H [email protected] Department of Chemistry and Biohealth Product Research Center, Inje University, Kimhae 621-749, S Korea2 School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China3 Department of Chemistry, Pusan National University, Pusan, 609-735, S Korea4 Department of Microbiology, Inje University, Kimhae 621-749, S Korea5 Department of Medical Laboratory Science, Inje University, Kimhae 621-749, S Korea6 College of Pharmacy, Pusan National University, Pusan 609-735, S Korea2004 30 9 2004 4 70 70 11 3 2004 30 9 2004 Copyright © 2004 Jiang 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
SV40 DNA replication system is a very useful tool to understand the mechanism of replication, which is a tightly regulated process. Many environmental and cellular factors can induce cell cycle arrest or apoptosis by inhibiting DNA replication. In the course of our search for bioactive metabolites from the marine sponges, psammaplin A was found to have some anticancer properties, the possible mechanism of which was studied.
Methods
Cell viability was determined by Cell Counting Kit-8 (CCK-8) to count living RAW264.7 cells by combining 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium (WST-8) and 1-methoxy-phenazine methosulfate (1-methoxy-PMS). The effect of psammaplin A on DNA replication was carried out in SV40 DNA replication system in vitro. The activities of topoisomerase I and polymerase α-primase were measured by the relaxation of superhelical plasmid DNA and the incorporation of [3H]dTTP to the template respectively. The ssDNA binding activity of RPA was assessed by Gel Mobility Shift Assay (GMSA).
Results
We have found that psammaplin A delivers significant cytotoxic activity against the RAW264.7 cell line. It was also found that psammaplin A could substantially inhibit SV40 DNA replication in vitro, in which polymerase α-primase is one of its main targets.
Conclusion
Taken together, we suggest that psammaplin A-induced cytotoxicity may correlate with its inhibition on DNA replication. Psammaplin A has the potential to be developed as an anticancer drug.
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Background
DNA replication in eukaryotic cells is a tightly regulated process [1]. The regulation of DNA replication is central to understanding the regulation of cell cycle and virus proliferation, events that have a direct impact on our understanding human disease. One critical component of cell cycle regulation is the initiation of DNA replication. The timing of initiation is precisely controlled and is sensitive to both environmental and cellular factors. If DNA replication is blocked by inhibitors or the template is damaged by radiation or other factors, signals are generated that can induce cell cycle arrest or apoptosis [2,3].
Much of what is currently known about the mechanism of DNA replication in eukaryotic cells has come from studying SV40 and related viruses. SV40 virus can use the host replication machinery for its own DNA replication together with the virally encoded SV40 T-antigen. SV40 T-Ag is a multifunctional regulatory protein with numerous biochemical activities, and it has been classified as a member of superfamily III helicase and can unwind dsDNA and RNA [4,5]. All other proteins are supplied by host cells. In replication, replication protein A (RPA) mediates unwinding of SV40 origin-containing DNA in the presence of SV40 T-Ag and the DNA polymerase α-primase complex (pol α-primase) [6,7], which is necessary for the initiation of SV40 DNA replication [8,9].
Psammaplin A is a symmetrical bromotyrosine-derived disulfide dimer that was originally isolated in 1987 from the Psammaplysilla sponge [10]. Early studies revealed that psammaplin A had general antibacterial and antitumor properties. In 1999, it was found that psammaplin A exhibited significant in vitro antibacterial activity against both Staphylococcus aureus (SA) and methicillin-resistant Staphylococcus aureus (MRSA), which was inferred to be the result of induced bacterial DNA synthesis arrest by psammaplin A through inhibiting DNA gyrase [11]. Given the increasingly rapid emergence of multi-drug resistant bacterial strains and the corresponding threat to public health, there is significant interest in the development of structurally novel antibacterial agents such as psammaplin A. Additionally, psammaplin A has been reported to exhibit certain inhibition of a number of enzymes including topoisomerase II (topo II) [12], farnesyl protein transferase [13], leucine aminopeptidase [13], and latest reported chitinase [14]. Among these enzymes, topo II, as one required protein for eukaryotic DNA replication, as well as bacterial DNA gyrase belongs to the topoisomerase family of enzymes responsible for the remolding of DNA topology. Since psammaplin A can inhibit bacterial DNA synthesis through DNA gyrase inhibition, and much of the basic enzymology of the eukaryotic replication fork has close homologies with its prokaryotic counterpart, we wonder whether psammaplin A also can induce eukaryotic DNA replication arrest or not.
We have reported that psammaplin A displayed significant cytotoxicity against human lung (A549), ovarian (SK-OV-3), skin (SK-MEL-2), CNS (XF498), and colon (HCT15) cancer cell lines [15]. In this paper, psammaplin A was found to have dose-dependent cytotoxicity on macrophage cell line. In order to clarify the possible mechanism of the cytotoxicity and also verify our conjecture of its possible action on DNA replication, the effect of psammaplin A on eukaryotic DNA replication was examined by using in vitro SV40 DNA replication system. According to our result that psammaplin A can induce eukaryotic DNA replication arrest through inhibiting some important replication proteins, we suggest that psammaplin A-induced cytotoxicity may correlate with its inhibition on DNA replication, and one of the main target molecules could be DNA polymerase α-primase.
Methods
Psammaplin A, proteins, cell extracts and DNA
Psammaplin A sample was a gift from a Dr. Jung's lab of Pusan National University. SV40 origin-containing circular duplex DNA (pUC-ori+), SV40 T-Ag, topoisomerase I (topo I), human DNA polymerase α-primase (pol α-primase), replication protein A (RPA), and HeLa extract were prepared as described previously [16].
Cell lines and chemicals
Media for cell culture including HY, DMEM and RPMI were purchased from the Sigma Chemical Co. (St. Louis, MO, USA) and Fetal Calf Serum (FCS) was from Gibco-BRL (Gaithersburg, MD, USA). Cell Counting Kit-8(CCK-8) was purchased from Dojin Laboratories (Kumamoto, Japan). The mouse macrophage cell line RAW264.7 was purchased from Korean Cell Line Bank (Seoul, Korea).
Cell viability assay
Cell viability was determined by CCK-8 to count living cells by combining WST-8 and 1-Methoxy PMS [17]. Briefly, macrophage cells (RAW264.7) were seeded into 96 well plates at an initial density of 105 cells/well. After incubation with the indicated concentrations of psammaplin A for 12 hr, 10 μl of kit reagent was added and incubated for a further 3 hr. Cell viability was obtained by scanning with a microplate reader at 450 nm.
SV40 DNA replication in vitro
The reactions were carried out as described previously [18]. In brief, the reaction mixtures (40 μl) included 40 mM creatine phosphate-di-Tris salt (pH 7.7), 1 μg of creatine kinase, 7 mM MgCl2, 0.5 mM DTT, 4 mM ATP, 200 μM UTP, GTP, and CTP, 100 μM dATP, dGTP, and dCTP, 25 μM [3H]dTTP (300 cpm/pmol), 0.6 μg of SV40 T-Ag, 0.23 μg of pUC-ori+, HeLa extracts, and psammaplin A as indicated. The reactions ran at 37°C for 2 hr, after which the acid-insoluble radioactivity was measured [18].
Topo I assay
Topo I was measured by the relaxation of superhelical plasmid DNA [19]. The 20 μl assay mixture contained 50 mM Tris-HCl (pH 7.5), 120 mM KCl, 10 mM MgCl2, 0.5 mM DTT, 0.5 mM EDTA, bovine serum albumin (30 μg/ml), pUC118 (20 μg/ml), topo I (1 unit), and various amount of the psammaplin A. After 30 min at 30°C, the reactions were stopped by the addition of 5 μl of 5% NaDodSO4/25% (wt/vol) Ficoll 400 (Pharmacia) containing 0.25 mg of bromophenol blue per ml. The samples were then loaded onto the agarose gel (0.8%) for electrophoresis followed by photography.
ssDNA binding assay
The assay was performed according to the published procedures [7]. The reaction mixture (20 μl) contained 50 mM Hepes-KOH (pH 7.5), 150 mM NaCl, 1 mM MgCl2, 0.5 mM DTT, 10% glycerol, 50 fmol of 5'-32P-labeled oligo(dT)50 (2200 cpm/fmol), plus the indicated amount of RPA, and was incubated for 15 min at room temperature. The complex was electrophoretically separated on a 5% polyacrylamide gel in 0.5 × TBE (89 mM Tris borate, 2 mM EDTA) at 15 V/cm. The gel was then dried and exposed to X-ray film.
Pol α-primase assay
DNA pol α-primase activities were assayed as described previously [20] with the following modifications. Reaction mixtures (30 μl) contained 40 mM creatine phosphate/di-Tris salt, pH 7.7, 1.0 μg of creatine kinase, 7 mM MgCl2, 0.5 mM DTT, 6 μg of bovine serum albumin, 4 mM ATP, 33 μM of [3H]dTTP (500 cpm/pmol), 0.1 μg of poly (dA)4500: oligo (dT)25 (20:1), DNA pol α-primase, and psammaplin A as indicated. After incubation at 37°C for 30 min, acid-insoluble radioactivity was determined [18].
Statistical analysis
Values are presented as mean ± SD. Data was initially analyzed by one-way analysis of variance (ANOVA) and comparison of groups was made using Turkey test (SPSS software).
Results
Effect of psammaplin A on the viability of macrophage cell line
As shown in Fig 1, psammaplin A is a symmetrical bromotyrosine-derived disulfide dimer, which exhibits in vitro antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). Psammaplin A is rather interesting owing to its two identical domains which are linked through a disulfide bridge. Macrophage cells are one of the key players in the early innate immune response, and they release inflammatory chemicals known as cytokines when they are activated. This sort of inflammation is not always a good thing, and overactive macrophage cells have been implicated in a number of human diseases, including arthritis and sepsis. When we studied the effect of psammaplin A on the viability of macrophage cell line RAW264.7, a reduced cell count was observed in the psammaplin A-treated cells and this decrease in the number of living cells also showed good dose-dependent (Fig 2).
Inhibition of SV40 DNA replication in vitro by psammaplin A
As it has been mentioned in the background, we wonder that psammaplin A has inhibitory effect on eukaryotic DNA replication or not. To verify this conjecture and also clarify the possible mechanism of psammaplin A-induced cytotoxicity, we examined the effect of psammaplin A on DNA replication using an in vitro SV40 DNA replication system. Addition of increasing amounts of psammaplin A quantitatively inhibited SV40 DNA replication with HeLa cytosolic extract (Fig 3).
Inhibition of replication by psammaplin A in a cell-free system could be mediated either by damaging the template or by modulating the activity of a protein (or proteins) that is required for replication. The former mechanism is unlikely, because we have directly checked the effect of psammaplin A on DNA and didn't find any detectable damages to the template (data not shown). In order to find what proteins in DNA replication were affected by psammaplin A, we checked topo I activity at first, which plays key roles in DNA replication, transcription, and recombination by forming transient DNA single-strand breaks and acting as DNA strand transferase. In addition, the topoisomerase is now considered to be an important cancer chemotherapeutic target. The inhibitory effect of psammaplin A on the catalytic activity of topo I was shown in Fig 4a. The plasmid DNA was in the superhelical form (lane 1), and topo I relaxed the supercoiled DNA (lane 2). Psammaplin A inhibited the relaxation by topo I strongly at a concentration of 125 μM.
Nicolaou and his colleagues reported that the DTT present in many enzyme assays could reduce the disulfide bond of psammaplin A to the corresponding free thiol [24]. In their experiment without DTT, psammaplin A exhibited no detectable inhibition of bacterial DNA gyrase up to 100 μg/ml. They suggested the weak inhibitory activity observed by the earlier authors could be attributed to the presence of the free thiol rather than the product itself. In our experiments detecting the effect of psammaplin A on topo I, no difference was found in the same gel between the reactions in the presence and absence of 0.5 mM DTT (Fig 4b).
In replication, RPA mediates unwinding of SV40 origin-containing DNA in the presence of SV40 T-Ag and topo I. It interacts with SV40 T-Ag and the DNA pol α-primase complex, which is necessary for the initiation of SV40 DNA replication [8,9]. Here, we examined the effect of psammaplin A on RPA's ssDNA-binding activity. As shown in Fig 5, RPA formed stable complexes with oligo(dT)50, which appeared as two distinct bands in the polyacrylamide gel. The ssDNA binding activity of RPA was inhibited by psammaplin A in a concentration-dependent manner, and 500 μM of psammaplin A totally inhibited the ssDNA-binding activity of RPA.
As described above, DNA pol α-primase complex is necessary for the initiation of SV40 DNA replication. To further investigate the inhibitory effect of psammaplin A in replication, we tested psammaplin A for inhibition of pol α-primase activity to see whether it's inhibitory effect on DNA replication correlate with pol α-primase activity. As shown in Fig 6, the activity of pol α-primase was inhibited by psammaplin A, and 40 μM of psammaplin A inhibited about 94% the activity of pol α-primase.
Discussion
Psammaplin A has exhibited inhibition on general bacterium, some actinomycetes and fungi, and it also has cytotoxicity toward several cancer cell lines. In our research, we found that psammaplin A deliver significant cytotoxic activity against macrophage cell line RAW264.7. Which process is mostly affected by psammaplin A in cell cycle? What is the mechanism of the inhibition? Enlightened by the inhibitory effects of psammaplin A on bacterial DNA synthesis, bacterial DNA gyrase and eukaryotic topo I, we investigated the effect of psammaplin A on DNA replication using SV40 replication in vitro system attempting to find out the target process and molecules of psammaplin A and have a glimpse on cell cycle regulation.
In our study, we found that psammaplin A inhibited SV40 DNA replication in vitro. In order to clarify the inhibition mechanism, further work were carried out. In SV40 DNA replication, three factors, SV40 T-Ag, RPA, and pol α-primase complex, are essential for initiation process. In the presence of topo I, SV40 T-Ag will continue to unwind the DNA to form a highly unwound DNA [21]. DNA synthesis with three factors and topoisomerase can be quite extensive [22]. We have suggested that psammaplin A might interfere with some molecules that are required to establish replication forks during the initiation reaction. To address this possibility, we asked whether psammaplin A inhibits topo I, RPA's ssDNA binding activity, and pol α-primase activity. In addition, the topo I is now considered to be important cancer chemotherapeutic target. In mammalian cells, actions of antitopoisomerase drugs on replication, transcription, and other processes ultimately activate pathways of programmed cell death [23]. Psammaplin A inhibited the DNA relaxation activity of topo I and the ssDNA binding activity of RPA in a dose-dependent manner, and up to 500 μM, psammaplin A can inhibit both the activities of topo I and RPA completely. On the other hand, psammaplin A significantly reduced pol α-primase activity at 40 μM. The above results indicate that major inhibition of SV40 DNA replication by psammaplin A may be due to the inhibition of pol α-primase activity. Here, we cannot rule out the possibility that psammaplin A inhibit the activity of SV40 T-Ag, because it is essential for initiation process.
It is puzzling that the DNA pol α-primase, RPA and topo I were readily inhibited by low concentration of psammaplin A whereas the SV40 DNA replication assay still showed about more than 80% DNA replication in the presence of 125 μM psammaplin A. In our opinion, at least three points could account for this discrepancy. First, the cell extract we have used to support in vitro DNA replication system includes a large number of proteins, while in topo I assay, RPA binding assay and pol α-primase assay, purified proteins were used. Many of the proteins in the crude extracts can affect each other by physical or functional interactions. For example, in the process of DNA replication initiation, RPA interacts with T-Ag and DNA pol α-primase, and it is believed that RPA can both stabilize the unwound DNA and stimulate DNA pol α. The universal protein-protein interactions in crude extracts make its working environment quite different from that of purified protein assay system. Second, even for the same protein, for example, pol α-primase, the concentration in the cell extract and in the purified pol α-primase assay is not comparable before any quantification of the replication proteins in the cell extract. Third, due to the active disulfide moiety in the structure of psammaplin A, it is possible that psammaplin A could interact with some particular cellular targets in the crude extract, which may lead to covalent modification of the biological targets and psammaplin A itself. Therefore, the free available concentration of psammaplin A in the crude extract might be different from that in purified protein assay system.
Different from the effect of psammaplin A on SV40 DNA in vitro replication, the significant inhibition of psammaplin A on the viability of macrophage RAW264.7 cells occurred at a relatively low concentration. There is the possibility that DNA replication might not be the single or primary event that affected by psammaplin A. It should be evident that the event of DNA replication in a living cell is more complicated than that in the crude extract system because of many existing cell cycle signals. So, it is still vague whether the ability of psammaplin A to inhibit cellular viability is correlated with its ability to inhibit DNA replication. In order to make it clear, it is necessary to check the effects of psammaplin A on other macromolecular synthesis (RNA synthesis and protein synthesis). Although the results in this paper do not clearly define the mechanism of cytotoxicity of psammaplin A, they have convincingly shown that psammaplin A possesses the abilities to inhibit DNA replication and some important replication proteins.
Because of the disulfide bridge linking two identical subunits in the structure of psammaplin A, in this study, we also paid attention to the potential reduction effect of DTT present in the topo I assay. Different from the report of Nicolaou [24], we didn't catch any difference between the reactions in the presence and absence of DTT. There exist two possibilities. One is that in our reaction system, DTT couldn't reduce psammaplin A to the corresponding free thiol. Comparing the in vitro assay conditions in this study, we can find that all these assays were performed in similar conditions (for example: pH 7.5~7.7, 0.5 mM DTT and Mg2+ ion environment). Given the mildness and tolerance of these reaction mixtures, we guess that psammaplin A is stable in the assays. Of course there is the second possibility that psammaplin A was reduced in the reaction, but the reduction product had nearly the same inhibition effect on topo I as the original compound. Further investigation aimed at this question need to be carried out.
Conclusions
Based on our results, we suggest that the cytotoxicity of psammaplin A might be related to the inhibitory effect it has on the fundamental cellular process-DNA replication, and one of the main target molecules of psammaplin A could be pol α-primase.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
YHJ and DKK conceived and designed the experiments and wrote the manuscript. YHJ performed all of the experiments. JHJ provided psammaplin A sample. EYA, SHR, JSP, HJY, SY, BJL and DSL participated in the conception, supervision, coordination and guidance of the study and manuscript preparation. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea. (02-PJ2-PG10-21601-0001).
Figures and Tables
Figure 1 The structure of psammaplin A.
Figure 2 Effect of psammaplin A on the viability of macrophage cell line RAW 264.7. Macrophage cells were treated with various concentrations of psammaplin A for 12 hr. Relative cell viability was determined by WST-8 and 1-Methoxy PMS and is shown as a percentage of living cells. Data are shown as means ± SD of three independent experiments. * Data are significantly different from control group at p < 0.001.
Figure 3 Effect of psammaplin A on SV40 DNA replication in vitro. Replication reaction comprised SV40 origin-containing DNA (pUC-ori+), SV40 T-Ag, HeLa cytosolic extract (100 μg), [3H]dTTP, and the indicated amounts of psammaplin A. Reaction mixtures were incubated at 37°C for 2 hr, after which the acid-insoluble radioactivity were measured. Data are shown as means ± SD of three independent experiments. Data are significantly different from control group at p < 0.05 (*)and at p < 0.001 (**).
Figure 4 Psammaplin A inhibited topo I catalytic activity. Topo I activity was measured by the relaxation of superhelical plasmid DNA. The assay mixture (20 μl) contained pUC118 (20 μg/ml), topo I (1 unit), and various amounts of the psammaplin A. After 30 min at 30°C, the reactions were stopped by the addition of 5 μl of stop solution. The samples were then loaded onto the agarose gel (0.8%) for electrophoresis followed by photography. (A) The reaction mixtures contain 0.5 mM DTT; (B) Compare the reactions in the presence and absence of 0.5 mM DTT.
Figure 5 Psammaplin A inhibited ssDNA binding activity of RPA. Indicated amount of either human RPA or a mixture of both RPA and psammaplin A were combined with 32P-labeled oligo(dT)50 and incubated for 15 min at room temperature. The protein-DNA complexes were then separated from unbound DNA by 5% polyacrylamide (acrylamide:bisacrylamide, 29:1) gel electrophoresis.
Figure 6 Effect of psammaplin A on pol α-primase activity. Indicated amounts of psammaplin A were added to the reaction mixtures, which included 0.1 unit of human pol α-primase complex, 4 mM ATP, 33 μM of [3H]dTTP, 0.1 μg of poly (dA)4500:oligo(dT)25(20:1), incubations were performed at 37°C for 30 min, followed by measurement of acid-insoluble radioactivity. Data are shown as means ± SD of three independent experiments. * Data are significantly different from control group at p < 0.001.
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| 15456519 | PMC524492 | CC BY | 2021-01-04 16:03:03 | no | BMC Cancer. 2004 Sep 30; 4:70 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-70 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-731547154810.1186/1471-2407-4-73Research ArticleCD155/PVR plays a key role in cell motility during tumor cell invasion and migration Sloan Kevin E [email protected] Brenda K [email protected] Jean K [email protected] Carol [email protected] Claudia [email protected] Marina [email protected] Jennifer E [email protected] Christine [email protected] David N [email protected] Leodevico L [email protected] Daniel G [email protected] Department of Physiology, Tufts University School of Medicine, Boston, MA, USA2 Xerion Pharmaceuticals, AG, Munich, Germany3 Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA2004 7 10 2004 4 73 73 28 7 2004 7 10 2004 Copyright © 2004 Sloan 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
Invasion is an important early step of cancer metastasis that is not well understood. Developing therapeutics to limit metastasis requires the identification and validation of candidate proteins necessary for invasion and migration.
Methods
We developed a functional proteomic screen to identify mediators of tumor cell invasion. This screen couples Fluorophore Assisted Light Inactivation (FALI) to a scFv antibody library to systematically inactivate surface proteins expressed by human fibrosarcoma cells followed by a high-throughput assessment of transwell invasion.
Results
Using this screen, we have identified CD155 (the poliovirus receptor) as a mediator of tumor cell invasion through its role in migration. Knockdown of CD155 by FALI or by RNAi resulted in a significant decrease in transwell migration of HT1080 fibrosarcoma cells towards a serum chemoattractant. CD155 was found to be highly expressed in multiple cancer cell lines and primary tumors including glioblastoma (GBM). Knockdown of CD155 also decreased migration of U87MG GBM cells. CD155 is recruited to the leading edge of migrating cells where it colocalizes with actin and αv-integrin, known mediators of motility and adhesion. Knockdown of CD155 also altered cellular morphology, resulting in cells that were larger and more elongated than controls when plated on a Matrigel substrate.
Conclusion
These results implicate a role for CD155 in mediating tumor cell invasion and migration and suggest that CD155 may contribute to tumorigenesis.
==== Body
Background
Metastasis is responsible for greater than 90% of cancer-related deaths [1]. It is therefore of great importance to develop therapies that limit this process. Cell migration plays a key role in invasion, an early step in metastasis, and proteins that regulate migration are often upregulated in tumor cells [2]. Cell migration also plays a key role in the dispersal of tumor cells within a tissue. In glioblastoma, the most aggressive form of brain cancer, tumor cells disperse so extensively that common treatment approaches such as resection or radiation therapy are not effective in checking progression [3].
Both invasion and dispersal are complex processes that require migration of individual cells from the tumor core into surrounding tissue and the extracellular matrix. Cell migration requires a coordinated orchestration of complex events including polarization, protrusion, adhesion, de-adhesion, and retraction [4]. Many cell-surface proteins are involved in regulating migration. Growth factor receptors receive environmental cues and initiate signaling cascades resulting in polarization and directional migration [5]. Cell adhesion molecules (CAMs) such as integrins, cadherins, and immunoglobulin family proteins, mediate adhesion and deadhesion between a cell and its neighbors or the extracellular matrix (ECM) and can also contribute to polarization and directional motility in response to soluble ECM proteins. CAMs sit at the top of many signaling cascades that regulate actin and microtubule dynamics through Rho family GTPases [2]. While many proteins have been described to play a role in cell migration, the mechanisms through which they act remain unclear [2]. Establishing which of these proteins are required for tumor invasion and migration is an important first step to developing therapeutics aimed at limiting the metastasis or dispersal of tumor cells.
In the post-genome era, global strategies are being developed to identify new players in complex biological processes such as tumor cell invasion. Microarray experiments have identified many differentially expressed genes that may contribute to enhanced invasion [6-10], but these correlative expression changes can only suggest functional importance. RNAi screens for cancer-relevant phenotypes have already identified several new gene targets [11,12]. Such approaches are limited, however, by the availability of genome-wide RNAi libraries, the possibility of genetic compensation following chronic inactivation, and the inability of RNAi to knockdown stable proteins with slow turnover. Proteomic screens have the advantage of being able to assess the proteome in high throughput but do not directly address function [13].
To complement these approaches, we have developed a high-throughput, acute protein inactivation strategy called fluorophore-assisted light inactivation (FALI) that allows for direct assessment of protein function through the systematic inactivation of proteins [14-16]. We have previously applied this approach on a smaller scale to identify surface proteins important for tumor cell invasion and have established a role for extracellular Hsp90 in activating metalloproteinases required for invasion [16]. Here we used a larger, unbiased functional proteomic screen to demonstrate that CD155, the receptor for poliovirus, contributes to tumor cell invasion by regulating cell migration.
Methods
Cells
HT1080 human fibrosarcoma, Hs27 human fibroblasts, and U87MG human glioblastoma cells were obtained from ATCC. Normal human astrocytes were obtained from Cambrex Bioproducts. Cells were maintained in DMEM supplemented with 10% fetal bovine serum and penicillin/streptomycin (Invitrogen; HyClone). HT1080 cells were additionally supplemented with 0.1 mM non-essential amino acids (Invitrogen). All cells were grown at 37°C under a humidified 7% CO2 atmosphere.
Antibodies
Monoclonal antibodies used for FALI were as follows: β1-integrin (JB1, Chemicon); CD155 (D171, Neomarkers; pv404.19, Beckman Coulter). Two additional CD155-specific monoclonal antibodies (5D11, ID8) were generated by fusing specific binding scFvs selected from our library to a human IgG backbone. ScFv 1A2 was used as a negative control in the migration assay. 1A2 was selected for binding to the surface of HT1080 cells (see scFv Library Generation) and was confirmed to bind to the surface of HT1080 and U87MG cells by immunocytochemistry, though its protein target is not known. Antibodies used for immunocytochemistry and immunoblotting were as follows: CD155 (D171; CD155 rabbit polyclonal, gift of Dr. Eckard Wimmer); αv-integrin (AB1930, Chemicon); ErbB2 (06-562, Upstate). Actin was visualized with rhodamine-phalloidin (Molecular Probes). Fluorophore-labeled secondary antibodies were obtained from Molecular Probes; peroxidase-labeled secondary antibodies were from Cell Signaling.
scFv Library Generation
Spleen RNA was harvested from HT1080 immunized mice as described [16]. Immunoglobulin cDNA was synthesized using a primer mix and variable regions amplified using specific primers. Products were cloned into the phage display vector pXP10 and transfected into E. coli TG-1 resulting in a library of 107 independent clones. HTl080-specific scFvs were selected by immunopanning phage against fixed HT1080 lysate, resulting in 2760 binders. These scFvs were recloned into expression vector pXP7 (containing his- and E-tags) and expressed in TG-1 cells. Bacterial lysates containing scFv were prepared and tested for binding to the surface of fixed HT1080 cells by ELISA. This additional round of selection yielded 595 HT1080 surface binders. These scFvs were confirmed to bind to the surface of HT1080 by immunocytochemistry on live cells. For FALI/screening, his-tagged scFvs were purified from bacterial lysates using Ni-NTA Superflow resin (Qiagen).
FALI
Antibodies or scFvs were conjugated with FITC (Molecular Probes) at pH 9.5 at room temperature as previously described [17]. Cells were detached with Versene (Invitrogen) and resuspended in serum-free, phenol red-free HBSS (Invitrogen). Cells were incubated with FITC-conjugated antibodies for 1 h at room temperature with gentle rocking and were then transferred in at least sextuplet to replicate clear, flat-bottomed 96-well plates on ice. One plate was illuminated for 1 h with 300 W (1 × 105 lux) blue-filtered light (Brilliant Blue #69, Roscolux) using a high-powered slide projector (Ektagraphic III, Kodak). A replicate control plate was kept in the dark for 1 h.
Invasion and migration assays
FALI invasion assays with HT1080 cells were done as described previously [14,16] but used the scFv library described above. 1 × 105 cells were labeled with cell tracker orange, incubated with 20 μg/ml of FITC-labeled scFv, irradiated (or kept in the dark) with blue-filtered light, and 5 × l04 cells were loaded onto matrigel-coated (4 μg, top) polycarbonate membranes (8 μM, 96-well, Neuroprobe). Each sample was assayed in triplicate in at least two independent experiments. scFvs that showed a significant change in invasion (p < 0.01 and change > 2 standard deviations) were assayed a third time with a new scFv preparation. U87MG and HT1080 migration assays were performed essentially as above, but excluded matrigel and used 8μM, 96-well Fluoroblok membranes (BD Biosciences). Cell invasion or migration was quantified using a fluorescent plate reader (SpectraFluor Plus, Tecan) and confirmed by visualization under an inverted fluorescent microscope.
Immunoprecipitation and Mass Spectrometry
To identify scFv protein targets, candidate scFv genes were re-cloned into expression vector pXP14, containing a strep-tag. Purified scFvs were coupled to StrepTactin Sepharose (50 μg/50 μl resin) and the washed scFv-beads were added to 1 mg HT1080 lysate. The scFv-target complexes were eluted (10 mM D-desthiobiotin, 0.1% Tween 20 in PBS) and the immunoprecipitated proteins were analyzed by SDS-PAGE and silver staining. In a parallel experiment, the immunoprecipitated proteins were subjected to deglycosylation using N-glycosidase F prior to SDS-PAGE analysis. Stained bands were excised and subjected to in-gel tryptic digestion. The peptide fragments were extracted from the gel, desalted on ZipTip μC18, the eluted peptides spotted on a Teflon-coated MALDI target, let dry and overlayed with 1 μl of a 3.5 mg/ml solution of α-Cyano-hydroxycinnamic acid. The samples were analyzed on a STR-DE Voyager MALDI mass spectrometer (Applied Biosystems) and the obtained peptide masses were used for protein identification via peptide mass fingerprint, searching all entries for the species Homo sapiens in the NCBI and SwissProt databases. Alternatively, the extracted peptide fragments were analyzed by nano electrospray mass spectrometry (nanoES-MS) on a Q-STAR QqTOF mass spectrometer (PE SCIEX). Relevant ions were selected for CID (collision induced dissociation)-MS and the obtained fragment ion data used for Peptide Sequence Tag database search.
CD155 siRNA
A double stranded siRNA oligonucleotide targeting CD155 (5'-CAACUUUAAUCUGCAACGUdTdT-3') was chemically synthesized (Dharmacon Research) and transfected into HT1080 cells using Oligofectamine (Invitrogen) following manufacturers instructions using 200 nM siRNA per 10 cm dish. Cells were incubated with siRNA in OptiMEM (Invitrogen) for 6 hrs after which time normal growth media was added. Cells were then incubated for 48–72 h to achieve >80% knockdown of CD155. Control cells were transfected with a scrambled siRNA oligonucleotide at matching concentration. Cells were then inserted into the migration assay described above or used for morphology experiments.
Cell morphology measurements
HT1080 cells were co-transfected with siRNA (scramble control or CD155-specific) and fluorescently labeled oligonucleotide (10 nM, Sequitur) for 48 hours. Cells were detached with Versene, and 5–15 × 103 cells were loaded onto glass coverslips coated with Matrigel (2μg/ml, BD Biosciences). After 2 hours of incubation at 37°C/7% CO2, cells were fixed and immunostained for CD155 using mAb D171. Cells were visualized with a Nikon Diaphot 200 microscope and images taken of >100 transfected cells (containing fluorescent oligo). Analysis of images was performed with OpenLab software (Improvision). Measurements of cell area, perimeter, and shape were made for each condition.
Wound healing assay
8-chamber slides (Falcon) were coated with matrigel (2 μg/ml) and blocked with FCS. 100,000 cells were plated in each well and grown to confluency. A 20-gauge needle was used to create a linear wound and cells allowed to recover for 3 h at 37°C. Cells were fixed and processed for immunocytochemistry as described below.
Immunocytochemistry
Cells were fixed in PBS/4% paraformaldehyde/4% sucrose, blocked in PBS/0.01% triton-x-100/10%FBS, and incubated in primary antibody for 1 h at rt. Appropriate species-specific secondary antibodies conjugated to Alexa 488 or 594 were used to visualize antibody staining on a Leica SP2 confocal microscope using LCS software. Antibodies: CD155 (D171), αv-integrin (AB1930), actin (rhodamine-phalloidin, Molecular Probes), ErbB2 (6562). Secondary antibodies alone were used to control for non-specific staining. For primary tissue, paraffin sections from a human tissue library were stained with biotinylated anti-CD155 human mAb 1D8 at 200 μg/ml and visualized with streptavidin-hrp and NovaRed substrate. For glioblastoma tumors, we used tissue microarrays, made from 20 primary tumors arrayed in quadruplicates of 1 mm cores with a Beecher Instrument arrayer. Positive staining was visualized with DAB substrate.
Immunoblotting
Pelleted and PBS-washed cells were lysed in NP40 lysis buffer (0.2% NP-40, 150 mM NaCl, 20 mM Tris pH7.5, 10% glycerol) with protease inhibitors (Roche) at 4°C. Lysates were cleared by centrifugation and quantified using the DC Protein Assay (BioRad). 30 μg lysate was separated on a 10% SDS-PAGE gel and transferred to a nitrocellulose membrane. Membranes were blocked in 5% nonfat dry milk in PBS and probed with primary antibody overnight. Antibody binding was detected with peroxidase-conjugated secondary antibodies (Cell Signaling) and visualized using ECL substrate (PerkinElmer).
Results
Functional proteomic screen reveals a role for CD155/PVR in tumor cell invasion and migration
We generated a recombinant single chain variable fragment (scFv) antibody library that recognized proteins on the surface of HT1080 fibrosarcoma cells, a highly invasive tumor cell line. ScFvs were selected from a phage display library generated from mice immunized with fixed HT1080 cells [16]. Genes encoding the selected scFvs were re-expressed as scFv antibodies and selected by ELISA for HT1080 surface binding. The selected scFvs were conjugated to fluorescein and used to acutely inactivate their protein targets by fluorophore-assisted light inactivation (FALI). The invasiveness of FALI- and control-treated cells was then compared using a 96-well transwell assay incorporating a matrigel-coated 8μM filter above a serum chemoattactant (Fig. 1a). 338 scFvs were screened in triplicate using this FALI-invasion assay. 15 scFvs caused a significant reduction of invasion compared to non-FALI controls (p < 0.01, unpaired t-test) and an amplitude change of greater than twice the average standard deviation. The majority of scFvs screened (323) had no significant effect on invasion and serve as an internal control. The 15 positive scFvs were re-screened using fresh scFv preparations in triplicate, and six scFvs were selected that continued to satisfy both criteria of significance (Fig. 1b). Sequencing of the scFv cDNAs revealed two unique groups of clones. The first group (I) contained a single scFv binder while the second group (II) included 5 scFvs with identical or nearly identical variable heavy and light chains. Protein targets of the two scFv groups were immunoprecipitated and identified by mass spectrometry.
The target of group I was α3βl-integrin, a promiscuous extracellular matrix binder that plays multiple roles in cell adhesion, morphology, migration, and invasion [18]. Identifying α3βl-integrin demonstrates the ability of our screen to reveal proteins with a confirmed role in tumor invasion. The target of group II was CD155, the poliovirus receptor. CD155 is a member of the nectin subclass of immunoglobulin domain proteins whose cellular function has not yet been established. To confirm the identification of CD155 as a mediator of invasion, we repeated the invasion assay using a previously characterized monoclonal antibody specific for CD155 (D171). FALI of CD155 significantly inhibited invasion of HT1080 cells by 32% (p < 0.01, t-test; Fig. 2a), consistent with the screening result.
In order to determine whether this result was specific to invasion through matrigel or due to effects on cell migration, we repeated the transwell experiment in the absence of matrigel using several different CD155-specific monoclonal antibodies, either previously characterized (D171, pv404) or newly generated by fusing the variable regions from our CD155-binding scFvs to a human IgG backbone (5D11, 1D8). FALI of CD155 significantly inhibited transwell migration by 20 to 23% (p < 0.01, t-test; Fig. 2b), suggesting a role for CD155 in cell migration that is responsible for the bulk of its contribution to invasion. FALI in the absence of antibody or in the presence of scFv 1A2, which had no effect in the invasion screen, did not alter migration. FALI of CD155 did not affect cellular viability or proliferation (data not shown). FALI with combinations of CD155 binders did not enhance the inhibition of migration (Fig. 2b), suggesting that inactivation was maximal or that the antibodies bound and saturated a common epitope.
To further validate the role of CD155 in tumor cell migration, we developed a siRNA duplex targeting CD155 mRNA as a complementary, chronic means of protein inactivation. Knockdown of CD155 mRNA yielded ~90% depletion of CD155 protein at 72 h and a 23% reduction in migration compared to control cells transfected with a scrambled siRNA duplex (p < 0.01, t-test; Fig. 2c). The extent of migration inhibition due to siRNA was equal to the inhibition seen by FALI, supporting the specificity of the antibodies. The observed changes in migration were not due to changes in survival or proliferation as measured by an MTS viability assay (data not shown). Taken together, the results from FALI and siRNA knockdown of CD155 clearly establish a role for CD155 in tumor cell invasion and migration.
CD155/PVR protein is highly expressed in cancer cells and primary tumors compared to normal counterparts
Since our screen was performed using a library selected for binding to HT1080 surface proteins, and thus might have a tendency to target proteins upregulated in these cells, we profiled CD155 expression in these and other cells. Lysates prepared from normal fibroblasts, fibrosarcoma, normal astrocytes, and GBM cells were tested for expression of CD155 by immunoblot using a polyclonal CD155 antibody (gift of Eckard Wimmer).
High levels of CD155 expression were observed in HT1080 and U87MG cells whereas the protein was only weakly expressed in their non-tumor counterparts (Fig. 3a). Since HT1080 and U87MG cells are highly invasive while hs27 cells and normal astrocytes are not (personal observation), these results suggest that upregulation of CD155 may contribute to an invasive phenotype.
Given our finding that CD155 appeared to be overexpressed in our model tumor cell lines, we evaluated CD155 expression levels in normal and cancerous human tissue. We performed immunohistochemistry on paraffin sections taken from a tissue library using mAb 1D8. In normal tissue we observed moderate staining in kidney, plasma cells, liver, lung, theca interna of the ovary, and testis (data not shown). No staining was observed in normal adrenal, bladder, brain, breast, colon, heart, pancreas, placenta, prostate, skin, skeletal muscle, small intestine epithelium, spleen, stomach, thymus, thyroid, or uterus (at least two samples examined per tissue). In cancer tissue, we observed extensive staining in a subset of samples taken from several different tumor types (Fig. 3b). These included prostate carcinoma (4 out of 10 samples examined), renal cell carcinoma (4/10), pancreatic carcinoma (7/10), colon carcinoma (2/10), ovarian carcinoma (2/10), non-small cell lung carcinoma (1/10), and breast carcinoma (1/10). Since CD155 had previously been suggested to be upregulated in glioblastoma (GBM) tumors [19], we performed immunostaining on a tissue array to examine CD155 protein expression across twenty different GBM tumor samples. Staining was observed in eight of the samples (Fig. 3c). Two types of positive staining were evident: scattered positive cells within a predominantly negative sample (5/20), or diffuse staining across many cells in the sample (3/20). Collectively, these data indicate that CD155 expression is frequently elevated in primary tumors. Since normal and cancerous tissue samples were not collected from the same patient, we cannot determine if elevated CD155 expression correlates with tumorigenesis, but speculate that such an association may exist.
CD155/PVR is recruited to the leading edge of migrating tumor cells and colocalizes with actin and αv-integrin
To address the role of CD155 in migrating cells, we employed a modified wound-healing assay and examined the sub-cellular localization of CD155. HT1080 cells were plated onto chambered tissue culture slides coated with a thin layer of Matrigel ECM substrate and grown to near confluence. A linear wound was made using a 20-gauge needle and the cells were allowed to recover for 3 h. Cells were fixed and immunostained to visualize CD155. CD155 was found to preferentially localize to the leading edge of cells, though some staining in trailing edges and cell-cell contacts could also be seen (Fig. 4a). In cells plated in isolation, where directionality could not be established, CD155 was consistently observed at some but not all peripheral edges of cells (data not shown). These results suggest that CD155 is recruited to the leading edge of migrating cells where it may be involved in directional motility.
Given our findings that CD155 was important for cell migration and that it localized to the leading edge of migrating cells, we asked whether CD155 might co-localize with other proteins known to be involved in motility. We used immunofluorescence and confocal microscopy to visualize CD155 along with actin and αv-integrin. CD155 colocalized extensively with actin ruffles at the leading edge of migrating cells (Fig. 4b), suggesting a potential link between CD155 and the actin cytoskeleton. CD155 also colocalized with αv-integrin, a known mediator of ECM adhesion [20], but not the epidermal growth factor receptor family member ErbB2, a mediator of growth factor signaling [21]. Thus, CD155 is associated with key players in substrate adhesion at the leading edge, and may be working in concert to mediate motility.
CD155/PVR influences cellular morphology
The previous section showed that CD155 localizes to the leading edge of migrating cells and colocalizes with known mediators of motility. Since cell shape changes are often associated with changes in motility and/or adhesion, we next investigated whether knockdown of CD155 by RNAi affected cellular morphology. HT1080 cells were transfected with either a CD155-specific or scrambled control siRNA (200 nM) along with a fluorescent, non-specific oligo (10 nM) to identify transfected cells. 48 hours after transfection, cells were plated at low density onto coverslips that had previously been coated with Matrigel, fixed, and immunostained for CD155 to confirm protein knockdown. Only cells containing fluorescent oligo were selected for analysis. CD155 knockdown cells had significantly larger perimeters and appeared more irregular in shape than control cells (Fig. 5). These findings suggest that CD155 has a role in cell size and shape, perhaps by regulating adhesion of cells to their substrate.
CD155/PVR regulates migration of glioblastoma cells
Our studies so far have been conducted using HT1080 fibrosarcoma cells. To assess the generality of CD155 function in cancer, we asked whether it could regulate the migration of other tumor cells in vitro. Our expression studies suggested that CD155 was upregulated in a subset of glioblastoma (GBM) tumors for which migratory behavior is poorly understood [3]. U87MG GBM cells were found to express high levels of CD155 (Fig. 3a). Knockdown of CD155 protein by FALI in U87MG cells resulted in a significant (16 to 22%) decrease in transwell migration towards a serum chemoattractant (p < 0.01, t-test; Fig. 6). FALI in the absence of antibody or in the presence of scFv 1A2, which binds to the surface of U87MG cells, did not alter migration. Cellular viability and proliferation were unchanged (data not shown). These results were consistently reproduced using multiple independent monoclonal antibodies targeting CD155 (D171, pv404, 5D11, 1D8). FALI with combinations of these antibodies did not enhance the inhibition of migration (Fig. 6), similar to our observations in HT1080 cells. These results demonstrate a role for CD155 in regulating migration across multiple tumor cell types.
Discussion
We developed a functional proteomic screen to identify surface proteins involved in tumor cell invasion [16] and here have expanded it to interrogate a library of 338 single chain phage display antibodies. One of the proteins identified as a mediator of invasion was CD155, the poliovirus receptor. This work reveals a novel role for CD155 as a mediator of tumor invasion that is likely due to its function in cell migration.
Knockdown of CD155 by FALI or by RNAi resulted in impaired in vitro migration and a pronounced change in cellular morphology with cells becoming more elongated and irregular in shape. CD155 localizes to the leading edge of migrating tumor cells and co-localizes with actin ruffles and αv-integrin, suggesting that CD155 may act in motility and/or cell-substrate adhesion. We also observed elevated expression of CD155 in a number of different cancer cell lines and primary tumors, suggesting a link between CD155 and tumorigenesis.
The endogenous function of CD155 is not well understood. CD155 is a type I transmembrane glycoprotein first identified based on its ability to mediate the binding of poliovirus to host cells [22]. It is a member of the immunoglobulin (Ig) superfamily and belongs to a subclass that contains three Ig-like domains (V-C2-C2). This subclass includes the nectins and several nectin-like proteins including the rodent Tage4 gene [23]. Nectins have been implicated in organizing cell-adherens junctions through homo-and heterophillic adhesion [24,25]. While nectins bind to the actin cytoskeleton through afadin, CD155 does not [24], suggesting that CD155's cellular role is distinct from that of nectins.
Several proteins have been found to interact with CD155. The ECM protein vitronectin binds CD155 in vitro suggesting that CD155 may mediate cell-substrate adhesion [26]. Our findings that CD155 co-localizes with αv-integrin, a receptor for numerous ECM proteins including vitronectin, is consistent with previous reports [23] and suggests a functional role for CD155 in mediating adhesion. Activation of integrins leads to assembly of focal adhesion complexes that stabilize cellular interaction with its substrate through intracellular signaling and rearrangement of the actin cytoskeleton [27]. Our experiments showed that loss of CD155 inhibited migration and induced cell spreading. This phenotype is similar to that observed in F397-FAK fibroblasts in which focal adhesions are enhanced and cell spreading is increased [28]. We speculate that CD155 could be involved in modulating integrin/substrate interactions leading to decreased adhesion or increased turnover of focal adhesions. Another Ig-domain containing protein, CD47, has been shown to bind to αv-integrin and is present in early adhesion complexes at the leading edge of spreading melanoma and human vascular endothelial (HUVEC) cells [29]. CD47 appears to regulate integrin activation and contribute to integrin-mediated adhesion events [30]. CD155 has also been shown to interact with nectin-3 [23], the dynein motor protein Tctex-1 [31], and also to reside proximal to CD44 [32]. Future studies will address the importance of these interactions for cancer cell migration.
Our results, in which CD155 inhibition reduces migration, are also consistent with a model in which transient interactions between CD155 and ECM result in pro-migratory signals. For example, it is known that binding of integrins to ECM proteins induces pro-migratory signaling through clustering of associated kinases [33]. CD155 itself may transduce signals when bound to ECM proteins or could be involved in the clustering of larger complexes. During preparation of this manuscript, Oda et al. reported that crosslinking of exogenously expressed CD155 resulted in tyrosine phosphorylation of its cytoplasmic tail, and concurrent reductions in focal adhesion kinase (FAK) and paxillin phosphorylation in NIH3T3 mouse fibroblasts [34]. Crosslinking of CD155 also resulted in decreased adhesion to fibronectin, a reduction in the number of focal adhesions, and an increase in migration [34]. It is possible that overexpression of CD155 in cancer cells drives dimerization of CD155 in the absence of ligand, resulting in decreased adhesion and increased migration as well as other signaling events.
CD155 expression has been reported widely to be restricted to primates [35,36], but recent work suggests that Tage4 may be a rodent ortholog [37,38]. At the amino acid level, Tage4 shares only 42% homology with CD155 [38] and rodents are not susceptible to polio virus infection [22,39]. However, Tage4 shares the extracellular structure of CD155 and the two genes reside in syntenic chromosomal regions [38]. Emerging data suggest functional similarities between the two proteins. Tage4 has been shown to bind to both nectin-3 and vitronectin [37,38] and also appears to colocalize with αvβ3-integrin [37]. Recently, Tage4 has been implicated in cell migration [37]. Overexpression of Tage4 led to increased migration of murine L cells in a serum- and integrin-dependent manner while Tage4 mutants inhibited motility [37]. Furthermore, V12Ras-transformed NIH3T3 cells, which form tumor nodules in the lungs of nude mice, were found to express elevated levels of Tage4 and expression of a dominant-negative Tage4 inhibited the ability of these cells to form nodules [37]. Due to low sequence conservation and the lack of functional data for CD155, it has been difficult to resolve whether Tage4 and CD155 are true orthologs. Our identification of a role for CD155 in tumor cell migration supports the idea that these proteins are functionally related. Thus, it will be interesting to compare the two proteins in future studies in order to better define the mechanism of action for CD155 both in normal cells and in cancer states.
We observed high levels of CD155 protein expression in a subset of several different types of primary tumor. Previously, expression of the CD155 gene had been reported to be upregulated in colon cancer [40] and possibly glioblastoma [19]. Here we have extended that observation at the protein level to several additional tumor types, including cancers of the prostate, kidney, pancreas, lung, ovary, breast, and brain, suggesting a much broader role for CD155 in tumorigenesis. A search of the EST and SAGE library databases maintained by the Cancer Genome Anatomy Project (CGAP; ) using the unique identifier AACCACCCAG supports the idea that expression of the CD155 gene may be elevated in several tumor types including colon, brain, kidney, pancreas, lung, and stomach (Table 1). Our finding that CD155 is involved in tumor cell migration in fibrosarcoma and glioblastoma cells implicates CD155 as a mediator of metastasis and dispersal. The selective expression of CD155 in tumors compared to normal tissue further supports the idea that targeted inhibition of CD155 could serve as a useful therapeutic approach to limit the spread of tumor cells in vivo. Very recently, Ochiai et al. reported that CD155 expression is upregulated in several breast cancer cell lines and primary breast tumors and demonstrated that an oncolytic poliovirus recombinant delivering a toxic payload could selectively kill breast cancer cells [41]. Our findings suggest that such a therapeutic approach could also have value in treating other cancer types. Since elevated expression of CD155 was detected in a subset of samples from the examined tumor types, it is possible that CD155 expression may represent a late-stage event in tumorigenesis. It is also possible that CD155 could lie in one of several different oncogenic pathways. Further research is necessary to determine if and how CD155 contributes to cancer progression in vivo.
This work extends the utility of our FALI-based functional screening approach in identifying proteins with a role in tumor cell invasion. Our previous screen identified an extracellular role for Hsp90 in regulating tumor cell invasion through regulation of MMP2 activity [16]. In contrast, this screen has identified a role for CD155 in regulating tumor cell motility. Thus, our approach can yield novel mediators of tumor cell invasion that are involved either directly in invasion or more generally in cell migration. Future applications of this technology will likely yield additional validated targets and open new avenues for research.
Conclusions
In summary, we have applied a novel, functional proteomic approach to identify proteins that mediate tumor cell invasion and have identified a novel role for CD155 in regulating cancer cell invasion and migration. We suggest that CD155 may control migration by regulating cell-substrate adhesion. We have further shown that CD155 is commonly expressed at high levels in primary tumors and speculate that it may contribute directly to tumorigenesis by enhancing cancer cell migration in vivo.
Competing interests
KS, BE, JS, MS, JR, DL: none declared
CZ, CT, CU, LI, DJ: received funding (research support or salaries) from Xerion Pharmaceuticals, AG.
Authors' contributions
KS contributed to study design, data interpretation, carried out the migration experiments with FALI and RNAi and the characterization of CD155 expression, and drafted the manuscript. BE contributed to study design, data interpretation, carried out the invasion screen and morphology experiments, and helped revise the manuscript. JS contributed to the invasion screen. CZ and CU generated and characterized the scFv library. CT performed the IP and mass spec to identify CD155. MS helped with the morphology experiments. JR and DL performed the GBM tissue staining and analysis and provided helpful discussion. LI and DJ contributed to study design, data interpretation, and editing of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank S. Schonhoff for critical reading of the manuscript. This work was supported in part by grants from the National Cancer Institute, the Goldhirsh Foundation, and a National Institute of Health program grant to the Gastroenterology Research on Absorptive and Secretory Processes (GRASP) core facility. KES and BKE were supported by a National Institutes of Health training grant.
Figures and Tables
Figure 1 Proteomic screen reveals a role for CD155 in tumor cell invasion. (a) Mice were inoculated with fixed HT1080 cells. A phage display library was generated from immunoglobulin genes (VH and VL) to generate recombinant scFvs (107 phage). Phage were immunopanned against HT1080 cells, and 2,760 binders were isolated. 595 of the resulting scFvs were further selected as HT1080 surface binders. These surface binders were conjugated to FITC and used to inactivate their protein targets by fluorophore-assisted light inactivation (FALI). The invasiveness of FALI- and control-treated cells was then compared using a high-throughput transwell assay in which cells were challenged to invade through a matrigel-coated 8βM filter towards a serum chemoattactant. Protein targets of candidate scFvs were identified by immunoprecipitation and mass spectrometry. (b) 338 scFvs were screened in triplicate in two independent experiments using the FALI-invasion assay. 6 scFvs caused a significant reduction of HT1080 invasion compared to non-FALI controls (p < 0.01, unpaired t-test and an amplitude change of greater than twice the average standard deviation).
Figure 2 CD155 mediates tumor cell invasion and migration. (a) FALI of CD155 resulted in a significant inhibition of HT1080 transwell invasion through matrigel towards a serum chemoattractant (Δ invasion ± FALI: no Ab 19 ± 6%; β1-int 32 ± 4%; CD155 32 ± 11%). (b) FALI of CD155 using multiple mAbs resulted in a significant inhibition of HT1080 transwell migration towards a serum chemoattractant (Δ migration ± FALI: no Ab l ± 4%; β1-int 30 ± 6%; 1A2 (control, surface binding scFv) 1 ± 3%; CD155: D171 23 ± 4%; pv404 20 ± 3%; 5D11 22 ± 4%; 1D8(20) 21± 2%; 1D8(40) 23 ± 4%; CD155pool 22 ± 1%). FALI with CD155 mAb pools did not yield greater inhibition, suggesting maximal inactivation or that the binders share a common epitope. (c) CD155-specific siRNA duplexes were used to knockdown protein expression by about 90% by 72 h. Knockdown of CD155 in HT1080 cells resulted in a significant decrease in transwell migration compared to control cells transfected with a scrambled duplex (23 ± 4%). * indicates p < 0.01, t-test.
Figure 3 CD155 is highly expressed in cancer cells and primary tumors. (a) Lysates from normal and cancer-derived cell lines were immunoblotted for CD155. CD155 expression was elevated in fibrosarcoma cells (HT1080) vs. normal fibroblasts (Hs27) and also in glioblastoma cells (U87MG) vs. normal human astrocytes (NHA). Antibody specificity is demonstrated using HT1080 cells in which CD155 expression is knocked down by RNAi. β-actin is shown as a loading control. (b) CD155 protein expression was examined by immunocytochemistry across a panel of normal and cancer tissue samples. In normal tissue we observed moderate staining in kidney, plasma cells, liver, lung, theca interna of the ovary, and testis. No staining was observed in normal adrenal, bladder, brain, breast, colon, heart, pancreas, placenta, prostate, skin, skeletal muscle, small intestine epithelium, spleen, stomach, thymus, thyroid, or uterus. In cancer tissue, significant staining was observed in prostate, renal cell, and pancreatic carcinomas as well as in colon, non-small cell lung, ovarian, and breast carcinomas. (c) High CD155 protein expression was also observed in glioblastoma tumor tissue. Two types of staining were evident: scattered positive cells (arrows) in a primarily negative sample, or diffuse staining across many cells in a sample.
Figure 4 CD155 is recruited to the leading edge of migrating cells and colocalizes with actin and αv-integrin. (a) HT1080 cells were grown to near confluency on a matrigel substrate. A linear wound was made with a 20 g needle and cells allowed to recover for 3 h. Cells were fixed and immunostained for CD155. The majority of CD155 appeared to localize to the leading edge of migrating cells. (b) CD155 colocalized extensively with actin and αv-integrin at the leading edge of HT1080 cells migrating on a matrigel substrate. CD155 did not appear to colocalize with the growth-factor receptor ErbB2.
Figure 5 CD155 regulates cellular morphology. HT1080 cells were transfected with siRNA targeting CD155 or with a scrambled control duplex and plated on matrigel after 48 h. Cells were fixed and immunostained for CD155 expression before images were acquired for morphometric analysis. (a) Schematic showing measurement parameters. (b) Chang plot showing cell perimeter measurements. Perimeters values are expressed as percent of the average control perimeter. As a population, CD155 knockdown cells (dark circles) exhibited a significant increase in perimeter compared to controls (light circles). (c) Chang plot showing cell shape measurements. Cell shape was defined using the equation (4 × cell area) / cell perimeter2, where 1 indicates a perfect circle and smaller values indicate a more irregular shape. CD155 knockdown cells (dark circles) were more irregular in shape than scrambled controls (light circles). Perimeter: Δ avg = +18%; shape: Δ avg = -13%;p < 0.001, t-test; n = 120 scramble siRNA and 112 CD155 siRNA.
Figure 6 CD155 mediates motility of GBM cells. FALI of CD155 using multiple mAbs resulted in a significant inhibition of U87MG GBM cell transwell migration towards a serum chemoattractant (no Ab 3 ± 3%; β1-int 47 ± 3%; 1A2 (control, surface binding scFv) 3 ± 2%; CD155: D171 17 ± 2%; pv404 22 ± 2%; 5D11 16 ± 3%; IDS 17 ± 2%; CD155pool21 ± 4%; * indicates p < 0.01, t-test). FALI with CD155 mAb pools did not yield greater inhibition, suggesting maximal inactivation or a common binding site.
Table 1 Results of CGAP EST and SAGE library search for CD155
EST data (tags/200,000)
Tissue Normal Cancer
Lung 4 37
Genitourinary 0 35
Head and neck 0 27
Stomach 0 23
Colon 0 22
Pancreas 0 20
Kidney 3 17
Bone 0 14
Cervix 0 10
SAGE data (pos libraries/ total libraries)
Tissue Normal Cancer
Brain 0/8 21/63
Lung 0/1 2/3
Pancreas 0/1 2/2
Kidney 0/1 1/1
Thyroid 0/1 1/1
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| 15471548 | PMC524493 | CC BY | 2021-01-04 16:03:03 | no | BMC Cancer. 2004 Oct 7; 4:73 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-73 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-281545856910.1186/1471-244X-4-28Research ArticleEfficacy of two once-daily methylphenidate formulations compared across dose levels at different times of the day: Preliminary indications from a secondary analysis of the COMACS study data Sonuga-Barke Edmund JS [email protected] James M [email protected] David [email protected] Heleen H [email protected] Simon J [email protected] The Developmental Brain-Behaviour Unit, University of Southampton, Southampton, SO17 1BJ. UK2 Child Development Center, University of California at Irvine, Irvine, CA 19722. USA3 The Department of Psychiatry, The University of Dundee, Dundee, DD1 9SY. UK4 Medical and Regulatory Affairs, Celltech Americas, Inc., Rochester, NY 14603. USA2004 30 9 2004 4 28 28 9 6 2004 30 9 2004 Copyright © 2004 Sonuga-Barke et al; licensee BioMed Central Ltd.2004Sonuga-Barke 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
Methylphenidate (MPH) is commonly prescribed in the treatment of Attention-Deficit/Hyperactivity Disorder or ADHD. Concerta and Metadate CD are once-daily formulations of MPH using different delivery mechanisms resulting in different pharmacokinetic profiles. A recent study (COMACS) showed that for near-milligram (mg) equivalent daily doses, Metadate CD provides greater symptom control in the morning (1.5 through 4.5 hours post-dose), while Concerta provides greater control in the early evening (12 hours post-dose). Non-inferential comparison of effects for different dose levels of the two formulations suggested that equivalent levels of morning symptom control could be obtained with lower daily doses of Metadate CD than Concerta; the situation being reversed in the evening. The current paper presents a secondary analysis that provides a statistical test of these observations.
Method
The COMACS study was a multi-center, double-blind crossover study of Metadate CD, Concerta and placebo with each treatment administered for 1 week. Children were assigned on the basis of their pre-trial dosage to either high (Metadate CD 60 mg; Concerta 54 mg), medium (Metadate CD 40 mg; Concerta 36 mg) or low doses (Metadate CD 20 mg; Concerta 18 mg) of MPH, and attended a laboratory school on the 7th day for assessment at 7 sessions across the day. For the post-hoc comparisons across dose levels presented here, total SKAMP scores with the active treatments (adjusted for placebo response) were analyzed using an analysis of covariance, with a combined measure modeling placebo response across all time period as the covariate.
Results
Symptom control from 1.5 through 6.0 hours post-dose was as good with lower doses of Metadate CD (20 and 40 mg) as with higher doses of Concerta (36 and 54 mg, respectively). Lower daily doses of Concerta (18 and 36 mg) and higher doses of Metadate CD (40 and 60 mg, respectively) gave equivalent control at 7.5 and 12 hours with Metadate CD giving better control from1.5 through 6.0 hours post-dose.
Conclusions
Different delivery profiles of Metadate CD and Concerta can be exploited to limit total daily exposure to MPH while at the same targeting a specific, especially clinically significant, period of the day. These results need to be confirmed in a study in which children are randomly allocated to different dose levels of the two formulations and plasma MPH concentrations are assessed simultaneously.
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Background
Attention Deficit /Hyperactivity Disorder (ADHD) is a relatively common early onset developmental condition characterised by a pervasive and persistent pattern of age inappropriate and debilitating inattention, impulsiveness and overactivity. It is reported to affect between 3 and 6 percent of the childhood population and, if untreated, to be associated with a poor prognosis in adolescence and adulthood [1,2]. Methylphenidate (MPH) remains a pharmacological treatment of first choice for children with ADHD [3]. Historically, effective 'all-day' management of symptoms has relied on the use of multiple doses (typically two or three) of immediate release (IR) MPH spread out across the day (early morning, midday and evening)[4]. The use of IR formulations in this way combines all-day coverage with the opportunity to tailor doses at different times of the day to meet the specific needs of children. However, there is evidence that multiple dosing leads to problems with adherence especially during the school day where children receiving medication may feel stigmatised by their classmates [5]. Once-a-day sustained release (SR) formulations have been licensed in the US for some time but the early formulations were not widely used because of the perceived lack of efficacy especially with regard to speed of onset [6]. In the last few years a second generation of more effective formulations (referred to here as extended release formulations – ER) have been licensed. These exploit a range of different delivery technologies and offer smooth patterns of symptom control across the day [7-9]. These new formulations represent a major advance in the clinical management of the condition and are popular with both patients and clinicians. Various ER formulations have been designed each with a different pharmacokinetic (PK) and pharmacodynamic (PD) profile that results in differing patterns of duration and timing of effect. Thus they have the potential to provide clinicians with the opportunity to simplify the dosing regime without loosing the ability to tailor treatment to the clinical profile of an individual patient. In order to exploit this opportunity clinicians need to be able to make informed decisions about the comparative benefits of differing doses of different formulations with different PK/PD profiles. Unfortunately, to date, there have been few head-to-head trials of these new ER formulations that provide the information required for this.
We recently reported the results of a randomised, placebo-controlled, head-to-head comparison of the pharmacodynamic (PD) properties of near mg-equivalent daily doses of two safe and effective [10,11] ER formulations of MPH in children (the COMACS study; [12]). Concerta (CON) was designed to replace three-times-a-day (TID) IR MPH and provide twelve-hour symptom coverage. It consists of an insoluble OROS™ tablet with an IR drug over coat. Twenty two percent of the dose is in the IR overcoat and 78% in the ER core, which is released, by an osmotic pump process [7,10]. Metadate CD (MCD) has a profile more in keeping with a two-times-a-day (BID) regime of IR MPH. It consists of a soluble capsule containing a mixture of IR MPH beads (30% of the total dose) and ER MPH beads coated with a controlled-release polymer to deliver MPH gradually over the extended period (70% of the total dose) [8,11]. Low (20 mg MCD and 18 mg CON), medium (40 mg MCD and 36 mg CON), and high (60 mg MCD and 54 mg CON) doses are available for each formulation and have been demonstrated to deliver equivalent levels of total exposure to MPH in adults [13] (as shown by comparable AUCs and Cmax). Recently a 10 and 30 mg dose of MCD and a 27 mg dose of CON were licensed, but these were not available during the COMACS study. As a result of the design differences, MCD releases 50% more IR MPH in the initial bolus delivery process than CON for a near mg-equivalent total daily doses. Furthermore although the amount of ER MPH is the same for near mg-equivalent doses the pattern of delivery differs with MPH release front-loaded with MCD (starting at 1 hour) and more back-loaded with CON.
The different IR/ER ratios and time-course for dissolution of the two extended-release portions of the formulations result in distinctly different plasma concentration vs. time profiles in adults when compared at near-mg equivalent total doses [13]. Concentrations of MPH are significantly higher for MCD than for CON for up to 6 hours after dosing, and, by contrast, concentrations of MPH are significantly higher for CON from 8 through 12 hrs after dosing. Differences in plasma concentration vs. time profiles are expected to occur in children as the PK of MPH in adults and children are qualitatively similar (i.e. there are no reported age-related differences in absorption, distribution, metabolism and excretion of MPH between these populations [14-16]). In agreement, the plasma concentration vs. time profile for MCD in children is consistent with that observed in adults [8]. Results of the COMACS study demonstrated that the PD patterns of the two formulations in children mirrored their expected differences in plasma concentration vs. time profiles. This meant that at each of the three near-mg equivalent daily dose levels, MCD produced a greater reduction in symptoms during the morning (up to six hours from drug administration) while CON produced superior control in the early evening (i.e., at 12 hours post-dose). However, the two formulations were equivalent in their effects during the afternoon between 6 and 7.5 hours post dose [12].
The publication of the main analysis of the COMACS study data has led to a number of suggestions for alternative analyses that would usefully address questions of the clinical utility of the two formulations. Although from a scientific point of view the aim of the COMACS study – to compare across-the-day PD profiles of the two formulations – was best served by a comparison of bio-equivalent (i.e. AUC-equivalent) total daily doses, other strategies for selecting comparator doses could have been legitimately followed. For instance, it has been suggested that comparators could have been matched on the basis of the size of the IR component dose rather than the total daily dose (see Table 1 for IR and ER components of the MCD and CON). In this regard it is interesting that an informal comparison of the relative efficacy of MCD and CON at each dose level suggested that equivalent morning symptom control would possibly be obtained at doses with a near mg-equivalent IR component but with a lower total daily dose of MCD than CON, while the situation was reversed in the evening (that is, lower daily doses of CON could have equivalent efficacy to higher doses of MCD). In each case, the COMACS study data suggested that this targeted control would be gained at the cost of efficacy at other point(s) during the day. These across-the-day-changes in patterns of the relative efficacy of the two formulations are consistent with what would be expected on the basis of their IR/ER ratios, their drug delivery mechanisms, and their resulting predicted plasma concentration vs. time profiles.
Table 1 Amounts of Immediate-Release (IR) and Extended-Release (ER) Methylphenidate in Available Dosage forms of Metadate CD and Concertaa
IR MPH ER MPH
18 mg CON 4 mg 14 mg
20 mg MCD 6 mg 14 mg
36 mg CON 8 mg 28 mg
40 mg MCD (2 × 20 mg capsules) 12 mg 28 mg
54 mg CON 12 mg 42 mg
60 mg MCD (3 × 20 mg capsules) 18 mg 42 mg
aRecently, a 10- and 30-mg dose strength of Metadate CD and a 27-mg dose strength of Concerta have become available. These were not available at the time of the COMACS study.
These observations could be clinically important because it is expected that patients and their families may be willing to trade some degree of symptom control at certain times of the day if that allows them to reduce the overall dose of MPH. In such cases, clinicians will seek a dosing profile that allows them to target a specific period of the day that is especially important for a particular patient. By doing this they can retain the effectiveness of a higher dose component during these selected periods of the day while limiting total daily MPH exposure. For instance, in some cases clinicians, patients and parents may seek symptom control in the evening while being less concerned about the daytime. In other cases they may wish to focus on morning and afternoon symptom control.
Because the main aim of the COMACS study design was to compare the PD profiles of MCD and CON across the day, initial analyses were limited to within-subject comparisons between equivalent total daily doses (low, medium and high) of the formulations. Thus this primary analysis of the data did not include a cross-dose analysis, which would allow the relative efficacy of different daily doses of MCD and CON to be directly tested at different times of the day. The present paper presents a statistical cross-dose analysis of the efficacy of MCD and CON at different times of the day in order to test these observations directly.
The ideal way to test across-dose comparisons of different medications is to randomise patients to different dosing strata. In the COMACS study children were assigned to dosing strata on the basis of pre-trial dose levels. However, because the COMACS study was designed to have similar and substantial numbers of children in each dosing strata the data set still offers the opportunity for a preliminary post-hoc exploration of the PD profiles of different doses of MCD and CON. Furthermore, the inclusion of the placebo arm in the COMACS design enabled us to directly address one of the possible major confounds associated with across dose comparisons in this sort of stratified design. An inspection of the published COMACS data revealed that the placebo scores measured at different sessions across the day were higher in the high dose group, than the medium or low dose groups. This suggests that children on higher doses may have had a more severe expression of the disorder making cross-group comparisons complicated. In the analysis reported in the current paper these placebo scores were used to adjust the treatment outcome scores to take account of this.
A number of specific across-dose comparisons were made. First, the lower daily doses (20 mg and 40 mg) of MCD were compared to the higher daily doses of CON (36 mg and 54 mg respectively). The comparison between MCD 20 mg and CON 36 mg is of particular interest to clinicians as these are the suggested dose substitutions for IR MPH 10 mg BID and 10 mg TID, respectively. On the basis of the initial observations of the COMACS study it was predicted that MCD 20 and CON 36 mg, and MCD 40 and CON 54 mg would produce equivalent control in the morning (from 1.5 through 4.5 hours post-dose). In contrast, at these dose levels it was predicted that CON would produce significantly greater effect as the day wore on (from 6 hours onwards). Second, lower daily doses (18 mg and 36 mg) of CON were compared with higher doses of MCD (40 mg and 60 mg respectively). We tested the prediction that at these dosing levels MCD would demonstrate greater efficacy only in the morning and early afternoon (from 1.5 through 6 hours); so that despite the lower total daily dosing levels, CON, designed to be effective over a 12 hour period, would still be more effective than MCD, designed to be effective over an 8 hour period, at reducing symptoms in the early evening (i.e., at 12 hours).
Methods
Clinical materials
Metadate® CD (methylphenidate HCl, USP) Extended-Release Capsules (MCD) were obtained from Eurand Americas, Inc (Vandalia, OH), while Concerta® (methylphenidate HCl) Extended-release Tablets (CON) were obtained from Alza Corporation (Mountain View, CA). For a detailed description of the preparation of clinical materials see Swanson et al., 2004 [12].
Patients
Six to 12 year old children, with interview-confirmed-diagnoses of ADHD who were being treated with MPH in doses of between 10 to 60 mg/day (5 mg to 20 mg per administration, one to three times a day) were recruited for the trial. Children were deemed otherwise healthy on the basis of an extensive medical history and physical examination. Children were excluded if they had an IQ below 80 or the inability to follow or understand study instructions. Other standard exclusion criteria for MPH drug trials applied [12]. Children provided signed assent, and their legal guardians signed an IRB-approved consent form. A total of 214 patients were screened for participation into the study and 184 patients (74 percent of which were male) were stratified across the three dose levels based on their previously established clinical doses of MPH. Eighty-two percent of the patients met the criteria for ADHD-Combined Type with a further 15 percent meeting the criteria for Inattentive Type. Approximately 25% of children had a co-morbid condition (e.g., anxiety and oppositional defiant disorder). At prescreening, approximately 91% of the patients were on once-a-day dosing regimens. Of the remainder 7.6% were taking BID and 1.6% TID IR MPH. Of the 184 subjects entering the study, 157 received all three levels of treatment and participated in all seven classroom sessions. The demographic characteristics of the sample of patients that completed all three treatments (n = 157) were not different than those reported for the full sample.
Design
This was a ten-site, double-blind, placebo-controlled crossover study comparing three treatment conditions: MCD, CON, and placebo (PLA). The study was conducted in accordance with the principle of the Declaration of Helsinki and its amendments and the International Committee on Harmonization Guidelines on Good Clinical Practice. Dose-level assignment was made according to pre-existing daily dosing requirement for MPH and children remained at the dose-level assigned for the entire study duration. Children treated with low doses (≤ 15 mg/day IR or 20 mg/day ER) of MPH were randomized to receive a daily dose of MCD 20 mg, CON 18 mg or PLA; those treated with medium doses (>10 to ≤ 30 mg/day IR or >20 to ≤ 40 mg/day ER) of MPH were randomized to receive MCD 40 mg, CON 36 mg or PLA; and children treated with high doses (> 30 mg/day IR or >40 mg/day ER) of MPH were randomized to receive MCD 60 mg, CON 54 mg, or PLA. Each of the three treatments was administered for 7 days (in randomized order) without an intervening washout period. Assessments took place in the laboratory school on Days 7, 14, and 21 (for a detailed description of the laboratory classroom day see Swanson et al., 2004 [12]). Two trained observers assessed patients during each classroom session on the Swanson, Kotkin, Atkins, M/Flynn, Pelham Scale (SKAMP; [17,18]) on the basis of a 1.5-hour cycle of activities with separate assessments of Attention and Deportment being made at 0, 1.5, 3.0, 4.5, 6.0, 7.5 and then 12 hours after drug administration. Because Attention and Deportment scores were highly correlated in the original COMACS analysis, these subscales were combined in the current analysis for ease of presentation.
Statistical analyses
A preliminary factorial Analysis of Variance (ANOVA) using the SPSS General Linear Model was conducted in order to confirm that the pattern of effects of Treatment (CON, MCD, PLA), Dose (low, med, high) and Session (0, 1.5, 3.0, 4.5, 6, 7.5 and 12 hours) found in the Swanson et al. (2004) paper [12] held when the individual SKAMP Attention and Deportment scales were combined to provide a composite score. In order to test the specific across-dose predictions Analysis of Covariance (ANCOVA) was used to make the four comparisons outlined above. In each case, Treatment/Dose (analysis 1-MCD 20 mg vs. CON 36 mg; analysis 2-MCD 40 mg vs. CON 54 mg; analysis 3-MCD 40 mg vs. CON 18 mg; analysis 4-MCD 60 mg vs. CON 36 mg) was the between-subjects independent factor. Session was the within- subject factor, and the dependent variable was total SKAMP score. Children's active drug SKAMP scores were adjusted to take account of their behaviour on PLA using a weighted combined SKAMP score for all observation points. Weighting was determined by principle components analysis and was similar for each observation point. Other between-subjects factors included in the initial COMACS study data analysis (including Site and Sequence of Drugs) were excluded from the current analysis. The GLM option that utilizes data from just those subjects with complete data (i.e., those cases without missing data) was selected for each separate analysis.
Results
Preliminary analysis
Table 2 shows the total SKAMP scores for each observation sessions at each dose level of CON, MCD and placebo. There were significant main effects of treatment, F (2, 306) = 92.06; p < 0.001, and session, F (6, 918) = 34.70; p < 0.001, and an interaction between treatment and session, F (12, 1836) = 45.21; p < 0.001. Planned comparisons demonstrated that the relative efficacy of the two formulations in relation to PLA was as described by Swanson et al. [12] for separate SKAMP deportment and attention scales: CON = MCD < PLA at the time of dose delivery, MCD > CON > PLA for 1.5, 3 and 4.5 hours, MCD = CON > PLA for session 6 and 7.5 hours and MCD = PLA < CON at 12 hours. For a discussion on the superiority of placebo immediately after dosing, the reader is referred to Swanson et al [12].
Table 2 Mean (± SD) SKAMP Total Scores at Each Observation Session for Each Treatment at Each Dose Levela
MCD CON PLA
Observation Session (hrs) Low Med High Low Med High Low Med High
0 18.48 (11.82) 20.88 (12.95) 19.91 (13.15) 18.04 (10.13) 19.14 (12.14) 21.47 (13.06) 13.58 (9.72) 16.02 (11.84) 13.96 (11.14)
1.5 11.44 (7.99) 10.98 (8.62) 6.55 (5.85) 14.04 (9.85) 14.86 (12.01) 11.34 (9.71) 19.10 (12.83) 19.47 (12.56) 18.88 (13.48)
3.0 12.57 (9.92) 11.03 (9.66) 7.31 (6.10) 16.44 (12.43) 15.29 (12.72) 12.62 (11.00) 21.47 (14.61) 20.98 (14.11) 22.11 (14.10)
4.5 13.46 (11.53) 12.39 (10.32) 9.15 (8.62) 17.55 (13.37) 15.09 (12.60) 13.55 (11.91) 20.23 (11.92) 22.09 (15.46) 23.44 (12.55)
6.0 16.08 (13.27) 14.47 (11.53) 10.30 (9.71) 17.00 (12.12) 14.28 (11.73) 12.04 (11.62) 22.98 (12.79) 22.15 (13.91) 26.02 (14.56)
7.5 15.85 (11.21) 17.26 (13.63) 14.29 (12.55) 18.62 (12.66) 15.19 (13.47) 13.47 (12.97) 23.54 (12.96) 23.13 (14.72) 24.48 (14.68)
12.0 20.44 (13.75) 20.28 (15.02 19.85 (14.41) 16.90 (13.36) 17.81 (13.84) 16.74 (14.98) 19.45 (13.46) 20.73 (13.46) 22.02 (15.17)
aLower SKAMP scores indicate greater efficacy
CON = Concerta; MCD = Metadate CD; PLA = placebo. Low = Low Dose (CON 18 mg; MCD 20 mg), Med = Medium Dose (CON 36 mg; MCD 40 mg), Hi = High Dose (CON 54 mg ; MCD 60 mg).
Comparison across dose levels
The PLA adjusted scores for the total SKAMP score relating to the specific cross-dose comparisons are presented in Figures 1a through 1d. The number of patients included in specific analysis were as follows: MCD 20 vs. CON 36 – 58 vs. 53; MCD 40 vs. CON 54 – 55 vs. 47; MCD 40 vs. CON 18 – 55 vs. 57; and MCD 60 vs. CON 36 – 49 vs. 53.
Figure 1 Mean (± SE) of placebo-adjusted total SKAMP scores for each comparison. Lower SKAMP scores indicate greater efficacy. Asterisks indicate MCD was significantly better than CON (p < 0.05) while crosses indicate CON was significantly better than MCD (p < 0.05).
Lower daily doses of MCD than CON
The comparison of MCD 20 mg and CON 36 mg revealed no overall difference between treatments, F (1,108) = 0.001; ns. There was an effect of session, F (6,648) = 15.08; p < 0.001 and an interaction between treatment and session F (6,648) = 3.98; p < 0.01. The two treatments did not differ at the time of dose administration or 3, 4.5, 6 and 7.5 hours after it (Fs < 2.5). MCD was superior to CON at 1.5 hours, F (2, 110) = 4.48; p < 0.05, while CON was superior to MCD at 12 hours, F (2,109) = 3.94; p < 0.05. The comparison of MCD 40 mg and CON 54 mg revealed no overall difference between treatments, F (1,99) = 1.05; ns. There was an effect of session F (6,594) = 36.20; p < 0.001 and an interaction between treatment and session, F (6,594) = 3.72; p < 0.01. There was no difference between treatments at the time of administration or at 1.5, 3, 4.5 and 6 hours (Fs < 3.58; ns). However, CON was superior to MCD at 7.5, F (1,100) = 6.26; p < 0.05, and 12 hours, F (1,99) = 4.55; p < 0.05.
Lower daily dose of CON than MCD
For the comparison of MCD 40 mg and CON 18 mg there was a main effect of treatment with MCD being associated with lower (i.e., better) SKAMP scores than CON, F(1,109) = 6.50, p < 0.05. There was also an effect of session, F(6,654) = 18.61, p < 0.001 and an interaction between treatment and session, F(6,654) = 8.32; p < 0.001. MCD gave lower (better) scores at 1.5 (F(1,111) = 10.02; p < 0.005), 3 (F(1,110) = 18.36; p < 0.001), 4.5 (F(1,111) = 16.16; p < 0.001), and 6 (F(2,111) = 5.94; p < 0.05) hours. However, there was no difference between the two formulations at 7.5 and 12 hours, Fs < 2.75. MCD 60 mg also gave significantly lower scores than CON 36, F (1,99) = 14.03; p < 0.001. Once again there was an effect of session, F (6,594) = 35.38; p < 0.001, and an interaction between treatment and session, F (6,594) = 10.55; p < 0.001. MCD gave significantly lower scores at 1.5 (F (1,100) = 35.01; p < 0.001), 3 (F (1,100) = 30.99; p < 0.001), 4.5 (F (1,99) = 18.23; p < 0.001) and 6 (F (1,100) = 9.66; p < 0.005) hours but not at 7.5 and 12 hours, Fs < 0.55.
Discussion
CON and MCD are once-daily formulations of MPH using different drug delivery mechanisms resulting in different plasma concentration vs. time profiles in both adults [13] and children [8]. In the COMACS study we compared the PD profiles of equivalent daily doses of the two formulations in children with ADHD [12]. The primary analysis was restricted to a comparison of the relative efficacy of the two formulations within subjects and dosing strata (i.e., across AUC-equivalent total daily doses). As predicted by the PK-PD model proposed by Swanson [19], MCD provided greater symptom control in the morning (from 1.5 through 4.5 hours post-dose), while CON gave greater control in the early evening (at 12 hours post-dose). However, on the basis of the IR/ER ratios of the two formulations, the expected plasma concentration vs. time profiles, and the informal observation of the relative efficacy of the different formulations made between subjects across dosing levels in the COMACS study, it was predicted that a lower total daily dose of MCD (i.e., 20 and 40 mg) would give equal levels of symptom control in the morning when compared to CON at the next highest dosing strata (i.e., 36 and 54 mg); the situation being reversed for CON in the later part of the afternoon and in the evening. Specifically, it was predicted that MCD 20 mg and 40 mg would give similar levels of symptom control to that provided by CON 36 mg and 54 mg, respectively, from 1.5 through 4.5 hours post dose. This would reflect the fifty percent higher relative proportion of IR delivery in MCD compared to CON. In contrast, the higher ER doses of CON would give significantly better control between 6 and 12 hours.
There was only partial support for these predictions. Not only was there no significant difference between placebo adjusted total SKAMP scores for MCD 40 mg and CON 54 mg from 1.5 through 4.5 hours, but also at 6.0 hours post-dose. In addition, comparison at the lower dose levels (MCD 20 mg and CON 36 mg) gave a surprising result: MCD appeared to be associated with a stronger effect and a more rapid onset of action than CON during the very early period post-dose. This was indicated both by the absolute values (i.e., the intercept) for the two formulations at 1.5 hours post-dose and the steeper slopes for MCD than CON between 0 and 1.5 hours. This was a particularly unexpected finding because at the dose levels being compared, MPH available from the IR component of MCD (6 mg) was less than that available for CON (8 mg). Given that the active drug is the same in the two formulations and assuming that clinical efficacy reflects MPH serum concentrations as has been proposed by Swanson [19] this would suggest that non-drug related factors may be involved. Non-drug factors may include those associated with the speed of dissolution and absorption associated with the different delivery mechanisms of the two formulations. For instance, it may be that the IR beads of MCD are associated with a higher rate of dissolution than the CON MPH overcoat, although there is no reason to expect this. Alternatively, the rate of dissolution of the IR components of the two formulations may be equivalent but the distinction between the IR and ER components may be less clear-cut in MCD than CON. This would produce early exposure to MPH from some ER beads in addition to IR beads during the early part of the day. Dissolution studies support this by suggesting that while the ER components of MCD and CON both start to release MPH at approximately 1 hour post-dose, the MCD ER MPH component seems to be more front-end loaded, while CON's ER MPH component seems to be more back-end loaded. MCD drug delivery technology, may therefore, be more efficient at delivering IR components at low doses, and this requires further investigation.
The predictions relating to the efficacy of these different dose comparisons during the latter part of the day were confirmed only at the 12 hour testing session for the lower doses (CON 36 mg vs. MCD 20 mg) and the 7.5 and 12 hours sessions for the higher doses (CON 54 mg vs. MCD 40 mg). Given their expected plasma concentration vs. time profiles in children, one would expect greater efficacy from CON than MCD whatever the dose comparison being made. The 7.5 hour post-dose period is less clear-cut in terms of the relative benefit of the two formulations.
The second set of predictions related to the value of lower daily doses of CON (18 mg and 36 mg) to provide equivalent symptom coverage compared to higher doses of MCD (40 mg and 60 mg, respectively) in the late afternoon and superior coverage in the evening. The predictions for late afternoon were supported at both the higher and lower cross-dose comparisons with lower doses of CON giving equivalent control at 7.5 hours. However, interestingly, at 12 hours the higher doses of MCD remained equivalent to CON. This result is in keeping with the idea, supported by the expected plasma concentration and known dissolution data that MCD continues to release MPH up to 12 hours post-dose. As expected, the higher doses of MCD had a greater effect between dosing through 6 hours post dose.
Given design limitations it is possible that these effects are not 'real' effects related to dose and treatment type but are related to differences between the types of children assigned to different dose levels. The way in which the children were assigned to dose level meant that across-dose comparisons of different treatments could be subject to a number of confounds. First, it could be that children were placed on higher doses because they had a more severe form of ADHD. In the present study we attempted to deal with the possible confounds that such an approach to dose assignment might bring by using the placebo SKAMP score (across all time periods) as a covariate. This score was included as a proxy for severity of ADHD in order to control for the possibility that children assigned to the higher-dose level had a more severe form of the condition.
A second possible confound, not corrected for by controlling for placebo scores, relates to the child's sensitivity to MPH: Children may have been prescribed higher pre-study doses of MPH because they were less sensitive to MPH and did not respond to the lower dose. Such variations in sensitivity could be independent of overall severity of the disorder and therefore constitute a second possible confound. If this were the case then an across-dose comparison would be between more- and less-sensitive children. There are two pieces of evidence that argue against this as an explanation for the current results. First, Swanson et al. [12] reported that the between-subject factor of dose was significant on SKAMP Attention scores in the COMACS study. If dose levels were determined by MPH sensitivity such an effect would not have been expected. Second, there is no reason to believe that sensitivity to MPH should favor one formulation at one time (i.e., MCD in the morning) and the second formulation at another time (i.e., CON in the evening). Differential MPH sensitivity, therefore, seems an unlikely explanation for the current pattern of results. It is also possible that basing the study dose level on pre-study total daily dose may have had differential effects for children receiving TID and BID doses of IR preparations prior to the study. Children on, for instance, two tablets of 10 mg IR given early in the morning and then in the afternoon would be assigned to the low dose strata of 18 mg of CON thought to be equivalent to 5 mg IR tablets taken TID. In this case, children, although receiving AUC-equivalent daily doses, would be receiving a lower morning (5 mg rather 10 mg) and afternoon dose (5 mg rather 10 mg) and a new evening dose (5 mg as opposed to no dose) during the study relative to their pre-study MPH daily treatment. However, in the current study, 91% of children were on once-a-day dosing prior at prescreening, and it is therefore difficult to estimate the effects of this factor on the current across-dose comparison.
Taken together, the existence of these confounding factors and complications means that while these data provide an initial indication of the relative efficacy of different doses of CON and MCD at different points across the day they should be treated with a certain degree of caution, and the results should be confirmed in a study where subjects are randomized to dose level rather than being assigned to it on the basis of their pre-study MPH daily dose and plasma levels of MPH are assessed simultaneously.
Conclusions
From the point of view of the practicing clinician the results of this study further highlight the value of having available different ER MPH formulations with different expected plasma concentration vs. time profiles offering different patterns of efficacy over different periods of the day. Given the established dose-response relationship between MPH and side effects, clinicians and parents may wish to limit total daily MPH intake as far as possible while maintaining a tolerable level of efficacy over the day as a whole and/or targeting a particularly important period of the day for a particular patient. This head-to-head study makes a first step towards providing the systematic evidence base on which to make such decisions.
Competing interests
EJB and DC are consultant for and/or have received support from Celltech, McNeil, Jannsen Cilag and Eli Lilly. JMS is a consultant for Celltech, McNeil and Eli Lilly. SJH and HHD are employees of Celltech Americas, Inc.
Authors' contributions
JMS, DC, SJH and EJB participated in the design of this post-hoc analysis while EJB performed the statistical analysis. HHD and EJB prepared the manuscript. All authors contributed discussion and read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was funded by Celltech Pharmaceuticals, Inc.
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| 15458569 | PMC524494 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Sep 30; 4:28 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-28 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-441546961710.1186/1471-2458-4-44Research ArticleThe validity of the SF-36 in an Australian National Household Survey: demonstrating the applicability of the Household Income and Labour Dynamics in Australia (HILDA) Survey to examination of health inequalities Butterworth Peter [email protected] Timothy [email protected] Centre for Mental Health Research, The Australian National University, ACT 0200 Australia2 Strategic Policy and Knowledge Branch, Department of Family and Community Services, Canberra, Australia2004 7 10 2004 4 44 44 19 5 2004 7 10 2004 Copyright © 2004 Butterworth and Crosier; licensee BioMed Central Ltd.2004Butterworth and Crosier; 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 SF-36 is one of the most widely used self-completion measures of health status. The inclusion of the SF-36 in the first Australian national household panel survey, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, provides an opportunity to investigate health inequalities. In this analysis we establish the psychometric properties and criterion validity of the SF-36 HILDA Survey data and examine scale profiles across a range of measures of socio-economic circumstance.
Methods
Data from 13,055 respondents who completed the first wave of the HILDA Survey were analysed to determine the psychometric properties of the SF-36 and the relationship of the SF-36 scales to other measures of health, disability, social functioning and demographic characteristics.
Results
Results of principle components analysis were similar to previous Australian and international reports. Survey scales demonstrated convergent and divergent validity, and different markers of social status demonstrated unique patterns of outcomes across the scales.
Conclusion
Results demonstrated the validity of the SF-36 data collected during the first wave of the HILDA Survey and support its use in research examining health inequalities and population health characteristics in Australia.
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Background
While much health research focuses on objective outcome measures such as mortality or morbidity defined through clinical assessment, there is an increasing emphasis on self-reported measures of health status and health-related quality of life. Self-reported measures of health status have been included in epidemiological and community-based survey research. Their use reflects the importance of considering the patients' point of view and the multidimensional nature of health [1-3].
The Medical Outcomes Study Short Form (SF-36) is one of the most widely used, self-completion measures of health status. It was developed to meet the psychometric standards necessary for group comparisons, to enable profiling of functional health and well-being, and to quantify disease burden [3]. It comprises 36 items of which all but one are used to measure eight important health concepts that are frequently examined through health surveys. These eight concepts or scales are: Physical Functioning; Role-Physical (interference with work or other daily activities due to physical health); Bodily Pain; General Health; Vitality; Social Functioning (interference with normal social activities); Role-Emotional (interference with work or other daily activities due to emotional problems); and Mental Health (symptoms associated with anxiety and depression and measures of positive affect). In addition, the eight scales yield two summary scales of health, relating to physical (the Physical Component Summary: PCS) and mental (the Mental Component Summary: MCS) functioning and well-being.
The SF-36 was first adapted for use in Australia in 1992, as part of the International Quality of Life Assessment (IQOLA) Project [4]. Previous research has demonstrated the validity of the SF-36 for use by Australian respondents using samples from Canberra and New South Wales [2,4]. This has involved assessment of the psychometric properties of the Australian form of the SF-36, evaluation of internal consistency and reliability, and demonstration of content and construct validity. There are considerable Australian data on the SF-36 from large National samples. In 1995, a subset of National Health Survey respondents (around 18,800 adults) completed the SF-36 and the Australian Bureau of Statistics published Australian population norms [5]. The SF-36 was also included in the Women's Health Australia survey, with data collected from a sample of around 41,500 women aged 18–22, 45–49, and 70–74 [6].
In 2001, the SF-36 was included in the first wave of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. This is an Australia-wide survey of approximately 7,680 households, comprising around 14,000 people aged 15 and over. The HILDA Survey is the first longitudinal household survey in Australia and is designed to provide a sound evidence base to support research and analysis of income, labour market and family dynamics. As such, it is a critical resource for social policy development. The inclusion of the SF-36 in the HILDA Survey will enable investigation of the interaction between social, economic and health measures.
There is an extensive body of research demonstrating socio-economic inequalities in the distribution of physical and mental health problems [7-9]. The SF-36 has been utilised in research on health inequalities and this research has shown that the SF-36 scales are differentially associated with markers of social-economic circumstance [10-13]. A focus of our research has been on the nature of disadvantage and social exclusion associated with welfare receipt and specifically the association between welfare receipt and mental health problems [14]. The HILDA Survey provides a valuable dataset with which to investigate this research topic. As such, it is critical to firstly ascertain the validity of the data within the HILDA Survey. Further, demonstrating the validity of the SF-36 scales collected through the survey is critical for other researchers and policy analysts who will utilise the HILDA dataset.
The aim of this paper is to evaluate the psychometric properties and criterion validity of the HILDA SF-36 data. We have used the manuscript by Sanson-Fisher and Perkins [4] as a framework for the analyses reported in the first section. This follows the standard IQOLA validation procedures [15]. The analysis examines the reliability and validity of the eight SF-36 scales and the PCS and MCS scales. We then compare the SF-36 results obtained from the HILDA Survey with other Australian estimates to assess the representativeness of the data. We also evaluate the criterion validity of the HILDA Survey SF-36 data. We do this by 1) looking for convergent validity, in which scales measuring similar or related constructs demonstrate a positive association, and divergent validity whereby unrelated scales and measures are not associated; and 2) examining the profile of SF-36 results across a range of measures associated with health, disability and social functioning, demographic characteristics as well as a focus on a number of measures of socio-economic circumstances.
Methods
Data source
Data are from the first wave of the HILDA Survey (Release 1.0), a nationally representative household panel survey. The HILDA Survey was funded by the Australian Department of Family and Community Services and managed by a consortium led by the Melbourne Institute of Applied Economic and Social Research at the University of Melbourne. The survey utilised a multi-stage sampling approach (sampling households within Census Collection Districts) and was stratified by State and part-of-State.
Four survey instruments were included in Wave 1. A Household Form and Household Questionnaire were completed during a personal interview with one adult member of each household. The Person Questionnaire, also administered during the personal interview, was conducted with all adult household members. Finally, the Self-Completion Questionnaire (SCQ) was provided to all respondents to the Person Questionnaire and was collected at a later date or returned by post. The SF-36 was included as the first element in the SCQ. Fieldwork for wave one of the HILDA Survey was conducted between August 2001 and January 2002.
A total of 7,682 households responded to the survey (a household response rate of 66 per cent). Within these households, there were 15,127 eligible adults. Of this group 13,969 (92%) completed the Person Questionnaire and 13,055 (86%) completed and returned an identifiable SCQ.
There are some differences between the characteristics of respondents to the HILDA Survey and population estimates from the Australian Bureau of Statistics. However, these discrepancies are not large enough to discredit the data and the differences in rates of response across both sex and location are corrected by applying population weights [16].
Analysis
The first set of analyses evaluated the reliability and validity of the SF-36 scales in the HILDA Survey. Given that these analyses were concerned with the internal structure of the data rather than representativeness, we ignored the clustered and stratified nature of the data and did not take population weights into account. These analyses were conducted using SPSS version 11.7.
Item-internal consistency assessed the extent to which each item measures what its associated scale measures. A correlation of 0.40 (corrected for overlap) or greater demonstrates adequate item-internal consistency [17]. Item-discriminant validity was also assessed. This is demonstrated when an item correlates significantly more strongly with the scale it contributes to rather than with any other scale. We use the multitrait/multi-item correlation matrix approach in which the correlation of each item with each scale is examined [18]. In this approach, the correlation between each item and its own scale is corrected for overlap: that is, the scale is calculated without the specific item in question to avoid inflating the correlation. The extent to which item-scale correlations within a scale were equal was also assessed, as was the approximate equality of item means and standard deviations.
To assess the internal consistency of scale scores, Cronbach's alpha coefficients were assessed, with a criterion of 0.7 used to define adequate internal consistency. Descriptive statistics for the eight SF-36 scales and the two summary scales were calculated, including the mean, median, range, standard deviation, skewness, kurtosis, and the per cent scoring at the lowest value (floor) and the highest value (ceiling).
Construct validity of the SF-36 in the HILDA Survey was also evaluated using the principal components method of factor analysis. Results were compared with the factor structure obtained from other analysis of the SF-36 scales.
The second set of analyses also examined a form of construct validity, assessing the extent to which scores on the SF-36 scales were associated with other criteria. We included in our analysis a number of measures of health and disability, ratings of satisfaction with life and health, and measures of stressful employment conditions and persistent feelings of loneliness. In addition to examining the profiles across the eight SF-36 scales, we also examined the relationship between these criterion measures and the PCS scale and the MCS scale, which were calculated according to the standard procedure outlined by Ware, Kosinski and Keller [19].
For these analyses it was critical to take account of the complex survey design. We therefore utilised the svy procedures of STATA to account for the clustered and stratified nature of the data, and to facilitate the use of weights to overcome differential response rates and to replicate Australian population parameters. The HILDA Survey dataset contains person-level weights which, when applied to individual survey respondents, adjusts for the unequal probabilities of selection and completion of the Person Questionnaire. However, these weights do not adjust for the further attrition associated with the SCQ. This is critical as our analysis is focused upon measures from this questionnaire. We therefore, conducted a logistic regression analysis to predict completion of the SCQ using the same predictors used to derive the HILDA person-level weights (geographic location, labour-force status, sex, age, number of adults in household, number of children in household, marital status, English language ability, and dwelling type) [20]. The probabilities of responding derived from this analysis were used to adjust the person-level weights. Utilising these adjusted weights produced accurate population estimates from the respondents who completed the SCQ (similar to the estimates for Personal Questionnaire respondents using the original person-level weights).
We also examine the correspondence between SF-36 results from the HILDA Survey and data from another, large-scale Australian survey to assess the representativeness of the data. We contrast the means for each of the SF-36 scales from the HILDA Survey with those obtained by the Australian Bureau of Statistics through the National Health Survey conducted in 1995 [5]. To facilitate evaluation of potential differences between means, two sample t-test statistics were calculated using a pooled variance approach.
Demographic, socio-economic and health measures
Based on previous analysis of the SF-36, we expected results across the eight scales to differ according to subgroups identified by demographic and socio-economic characteristics, including age, sex, marital status, educational attainment, housing tenure and employment status. We also examined differences associated with receipt of welfare payments, a focus of our research endeavours [14]. The analyses also include a range of measures related to health and life circumstances drawn from other scales and instruments included in the HILDA Survey.
Long-term health condition
One question from the Person Questionnaire asked respondents if they experienced a disability or health condition that had lasted, or was likely to last 6 months or more, and restricted their everyday activities. A showcard listing examples of health conditions, impairments and disabilities was presented as a prompt for respondents. Responses were either Yes or No.
Limited ability to work
Those identifying a disability or condition were asked to rate whether this limited the type or amount of work they were able to undertake. Response categories were No, Yes and Can Do Nothing.
Satisfaction with health
In a series of questions from the Person Questionnaire, respondents were asked to rate their satisfaction with a range of life circumstances using an eleven-point scale with descriptive anchors at either end (0 = totally dissatisfied; 10 = totally satisfied). Responses were categorised as either dissatisfied (if the rating was 4 or less) or satisfied/ mixed (if the rating given was at the midpoint 5 or higher).
Satisfaction with life
Respondents were also asked, all things considered, how satisfied they were with their life. Responses were categorised as above.
Job stress
The SCQ included a series of statements about current job characteristics, to which respondents rated their level of agreement or disagreement using a seven-point scale anchored at either end (1 = strongly disagree; 7 = strongly agree). One item asked respondents to rate whether they feared that the amount of job stress would make them physically ill. Respondents were categorised as experiencing significant job stress if they agreed with this statement (a rating of 5 or greater).
Social isolation
The final measure was also drawn from the SCQ. Using the same scale as the job stress measure, respondents were presented with a series of statements about their level of social support. Respondents were categorised as lonely if they agreed (rating of 5 or above) with the statement "I often feel very lonely".
Based on previous research using the SF-36, we anticipate that increasing age will be associated with poorer physical health, and that women will demonstrate poorer health than men as measured on all SF-36 scales with the possible exception of the General Health scale [5]. Those respondents who are divorced or separated are expected to demonstrate poorer health than those currently married or in a de facto relationship, with the greatest differences observed on the scales related to social functioning and mental health [21]. For all of the socio-economic measures, we expect to find a socio-economic gradient for all SF-36 scales, though the pattern may differ for different measures. For example, Sullivan and Karlsson found that educational level was more strongly associated with the measures of physical health whereas employment status was more strongly associated with mental health. Also based on results such as those reported by Sullivan and Karlsson, it was expected that reported experience of a long-term health condition, limited ability to work and satisfaction with health would be more strongly associated with SF-36 scales related to physical health, while job stress, social isolation (loneliness) and satisfaction with life would be more strongly associated with the scales loading on the mental health factor.
Results and discussion
Sample statistics
Of the 13,055 respondents with identifiable SCQs, 53 percent were female. The age of respondents ranged from 15 to age 90 or greater (reported age was capped at 90 in the survey data), with an average age of 43 years (SD = 17.25).
A total of 11,264 respondents (86.3%) completed all SF-36 items, with a further 1,326 (10.1%) having 5 or fewer missing items. Overall, the mean number of missing items per person was 0.69, and the median was 0. Details of non-responses/missing values for each item are presented in Figure A1 of the Attached file. The rate of missing values was below 3.5 percent for all items. The highest rate of missing values was evident for items from the Physical Functioning scale, followed by the Role Physical, General Health and Role Emotional scales. Scales were calculated using standard SF-36 scoring procedures, whereby missing values were replaced by scale means where valid responses were available for at least half of the scale items. Therefore, the number of respondents with valid scale scores ranged from 12,686 for the Role Emotional scale to 13,031 for the Social Functioning scale. Analyses were conducted with the maximum number of respondents possible and, therefore, varies across the scales.
Tests of scaling assumptions
Table 1 demonstrates that the range of item-scale correlations within each scale were moderate to strong (see item-internal consistency column). Indeed, all item-scale correlations were greater than the recommended correlation of 0.40 for adequate item-internal consistency [17]. The widest range of item-scale correlations was observed for the General Health scale (0.47 – 0.77), with the weakest item-scale correlations evident for items 11a and 11c (see Table A1 in Additional file). Nonetheless, the item-scale correlations were reasonably similar within each scale.
Table 1 Results of item scaling tests and reliability estimates for SF-36 scales.
Range of item correlations
No. of items Item-internal consistency1 Item-discriminant validity2 Scale Reliability3
SF-36 scale
Physical Functioning 10 0.55 – 0.81 0.14 – 0.55 0.93
Role Physical 4 0.76 – 0.81 0.24 – 0.60 0.90
Bodily Pain 2 0.80 0.32 – 0.67 0.87
General Health 5 0.47 – 0.77 0.22 – 0.57 0.82
Vitality 4 0.63 – 0.67 0.24 – 0.59 0.83
Social Functioning 2 0.71 0.42 – 0.60 0.83
Role Emotional 3 0.68 – 0.72 0.27 – 0.54 0.83
Mental Health 5 0.54 – 0.69 0.15 – 0.57 0.82
1 Correlations between items and scale corrected for overlap.
2 Correlations between items and other scales.
3 Internal-consistency reliability (cronbach's alpha).
In order to assess item-discriminant validity, items that make up a scale were correlated with other scale constructs. Item-discriminant validity is demonstrated when an item correlates higher with its own scale than with other scales. While there was some overlap in correlations between items from one scale and other scales (compare the range of item correlations for the item-internal consistency and item-discriminant validity columns in Table 1), these were relatively minor. At the level of the individual items, it is apparent that all items were more strongly correlated with their own scale than with other scales, and that this difference was statistically significant for all but one item (see Table A.2 in Additional file). Thus, Table A.3 demonstrates the achievement of 100% scaling success according to accepted SF-36 methods.
Reliability
Table 1 also shows that all SF-36 scales demonstrated acceptable internal consistency, with Cronbach's alpha ranging from 0.82 (Mental Health and General Health) to 0.93 (Physical Functioning). These reliability scores are similar to those reported in previous Australian research [2,4].
Descriptive statistics for scales
In accordance with the standard scoring procedures [19], the eight scales of the SF-36 were constructed by aggregating 35 of the 36 individual items (the excluded item measures self-reported health transition). Each of the 35 items contributed to one scale, with each scale comprising 2 to 10 items. The range of scores possible on each of the eight scales was from 0 to 100, with 100 representing optimal functioning as measured by the SF-36.
The (unweighted) means for the eight scales ranged from 60.87 (Vitality) to 83.19 (Physical Functioning; see Table 2). All scales were found to be negatively skewed with the Physical Functioning, Role Emotional, Role Physical and Social Functioning scales moderately skewed. Ceiling effects were high for the Role Emotional (73.3%) and Role Physical (68.8%) scales and moderate for the Social Functioning (49.4%), Physical Functioning (33.8%) and Bodily Pain (32.9%) scales. The greatest prevalence of floor effects was observed for the Role Physical (12.4%) and Role Emotional (9.9%) scales. Minimal ceiling and floor effects were observed for the Vitality, Mental Health and General Health scales. This pattern is similar to that obtained with previous assessment of the SF-36 [4]. Descriptive statistics for the two summary scores (the Physical and Mental Component Summary scores) are also consistent with previous findings.
Table 2 Descriptive statistics for SF-36 scales.
SF-36 scale1 No. of items Mean Median Range SD Skewness Kurtosis Floor (%) Ceiling (%)
PF 10 83.19 95.00 0–100 23.11 -1.70 2.10 0.5 33.8
RP 4 79.11 100.00 0–100 35.58 -1.42 0.40 12.4 68.8
BP 2 74.30 84.00 0–100 25.19 -0.81 -0.20 0.9 32.9
GH 5 70.02 72.00 0–100 21.14 -0.80 0.16 0.2 6.1
VT 4 60.87 65.00 0–100 19.84 -0.59 -0.01 0.4 1.1
SF 2 81.96 87.50 0–100 23.55 -1.29 0.86 0.6 49.4
RE 3 82.13 100.00 0–100 33.08 -1.64 1.16 9.9 73.3
MH 5 73.92 76.00 0–100 17.42 -0.98 0.88 0.1 3.3
PCS 21 49.17 52.55 3.61–71.87 10.33 -1.19 0.86 0.0 0.0
MCS 14 49.92 52.74 4.45–73.94 9.95 -1.18 1.24 0.0 0.0
1 PF = Physical Functioning; RP = Role Physical; BP = Bodily Pain; GH = General Health; VT = Vitality; SF = Social Functioning; RE = Role Emotional; MH = Mental Health; PCS = Physical Component Summary; MCS = Mental Component Summary.
Principal Components Analysis
Principal Components Analysis was conducted to examine the underlying structure in the SF-36. The analysis supported the two factor solution. Two factors had eigenvalues greater than one. The two factor solution was also consistent with the pattern of results evident in the scree plot. The two factors accounted for 69% of the total variance in the 8 SF-36 scales. The first factor accounted for 56% of the variance and the second factor accounted for an additional 13%. The total variance accounted for by the two-factor solution was similar to that found in other studies. For example, in a ten country comparison of the factor structure of the SF-36, Ware and colleagues report that the two factor solution accounted for between 66 and 72 percent of total variance [22]. Previous Australian analyses also found that the two factors explained around 70 percent of total variance [4,6].
Table 3 shows the correlations between the SF-36 scales and the rotated components. We used the varimax method to obtain orthogonal factors. Across the eight scales, the percentage of variance within each scale explained by the two-factor solution (commonalities) ranged from 0.57 to 0.82. Again, this is consistent with previous international studies [22]. It is apparent from Table 3 that our two-factor solution is also consistent with the previously reported factor structure, identifying scales that relate to physical and mental health. The scales that correlated strongly with the first factor were Mental Health (0.90), Vitality (0.74), Role Emotional (0.71) and Social Functioning (0.71). The Physical Functioning (0.11), Role Physical (0.28) and Bodily Pain (0.31) scales correlated weakly with this factor. This factor was subsequently labelled mental health. With regard to the second factor, the Physical Functioning (0.84), Role Physical (0.81) and Bodily Pain (0.77) scales correlated strongly, while the Mental Health (0.08) and Role Emotional (0.25) scales correlated weakly. This factor was labelled physical health.
Table 3 Rotated principal components associations between the eight SF-36 scales and the mental and physical health components. Commonalities (h2) are also presented.
Rotated principal components
SF-36 scale Mental Physical h2
Physical Functioning .106 .842 0.72
Role Physical .278 .806 0.73
Bodily Pain .307 .771 0.69
General Health .475 .627 0.62
Vitality .744 .361 0.68
Social Functioning .709 .465 0.72
Role Emotional .714 .246 0.57
Mental Health .900 .079 0.82
As with most international comparisons (though see [23,24] using Taiwanese and Japanese populations), the factor loadings associated with General Health were stronger for the physical health factor. We also found this pattern of results, though the relationship observed between the General Health scale and the mental health factor was at the upper end of the range of international data. The opposite pattern is generally demonstrated for the Vitality scale, with it loading most strongly on the mental health factor. One previous Australian study [4] found that the Vitality scale loaded more strongly on the physical health factor. The current results are more consistent with the body of international evidence.
Normative comparisons
Table 4 presents population estimates of the mean and standard deviation for each of the SF-36 scales and the Physical and Mental Component Summary scores. These results take account of the complex survey design and population weights. Also presented are the corresponding results from the ABS 1995 Health Survey.
Table 4 Population estimates of mean SF-36 Scale scores and Component Summary Scores (with N and standard deviation) from the HILDA Survey. Means (and sd) from ABS (1995) are also presented, together with estimate of independent t test to assess differences between estimates.
HILDA Population (weighted) Australian population norms1
Mean (sd) N Mean (sd) N t test
Physical Component Summary Score 2 49.23 (10.6) 12 320
Mental Component Summary Score 2 49.79 (10.3) 12 320
Scales
Physical Functioning 82.49 (24.0) 12 760 82.6 (23.9) 18 734 0.40
Role Physical 79.06 (36.2) 12 714 79.9 (35.1) 18 710 2.06
Bodily Pain 73.89 (25.6) 12 977 76.8 (25.0) 18 699 10.09
General Health 69.54 (21.6) 12 718 71.6 (20.3) 18 715 8.60
Vitality 60.72 (20.0) 12 938 64.5 (19.8) 18 728 16.64
Social Functioning 81.14 (24.0) 13 031 85.0 (22.5) 18 789 14.64
Role Emotional 81.83 (33.8) 12 686 82.9 (32.3) 18 620 2.82
Mental Health 73.42 (17.7) 12 930 75.9 (17.0) 18 676 12.55
1 From ABS, 1995
2 Physical and mental summary scores were calculated differently in the ABS publication (using factor scores from principle component analysis and stardardised to have a mean of 50 and sd of 10). Therefore, it is not appropriate to directly compare with the HILDA data derived using the standard scoring procedures.
The results obtained are broadly consistent with the previous Australian national data. The means are of a similar magnitude. Direct comparison of the means, assessed using independent t tests (see [6]), indicated that the differences observed were statistically significant (at the .01 level) for six of the eight scales. The mean scores from the HILDA Survey were consistently lower than those obtained in the ABS Health Survey. However, the greatest differences observed on the individual scales (VT and SF) were less than four points and Ware et al. [19] suggest that a difference of 5 points or more indicates clinical or social meaningfulness.
While relatively small, the significant differences in mean SF-36 scale scores from these two national surveys are intriguing. The most marked differences were on scales related to mental health, while the scales most strongly related to physical health (the Physical Functioning and Role Physical scales) were those where no difference between surveys was observed. It may be that the differences observed reflect different methodological approaches adopted in the two surveys, or could be due to non-sampling error. Alternatively, it could reflect a real change in the Australian population between 1995 and 2001, with poorer reported mental health occurring in more recent times. This may be the case, as changes were restricted to the measures related to mental health and therefore are not simply a general change in response bias.
There is evidence which corroborates the apparent increased prevalence of mental health problems in the community. Comparison of the 1997 Australian Survey of Mental Health and Wellbeing and the 2001 National Health Survey, both of which included the 10 item Kessler Psychological Distress Scale, showed the rates of substantial psychological distress had increased by over one percent [25]. In the UK, comparison of the results from the 1993 and 2000 Psychiatric Morbidity Surveys found that, while there was no difference in the overall rate of neurotic disorders amongst adults, there was a significant increase in the prevalence among men [26]. Further investigation of this finding and possible explanations (e.g. increasing mental disorders, reduced stigma and increased likelihood of disclosure) is warranted.
Comparison of groups
The data presented in Table 5 show the profile of mean SF-36 scores across a range of demographic and socio-economic characteristics. There were small but significant sex differences across most of the SF-36 scales and summary measures. Females consistently demonstrated slightly poorer health than males on all measures apart from the General Health scale, including the physical and mental summary scores. This pattern of results precisely replicates the pattern found in the previous ABS National Health Survey [5].
Table 5 Mean SF-36 Scale and Component Summary Scores, by measures of demographic and socio-economic circumstances. The F statistic associated with each comparison is indicated, together with minimum number of respondents in each cell (in brackets).
PCS MCS PF RP BP GH VT SF RE MH
Gender
Male (5817) 49.49 50.45 83.84 80.23 74.96 69.51 62.63 82.21 83.46 74.61
Female (6503) 48.97 49.15 81.18 77.90 72.84 69.57 58.85 80.09 80.22 72.25
F(1,475) 8.19 53.82 44.89 14.56 24.03 0.03 121.39 27.94 30.55 62.19
** *** *** *** *** ns *** *** *** ***
Age
18–25 (2135) 53.23 48.26 91.75 88.48 80.26 73.38 62.53 82.65 83.09 71.64
26–39 (3535) 52.16 48.88 89.17 86.40 78.71 73.85 60.97 82.50 83.91 72.80
40–55 (3676) 49.56 49.99 84.28 80.82 73.70 69.49 60.86 81.71 83.35 73.74
56–65 (1452) 44.47 51.51 72.87 69.27 66.23 63.72 60.87 79.87 80.42 74.44
66–75 (992) 41.70 52.92 65.10 61.29 64.59 62.26 59.47 79.61 78.90 76.14
75+ (530) 35.85 51.31 49.49 39.71 57.64 55.57 53.21 70.22 62.76 75.01
F(5,451) 324.72 29.94 302.04 210.84 96.79 85.05 15.69 17.57 25.92 8.15
*** *** *** *** *** *** *** *** *** ***
Marital status
Married/de facto (7928) 48.85 50.73 82.36 79.15 73.68 69.62 61.08 82.97 83.74 74.93
Divorced/separated (1013) 47.01 47.50 77.23 71.74 67.25 65.87 57.02 72.82 73.94 69.29
F(1.475) 18.74 59.43 29.33 28.06 31.20 19.39 25.41 94.83 52.03 60.11
*** *** *** *** *** *** *** *** *** ***
Educational attainment
Yr 12 or above (7975) 50.29 49.92 85.59 81.92 75.86 71.22 61.46 82.40 83.43 74.17
Yr 11 or less (4041) 47.15 49.56 76.64 73.48 70.22 66.32 59.31 78.82 78.74 72.00
F(1.475) 159.19 2.48 235.31 112.92 95.55 102.68 24.48 42.39 40.34 28.45
*** ns *** *** *** *** *** *** *** ***
Employment status
Employed (7741) 51.77 50.25 89.04 87.14 78.54 73.40 62.57 85.13 86.53 74.87
Unemployed (520) 51.22 47.08 85.07 82.37 76.28 70.67 62.85 77.57 76.39 68.72
F(1.471) 1.58 36.90 17.16 10.00 3.69 6.47 0.10 46.04 36.74 44.94
ns *** *** ** ns * ns *** *** ***
Housing status
Own/mortgage (9281) 49.12 50.43 82.58 78.86 74.20 69.99 61.16 82.54 83.10 74.48
Rent (3039) 49.53 47.97 82.24 79.63 73.01 68.26 59.46 77.15 78.18 70.38
F(1.475) 1.77 92.54 0.20 0.75 2.79 6.35 11.43 72.65 36.32 81.86
ns *** ns ns ns * *** *** *** ***
Receipt of welfare payments (working age respondents)
Non-recipients (8373) 51.75 50.16 88.93 86.86 78.45 73.25 62.73 84.87 86.09 74.66
Welfare recipients (2091) 46.57 46.16 76.22 68.35 66.22 62.71 55.48 70.35 71.10 66.50
F(1,473) 259.15 174.85 305.32 300.00 223.36 274.19 147.06 431.71 241.05 221.56
*** *** *** *** *** *** *** *** *** ***
Degrees of freedom for F statistics based on number of clusters (primary sampling units) and number of strata
* p < .05, ** p < .01, *** p < .001
Age effects were also consistent with expectations. While those in older age groups reported poorer levels of health across most SF-36 scales, the decline was most pronounced in the measures related to physical health (Physical Health, Bodily Pain). In contrast to this pattern, however, both the Mental Health scale and Mental Component Summary Score showed improved mental health with increasing age, apart from a slight decline evident in the oldest (75+) age group. This is consistent with epidemiological survey results using a variety of mental health measures [27]. Health differences associated with current marital status (comparing partnered respondents with those who were separated or divorced) were evident across all scales. The greatest differences were observed in the Social Functioning and Role Emotional scales.
The analysis showed health was associated with a range of measures of social status, however the profile across the SF-36 scales differed for the different measures. Consideration of educational attainment showed that those respondents who had less than secondary education had significantly poorer physical health, most evident on the Physical Functioning scale. Current unemployment (compared to full or part-time employment) was significantly associated with poorer health on a number of scales, but particularly the measures loading on the mental health factor (Social Functioning, Role Emotional and Mental Health). This too, is consistent with the extensive unemployment literature [28]. Housing tenure (rental housing vs other) was not related to the SF-36 physical health scales. However, reliance on rental housing was associated with poorer mental health (lower means on Vitality, Social Functioning, Role Emotional, and Mental Health). While Australian welfare payments are universally available, eligibility is highly targeted. Therefore, welfare receipt appears to be a good proxy for poor financial circumstances. In addition, a subset of the welfare population are those with severe disabilities that prevent work. Consistent with this, we found that, amongst working age respondents, receipt of an Australian income support payment was strongly associated with both poorer physical and mental health.
The final set of comparisons examined the association between SF-36 scales and respondents' health and social circumstances as measured by other scales and items included in the HILDA Survey (Table 6). Consistent with expectations, respondents who reported a long-term health condition or disability demonstrated lower mean scores on all SF-36 scales, but particularly so on the scales loading on physical health (Physical Functioning, Role Physical, Bodily Pain and General Health). A similar pattern of associations was observed when examining the circumstances of those with health conditions that impacted on their ability to work. The greater influence on work ability was associated with poorest physical health as measured by SF-36 scales. Reported satisfaction with health was most strongly associated with General Health and physical health measures more generally, while overall satisfaction with one's life was more highly associated with the SF-36 scales related to mental health.
Table 6 Mean SF-36 Scale and Component Summary Scores, by other measures of health and social situation. The F statistic associated with each comparison is indicated, together with minimum number of respondents in each cell (in brackets).
PCS MCS PF RP BP GH VT SF RE MH
Presence of long term health condition or disability
No (9549) 52.16 50.58 88.57 88.09 80.14 74.85 64.19 85.87 86.55 75.34
Yes (2771) 38.89 47.02 61.99 47.91 52.96 51.40 49.10 65.35 65.48 66.94
F(1,475) 1797.99 163.21 1324.59 1367.80 1621.36 1777.15 892.23 858.84 455.68 340.62
*** *** *** *** *** *** *** *** *** ***
If so, does condition limit ability to work?
No (761) 47.69 49.20 80.13 75.08 70.20 65.01 58.66 79.67 78.80 72.31
Yes (1969) 35.76 46.23 56.05 38.30 46.92 46.57 45.72 60.36 60.98 65.08
Can do nothing (41) 28.00 45.10 31.05 22.33 37.13 37.61 41.20 51.72 44.01 61.01
F(2,460) 475.53 15.92 361.10 275.57 272.20 235.87 110.07 159.16 65.12 35.12
*** *** *** *** *** *** *** *** *** ***
Satisfaction with health
Satisfied/mixed (11182) 50.22 50.40 84.65 82.24 76.19 71.88 62.43 83.26 84.14 74.65
Not satisfied (1018) 39.71 43.95 62.67 48.61 52.82 47.63 45.23 61.96 58.97 62.24
F(1,475) 501.04 204.52 391.67 477.58 516.14 672.21 503.83 435.33 250.56 270.29
*** *** *** *** *** *** *** *** *** ***
Satisfaction with life
Satisfied/mixed (11937) 49.40 50.22 82.94 79.90 74.51 70.23 61.42 82.07 82.87 74.21
Dissatisfied (341) 43.99 37.17 69.34 53.93 56.02 49.22 40.10 53.42 51.37 50.20
F(1,475) 48.84 265.85 64.26 84.88 98.41 196.47 275.57 257.92 125.10 364.73
*** *** *** *** *** *** *** *** *** ***
Report that job stress makes physically ill
Not agree, neither (6533) 51.82 51.12 89.11 87.80 79.22 74.59 64.24 86.25 88.23 76.08
Agree (1255) 49.89 44.72 84.90 76.61 71.08 65.19 53.41 74.52 70.55 66.04
F(1,472) 40.41 290.07 39.02 98.10 89.70 174.22 267.47 208.13 200.02 269.57
*** *** *** *** *** *** *** *** *** ***
Often Lonely
Disagree/mixed (9506) 49.74 51.33 84.16 81.58 75.93 71.65 63.04 84.27 85.63 76.17
Agree (2454) 47.45 43.87 77.37 69.90 66.63 61.96 52.26 70.07 67.19 63.14
F(1,475) 63.52 722.01 100.92 147.99 196.06 315.39 486.49 514.90 424.96 763.49
*** *** *** *** *** *** *** *** *** ***
Degrees of freedom for F statistics based on number of clusters (primary sampling units) and number of strata
* p < .05, ** p < .01, *** p < .001
The final two criterion measures (job stress and loneliness) were expected to be most strongly associated with poorer mental health outcomes. Consistent with expectations, those respondents in work who reported that their experience of job stress made them physically ill reported poorest health on the Vitality and Mental Health scales, while those who reported often feeling lonely had low scores on the Mental Health, Social Functioning and Vitality scales.
Conclusions
This analysis has demonstrated the validity of the SF-36 data collected through the first wave of the HILDA Survey. The eight scales were shown to be psychometrically sound, with good internal consistency, discriminant validity and high reliability. The results supported the underlying two-factor structure, with factors related to physical and mental health. The pattern of factor loadings, variance explained, and the profile and magnitude of the scale means were consistent with previous Australian and international results. There was, however, an indication that the estimated population means on several of the SF-36 scales, particularly on scales loading on the mental health factor, were lower in the HILDA Survey than previous Australian national data collected through the National Health Survey. Self-report measures of health status can be subject to a range of biases, such as reporting bias or cohort effects. They may also be influenced by contextual factors, such as the setting in which the measure is conducted. Further consideration of these different data sources, and an examination of possible methodological and other differences, is warranted.
With respect to the relationship between SF-36 scales and external criteria, we demonstrated a pattern of results consistent with expectations and previous research. We examined measures having differential effects on physical and mental health (e.g., disability and health satisfaction vs job stress, loneliness, and life satisfaction). The relationship between demographic characteristics (age, sex, marital status) and SF-36 scales was consistent with previous data. Finally, we focused on health inequalities, using a range of markers of social status and examining their relationship with the SF-36 scales. While each marker of social status was associated with poorer health, the dimensions of health related to each social variable differed. Whereas educational attainment was most strongly associated with poorer physical health, housing tenure and employment status were related to mental health scales. The measure of welfare receipt, which is associated with economic hardship, was strongly associated with both poorer physical and mental health.
The application of the probability weights available with the HILDA Survey data ensures the accuracy of the point-estimates of the population parameters, such as the mean, by correcting for non-response and design characteristics. However, it is also important to recognise the impact of the survey design (clustering and stratification) on the calculation of standard errors. As the sampling units in the HILDA Survey are Census Collection Districts and, within that, households, the data contradict assumptions of independence and should not be treated as a simple random sample drawn from the population. We expect more similarity between individuals within Census Collection Districts and households than between those drawn at random from the population and if these characteristics of the survey design are not taken into account the estimates of standard error will overstate accuracy.
Calculation of the design effect provides an indication of the increase in error associated with the complex survey design, representing the ratio of the variance for the complex design to that obtained assuming a simple random sample. These measures range from 1.4 (for Role Emotional scale) to 3.1 (for the Physical Functioning scale). Thus, whereas the 95 percent confidence interval around the population estimate for Physical Functioning Scale (84.49) would have been 82.04 to 82.94 had we not taken the survey design into account, our estimate was 81.77 to 83.21. The design effects were greater for the scales assessing physical health than for those loading on mental health. This suggests physical health more strongly clusters within households and areas than does mental health, perhaps as a consequence of the clustering of age (design effect of 3.8) and the strong relationship between age and physical health and disability. Note that the design effects are not evident in the population estimates in Table 4 as these figures were corrected to provide an estimate of the population mean and population standard deviation. Nonetheless, the design effects highlight the need to utilise appropriate statistical techniques in analysis of the HILDA Survey data.
This analysis has provided evidence that the SF-36 data collected in the HILDA Survey is valid and supports its use as a general outcome measure of physical and mental health status. It also suggests that the results obtained using the SF-36 in the HILDA Survey can be interpreted by reference to published SF-36 normative data and comparison with previous research findings. Most critically, from our perspective, the analysis provides initial support for the proposition that the SF-36 scales and component summary scores are related in a meaningful and interpretable manner to measures of social status. This supports the use of the HILDA Survey data in our analysis of health inequalities and the effects of social exclusion in Australia.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
PB and TC participated in all aspects of the preparation of this manuscript, including data analysis, preparation of various sections of the manuscript, and commenting on drafts.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Additional file SF36 HILDA validation.pdf – provides tables and figures used to determine item-discriminant validity and to explore the pattern of missing data across SF-36 items.
Click here for file
Acknowledgements
We thank Bryan Rodgers for comments on an earlier draft of this manuscript.
Funding for PB's position was provided by Program Grant No. 179805 from the National Health and Medical Research Council.
The opinions, comments and/or analysis expressed in this paper are those of the authors and do not necessarily represent the views of the Minister for Family and Community Services or the Department of Family and Community Services, and cannot be taken in any way as expressions of government policy.
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| 15469617 | PMC524495 | CC BY | 2021-01-04 16:28:48 | no | BMC Public Health. 2004 Oct 7; 4:44 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-44 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-191547655610.1186/1472-6920-4-19Research ArticleDescription and evaluation of an EBM curriculum using a block rotation Thom David H [email protected] Julie [email protected] Peter S [email protected] Peter [email protected] Department of Family and Community Medicine, University of California, San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Building 80/83, San Francisco, CA 94110, USA2 Barnett-Briggs Medical Library, University of California, San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Building 30, San Francisco, CA 94110, USA2004 11 10 2004 4 19 19 26 4 2004 11 10 2004 Copyright © 2004 Thom et al; licensee BioMed Central Ltd.2004Thom 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 previous authors have emphasized the importance of integrating and reinforcing evidence-based medicine (EBM) skills in residency, there are few published examples of such curricula. We designed an EBM curriculum to train family practice interns in essential EBM skills for information mastery using clinical questions generated by the family practice inpatient service. We sought to evaluate the impact of this curriculum on interns, residents, and faculty.
Methods
Interns (n = 13) were asked to self-assess their level of confidence in basic EBM skills before and after their 2-week EBM rotation. Residents (n = 21) and faculty (n = 12) were asked to assess how often the answers provided by the EBM intern to the inpatient service changed medical care. In addition, residents were asked to report how often they used their EBM skills and how often EBM concepts and tools were used in teaching by senior residents and faculty. Faculty were asked if the EBM curriculum had increased their use of EBM in practice and in teaching.
Results
Interns significantly increased their confidence over the course of the rotation. Residents and faculty felt that the answers provided by the EBM intern provided useful information and led to changes in patient care. Faculty reported incorporating EBM into their teaching (92%) and practice (75%). Residents reported applying the EBM skills they learned to patient care (86%) and that these skills were reinforced in the teaching they received outside of the rotation (81%). All residents and 11 of 12 faculty felt that the EBM curriculum had improved patient care.
Conclusions
To our knowledge, this is the first published EBM curriculum using an individual block rotation format. As such, it may provide an alternative model for teaching and incorporating EBM into a residency program.
==== Body
Background
Evidence-based medicine (EBM) strives to provide a systematic approach to integrating the best research evidence with clinician expertise and patient preferences to provide better patient care [1]. While the potential for answering clinical questions using online resources is high [2], a recent study found resident physicians rarely used web-based or other evidence-based sources to answer clinical questions, preferring instead another person or a pocket reference [3]. Many authors have argued for the importance of teaching EBM skills during residency training, and several have cited evidence to support the desirability of integrating EBM training with other aspects of the residency program [4-8]. Such a curriculum presents several challenges, including finding sufficient time to teach EBM skills to interns and developing ways to integrate and reinforce EBM among residents and faculty.
While there have been several studies of residency EBM curricula [4,9-11], none, to our knowledge, has operated in the framework of an intern block rotation. In this paper we describe an EBM curriculum based on an individual block rotation and designed to integrate and reinforce EBM skills throughout the residency program; we also report on its evaluation by interns, residents and faculty.
Methods
Setting
The UCSF family practice residency program is based at San Francisco General Hospital (SFGH), a large county hospital serving the urban poor. The program runs a busy family practice inpatient service (at SFGH) as well as the Family Health Center, an outpatient clinic that includes both continuity practices and acute care services. Within this setting, we formulated 3 primary goals for our EBM curriculum: (1) to teach interns basic EBM concepts and skills; (2) to disseminate and reinforce EBM skills to second and third year residents and faculty; and (3) to apply EBM to the care of patients. This study was approved by the UCSF Committee on Human Research.
Curriculum
The EBM curriculum for UCSF family practice residents began, in its current form, summer of 2001. The core of the curriculum is a 2-week, individual EBM rotation for interns. In contrast to the usual format of multiple lectures or learning modules scattered throughout residency, the two-week individual block format provides for a concentrated time in which to learn EBM. It also allows tailoring of the rotation to fit the residents' backgrounds and interests. During each of the two weeks, residents have 3 half-day clinics, with the remainder of the time available for EBM. The EBM portion of the rotation fulfills the ACGME requirements for resident research and scholarly activity.
The rotation begins with a meeting with the EBM faculty Director (DT) during which the structure and goals of the rotation are communicated, the intern's knowledge and experience related to EBM are assessed by review of his or her experience and by a pre-rotation test of EBM skills and knowledge (described below). Interns receive reference materials [1,12] and a binder including detailed instructions for the rotation and key articles. The intern, together with the rotation faculty director (DT) and the medical librarian co-director (JH), attends family practice inpatient service rounds to obtain one or two clinical questions directly bearing on the care of one or two patients. With faculty guidance, questions are then formatted into the standard EBM structure identified by the acronym PICO (for patient/problem, intervention, comparison, and outcome). The entire process generally takes 5 to 10 minutes and includes modeling of how to formulate an appropriate 'answerable' question by senior residents and the inpatient attending faculty. The question(s) generated are then used by the librarian Co-director as material for a tutorial on developing search strategies and using high-quality web-based EBM resources. The emphasis of the tutorial is to introduce the intern to the concept of information mastery [5]. An initial assessment of the intern's searching experiences and use of EBM resources helps to tailor the tutorial session. In the tutorial, the intern is introduced to essential EBM concepts, including clinical question development, levels of evidence search strategies, and appraisal techniques [13], and learns to translate the 'answerable' clinical question into a 'searchable' one. Based on the type of clinical question, the intern reviews the possible levels of evidence and study designs and formulates a valid search strategy. As the intern searches for the evidence, the medical librarian provides input into alternative search strategies and information resources. The results of this and any subsequent searches are discussed with the faculty EBM Director and a 1-page response is developed in the form of a critically appraised topic or CAT [1]. Each CAT is structured to include the 'clinical bottom line' answer to the question; the clinical scenario that generated the questions; a summary of the evidence; a critical appraisal of the evidence; and citations. Results are presented to and discussed with the inpatient team. CATs are stored at the EBM website for future reference by residents and faculty [14].
During the first week, the intern completes a web-based EBM tutorial [15] which covers critical evaluation of articles about diagnosis, therapy, prognosis, meta-analysis and decision making, and how to ask clinical questions. In the second week the intern receives another 1 or 2 clinical questions from the inpatient service which are searched and answered as above. The intern also prepares and presents a journal club, which is attended by faculty, residents and medical students. A research article is selected by the intern (with guidance from the EBM Director), which could potentially change a primary care practice around a clinical question. The article is critically reviewed using an EBM approach [1,12], presented and discussed by the group.
During the initial 3 months of the curriculum, interns completed a written test of their EBM skills and knowledge before and immediately following the rotation. The tests were adapted, with permission, from a similar instrument developed by Sean Schafer, MD and Katie Ramos, PhD at the University of California Fresno Family Practice Residency Program [16]. Scores improved post-rotation in all 3 areas tested: EBM terms and concepts 81% to 97%; quantitative skills 51% to 80%; question formulation and searching 71% to 92%, with the total score increasing from 63% to 87%.
As others have noted, progress in the use of EBM depends on the availability of information support services to resident and faculty at the point of patient care [17]. In conjunction with our EBM rotation we have also improved support for residents and faculty searching for evidence-based answers to clinical questions, through the development of our EBM-filtered information support website [15], access to a librarian information specialist (JH), and provision of PDAs to our interns who did not have their own.
We estimate that training 13 interns per year requires approximately 70 hours of librarian time and 200 hours of the faculty Director's time. In the event that the medical librarian Co-Director (JH) is not available, the faculty Director (DT) provides coverage for this portion of the rotation. Absence of the faculty Director for one or two days can usually be covered by schedule modifications and communication by telephone and e-mail. An extended absence of the faculty Director requires another faculty member assuming supervisory responsibility.
Evaluation
We evaluated our EBM curriculum with respect to our 3 primary goals using pre- and post-rotation questionnaires (completed by each EBM intern) and by a survey of family practice residents and faculty.
The EBM intern questionnaires consisted of pre- and post-rotation self-assessments of confidence in EBM knowledge and skills (Goal 1). Self-confidence in EBM knowledge and skills was assessed by asking the intern to rate his or her level of comfort from 1 = very uncomfortable to 5 = very comfortable for (1) MEDLINE searching to answer a clinical question; (2) use of Web-based EBM resources to answer a clinical question; and (3) use of EBM principles to critically evaluate articles. At the end of the rotation interns were asked to identify the most useful and least useful aspects of the rotation and what could make the rotation better. In addition, residents completed and returned to the Residency Coordinator a standard evaluation of the rotation.
We evaluated the dissemination and reinforcement of EBM skills and knowledge to residents and faculty (Goal 2) and the application of EBM to patient care (Goal 3) by surveying residents (all of whom had previously completed the EBM rotation as interns) and faculty. These surveys were distributed and collected by a program assistant. Surveys were labeled with a code number for each resident and faculty and results were reported in aggregate to provide anonymity. The resident survey asked "How frequently have you continued to apply EBM concepts and tools from the rotation to answer clinical questions?" and "How frequently have EBM concepts and tools been reinforced via teaching by faculty or senior residents?" For both questions, response options were 1 = never, 2 = seldom (less than once per month on average); 3 = occasionally (1 to 3 times per month on average); 4 = often (1 or 2 times a week on average); and 5 = frequently (3 or more times per week on average). For faculty and residents who had had a clinical question answered by the EBM intern on the inpatient service were asked. "How often did the EBM answer provide useful information?" and "How often did the answer change your management of a patient?" For both questions, response options were 1 = less than 25% of the time; 2 = 25% to 75% of the time; and 3 = more than 75% of the time. Finally, all faculty and residents were asked if they believed that "the presence of the EBM rotation improves the quality of patient care within the family practice residency program."
Results
During the period from July 1, 2001 to September 30, 2003, 30 EBM interns presented 30 journal clubs and generated 74 CATs in response to questions from the inpatient service. Journal club presentations and CATS are electronically archived and make accessible via the residency website [14]. Interns rated their experience during the rotation a median of 7 (superior) on a scale from 1 to 9. Written comments were also elicited and showed that one-on-one meetings with the EBM faculty (DH and JH) and exposure to web-based EBM resources were consistently valued. The only areas suggested for improvement were (1) to drop written material that repeated content of website tutorial (which we did) and (2) to have back-up EMB questions in the event that an appropriate question cannot be generated by the inpatient service (we have used this option only rarely). As shown in Table 1, residents' confidence in their level of EBM knowledge and skills significantly increased from prior to the rotation in all 3 areas assessed (Goal 1).
Table 1 Comparison of interns' confidence* in EBM skills pre- and post-rotation (n = 10)
Mean Pre-Rotation Score Mean Post-Rotation Score Difference P-value
Use of PubMed/Medline 4.11 4.78 +0.67 <.01
Use of other Web-based EBM resources 2.74 4.67 +1.93 <.001
Use of EBM tools and principles (critical appraisal) 2.58 4.44 +1.86 <.001
Mean confidence score 3.14 4.63 +1.39 <.001
* Assessed from 1 = very uncomfortable to 5 = very comfortable
Surveys to evaluate goals 2 and 3 were completed by 21 of 25 residents (response rate of 84%) and 12 of 13 faculty (response rate or 92%). As seen in Table 2, 86% of residents reported that they have continued to apply EBM concepts and tools learned in the rotation to clinical questions at least occasionally and 81% reported having had EBM concepts and tools reinforced occasionally or often by faculty or senior residents. Eleven of the 12 faculty (92%) agreed that the EBM curriculum had increased their use of EBM concepts and tools in teaching and 75% felt that the curriculum had increased their use of EBM concepts and tools in their own clinical practice.
Table 2 Residents' report of how often they applied EBM concepts and tools to patient care, and how often EBM concepts and tools are reinforced by senior residents and faculty (n = 21)
Item Never Seldom (< 1/month) Occasionally (1 to 3 times/month) Often (1 to 2 times/week) Frequently (≥3 times/week)
N (%) N (%) N (%) N (%) N (%)
Applied to patient care 0 (0) 3 (14) 7 (33) 8 (38) 3 (14)
Reinforced by senior residents and faculty 0 (0) 4 (19) 12 (57) 5 (24) 0 (0)
As shown in Table 3, most residents and inpatient attending faculty felt that the EBM answer to the inpatient clinical question provided useful information 25% to 75% of the time. Only 1 resident (and none of the faculty) reported the EBM answer provided useful information less than 25% of the time. The majority of the residents (65%) and faculty (83%) reported that the information led to a change in patient management 25% to 75% of the time, with the remainder reporting a change in management less than 25% of the time. All residents and 11 of the 12 faculty (92%) agreed that the EBM rotation had improved the quality of patient care within the residency program.
Table 3 Percent of time that the answer to EBM question provided useful information or led to a change in patient management on the Family Practice Inpatient Service
Item Residents (n = 20*) Faculty (n = 6**)
<25% 25% to 75% >75% <25% 25% to 75% >75%
N (%) N (%) N (%) N (%) N (%) N (%)
Provided useful information 1 (5) 12 (60) 7 (35) 0 (0) 3 (50) 3 (50)
Led to a change management of the patient 7 (35) 13 (65) 0 (0) 1 (17) 5 (83) 0 (0)
* One Resident, who joined in the second year, had not been on the inpatient service during a time when an EBM intern was present.
** Answered by the 6 faculty members who attend on the inpatient service.
Discussion
While the above findings support the acceptance and perceived utility of our EBM program, they do not provide a formal measure of its effectiveness. Changes in resident knowledge, skills and confident were measured over a short period of time. The frequency of reinforcement of EBM and the impact of EBM on clinical care was by resident and faculty report and may have been biased. No attempt was made to observe changes in physician behaviors or patient outcomes. There were no measures of EBM use prior to the introduction of the EBM rotation and no comparison group was available. It is therefore not possible to objectively determine to what degree current levels of awareness and utilization of EBM are the result of the rotation. It is also not possible to separate out the effects of the 2-week EBM rotation from the adjunct changes of establishing a medical information website or promoting the use of PDAs, except to the extent that the questions asked specifically about the 2-week EBM rotation.
Our block EBM rotation differs substantially from the approaches reported in previous studies [9,11,16] in that it utilizes a concentrated, individual experience that combines formal learning (via tutorials and a web-based course) with immediate application of EBM to answer an important clinical questions for individual patients – the target goal for EBM training. An advantage of our approach is the ability to tailor the curriculum to the background and needs of each intern and to provide interns with dedicated time during which they can rapidly acquire EBM knowledge and skills and apply them to "real time" clinical questions under the supervision of a faculty member and a librarian information specialist. We believe that our curriculum could be implemented by any faculty member with a working knowledge of EBM. The website tutorial [15] provides the core didactic portion for the rotation. Moreover, directing the rotation naturally increases the experience and expertise of the faculty member involved.
Interns reported greater confidence in their search skills after the two week rotation. Two randomized controlled trials and a controlled before-after study have demonstrated benefits of training in electronic search skills [17-19]. Interns also reported significantly greater confidence in their ability to apply EBM knowledge and principals in critical appraisal. This is consistent with previous studies that have found training improves critical appraisal skills for residents and practicing physicians [20].
One study that evaluated the impact of a 1-month pilot program to use EBM methods on an inpatient service, reported results roughly similar to ours [21]. Our curriculum also includes the archiving of EBM answers in a standardized format for future reference by residents and faculty. While our study did not include an evaluation of the usefulness of the archived EBM answers generated by the rotation, archiving EBM answers for web-based access has been shown to provide a useful resource for resident physicians in an internal medicine residency program [22].
Previous studies have found only small increases in residents' knowledge and skills from journal clubs alone, leading to the suggestion that journal clubs should be used as a component of EBM training rather then being 'stand alone' activities [6-8]. While we did not attempt to evaluate the effects of the journal club per se, including it as a component of our EBM curriculum is concordant with this suggestion.
Other investigators have reported educational interventions aimed at improving faculty knowledge and skills in medical informatics and EBM. One study reported increases in faculty self-rating of EBM skills following an intervention consisting of 2 half-day workshops and substantial amount of individual mentoring [23]. We found that faculty reported our EBM rotation has increased their use of EBM in their clinical practice, as well as their teaching of EBM; this was echoed by residents. While it is difficult to compare the two studies, our findings suggest that integrating EBM into the residency via resident training may improve faculty application of EBM to clinical care and in their teaching, and may be a cost-effective way to reinforce faculty EBM skills.
Our EBM curriculum, based on an individual 2-week EBM rotation in the first year, appears to be successful in increasing resident's EBM skills and confidence. In addition, resident and faculty both perceive EBM as being incorporated and reinforced beyond the rotation, and that the presence of EBM in the residency improves the quality of patient care. We hope that our experience provides a useful model for teaching and integrating EBM into a busy, resource-limited, family practice residency.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
All authors participated in the development of the curriculum. DT conceived of and conducted the evaluation. JH and PSS reviewed the survey instrument. JH and PSS reviewed and suggested changes to the paper. JH drafted the subsection describing the teaching the use of electronic. All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors would like to acknowledge Juliana Fung for her assistance in the preparation of this manuscript.
==== Refs
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| 15476556 | PMC524496 | CC BY | 2021-01-04 16:30:53 | no | BMC Med Educ. 2004 Oct 11; 4:19 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-19 | oa_comm |
==== Front
BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-191547655610.1186/1472-6920-4-19Research ArticleDescription and evaluation of an EBM curriculum using a block rotation Thom David H [email protected] Julie [email protected] Peter S [email protected] Peter [email protected] Department of Family and Community Medicine, University of California, San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Building 80/83, San Francisco, CA 94110, USA2 Barnett-Briggs Medical Library, University of California, San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Building 30, San Francisco, CA 94110, USA2004 11 10 2004 4 19 19 26 4 2004 11 10 2004 Copyright © 2004 Thom et al; licensee BioMed Central Ltd.2004Thom 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 previous authors have emphasized the importance of integrating and reinforcing evidence-based medicine (EBM) skills in residency, there are few published examples of such curricula. We designed an EBM curriculum to train family practice interns in essential EBM skills for information mastery using clinical questions generated by the family practice inpatient service. We sought to evaluate the impact of this curriculum on interns, residents, and faculty.
Methods
Interns (n = 13) were asked to self-assess their level of confidence in basic EBM skills before and after their 2-week EBM rotation. Residents (n = 21) and faculty (n = 12) were asked to assess how often the answers provided by the EBM intern to the inpatient service changed medical care. In addition, residents were asked to report how often they used their EBM skills and how often EBM concepts and tools were used in teaching by senior residents and faculty. Faculty were asked if the EBM curriculum had increased their use of EBM in practice and in teaching.
Results
Interns significantly increased their confidence over the course of the rotation. Residents and faculty felt that the answers provided by the EBM intern provided useful information and led to changes in patient care. Faculty reported incorporating EBM into their teaching (92%) and practice (75%). Residents reported applying the EBM skills they learned to patient care (86%) and that these skills were reinforced in the teaching they received outside of the rotation (81%). All residents and 11 of 12 faculty felt that the EBM curriculum had improved patient care.
Conclusions
To our knowledge, this is the first published EBM curriculum using an individual block rotation format. As such, it may provide an alternative model for teaching and incorporating EBM into a residency program.
==== Body
Background
Evidence-based medicine (EBM) strives to provide a systematic approach to integrating the best research evidence with clinician expertise and patient preferences to provide better patient care [1]. While the potential for answering clinical questions using online resources is high [2], a recent study found resident physicians rarely used web-based or other evidence-based sources to answer clinical questions, preferring instead another person or a pocket reference [3]. Many authors have argued for the importance of teaching EBM skills during residency training, and several have cited evidence to support the desirability of integrating EBM training with other aspects of the residency program [4-8]. Such a curriculum presents several challenges, including finding sufficient time to teach EBM skills to interns and developing ways to integrate and reinforce EBM among residents and faculty.
While there have been several studies of residency EBM curricula [4,9-11], none, to our knowledge, has operated in the framework of an intern block rotation. In this paper we describe an EBM curriculum based on an individual block rotation and designed to integrate and reinforce EBM skills throughout the residency program; we also report on its evaluation by interns, residents and faculty.
Methods
Setting
The UCSF family practice residency program is based at San Francisco General Hospital (SFGH), a large county hospital serving the urban poor. The program runs a busy family practice inpatient service (at SFGH) as well as the Family Health Center, an outpatient clinic that includes both continuity practices and acute care services. Within this setting, we formulated 3 primary goals for our EBM curriculum: (1) to teach interns basic EBM concepts and skills; (2) to disseminate and reinforce EBM skills to second and third year residents and faculty; and (3) to apply EBM to the care of patients. This study was approved by the UCSF Committee on Human Research.
Curriculum
The EBM curriculum for UCSF family practice residents began, in its current form, summer of 2001. The core of the curriculum is a 2-week, individual EBM rotation for interns. In contrast to the usual format of multiple lectures or learning modules scattered throughout residency, the two-week individual block format provides for a concentrated time in which to learn EBM. It also allows tailoring of the rotation to fit the residents' backgrounds and interests. During each of the two weeks, residents have 3 half-day clinics, with the remainder of the time available for EBM. The EBM portion of the rotation fulfills the ACGME requirements for resident research and scholarly activity.
The rotation begins with a meeting with the EBM faculty Director (DT) during which the structure and goals of the rotation are communicated, the intern's knowledge and experience related to EBM are assessed by review of his or her experience and by a pre-rotation test of EBM skills and knowledge (described below). Interns receive reference materials [1,12] and a binder including detailed instructions for the rotation and key articles. The intern, together with the rotation faculty director (DT) and the medical librarian co-director (JH), attends family practice inpatient service rounds to obtain one or two clinical questions directly bearing on the care of one or two patients. With faculty guidance, questions are then formatted into the standard EBM structure identified by the acronym PICO (for patient/problem, intervention, comparison, and outcome). The entire process generally takes 5 to 10 minutes and includes modeling of how to formulate an appropriate 'answerable' question by senior residents and the inpatient attending faculty. The question(s) generated are then used by the librarian Co-director as material for a tutorial on developing search strategies and using high-quality web-based EBM resources. The emphasis of the tutorial is to introduce the intern to the concept of information mastery [5]. An initial assessment of the intern's searching experiences and use of EBM resources helps to tailor the tutorial session. In the tutorial, the intern is introduced to essential EBM concepts, including clinical question development, levels of evidence search strategies, and appraisal techniques [13], and learns to translate the 'answerable' clinical question into a 'searchable' one. Based on the type of clinical question, the intern reviews the possible levels of evidence and study designs and formulates a valid search strategy. As the intern searches for the evidence, the medical librarian provides input into alternative search strategies and information resources. The results of this and any subsequent searches are discussed with the faculty EBM Director and a 1-page response is developed in the form of a critically appraised topic or CAT [1]. Each CAT is structured to include the 'clinical bottom line' answer to the question; the clinical scenario that generated the questions; a summary of the evidence; a critical appraisal of the evidence; and citations. Results are presented to and discussed with the inpatient team. CATs are stored at the EBM website for future reference by residents and faculty [14].
During the first week, the intern completes a web-based EBM tutorial [15] which covers critical evaluation of articles about diagnosis, therapy, prognosis, meta-analysis and decision making, and how to ask clinical questions. In the second week the intern receives another 1 or 2 clinical questions from the inpatient service which are searched and answered as above. The intern also prepares and presents a journal club, which is attended by faculty, residents and medical students. A research article is selected by the intern (with guidance from the EBM Director), which could potentially change a primary care practice around a clinical question. The article is critically reviewed using an EBM approach [1,12], presented and discussed by the group.
During the initial 3 months of the curriculum, interns completed a written test of their EBM skills and knowledge before and immediately following the rotation. The tests were adapted, with permission, from a similar instrument developed by Sean Schafer, MD and Katie Ramos, PhD at the University of California Fresno Family Practice Residency Program [16]. Scores improved post-rotation in all 3 areas tested: EBM terms and concepts 81% to 97%; quantitative skills 51% to 80%; question formulation and searching 71% to 92%, with the total score increasing from 63% to 87%.
As others have noted, progress in the use of EBM depends on the availability of information support services to resident and faculty at the point of patient care [17]. In conjunction with our EBM rotation we have also improved support for residents and faculty searching for evidence-based answers to clinical questions, through the development of our EBM-filtered information support website [15], access to a librarian information specialist (JH), and provision of PDAs to our interns who did not have their own.
We estimate that training 13 interns per year requires approximately 70 hours of librarian time and 200 hours of the faculty Director's time. In the event that the medical librarian Co-Director (JH) is not available, the faculty Director (DT) provides coverage for this portion of the rotation. Absence of the faculty Director for one or two days can usually be covered by schedule modifications and communication by telephone and e-mail. An extended absence of the faculty Director requires another faculty member assuming supervisory responsibility.
Evaluation
We evaluated our EBM curriculum with respect to our 3 primary goals using pre- and post-rotation questionnaires (completed by each EBM intern) and by a survey of family practice residents and faculty.
The EBM intern questionnaires consisted of pre- and post-rotation self-assessments of confidence in EBM knowledge and skills (Goal 1). Self-confidence in EBM knowledge and skills was assessed by asking the intern to rate his or her level of comfort from 1 = very uncomfortable to 5 = very comfortable for (1) MEDLINE searching to answer a clinical question; (2) use of Web-based EBM resources to answer a clinical question; and (3) use of EBM principles to critically evaluate articles. At the end of the rotation interns were asked to identify the most useful and least useful aspects of the rotation and what could make the rotation better. In addition, residents completed and returned to the Residency Coordinator a standard evaluation of the rotation.
We evaluated the dissemination and reinforcement of EBM skills and knowledge to residents and faculty (Goal 2) and the application of EBM to patient care (Goal 3) by surveying residents (all of whom had previously completed the EBM rotation as interns) and faculty. These surveys were distributed and collected by a program assistant. Surveys were labeled with a code number for each resident and faculty and results were reported in aggregate to provide anonymity. The resident survey asked "How frequently have you continued to apply EBM concepts and tools from the rotation to answer clinical questions?" and "How frequently have EBM concepts and tools been reinforced via teaching by faculty or senior residents?" For both questions, response options were 1 = never, 2 = seldom (less than once per month on average); 3 = occasionally (1 to 3 times per month on average); 4 = often (1 or 2 times a week on average); and 5 = frequently (3 or more times per week on average). For faculty and residents who had had a clinical question answered by the EBM intern on the inpatient service were asked. "How often did the EBM answer provide useful information?" and "How often did the answer change your management of a patient?" For both questions, response options were 1 = less than 25% of the time; 2 = 25% to 75% of the time; and 3 = more than 75% of the time. Finally, all faculty and residents were asked if they believed that "the presence of the EBM rotation improves the quality of patient care within the family practice residency program."
Results
During the period from July 1, 2001 to September 30, 2003, 30 EBM interns presented 30 journal clubs and generated 74 CATs in response to questions from the inpatient service. Journal club presentations and CATS are electronically archived and make accessible via the residency website [14]. Interns rated their experience during the rotation a median of 7 (superior) on a scale from 1 to 9. Written comments were also elicited and showed that one-on-one meetings with the EBM faculty (DH and JH) and exposure to web-based EBM resources were consistently valued. The only areas suggested for improvement were (1) to drop written material that repeated content of website tutorial (which we did) and (2) to have back-up EMB questions in the event that an appropriate question cannot be generated by the inpatient service (we have used this option only rarely). As shown in Table 1, residents' confidence in their level of EBM knowledge and skills significantly increased from prior to the rotation in all 3 areas assessed (Goal 1).
Table 1 Comparison of interns' confidence* in EBM skills pre- and post-rotation (n = 10)
Mean Pre-Rotation Score Mean Post-Rotation Score Difference P-value
Use of PubMed/Medline 4.11 4.78 +0.67 <.01
Use of other Web-based EBM resources 2.74 4.67 +1.93 <.001
Use of EBM tools and principles (critical appraisal) 2.58 4.44 +1.86 <.001
Mean confidence score 3.14 4.63 +1.39 <.001
* Assessed from 1 = very uncomfortable to 5 = very comfortable
Surveys to evaluate goals 2 and 3 were completed by 21 of 25 residents (response rate of 84%) and 12 of 13 faculty (response rate or 92%). As seen in Table 2, 86% of residents reported that they have continued to apply EBM concepts and tools learned in the rotation to clinical questions at least occasionally and 81% reported having had EBM concepts and tools reinforced occasionally or often by faculty or senior residents. Eleven of the 12 faculty (92%) agreed that the EBM curriculum had increased their use of EBM concepts and tools in teaching and 75% felt that the curriculum had increased their use of EBM concepts and tools in their own clinical practice.
Table 2 Residents' report of how often they applied EBM concepts and tools to patient care, and how often EBM concepts and tools are reinforced by senior residents and faculty (n = 21)
Item Never Seldom (< 1/month) Occasionally (1 to 3 times/month) Often (1 to 2 times/week) Frequently (≥3 times/week)
N (%) N (%) N (%) N (%) N (%)
Applied to patient care 0 (0) 3 (14) 7 (33) 8 (38) 3 (14)
Reinforced by senior residents and faculty 0 (0) 4 (19) 12 (57) 5 (24) 0 (0)
As shown in Table 3, most residents and inpatient attending faculty felt that the EBM answer to the inpatient clinical question provided useful information 25% to 75% of the time. Only 1 resident (and none of the faculty) reported the EBM answer provided useful information less than 25% of the time. The majority of the residents (65%) and faculty (83%) reported that the information led to a change in patient management 25% to 75% of the time, with the remainder reporting a change in management less than 25% of the time. All residents and 11 of the 12 faculty (92%) agreed that the EBM rotation had improved the quality of patient care within the residency program.
Table 3 Percent of time that the answer to EBM question provided useful information or led to a change in patient management on the Family Practice Inpatient Service
Item Residents (n = 20*) Faculty (n = 6**)
<25% 25% to 75% >75% <25% 25% to 75% >75%
N (%) N (%) N (%) N (%) N (%) N (%)
Provided useful information 1 (5) 12 (60) 7 (35) 0 (0) 3 (50) 3 (50)
Led to a change management of the patient 7 (35) 13 (65) 0 (0) 1 (17) 5 (83) 0 (0)
* One Resident, who joined in the second year, had not been on the inpatient service during a time when an EBM intern was present.
** Answered by the 6 faculty members who attend on the inpatient service.
Discussion
While the above findings support the acceptance and perceived utility of our EBM program, they do not provide a formal measure of its effectiveness. Changes in resident knowledge, skills and confident were measured over a short period of time. The frequency of reinforcement of EBM and the impact of EBM on clinical care was by resident and faculty report and may have been biased. No attempt was made to observe changes in physician behaviors or patient outcomes. There were no measures of EBM use prior to the introduction of the EBM rotation and no comparison group was available. It is therefore not possible to objectively determine to what degree current levels of awareness and utilization of EBM are the result of the rotation. It is also not possible to separate out the effects of the 2-week EBM rotation from the adjunct changes of establishing a medical information website or promoting the use of PDAs, except to the extent that the questions asked specifically about the 2-week EBM rotation.
Our block EBM rotation differs substantially from the approaches reported in previous studies [9,11,16] in that it utilizes a concentrated, individual experience that combines formal learning (via tutorials and a web-based course) with immediate application of EBM to answer an important clinical questions for individual patients – the target goal for EBM training. An advantage of our approach is the ability to tailor the curriculum to the background and needs of each intern and to provide interns with dedicated time during which they can rapidly acquire EBM knowledge and skills and apply them to "real time" clinical questions under the supervision of a faculty member and a librarian information specialist. We believe that our curriculum could be implemented by any faculty member with a working knowledge of EBM. The website tutorial [15] provides the core didactic portion for the rotation. Moreover, directing the rotation naturally increases the experience and expertise of the faculty member involved.
Interns reported greater confidence in their search skills after the two week rotation. Two randomized controlled trials and a controlled before-after study have demonstrated benefits of training in electronic search skills [17-19]. Interns also reported significantly greater confidence in their ability to apply EBM knowledge and principals in critical appraisal. This is consistent with previous studies that have found training improves critical appraisal skills for residents and practicing physicians [20].
One study that evaluated the impact of a 1-month pilot program to use EBM methods on an inpatient service, reported results roughly similar to ours [21]. Our curriculum also includes the archiving of EBM answers in a standardized format for future reference by residents and faculty. While our study did not include an evaluation of the usefulness of the archived EBM answers generated by the rotation, archiving EBM answers for web-based access has been shown to provide a useful resource for resident physicians in an internal medicine residency program [22].
Previous studies have found only small increases in residents' knowledge and skills from journal clubs alone, leading to the suggestion that journal clubs should be used as a component of EBM training rather then being 'stand alone' activities [6-8]. While we did not attempt to evaluate the effects of the journal club per se, including it as a component of our EBM curriculum is concordant with this suggestion.
Other investigators have reported educational interventions aimed at improving faculty knowledge and skills in medical informatics and EBM. One study reported increases in faculty self-rating of EBM skills following an intervention consisting of 2 half-day workshops and substantial amount of individual mentoring [23]. We found that faculty reported our EBM rotation has increased their use of EBM in their clinical practice, as well as their teaching of EBM; this was echoed by residents. While it is difficult to compare the two studies, our findings suggest that integrating EBM into the residency via resident training may improve faculty application of EBM to clinical care and in their teaching, and may be a cost-effective way to reinforce faculty EBM skills.
Our EBM curriculum, based on an individual 2-week EBM rotation in the first year, appears to be successful in increasing resident's EBM skills and confidence. In addition, resident and faculty both perceive EBM as being incorporated and reinforced beyond the rotation, and that the presence of EBM in the residency improves the quality of patient care. We hope that our experience provides a useful model for teaching and integrating EBM into a busy, resource-limited, family practice residency.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
All authors participated in the development of the curriculum. DT conceived of and conducted the evaluation. JH and PSS reviewed the survey instrument. JH and PSS reviewed and suggested changes to the paper. JH drafted the subsection describing the teaching the use of electronic. All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors would like to acknowledge Juliana Fung for her assistance in the preparation of this manuscript.
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| 15473899 | PMC524497 | CC BY | 2021-01-04 16:29:56 | no | BMC Int Health Hum Rights. 2004 Oct 8; 4:4 | latin-1 | BMC Int Health Hum Rights | 2,004 | 10.1186/1472-698X-4-4 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-321547390110.1186/1475-925X-3-32ResearchFeasibility of rapid and automated importation of 3D echocardiographic left ventricular (LV) geometry into a finite element (FEM) analysis model Verhey Janko F [email protected] Nadia S [email protected] Department of Medical Informatics, University Hospital Goettingen, Robert-Koch-Straße-40, 37075-Göttingen, Germany2 Department of Anesthesiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA2004 8 10 2004 3 32 32 7 9 2004 8 10 2004 Copyright © 2004 Verhey and Nathan; licensee BioMed Central Ltd.2004Verhey and Nathan; 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
Finite element method (FEM) analysis for intraoperative modeling of the left ventricle (LV) is presently not possible. Since 3D structural data of the LV is now obtainable using standard transesophageal echocardiography (TEE) devices intraoperatively, the present study describes a method to transfer this data into a commercially available FEM analysis system: ABAQUS©.
Methods
In this prospective study TomTec LV Analysis TEE© Software was used for semi-automatic endocardial border detection, reconstruction, and volume-rendering of the clinical 3D echocardiographic data. A newly developed software program MVCP FemCoGen©, written in Delphi, reformats the TomTec file structures in five patients for use in ABAQUS and allows visualization of regional deformation of the LV.
Results
This study demonstrates that a fully automated importation of 3D TEE data into FEM modeling is feasible and can be efficiently accomplished in the operating room.
Conclusion
For complete intraoperative 3D LV finite element analysis, three input elements are necessary: 1. time-gaited, reality-based structural information, 2. continuous LV pressure and 3. instantaneous tissue elastance. The first of these elements is now available using the methods presented herein.
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Background
Intraoperative TEE is currently available in most cardiac surgical operating rooms. In some centers, intraoperative 3D echocardiography is used to evaluate geometry and to plan surgical interventions prior to LV remodeling surgery. However, quantitation of LV geometry is limited to rather imprecise measures such as ejection fraction. Thus the cardiac surgeon has no sophisticated, immediate, quantitative analysis of the preoperative 3D LV geometry. Intraoperative quantitative analysis of the dynamic behavior of the LV might provide optimal information upon which to base precise patient-specific planning of the surgical intervention, as well as to assess the adequacy of the completed surgical repair.
Because the LV cannot be realistically described by a symmetric mathematical model, the modern approach consists of using a FEM mesh which approximates LV geometry [1] or whole heart geometry [2].
Initial attempts at FEM in the heart have been carried out with 3D segmentation and tracking using sophisticated and expensive cardiac MRI [3]. MRI is impractical in the cardiac surgical operating room and is complicated by the fact that the LV and the papillary muscles are active materials, behaving differently during systole and diastole. An ideal model would provide material properties specific to each patient as first mentioned by McCulloch [4], but untill now patient-specific modeling in the operating room is not been possible.
FEM modeling of 3D intraoperative echo data provides an excellent tool for incorporating material properties, volumetric data and boundary pressures to more accurately record and then to simulate LV dynamic performance. Accurate simulation will be the foundation of surgical planning. The limitation until now in applying FEM intraoperatively has been the technical complexity of this technique. The purpose of this study is to take the first step towards introducing FEM into the operating room environment. The goal is to facilitate transfer of geometric data from 3D ultrasound data set into FEM.
Methods
After obtaining institutional review board approval, LV images from clinical TEE data sets were obtained in five patients via the midesophageal window using a Philips 5500/7500 or and Acuson Sequoia ultrasound system. After induction of general anesthesia and airway protection, the esophagus was intubated using an omniplane TEE probe. 3D TEE data sets of the LV structures including mitral annulus and leaflets, chordae tendinae, papillary muscles and ventricular wall were obtained using the automated Philips/Acuson acquisition protocol at 10° increment. Images were gaited for both beat-to-beat variability and respiratory motion. In order to facilitate acquisition in the shortest possible timeframe, ventilation was modified to provide a tidal volume of 5*10-6 m-3 kg-1 at a respiratory rate sufficient to maintain end-tidal CO2 levels between ~4.4*103 m kg-1 s-1 and ~5.1*103 m kg-1s-1.
All images were stripped of patient identifiers. For LV geometry reconstruction, the TomTec LV-Analysis TEE© software module [5] was employed. This software runs on a standard Dell Inspirion laptop computer with Microsoft Windows™ 2000 operating system which imports, analyzes, reports and archives the time-resolved 3D-ultrasound data. The TomTec system automatically detects endocardial borders and produces a 3D shell reconstruction of the LV [5]. It also provides for an analysis of global and regional LV parameters in which a landmark-setting method is used (see Fig. 1).
Figure 1 Scheme of the LV (left ventricle). Section through left atrium and ventricle shown schematically. In the LV Analysis TomTec TEE program, three landmarks are taken from each second frame per data set. This means that each 10° a frame is taken as the sampling point for the LV Analysis TEE program. AV is aortic valve, MV is mitral valve and Ap is apex.
The first landmark was set in the middle of the mitral valve at the level of its annulus. Care was taken to avoid having the mitral valve cusps cross this landmark. Two additional landmarks were placed in the middle of the aortic valve at the level of its annulus and at the endocardial level of the LV apex. With this landmarking procedure, a time-resolved LV geometric analysis with 18 models per heart cycle was obtained (see Fig. 2).
Figure 2 Screenshot-TomTec. Screenshot of the workspace of the TomTec LV Analysis TEE program. The LV is segmented using color coding in (c). In (a) the LV model is shown in 3D as calculated from the sampling points set according to Fig. 1. The shadowed plane in (a) indicates the position of the actual original US gray-value frame in 3d as shown in (b). In (d) the volume content is displayed in terms of the actual model step indicating the actual phase with a green line. The screenshot of the actual phase shows the LV model at near systole.
The rendered LV geometry resulting from the TomTec analysis tool was transferred to an ABAQUS input file using software written in Delphi. In this program, the TomTec file structure was reformatted to an ABAQUS system (version 6.3) input file based on standard ABAQUS FEM elements.
ABAQUS creates a time series of LV model files and requires continuous intraventricular pressure and tissue elastance parameters to process the model. For this analysis LV pressure was modeled using wave forms obtained from Columbia University's HeartSim© cardiac simulator [6]. A single tissue elastance parameter was applied. These modeled values were used to demonstrate the concept. Actual values will be needed for accurate simulations.
The time required for each step in this process was recorded for each patient data set.
Results
Both, the Philips Sonos 5500/7500 or the Acuson Sequoia ultrasound systems required less than 10 minutes acquistion time per patient. The application of the TomTec LV analysis algorithms with manual placement of the necessary landmarks took approximately 7 minutes per patient. In five patient data sets conversion from TomTec data to the FEM model was carried out in less than a minute for a heart cycle using the conversion tool MVCP FemCoGen© [7]. ABAQUS processing time on the above computer was 20 seconds per sequence or approximately 6 minutes per patient. Total time for the procedure was approximately 24 minutes per patient (see Table). Fig. 3 shows the ABAQUS FEM program system interface (ABAQUS/viewer) including the LV models in default mesh mode. All 774 triangles of the FEM mesh from the diastolic state (14th image out of a set of 18 images per heart cycle) are displayed and can be visualized using ABAQUS viewer options. Each mesh element can be analyzed separately. This is shown in Fig. 4: In Fig. 4a and 4b the rendered LV using a standard constant-shading model is displayed in systolic and diastolic states. Fig. 4c and 4d show both the FEM mesh and the normal vectors orthogonally placed (orthonormals) on each triangle indicating the force direction. These figures demonstrate the quantification of movement during the heart cycle directly using modeled continuous LV pressure and tissue elastance parameters.
Table 1 Data acquisition and processing times Data acquisition and processing times
Data set Ultrasound acqusition [s] TomTec analysis [s] FemCoGen transfer [s] Abaqus processing [s]
1 715 475 50 355
2 580 364 45 340
3 670 320 55 390
4 640 390 45 410
5 597 423 45 430
Mean ± (standard deviation) [s] 640.4 ± 41.7 394.4 ± 43.7 48 ± 3.6 385 ± 30
Total time [s] 1467.8 ± 29.7
Figure 3 LV in finite element analysis program. Left ventricle FEM model in ABAQUS FEM program interface. Shown is the LV in diastole. At the top of the mesh is the aortic valve depicted as a cavity. The LV apex appears at the bottom of the mesh.
Figure 4 Pressure direction at systole and diastole. Rendered LV at systole on the left (a) and (c) and diastole on the right (b) and (d). Shown is the mesh generated with FEM program including all 774 FEM elements rendered with a standard constant shading model in (a) and (b). (c) and (d) show the mesh together with the surface vectors (normals) orthogonally placed on each element (triangle) indicating the pressure directions.
Discussion
The general intention of this study was to demonstrate the feasibility of transporting individual patient's LV geometry data into a FEM model. Standard laptop computer technology was utilized to accomplish the transfer from common TEE-machines (Philips Sonos 5500/7500 and Acuson Sequoia).
The software running on the laptop was the commercially available TomTec LV Analysis TEE© package and ABAQUS FEM system, plus the recently developed MVCP FemCoGen©. Accomplishing this transfer will form the foundation for intraoperative surgical planning and quantitative outcome assessment of valvular and LV reconstructive surgery.
The scope of this study was to produce a prototype in which the feasibility of the method could be assessed. In a fully operational system, we could postulate clinical applications such as enhanced/automated wall motion abnormality detection, assessment of regional relaxation which encompases the entire ventricle, assessment and guidance of ventricular remodeling operations, and serial assessment of recovery of regional wall function post myocardial stunning.
FEM meshes have been used for approximately 30 years [8] in the analysis of many anatomical structures and organs e.g such as major vessels [9,10], heart valves [11] and ventricles [12], lung [13], corneoscleral shell [14], plastic and reconstructive craniofacial surgery [15] and the femur [16]. A FEM model can be created to determine the deformation of the LV loaded by intraventricular pressure. Steady-state fluid dynamics and structural analyses can be carried out using commercial codes based on FEM [17]. At a sequence of time-steps of the cardiac cycle, the model can be considered to be a quasi-incompressible transversely isotropic hyperelastic material based on the analysis of Feng [18]. Until now, biomechanical cardiac FEM models have been based on simplified ellipsoidal and cylindrical geometries [18]. A FEM created in this way is not patient-specific and does not accurately represent precise regional deformations in the LV loaded by intraventricular pressure. The method described here will allow patient specifity and the precise representation of deformation. Our method would be applicable to the "live 3D" systems assuming that the entire ventricle could be seen throughout the cardiac cycle in the transthoracic (or epicardial) matrix array acquisition. This would be most feasible in small adults and children and can be proved in further studies.
The total time required for acquisition to a completed FEM model was approximately 24 minutes and can be accomplished during the time period when the patient is being prepared for cardiopulmonary bypass (generally 1 to 1.5 h). Thus the feasilbility in terms of duration is clearly demonstrated.
In terms of procedure accuracy, reproducibility and duration, the primary limitation is the dependence of the TomTec software on manual entries of the three registration landmarks. This requirement is iterative. Manual entries must be done for multiple frames within the TEE data sets. Inter- and intraobserver variability is a general problem for ultrasonic imaging. The validation of the TomTec border detection has not been published. TomTec LV Analysis TEE© Software is under review by the US FDA, but despite of lack of validation TEE is the only practical technology in the cardiac operating room for the forseeable future. Ultrasound tissue Doppler technologies may be developed in the future to allow automation of the registration process.
A limitation of the present study is that it is focused on the deployment of the transfer method. The entire process will require extensive validation. The validation strategy will most likely involve comparision with preoperative cardiac MRI as well as comparison with bypass and post bypass tissue geometry in the same patients. Creating models from MRI based data sets analogous to the TomTec LV analysis and transfering these models to ABAQUS might lead to a new validation strategy which is not been possible up to now. The tool for modeling presented here facilitates vector-subtraction analysis for different points within the cardiac cycle. Quantification is therefore immediately available for both global and regional wall motion, shape and volume analysis. The future use of such instantaneous analysis has a number of potential applications for LV function assessment and surgical planning. This technology could enable a comprehensive automated regional wall motion analysis. A significant challenge in the evaluation and management of patients with coronary artery disease is determining the viability of myocardium. A biomechanical FEM of the LV myocardium can be imported to evaluate dynamic mechanical properties of regions of the myocardium. This approach could provide the basis for a new index of regional myocardial viability.
Conclusions
For complete intraoperative 3D LV finite element analysis, three input elements are necessary: 1. time-gaited, reality-based structural information, 2. continuous LV pressure and 3. instantaneous tissue elastance. The first of these elements is now available using the methods presented herein. The later two parameters will be required for robust modeling and analysis. Pressure data will be easily available in the cardiac operating room. Strategies for computing elastance are presently under development. With all three parameters, it will be possible to begin to develop the computational strategies which will allow virtual procedures to be performed utilizing 3D display technology and a haptic-feedback robotic "instruments". Whether this new intraoperative information will be useful in assessing the effectiveness of surgical interventions such as LV remodeling remains to be studied.
FEM analysis has not been feasible for LV in the intraoperative setting. The major roadblock was the complexity and the practicality of transfer of structural 3D data to a FEM analysis program. This study describes a method to rapidly transfer 3D structural data from the TEE device into a FEM analysis program. Once mesured pressure and calculated elastance are added to the model, near real-time dynamic stress-strain information in the operating room will be achievable.
Authors' contributions
JFV did the technical part implementing the FEM model in ABAQUS®, NSN did the data acquisition and the medical part. Both authors read and approved the final manuscript.
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| 15473901 | PMC524498 | CC BY | 2021-01-04 16:37:32 | no | Biomed Eng Online. 2004 Oct 8; 3:32 | utf-8 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-32 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-261546960510.1186/1476-4598-3-26ReviewMolecular regulation of pancreatic stellate cell function Jaster Robert [email protected] Department of Medicine, Division of Gastroenterology, Medical Faculty, University of Rostock, E.-Heydemann-Str. 6, 18057 Rostock, Germany2004 6 10 2004 3 26 26 10 3 2004 6 10 2004 Copyright © 2004 Jaster; licensee BioMed Central Ltd.2004Jaster; 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.
Until now, no specific therapies are available to inhibit pancreatic fibrosis, a constant pathological feature of chronic pancreatitis and pancreatic cancer. One major reason is the incomplete knowledge of the molecular principles underlying fibrogenesis in the pancreas. In the past few years, evidence has been accumulated that activated pancreatic stellate cells (PSCs) are the predominant source of extracellular matrix (ECM) proteins in the diseased organ. PSCs are vitamin A-storing, fibroblast-like cells with close morphological and biochemical similarities to hepatic stellate cells (also known as Ito-cells). In response to profibrogenic mediators such as various cytokines, PSCs undergo an activation process that involves proliferation, exhibition of a myofibroblastic phenotype and enhanced production of ECM proteins. The intracellular mediators of activation signals, and their antagonists, are only partially known so far. Recent data suggest an important role of enzymes of the mitogen-activated protein kinase family in PSC activation. On the other hand, ligands of the nuclear receptor PPARγ (peroxisome proliferator-activated receptor γ) stimulate maintenance of a quiescent PSC phenotype. In the future, targeting regulators of the PSC activation process might become a promising approach for the treatment of pancreatic fibrosis.
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Review
Excessive production of connective tissue molecules forming the extracellular matrix (ECM) is a pathological process relevant to diseases of many organ systems, including liver, lung, kidney, bowel and pancreas. The resulting fibrosis frequently leads to a progressive loss of specific organ functions. In the past two decades, fibrogenesis has been intensively studied by a large number of laboratories, and a great deal of scientific information has been accumulated regarding the pathogenesis of fibrosis in various organs. Until a few years ago, pancreatic fibrosis, however, remained an exception: although known for a long time as a central pathological feature of both chronic pancreatitis and pancreatic cancer [1,2], its cellular and molecular basics remained obscure. This situation has changed significantly since the identification of a fibroblast-like cell type in the pancreas with close similarities to hepatic stellate cells (HSCs; also called Ito cells) [3,4], the predominant source of ECM in the fibrotic liver [5,6]. In the meantime, it has become increasingly clear that these stellate cells of the pancreas (named pancreatic stellate cells; PSCs) are the principle effector cells in pancreatic fibrosis. In the following sections, I will focus on (I) the current understanding of the role of PSCs in fibrogenesis, (II) extracellular signals involved in PSC activation, (III) intracellular mediators of activation signals in PSCs, (IV) future directions of research, and (V) activated PSCs as a target for antifibrotic therapies.
Pancreatic stellate cells and their role in pancreatic fibrogenesis
Both chronic pancreatitis and pancreatic cancer are accompanied by an organ fibrosis [1,2]. The progressive replacement of pancreas-specific tissue by ECM-rich connective tissue leads to the development of an exocrine and endocrine insufficiency of the gland. So far, specific therapies to prevent, retard or even reverse this process are not available.
Fibroblast activation has been reported to be a common event in pancreatitis already more than a decade ago [7-9], but the basic matrix producing cell type in the pancreas remained to be identified. In 1997, Saotome et al. [10] described the isolation of periacinar fibroblast-like cells from human pancreas. The cells displayed some characteristics of activated myofibroblasts, e.g. expression of α-smooth muscle actin (α-SMA) and synthesis of ECM proteins. One year later, Bachem et al. [3] and Apte et al. [4] found that vitamin A-storing cells resembling hepatic stellate cells can be isolated from human and rat pancreas. In the healthy organ, PSCs comprise about 4% of all pancreatic cells and show a periacinar distribution. They can be identified by the presence of retinoid-containing cytoplasmic lipid droplets and by immunostaining for cytoskeletal proteins such as desmin and glial fibrillary acidic protein [4]. In culture, pancreatic stellate cells readily grow [4] and change from a quiescent phenotype to a myofibroblast-like cell expressing α-SMA and producing large amounts of the ECM proteins collagen type I and III, fibronectin as well as laminin [3]. This activation process is accompanied by a loss of the characteristic retinoid-containing fat droplets [3,4]. Together, these in vitro data gave rise to the hypothesis that PSCs might play a pivotal role in pancreatic fibrogenesis.
In the meantime, this hypothesis has been supported by the results of several in vivo studies using experimental models of pancreatic fibrosis: Infusion of trinitrobenzene sulfonic acid (TNBS) into the pancreatic duct of rats causes a pancreatic necroinflammation followed by fibrosis [11]. In TNBS-treated rats, areas of pancreatic fibrosis colocalized with α-SMA-positive cells, suggesting the presence of activated PSCs. Furthermore, dual staining techniques indicated that these α-SMA-positive cells were the main source of collagen in the fibrotic pancreas [12]. Importantly, very similar data were obtained when pancreatic tissue from patients with chronic pancreatitis was analyzed [12]. Another well-established model of fibrogenesis involves the administration of a single intravenous dose of dibutyltin dichloride (8 mg/kg body weight), resulting in the development of a chronic pancreatitis associated with fibrosis [13]. Time course studies of DBTC-induced chronic pancreatitis revealed an early activation of PSCs that preceded development of fibrosis [14]. In mice, repeated intraperitoneal application of supraphysiological cerulein doses causes a pancreatic injury and, subsequently, fibrosis [15,16]. In agreement with the data mentioned above, collagen gene expression was colocalized to PSCs [16]. Overexpression of transforming growth factor-beta (TGF-β) 1 in transgenic mice has been shown to be associated with increasing deposition of ECM in the pancreas. In parallel with the development of fibrosis, the number of PSCs in the pancreas increased [17].
Recently, it has also been suggested that PSCs contribute to regeneration early after acute necrotising pancreatitis in humans [18].
Together, in vitro and in vivo data suggest that PSCs are essentially involved in the development of pancreatic fibrosis.
Extracellular signals involved in pancreatic stellate cell activation
Based on the results of various recent studies, extracellular factors involved in PSC activation may be divided into two major groups: (I) cytokines/growth factors [3,19-22] and (II) ethanol and its metabolites, most of all acetaldehyde [23].
Cytokines stimulating PSC activation include platelet-derived growth factor (PDGF) [3,19,21,22], the TGF-β family members TGF-β1 [3,19,21,22] and activin A (24), TGF-alpha [3,22], basic fibroblast growth factor [3,22], tumor necrosis factor-α (TNF-α) [22], interleukin (IL)-1 [20] and IL-6 [20]. While TGF-β1 efficiently promotes ECM synthesis [3,19,21,22], PDGF is considered to be the most effective mitogen [22]. Furthermore, PDGF also enhances the migratory capacity of PSCs [25]. Potential sources of cytokines stimulating PSC activation in the inflamed pancreas are, for example, activated macrophages (secreting TGF-β1) [26], platelets (containing PDGF and TGF-β1) [21], and possibly acinar cells (expressing, among other cytokines, TNF-α [27], IL-1 and IL-6 [28]). Importantly, PSCs themselves are capable of synthesizing cytokines such as TGF-β1 [29,30], activin A [24] and IL-1 [31]. These observations suggest the existence of autocrine loops that may contribute to the perpetuation of PSC activation after an initial exogenous signal, thereby promoting the development of fibrosis.
Recent studies have also implicated the pancreatic renin-angiotensin system [32,33] in pancreatic fibrogenesis. Thus, application of the angiotensin-converting enzyme inhibitor lisinopril [34], as well as the angiotensin II receptor antagonist candesartan [35], suppressed pancreatic inflammation and fibrosis in an animal model of spontaneously occurring chronic pancreatitis, Wistar Bonn/Kobori rats. In angiotensin II receptor type 1a-deficient (AT1a(-/-)) mice, pancreatic fibrosis induced by repeated episodes of acute pancreatitis (following cerulein injections) was found to be attenuated [36]. In vitro, angiotensin II (ATII) stimulates PSC proliferation [37,38] and induces cell contraction [38].
Cytokines that act as antagonists of PSC activation have not been systematically studied so far. Recently, it has been shown that IFN-α protects hepatic stellate cells from lipid peroxidation by enhancing biological activities against oxidative stress, resulting in an inhibition of activation [39]. Furthermore, antiproliferative effects of IFN-α [40], IFN-β and IFN-γ [41] on HSCs have been reported. On the other hand, IFN-α also inhibits spontaneous apoptosis of activated HSCs [40]. The effects of interferons on pancreatic fibrogenesis remain to be characterized.
Although it is known for a long time that chronic pancreatitis, associated with fibrosis, is a serious complication of alcohol abuse, the pathogenesis of alcoholic pancreatitis still remains to be fully elucidated [reviewed in [42]]. In recent studies, the question has been addressed how long-term alcohol consumption is linked to PSC activation and fibrosis. It has been proposed that the profibrogenic effects of ethanol are in part mediated by PSC-activating proinflammatory cytokines released during episodes of alcoholic pancreatitis (associated with necroinflammation) [43]. Furthermore, in vitro data suggest that ethanol directly acts on PSCs and induces activation [23]: Cultured PSCs respond to ethanol application by increased α-SMA expression and collagen synthesis. Stimulatory effects of ethanol were detectable both in already activated and still quiescent PSCs. The cells express alcohol dehydrogenase, indicating that they are capable of ethanol oxidation and generation of its metabolite acetaldehyde. Very likely, induction of oxidant stress in PSCs contributes to the profibrogenic effects of ethanol [23]. Although the exact chain of events linking ethanol abuse to pancreatic inflammation and PSC activation remains to be described, it is likely that both direct and indirect (cytokine-mediated) effects of ethanol on PSCs are involved in the development of pancreatic fibrosis.
Intracellular transduction of activation signals
In the past two years, analysis of signal transduction pathways regulating PSC function has become a focus of attention. As detailed below, identification of signaling molecules that play a crucial role in PSC activation is a promising approach for the development of therapeutic strategies to inhibit pancreatic fibrosis. It is therefore envisaged that the systematic elucidation of signaling pathways in PSCs will also be one of the most important issues for future research. So far, research regarding intracellular signaling in PSCs has focused on two main aspects: the role of enzymes of the mitogen-activated protein kinase (MAPK) family and the transcriptional control of PSC activation.
MAPKs are a family of serine/threonine specific protein kinases with a wide range of biological functions in the regulation of fundamental cellular processes, including gene expression, proliferation and cell survival/apoptotic cell death [44-46]. In mammalian cells, three major MAPK families (extracellular signal-regulated kinases [ERKs], c-Jun N-terminal kinase [JNK] and p38) have been identified [45], and all of them have recently been studied with respect to the regulation of PSC activation. The best-characterized ERKs, ERK 1 and 2, are activated through a well-established pathway (induced by many growth factors) that involves, among several other cytosolic proteins, the small G-protein Ras and the serine/threonine-specific protein kinase Raf-1 [45]. In the process of PSC activation induced by sustained culture, ERK 1/2 activation is an early event that precedes exhibition of a myofibroblastic phenotype [47]. The strong PSC mitogen PDGF induces an activation of ERK 1/2, and inhibition of signaling through the Ras-Raf-ERK signaling cascade attenuates PSC proliferation [47-49]. It has also been shown that exposure of PSCs to ethanol and acetaldehyde is accompanied by a fast [50] and long-lasting [51] ERK 1/2 activation.
The other two major MAP kinase pathways, involving JNK and p38, are well-established mediators of signals induced by pro-inflammatory cytokines and cellular stressors (e.g., oxidant stress, UV irradiation) [52]. In PSCs, both JNK and p38 are activated in response to ethanol/acetaldehyde exposure [50,51]. Inhibition of p38 enzymatic activity interferes with ethanol-induced myofibroblastic transdifferentiation of PSCs [51]. The p38 signaling pathway has also been implicated in the mediation of the mitogenic PDGF effect and in the induction of PSC activation induced by sustained culture [53]. Incubation of freshly isolated PSCs with the JNK inhibitor SP600125 attenuates proliferation of the cultured cells [54]. MAP kinase pathways have also been shown to be involved in ATII signaling in PSCs [37,38]. Together, these data support the hypothesis that MAPKs are key mediators of activation signals in PSCs.
Two other intracellular signal transduction pathways that have recently been studied regarding their role in PSC activation are the phosphatidylinositol 3 (PI 3)-kinase and the Rho-Rho kinase (ROCK) pathway. The results suggest that PI 3-kinase activity is required for PDGF-stimulated PSC migration but not proliferation [49,55]. The Rho-ROCK pathways was shown to be involved in the activation process of PSCs in vitro by regulating the actin cytoskeleton [56].
Cytokine and growth factor receptors exert their effects on the expression of target genes through signaling cascades that regulate the activity of a characteristic set of transcription factors. Recently, the group of the author has analyzed the activation profiles of activator protein (AP)-1 [57,58], signal transducer and activator of transcription (STAT) 3 [59] and nuclear factor (NF)-κB [60,61] in the course of PSC activation induced by sustained culture. AP-1 and NF-κB displayed an earlier maximum of DNA binding activity than STAT3 [62]. Further experiments revealed that phenotypic transition of PSCs towards myofibroblasts was accompanied by characteristic changes of AP-1 complex composition (increase of the JunD content relative to the one of JunB) [62]. DNA binding of AP-1 in PSCs is induced by PDGF, suggesting AP-1 activation as an important step in the process of PSC activation [47].
In the transduction of TGF-β receptor-derived signals into the nucleus, Smad transcription factors play a central role [63,64]. Studies by Ohnishi and co-workers revealed that TGF-β1 stimulated PSC activation (indicated by increased α-SMA expression) in a Smad2-dependent manner, while Smad3 was required for TGF-β1-induced growth inhibition [65]. Interestingly, exogenous TGF-β1 increased TGF-β1 mRNA expression in PSCs through an ERK-dependent but Smad2/3-independent pathway. Together, these data suggest distinct roles of Smad2-, Smad3- and ERK-dependent pathways in TGF-β1 regulation of PSC functions. Based on recently published data on HSC biology [66], it can be hypothesized that Smad7, a negative regulator of TGF-β signaling, might act as a transcriptional inhibitor of PSC activation, but so far experimental evidence has not been presented.
Recent studies have implicated the nuclear hormone receptor peroxisome proliferator-activated receptor γ (PPARγ) in the inhibition of stellate cell activation in liver [67-69] and pancreas [69]: The PPARγ ligands 15-deoxy-Δ12,14-prostaglandin J2 and troglitazone (an antidiabetic drug of the thiazolidinedione group) act as antagonists of PSC activation in vitro that decrease cell proliferation and expression of α-SMA [70]. In Wistar Bonn/Kobori rats, troglitazone attenuates pancreatic inflammation and fibrosis [71]. The antifibrotic effect of the drug, however, was found to be in part mediated via a PPARγ-independent mechanism [72]. Thus, the precise role of PPARγ in pancreatic fibrogenesis remains to be elucidated in further studies.
Open questions with respect to PSC biology and pathology
While the role of activated PSCs in pancreatic fibrosis is well established, the physiological functions of their quiescent precursors are less well understood. Importantly, PSCs are not only a source of ECM but also of matrix-degrading enzymes of the MMP (matrix metalloproteinases) family and their inhibitors (tissue inhibitors of matrix metalloproteinases, TIMPs). Thus, PSCs have been shown to secrete MMP-2, MMP-9 and MMP-13 and to express TIMP-1 and TIMP-2 [73]. It appears therefore likely that PSCs participate in the regulation of matrix turnover in the healthy pancreas.
The embryonic origin of PSCs still remains to be determined. Very recently, Seaberg et al. [74] reported the clonal identification of multipotent precursors from adult mouse pancreas that generate neural and pancreatic lineages, including β-like cells and pancreatic stellate cells. With regard to PSC biology, one implication of this pioneer study is that PSCs share with exocrine and endocrine pancreatic lineages a common progenitor cell. Kruse et al. [75] have described the isolation and culture of undifferentiated pancreatic cells, capable of extended self-renewal and spontaneous differentiation into cells of all three germ layers. The relationships between these cells, which were described as stellate-like cells, and PSCs are currently unknown and should be further studied using clonal cell populations.
Until now, the physiological consequences of vitamin A-storage in PSCs remain unclear. It has recently been shown by the group of the author that the vitamin A derivate all-trans retinoic acid has complex effects on PSC function and acts, at least in part, as an antagonist of the activation process [76]. It is, therefore, tempting to speculate that retinoic acids, through the binding to their nuclear receptors and the regulation of gene expression, are involved in the maintenance of a quiescent PSC phenotype. In this scenario, the loss of retinoids in the course of PSC activation might be not an epiphenomenon but an essential prerequisite.
In the past, research regarding PSC biology has almost exclusively focused on the molecular basics of the activation process. However, given that participation in regeneration after pancreatic injury is an important function of activated PSCs, it is apparent that a disturbance of the inactivation or elimination of activated PSCs, rather than PSC activation itself, is the pathological process that leads to fibrosis. So far, it has not been systematically studied whether activated PSCs are capable of returning into a quiescent stage after fulfilling a repair function. Alternatively, elimination by apoptosis might be important in terminating the wound-healing response after pancreatic injury [77].
Finally, work on the complex relationships between PSCs and pancreatic tumor cells is still in its infancy. Very likely, activation of PSCs not simply accompanies tumor progression but plays an active role in this process. Thus, it has been recently been shown that pancreatic cancer growth and progression is accelerated through complex functional interactions between carcinoma cells and PSCs [78]. Furthermore, the increased deposition of connective tissue in pancreatic carcinoma was suggested to be the result of a paracrine stimulation of PSCs by cancer cells [79]. Interestingly, TGF-β1-transfected pancreatic tumor cells have been demonstrated to induce a rich stroma after orthotopical transplantation in the nude mouse pancreas [80]. Considering the established role of TGF-β1 in PSC activation [3,19,21,22], it appears likely that the cytokine is a key effector in tumor-associated pancreatic fibrosis. It is easy to predict that the further analysis of PSC activation in pancreatic cancer will be an important research area in the future.
Studies on PSC biology are still hampered by the limited availibility of primary cells. Possibly, recently established pancreatic stellate cell lines [81,82] will be helpful in overcoming this problem.
Therapeutic implications
Given that activated PSCs are the principle effector cells in pancreatic fibrosis, targeting PSCs might become a promising therapeutic approach. Principle strategies that can be envisaged include an interruption/reversion of the activation process as well as an elimination of activated PSCs, e.g. through an induction of apoptosis. So far, potential antifibrotic drugs have been mainly tested in models of liver fibrosis (reviewed in [83]). The existence of common mechanisms in the development of liver and pancreatic fibrosis (particularly, the key role of activated stellate cells), however, suggests that at least some of these drugs may also be effective inhibitors of fibrogenesis in the pancreas. In this regard, the efficiency of substances interfering with the action of stellate cell mitogens (e.g., PDFG), or cytokines stimulating ECM synthesis (especially TGF-β), should be tested in animal models of pancreatic fibrosis. The inhibitory effects of an angiotensin-converting enzyme inhibitor [34], as well as an ATII receptor antagonists [35], on pancreatic fibrosis need to be further evaluated. Interesting candidates are also cytokines that display inhibitory effects on hepatic stellate cell activation, such as interferons.
As described above, studies on the regulation of PSC activation at the intracellular level have identified key mediators of stimulatory and inhibitory signals. Targeting molecules such as PPARγ, MAP kinases, PI 3-kinase, or Smad proteins might become an important approach for the treatment of pancreatic fibrosis in the future. Further progress in the development of antifibrotic therapies can be expected from the ongoing elucidation of the molecular principles of PSC activation.
Conclusions
PSCs play a crucial role in pancreatic fibrogenesis (Figure 1). Ethanol metabolites and cytokines such as PDGF and TGF-β are key activators of PSCs. The intracellular regulation of PSC activation is incompletely characterized. MAP kinase signaling cascades are involved in the transduction of activation signals, while PPARγ ligands induce a quiescent PSC phenotype. The recent progress in the understanding of the cellular and molecular basics of pancreatic fibrosis will facilitate the development of therapeutic strategies to inhibit pancreatic fibrosis.
Figure 1 Pancreatic stellate cell activation in chronic pancreatitis and pancreatic cancer. Pancreatic stellate cells are activated by profibrogenic mediators, such as ethanol metabolites and cytokines/growth factors. Perpetuation of stellate cell activation under persisting pathological conditions results in pancreatic fibrosis.
Acknowledgement
This work was supported by a grant from the Bundesministerium für Bildung und Forschung (01ZZ0108).
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| 15469605 | PMC524499 | CC BY | 2021-01-04 16:36:33 | no | Mol Cancer. 2004 Oct 6; 3:26 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-26 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-271547154410.1186/1476-4598-3-27ResearchGene expression profiling of epithelial ovarian tumours correlated with malignant potential Warrenfeltz Susanne [email protected] Stephen [email protected] Susmita [email protected] Eileen T [email protected] Benedict [email protected] John F [email protected] Genetics Department, University of Georgia, Athens Georgia 30602, USA2 Ovarian Cancer Institute, Atlanta Georgia, 30342, USA3 Computer Science, University of Georgia, Athens, Georgia 30602, USA4 Mathematics and Statistics, Georgia State University, Atlanta, Georgia 30303, USA2004 7 10 2004 3 27 27 25 6 2004 7 10 2004 Copyright © 2004 Warrenfeltz et al; licensee BioMed Central Ltd.2004Warrenfeltz 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
Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression.
Results
Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location.
Conclusion
Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events.
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Background
Epithelial ovarian cancer is the fifth leading cause of death for women in the United States [1]. Although early stage ovarian cancer can be effectively treated, symptoms of early disease are sufficiently vague that accurate diagnosis is often delayed until the cancer has progressed into more advanced stages [2]. Treatment of early staged tumours (I through IIa) is associated with a 5-year survival rate of approximately 95% while survival rates drop to less than 30% when diagnosis is delayed until later stages (stage IIb through IV). To improve these statistics, effective early diagnosis and treatment strategies must be developed. Further knowledge of the genes and gene functional pathways involved in ovarian cancer are needed in order to develop these strategies.
Microarray technology has revolutionised the study of gene function by providing "snapshots" of global gene expression patterns from different normal and diseased tissues over multiple stages of development. Nowhere has the impact of this technology been more pronounced than in the field of cancer biology where gene expression profiling has been successfully used to objectively classify tumours and, in some instances, identify novel tumour sub-types [3]. Microarray analyses have also been instrumental in the elucidation of new biological pathways that may be involved in tumour development, as well as, in the identification of new biomarkers of the disease and potential targets of therapeutic intervention.
Previous microarray studies of ovarian cancers have focused on the characterisation of differences between normal ovarian epithelial cells (and cell lines) and various types and stages of ovarian tumours [4-10]. In this study, we focus on characterising differences between benign adenomas, borderline tumours of low malignant potential and malignant adenocarcinomas in order to identify changes associated with the acquisition of malignancy and to avoid the technical difficulties associated with obtaining sufficient amounts of normal ovarian surface epithelium. The ovarian tumour tissue samples used in these microarray studies were chosen to accurately represent the range of malignant potential observed clinically.
We report here the results of applying clustering and statistical analyses to the microarray expression profiles of 18 ovarian tumours. Our findings indicate that gene expression profiling distinguished properly classify 92% of tumours in this study as benign or malignant. Samples taken from ovarian cancer patients who had been treated with chemotherapy prior to surgery were found not to cluster as a distinct group but rather with either the benign or malignant (not pre-treated) tumours. Chemotherapy patients whose tumours clustered with the benign group remained disease free for the duration of the study as evidenced by continued normal serum CA-125 levels. Profiling the functional categories of co-ordinately expressed genes revealed significant correlation between increased malignant potential and loss of IGF binding proteins, and cell adhesion molecules. In several instances co-ordinately expressed genes sharing functional categories also correlated with chromosomal location.
Results
Unsupervised clustering of gene expression profiles can reliably identify ovarian tumour types
To determine if gene expression profiling can distinguish between histologically determined tumour types, we analysed the profiles of 13 ovarian tumours (Table 1) by performing clustering using self-organising maps (SOM) and unsupervised hierarchical clustering (UHC). Self-organising maps are a type of mathematical cluster analysis used to recognise and classify features in complex multi-dimensional data [11]. SOMs group samples into a user-defined number of clusters based on the similarity of the gene expression profiles. The set of thirteen samples was comprised of four benign adenomas (a_64, a_77, a_97, a_159), four borderline tumours of low malignant potential (b_15, b_65, b_72, b_120) and five malignant adenocarcinomas (c_2, c_4, c_23, c_66, c_79). Analysing all 12,590 probe set values from the 13 samples into four groups resulted in 92% of the samples being grouped into clusters consistent with their histopathological classification (Figure 1a). One cluster (cluster 0) contained only adenocarcinomas, two clusters (clusters 1 and 2) contained only borderline tumours, and one cluster (cluster 3) contained all of the benign adenomas and one adenocarcinoma sample. In addition, the UHC of the entire data set (Figure 1b) produced essentially the same clusters as determined by SOM. The only difference between the SOM and UHC results was the stratification of borderline tumours, which are known to be a heterogeneous group of tumours. The SOM clustered the four borderline samples into one group of three borderlines (b_65, b_15, b_72) and one solitary sample (b_120) (Figure 1a). However, the UHC clustered the four borderline samples into one group containing b_15 and b_120, and one group containing b_65, and b_72. Since c_79 was consistently misclassified, a second tissue sample of c_79 was analysed by microarray and clustered as above. This independently obtained expression profile for c_79 produced the same results.
Table 1 Tissue Sample Information
Tumor ID Malignant Potential Available Histological Information Stage Chemo
a_64 benign Serous cystadenofibroma - -
a_77 benign Serous cystadenofibroma - -
a_97 benign Serous cystadenoma - -
a_159 benign Serous cystadenofibroma - -
b_15 low/borderline Serous papillary adenocarcinoma III -
b_65 low/borderline Mucinous adenocarcinoma II -
b_72 low/borderline Mucinous adenocarcinoma I -
b_120 low/borderline Serous papillary adenocarcinoma II -
c_2 invasive malignant Serous papillary adenocarcinoma IIb -
c_4 invasive malignant Serous papillary adenocarcinoma III -
c_23 invasive malignant Serous papillary adenocarcinoma IIIa -
c_66 invasive malignant Serous papillary/endometroid adenocarcinoma IV -
c_79 invasive malignant Serous papillary carcinoma III -
cc_9 invasive malignant Serous papillary carcinoma III Yes*
cc_29 invasive malignant Serous papillary carcinoma III Yes#
cc_36 invasive malignant Serous papillary adenocarcinoma IIIc Yes*
cc_76 invasive malignant Serous papillary adenocarcinoma IIIa Yes*
cc_94 invasive malignant Serous carcinoma III Yes*
* Taxol/Carbo 3X prior to surgery #Taxol/Carbo 4X prior to surgery
Figure 1 Cluster analysis of ovarian tumour expression profiles. Gene expression profiles were obtained from eighteen ovarian tumours. The profiles were analysed by clustering methods in several groups: (a) self organizing map of he thirteen patients not receiving chemotherapy prior to tissue collection, (b) hierarchical clustering of the same 13 patients, (d) self organizing maps of all eighteen patients and (e) hierarchical clustering of all 18 patients. Marker analysis (c) identified the top ten gene most highly correlated with clusters resulting from (a) and (b).
Since many of the genes in our data set display no differential expression across the 13 tumours, their contribution to the SOM is negligible and can be considered noise. Removing genes whose expression pattern displayed insignificant variation (low standard deviation) across all samples, we reduced the data set to1000 probe sets. After removing probe sets representing the same gene, the reduced data set contained expression values representing 700 genes. The SOM and UHC of the reduced data set yielded identical clusters to those obtained using the entire data set (Figure 1a and 1b).
To determine the genes most highly correlated with each cluster identified by the SOM analysis, we performed a marker analysis (Figure 1c) on the reduced set of 700 genes. Marker analysis helps the user discover which genes are most closely correlated with a cluster and provides a measure of how significant that correlation is for each gene. Marker analysis measures the contribution of each gene to the SOM groupings based on a signal to noise ratio calculated from the difference in each gene's mean expression scaled by the sum of the standard deviations across all samples. To avoid having one cluster containing only one sample in the marker analysis, we grouped clusters cluster 1 and cluster 2 containing the borderline samples together, creating three clusters (Figure 1c) consisting of the benign adenomas and c79 (SOM_a), borderline adenocarcinomas (SOM_b), and the malignant adenocarcinomas (SOM_c). Genes highly correlated with each SOM group were expressed strongly in the tumour type associated with that SOM group and poorly expressed in the other SOM groups. It is interesting to note that the 10 genes highly correlated with SOM_a were expressed at intermediate levels in borderline tumours. Similarly, the 10 genes highly correlated with SOM_b were expressed at intermediate levels in the adenocarcinomas of SOM_c.
Gene expression profiles are correlated with recurrence
Five of the cancer patients in our study were treated with chemotherapy prior to surgery. We added the microarray profiles of these patient samples to our analysis in order to determine if they would cluster into a new distinct group or into one or more of the existing groups. The SOM (Figure 1d) and UHC (Figure 1e) clusters resulting from the analysis of all data (12,590 expression values) from all eighteen samples into four clusters differ only in the stratification of the borderline samples. The addition of the five samples from patients who received chemotherapy prior to surgery did not change the cluster assignments of the original thirteen samples. Clustering of the reduced set of 700 genes (see above), resulted in the same patterns of clustering as determined using the entire set of 12,590 expression values (Figure 1d and 1e). Interestingly, the five samples from patients pre-treated with chemotherapy did not cluster together in a distinct group but rather were dispersed among the existing four clusters. Samples cc_29 and cc_76 clustered with the malignant adenocarcinomas, while samples cc_36 and cc_9 clustered with the benign adenomas. Sample cc_94 clustered with the borderline tumours.
In an initial effort to test the possible clinical significance of the differential clustering of samples obtained from patients pre-treated with chemotherapy, we examined the post-operative history of these patients. One commonly used indicator of recurrence is the level of Cancer Antigen-125 (CA-125) in the blood [12,13]. Although post-operative CA-125 levels were initially lowered to a significant extent in all of the patients pre-treated with chemotherapy, the levels remained consistently low in only those patients (cc_36, cc_9) whose microarray profiles clustered with the benign adenomas (Figure 2). The remaining patients displayed periodic recurrence requiring additional chemotherapy.
Figure 2 CA-125 levels of patients receiving chemotherapy prior to tissue collection. CA-125 levels of patients with cancer-like profiles (a) and adenoma-like profiles (b) were normalized to the earliest post-surgery reading. CA-125 level for patients 76 and 29, two patients receiving chemotherapy prior to tissue collection, spike dramatically at about 700 days post surgery. CA-125 levels for patients 9 and 36 remained low through 700 days past surgery.
Significant differences in gene expression are associated with different ovarian tumour types
To identify genes whose differential expression correlate with malignant potential, we performed a statistical analysis comparing the expression profiles of the three tumour types examined in this study (benign adenoma, low malignant potential borderline adenocarcinoma, and malignant adenocarcinoma). Malignant adenocarcinoma sample c_79 was excluded from this analysis since both the SOM and UHC classification methods identified this sample as an outlier of the malignant adenocarcinoma group (see above). The F statistic was used to test equality of group means [14]. Genes whose group means were identified as significantly different (p ≤ 0.001, 299 genes) in the ANOVA analysis were further analyzed using multiple comparison methods to determine which means differ from each other. The differences between group means for all pairwise combinations of groups were calculated and compared to the least significant difference. Genes were declared differentially expressed if the pairwise difference between group means was greater than the least significant difference. Probe sets duplicated between pairwise comparisons and probes sets with a fold change value below 2.0 were removed, leaving 163 unique genes differentially expressed between the tumour groups. The 15 differentially expressed genes with highest statistical significance are presented in Table 2. The gene name, gene symbol, chromosomal location, functional classification, ANOVA rank and p-value of each of these 163 genes are attached as additional file 1 (complete list.txt).
Table 2 Highest 15 statistically significant genes via ANOVA analysis, their fold change and p-values.
Affy ID Gene Name Gene Symbol FC a:b FC a:c FC c:b ANOVA p-value
1651_at ubiquitin-conjugating enzyme E2C UBE2C 1.16(b) 4.35(c) 3.75(c) 1.2E-07
41583_at flap structure-specific endonuclease 1 FEN1 1.27(b) 3.86(c) 3.04(c) 1.9E-07
31888_s_at tumour suppressing subtransferable candidate 3 TSSC3 3.46(b) 8.15(c) 2.36(c) 2.7E-07
34715_at forkhead box M1 FOXM1 1.06(b) 2.66(c) 2.5(c) 7.5E-07
39109_at chromosome 20 open reading frame 1 C20orf1 1.29(b) 4.91(c) 3.80(c) 2.9E-06
37985_at lamin B1 LMNB1 1.19(b) 3.19(c) 2.69(c) 2.9E-06
41451_s_at SAR1 protein SAR1 1.07(b) 2.27(c) 2.13(c) 3.2E-06
37015_at aldehyde dehydrogenase 1 family, member A1 ALDH1A1 1.86(a) 10.76(a) 5.79(b) 3.3E-06
527_at centromere protein A, 17kDa CENPA 1.08(b) 3.47(c) 3.2(c) 3.9E-06
40619_at ubiquitin carrier protein E2-EPF 1.51(b) 2.74(c) 1.81(c) 4.6E-06
32332_at isocitrate dehydrogenase 2 (NADP+), mitochondrial IDH2 1.28(b) 4.36(c) 3.40(c) 5.4E-06
1058_at WAS protein family, member 3 WASF3 2.03(a) 2.56(a) 1.27(b) 5.7E-06
1943_at cyclin A2 CCNA2 1.05(a) 2.07(c) 2.17(c) 6.4E-06
2039_s_at FYN oncogene related to SRC, FGR, YES FYN 1.05(b) 3.11(a) 2.97(b) 6.4E-06
1868_g_at CASP8 and FADD-like apoptosis regulator CFLAR 0.97(b) 1.78(c) 1.83(c) 7.7E-06
Hierarchical clustering was performed to visualise gene expression across tumour types for each of these 163 genes. All 12 tumours were correctly assigned as shown by the dendogram above the gene expression colour plot (Figure 3a). Several features within the gene expression colour plot are worthy of note (Figure 3b,3c,3d,3e,3f). Thirteen genes (Figure 3b) showed high expression in both adenoma and borderline. For forty genes expression levels in borderline tumours was intermediate between adenoma and cancer (Figure 3c). Eight genes were highly expressed in either adenoma (3 genes, Figure 3c) or borderline (5 genes, Figure 3d). And finally, thirteen genes showed high expression in both cancer and borderline (Figure 3e).
Figure 3 Patterns of differential expression for the 163 genes of highest statistical significance. The 300 probe sets with the lowest p-values in the ANOVA analysis were filtered for duplicate genes and fold change <2.0. The remaining 163 genes were subjected to hierarchical clustering to reveal correlated expression (a). Thirteen genes showed high expression in both benign adenomas and borderline tumours (b). Borderline tumours showed intermediate levels of expression for forty genes (c). Three genes were high only in benign adenoma (d). Five genes showed high expression in borderline tumours only (e). And 13 genes were high in both malignant adenocarcinomas and borderline tumours (f).
To independently test the validity of the differential expression patterns determined by microarray, we measured the expression patterns of 3 representative genes in 6 tissue samples using quantitative real time RT-PCR [15]. Genes were selected from the microarray data set to represent a spectrum of statistical significance (Table 3). In all cases, the results of the quantitative RT-PCR analyses confirmed the differences detected in the microarray studies (Figure 4).
Table 3 Genes expression changes verified with quantitative RT-PCR.
Gene Name Gene Symbol ANOVA rank p-value
ubiquitin-conjugating enzyme E2C UBE2C 1 1.18E-7
cadherin 2, type 1, N-cadherin CDH2 177 0.00042
oviductal glycoprotein 1, 120 kDa OVGP1 739 0.0058
Figure 4 RT-PCR validation of microarray results. OVGP1 expression (a), CDH2 expression (b) and UBE2C expression as measured by RT-PCR (◆) and microarray (■). Patterns of change in expression were the same for each method.
Functionally related genes display correlated changes in expression between benign malignant tumours
Two expression subgroups were evident in the list of 163 differentially expressed genes (Figure 3a): genes with low expression in cancer (first 61 genes of colour plot) and genes with high expression in cancer (last 102 genes of colour plot). To examine the possibility that these subgroups also correlate with differential gene function, we applied two functional profiling programs, EASE [16] and Onto Express [17]. Searching the gene ontology assignments for all genes in a list, these programs identify and assign statistical significance to the over-represented gene functional categories identifying common biological processes, molecular functions, cellular and chromosomal locations shared by genes in a list. Functional profiling revealed that the expression subgroups exhibited distinctly different gene functions (Figure 5). Genes in the expression subgroup with high expression in cancer were intracellular whereas the genes in the low expression subgroup were extracellular. Genes whose gene products function during cell proliferation and DNA metabolism dominate the high expression subgroup. On the other hand, gene products involving insulin-like growth factor binding, regulation of cell growth, cell-cell adhesion, and calcium transport activity were associated with the low expression subgroup.
Figure 5 Over-represented functional categories of differentially expressed genes. Differentially expressed genes with low expression in cancer were able to bind insulin-like growth factor (p < 6 × 10-6) or functioned in cell adhesion (p < 0.012) and calcium channel activity (p < 0.02). Genes with high expression in cancer functioned in nuclear division (p < 8 × 10-8), mitosis (p < 5 × 10-5), and DNA metabolism (p < 3 × 10-8).
Discussion
Microarray profiles of ovarian tumours are of potential diagnostic and prognostic significance
Gene expression profiling via microarray technology has previously been shown to be an effective tool for the objective classification of established tumour types [18,19] and in some instances, for the identification of previously unrecognized tumour sub-types [20]. Applied to ovarian cancer, gene expression profiling has aided in distinguishing clear cell carcinomas [8,9], characterizing advanced stage ovarian cancer [5,6], and identifying genes differentially expressed between normal and cancerous ovarian tissue [4,7,10]. The experiments presented here were designed to elucidate gene expression changes in ovarian tumours of differing malignant potential. In many instances, genes that we identified as differentially expressed across malignant potential were previously determined to be differentially expressed between normal and cancerous ovarian tissue including ERBB3 [10], ubiquitin carrier protein[10], and E-cadherin [4]. We also correctly classified 92% of tumours from patients who did not receive chemotherapy prior to surgery into their proper histopathological group. These results are consistent with earlier findings and indicate that gene expression profiling can effectively distinguish between malignant and benign ovarian tumours.
One particularly promising result emerging from our study is that expression profiling may be useful in predicting recurrence in patients treated with chemotherapy prior to surgery. We find that the microarray patterns of ovarian adenocarcinomas obtained from patients treated with chemotherapy prior to surgery clustered either with the benign tumours or with the malignant adenocarcinomas. Serum CA-125 levels indicate that patients whose samples clustered with the benign tumours have remained disease free for more than 3 years after surgery while those patients whose samples clustered with the malignant tumours recurred within 2 years of the initial treatment. Clearly, the testing of additional patient samples will be needed before definitive conclusions can be drawn. However, the preliminary results are consistent with the hypothesis that gene expression profiles of samples removed on the day of surgery may predict recurrence and would therefore be an indicator of the long-term effectiveness of chemotherapy administered to patients prior to surgery.
Expression profiles indicate that borderline tumours are not a distinct disease
Our microarray data are, in general, most consistent with the hypothesis that borderline ovarian tumours represent an intermediate stage between the benign and malignant tumours. Borderline tumours of the ovary display many but not all characteristics of malignancy including nuclear atypia and increased mitotic count, usually in the absence of stromal invasion [21-24]. Whether the borderline tumour is a precursor to the fully malignant ovarian carcinoma or a disease distinct from invasive carcinomas is a topic that has been debated since the International Federation of Gynaecologic Oncology added the borderline tumour to the classification of ovarian tumours in 1972. Distinct disease states are expected to show discrete gene expression patterns when analysed by microarray [3]. Our analysis identified only 5 genes (Figure 3e) with increased expression distinctly correlated with borderline tumours. On the other hand, for 40 of the 163 genes displaying a significant change in expression between benign and malignant ovarian tumours, borderline tumours display an intermediate expression level (Figure 3c). In all other cases, (118 genes) borderline expression mimicked either the benign adenoma (102 genes) or malignant adenocarcinoma (16) tumours. Thus, for these genes, borderline tumours appear to be a transitional state between the benign and malignant state. The five genes identified as characteristic of borderline tumours (Figure 3e) may constitute a reliable marker of borderline tumours. Interestingly, two of these genes, AGR2 and NPTX2, are physically linked to one another, mapping to p21.3 on chromosome 7.
Many genes displaying altered patterns of expression between benign and malignant ovarian tumours are genetically linked
Genes physically linked to one another shared changes in gene expression between tumour types. For those cases where linked genes displayed a significant reduction in expression in malignant vs. benign tumours (Table 4), at least three explanations are possible. Perhaps the most likely explanation is that the change is due to a small deletion in a chromosomal region encompassing the affected alleles. Such deletional events are believed to be at the basis of the "loss of allele" (LOA) phenomenon, which is known to be a relatively common event in tumour development [25-27]. Another possibility is that these co-ordinated reductions in gene expression are due to regional changes in chromatin structure resulting in the reduced access of transcription factors to genes. Such epigenetic changes are typically associated with the hypermethylation of so-called "CpG islands" in or around genes [28-30]. Indeed, it has been well documented that the silencing of many tumour suppresser genes and genes involved in DNA repair and apoptosis in cancer cells is the consequence of DNA hypermethylation [31,32]. The third possibility is that the coordinated reductions are the result of completely independent mutational events. However, the probability that such independent events would repeatedly occur at linked loci seems low.
Table 4 Co-ordinately expressed genes sharing chromosomal location
Gene Symbol Location Expression in cancer Function
KIF2C 1p34.1 Up Nuclear division/mitosis
CDC20 1p34.1 Up Regulation of cell growth
PMSB2 1p34.2 Up Protein Catabolism
UBE2C 20q13.12 Up Nuclear division/mitosis
STK6 20q13.2-q13 Up Signal Transduction
RGS19 20q13.3 Up Nuclear division/mitosis
PDGFRA 4q11-q13 Down Regulation of cell growth
IGFBP7 4q12 Down Regulation of cell growth
HNRPDL 4q13-21 Down RNA binding
FYN 6q21 Down Calcium ion transport
LAMA2 6q22-q23 Down Cell adhesion
CTGF 6q23.1 Down Cell adhesion
FOXM1 12p13 Up Transcriptional regulation
CCND2 12p13 Down Nuclear division/mitosis
ERBB3 12p13 Up Signal Transduction
We also observed a co-ordinated increase in gene expression of physically linked genes in the malignant samples in several cases (Table 4). These changes may have been due to regional duplication or amplification events. Examples of such events have been previously documented in cancer cells [33-35]. It is also possible that at least some of these co-ordinated increases in gene expression are the consequence of regional hypomethylation events resulting in a more open chromatin configuration and a consequent increase in transcription factor accessibility. Genes located in proximity to transposable element sequences may be more prone to such epigenetic events [36].
In a few instances genes that were physically linked displayed opposing changes in gene expression between benign and malignant tumours (Table 4). It is possible that these disparate changes were due to independent mutational events or, perhaps more likely, to a regional relaxation of chromatin structure that permitted increased access of both positive and negative transcription factors.
Our finding that a number of the genes displaying a significant difference in expression among malignant and non-malignant tumours indicates that some caution must be taken in the functional interpretation of microarray results. For example, significant changes in the expression of only one gene in a physically linked group may be of functional significance although correlated changes in gene expression may result from a regional effect.
Malignant ovarian tumours display expression profiles consistent with previously established features of cancer cells
Two common features of malignant cancer cells are increased cell proliferation and loss of cell adhesion [37]. Consistent with these general features, we found that the majority of differentially expressed genes with high expression in the malignant tumours belonged to functional categories associated with DNA metabolism and cell proliferation. We also report here that insulin-like growth factor binding, cell adhesion and calcium ion transport were gene functional categories over-represented among the genes significantly under-expressed in ovarian cancer.
The IGF system is a complex network of molecules involved in the normal growth and development of many cell types [38]. Disregulation of the IGF system through over-stimulation of the IGF1 receptor (IGF1R) has been implicated in tumour development and maintenance of the transformed phenotype [39,40]. The functional consequences of IGF1R over-stimulation include increased cell proliferation, cell survival and regulation of cell adhesion. The six specific IGF binding proteins (IGFBP-1 through -6) bind IGF in the serum and extracellular matrix, thereby reducing the bioavailability of IGF1 for receptor binding, as well as downstream signalling. Recently, elevated serum levels of IGFBP-2 at diagnosis were correlated with the likelihood of relapse, confirming the prognostic value of serum IGFBP-2 in choosing aggressive treatments for these patients[41]. Measuring serum levels of IGFBP-3 and IGF1 of healthy women proved useful in predicting a woman's risk of ovarian cancer [42]. Our analysis demonstrated significantly lower expression of IGFBP-4, -5, and -7 in the malignant adenocarcinomas than in the benign adenomas or borderline tumours (Figure 6b). IGFBP-2 and -3 were highly expressed but not differentially expressed across the tumour types. Furthermore, no IGF binding proteins appeared in the list of genes significantly up-regulated in cancer tissue. These findings suggest that loss of expression of IGFBPs in adenocarcinomas increases IGF signalling and its functional consequences, processes clearly associated with the clinical phenotypes of ovarian adenocarcinomas.
Figure 6 Microarray expression of cadherins and insulin-like growth factor system genes. E-cadherin expression contributes about equally to the cadherin distribution in benign adenomas but is the dominant component of the cadherin distribution in malignant adenocarcinomas (a). Insulin-like growth factor system components show lower expression in the adenocarcinomas relative to benign adenomas and borderline tumours (b). P-values associated with differential expression as analysed by ANOVA analysis are shown above each genes column graph.
Over-expression of certain members of the IGF system increased sensitivity to IGF1 signaling in breast cancer cells [43] leading to increased cell proliferation. Insulin receptor substrate 1 (IRS1) is one member of the IGF system whose over-expression potentiated the effects of IGF1. Interestingly, IRS1 was significantly up-regulated in the benign and LMP tumours of our study (Figure 2). Considering the documented ability of the IGFBP's to reduce bioavailability of IGF1, increased expression of IGFBP's would be an appropriate cellular response to increased expression of IRS1.
Loss of cell adhesion molecules (CAM) is one mechanism proposed to induce the tissue invasion and metastatic capabilities acquired by cells during tumourigenesis[37]. Intra-abdominal spread of ovarian cancer via peritoneal implants is a hallmark of advanced stage ovarian cancer and can be linked to loss of cell-cell adhesion [44]. Our findings support the theory that loss of CAM in ovarian cancer is instrumental in cancer progression.
Cell-cell adhesion is often mediated through the cadherins, a family of transmembrane glycoproteins that require calcium to perform their adhesive functions. Well-documented changes in cadherin subtype expression correlate with the progression of breast and prostate cancer [45]. Recently, differences in the profile of cadherin subtypes expressed in normal and cancerous ovarian tissue were also shown to correlate with disease progression [46]. Support for cadherin switching in ovarian tumours is evident in our microarray data. Expression of N-cadherin (N-cad) and cadherin-11 (CDH11), the dominant subtypes in normal ovarian surface epithelium, were significantly higher in the benign and LMP tumours of our study than in the adenocarcinomas (Figure 4a). The intensity of change in expression between the benign adenomas and malignant adenocarcinomas for N-cad and CDH11 were 3.9 and 7.8 fold respectively, and both genes appeared in the list of top 163 differentially expressed genes. The LMP tumours in our study expressed N-cad and CDH11 at levels intermediate to adenomas and adenocarcinomas, suggesting an integral role for these cadherins in transformation to a malignant phenotype. Expression of E-cadherin, a major subtype seen in adenocarcinomas, increased approximately 2 fold from a benign tumour to either LMP or the adenocarcinomas (Figure 6a). This data documents the switch from a normal-like distribution of N-cad and CDH11 in the benign and LMP tumours to a cancerous profile dominated by E-cad expression.
Since cadherins are calcium-dependent cell adhesion molecules [44]and increased dietary intake of calcium correlates with a reduced risk of ovarian cancer [47], it is also interesting that calcium transport and calcium channel activity are gene functions that we found correlated with genes under-expresses in the adenocarcinomas. Thus it is also possible that altered functionality of the cadherins through changes in calcium availability, a parameter not measurable with microarray, may be involved in increasing a tumours' malignant potential.
Conclusions
Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Genes displaying co-ordinated changes in gene expression were often genetically linked suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic changes. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. A significant portion of the differentially expressed genes exhibited expression levels in borderline samples intermediate between benign adenomas and malignant adenocarcinomas, suggesting the borderline tumours are a transitional state between benign and malignant tumours.
Methods
Tumour Samples and RNA Isolation
A set of 18 primary ovarian tumours was obtained from the Ovarian Cancer Institute. This set of tumours was comprised of 4 benign cystadenofibromas, 4 carcinomas of low malignant potential (borderline carcinomas), 5 adenocarcinomas, and 5 adenocarcinomas from patients who received chemotherapy prior to surgery. This study was approved by the Institutional Review Board of the University of Georgia and of Northside Hospital (Atlanta), from which the samples were obtained Tissue was collected at the time of initial surgery and preserved in RNA Later (Ambion) within one minute of collection. For RNA isolation, each tissue (50 ± 25 mg) was homogenized on ice in 1.5 ml Trizol (Molecular Research Corporation) with a polytron homogenizer for about 30 seconds. RNA was isolated from the crude homogenate according to the manufacturer's protocols (Trizol, Molecular Research Corporation) with the following specifics. Linear polyacrylamide (5 μl) was added prior to homogenization to aid in RNA precipitation. Total RNA was further purified over an RNEasy (Qiagen) column using the manufacturer's cleanup protocol.
Microarray Hybridization
Biotinylated target cRNA was generated according to the Affymetrix Technical Manual. In brief, 5–10 μg total RNA was converted to double stranded cDNA using Supercript II (Invitrogen). The cDNA was cleaned by phenol/chloroform extraction and ethanol precipitation. In vitro transcription of the cDNA with the High Yield RNA Transcript Labeling Kit (Enzo) yielded 50–100 μg of biotin labeled cRNA target. The cRNA was fragmented in a metal catalyzed acid hydrolysis to a length of 20–200 bp (by electrophoresis) and the fragmented cRNA was hybridized to the Affymetrix array (U95Av2) for 16 hours at 45C. Hybridized arrays were washed, stained and scanned according to the Affymetrix technical manual.
Microarray Data Handling and Manipulation
Signal values were generated in two ways. Affymetrix signal values were generated from the .CEL file using the Affymetrix software MAS 5.0. The overall intensity of each array was scaled to an average intensity of 500. These normalized signal values were exported to Excel (Microsoft) for further analysis labeled the Affy-data set. Robust multi-array analysis (RMA) signal values were generated from the .CEL file using the espresso wrapper in the Affy library of the Bioconductor package in the R-statistical environment. The parameters of PM correction, background correction, normalization, and summary method were set to PM only, RMA, quantile, and median polish, respectively. The normalized signal values were exported to Excel for further analysis and will be referred to in this paper as the RMA-data set. For each data set, Affy-data set and RMA-data set, Pearson correlation coefficients were calculated (Microsoft, Excel) for a_97 vs. all other arrays. Higher correlation and lower standard deviation from the mean within groups was seen with the RMA data set, suggesting higher quality data.
Clustering
Raw data output from the Affymetrics MicroArray reader is transformed into expression level values using the RMA method [48,49]of the "affy" package in the Bioconductor suite of the R statistical environment, and a text output file generated. This text file is then transformed into a *.gct file for input into the GeneCluster program. GeneCluster (Whitehead Insitute, ) was used to cluster the dataset on both samples and genes. Except where described below, default parameters were used. The SOM feature of GeneCluster was employed, and various values were explored for the "Cluster Range" and "Iterations" parameters. The 'Cluster Range' parameter sets the geometry of the clusterings that will be performed on the data. For instance, if 2–3 is entered, two cluster sets are produced. One has two clusters and the other three clusters. When entering a number, any set of factors of that number will create a clustering. If 9 is entered, a linear set of 9 clusters and a 3 × 3 matrix of clusters are produced. Marker analysis was also performed in GeneCluster, again using the default parameters.
Quantitative RT-PCR
Total RNA (2 μg) from ovarian tissue was converted to cDNA using Superscript III (Invitrogen) primed with random hexamers under conditions described by the supplier. cDNA from this reaction was used directly in the Quantitative RT-PCR analysis. TaqMan probes and gene specific primers for three genes (RPL-29, UBE2C, OVGP1, and CDH2) were obtained from Applied Biosystems' Assay on Demand. The mRNA levels of the three genes were measured in 6 ovarian tumours and one normal ovary on the ABI Prism 7700 Sequence Detection System. PCR was performed using the TaqMan Universal PCR MasterMix (Applied Biosystems), according to the manufacturer's protocols with standard PCR cycling steps. Using RPL29 as a housekeeping gene and the normal human ovary RNA as a reference sample, the expression levels of UBE2C, OVGP1 and CDH2 were calculated according to the 2-ΔΔCt method[15]. The Ct values of triplicate RT-PCR reactions were averaged for each gene in each cDNA sample. For each tissue sample assayed, the average Ct value for the gene of interest (UBE2C, CDH2 and OVGP1) was subtracted from the average Ct value of the housekeeping gene (RPL29) to obtain the ΔCt value. The ΔCt value of the reference sample was subtracted from that of the tumours to obtain the ΔΔCt value.
Data Filtering and Statistical Analysis
The RMA normalised data set was analysed for probe sets likely to be absent in all samples. Probe sets whose maximum RMA normalised value across all samples was less than 5.2 were removed from further analysis. Analysing the remaining 10,520 probe sets, we applied an analysis of variance (ANOVA) to test the hypothesis that the mean expression values for all groups (adenoma, borderline and cancer) are equal. For each gene, the within group and between group variation was calculated and used to generate the F statistic and subsequent p-values [14]. Adjusted p-values were also calculated using Holm's method. The ranking of genes in order of significance was exactly the same for the un-adjusted and adjusted methods. However, the adjusted p-values were 1000 fold higher than the unadjusted p-values and only the top 50 genes were considered significant (p < 0.05). Since RT-PCR confirmed differential expression down to the 739th statistically significant gene (see results section), we continued the analysis on the top 300 statistically significant genes. Genes whose groups means were identified as significantly different (p ≤ 0.001, 299 genes) in the unadjusted ANOVA were further analysed using Fisher's Least Significant Difference multiple comparison method. The differences between group means for all pairwise combinations of groups were calculated and compared to the least significant difference. Genes were declared differentially expressed if the pairwise difference between group means was greater than the least significant difference. Probe sets duplicated between pairwise comparisons and probes sets with a fold change value below 2.0 were removed, leaving 163 unique genes differentially expressed.
Functional Profiling
Genes found to be differentially expressed in the statistical analysis were divided into two lists: genes overexpressed in cancer, and genes underexpressed in cancer. Each list was analyzed for over-represented functional categories based on molecular function, biological process, cellular component and chromosomal location using two different freeware programs: EASE [16]and OntoExpress [17]. Given a list of genes, EASE forms subgroups of genes based on the functional categories assigned to each gene. EASE assigns a significance level to the functional category based on the probability of seeing the number of subgroup genes within a category given the frequency of genes from that category appearing on the microarray. The 'EASE score' is the upper bound of the distribution of Jacknife Fisher exact probabilities. Onto Express identifies overrepresented gene functional categories in a manner similar to EASE and was used to verify results obtained from EASE. All information on chromosomal location was obtained from Onto Express since EASE does provide information on chromosomal location.
Authors' contributions
SW performed microarray, basic statistical analysis and hierarchical clustering. SP performed SOM clustering and marker analysis. SD contributed to the statistical analysis. BB collected tissue and serum. EK directed SW and SP. JM contributed to the linkage and CA-125 analyses and directed SW.
Supplementary Material
Additional File 1
The file complete_list.xls contains the gene name and chromosomal location for the 163 genes determined to be differentially expressed in this study.
Click here for file
Acknowledgements
This work was supported by the Ovarian Cancer Institute and awards from the Georgia Cancer Coallition and the Georgia Research Alliance.
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Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-3-221547154010.1186/1476-511X-3-22ReviewThe pivotal role of cholesterol absorption inhibitors in the management of dyslipidemia Al-Shaer Moutasim H [email protected] Nabil E [email protected] Ehab S [email protected] Department of Internal Medicine, and Human Cardiovascular Physiology Laboratory University of Iowa College of Medicine Iowa City, Iowa 52242-1009, USA2004 7 10 2004 3 22 22 23 9 2004 7 10 2004 Copyright © 2004 Al-Shaer et al; licensee BioMed Central Ltd.2004Al-Shaer 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.
Elevated low-density lipoprotein (LDL)-cholesterol is associated with a significantly increased risk of coronary heart disease. Ezetimibe is the first member of a new class of selective cholesterol absorption inhibitors. It impairs the intestinal reabsorption of both dietary and hepatically excreted biliary cholesterol. Ezetimibe is an effective and safe agent for lowering LDL-C and non HDL-C. Short term clinical trials have established the role of ezetimibe monotherapy and its use in combination with statins. Furthermore, ezetimibe and statin combination therapy increased the percentage of patients who achieved their LDL-C treatment goal.
Studies using surrogate markers of atherosclerosis have suggested a possible role of ezetimibe in combating atherosclerosis. Ezetimibe provides an effective therapeutic strategy for the management of homozygous familial hypercholesterolemia (HoFH) and sitosterolemia. The lack of outcomes and long term safety data is attributed to the relatively recent introduction of this medication.
EzetimibeZetiaCholesterol Absorption InhibitorsDyslipidemiaHyperlipidemiaHypercholesterolemiaAtherosclerosisSitosterolemiaPleiotropic Effects of Statinshomozygous familial hypercholesterolemia
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Background
Over 60 million Americans suffer from cardiovascular disease (CHD). The incidence of CHD and stroke has been on the rise partly because of the increase in life expectancy and the explosive epidemic of diabetes and the metabolic syndrome [1]. CHD is responsible for about 38% of the overall mortality in the United States making it the number one killer of Americans [2].
Animal and human studies have established the role of cholesterol in the development and progression of atherosclerosis. LDL-cholesterol (LDL-C) constitutes approximately 60–70 % of total serum cholesterol. Epidemiological studies directly implicated LDL-C to the development of atherosclerosis and CHD. Furthermore, LDL-C level appears to be directly related to the development and recurrence of CHD [3].
Animal studies suggested a protective effect of low LDL-C against atherosclerosis [2]. Multiple human trials examining the relationship of LDL-C lowering in primary and secondary prevention of CHD have demonstrated the impact of reducing LDL-C levels on decreasing CHD and CHD related mortality [4-8].
Most of the landmark CHD prevention trials involved the use of statin medications. LDL-C remains the primary target of treatment in most instances, and statins are the mainstay of LDL-C lowering treatment [9].
The National Cholesterol Education Program/ Adult Treatment Panel III (NCEP/ATP III) updated guidelines (table 1) for detection and treatment of dyslipidemia envisioned LDL-C below 100 mg/dL to be optimal for all patient risk categories. These more aggressive guidelines resulted in doubling of the number of patients that are not at target LDL-C levels as compared to previous guidelines [2]. Recent NCEP/ATP III update data suggested even lower LDL-C levels than previously advocated, making it harder to achieve the treatment in many instances and recommended the use of combination therapy if needed to help achieve the treatment targets. The NCEP/ATP III update emphasized "the lower, the better" hypothesis [10].
Table 1 Synopsis of the updated ATP III LDL-C Goals and Cut-points for TLC and Drug Therapy in Different Risk Categories and Proposed Modifications Based on Recent Clinical Trial Evidence
Risk Category Goal TLC Drug Therapy
High risk: CHD or CHD risk equivalents (10-year risk >20%) < 100 mg/dL (optional goal: <70 mg/dL) ≥ 100 mg/dL ≥ 100 (<100 mg/dL: consider drug options)
Moderately high risk: 2+ risk factors (10-year risk 10% to 20%) < 130 mg/dL ≥ 130 mg/dL ≥ 130 mg/dL(100–129 mg/dL; consider drug options)
Moderate risk: 2+ risk factors (10-year risk <10%) < 130 mg/dL ≥ 130 mg/dL ≥ 160 mg/dL
Lower risk: 0–1 risk factor < 160 mg/dL ≥ 160 mg/dL ≥ 190 mg/dL (160–189 mg/dL: LDL-lowering drug optional)
Cholesterol Absorption inhibitors
Ezetimibe, a cholesterol absorption inhibitor, is the first agent of a new class of lipid-lowering compounds that selectively inhibits the intestinal absorption of cholesterol and related phytosterols. Ezetimibe undergoes extensive glucuronidation to an active metabolite in the intestinal mucosa [11]. Ezetimibe acts on brush border of the small intestine and decreases biliary and dietary cholesterol from the small intestine uptake into the enterocytes. Ezetimibe is primarily metabolized in the small intestine and liver via glucuronide conjugation with subsequent biliary and renal excretion [12]. Ezetimibe does not affect the absorption of fat-soluble vitamins, triglycerides, or bile acids [13].
After oral administration, ezetimibe is absorbed and extensively conjugated to a pharmacologically active phenolic glucuronide (ezetimibe-glucuronide) [14], the drug and its metabolite have a half-life of approximately 22 hours [8]. Concomitant food administration (high fat or non-fat meals) had no effect on the extent of absorption of ezetimibe when administered in the 10-mg clinical dose [15]. Ezetimibe and ezetimibe-glucuronide are highly bound (>90%) to human plasma proteins [16].
Plasma concentrations for total ezetimibe were about 2-fold higher in older individuals (>65 years), levels were similar in adolescents to healthy adults and may be higher in women than in men [8]. In patients with severe renal disease, ezetimibe level was increased approximately 1.5-fold, compared to healthy controls [17]. Ezetimibe had no significant effects on the bioavailability of warfarin, fenofibrate, HMG CoA reductase inhibitors, or digoxin [16,18-20].
Adverse experiences were reported in approximately 2% of patients treated with ezetimibe and included fatigue, arthralgia, diarrhea, abdominal pain and back pain. Angioedema and rash were reported after general clinical use of this medication [16]. With co-administration of ezetimibe and statins the adverse event profile was similar to that for statins alone. In a recently published case report, the authors described two patients whose creatinine kinase (CK) increased after the addition of ezetimibe to statin therapy causing one of the patients to experience myalgia and tendinopathy. This finding raises the question of whether ezetimibe can be implicated in precipitating increased risk of stain-associated myopathy [21].
The Role of Ezetimibe in Clinical Practice
Indications for use
1. Primary Hypercholesterolemia (heterozygous familial and non-familial), Ezetimibe is indicated in this case for use as both mono and combination therapy.
2. The reduction of elevated total-C and LDL-C levels in patients with homozygous familial hypercholesterolemia (HoFH) either as primary or as an adjunct to other lipid-lowering treatments.
3. In patients suffering from Homozygous Sitosterolemia, as adjunctive therapy to diet for the reduction of elevated sitosterol and campesterol levels in patients with homozygous familial sitosterolemia.
Monotherapy
Multiple studies conducted to examine the effects of ezetimibe monotherapy have concluded that this drug was effective in lowering LDL-C versus placebo.
Analysis of multicenter, double-blind, placebo-controlled trials demonstrated that ezetimibe at the 10 mg once daily clinically approved dose significantly modified cholesterol and cholesterol subtypes in patients with hypercholesterolemia when compared to placebo. Ezetimibe significantly lowered total-Cholesterol (TC) (12 %), LDL-C (18 %), apolipoprotein B (Apo B) (15 %), and triglycerides (TG) (7%) and increased high density lipoprotein (HDL-C) (3.5%) [22-24]. Lipoprotein (a) [Lp (a)] was not significantly affected by Ezetimibe 10 mg once a day treatment [25].
In a case series report, we analyzed the effects of Ezetimibe on cholesterol particle size and number using NMR technology (Lipo science, Raleigh, NC). We found that Ezetimibe lowered cholesterol particle number by a mean 26 % and had no significant effect on cholesterol particle size [26].
The effects of ezetimibe on cholesterol and its subtypes were not influenced by risk-factor status, gender, age, race, time of administration, or baseline lipid profile [22]. The overall incidence of adverse effects with ezetimibe monotherapy was similar to placebo.
Combination therapy
The struggle to achieve the NCEP/ATP III guidelines LDL-C goals through primary utilization of statins is often frustrating for the clinician. In a study to examine the efficacy of statin titration on attainment of LDL-C goal, the authors concluded that for high risk patients, approximately half were able to achieve their LDL-C goal at the appropriate statin starting dose, and only one third of the titration group were able to achieve the NCEP/ATP III cholesterol goal [27]. Now with the very recent publication of the update to the NCEP/ATP III, clinicians are faced with even lower goals of LDL-C, Making combination therapy a must in more cases than previously advocated by the NCEP/ATP III [10].
HMG-CoA reductase inhibitors (statins) act on the rate-limiting step to inhibit HMG-CoA conversion to mevalonate, effectively decreasing LDL-C synthesis. They result in a decrease of LDL-C ranging between 30–60 %, depending on the individual statin and the dose administered. Statin induced LDL-C lowering appears to be effective in reducing CHD and CHD related mortality and morbidity. The extent of CHD and CHD related events reduction is proportionate to the extent of LDL-C reduction [10,28]. LDL-C reduction trials have demonstrated a reduction in CHD related events by approximately 20–40% [4-8].
Reasons to initiate combination therapy to treat hyperlipidemia include: further LDL-C lowering, reducing side effects related to higher doses of statins, modifying other risk factors besides LDL-C such as HDL-C and TG.
Increasing the dose of statins has a limited effect on reducing LDL-C, as it is well established that doubling the dose of a statins leads to a 5 % more reduction in total TC and 7 % more reduction in LDL-C with each doubling [29].
Although statins have demonstrated similarity in CHD related events, they are heterogeneous not only in LDL-C lowering efficacy but also in their safety profiles. The bulk of the statins effect on LDL-C occurs at the initial recommended dose and they are safer when used at doses below the maximal recommended dose.
Statins are the most effective drugs known to modify LDL-C, but in terms of HDL-C and TG modifying capacity, other classes of lipid lowering medications used alone or in combination with statins offer higher efficacy.
The metabolism of cholesterol is an intricate process that involves both produced and ingested cholesterol. The mechanism of action of HMG-CoA reductase inhibitors affects the production of cholesterol, whereas that of cholesterol absorption inhibitors affects absorbed cholesterol, thereby offering potential synergism of action when the medication are used in combination. Trials examining the efficacy have demonstrated synergism and consistency in LDL-C lowering in the absence pharmacokinetic interaction between the statins and ezetimibe.
In a relatively large, multicenter study, involving patients with primary hypercholesterolemia already receiving statin monotherapy (but who had not met their NCEP ATP II target LDL-C goal), patients were randomized to receive either ezetimibe or placebo in addition to their current statin therapy. At the conclusion of this 8 week study, the ezetimibe and statin groups were found to have a significantly lower total-C, LDL-C, Apo B, and TG, and increased HDL-C when compared to the statin only and placebo groups. Furthermore, LDL-C reductions induced by ezetimibe were generally consistent across all statins groups [30].
Another multicenter, double-blind, randomized trial examined the effects of ezetimibe on patients suffering from (HoFH). At the initiation of the trial patients were receiving either atorvastatin or simvastatin. The addition of ezetimibe reduced LDL-C by an additional 20.5 % in contrast to only 6.7 % reduction that resulted from doubling the statin dose [31]. Similar results were demonstrated in high-risk patients with familial heterozygous hypercholesterolemia (HeFH) [32].
The addition of ezetimibe to statins is superior to treatment with statins alone in lowering non-HDL-C, ezetimibe co-administered with simvastatin lowered non-HDL-C by 47.1% whereas, simvastatin monotherapy lowered non-HDL-C by 33.6% when results were pooled across different doses [33].
In terms of modifying risk factors other than LDL-C, the co-administration of ezetimibe with statins had a more favorable effect on HDL-C and TG when compared to statins therapy alone [33]. In conclusion, the addition of ezetimibe to statins produced further lowering of LDL-C of approximately 15–20 % with no apparent increase in side effects. This effect was superior to that observed by doubling the dose of the statins. Furthermore, the lowering produced was consistent.
Ezetimibe co-administration with fibric acid derivatives was examined in a randomized, evaluator-blind, placebo-controlled, parallel-group study of 32 healthy hypereholesterolemics. Ezetimibe co-administration with fenofibrate was found to produce clinically significant reductions in LDL-C (36.3%) compared to the fenofibrate group (22.3%) with a more favorable TG and HDL-C profile [34].
Sitosterolemia
Sitosterolemia is a rare inherited disorder caused by mutation in either the ABCG5 or ABCG8 genes located on chromosome (2p21) [35]. First described by Bhattacharyya and Connor in 1974 in two sisters of German and German-Swiss ancestry with normal mental development. The patients presented with tendinous and tuberous xanthoma and elevation of beta-sitosterol, campesterol and stigmasterol (plant sterols) in the blood [36]. Affected individuals have increased intestinal absorption of plant sterols (mainly sitosterol) that are usually absorbed in minute amounts in normal individuals. Additionally, these patients have diminished clearance of plant sterols, leading to very high levels of plant sterols in the plasma. Patients suffering from sitosterolemia have severely depressed hepatic cholesterol biosynthesis, and decreased levels of HMG-CoA reductase enzyme [37].
Clinical manifestations include: tendon and tuberous xanthomas, episodes of hemolysis, accelerated atherosclerosis, and premature coronary artery disease. It is important to note that close to 50 % of these patients have normal cholesterol levels [38-41].
A recently reported trial demonstrated that treatment with ezetimibe reduces plant sterol levels in patients with sitosterolemia. The authors reported a decrease in sitosterol concentrations by 21% and campesterol by 24 % [42].
Ezetimibe and atherosclerosis
It is generally accepted that atherosclerosis is an inflammatory disorder. It is believed that the atherosclerotic process begins with endothelial cell activation, which is triggered by multiple factors such as oxidized lipoproteins. Cholesterol lowering agents as a group have demonstrated great efficacy in prevention and cessation of the progression of atherosclerosis. The efficacy of ezetimibe in monotherapy or in combination on CHD morbidity and mortality has not been well established.
One of the unique features of cholesterol absorption inhibitors is their ability to modify post-prandial hyperlipidemia. There is increasing evidence that post-prandial lipoproteins (particularly cholesterol-rich chylomicron remnant) are atherogenic. Ezetimibe has the potential to reduce the cholesterol content of chylomicrons by up to 60% [44], which may lead to a lower atherogenic potential of chylomicron remnants [43].
High-sensitivity C-reactive protein (hs-CRP) is an inflammatory mediator whose levels correlate with increased coronary risk. Ezetimibe co-administered with simvastatin resulted in significant incremental decreases in hs-CRP in patients with primary hypercholesterolemia. Changes in individual lipid parameters did not explain the observed decreases in hs-CRP and were possibly consistent with an additional anti-inflammatory effect compared with simvastatin monotherapy [45].
In a prospective trial to study effects of ezetimibe co-administered with atorvastatin in patients with primary hypercholesterolemia, ezetimibe plus atorvastatin significantly provided an additional (10%) lowering of hs-CRP versus atorvastatin alone [46].
The unanswered questions
Ezetimibe and its class of cholesterol absorption inhibitors are new, and there is a lack of outcomes data to explore whether its cholesterol modifying effects will translate to lower CHD mortality and morbidity. The safety of this medication has not yet been established with long term trials data as most of the studies conducted were short term. With the advent and increased utilization of combination therapy in the management of dyslipidemia, further trials are needed to explore the efficacy, indications and safety profile of ezetimibe use in combination with Peroxisome proliferator-activated receptors (PPARs), niacin and bile acid resins.
The increased popularity of special weight loss diets such as the high protein diet, poses questions of whether such diets will alter the efficacy or safety of cholesterol absorption inhibitors.
Finally, the efficacy of statins in reducing CHD related events has lead to the controversial hypothesis regarding whether or not statins poses a pleiotropic (non lipoprotein) effect. If a pleiotropic effect exists, one might argue that a statin at a higher dose might be more beneficial than combination therapy producing the same effect.
Conclusions
Ezetimibe is the first clinically approved cholesterol absorption inhibitor. It is effective in lowering LDL-C as monotherapy or in combination with statins. The use of combination LDL-C lowering medication is expected to become a much more common modality of treatment, especially after the recent NCEP/ATP III update. Ezetimibe offers further lowering of LDL-C and non HDL-C that is consistent and probably safer than increasing the dose of the individual statin. It also provides another effective treatment option for HoFH and sitosterolemia patients. Because of its recent introduction, we still lack both outcomes and long term safety data.
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| 15471540 | PMC524501 | CC BY | 2021-01-04 16:39:18 | no | Lipids Health Dis. 2004 Oct 7; 3:22 | utf-8 | Lipids Health Dis | 2,004 | 10.1186/1476-511X-3-22 | oa_comm |
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J Negat Results BiomedJournal of Negative Results in Biomedicine1477-5751BioMed Central London 1477-5751-3-41548259910.1186/1477-5751-3-4ResearchSerum S100B in primary progressive multiple sclerosis patients treated with interferon-beta-1a Lim Ee Tuan [email protected] Axel [email protected] Siobhan M [email protected] Daniel R [email protected] Geoff [email protected] Ed J [email protected] David H [email protected] Alan J [email protected] Gavin [email protected] Department of Neuroinflammation, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK2 NMR Research Unit, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK3 Medical Statistics Unit, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK2004 13 10 2004 3 4 4 18 5 2004 13 10 2004 Copyright © 2004 Lim et al; licensee BioMed Central Ltd.2004Lim 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.
S100B belongs to a family of calcium-binding proteins implicated in intracellular and extracellular regulatory activities. This study of serum S100B in primary progressive multiple sclerosis (PPMS) is based on data obtained from a randomized, controlled trial of Interferon β-1a in subjects with PPMS. The key questions were whether S100B levels were associated with either disability or MRI findings in primary progressive MS and whether Interferon β-1a has an effect on their S100B levels. Serial serum S100B levels were measured using an ELISA method. The results demonstrated that serum S100B is not related to either disease progression or MRI findings in subjects with primary progressive MS given Interferon β-1a. Furthermore there is no correlation between S100B levels and the primary and secondary outcome measures.
serum S100Bprimary progressive multiple sclerosisInterferon β-1amagnetic resonance imaging
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Introduction
S100B belongs to a family of calcium-binding proteins implicated in intracellular and extracellular regulatory activities [2]. Intracellularly, it exhibits regulatory effects on cell growth, differentiation, cell shape and energy metabolism. Extracellularly, S100B stimulates neuronal survival, differentiation, astrocytic proliferation, neuronal death via apoptosis, and stimulates (in some cases) or inhibits (in others) activity of inflammatory cells.
Several studies suggest that S100B has a role in the pathogenesis of multiple sclerosis (MS). Phenotypically and functionally similar T cells specific against S100B can be detected in the peripheral blood of MS patients making S100B a putative candidate auto-antigen in MS [15]. Furthermore, S100B may act as a cytokine [2,10,11] and in vitro studies show that, at high levels, S100 can induce the neuronal expression and secretion of pro-inflammatory interleukin-6. In addition, elevated levels of S100B have been detected in the cerebrospinal fluid (CSF) of MS patients during acute phases or exacerbations of the disease [10] and it has therefore been proposed that elevated S100B may be indicative of active cell injury [11].
Interferon-β (IFN-β) is effective in reducing relapse rate in relapsing-remitting [6,14,17] and secondary progressive MS [3] but the mechanisms behind the beneficial action of IFNβ are not fully understood. Two potential sites of action are on cytokine production [1,4,12] and on the entry of leukocytes into the CNS [8,9,16,18].
In this clinically negative phase II study [7], we assessed the effect of IFNβ-1a on serum levels of S100B at 3-month intervals in subjects with primary progressive MS (PPMS). The key questions were whether serum S100B levels correlated with disability or MRI findings in patients with PPMS, and whether IFN-β has an effect on levels of serum S100B.
Methods
Patients and examination
Fifty patients with PPMS were recruited in a phase II trial of IFNβ-1a (Avonex®, Biogen) and were assessed three monthly over a study period of 2 years. Fifteen of these patients were treated with IFNβ-1a 30μg intramuscularly (im) weekly (IFN30), 15 received IFNβ-1a 60μg im weekly (IFN60) and 20 with placebo. IFNβ-1a was reduced to half dose in 5 subjects receiving 60μg im weekly, and in 2 subjects receiving IFNβ-1a 30μg im weekly. Seven subjects withdrew from treatment [7] (see Figure 1).
Figure 1 Fifty subjects with PPMS were randomised in a phase II trial of Interferon β-1a and were assessed 3 monthly over a 2-year study period. n = number of subjects with PPMS
Neurological examination was performed at each visit and disability was measured using Kurtzke's expanded disability status scale (EDSS). Progression was defined as a sustained (3 months apart) increase of at least 1.0 on the EDSS scale between 0 to 5 and 0.5 for subjects with EDSS score of 5.5 and above.
Fourteen healthy subjects served as controls.
All subjects provided informed consent prior to their inclusion in the study. This study was approved by the ethics committee and has therefore been performed with the ethical standards laid down in the 1964 Declaration of Helsinki.
MR imaging and analyses
MRI was performed at baseline and 6 monthly for 2 years. Only baseline and year 2 data were included in this study. Brain and spinal cord atrophy, ventricular volume, T1 and T2 lesion load were measured as described elsewhere [7].
Serum S100B levels
Serum samples were centrifuged and stored at -20°C. Serum S100B levels were quantified using a modified ELISA method as previously described by Green et al. [5]. Ninety-six-well plates were coated with 100μl 0.05 M carbonate buffer containing 10μl monoclonal anti-S100B (Affiniti Research Products, Exeter, UK). The plates were washed with 0.67 M barbitone buffer containing 5 mM calcium lactate, 0.1% BSA and 0.05% Tween and then blocked with 2% BSA and washed again. Diluted serum (1:1) in 0.67 M barbitone buffer containing 5 mM calcium lactate was added in duplicate. After incubation and wash 0.1% HRP conjugated polyclonal anti-S100B (Dako, Copenhagen, Denmark) was used as detecting antibody. The OPD colour reaction was stopped with 1 M hydrochloric acid and the absorbance read at 492 and 405 nm. The antigen concentration was calculated against a standard curve ranging from 0.01 to 2.5 ng/ml.
Statistical analyses
Median, interquartile range and significance of group differences (Mann-Whitney U tests) were evaluated. Changes of serum level over time were examined using variance components regression models of serum response variable on time as predictor, with random subject-specific intercepts and fixed common slopes. Curvature was assessed using a quadratic term in time; modification of curve over time by treatment was assessed using additional terms for treatment and treatment by time interaction in the model. Two sets of treatment terms were used: i) indicators of assigned weekly dose ii) average weekly dose over follow-up (including changes to dose regime) as continuous variable. Modification of the curve over time by MRI variable values were similarly examined using terms for MRI variable and MRI variable by time interaction.
Direct associations between serum level and MRI/clinical variables were examined by regression models of 24 month serum on 24 month MRI variable, adjusting for baseline serum and MRI values (this type of model takes into account change from baseline), with additional terms for treatment and treatment by MRI variable interaction, the latter to assess possible modifications of the relationship by treatment.
Software used were the SPSS software package (version 11.0 for Windows) and Stata 7.0 (Stata Corporation. Stata Statistical Software: Release 7.0. College Station, Texas, USA).
Results
Serum S100B between subjects with PPMS and controls
The median and interquartile ranges for all subjects are described in Table 1. There were no significant differences between any of the groups in relation to age. When comparing S100B levels at baseline of subjects with PPMS and controls, the difference was not statistically significant (p = 0.3).
Table 1 Age and serial serum S100B levels expressed as median (interquartile range). n = number of subjects; mo, months; N/A, non-applicable.
Control (n = 14) Placebo (n = 20) IFN30 (n = 15) IFN60 (n = 15)
Male:Female 6:8 n = 14 15:5 n = 20 10:5 n = 15 7:8 n = 15
Age (years) 32 (29–44) n = 14 43 (36–51) n = 20 51 (39–53) n = 15 52 (43–54) n = 15
S100B-0mo 0.08 (0.08–0.10) n = 14 0.09 (0.02–0.10) n = 20 0.06 (0.04–0.10) n = 12 0.07 (0.04–0.10) n = 14
S100B-3mo N/A 0.08 (0.05–0.10) n = 20 0.06 (0.05–0.10) n = 15 0.07 (0.04–0.10) n = 14
S100B-6mo N/A 0.10 (0.03–0.20) n = 18 0.07 (0.04–0.10) n = 15 0.07 (0.04–0.10) n = 14
S100B-9mo N/A 0.08 (0.03–0.10) n = 18 0.05 (0.02–0.10) n = 15 0.06 (0.04–0.10) n = 14
S100B-12mo N/A 0.07 (0.03–0.10) n = 17 0.08 (0.05–0.10) n = 15 0.08 (0.04–0.10) n = 14
S100B-15mo N/A 0.07 (0.02–0.10) n = 19 0.07 (0.04–0.10) n = 14 0.09 (0.04–0.10) n = 14
S100B-18mo N/A 0.07 (0.04–0.10) n = 18 0.06 (0.02–0.09) n = 13 0.09 (0.03–0.20) n = 14
S100B-21mo N/A 0.06 (0.05–0.10) n = 18 0.08 (0.04–0.10) n = 14 0.08 (0.04–0.20) n = 13
S100B-24mo N/A 0.07 (0.02–0.10) n = 18 0.07 (0.05–0.10) n = 15 0.06 (0.04–0.10) n = 13
Serum S100B change over time
There was no change over time in the serum S100B levels. The shape of the serum trajectory did not vary between the treatment regimes, i.e. placebo vs. IFN30 vs. IFN60.
Serum S100B versus Clinical and MRI parameters (Table 2)
There was no evidence that the 24-month serum S100B values were associated with either changes in the T1 or T2 loads, or ventricular or cord volumes at 24 months, after adjusting for the baseline values of each subject. There was no correlation with disease progression on the EDSS. There was also no evidence that these relationships were modified by treatment assignment (intention-to-treat analysis) (Table 2) or the overall average dose, which included the changes to treatment regime (non-intention-to-treat analysis) (Table 2).
Table 2 Serum S100B versus Clinical and MRI variables. Estimated mean change in 24-month serum S100B associated with unit increase in mean value of T1 and T2 lesion load, ventricular and spinal cord volume, adjusted for baseline values of both serum S100B and of MRI parameters. Baseline adjustment ensures that the coefficient assesses the 'effect' of the 24-month MRI parameters value relative to its baseline. * Test of treatment interactions with row variable.
Variable Coefficient P-value 95% Confidence Interval (CI) P-value for treatment modification*: Assignment average dose
24 month T1 load -4 × 10-6 0.35 -1 × 10-5, 4 × 10-6 0.76 0.59
24 month T2 load -3 × 10-6 0.16 -7 × 10-6, 1 × 10-6 0.57 0.89
24 month ventricular volume 7 × 10-7 0.75 -3 × 10-6, 5 × 10-6 0.46 0.24
24 month cord volume -2 × 10-3 0.54 -9 × 10-3, 5 × 10-3 0.58 0.88
Discussion
These results suggest that serum S100B levels in patients with PPMS were not affected by intramuscular IFNβ-1a and that there was no observable change in S100B over time. Furthermore, we did not observe any correlation between S100B levels and clinical disability or between S100B and quantitative MRI measures.
This study therefore suggests Although there is evidence that S100B elevation in MS is related to inflammatory activity [10,11,13], this study has shown that S100B was not sensitive to disease progression in PPMS. This supports the view that PPMS is less inflammatory than other forms of MS and that serum S100B would be ineffective as a surrogate marker of disease progression in this subgroup.
It would be valuable to identify surrogate markers of clinical progression in PPMS to aid the development of effective therapeutic intervention, since clinical trials with a disability endpoint are very large and resource consuming. It is possible that such markers would need to be less related to acute inflammation and more dependant on other neuropathology such as axonal loss and regeneneration.
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| 15482599 | PMC524502 | CC BY | 2021-01-04 16:37:33 | no | J Negat Results Biomed. 2004 Oct 13; 3:4 | utf-8 | J Negat Results Biomed | 2,004 | 10.1186/1477-5751-3-4 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-561546181610.1186/1477-7525-2-56ResearchAssociation of changes in health-related quality of life in coronary heart disease with coronary procedures and sociodemographic characteristics Veenstra Marijke [email protected] Kjell I [email protected] Arnfinn [email protected] Knut [email protected] Norwegian Health Services Research Centre; Quality Evaluation Department, P.O. Box 7004, St. Olavs plass 0130, Oslo, Norway2 Department of Medicine, Akershus University Hospital, Nordbyhagen, Norway2004 4 10 2004 2 56 56 21 7 2004 4 10 2004 Copyright © 2004 Veenstra et al; licensee BioMed Central Ltd.2004Veenstra 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
Few studies have focused on the association between the sociodemographic characteristics of a patient with the change in health-related quality of life (HRQOL) following invasive coronary procedures, and the results remain inconclusive. The objective of the present study was to measure the temporal changes in HRQOL of patients with coronary heart disease, and assess how these changes are associated with invasive coronary procedures and sociodemographic characteristics.
Methods
This was a prospective study of 254 patients with angina pectoris and 90 patients with acute coronary syndrome. HRQOL was assessed with the multi-item scales and summary components of the SF-36, both 6 weeks and 2 years after baseline hospitalization in 1998. Paired t-tests and multiple regression analyses were used to assess temporal changes in HRQOL and to identify the associated factors.
Results
Physical components of HRQOL had improved most during the 2 years following invasive coronary procedures. Our findings indicated that patients with angina pectoris who were younger, male, and more educated were most likely to increase their HRQOL following invasive coronary procedures. When adjusting for baseline HRQOL scores, invasive coronary procedures and sociodemographic characteristics did not explain temporal changes in patients with acute coronary syndrome, possibly due to higher comorbidity.
Conclusion
Sociodemographic characteristics should be taken into account when comparing and interpreting changes in HRQOL scores in patients with and without invasive coronary procedures.
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Background
In the medical treatment of patients with coronary heart disease (CHD), invasive coronary procedures – such as percutaneous coronary intervention – are effective in reducing mortality and morbidity [1,2]. An important supplementary outcome of medical interventions and the processes of health care is health-related quality of life (HRQOL) [3,4]. There is a strong interest in differences in the care and outcomes between sociodemographic groups to optimize population health. Differential temporal changes in HRQOL between diverse sociodemographic groups may be of interest in secondary prevention programs to maximize the benefit from treatment for CHD.
Most studies investigating the association between sociodemographic characteristics and HRQOL in patients with CHD focus on cross-sectional group comparisons [5-11]. Longitudinal studies on the association between sociodemographic characteristics and HRQOL have indicated that being female [12-14] and lower socioeconomic status [15] are associated with less temporal improvement in HRQOL. These studies, however, lacked information on medical interventions and focused on short-term changes lasting only up to 1 year. Studies assessing the effect of invasive coronary procedures on HRQOL, have shown that HRQOL improves after intervention [16-20] to levels similar to population norms [21-23]. Some of these studies were clinical trials and involved very selective populations.
Only few studies have focused on the association of sociodemographic variables with temporal changes in HRQOL following invasive coronary procedures. A recent observational study indicated that higher income is associated with greater improvement in physical HRQOL following invasive coronary procedures [24]. Improvements in physical HRQOL appear to be unrelated to the age of patients [25], whereas elderly patients exhibit a stronger improvement in mental HRQOL after medical intervention [24,26]. However, the association of sex and educational attainment with changes in HRQOL following invasive coronary procedures remains inconclusive.
In the present study of patients with CHD, we aimed to (i) describe the effect of invasive coronary procedures on different domains of HRQOL at both 6 weeks and 2 years after baseline hospitalization, (ii) assess the association between sociodemographic characteristics and temporal changes in HRQOL.
Methods
Baseline data in the present study were derived from the Norwegian study on outcomes research and quality improvement (RESQUA), a cross-sectional postal survey of HRQOL and the experiences of patients receiving hospital care [27]. All patients from surgical and internal medicine wards at 17 hospitals (4 teaching hospitals, 6 central hospitals, and 7 local hospitals) between October and December 1998 were sent a questionnaire 6 weeks after hospital discharge. No response within 4 weeks triggered one reminder. Patients younger than 16 years and those registered as dead at discharge were excluded from the study.
Participants with CHD discharged from internal medicine wards were selected for a follow-up postal survey approximately 2 years later, in October and November 2000. We used information on primary and secondary diagnoses from the patient-administration systems of the hospitals, and included patients with acute coronary syndrome (ICD-9 410.xx and 411.xx) and angina pectoris (ICD-9 413.xx). Patients with chronic heart failure (ICD-9 428.xx) as the primary diagnosis were classified as angina pectoris or acute coronary syndrome dependent on their secondary diagnoses.
Measures
Health-related quality of life
HRQOL was measured by the Norwegian version 1.2 of the Short-Form 36 (SF-36), a widely used generic health status measure that enables comparison with normative scores [28,29]. The scales and items of the SF-36 have satisfactory reliability, validity, and responsiveness, also in patients with CHD [3,10,30,31]. Single items of the SF-36 are transformed and aggregated into eight multi-item scales: Physical Functioning, Physical Role Limitations, Bodily Pain, General Health Perceptions, Vitality, Social Functioning, Emotional Role Limitations, and Mental Health. The resulting summated rating scales range from 0 to 100, with higher scores indicating better health.
To estimate the potential impact of CHD on HRQOL, we compared the SF-36 scores from the patients in our study with normative data from the Norwegian general population [32]. Norm scores for the eight multi-item scales were adjusted to reflect age and sex distributions similar to those of the patients in the present study. These adjusted norm data for the eight multi-item scales were used to calculate the standardized Physical (PCS) and Mental (MCS) Component Summary scores [33].
Procedures, sociodemographics, and comorbidity
Invasive coronary procedures referred to diagnostic and therapeutic procedures, such as coronary angiography, which contributes to diagnosis of potential coronary artery disease, and when followed by angioplasty, it can contribute to a relief from chest-pain as well as improve the prognosis in high-risk patients. Procedure codes were derived from the patient-administration systems of the hospitals in 1998 (Classification of Operations; version 3, 1995). We defined invasive coronary procedures as a dichotomous variable, differentiating between patients with and without invasive coronary procedures during baseline hospitalization in 1998. The age and sex data were also derived from the administration systems in 1998. Information about the highest level of educational attainment was obtained from self-reported data in the 1998 postal survey. This variable was heavily positively skewed, and we therefore created two groups: (1) below and equal to, and (2) above the median value in our cohort. As a crude estimate of the degree of comorbidity for each patient, we used the total number of secondary diagnoses registered in the administration databases during baseline hospitalization in 1998.
Statistical analyses
Changes in HRQOL were only assessed in patients who had valid scores on all multi-item scales both in 1998 and 2000. We used χ2-statistics or the t-test for independent samples to analyze the extent of selective attrition, and differences in the use of invasive coronary procedures across characteristics of respondents.
Temporal changes in HRQOL were analyzed with paired-samples t-tests. As a measure of the minimally important difference in intra-individual scores, we calculated the standardized response mean, a distribution-based approach that compares temporal change by the standard deviation of change [34]. Standardized response means of 0.2–0.5, 0.5–0.8, and > 0.80 are regarded as small, moderate, and large, respectively [35].
Additionally, we applied multivariate linear regression analyses to determine the association of invasive coronary procedures and sociodemographic factors with PCS and MCS scores 2 years after baseline hospitalization. By including baseline PCS and MCS scores in the regression model, the regression coefficients of invasive coronary procedures and sociodemographic factors indicate the one unit increase in 2-year PCS and MCS scores, provided that baseline scores are held constant.
All analyses were performed separately in patients with angina pectoris and acute coronary syndrome. We chose a 5% statistical significance level. The Regional Medical Research Ethics Committee, the Data Inspectorate, and the Norwegian Board of Health approved the study.
Results
A total of 1,534 patients with CHD were sent a questionnaire in 1998, and 1,059 (69%) responded. In 2000, 700 patients with valid HRQOL scores were sent a follow-up questionnaire (and, where necessary, one reminder), and 473 patients responded. After excluding 38 patients who had recently died and 9 patients with an unknown address, the adjusted response rate in the follow-up study was 72% (Figure 1).
Figure 1 Flow chart describing attrition in the cohort of patients with coronary heart disease
A total of 254 patients with angina pectoris and 90 patients with acute coronary syndrome had valid MCS and PCS scores both in 1998 and 2000, of which 108 patients with angina pectoris and 41 patients with acute coronary syndrome underwent an invasive coronary procedure. The majority underwent catheterization (N = 79) or percutaneous coronary intervention (N = 45). Twenty-one patients underwent coronary bypass surgery and the remaining patients (N = 4) underwent other medical procedures related to the cardiovascular system. Compared to the original cohort of patients, patients with valid HRQOL scores both in 1998 and 2000, more often had undergone invasive coronary procedures, were male, younger, and had lower comorbidity (Table 1). Compared to the cohort with valid responses in 1998, attrition was associated with age, educational attainment and comorbidity. Angina pectoris patients who responded both in 1998 and in 2000 had higher PCS scores in 1998 compared to nonrespondents to the follow-up survey (mean HRQOL score 42 versus 39; P < 0.001). Among patients with valid HRQOL scores both in 1998 and 2000, women, elderly patients, and patients with higher comorbidity had fewer invasive coronary procedures during the baseline hospitalization (Table 2). Educational attainment was not associated with invasive coronary procedures.
Table 1 Baseline characteristics of respondents and non-respondents
I. Total II. Non-respondents at 6 weeks III. Respondents at 6 weeksa IV. Respondents at 6 weeks & 2 yearsa Comparison Column IV and total nonresponse (P-value) Comparison Column IV and nonresponse at 6 weeks (P-value)
N = 1534 N = 475 N = 700 N = 344
Age, mean (SD) 69 (12) 71 (12) 65 (11) 64 (10) <0.001 0.001
Gender (% women) 34 44 29 27 0.001 0.2
> 10 years education (%) 44 49 0.014
> 1 diagnosis (%) 65 72 60 56 0.001 0.039
Emergency admission (%) 70 80 62 58 <0.001 0.07
Acute Coronary Syndrome (%) 31 32 28 26 0.047 0.4
Invasive Coronary Procedure (%) 33 22 40 43 <0.001 0.06
Teaching Hospital (%) 54 48 57 59
Central Hospital (%) 26 27 25 22 0.042 0.123
Local Hospital (%) 20 25 18 19
aRespondents to all multi-item scales of the SF-36
Table 2 Invasive coronary procedures (ICP) according to characteristics of baseline hospitalization and sociodemographic characteristics in patients with angina pectoris and acute coronary syndrome
Angina pectoris Acute coronary syndrome
No ICP ICP No ICP ICP
Baseline characteristics N = 146 N = 108 N = 49 N = 41
Sex
Men (%) 70 77 73 76
Women (%) 30 23 27 24
Education
≤ 10 years (%) 53 52 53 44
> 10 years (%) 47 48 47 56
Age (mean; (SD)) 64 (11) 61 (9) 68 (9) 62 (10)
Type of hospital
Teaching (%) 34 98 31 90
Central (%) 33 2 36 10
Local (%) 33 0 34 0
Length of hospital stay (mean (SD)) 3.8 (3.1) 3.9 (3.7) 8.5 (4.8) 7.1 (7.3)
No. of diagnoses (mean (SD)) 1.9 (1.1) 1.6 (0.9) 2.9 (1.2) 2.2 (1.0)
Six weeks after hospitalization, patients with angina pectoris and acute coronary syndrome had lower scores compared to the Norwegian norm data in all domains of HRQOL (Table 3 [see Additional file 1]). Patients without invasive coronary procedures exhibited the largest differences, particularly in domains referring to physical aspects of HRQOL: Physical Role Limitations, Emotional Role Limitations, and Bodily Pain; but also in General Health Perceptions. Two years after the baseline hospitalization in 1998, scores on all multi-item scales were still below the scores of the norm population.
Over the 2 years analyzed, Physical Role Limitations (P = 0.001) and Social Functioning (P = 0.003) improved in angina pectoris patients without invasive coronary procedures, corresponding to a small effect size (Table 3). Physical Functioning (P = 0.03), Physical Role Limitations (P < 0.001), and Bodily Pain (P = 0.03) improved in patients with angina pectoris undergoing invasive coronary procedures. The change in Physical Role Limitations corresponded to a moderate effect size. A significant deterioration was found in General Health Perceptions (P = 0.04).
In patients with acute coronary syndrome without invasive coronary procedures, Physical Role Limitations (P = 0.003), Social Functioning (P = 0.005), and Emotional Role Limitations (P = 0.009) significantly improved. Physical Role Limitations (P = 0.001) improved in patients with acute coronary syndrome undergoing invasive coronary procedures.
Patients with invasive coronary procedures showed a small improvement in PCS scores (P = 0.034 for angina pectoris and P = 0.015 for patients with acute coronary syndrome). MCS scores remained stable during the 2 years of follow-up; only patients with angina pectoris without an invasive coronary procedure experienced a small improvement in MCS scores (P = 0.007).
Multiple linear regression analyses revealed that, after taking baseline PCS scores into account, invasive coronary procedures and being younger, male, and more educated were significantly associated with higher PCS scores in 2000 in patients with angina pectoris (Table 4 [see Additional file 2]). For these patients, being older was significantly associated with higher MCS scores in 2000. In patients with acute coronary syndrome, PCS scores and MCS scores in 2000 were significantly associated only with baseline scores, and not with invasive coronary procedures or sociodemographic characteristics.
Discussion
In the present study, most improvement was found in the physical components of HRQOL 2 years following invasive coronary procedures. These results support the findings of Krumholz et. al. [21] that the SF-36 scale for Physical Role Limitations was most responsive after elective coronary angioplasty. Furthermore, in patients with angina pectoris, PCS scores improved more among those who were male, younger, and more educated, independently of invasive coronary procedures. One explanation for this, as suggested by some previous studies, is related to differences in disease severity: women and patients from disadvantaged socioeconomic strata may have more extensive coronary disease at the onset of symptoms [12,13,36]. Additionally, undesirable events and adverse experiences might have stronger negative emotional consequences in this group [37], suggesting worse adaptation to the long-lasting physical limitations of CHD and a greater risk of recurrent events [36].
When adjusting for baseline scores, invasive coronary procedures and sociodemographic characteristics did not explain any additional variation in PCS and MCS scores 2 years after hospitalization in patients with acute coronary syndrome. This may be due to the relatively small sample size. An alternative explanation is that patients with acute coronary syndrome exhibited higher comorbidity that could limit the effect of invasive coronary procedures on HRQOL, and accordingly, the sensitivity of the SF-36 in detecting differences [20].
Our results demonstrated that invasive coronary procedures and sociodemographic characteristics were weakly associated with MCS scores and indicated small deviations from the population norm, which corresponds to previous findings in patients with CHD [6,38]. This may be attributable to health care having less impact on mental health than on physical health. An alternative explanation refers to the construction of the SF-36 MCS and PCS measures. The scores of these component scales are calculated using all eight multi-item scales with factor score coefficients derived from factor analysis with orthogonal rotation, thereby defining that PCS and MCS are uncorrelated. Mean scores on the multi-item scales that are below the population mean will contribute to component scores opposite to the direction defined by the factor score coefficient [39]. In our study, the low scores of Physical Role Limitations contributed negatively to PCS and positively to MCS. Hence, MCS scores were probably inflated by poor physical health. The RAND-36 has been suggested as an alternative method for computing PCS and MCS scores that avoids the orthogonal approach of the SF-36 [40,41].
Other factors may have influenced our results, for example selective attrition. In our study, the respondents to both surveys represent a survivor cohort, and hence attrition may have reduced the temporal changes in SF-36 scores and possibly lead to underestimation of the associations with invasive coronary procedures and sociodemographic factors. Moreover, the use of self-administered and postal questionnaires may have contributed to missing SF-36 items, especially in elderly subjects who are associated with a higher frequency of missing values for items used to score physical and emotional role functioning [24]. The appropriateness of the SF-36 for use in elderly populations with expected low response rates, reduced cognitive functioning, and shifts in conceptualizations of subjective health, has been discussed previously [32]. Consequently, caution should be exercised when employing norms among people aged 70 years and older.
Another limitation of our study is the lack of HRQOL data before the baseline hospitalization in 1998, which prevented us from assessing the full impact of invasive coronary procedures on subsequent HRQOL and its association with sociodemographic characteristics. We also did not examine the influence of use of medical services after the baseline hospitalization. Finally, coronary patients and invasive procedures were defined by registry data from the patient-administration systems of hospitals, which might be inaccurate and mask some of the underlying clinical differences that could influence the HRQOL results.
Our findings indicated that patients with angina pectoris who were younger, male, and more educated were most likely to increase their HRQOL following invasive coronary procedures. In patients hospitalized for acute coronary syndrome, temporal change in HRQOL was not associated with invasive coronary procedures and sociodemographic characteristics, possibly due to higher comorbidity. In a usual care setting the occurrence of invasive coronary procedures varies with sociodemographic characteristics [42,43]. The association of both sociodemographic variables and invasive coronary procedures with HRQOL outcomes makes it imperative to take these into account when comparing and interpreting change scores to reduce the risk of spurious findings.
Authors' contributions
MV carried out the follow-up study, analyzed the data, and drafted the manuscript. KIP performed the baseline survey. AR participated in the design of the study. KS participated in the design and coordination of the study. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Table 3: SF-36 multi-item and summary scales 6 weeks and 2 years after baseline hospitalization in patients with angina pectoris and acute coronary syndrome according to invasive coronary procedures (ICP). Norwegian norm data and 2-year change scores
Click here for file
Additional File 2
Table 4 Predictors of Physical Component Summary (PCS) and Mental Component Summary (MCS) scores 2 years following the baseline hospitalization in patients with angina pectoris and acute coronary syndrome. Multivariate linear regression analysis; unstandardized regression coefficients (B) and 95% Confidence Intervals (CI)
Click here for file
Acknowledgement
This study was supported by grants from the Ministry of Health and the Research Council of Norway. We thank Tomislav Dimoski for developing software for quality assurance and patient selection.
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| 15461816 | PMC524503 | CC BY | 2021-01-04 16:38:12 | no | Health Qual Life Outcomes. 2004 Oct 4; 2:56 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-56 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-701545011910.1186/1477-7827-2-70ResearchComparison of Misoprostol and Dinoprostone for elective induction of labour in nulliparous women at full term: A randomized prospective study Papanikolaou Evangelos G [email protected] Nikos [email protected] Aikaterini [email protected] Styliani [email protected] Christina [email protected] Theodoros [email protected] Evangelos [email protected] Konstantinos [email protected] Department of Obstetrics and Gynecology, University Hospital of Ioannina, Medical School of Ioannina, Ioannina, Greece2 Department of Neonatology, University Hospital of Ioannina, Medical School of Ioannina, Ioannina, Greece2004 27 9 2004 2 70 70 12 7 2004 27 9 2004 Copyright © 2004 Papanikolaou et al; licensee BioMed Central Ltd.2004Papanikolaou 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 randomized prospective study was to compare the efficacy of 50 mcg vaginal misoprostol and 3 mg dinoprostone, administered every nine hours for a maximum of three doses, for elective induction of labor in a specific cohort of nulliparous women with an unfavorable cervix and more than 40 weeks of gestation.
Material and Methods
One hundred and sixty-three pregnant women with more than 285 days of gestation were recruited and analyzed. The main outcome measures were time from induction to delivery and incidence of vaginal delivery within 12 and 24 hours. Admission rate to the neonatal intensive care unit within 24 hours post delivery was a secondary outcome.
Results
The induction-delivery interval was significantly lower in the misoprostol group than in the dinoprostone group (11.9 h vs. 15.5 h, p < 0.001). With misoprostol, more women delivered within 12 hours (57.5% vs. 32.5%, p < 0.01) and 24 hours (98.7% vs. 91.4%, p < 0.05), spontaneous rupture of the membranes occurred more frequently (38.8% vs. 20.5%, p < 0.05), there was less need for oxytocin augmentation (65.8% vs. 81.5%, p < 0.05) and fewer additional doses were required (7.5% vs. 22%, p < 0.05). Although not statistically significant, a lower Caesarean section (CS) rate was observed with misoprostol (7.5% vs. 13.3%, p > 0.05) but with the disadvantage of higher abnormal fetal heart rate (FHR) tracings (22.5% vs. 12%, p > 0.05). From the misoprostol group more neonates were admitted to the intensive neonatal unit, than from the dinoprostone group (13.5% vs. 4.8%, p > 0.05). One woman had an unexplained stillbirth following the administration of one dose of dinoprostone.
Conclusions
Vaginal misoprostol, compared with dinoprostone in the regimens used, is more effective in elective inductions of labor beyond 40 weeks of gestation. Nevertheless, this is at the expense of more abnormal FHR tracings and more admissions to the neonatal unit, indicating that the faster approach is not necessarily the better approach to childbirth.
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Introduction
Induction of labor is carried out for maternal and fetal indications. One of the most common indications is prolonged pregnancy [1]. Recent studies have suggested that by continuing pregnancy beyond 41 weeks, there is a statistically significant higher perinatal morbidity and mortality as well as an increased risk to the mother [2,3]. Thus, there is a growing body of evidence suggesting the elective induction of labor at 41 weeks of gestation instead of expectant management [4-6].
Prostaglandin analogues, dinoprostone (PGE2) and misoprostol (PGE1), are widely used in "induction of labor" practice for ripening the cervix and stimulating uterine contractions in order to achieve vaginal delivery. Although dinoprostone has been approved by the FDA for cervical ripening in women at or near term, misoprostol is not currently approved for such use by the FDA, although it has the advantages of lower cost, no need for refrigeration and probably higher efficacy.
Several studies have demonstrated a higher efficacy of vaginally administered misoprostol compared to vaginal dinoprostone for both cervical ripening and labor induction [7-19]. The Cochrane Pregnancy and Childbirth Group, having reviewed 45 randomized studies, concluded that vaginal misoprostol (25 to 100 mcg) was more effective than oxytocin or dinoprostone at the usual recommended doses for induction, but with increased rates of uterine hyperstimulation both with and without associated fetal heart rate (FHR) changes, as well as meconium stained fluid [9]. Most of the studies included in the previous meta-analysis used the 50 mcg dose for misoprostol at a maximum interval of six hours between the repeated doses, always resulting in higher rates of hyperstimulation.
However, it is difficult to interpret previously published studies comparing misoprostol with dinoprostone for induction of labor since the majority of them are not double-blinded [9] and they have included both complicated and uncomplicated pregnancies, multiparous women with nulliparous as well as a wide gestational age (GA) range (37–42 weeks). Moreover, to reduce the risk of side effects, one can either decrease the dose of the drug [7,19] or prolong the dosage interval [14,15,19]. In addition, Alexander et al. have recently shown that in prolonged pregnancies it was not the induction per se that would increase the risk for caesarean section (CS), but patients related risk factors such as nulliparity and undilated cervix and the use of epidural analgesia [20].
This study was undertaken to compare the efficacy of vaginal misoprostol (50 mcg) with that of vaginal dinoprostone (3 mg) when both are administered at an interval of nine hours between repeated doses in a well-homogenized cohort of full term pregnancies (nulliparous women with an unfavorable cervix and without pregnancy complications).
Material and Methods
Between March 1, 2001 and July 16, 2003, 163 women were recruited for the study: 80 women in the misoprostol group and 83 women in the dinoprostone group. All of the women were recruited at Ioannina University Hospital, a tertiary referral center for high-risk pregnancies, with about 1600 deliveries a year. The Ethics Committee of the University of Ioannina approved the study and all participants gave their written informed consent after they had been made aware of the purpose of the study. Although the main indication was prolonged pregnancy, some of the inductions were performed at the patient's request after consultation at 40 weeks of gestation, (without any medical indications) and only if they had not delivered by the 285th day of gestation. A sequence from a computerized random number generator was used for the allocation of patients to each group. The vaginal administration of prostaglandins was performed by one of the resident doctors on duty, who was not involved in managing these women in labor or delivery. The study was double blind, since the patients were not aware of which type of medication was used, and the deliveries were then performed by two gynecologists blinded to the induction regimen utilized.
Inclusion criteria were: 1) age>18 years old 2) nulliparity, 3) accurate dating of gestation, including crown rump length (CRL) measurements in the first trimester of pregnancy, 4) singleton viable pregnancy, 5) gestational age ≥ 285 days, 6) cephalic presentation, 7) unfavorable cervical status defined as a Bishop score (BS) of ≤5, 8) intact membranes, 9) reactive non-stress test (NST). Exclusion criteria were: 1) known contraindications to receiving prostaglandins, 2) placenta previa, 3) prior uterine surgery and 4) any antenatal complications.
GA was estimated by ultrasound biometry (via CRL measurements in the first trimester of pregnancy) in cases where there were more than 3 days difference from that obtained from the last menstrual period (LMP) [21]. Uterine tachysystole was defined as more than five contractions per 10 minutes, uterine hypertonus as when one contraction lasted more than 2 minutes and hyperstimulation syndrome as the presence of non-reassuring FHR tracing combined with either tachysystole or hypertonus. Non-reassuring FHR patterns were defined as persistent or recurring episodes of severe variable decelerations, late decelerations, prolonged fetal bradycardia or a combination of decreased beat-to-beat variability and a decelerative pattern [22].
A NST to ensure the well-being of the fetus was performed for each patient at the time of recruitment and admission to the hospital (at least 285 days of gestation) and one hour before the application of the prostaglandin. After the reassessment of the cervical BS, either 50 mcg misoprostol, or 3 mg dinoprostone was administered in the posterior vaginal fornix at 23:00 hours. The NST was repeated (duration of two hours) after 1 h and 5 h. If the woman was in active labor, the membranes spontaneously ruptured or the FHR not reassuring, the patient was transferred to the labor room. Otherwise, a second BS evaluation was carried out the next morning at 08:00 am (after 9 hours). If the cervix was favorable, (BS ≥ 5), the patient was admitted to the labor ward where oxytocin augmentation was carried out if the uterine contractions were unsatisfactory and amniotomy was performed when appropriate. If the cervix was still unfavorable, a second dose of misoprostol or dinoprostone was given and the same evaluation steps as described above were followed. After a total of 18 h had elapsed, non-responders were given a third dose of prostaglandin. When the third dose was insufficient for initiating spontaneous labor, a trial of labor was offered with oxytocin infusion and if no progress was achieved within 6 hours (based on digital assessment of the BS), the patient underwent a CS.
The outcome measures were divided into "obstetrical" and "neonatal". The primary outcome measures were time from induction to delivery and incidence of vaginal delivery within 12 and 24 hours; the secondary outcomes were the CS rate, the need for oxytocin augmentation, the incidence of meconium stained amniotic fluid, the incidence of uterine tachysystole, abnormal FHR tracings, maternal morbidity, the admission to neonatal intensive care within 24 hours and neonatal arterial cord ph, base deficit.
Statistical analysis was performed using SPSS version 11 software. The Chi square test and Fisher's exact test were used to analyze nominal variables in the form of frequency tables. Normally distributed (Kolmogorov-Smirnov Test with Lilliefors correction) metric variables were tested by the T-test for independent samples, while non-normally distributed metric variables were analyzed by the Mann-Whitney U test. All tests were two-tailed with a confidence level of 95% (p < 0.05). Values are expressed as mean ± standard error (SEM).
Results
The two groups were comparable in terms of patients' age (28.1 years vs.27.5, p > 0.05) and indication for induction (prolonged pregnancy 81.2% vs.78.3%, p > 0.05; social 18.8% vs. 21.7 %,) in the misoprostol and dinoprostone groups, respectively. Gestational age (286 days, range:285–292) and the preinduction BS (2.7 ± 0.1) in the misoprostol group were also comparable to the dinoprostone group (286 days, range:285–293) and (2.9 ± 0.1), respectively.
Obstetrical outcome
The induction-delivery interval was significantly shorter (11.9 h vs. 15.6 h, p < 0.001) in the misoprostol group, with even less need for a second or third dose (7.5% vs. 22%, p < 0.05) compared to dinoprostone. With misoprostol, more women delivered within 12 h (57.5% vs. 32.5%, p < 0.01) and almost all of the women delivered within 24 h (98.8% vs. 91.6%, p < 0.05). In addition, spontaneous rupture of the membranes occurred more often after the administration of misoprostol (p < 0.05) and there was a reduced need for oxytocin augmentation in labor: 65.8% vs. 80.7% with dinoprostone (p < 0.05). However, uterine tachysystole (p < 0.05)) and meconium stained amniotic fluid (p > 0.05) occurred more often in the misoprostol group as did abnormal heart rate tracing (22.5% vs.12%, p > 0.05) (Table 1).
Table 1 Obstetrical Outcomes
Misoprostol n = 80 (%) Dinoprostone n = 83 (%) Statistical significance
Time from induction to delivery (h ± SEM) 11.9 ± 0.6 15.6 ± 0.7 p < 0.001
Delivery < 12 h 46 (57.5%) 27 (32.5%) p < 0.01
Delivery < 24 h 79 (98.8%) 76 (91.6%) p < 0.05
Number of doses
Single dose 74 (92.4%) 65 (78.3%) p < 0.05
Second dose 6 (7.5%) 17 (20.5%)
Third dose 0 (0%) 1 (1.2%)
Required oxytocin augmentation 53(65.8%) 67(80.7%) p < 0.05
Spontaneous rupture of membranes 31 (38.8%) 17 (20.5%) p < 0.05
Meconium stained AF 15 (18.8%) 7 (9.6%) NS
Abnormal FHR 18 (22.5%) 10 (12%) NS
Uterine Tachysystole 10 (12.6%) 3 (3.6%) p < 0.05
Uterine Hyperstimulation 2 (2.5%) 1 (1.2%) NS
FHR = fetal heart rate.
AF = amniotic fluid
NS = not significant (p > 0.05)
SEM = standard error of the mean
In both groups, the majority of women had vaginal delivery, 92.5% with misoprostol, and 86.7% with dinoprostone. There was no statistically significant difference between the two groups with regard to the CS rate (Table 2). There were no uterine ruptures or other major maternal complications resulting from the use of either of the prostaglandins. There was only one wound infection with dinoprostone, one woman in each group had delayed discharge due to persistent pyrexia and two women in the dinoprostone group required uterine packing (insertion of tampons within the uterine cavity) due to postpartum bleeding.
Table 2 Mode of delivery and indications for Caesarean section
Misoprostol n = 80 (%) Dinoprostone n = 83 (%) Statistical significance
Vaginal 74 (92.5%) 72 (86.7%) NS1
Spontaneous vaginal 46 (57.5%) 52 (62.6%) NS
Vacuum assisted vaginal 28 (35.0%) 20 (24.1%) NS
Caesarean section 6 (7.5%) 11 (13.3%) NS
Nonreassuring FHR2 4 (5.0%) 6 (7.2%) NS
Failed induction 0 (0.0%) 1 (1.2%) NS
Lack of labor progress 1 (1.3%) 2 (2.4%) NS
Cephalopelvic disproportion 1 (1.3%) 2 (2.4%) NS
1NS = not significant
2FHR = fetal heart rate.
Neonatal outcome
More neonates in the misoprostol group had first minute Apgar scores lower than 7 (12.6% vs. 6.1%, p > 0.05), or needed neonatal resuscitation (11.4% vs. 9.9%, p > 0.05) but none of the babies had birth asphyxia [23]. The mean cord pH and the base deficit were comparable in the two groups. No neonate had meconium aspiration syndrome. Two neonates in the dinoprostone group had clavicle fracture (Table 3). There was no statistically significant difference in the number of neonates admitted to neonatal intensive care within 24 hours after delivery, between the misoprostol and dinoprostone groups (6.3% vs. 3.6% p > 0.05) (Table 4).
Table 3 Neonatal Outcomes
Misoprostol n = 80 (%) Dinoprostone n = 83 (%) Statistical significance
Birth weight (g) 1 3275 ± 430 3373 ± 390 NS
Perinatal death 0 1(1.2%) NS
Neonatal resuscitation 9 (11.3%) 9 (10.8%) NS
O2 Supplementation 1 2
Ambou ventilation 7 6
Intubation in labor room 1 1
Apgar score < 7
1 min 10 (12.5%) 5 (6.0%) NS
5 min 1 (1.3%) 0
Cord blood pH (arterial)1 7.28 ± 0.05 7.27 ± 0.05 NS
Base deficit 1 5.0 ± 2.3 5.7 ± 3.2 NS
7.01 < cord pH < 7.20 3 (3.8%) 4 (4.8%) NS
10 < base deficit < 16 1 (1.3%) 2 (2.5%) NS
Hyperbilirubinemia 2 9 (11.3%) 5 (6.0%) NS
Birth trauma 3 0 2 (2.5%) NS
1Values expressed as mean ± SD
2Excluding pathological causes of icterus
3Both were clavicle fractures
Table 4 Admission to Neonatal Intensive Care Unit
N (%) DA1 Delivery2 Indication Diagnosis HDs3
Within 24 hours
Misoprostol† 5 (6.3%) 01 VVD Rule out infection Elevated CRP4WBC5 07
01 VVD Respiratory distress Respiratory infection 11
01 CS Rule out asphyxia Infection 10
01 SVD Respiratory distress Work up for infection 03
01 VVD Respiratory distress Work up for infection 04
Dinoprostone† 3 (3.6%) 01 VVD Respiratory distress Atelectasis 06
01 SVD Rule out asphyxia Infection 10
01 SVD Respiratory distress Respiratory infection 10
After 24 hours
Misoprostol† 6 (7.2%) 02 VVD Rule out infection WBC in CSF6 10
07 VVD Hyperbilirubinemia Urinary infection 07
04 SVD Hyperbilirubinemia Icterus 04
08 SVD Infection Respiratory infection 10
03 SVD Feeding difficulty WBC in CSF 20
05 CS Hyperbilirubinemia Icterus 04
Dinoprostone† 1 (1.2%) 17 SVD Fever WBC in CSF 15
1 DA = day of admittance
2 SVD = Spontaneous Vaginal Delivery, VVD = vacuum assisted vaginal delivery, CS = Caesarean section
3 HDs = hospitalization days
4 CRP = C-reactive protein
5 WBC = white blood cell
6 CSF = cerebrospinal fluid
† not significant (p > 0.05)
A 28-year-old woman at 41 weeks of gestation had an unexplained stillbirth after receiving a single dose of dinoprostone. Seven hours later she had a cardiotocogram without abnormal FHR patterns and regular contractions of the uterus were evident. We decided to move her to the labor ward and within half an hour of entry, no cardiac activity of the fetus was found. During these 30 minutes FHR monitoring had been discontinued, as it was not included in the study design. We decided to let her attempt vaginal delivery. An amniotomy was performed and the amniotic fluid was found to be clear and a vaginal delivery was achieved within 6 hours. Direct examination of the fetus, the placenta and the umbilical cord (UC) showed only a thin UC with excess twisting around its axis. The anatomopathology examination of the fetus revealed no abnormality except a microscopically decreased Wharton's jelly.
Discussion
Nowadays, induction of labor is more widely used than ever before [24,25]. Recent studies have shown that this increase is mainly due to a rise of inductions for marginal or elective reasons. The common indications are elective induction and postdate pregnancy often applied to gestations of 40 to 41 weeks [1,25]. Mongelli et al. have also shown that for the detection of post-maturity there is no advantage in using menstrual dates when ultrasound biometry is available [26]. Women may experience distress when labor has not started by the expected date [27] and obstetricians have to withstand pressure from these patients as well as the temptation to use prostaglandins earlier. Appropriate evaluation of the pregnancy and consultation with such patients will lead to the correct selection of those who will benefit most from a labor induction, thus eliminating the risk of post-maturity to the fetus without inducing fetal distress during labor.
To the best of our knowledge, the present study is the only one that compares misoprostol and dinoprostone in such well-homogenized groups. All of the women were nulliparous with intact membranes and at more than forty weeks' gestation with no antenatal complications and all had an unfavorable cervix. In these carefully selected patients, misoprostol at the dose used not only shortened the time between induction and delivery (11.9 vs. 15.6 h), but it also was significantly more effective than dinoprostone. The positive point was that this result was achieved with a very low CS rate even in the dinoprostone group, (7.5%, and 13.3%), respectively. A difference of 5% in favor of misoprostol, although not statistically significant, might have clinical importance in terms of patient health and cost effectiveness. Although in the recent large meta-analysis [9] published by the Cochrane Library, the CS rates were inconsistent, they tended to be lower with misoprostol; an earlier study by Sanchez-Ramos et al. found a statistically significant difference in favor of misoprostol [28]. In addition, our results for this GA window are reassuring with regard to concerns that have been raised from previous retrospective studies reporting an increased risk of Caesarean delivery in nulliparous women when elective inductions are performed [29,30].
Even though misoprostol improves the kinetics of labor during induction in a more efficient way than dinoprostone, concerns persist with respect to intrapartum fetal "wellbeing". In order to avoid uterine hyperstimulation and abnormal FHR tracings, we used for first time in the literature, a 9 h interval between the prostaglandin doses. Although we indeed achieved a low rate of uterine hyperstimulation syndrome (2.5% with misoprostol and 1.2% with dinoprostone, respectively), we still noticed a trend towards a high rate of abnormal FHR tracings during induction with misoprostol. Our findings, in accordance with the previous Cochrane metanalysis [9], showed that with misoprostol there was an increased probability of meconium staining of amniotic fluid as well as of uterine tachysystole and of abnormal FHR tracings. In the misoprostol group, the majority of women also underwent either a CS or a vacuum operative delivery due to non-reassuring FHR. If neonatal outcomes such as neonatal resuscitation, low Apgar score in the first minute and admittance to the neonatal unit within the first 24 hours (none of the above were statistically significant but they were more frequent with misoprostol) are taken into account, misoprostol may increase these complications in labor. Thus, although our sample size cannot determine safety, misoprostol use is associated with a higher chance of admittance to the neonatal unit within 24 hours even in the absence of asphyxia. This evidence indicates that the faster approach to childbirth is not necessarily the better one.
Attempting an explanation to the aforementioned side effects of misoprostol use and taking into account other reports [9,31,32], it appears that the increase in clinically relevant adverse effects is not only misoprostol related but it may be dose dependent. Lyons et al. have recently shown in term pregnant rats that a higher dose of misoprostol is needed to induce PGE2 secretion in the cervix than in the myometrium, and furthermore that EP3 receptors (prostaglandin E2 receptors) are differentially expressed in the myometrium (increased) than in the cervix (unaltered) in response to misoprostol [33]. The above findings indicate that misoprostol not only acts better on the myometrium than on the cervix, but an even higher dose is needed in order to ripen the cervix. Thus, it seems reasonable that increasing the interval between repeated misoprostol doses should reduce the risk of an asynchrony between a well or even hyper-stimulated uterus and a still not efficiently ripened cervix. Misoprostol probably has a large inter-patient variability in terms of pharmacokinetics, but it is also probable that the 50 mcg dosage may induce asynchrony between immature cervix effacement and uterine contractions, resulting in a more rapid but also more "stressful" labor. Based on these findings, we would propose, in future, a slight modification of the misoprostol protocol used in this study. An initial lower dose of misoprostol (20–25 mcg), followed by 50 mcg should be considered in trying to achieve priming of the cervix without inducing such high uterine contractility and neonatal complications. Indeed, in a recent study comparing 25 mcg misoprostol with 1 mg dinoprostone administered vaginally every four hours, the admission rate to neonatal intensive unit was significantly lower in the misoprostol group [34].
It still has to be mentioned that in many of our participants, the vertex was not engaged in the pelvic inlet on the day of admittance and this should have been included as an independent risk factor in the initial study design. The exact cause of the stillbirth in the dinoprostone group remains unclear, emphasizing thus, the need for continuous FHR monitoring during labor induction if regular uterine contractions persist [35,36].
Conclusions
To conclude, 50 mcg misoprostol at a 9 h interval is more highly effective in promoting cervical ripening and in inducing labor, compared to dinoprostone. However, certain aspects concerning fetal well being during labor induction remain questionable. Larger prospective studies comparing elective induction to expectant management after a completed 40-week gestation (on the basis of early ultrasound biometry) might reveal a subgroup of women, such as nulliparous with an unfavorable cervix, who might benefit from an elective induction, preferably with a 25 mcg misoprostol initial dose.
Authors' contributions
E.P: conceived of the study, participated in the sequence alignment, performed the statistical analysis and drafted the manuscript.
N.P: conceived of the study, and performed the labor inductions
A.D: performed the neonatal examination and follow up
S.A: performed the neonatal examination and participated in the neonatal data analysis
C.V: was the midwife involved in women allocation, medications preparation and labour data registration
T.S: conceived of the study and data analysis
E.P: conceived of the study and coordinated the study
K.Z: conceived of the study, performed the labor inductions and coordinated the study
Acknowledgements
The authors would like to thank all of the patients who participated in the trial, the midwifes in the antenatal clinic and the labor ward and the doctors and nurses in the neonatal intensive care unit, whose involvement made this study possible.
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| 15450119 | PMC524504 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Sep 27; 2:70 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-70 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-721547390710.1186/1477-7827-2-72ResearchLeptin receptor in the chicken ovary: potential involvement in ovarian dysfunction of ad libitum-fed broiler breeder hens Cassy Sandrine [email protected] Sonia [email protected] Sabine [email protected] Nicole [email protected] Anne [email protected] Sophie [email protected] Institut National de la Recherche Agronomique, Station de Recherches Avicoles, 37380 Nouzilly, France2004 8 10 2004 2 72 72 29 6 2004 8 10 2004 Copyright © 2004 Cassy et al; licensee BioMed Central Ltd.2004Cassy 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.
In hens, the ovarian follicles committed to ovulation are arranged in an ordered follicular hierarchy. In standard broiler breeders hens genetically selected for high growth rate the reproductive function is clearly dysfunctional. Feed restriction is needed during reproductive development to limit the formation of excessive numbers of ovarian yellow follicles arranged in multiple hierarchies. To determine whether leptin is involved in the nutritional and reproductive interactions controlling follicular hierarchy in hens, blood leptin levels and ovarian expression of the leptin receptor mRNA were determined during follicle maturation in three chicken lines; a slow growing broiler "Label" genotype without reproductive dysfunction, a fast growing "Standard" genotype fed ad libitum or restricted and a fast growing "Experimental" line with intermediate reproductive performance levels. Whereas expression of the leptin receptor mRNA did not change in the theca, it clearly decreased with follicular differentiation in the granulosa of slow growing hens. In fast growing standard hens fed ad libitum and presenting significant reproductive dysfunction, the decrease was disrupted and dramatic up-regulation of granulosa cell expression of the leptin receptor was observed. On the other hand, feed restriction decreased the overall level of expression of the leptin receptor mRNA and restored the decrease with follicular growth. The level of expression of the leptin receptor probably modulates the action of leptin on follicular differentiation. Since blood leptin and other metabolic factors were not affected by the genotype or by nutritional state, the factors involved in the regulation of leptin receptor gene expression remain to be determined. This study demonstrates the involvement of leptin in the nutritional control of reproduction in birds. Leptin action on the ovary probably controls follicular hierarchy through the regulation of steroidogenesis.
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Background
The ovary of the mature hen contains a hierarchy of yellow yolky follicles and several thousand smaller follicles from which the large yolky follicles are recruited. The yellow follicles are arranged in a size hierarchy and are committed to ovulation. In each follicle, the granulosa cells are surrounded by theca tissue and are separated from it by a basement membrane. Each compartment of the largest yellow follicles (theca and granulosa cells) can be anatomically separated to follow the individual functions of these two ovarian compartments in follicle growth and differentiation. At an early stage of follicular development the small ovarian follicles produce estrogens and androgens. As follicles begin to sequester yolk their production of estrogens from theca cells decreases to become very low at ovulation. As follicles are recruited into the yolky follicular hierarchy, estrogen and androgen production by theca cells diminishes and production of progesterone by granulosa cells increases. The largest F1 yellow follicle then attains the highest progesterone production at the time of ovulation.
In most domestic animals, reproductive function is considerably affected by nutrition [1,2]. Various hormones, including growth hormone, insulin-like growth factors (IGFs) and insulin, have been proposed as potential mediators affecting reproductive function [3]. However, the interactions between the reproductive endocrine axis and the metabolic axis have not been clearly determined. Leptin represents also a good candidate for such reproductive-metabolic interactions. Leptin, the protein hormone synthesized and secreted mainly by adipose tissue, has primarily been shown to regulate food intake and energy expenditure (for review see [4-6]). Recent studies have demonstrated that leptin may also be involved in the regulation of reproductive mechanisms in human and rat ovaries [7-10]. Exogenous leptin can rescue reproductive function in ob/ob leptin-deficient mice that are not only obese but also infertile. This leptin action is independent from weight loss since feed restriction in ob/ob female mice fails to restore fertility [11]. Leptin can also advance the onset of puberty or at least reverse the delay caused by feed restriction in rodents [12,13]. In chickens, leptin attenuates the negative effects of fasting on ovarian function. Injections of leptin during fasting delays cessation of egg laying, attenuates regression of yellow hierarchical follicles, alters ovarian steroidogenesis and limits apoptosis [14]. Leptin exerts its effect by binding to a receptor which belongs to the cytokine receptor super-family [15]. The chicken leptin receptor has been cloned and sequenced [16,17]. Its expression at the level of the ovary [14,17] suggests that leptin might act directly on the ovary to regulate chicken reproductive function.
Standard broiler breeders have been submitted to high selection pressure on growth and feed efficiency. Male traits have been favoured resulting in poorer reproductive performances of the hens. As a result of such selection, broiler breeder hens are subject to metabolic disorders and reproductive dysfunction. Overfeeding during reproductive development is associated with the formation of excessive numbers of ovarian yellow follicles which can be arranged in multiple hierarchies, with increased production of unsettable eggs [18-21]. Severe feed restriction during rearing reduces the production of yellow follicles, the incidence of double ovulation and considerably improves the laying rate [19]. Up-regulation of yellow follicles has been related to excessive recruitment and rapid growth rate of follicles to maturity, especially under ad libitum feeding [19]. However, the mechanisms that regulate these processes are still not fully explained.
This study investigated the potential involvement of leptin and its receptor in ovarian abnormalities observed in broiler breeder hens fed ad libitum. We aimed to determine the evolution of leptin receptor gene expression in both granulosa and theca cells from the four largest yellow follicles of 32 week-old hens during the laying period. The effects of genotype and diet on plasma leptin levels and ovarian expression of the leptin receptor gene were also quantified. For this purpose standard broiler breeder hens fed ad libitum or feed-restricted were compared to a French "Label" genotype and a dwarf "Experimental" line. Compared to fast growing standard broiler breeders, the French "Label" is a dwarf, slow-growing broiler genotype and the "Experimental" line is a dwarf genotype with a growth potential of progeny chicks close to that of the standard broiler chicks. The "Experimental" line is specifically selected for reproductive traits and viability at partial expenses of growth performances. The "Label" line tolerates ad libitum feeding of breeders and does not have reproductive problems under ad libitum feeding whereas the «Experimental» line can be fed ad libitum on a low energy diet during the growing period but presents more reproductive problems than the Label genotype but less than the standard genotype fed ad libitum.
Methods
Animals
Three lines of broiler breeder hens supplied by Hubbard primary breeder (Chateaubourg, France) were used in this experiment. The S line is a standard fast growing broiler, the French Label (L) line is a slow growing broiler breeder strain used for the quality market and the experimental (E) line is a broiler breeder strain bearing the "dw" dwarf gene. This strain has a decreased need for rationing. S and L hens were given the same regime in accordance with Hubbard nutritional recommendations (2724 kcal/kg) in fine meal form. During the growing period (0–20 weeks), half of the S hens were feed restricted (SR) on the same diet in order to match a reference body weight curve provided par Hubbard primary breeder, the other half (SA) and all the L hens were fed ad libitum. The feed intake was equivalent to 37% of the SA group up to point of lay [22]. A special diet was designed for the E hens, consisting of a series of finely ground meal diets with a lower energy content (2550 kcal/kg). The interest of the E group is mainly practical, it represents an actual alternative for severely restricted standard broiler breeder hens [22]. Transition between the grower and breeder feed occurred at 20 weeks of age for the ad libitum-fed hens (L, SA and E). Transition occurred at the beginning of laying for the restricted hens that were then allowed ad libitum access to food.
At 32 weeks of age, blood samples were collected from 12 hens of each experimental group (SA, SR, E and L) and six hens were sacrificed by an overdose of pentobarbital (Sanofi-Santé Animale, Libourne, France). The ovaries and liver were immediately removed. Granula and theca compartments from the first (F1), second (F2), third (F3) and fourth (F4) largest ovarian yellow follicles were dissected as previously described [23]. Since SA birds presented a greater average number of yellow follicles per ovary (9.36, 8,00, 7.42, and 6.33 yellow follicles/ovary for the SA, SR, E and L hens respectively) and a higher proportion of pairs of yellow ovarian follicles undergoing simultaneous development [22], follicles were assigned to the same follicular rank when the difference of weight between two follicles was of 0.4 g or less. In that case, one follicle per pair was collected and dissected. Tissues were immediately snap frozen in liquid nitrogen and stored at -80°C until used for total RNA extraction. This experiment was carried out with due regard to the legislation governing ethical treatment of animals, and investigators were certificated by the French government to carry out animal experiments.
RNA extraction and leptin receptor RT-PCR
Total RNA was extracted from liver, granulosa and theca cells using RNA InstaPure (Eurogentec, Angers, France) according to the manufacturer's recommendations. After DNAse treatment using Ambion's DNA-free kit (Clinisciences, Montrouge, France), 2 μg of total RNA were reverse-transcribed (RT) in a final volume of 20 μl using RNAse H- MMLV reverse transcriptase (Superscript II, Invitrogen, Cergy Pontoise, France) and random hexamer primers (Promega, Charbonnières, France). cDNA was then diluted to 1:8.
For normal PCR amplification, five microliters of the RT reaction were amplified for 35 cycles in a 50 μl reaction volume containing 2.5 units of Taq DNA Polymerase (Amersham Biosciences, Orsay, France), 2.5 mM MgCl2, 0.2 mM dNTPs (Promega, Charbonnieres, France) and 0.2 μM of each forward and reverse primer. Leptin receptor forward (5'-GTC CAC GAG ATT CAT CCC AG-3') and reverse (5'-CCT GAG ATG CAG AGA TGC TC-3') primers were chosen according to the previously determined sequence of the chicken leptin receptor cDNA [17]. This pair of primers amplifies a 271 bp cDNA fragment located in the coding sequence of the extra-cellular domain. The amplification conditions were as follows: denaturation at 94°C for 30 sec, annealing at 60°C for 30 sec and primer extension at 72°C for 60 sec. After final extension at 72°C for 10 min, PCR products were resolved on 1.5% agarose gel containing ethidium bromide.
Real-time RT-PCR
Real-time RT-PCR was performed as previously described [24]. Briefly, forward leptin receptor primer 5'-GCATCTCTGCATCTCAGGAAAGA-3' and reverse leptin receptor primer 5'-GCAGGCTACAAACTAACAAATCCA-3'(nucleotides 362 to 448 of the chicken leptin receptor cDNA sequence) [16,17] were designed to be intron-spanning to avoid co-amplification of genomic DNA using Primer Express Software (Applied Biosystems, Courtaboeuf, France). A 20 μl master mix containing 12.5 μl SYBR Green PCR Master Mix, 1 μl forward primer (300 nM), 1 μl reverse primer (300 nM) and 5.5 μl water was prepared to perform real-time PCR (Applied Biosystems, Courtaboeuf, France). Five microliters of cDNA dilution was added to the PCR Master Mix to a final volume of 25 μl. The following PCR protocol was used on the ABI Prism 7000 apparatus (Applied Biosystems, Courtaboeuf, France): initial denaturation (10 min at 95°C), followed by a two-step amplification program (15 sec at 95°C, followed by 1 min at 60°C) repeated 40 times. Quantification was performed using ABI integrated software as previously described [24]. 18S ribosomal RNA was chosen as the reference gene. The level of 18S RNA was determined using the Pre-developed TaqMan Ribosomal RNA control kit (Applied Biosystems, Courtaboeuf, France) according to the manufacturer's recommendations. The results were expressed as the leptin receptor mRNA/18S RNA ratio. Each PCR run included a no template control and replicates of control and unknown samples. Runs were performed in triplicate.
Plasma lipid, glucose and hormone analyses
Total cholesterol, phospholipid, and triglyceride plasma concentrations were calculated with "Cholesterol RTU", "Phospholipides Enzymatique PAP 150", and "Triglycérides Enzymatique PAP 150" kits (bioMérieux, Charbonnières les Bains, France) according to the manufacturer's recommendations. Plasma glucose levels were measured by the glucose oxidase method using an automated analyzer. Plasma insulin levels were determined by a radioimmunoassay with a guinea pig anti-porcine insulin antibody using chicken insulin as the standard [25]. Plasma concentrations of leptin were determined by a multi-species leptin RIA kit (LINCO Research Inc, CliniSciences, Montrouge, France) according to the recommendations of the manufacturer.
Statistical analysis
The results of plasma lipid, glucose and hormone analyses as well as leptin receptor mRNA expression in the liver were analyzed by one-way ANOVA and means were compared by Student Newman Keuls multiple comparison test. The effects of the groups of birds (E, L SA and SR), follicular rank and possible interaction on the logarithm of leptin receptor mRNA levels were tested by two-way ANOVA using the General Linear Model (GLM) procedure of SAS (SAS Institute, 1999. SAS User' Guide, Version 8 ed. SAS Institute Inc., Cary, NC). An additional effect of the subject was introduced into the model in order to take into account the fact that measurements of leptin receptor mRNA expression for the different follicular ranks were performed on the same animal. Pairwise comparisons of means for each significant effect of the ANOVA were performed by Scheffe test with the least means squares statement of the GLM procedure. The level of significance was set at P < 0.05.
Results
Leptin receptor mRNA expression in granulosa and theca cells
We demonstrated the expression of leptin receptor mRNA in granulosa and theca cells from the three genotypes fed ad libitum or restricted for the S line. The expression of leptin receptor mRNA for both ovarian cells was detected in each hierarchical yellow follicle studied (F1 to F4) (Figure 1).
Figure 1 Amplification by RT-PCR of leptin receptor mRNA (271 bp) in granulosa and theca cells of the four largest yellow follicles (F1, F2, F3 and F4) inbroiler breeder hens from a dwarf Experimental line (E), a French Label line (L) and a Standard line fed ad libitum (SA) or restricted (SR).
Evolution of leptin receptor mRNA expression with follicular development
The evolution of expression of leptin receptor mRNA during follicle development was investigated in both granulosa and theca cells from F4 to F1 yellow follicles using real-time RT-PCR. Since leptin receptor mRNA levels did not follow a normal distribution (skewness of 5.48 and Kurtosis of 32.88), they were log transformed. The resulting distribution was closer to the normality with skewness of 0.87 and kurtosis of 0.69. Variance analysis was performed on transformed data.
As shown in Table 1 and Figure 2, expression of leptin receptor mRNA in granulosa cells from the L genotype clearly decreased as the follicle developed. Significant statistical differences were measured between hierarchical yellow follicles at each stage. In the E line, expression of the leptin receptor mRNA decreased between F4 and F1 yellow follicles. However the high variability of the expression of leptin receptor mRNA in F4 follicles prevented the decrease from reaching statistical significance. Compared to the L line, the level of expression of the leptin receptor in the granulosa cells was lower in the E group, especially for the F4 and F3 yellow follicles but statistical significance was reached only for the F4 follicles (Table 1). In the S line fed ad libitum, expression of the leptin receptor in the granulosa was dramatically up-regulated. This up-regulation was clearly evident in F4 F3 and F1 follicles. Wide variability was also observed in F4 and F3 follicles. Feed restriction of the standard hens (SR) induced a general decrease in the expression of leptin receptor mRNA. The overall level of expression of the leptin receptor mRNA measured in the SR line was similar to that observed in the L and E lines. Compared to the SA birds, feed restriction has restored the decrease in the expression of leptin receptor mRNA with follicle development.
Table 1 Leptin receptor mRNA levels normalized to the level of 18S rRNA (expressed as arbitrary units) in the granulosa of ovarian F1, F2, F3 and F4 yellow follicles.
E L SA SR ANOVA
F1 18.63 ± 5.26a 16.34 ± 2.26a 219.01 ± 72.23b 24.73 ± 4.20a P < 0.0001
F2 34.60 ± 6.18a 32.78 ± 4.73a 11.57 ± 4.31a 26.89 ± 3.89a NS
F3 29.78 ± 3.43a 92.80 ± 18.25a 1062.90 ± 580.82b 71.31 ± 20.92a P < 0.001
F4 91 ± 48.85a 290 ± 57.93b 844 ± 420.56b 135.22 ± 32.26a P < 0.01
Data are expressed as mean ± SEM (n = 6). Data with different letters are significantly different between groups.
Figure 2 Relative levels of leptin receptor mRNA assessed by real time RT-PCR in the granulosa cells of the four largest yellow follicles (F1, F2, F3 and F4) in broiler breeder hens from a dwarf Experimental line (E), a French Label line (L) and a Standard line fed ad libitum (SA) or restricted (SR). The results were corrected by the corresponding levels of 18S rRNA. Data are expressed as mean ± SEM, n = 6. Bars with different letters are significantly different within groups (P < 0.05).
Expression of leptin receptor mRNA in the theca cells remained stable during yellow follicle development, whatever the group of birds considered. Statistical analysis did not reveal any difference in leptin receptor mRNA expression between the 4 groups of birds (Figure 3).
Figure 3 Relative levels of leptin receptor mRNA assessed by real time RT-PCR in the theca cells of the four largest yellow follicles (F1, F2, F3 and F4) in broiler breeder hens from a dwarf Experimental line (E), a French Label line (L) and a Standard line fed ad libitum (SA) or restricted (SR). The results were corrected by the corresponding levels of 18S rRNA. Data are expressed as mean ± SEM, n = 6.
Expression of leptin receptor mRNA in the liver
In the liver, the expression of leptin receptor mRNA was up-regulated in S birds fed ad libitum. Feed restriction of S birds restored the level of expression of leptin receptor mRNA similar to that measured in the E and L birds (Figure 4).
Figure 4 Relative levels of leptin receptor mRNA assessed by real time RT-PCR in the livers of broiler breeder hens from a dwarf Experimental line (E), a French Label line (L) and a Standard line fed ad libitum (SA) or restricted (SR). The results were corrected by the corresponding levels of 18S rRNA. Data are expressed as mean ± SEM, n = 6. Bars with different letters are significantly different (P < 0.05).
Plasma lipid and glucose and hormone concentrations
At 32 weeks of age plasma leptin and insulin concentrations were found to be similar in the three genotypes. Food restriction of the standard hens did not alter plasma leptin, glucose or insulin levels (Table 2). Triglyceride, cholesterol and phospholipid levels were also measured. Cholesterol and phospholipid levels were not affected by genotype or diet. On the other hand, triglyceride levels seemed to be affected by the genotype. Lower triglycerides levels were measured in Standard birds (SA, SR). However, statistical significance was reached only for the restricted SR birds.
Table 2 Lipid and hormone plasma levels.
E L SA SR
Leptin (ng/ml) 1.68 ± 0.17 2.08 ± 0.23 2.06 ± 0.19 1.97 ± 0.21
Insulin (pmol/ml) 118.6 ± 17.9 101.1 ± 12.0 129.7 ± 29.7 128.3 ± 16.3
Glucose (mmol/l) 10.99 ± 0.39 10.60 ± 0.22 11.10 ± 0.22 10.71 ± 0.33
Triglycerides (mmol/l) 31.36 ± 1.89 33.48 ± 1.63 25.01 ± 3.41 19.15 ± 3.34*
Phospholipids (mmol/l) 12.10 ± 0.29 12.75 ± 0.47 10.53 ± 1.14 11.04 ± 1.24
Cholesterol (mmol/l) 9.16 ± 0.39 9.48 ± 0.40 8.40 ± 0.63 9.07 ± 0.77
Data are expressed as mean ± SEM (n = 12). * indicates statistical difference (P < 0.05)
Discusssion
Several studies conducted on theca and granulosa cells have shown that leptin may have direct negative effects on ovarian steroidogenesis in various mammalian species. Leptin inhibits insulin-induced progesterone and 17β-estradiol production by isolated bovine granulosa cells [26] and impairs the hormonally-stimulated in vitro release of 17β-estradiol by rat granulosa cells [27]. In granulosa cells from fertile women, leptin inhibits FSH and IGF-I stimulated estradiol production [28,29] Since leptin has a more potent inhibitory action of insulin-induced aromatase activity of granulosa cells from small than large follicles, it has been proposed that the numbers of leptin receptors in granulosa cells might decrease as follicles develop in order to make mature Graafian follicles less sensitive to the negative action of leptin [26,30]. As shown in this study and in previous reports [14,17], the leptin receptor was expressed in the hen ovary in both granulosa and theca cells, suggesting a direct action of leptin at the level of the ovary. It seemed that leptin might affect ovarian steroidogenesis in laying hens during fasting [14] but the involvement of leptin on steroidogenesis during normal follicle development remained to be determined. In this study we demonstrated that the direct action of leptin on the ovary might be modified during follicle development since the level of expression of its receptor clearly decreased during maturation of yellow follicles. This decrease was particularly evident in slow growing broiler breeder hens from the "Label" genotype and from the feed-restricted standard line. Given that fast growing chickens (ad libitum-fed standard and Experimental broiler breeder hens) have the highest reproductive problems, genetic or nutritional control of the growth rate might regulate ovarian leptin receptor gene expression and improve reproductive function. Such evolution of receptor expression in the follicular hierarchy has previously been shown for the FSH receptor. FSH-stimulated steroidogenesis declined during follicle maturation and was associated with a decrease in FSH receptor numbers [31]. Conversely, the expression of mRNA encoding the IGF-I receptor and the related efficacy of binding of IGF-I to granulosa cells increased as the follicle matured [32,33]. Since leptin receptor gene expression was modified during follicle development, leptin might also be involved in regulation of the follicular hierarchy and onset of preovulatory steroidogenesis, as has been proposed for gonadotrophins and growth factors including FSH and IGF-I.
Unlike mammals, progesterone in chickens is synthesized and secreted mainly by granulosa cells whereas theca cells generate estradiol [34]. Progesterone produced by granulosa cells from mature follicles provides the positive feedback necessary to stimulate a preovulatory surge of LH [35]. IGF-I has been involved in the regulation of ovarian steroidogenesis in both mammals and birds. IGF-I stimulates progesterone production from avian granulosa cells [36] whereas it up-regulates estradiol from mammalian granulosa cells [27-29]. Since leptin is considered to be an inhibitor of insulin and IGF-I action on steroidogenesis in mammals, leptin might have similar negative action in birds. Thus, the decrease in its receptor in the granulosa suggests that the inhibiting action of leptin would decrease during follicle development and consequently favours the stimulatory effect of gonadotrophins and IGF-I on follicular maturation. This hypothesis is also consistent with the weaker steroidogenic response of granulosa cell culture of ad-libitum fed standard broiler breeder hens when stimulated by IGF-I compared to granulosa cell culture from feed-restricted birds [37]. Moreover, Onagbesan et al (2004) demonstrated in an experiment similar to that performed in the present study and using the same genotypes that plasma progesterone levels were clearly affected in SA birds. They demonstrated that plasma progesterone levels remained relatively stable between 25 and 37 weeks of age in the E, L and SA birds with a significant lower level in the SA birds (2.2 ± 0.62 ng/ml for SA birds compared to 3.9 ± 0.36 and 4.2 ± 0.54 for L and E birds respectively). In restricted standard birds, plasma progesterone levels dramatically increased and reached values (3.8 ± 0.26 ng/ml) closed to that measured in the E and L lines (Onagbesan et al., 2004, data from progesterone levels were personal communication from Dr Onagbesan, Catholic University of Leuven, Belgium).
The erratic pattern of oviposition in standard broiler breeder hens fed ad libitum has been previously demonstrated to be related to abnormal maturation of steroidogenesis, particularly in the two largest yellow follicles [35]. Since F2 and F1 yellow follicles presented similar endocrine profiles, the preovulatory surge of LH probably triggers ovulation of the two largest follicles [21,36]. In this study we have shown that ad libitum feeding of broiler breeder hens dramatically up-regulated expression of the leptin receptor in the granulosa cells of yellow follicles and changed the evolution of expression of this receptor with follicle development. These results suggest a strong action of leptin on the ovaries of ad libitum fed birds. Feed restriction reduced the level of expression of the leptin receptor and on the whole restored the evolution of expression of the receptor with follicle maturation. Since ad libitum feeding affects the hierarchical endocrine order of the follicles, as a potential inhibitor of hormonally induced avian steroidogenesis leptin represented a good candidate to explain the affects of follicular hierarchy. Up-regulation of the expression of the leptin receptor gene was also demonstrated in the liver. This up-regulation may be related to the control of lipogenesis. The liver plays a key role in lipid metabolism and lipogenesis in avian species [38,39] and the standard broiler breeder hens were the fattest birds of this experiment.
Since expression of its receptor was dramatically up-regulated in SA hens, leptin probably played an important role in the increased number of large yellow follicles and abnormal follicle hierarchy. However the factors involved in regulation of the expression of the leptin receptor within the hen ovary remains to be determined. Among the plasma hormones and lipids analyzed in this study only triglycerides were found to be different between strains, with a lower level in the restricted standard broiler breeder hens that were also the leanest birds. Down regulation of the expression of the leptin receptor by homologous and heterologous signals have previously been demonstrated in both mammals [40,41] and chickens [24]. Leptin and insulin are able to down-regulate expression of the chicken leptin receptor in vitro. In the present study, plasma leptin and insulin levels were similar for each genotype and were not altered by feed restriction in the standard genotype. We have previously demonstrated that during the first 5 weeks of age, plasma leptin levels remained relatively stable in both broiler and layer chicken despite increased body weight [42]. However the absence of leptin levels differences may be related to the fact plasma leptin levels were measured at 32 weeks of age. SR birds were relaxed at the start of lay, switched to breeding feeding and allowed ad libitum access as the other groups of birds. We therefore suggested that leptin and insulin are probably not involved in the regulation of ovarian leptin receptor gene expression in ad libitum or feed-restricted standard broiler breeder hens.
Evidence of the regulation of expression of the leptin receptor gene in the granulosa related to follicle maturation and nutritional state strongly suggest that leptin played an important local and sequential role in the dysfunction of the follicular hierarchy observed in standard broiler breeder hens fed ad libitum. This study suggests that the level of expression of the leptin receptor regulates the action of its ligand at the level of the ovary. This provides an interesting perspective to understanding the physiological role of leptin in the ovary.
Acknowledgements
This work was supported by the "5th Framework Program Grant" from the European Community (QLRT-2000-1732). We thank Dr Elisabeth Duval (INRA, Nouzilly, France) for statistical analysis of the data and Dr Michel Duclos (INRA, Nouzilly, France) for critical reading of the manuscript.
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| 15473907 | PMC524505 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Oct 8; 2:72 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-72 | oa_comm |
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BMC GeriatrBMC Geriatrics1471-2318BioMed Central London 1471-2318-4-81545652110.1186/1471-2318-4-8Research ArticleModerate exercise may attenuate some aspects of immunosenescence Drela Nadzieja [email protected] Ewa [email protected] Piotr [email protected] Department of Immunology, Warsaw University, Warsaw, Poland2 Department of Recreation, Academy of Physical Education, Warsaw, Poland3 Department of Sports Medicine, Academy of Physical Education, Warsaw, Poland2004 29 9 2004 4 8 8 20 2 2004 29 9 2004 Copyright © 2004 Drela et al; licensee BioMed Central Ltd.2004Drela 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
Immunosenescence is related to the deterioration of many immune functions, which may be manifested in increased susceptibility to infection, cancer, and autoimmunity. Lifestyle factors, such as diet or physical activity, may influence the senescence of the immune system. It is widely accepted that moderate physical activity may cause beneficial effects for physical and psychological health as well as for the immune system activity in aged people.
Methods
Thirty elderly women aged 62 to 86 were subjected to a two-years authorized physical activity program. Peripheral blood lymphocytes distribution and the production of cytokines involved in the immune response development and regulation (IL-2, IL-4 and IFN-γ) were investigated. The same parameters were evaluated in two control groups of women: a sedentary group of 12 elderly women selected for the second round of the physical activity program and in a group of 20 sedentary young women. Flow cytometry methods were used for the examination of surface markers on peripheral blood lymphocytes and intracellular cytokines expression.
Results
The distribution of the main lymphocytes subpopulations in the peripheral blood of elderly women did not show changes after long-term moderate physical training. The percentage of lymphocytes expressing intracellular IL-2 was higher in the group of women attending 2-years physical activity program than in the control group of elderly sedentary women, and it was similar to the value estimated in the group of young sedentary women. There was no difference in the intracellular expression of IL-4 and IFN-γ between the active and elderly sedentary women.
Conclusions
Our results suggest that moderate, long-term physical activity in elderly women may increase the production of IL-2, an important regulator of the immune response. This may help ameliorate immunosenescence in these women.
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Background
The decline in immune function associated with ageing increases the risk for infectious diseases, autoimmune disorders and tumors occurrence. The scientific interest of many laboratories is focused on the ageing of the immune system and on agents, which may retardate this process. The most frequently mentioned features of immunosenescence characterized by lymphocytes surface markers expression are: shift from naive CD45 RA+ to memory CD45 RO+ T cells, increased level of CD4+ (mainly in peripheral tissues), increased level of NK cells, and decreased level of CD8+ and B cells [1-4]. Changes in the distribution of T cell subpopulations in the blood are considered rather not significant (with the exception of naïve/memory T cells). However data are still inconsistent and may be caused by the criteria used to select healthy old individuals and by differences between human populations depending on race or geographical region [5-8].
Changes in cytokine production are manifested by the shift from Th1 to Th2-type cytokine production and increased level of proinflammatory cytokines (TNF-α, IL-1β, IL-6) [9-12]. The decrease of T cells responsiveness to in vitro stimulation may result from the reduction of IL-2 secretion and IL-2R α chain expression [13]. The most influenced by advancing age is the cell-mediated immunity, which is directly attributed to age-associated involution of thymus gland [14,15]. Recently, the interest of many groups and medical centers is focused on the benefits of regular exercise by older people. These benefits include enhanced cardiovascular fitness, retention of muscle mass, reduction of risk factors associated with many life-threatening diseases. There is still poor documentation of the possibility to alter the activity of the immune system as a consequence of exercise in older population. It has been shown that natural immunity is strongly influenced by physical exercise [16,17]. There is some evidence that the plasma levels of various, mainly pro-inflammatory cytokines increase in response to strenuous exercise [18]. A new area of research points on the relationship between certain lifestyle factors (diet, physical activity) and immune senescence. The group of 30 elderly women was selected to participate in the program of physical activity supervised by the Academy of Physical Education in Warsaw (Poland). The purpose of this program was to evaluate the modulation of physiological and psychological parameters by moderate long-term training. The beneficial results of moderate training on different body functions created the idea to check selected parameters, which characterize the activity of the immune system. Blood lymphocyte subpopulations distribution and intracellular expression of cytokines, which are central regulators of the immune responses (IL-2, IL-4 and IFN-γ), in activated blood lymphocytes have been examined. The aim of these investigations was to answer the main question related to the effect of exercise in older people: can long-term, moderate exercise, attenuate changes attributed to aged immune system? Comparative studies have been performed between the group of elderly exercising women and two control groups: elderly women selected for the new round of physical activity program and young sedentary women.
Methods
Characteristics of the group
The group investigated consisted of 30 women aged 62 to 86 (mean age 73.2) with no contraindication of consistent workout found. This group of surveyed women had never attended regular organized exercise classes previously. The group attended 2-years authorized physical activity program aimed at taking into account the basic requirements for health regarding prophylaxis of diseases of the circulatory and respiratory systems and the kinetic system. The exercises were done twice a week for 50 minutes during 10 months of the year. The annual cycle has been concluded with a two-week trip with a profile of recreation and tourism. Each workout session was similar in nature with equal time for each exercise. The initial part (10 minutes) consisted of exercises preparing the body for physical exertion simply called "warm up" preferred to be done in higher positions. The main part (30 minutes) of different intensity consisted of: a/ workout while marching and standing – aimed at improving the condition of the circulatory and respiratory system, b/ workout in low positions – in squat, on hands and knees, lying on your side, in prone position – which intended to strengthen the kinetic system. The exercises finished with a 5 minutes "cool down" and relaxation exercises done in low positions such as lying and squat positions. The general rate of exercises was stimulated with music in character and rhythm, which was adapted to the age and abilities of those who participated in exercises and it, was from 100 to 120 bpm. The intensity of exercises was regulated additionally by the number of repetitions of particular exercises and series, the integration of the particular movements into the entire unit or set, the choice and the method of performing breathing exercises. During the exercises the heart rate was monitored and registered by a Polar Sport Tester. The pulse rate was not higher than 80% of the maximum load of exertion (100% of maximum load of effort during exercises = 200-age, according to the Worms rule). The work rate intensity was 60–80% of the maximum load.
Constant observation of the attendants and systematic medical examination allowed the individual immediate modifications of the load of exertion while the women were exercising. Psychophysical self-control and self-evaluation had been installed among the participants since the very first workout session based on: heart rate (this was observed before, during and after exercising), frame of mind estimation, and subjective grading of the level of difficulty of the exercises according to the Borg Scale. Participants of the exercises had been under permanent medical control. No health contraindications for attending the exercises were found during the two years observation of the participants in the workout classes. The beneficial results of the workout program have been discovered in the research works concerning: 1/ motor activity competence, 2/ mobility of joints, 3/ physical efficiency 4/ body component, 5/ post-urography, 6/ nourishment, 7/ internal evaluation, 8/ laboratory works, 9/ orthopedics, 10/ bone density evaluation, 11/ psychology [19,20].
The analysis of peripheral blood lymphocyte subpopulations and expression of intracellular cytokines were performed after 2-years physical activity program. As a control we used the group of selected 12 women (aged 60–72) starting the new round of the program (Ctrl A), and the group of 20 sedentary young healthy women 20–40 years old (Ctrl B).
Preparation of cells
Blood was collected from donors at 08.30 – 10.00 am. PBMCs were isolated from freshly drawn heparinized blood by Lymphocyte Separation Medium (GIBCO BRL) centrifugation. Mononuclear cells from the interface were collected and washed with PBS.
Peripheral blood lymphocyte subpopulations distribution
Blood lymphocyte subpopulations were assessed by surface markers expression. For immunofluorescence staining of human lymphocytes the Lysed Whole Blood Method was used according to Becton-Dickinson protocol. Standard set of monoclonal antibodies against surface antigens was used: Simultest™ CD3/CD8, Simultest™ CD3/CD4, Simultest™ CD3/CD19, Simultest™ CD3/CD16CD56, Simultest™ Leucogate™ (CD45/CD14) and Simultest™ Control γ1/γ2a (all reagents from Becton-Dickinson). The results are given as % of positively stained cells in the sample.
Identification of lymphocytes expressing intracellular cytokines
PBMCs were suspended in RPMI 1640 medium with glutamax II supplemented with 1 mM sodium pyruvate, 5 × 10-5 M 2-mercaptoethanol, 20 mM HEPES and 5 mcg/mL gentamicin (GIBCO BRL). For the induction of cytokines synthesis peripheral lymphocytes were activated by PMA and calcium ionophore A 23187 (Sigma) at concentrations 50 ng/mL and 250 ng/mL, respectively. Approximately 3 × 106 cells in culture medium were placed in 24-well tissue culture plates and incubated with stimulants for 14 hours at 37°C, 5% CO2. Monensin was used as the protein transport inhibitor and was added to the cultures for the last 5 hours incubation. Completed the incubation, the cells were harvested and tested for viability by trypan blue exclusion. For intracellular cytokines staining the IC Screen™ Intracellular Staining Kits for human IL-2, IL-4 and IFN-γ were used (Biosource Int.). Intracellular staining was performed according to the Biosource protocol. Positively stained cells were analyzed by CellQuest™ software on FACSCalibur (Becton-Dickinson). Results are presented as the percentage of cells expressing intracellular cytokines in total lymphocytes population.
Statistical analysis
Data have been expressed as arithmetical mean and SD. Analysis of variance (ANOVA) was used to determine significant differences between control and experiments. Significance was determined at P < = 0.05.
Results
Phenotypes examination of peripheral blood lymphocytes
The percentages of lymphocytes subpopulations characteristic for exercised women were presented separately for 16 women up to 70 years old, and 14 exercised women over 70 (Fig. 1). The decrease in mature CD3+ T cells with age is not a continuous process. The percentage of CD3 + lymphocytes is rather constant until the seventh decade and decreases after this period (21, 22). The CD3 level was the purpose to present phenotypes distribution separately for women over/up 70. The distribution of the main lymphocyte subpopulations did not change among the groups. Typical changes, however not statistically significant, in lymphocyte subsets distribution in peripheral blood of women over 70 years old have been observed: decrease of the percentage of CD3+ and CD8+ cells, and increase in the percentage of NK cells (CD16+CD56+).
Figure 1 Distribution of peripheral blood lymphocyte subpopulations. Ctrl A – elderly women starting with the physical activity program. After the program (up to 70) – up to 70 years old women after the physical activity program. After the program (over 70) – over 70 years old women after the physical activity program
Expression of intracellular IL-2, IL-4 and IFN-γ
The percentages of in vitro activated peripheral blood lymphocytes expressing intracellular IL-2 of older exercised vs. young sedentary women were similar. Both percentages were significantly higher compared to the value obtained for older women starting the physical activity program (Fig. 2). The percentages of lymphocytes positive for IL-4 were significantly higher in older vs. young women independently on physical training. Changes in the percentages of intracellular IFN-γ expressing lymphocytes were not observed. Representative results are also presented on density plots (Fig. 3).
Figure 2 Intracellular cytokines expression in activated peripheral blood lymphocytes. Ctrl A – elderly women starting with the physical activity program. After the program – elderly exercised women. Ctrl B – young, sedentary women. Statistically significant data from Ctrl A are denoted by black stars. Statistically significant data from Ctrl B are denoted by black triangles.
Figure 3 Intracellular cytokines expression in activated peripheral blood lymphocytes. A/ Ctrl A – elderly women starting with the physical activity program, B/ elderly exercised women, C/ Ctrl B – sedentary, young women. FL2 – lymphocytes positive for intracellular cytokines. Lymphocytes with intracellular cytokine expression are situated in the upper left (UL) part of the quadrant.
The exercise intensity basing on maximum heart rate and Borg scale
The exercise intensity was evaluated basing on heart rate, assuming that 80% of effort possibilities of exercising individuals (acc. to the formula 100% = 200-age) were not exceeded. When the exercise pulse was measured during training session it was stated that the average value of HR max amounted to 105,5 +/- 6,7 bpm (90–116 bpm). At the same time the participants of training group evaluated their effort by the subjective method according to the Borg scale. The average value of this evaluation was 11,2 +/- 1,1 points (9–13 points) so it means that the participants estimated their effort as fairly light. According to Borg the results of the subjective evaluation (in points) multiplied by 10 should correspond with the heart rate measured during the effort. Thus, the exercise intensity evaluated basing on the average value of HR max (105,5 bpm) was similar to the subjective evaluation of an effort acc. to the Borg scale (11,2 points × 10 = 112 bpm).
Discussion
Moderate physical activity may have several beneficial effects for physical and psychological health and for immune system activity in young and aged individuals. Strenuously performed exercise may cause harmful effects. The balance between beneficial and undesirable effects might be of great importance especially in older people. Exercise may mobilize NK cells, which may have originated from the liver [23]. Moderate endurance training results in increasing capacity to generate IFN-γ, but repeated exhausting exercise tends rather to down regulation of this cytokine [24]. Results of experiments performed on animal model revealed that exercised vs. control rats had greater numbers of leukocytes in the thymus, axial, and inguinal nodes. The percentage of CD4+ lymphocytes increased after exercise in the thymus, spleen and blood [25]. Results of our study did not show changes in the peripheral blood lymphocyte subpopulations distribution in the group of elderly exercised women. Cytokines generate signals required for the communication among cells of the immune system. IL-2, IL-4 and IFN-γ are multifunctional cytokines involved in the development and effectors functions regulation of T, B and NK cells [26-28]. The results of our study demonstrated that the percentages of lymphocytes expressing intracellular IL-4 were higher in both groups of older women (exercised and Ctrl A) than in the group of young sedentary woman (Ctrl B). One possible explanation is the higher level of memory lymphocytes in elderly [29]. The percentage of IFN-γ positive cells did not differ significantly among groups. The percentage of lymphocytes expressing intracellular IL-2 in the group of exercised women is higher from the value obtained for older sedentary women and similar to the value characteristic for young women. Normally the level of IL-2 estimated by ELISA decreased with ageing [13]. Increase in IFN-γ production and decrease in IL-2 production have been observed in old mice [30]. IL-2 is a multifunctional cytokine essential for T cells development in the thymus and for growth in the periphery. It is involved in the maintenance of lymphocyte homeostasis [31]. IL-2 deficiency may lead/facilitate multiorgan inflammation and the formation of autoantibodies of various specificities [32]. The exercise-induced increase of the percentage of IL-2 expressing lymphocytes may sustain the activity of NK cells and the generation of cytotoxic lymphocytes. In elderly humans changes in T memory versus T naïve cells is accompanied by diminished activity in IL-2 synthesis and elevated production of IL-4 and IFN-γ. It may be possible that the effect of moderate physical activity modulate the reactivity of T cells and other cells of the immune system despite physiological changes related to ageing. Recent data demonstrate that in vitro treatment of lymphocytes from old subjects with IL-2 reversed the impaired production of type-1 cytokines, restored the proliferative response of T cells and rescued from increased apoptosis [33]. Thus the increase in the percentage of lymphocytes expressing intracellular IL-2 may normalize the immune function dependent on this cytokine. Adequate IL-2 secretion is essential in naive CD4+ T cells proliferation in response to T cell receptor (TCR) stimulation and generation of effectors from naïve T lymphocytes pool [34]. Additionally, an antigen-independent process of ageing of T cells occurs because of lowered IL-2 production [35]. Thus, increased IL-2 production as the effect of moderate exercise may contribute in the restoration of naïve T cells pool. The results of investigations performed in other laboratories showed that peripheral blood lymphocytes isolated from active elderly demonstrated higher proliferative response to polyclonal mitogens and higher rates of IL-2, IFN-γ, IL-4 production than elderly sedentary subjects [36]. Experiments on animal models showed that Con A-activated splenocytes from exercised mice produced higher rate of IL-2 [37]. The results of experiments performed with exercised old mice demonstrated the increase of the naïve to memory T cell ration [38].
Our results demonstrate that moderate fitness training may have the potential to increase immune reactions in vivo in elderly women by modulating the expression of cytokines, which normally are depressed with age.
Conclusions
Moderate, long-term physical activity may modulate the synthesis of certain cytokines, which are important regulators of the immune response, and may ameliorate the status of the immune system in senescence. This amelioration may result in an enhancement of quality of life of older people.
List of abbreviations
IL – interleukin
Competing interests
The authors declare that they have no competing interests.
Authors' Contributions
EK is the author of the physical activity program and conceived of the idea for the study. PS and ND designed the study. ND drafted the manuscript and analyzed the data related to the immune system. EK and PS described the physical training program and data related to Borg scale.
All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
This investigation was supported by the Academy of Physical Education in Warsaw, Poland, Project No. DS 20.3.
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| 15456521 | PMC524506 | CC BY | 2021-01-04 16:30:12 | no | BMC Geriatr. 2004 Sep 29; 4:8 | utf-8 | BMC Geriatr | 2,004 | 10.1186/1471-2318-4-8 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-721546961810.1186/1471-2407-4-72Research ArticleClassification between normal and tumor tissues based on the pair-wise gene expression ratio Yap YeeLeng [email protected] XueWu [email protected] MT [email protected] XiangHong [email protected] YC [email protected] Antoine [email protected] HKU-Pasteur Research Centre, Dexter H.C. Man Building, 8 Sassoon Road Pokfulam, HongKong, China2 Cancer Biology Laboratory, Department of Anatomy, Faculty of Medicine. The University of HongKong, China3 Central Laboratory of the Institute of Molecular Technology for Drug Discovery and Sythesis, The University of HongKong, China4 Institute Pasteur, Unité de Génétique des Génomes Bactériens, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France2004 7 10 2004 4 72 72 9 1 2004 7 10 2004 Copyright © 2004 Yap 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
Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking.
Method
Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers.
Results
82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio.
Conclusion
The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer.
HepsinAdipsinMonocyte-derived neutrophil-activating proteinProstate cancerColon cancer
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Background
Tumor development is a process in which gene expression is modified, causing abnormal cell behaviour [1]. Many techniques have been developed to identify abnormalities of gene expression, as reflected by abundance of mRNA transcripts between normal and tumor. The completion of the Human Genome Project and advances in DNA-array technology have allowed highly parallel genetic analyses to take place on a genome-wide scale. They have revolutionized the way tumors are studied, and promised to provide a better and more thorough understanding of the underlying mechanisms for tumorigenesis. Eventually, they will lead to more comprehensive diagnosis/prognosis of tumor with more effective therapeutic interventions.
Despite its advantages, the DNA-array technology poses three major challenges that render the interpretation of expression data less efficient than expected. Firstly, the gene expression data is inherently variable due to various factors that either depend on biological factors that remain difficult to control (cross-contaminated samples of tumor and normal cells), or depend on difficulties in setting up of the experiment (RNA extraction) [2]. These drawbacks interfere with the subsequent array analysis aimed to identify reliable markers that best correlate with the tissue phenotypes. Efforts have been devoted to address these drawbacks by incorporating various raw data scaling, data filtering, normalization and improvement of the classifier algorithm [3]. Promising results have been reported claiming near-perfect classification accuracy [4]. However, the usually small number of samples per class in most studies and the highly biased cross validation procedures cast doubt on the classification accuracy in terms of their statistical significance [5]. This statistical constraint creates a further challenge for DNA-array technology where the number of features in arrays is in thousands while tissue samples are available in limited number. This causes high probability for any classification to be correct by chance alone. Thirdly, although it has been recently established that genes segregate into clusters of interacting networks [6] instead of acting as one single entity, most cancer DNA-array studies have only investigated single gene aberration (up/down-regulated) when comparing tumor expression profiles to their corresponding normal tissue controls. In an interesting study, B∅ and Jonassen tried to circumvent some of these difficulties by investigating genes in pairs. They demonstrated that gene pairs can be used to improve discrimination between different tissue classes [7]. This idea of studying genes in pairs, or even in higher order clusters, should be explored further to reveal new features of complex expression profiling datasets.
In this study, we introduced a novel data transformation meant to investigate relationships between pair-wise gene expression ratios and tissue phenotype within a given experiment. With this procedure, we aimed to discover strong cancer-related signals (features) that exist in the form of pair-wise ratios (or higher order relationship when we extend to N-feature model classifier for N>2) in a given sample, while improving the signal to noise ratio of the dataset by minimizing its coefficient of variation (CV). The underlying concept for adopting pair-wise gene expression ratios as the discriminating axes for tissue type classification is that an experiment is self-consistent (in terms of factors affected either by the biology of the phenomenon of interest, or of the experimental setting, or both). With this approach we could "subtract" correlated variations by considering the sample as a whole, without making inferences such as those needed for normalization. Basically, we avoided studying gene expression in an absolute term because this requires robust normalization method to account for arrays from different experiments, different platforms and different profiling technologies. By resorting to analyze features in the form of ratios, we attempted to minimize the effect of normalization and look for co-varying signals in each experiment.
Methods
Colon and prostate cancer datasets
The 62 colon cancer sample dataset is composed of measurements for 1,988 gene probes, of which 40 were labelled as tumor and 22 were labelled as normal. The samples were collected from patients, their RNAs were extracted and hybridised to Affymetrix Hum6000 arrays. Please refer to paper [8]. The normalized dataset can be downloaded at .
The 102 prostate cancer sample dataset is composed of measurements for 12,600 gene probes, of which 52 were labelled as tumor and 50 were labelled as normal. The samples were collected from patients, their RNAs were extracted and hybridised to Affymetrix U95Av2 arrays. Please refer to paper [9]. The normalised dataset can be downloaded at .
Both datasets were pre-processed to eliminate those probe pairs that showed significant fluctuation in their hybridisation signals (those greater than 3 standard deviation away from the mean for their ESTs, and the probes pairs that showed an overall higher intensity in their mismatch probe cells (MM) than their corresponding perfect match probe cells (PM); these probe pairs indicate non-specific hybridisation by background RNAs). Both datasets used average intensity as quantitative measurements of the level of gene expression. Base-10 logarithmic transformations were performed for each dataset.
Initial gene selection
For downstream classification analysis, we extracted only the genes whose expression pattern correlated strongly to the tissue phenotype. To achieve this, we first calculated the correlation coefficient ri (Equation 1) for each gene i using the full dataset, and ranked the genes according to their correlation coefficient ri. For the calculation of r, we assigned a number to each tissue phenotype: 1 for normal tissue and 10 for cancer tissue. After obtaining the correlation coefficients for all genes, we used a simple threshold value (|r|>0.4) to select the set of cancer-related genes. There were two reasons for set the threshold value at 0.4. When lower thresholds were used, we incorporated many genes that were not known to be cancer-related (data not shown). Furthermore, too many genes will later cause computer tractability problem when we calculate their pair-wise gene expression ratio for each tissue sample and later the N-feature model classifier. At |r|>0.4, we were able to account for most of previously known cancer related genes.
where V1 is a vector representing the gene expression pattern for gene #1; Vsample is the dichotomous representation of tissues; SV1and Ssample standard deviation of V1, Vsample; , are the mean of V1, Vsample.
Transforming the gene expression data to investigate the expression equilibrium between genes pairs
The raw expression data within a sample tissue was transformed into measurement of the pair-wise gene expression ratio for any combinatorial pairs of genes. For the 1,988 gene expression intensities for each sample (e1, e2...e1988), there are 1988C2 combinations (e1/e2, e1/e3...) of pair-wise gene expression ratios (Figure 1). This transformed matrix is referred to as M. Each row/column corresponds to a specific gene and the entry at the intersection of row X and column Y corresponds to the expression equilibrium between gene X and gene Y. Such matrix has a diagonal entry of value 1 because e1/e1 equals to unity.
Feature partitioning method [4] for classification of normal/tumor tissues using single gene expression
Regarding the Feature Partitioning Method (FPM), in order to discriminate between the normal/tumor tissues based on specific feature i (single gene expression), the first step is to determine the threshold value, Ti, that can optimally splits all the tissue samples into tumor and normal tissue. The FPM algorithm has a recursive version [4], in which a decision tree depicting the classification rules for tissue samples was generated recursively. Both methods differ in the way Tis are derived. Nonetheless, they are very intuitive and non-parametric in nature. Also, they restrict no priori distribution patterns for features used. We adopted the simple FPM for tissue classification where each feature was treated individually. There are two criteria for deriving a valid threshold value Ti for each feature. First, it has to delineate correctly (discriminating efficiency = 100%) the one-dimensional region (Rfeature_i) for either all the normal/tumor tissues using all tissue samples. Secondly, it has to minimize the percentage of false prediction for the other tissue type. Take gene #1659 for example. To fulfill the two aforementioned criteria, it was determined that the region greater than 63.7 (R#1659) incorporates all the tumor samples (Figure 2). It classified correctly all tumors (discriminating efficiency = 100%) with an overall false prediction of 13.9% in the normal set. This was performed repeatedly for all features until all the threshold values (Ti...all features) were determined.
Now, to classify an unknown sample using 2-feature model classifier, a combination of any two features and their corresponding pre-determined threshold values Tis (selected from Ti...all features for each dataset) were recruited. The outcome of the tissue class will be determined depending on whether one/both the expression values of the unknown sample fall completely in either the normal/cancer region (Rfeature_i). This is to say that if any of the two features from the unknown sample meets the criteria (Rfeature_i) to be either normal/tumor tissue type (based on our definition, Rfeature_i is a region with 100% discriminating efficiency for a specific tissue type), the unknown sample will be assigned to be normal/tumor respectively. This is repeated exhaustively for all possible combinations constituting of any two features. The procedure will be repeated for all tissue samples to evaluate the overall classification accuracy for 2-feature model classifier. In total, we evaluated the classification of tissue samples based on different combinations of N genes and investigated the classifiers up to 10-feature model classifier.
Classification of normal/tumor tissues using transformed datasets
The classification procedures and the two criteria for determining the threshold value were the same as explained in previous paragraph. The only difference here is that the definition of "feature" refers to pair-wise gene expression ratio derived from lower/upper triangular matrix of M. Take the ratio #1537/#1831 for example. To fulfill the two aforementioned criteria, it was determined that the region greater than 0.755 (R#1537/#1831) incorporates all the tumor tissue samples (Figure 2). It classifies correctly all tumor tissue samples with a false prediction of 6.4%. This is performed repeatedly for all entries in M until all the threshold values are determined.
Now, to classify an unknown sample using 2-feature model classifier, a combination of any two features (pair-wise gene expression ratio) and their corresponding pre-determined threshold values Tis (selected from Ti...all features for each dataset) were recruited. The outcome of the tissue class will be determined depending on whether one/both the expression values of the unknown sample fall completely in either the normal or cancer region (Rfeature_i). This is to say that if any of the two features (pair-wise gene expression ratio) from the unknown sample meets the criteria (Rfeature_i) to be either normal/tumor (based on our definition, Rfeature_i is a region with 100% discriminating efficiency for a specific tissue type), the unknown sample will be assigned to be normal/tumor respectively. This is repeated exhaustively for all possible combinations constituting of two features. The procedure will be repeated for all tissue samples to evaluate the overall classification accuracy for 2-feature model classifier. In total, we evaluated the classification of tissue samples based on different combinations of N genes and investigated the classifiers up to 10-feature model classifier.
Constructing the relationship tree for the top 25 genes
We calculated the cross correlation coefficient r (Equation 1) for all pair combinations of the top 25 genes listed in Table 6 and Table 7. Prior to the construction of a relationship tree for the top 25 genes for colon and prostate cancer, the cross-correlation coefficient was used to construct the pair-wise distance matrix D. Each entry in the pair-wise distance matrix was measured by the value of (1-r). Each row/column corresponds to a specific gene and an entry at the intersection of row X and column Y corresponds to the distance of gene expression between gene #X and gene #Y. Such matrix has a diagonal entry of value 0. Only the lower/upper triangular matrix of D is required to construct the relationship tree. After obtaining lower/upper triangular matrix of D, the neighbor-joining method (NJ) algorithm was used to construct the relationship tree [10].
Computer hardware and software
A Sun Fire 6800 Server with 24 CPUs (each running with a clock speed of 900 MHz) was employed throughout this study. The computation of correlation coefficient and classification procedures were implemented using the Matlab Technical Programming language (Matlab programs can be downloaded at .
Results
After initial gene selection, respectively 82 and 262 genes (|r|>0.4) were selected from the colon and prostate dataset for downstream analysis (Table 1 and Table 2). Topping the list in both tables were genes that have been found to be either over-expressed/under-expressed in tumors [11]. The first three genes most correlated to cancer in the colon dataset were heavy chain of non-muscle myosin, human monocyte-derived neutrophil-activating protein (MONAP) and human desmin genes. This agrees with the findings from [12,13] that used other statistical tests (z-score, t-test) in a comparable analysis. The heavy chain of non-muscle myosin, denoted as the embryonic smooth muscle myosin heavy chain (SMemb), was found to be down-regulated in cancer. It was also determined experimentally to be a target for the protein encoded by the metastasis-related mts-1 gene [14]. Furthermore, it was demonstrated recently by 5'RACE analysis that heavy chain of non-muscle myosin interacts with ALK genes that have tyrosine kinase activity and oncogenic properties [15]. The human monocyte-derived neutrophil-activating protein (MONAP, interleukin-8), was second on the list. It was significantly up-regulated in the tumor compared to the normal samples. This protein has been linked to the progression of several human cancer types [16]. It was believed that over-expression of MONAP plays an important role in tumor angiogenesis and tumor aggression. The human desmin gene is the third on the list, and it was found to be down-regulated in tumor. Interestingly, this gene also showed significantly reduced expression in other cancer types such as the melanoma cell line [17].
From the prostate dataset, the most cancer-correlated gene is the human hepatoma gene coding for serine protease hepsin. Brief literature search in PubMed showed that hepsin is a well-characterized transmembrane protease that is expressed at high level in tumor. Three separate studies identified hepsin as a significant cancer biomarker that can be used for cancer diagnosis [18]. The second gene on the list was the human mitochondrial matrix protein P1. This gene has been correlated to different cancer types with consistent up-regulation in tumor [13]. The third gene is the carcinoma-associated antigen GA733-2, which was among the 216 cancer markers identified by Ernst's group in Germany [19].
Effect of data transformation on coefficient of variation
To date, reliable markers with low coefficient of variation (CV) are generally lacking. Discovering robust cancer marker is crucial for the purpose of successful cancer diagnosis. We investigated the CV between samples after data transformation: the lowest CVs decreased to 16.5% in the colon dataset while it increased to 25.8% for the prostate dataset (Table 3 and Table 4). Topping the list for both dataset were the pair-wise gene expression ratio for genes #119/#54 (elongation factor 1-delta and 40S ribosomal protein S24) and #10614/#5871 (zq58b03.r1 Homo sapiens cDNA and nuclear matrix protein NXP2), which revealed informative pair-wise gene interaction in relation with their corresponding tissue phenotypes. They reflected how cell adjusts to their pair-wise product in response to physiological changes. Based on these observations, we found that the relative abundance between the numerator and denominator exhibited a strong mutual dependency, and had strong correlation to tissue phenotype. For pair-wise gene expression ratio #119/#54, the elongation factor 1-delta is involved in a sequence of events during the decoding of mRNA on the ribosome [20]. For the ratio of #10614/#5871, it corresponds to novel genes that do not yet have known function. A search in the DNA non-redundant (nr) database for gene #10614 yielded 83% DNA identity to a segment on chromosome 9. On the other hand, a search in non-redundant (nr) database for #5871 revealed 72.3% DNA identity to the cDNA of mouse that incorporates proteins involved in chromosome partitioning and cell decision [21].
Prior to data transformation the lowest coefficients of variations for single gene expression were 45.3% and 24.5% for colon and prostate datasets respectively. When using the data transformation we proposed, significant improvement was achieved in the colon dataset. Interestingly, this was followed by an improved data correlation to the tissue phenotype as well as to the classification efficiency. We did not observe a similar improvement of the CV, data correlation to tissue classes or classification efficiency in the prostate dataset.
Correlations of the single gene expression and pair-wise gene expression ratio
The distribution of correlation coefficients between genes and tissue phenotypes for the colon and prostate datasets is shown in Figure 3. The distributions are positively and negatively skewed for both datasets. The two red lines separate genes with |r| >0.4 from the bulk (Table 1 and 2). They retained respectively 82 and 262 genes from the colon and prostate datasets. To study the possible interaction between pair-wise genes, we estimated the statistical correlation of gene expressions. Both the distributions for the correlation coefficient and the extreme cases are shown in Figures 4 and 5. Both figures emphasize the true nature of gene-gene co-regulations – a complex biological mechanism, that most often has been over-simplified when we treat the gene expression as an independent variables [22]. For example, Figure 4 and Figure 5 suggested that the expressions of genes belonging to a common subset are most likely correlated to each other (e.g.: Gene #31 vs #119 in colon cancer (r = 0.95306) and gene #7775 vs #10749 in prostate cancer (r = 0.92922)). It should be pointed out that the two humps in the probability density function are not zero-centered, but concentrated at non-zero correlation r. For colon dataset, positive correlation was the dominant type. For prostate dataset, a balanced distribution in their gene correlation was observed. We determined that some improvement in tissue classification is achieved when pair-wise gene expression ratio was used as discriminating axes instead of using a single gene expression (Figure 2). The reason is that pair-wise gene expression ratio has higher correlation to tissue phenotype with lower CV (Table 5).
Gene expression and tissue type correlation
Several previous studies have already endeavored to identify correlations between specific gene expression and cancerous transformation [4,13,23]. In the present study, we identified several novel target genes that clearly distinguish the two different tissue phenotypes with high discriminating efficiency (>74%) (Table 6 and Table 8). Some of those have previously been documented in studies that did not involve expression profiling as cancer related genes (Human monocyte-derived neutrophil-activating protein (MONAP) and Human hepatoma mRNA for serine protease hepsin), others (Human gene for heterogeneous nuclear ribonucleoprotein (hnRNP), P24480 CALGIZZARIN, Human mitochondrial matrix protein P1, Human mRNA for aldose reductase and human adipsin) have not been identified from in-silico studies of tissue DNA-array expression data. The cancer related genes for colon and prostate cancer were ranked according to their discriminating predictive power. The list should provide hints for researchers during selection of molecular target for diagnostic, prognostic or attempts to cure the disease. Overall classification results and accuracies for each N-feature model classifier across two datasets were reported in Table 6, 7 and 8. In the following section, we will discuss a few important genes or pair-wise gene expression ratios from Table 6 and Table 7 that resulted in the optimum classification accuracy (Table 8B). They are the most efficient combination of discriminating axes for classifying tissue types because they delineate correctly all the normal/tumor tissues with the lowest percentage of false prediction.
For the sake of brevity, we will discuss three single gene expressions and two pair-wise gene expression ratios from colon cancer. For prostate cancer, two single gene expressions and two pair-wise gene expression ratios will be discussed.
For colon cancer single gene expression, three axes for discriminating tissue types are: 1) Human monocyte-derived neutrophil-activating protein (MONAP); 2) Human desmin gene and 3) Human cysteine-rich protein (CRP) gene. Their threshold values were determined to be 62.73, 2787.0 and 749.4 respectively.
For colon cancer pair-wise gene expression ratio, the two axes for discriminating tissue types are: 1) #1831/#1537 and 2) #753/#768. Their threshold values were reported to be 1.32 and 1.85 respectively.
For prostate cancer individual gene expression, the two axes for discriminating tissue types are: 1) Human hepatoma mRNA for serine protease hepsin and 2) Human adipsin. Their threshold values were reported to be 115.0 and 182.0 respectively.
For prostate cancer pair-wise gene expression ratio, the two axes for discriminating tissue types are: 1) #6185/#5840 and 2) #6185/#6749. Their threshold values were reported to be 2.69 and 2.55 respectively.
To illustrate graphically the result of tissue classification, two examples, each based on three genes or pair-wise gene expression ratios that altogether yielded the optimum classification efficiency for the prostate cancer are shown (Figure 6, Figure 7).
Constructing the relationship tree for top 25 gene for colon and prostate cancer
The relationship tree for top 25 genes listed in Table 6 and Table 7 were constructed based on the cross-correlation between gene expressions (Figure 8). We employed the established 'neighbor-joining' clustering method [10] to group different genes based on their correlated expression patterns across all tissue samples (meaning that genes expression that are correlated will appear in the same branch of the clustering tree), using a novel distance measurement to quantify how change in the expression for one gene interfered with that of another gene. The principle of this method is to cluster pairs of operational taxonomic units (OTUs [=neighbors of similar gene expression]) that minimize the total branch length at each stage of clustering of OTUs starting with a star-like tree. Figure 8 revealed two major clusters of genes. The first cluster corresponded to down-regulated genes, the second cluster represented up-regulated genes. Also, the most efficient discriminating axes (feature genes) reside at the basal position for each cluster. In bacteria many genes are co-expressed as single transcription units. This was used as a control study to validate the methodology of grouping genes, we implemented this distance measurement on bacteria gene arrays (B. subtilis and E. coli) and successfully determined the co-regulated operon gene structures (supplementary file #1).
Discussion
Data transformation to investigate pair-wise gene expression ratios
As the expression profiling technologies mature, the identification of significant cancer-related signals from noisy datasets (characterized by a high CV) remains a major challenge. In particular, a robust normalization method is critical to ascertain that arrays from two experiments are comparable with minimum noise prior downstream analysis. However, the existing normalization methods pose limitations due to the lack of good models to account for sources of experimental and biological variations [24]. Hoffmann et al. [25] employed different normalization methods to analyse the same dataset, and demonstrated that the numbers of genes detected as differentially expressed differed by a huge factor depending on which normalization methods used. The problem is exacerbated further by the presence of different array formats, experimental designs and methods.
Here, instead of resolving to single gene expression, that depends heavily on normalization, for tissue classification, we presented a transformation method that uses pair-wise gene expression ratios within the same experiment as the discriminating axes. By doing so, we aimed to minimize the influence of different normalization methods considering that an experiment is self-consistent with the same factors affecting all genes in the same fashion. The rationale is that even when the normalization methods differ between two array experiments, their pair-wise gene expression ratios within the same experiment will remain relatively stable. If reliable cancer-related signal, exist in the form of pair-wise gene expression ratio, were indeed discovered successfully, they will be relatively independent from the normalization method used on a dataset.
The improvement in CV (Table 3) and overall classification accuracy (Table 7) for colon dataset after introduction of data transformation signifies two implications: First, the transformation is able to increase the signal to noise ratio (SNR) of the cancer related signal because the resulted pair-wise gene expression ratios correlate stronger to tissue phenotype. Second, because the pair-wise gene expression ratios are less dispersed than single gene expression, using the pair-wise gene expression ratios to classify tissue types will be much more reliable and accurate (Table 8). Despite the benefits mentioned, this data transformation introduced a computational limitation due to the enormous amount of feature combinations to be processed, especially when N-feature model classifiers for N>4 are considered (If 100 features are selected, and 10-feature model classifier is investigated, the search space will be 100C10= 1.731030945644000 × 1013 different combination of features). As a result, more computation time will be required to search all possibilities. As an example, the discriminating axes that accounted for the optimum accuracy in 1 to 3-feature model classifier are reported in Table 9.
Regarding the high classification accuracy reported in Table 8, it should be stressed that this was achieved by involving all tissue samples during the derivation of the threshold value, Ti, in the feature selection procedure. In other word, instead of adopting the more conservative classification accuracy test where only a subset of tissue samples are used to derive a set of classification criteria (threshold values), we adjusted our methodology to use all tissue samples so that our results are unbiased (when comparing the outcome from single gene and pair-wise gene ratio) and in-line with our objective that is to compare the classification efficiency between single gene and pair-wise gene ratio. Admittedly, we have a noisy dataset whereby selecting a subset of tissue samples that are a representable population for the entire dataset remains a challenge [5] (given that we have a small and unbalanced dataset, particularly the colon dataset). Eventually, we might run into ambiguous/contradicting results using a different population subset of tissue samples. Furthermore, we might miss important features (single gene expression/ pair-wise gene expression ratio) because of the biased training dataset. By including all tissue samples for both studies (single gene and pair-wise gene ratio), we aimed to derive the most reliable threshold values and classified tissue samples based on them. Since the same methodology was applied for both studies, the comparison of classification efficiency is valid and will reflect how well each feature (single gene and pair-wise gene ratio) can be used to delineate tissue samples.
The implication derived from the classification results
For colon dataset, three axes for discriminating tissues are: 1) Human monocyte-derived neutrophil-activating protein (MONAP); 2) Human desmin gene and 3) Human cysteine-rich protein (CRP) gene. The association of the first two genes and cancer biology had been discussed earlier. We will discuss the Human cysteine-rich protein gene. The expression and induction of this protein has been associated with protection against DNA damage, oxidative stress and apoptosis [26]. In the colon dataset, we observed down-regulation of this protein in tumor. This suggested lack of protection against DNA damage.
For colon cancer pair-wise gene expression ratio, the two axes for discriminating tissues are: 1) #1831/#1537 and 2) #753/#768. Using these two axes, 98.4% of the tissue samples can be classified correctly. The expression ratio between #1831 (gelsolin precursor) and #1537 (vascular endothelial growth factor) was able to discriminate 93.6% of the total tissue data. The vascular endothelial growth factor was determined recently to be a plausible biomarker for colon cancer [27]. Gelsolin had been found to suppress tumorigenicity in different cancer samples, including lung, bladder and breast [28]. When they were used individually as a discriminating axis, they were only able to classify correctly 66.1% and 67.7% of all tissue samples. Furthermore, the expression ratio between #753 (Human cysteine-rich protein) and #768 (the macrophage migration inhibitory factor) was able to discriminate 90.3% of total tissue type. The human cysteine-rich protein was discussed in the previous section. The macrophage migration inhibitory factor (MIF) functions as a pluripotent cytokine involved in broad-spectrum pathophysiological events in association with inflammation and immune responses. Several reports, including ours, have suggested that MIF is also involved in tumorigenesis [29]. When they were used individually as single discriminating axis, they were only able to classify correctly 83.9% and 66.1% of all tissues.
For prostate cancer single gene expression, the two axes for discriminating tissues are: 1) Human hepatoma mRNA for serine protease hepsin, and 2) Human adipsin. The first gene was discussed in the previous paragraph. For the second gene, adipsin had also been suggested by Chow et al. [30] as a good cancer marker for studying the basic biology of cancer.
For prostate cancer pair-wise gene expression ratio, the two axes for discriminating tissues are: 1) #6185/#5840 and 2) #6185/#6749. Using these two axes, all tissue samples can be classified correctly. The expression ratio between #6185 (Human hepatoma mRNA for serine protease hepsin) and #5840 (Homo sapiens mRNA for KIAA1109 protein) was able to discriminate 92.2% of total tissues. The human hepatoma mRNA for serine protease hepsin had been determined to be an important marker for cancer cell development [11,18]. The KIAA1109 protein is an unknown protein in human chromosome four [31]. A homology search against the non-redundant databases yielded no significant hit to known genes. When they were used individually as a discriminating axis, they were only able to classify correctly 86.3% and 61.8% of all tissues. On the other hand, the expression ratio between #6185 (Human hepatoma mRNA for serine protease hepsin) and #6749 (Homo sapiens mRNA for KIAA1055 protein) was able to discriminate 90.10% of total tissues. The human hepatoma mRNA for serine protease hepsin was discussed in the previous section. The KIAA1055 protein is an unknown protein in human chromosome 15 [21,31]. A homology search against the non-redundant databases yielded 40.7% DNA identity to a novel human cDNA that had been found to function as a cancer inhibiting protein [21]. When they were used individually as a discriminating axis, they were only able to classify correctly 86.3% and 62.8% of all tissues.
Conclusion
By comparing the tissue classification methods based on the single gene expression and the pair-wise gene expression ratio in two microarray datasets, we reached the following conclusions:
1. The minimum coefficient of variation decreased from 45.33% to 16.53% for colon dataset but increased marginally from 24.54% to 25.78% in prostate dataset.
2. The correlation coefficient, r, of the discriminating axis that correlates maximally to the tissue phenotype improves from 0.63 to 0.79 and 0.71 to 0.75 in colon and prostate dataset respectively.
3. The optimum accuracy for 1-feature model classifier (using single gene or pair-wise gene expression ratio as discriminating axis) improved from 87.1% to 93.55% in colon dataset. In prostate dataset, nine out of the top 10 discriminating axes showed significant improvement. The mean accuracy for 1-gene classifier improved from 76.8% to 91.2% and 75.8% to 81.9% in both datasets.
4. The comparable classification accuracy achieved after data transformation suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio, especially prominent in the colon dataset.
5. Through the single gene analysis, we identified key biomarkers that agree with the findings by other researchers. In addition, study on gene-to-gene correlation and the classification outcome based on the pair-wise gene expression ratio suggested that genetic network within a cluster of cancer-related genes should be explored further.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
YLY proposed the idea, participated in the design, performed the statistical analysis and wrote the first draft of the manuscript.
AD participated in the design and overall coordination of this study as well as in the writing of the manuscript.
XWZ participated in the design of the study.
YCW, XHW and MTL participated during the revision phase of this study.
All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Indispensable support was provided by the doctoral fellowship from The University of Hong Kong (HKU) and well as the Hong Kong Innovation and Technology Fund (ITF), BIOSUPPORT Programme. Finally, we wish to thank Dr Ralf Altmeyer for his critical interest for this work as he came at the head of the HKU-Pasteur Research Centre.
Figures and Tables
Figure 1 Transformation of gene expression data.
Figure 2 Potential colon cancer gene markers: The expression of single gene and the transformed pair-wise gene expression ratio. Potential gene marker for colon cancer tissue (#1659-Human monocyte-derived neutrophil-activating protein (MONAP) mRNA). However, we observed that the pair-wise gene expression ratio (#1537/#1831- ratio between vascular endothelial growth factor and gelsolin precursor) has better discriminating efficiency as tabulated in Table 7. ('*' and 'o' represent normal and cancer tissue type respectively).
Figure 3 The histogram for correlation of coefficient r between single gene expression and the tissue types for the colon and prostate tissue cancer. The distribution shows coefficient of correlation between single gene expression and cancer phenotype. Their extrema of correlation coefficient |r|>0.4 (represented in red lines) were extracted for downstream data analysis.
Figure 4 The distribution of cross-correlation between two single gene expression patterns in colon dataset. The distribution shows the coefficient of correlation between expression patterns for any pair of gene markers. Their extrema scenarios were also plotted with their corresponding r value.
Figure 5 The distribution of cross-correlation between two genes expression patterns in prostate dataset. The distribution shows coefficient of correlation between any pair of gene markers. Their extrema plots of correlation coefficient were also plotted with corresponding r value.
Figure 6 Prostate dataset: an example showing the projection of 102 tissue samples on the top three discriminating axes of the single gene expression patterns. The gene numbers are shown as the axis labels. The threshold values Ti for normal tissues on each axis are tabulated on Table 7.
Figure 7 Prostate dataset: an example showing the projection of 102 tissue samples on the top three discriminating axes of the pair-wise gene expression ratio. The gene numbers are shown as the axis labels. The threshold values Ti for normal tissues on each axis are tabulated on Table 7.
Figure 8 Inter-relationship of gene expression gene expression for top 25 prostate cancer genes extracted from Table 8. The tree structure was derived using neighbor-joining algorithm [10]. Two clusters of gene expression were observed, namely the up-regulated (#6185) and down – regulated (#8554) genes in cancer tissues.
Table 1 Colon cancer: the gene retained for classification of tissue types. This table contains the genes and their descriptions. The key genes are selected based on how correlated their average intensity to the normal and tumor tissues. The genes are placed in the order of descending correlation coefficient r. Ten key genes are reported, the complete table can be downloaded at . Entire data for the experiment can be downloaded from .
No. on array Gene accession number with correlation >0.4 to cancer tissue type Info Correlation
481 R87126 MYOSIN HEAVY CHAIN, NONMUSCLE (Gallus gallus) 0.6327
1659 M26383 Human monocyte-derived neutrophil-activating protein (MONAP) mRNA, complete cds. 0.5853
241 M63391 Human desmin gene, complete cds. 0.5848
1760 H08393 COLLAGEN ALPHA 2(XI) CHAIN (Homo sapiens) 0.5760
1030 R36977 P03001 TRANSCRIPTION FACTOR IIIA ;. 0.5741
1411 J02854 MYOSIN REGULATORY LIGHT CHAIN 2, SMOOTH MUSCLE ISOFORM (HUMAN);contains element TAR1 repetitive element 0.5680
1759 J05032 Human aspartyl-tRNA synthetase alpha-2 subunit mRNA, complete cds. 0.5670
613 X12671 Human gene for heterogeneous nuclear ribonucleoprotein (hnRNP) core protein A1. 0.5583
365 Z50753 H.sapiens mRNA for GCAP-II/uroguanylin precursor. 0.5494
753 M76378 Human cysteine-rich protein (CRP) gene, exons 5 and 6. 0.5354
Table 2 Prostate cancer: the key gene retained for classification of tissue types. This table contains the genes and their descriptions. The key genes are selected based on how correlated their average intensity to the normal and tumor tissues. The genes are placed in the order of descending correlation coefficient r. Ten key features were shown, the complete table can be downloaded at . Entire data for the experiment can be downloaded from .
No. on array Gene probe with correlation >0.4 to cancer tissue type Info Correlation
6185 37639_at Cluster Incl. X07732:Human hepatoma mRNA for serine protease hepsin 0.7119
8965 37720_at Cluster Incl. M22382:Human mitochondrial matrix protein P1 (nuclear encoded) mRNA, complete cds M93036 /FEATURE=mRNA /DEFINITION=HUMGA7A08 0.7018
12148 575_s_at Human (clone 21726) carcinoma-associated antigen GA733-2 (GA733-2) mRNA 0.6917
6462 38634_at Cluster Incl. M11433:Human cellular retinol-binding protein mRNA 0.6514
10138 41288_at Cluster Incl. AL036744:DKFZp564I1663_r1 Homo sapiens cDNA 0.6367
12153 556_s_at M96233 /FEATURE=expanded_cds/DEFINITION=HUMGSTM4A Human glutathione transferase class mu number 4 (GSTM4) gene 0.6217
6866 39756_g_at Cluster Incl. Z93930:Human DNA sequence from clone 292E10 on chromosome 22q11-12. Contains the XBP1 gene for X-box binding protein 1 (TREB5), ESTs, STSs, GSSs and a putative CpG island 0.6201
4365 41468_at Cluster Incl. M30894:Human T-cell receptor Ti rearranged gamma-chain mRNA V-J-C region X14885 /FEATURE=mRNA /DEFINITION=HSTGF31 0.6193
10956 1767_s_at H.sapiens gene for transforming growth factor-beta 3 (TGF- beta 3) 0.6160
9172 38406_f_at Cluster Incl. AI207842:ao89h09.x1 Homo sapiens cDNA, 3 end / 0.6155
Table 3 Colon cancer: the coefficient of variation (CV) for the original dataset and transformed dataset. This table shows ten features with lowest coefficient of variation, the complete table can be downloaded at .
Colon Cancer Colon Cancer (Transformed)
Rank No. on array Gene Acession Name Coefficient of variation Rank Gene Acession Number Coefficient of variation
1 #39 T57619 45.33% 1 #119/#54 16.53%
2 #119 T51529 48.23% 2 #54/#119 17.19%
3 #54 T48804 48.61% 3 #39/#31 18.88%
4 #58 T71025 49.03% 4 #119/#31 19.85%
5 #365 Z50753 49.60% 5 #31/#39 19.86%
6 #26 T95018 49.79% 6 #31/#119 20.01%
7 #387 U30825 50.74% 7 #39/#119 20.64%
8 #64 H55758 52.48% 8 #119/#39 20.71%
9 #1760 H08393 52.92% 9 #54/#39 20.83%
10 #31 T61609 53.15% 10 #26/#119 21.50%
Table 4 Prostate cancer: the coefficient of variation (CV) for the original dataset and transformed dataset according to their rank. This table shows 20 data with lowest coefficient of variation, the complete table can be downloaded at .
Prostate Cancer Prostate Cancer (transformed)
Rank No. on array Gene Accession Name Coefficient of variation Rank Gene Acession Number Coefficient of variation
1 #5871 36845_at 24.54% 1 (#10614)/(#5871) 25.78%
2 #8965 37720_at 25.02% 2 (#7532)/(#6236) 26.75%
3 #8851 37367_at 26.75% 3 (#5871)/(#10614) 27.15%
4 #10614 33198_at 28.47% 4 (#5871)/(#10138) 27.52%
5 #8160 34877_at 28.98% 5 (#9599)/(#10138) 27.94%
6 #5840 36814_at 31.02% 6 (#7715)/(#8889) 27.98%
7 #5954 36928_at 31.77% 7 (#7532)/(#9288) 28.33%
8 #10138 41288_at 31.97% 8 (#8160)/(#10614) 28.41%
9 #6865 39755_at 32.00% 9 (#9424)/(#9599) 28.86%
10 #9599 39551_at 32.07% 10 (#7520)/(#10138) 29.14%
Table 5 Colon and prostate cancer: Ten key pair-wise gene expression ratios that are most correlated to tissue phenotype, the complete table can be downloaded at . They were determined to be accurate discriminating axes.
Colon Cancer Prostate Cancer
Gene Number Correlation Gene Number Correlation
#481/#67 0.7866 #4751/#6185 0.7454
#1831/#1537 0.7662 #9288/#6185 0.7393
#481/#269 0.7632 #8892/#6185 0.7383
#255/#1760 0.7545 #6185/#8851 0.7371
#481/#508 0.7534 #7532/#6185 0.7349
#481/#768 0.7495 #8136/#6185 0.7335
#1831/#1244 0.7482 #205/#5954 0.7291
#237/#1760 0.7468 #9059/#6185 0.7241
#1482/#1537 0.7460 #4432/#6185 0.7236
#481/#613 0.7369 #8965/#10614 0.721
Table 6 Colon cancer: the top 10 genes and pair-wise gene expression ratio used to discriminate the colon cancer tissue. This table is ranked with decreasing classification efficiency. The threshold values Ti for normal tissues are also provided together with classification efficiency. The list of top 25 genes can be downloaded from .
Colon Cancer-Original data Colon Cancer-Transformed data
Rank No. on array gene accession number gene info Threshold for normal tissue type, Tj discriminating efficiency*/% Ref Rank gene number on array Threshold for normal tissue type, Ti discriminating efficiency*/% Ref
1 #1659 M26383 Human monocyte-derived neutrophil-activating protein (MONAP) mRNA, complete cds. <62.7375 87.1% [12,13] 1 #1537/#1831 <0.75512 93.6% [27,28]
2 #753 M76378 Human cysteine-rich protein (CRP) gene, exons 5 and 6. >749.4075 83.9% [26] 2 #1831/#1537 >1.3243 93.6% [27,28]
3 #613 X12671 Human gene for heterogeneous nuclear ribonucleoprotein (hnRNP) core protein A1. <233.4162 82.3% [33] 3 #1827/#481 <0.074449 91.9% [14,15,39]
4 #569 T51571 P24480 CALGIZZARIN. SERINE/THREONINE-PROTEIN <309.3037 77.4% [34] 4 #1537/#1623 <1.0533 91.9% [27,40]
5 #1103 R97912 KINASE IPL1 (Saccharomyces cerevisiae) <70.2738 75.8% [35] 5 #1831/#1759 >1.4003 91.9% [28]
6 #1759 J05032 Human aspartyl-tRNA synthetase alpha-2 subunit mRNA, complete cds. <41.92 75.8% [36] 6 #1623/#1537 >0.94939 91.9% [27,40]
7 #241 M63391 Human desmin gene, complete cds. >2787.0425 75.8% [17] 7 #365/#1760 >3.3867 91.9% [41,42]
8 #818 R75843 TRANSLATIONAL INITIATION FACTOR 2 GAMMA SUBUNIT (Homo sapiens) <152.5662 74.2% [37] 8 #1759/#1831 <0.71414 91.9% [28]
9 #1960 T57468 FIBRILLARIN (HUMAN). <42.0225 74.2% [38] 9 #1760/#365 <0.29528 91.9%
10 #1281 H23544 GTP-BINDING NUCLEAR PROTEIN RNA (Homo sapiens) <103.2488 74.2% [20] 10 #481/#1827 >13.432 91.9% [14,15,39]
*discriminating efficiency using only single gene as discriminating axis
Table 7 Prostate cancer: the top 10 genes and pair-wise gene expression ratio used to discriminate the prostate cancer tissues. The threshold values Ti for normal tissues are also provided. The list of top 25 genes can be downloaded from .
Prostate Cancer-Original data Prostate Cancer-Transformed data
Rank No. on array Probe no gene info Threshold Ri discriminating efficiency* / % Ref Rank gene number on array Threshold Ri discriminating efficiency* / % Ref
1 6185 37639_at Cluster Incl. X07732:Human hepatoma mRNA for serine protease hepsin <115 86.3% [18] 1 #5840/#6185 >0.37168 84.6% [18,3]
2 10537 33121_g_at Cluster Incl. AF045229:Homo sapiens regulator of G protein signaling 10 mRNA <50 80.4% [43] 2 #6185/#5840 <2.6905 84.6% [18,31]
3 8965 37720_at Cluster Incl. M22382:Human mitochondrial matrix protein P1 (nuclear encoded) mRNA <238 80.4% [44] 3 #7775/#205 <0.22928 82.7% [50,51]
4 8554 36589_at Cluster Incl. X15414:Human mRNA for aldose reductase (EC 1.1.1.2) >35 79.4% [45] 4 #8631/#10234 <5.4561 82.7% [52,53]
5 9172 38406_f_at Cluster Incl. AI207842:ao89h09.x1 Homo sapiens cDNA >626 79.4% [46] 5 #10749/#11942 <0.34585 82.7% [54]
6 7067 40436_g_at Cluster Incl. J03592:Human ADP/ATP translocase mRNA <234 78.4% [47] 6 #10234/#8631 >0.18328 82.7% [52,53]
7 9850 40282_s_at Cluster Incl. M84526:Human adipsin/complement factor D mRNA >182 77.5% [30] 7 #8554/#6185 >0.39823 82.7% [18]
8 7066 40435_at Cluster Incl. J03592:Human ADP/ATP translocase mRNA, 3 end, clone pHAT8 M96233 /FEATURE=expanded_cds/DEFINITION=HUMGSTM4A Human <349 76.5% [47] 8 #11942/#10749 >2.8914 82.7% [54,55]
9 12153 556_s_at glutathione transferase class mu number 4(GSTM4) gene >152 76.5% [48] 9 #205/#7775 >4.3614 82.7% [50,51]
10 9093 38087_s_at Cluster Incl. W72186:zd69b10.s1 Homo sapiens cDNA >62 74.5% [49] 10 #6185/#8554 <2.5111 82.7% [18]
*discriminating efficiency using only single gene as discriminating axis
Table 8 Accuracy of N-feature model classifier. The optimum classification accuracy, the mean classification accuracy and the standard deviation for the N-feature classifier (N<11).
Colon cancer–Original expression data Colon cancer–Transformed expression data
Order or classifier Optimum Accuracy* / % Mean Accuracy* / % Standard Deviation Order or classifier Optimum Accuracy* / % Mean Accuracy* / % Standard Deviation
1 87.10% 76.77% 4.17% 1 93.55% 91.24% 1.22%
2 91.94% 83.33% 4.38% 2 98.39% 95.00% 1.95%
3 95.16% 87.07% 4.06% 3 98.39% 96.47% 1.53%
4 95.16% 89.37% 3.58% 4 98.39% 97.20% 1.23%
5 95.16% 90.88% 3.10% 5 98.39% 97.60% 0.99%
6 95.16% 91.94% 2.70% 6 98.39% 97.84% 0.83%
7 95.16% 92.72% 2.38% 7 98.39% 98.00% 0.70%
8 95.16% 93.31% 2.09% 8 98.39% 98.12% 0.60%
9 95.16% 93.78% 1.83% 9 98.39% 98.21% 0.50%
10 95.16% 94.15% 1.57% 10 98.39% 98.28% 0.40%
Prostate cancer–Original expression data Prostate cancer–Transformed expression data
Order or classifier Optimum Accuracy* / % Mean Accuracy* / % Standard Deviation Order or classifier Optimum Accuracy* / % Mean Accuracy* / % Standard Deviation
1 86.27% 75.82% 4.31% 1 84.62% 81.92% 2.28%
2 100.00% 91.27% 7.89% 2 98.39% 90.84% 4.18%
3 100.00% 95.98% 5.53% 3 100.00% 93.64% 3.40%
4 100.00% 97.91% 3.87% 4 100.00% 95.00% 2.94%
5 100.00% 98.86% 2.76% 5 100.00% 95.90% 2.68%
6 100.00% 99.38% 1.98% 6 100.00% 96.59% 2.51%
7 100.00% 99.67% 1.41% 7 100.00% 97.16% 2.36%
8 100.00% 99.83% 0.99% 8 100.00% 97.66% 2.23%
9 100.00% 99.90% 0.67% 9 100.00% 98.10% 2.09%
10 100.00% 99.96% 0.43% 10 100.00% 98.49% 1.94%
Table 9 The discriminating axes. The discriminating axes that accounted for the optimum accuracy in 1 to 3-feature model classifier.
Order or classifier Optimum Accuracy* / % Discriminating axes Order or classifier Optimum Accuracy* / % Discriminating axes
1 87.10% #1659 1 93.55% #1831/#1537
2 91.94% (#241)&(#1659) 2 98.39% (#753/#768) & (#1831/#1537)
3 95.16% (#241)&(#1659)&(#1759) 3 98.39% (#753/#768) & (#1831/#1537)&(#481/#1394)
Prostate cancer–Original expression data Prostate cancer–Transformed expression data
Order or classifier Optimum Accuracy* / % Discriminating axes Order or classifier Optimum Accuracy* / % Discriminating axes
1 86.27% (#6185) 1 84.62% (#6185/#5840)
2 100.00% (#6185)&(#9850) 2 100.00% (#6185/#5840)&(#6185/#6749)
3 100.00% (#6185)&(#9850)&(#12148) 3 100.00% (#6185/#5840)&(#6185/#6749)&(#7247/#7067)
* : best accuracy based on the specified number of gene/gene ratio as discriminating axes ****Please do not delete from here on, needed for the correct order of reference list****** [32-54]
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| 15469618 | PMC524507 | CC BY | 2021-01-04 16:02:59 | no | BMC Cancer. 2004 Oct 7; 4:72 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-72 | oa_comm |
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-4-151547656210.1186/1471-2377-4-15Research ArticleLack of association between vascular dementia and Chlamydia pneumoniae infection: a case-control study Chan Carusone Soo [email protected] Marek [email protected] William [email protected] Charlie H [email protected] Jim [email protected] Max [email protected] Judy [email protected] Tim [email protected] Stephanie [email protected] Mark [email protected] Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main St. W., L8N 3Z5 Hamilton, Canada2 Department of Pathology and Molecular Medicine, McMaster University, 1200 Main St. W., L8N 3Z5 Hamilton, Canada3 Hamilton Regional Laboratory Medicine Program, 50 Charlton Ave. E., L8N 4A6 Hamilton, Canada4 St. Peter's Centre for Studies in Aging, St. Peter's Hospital, 88 Maplewood Ave., L8M 1W9 Hamilton, Canada5 Department of Medicine, McMaster University, 1200 Main St. W., L8N 3Z5 Hamilton, Canada6 Department of Medical Microbiology and Immunology, University of Alberta, T6G 2H7 Edmonton, Canada2004 12 10 2004 4 15 15 3 6 2004 12 10 2004 Copyright © 2004 Carusone et al; licensee BioMed Central Ltd.2004Carusone 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 inflammation appears to play a role in the pathogenesis of vascular dementia. Given the association between Chlamydia pneumoniae and stroke, the possibility exists that previous exposure to C. pneumoniae may play a role in vascular dementia. The objective of this study was to determine if there was an association between serological evidence of C. pneumoniae infection or inflammatory markers with vascular dementia.
Methods
28 case-patients with vascular dementia at a geriatric clinic and 24 caregiver-controls were tested for C. pneumoniae IgG and IgA antibodies. The association between vascular dementia and C. pneumoniae titres as well as inflammatory markers was estimated by using both conditional logistic regression and stratified logistic regression.
Results
When matched cases were compared to controls, there was no significant difference in elevated C. pneumoniae specific IgG antibodies (titre ≥ 1:32), odds ratio [OR] 1.3 (95% confidence intervals [CI] 0.3 to 6.0), p = 0.71, or in elevated C. pneumoniae specific IgA antibodies (titre ≥ 1:16), OR 2.0 (95%CI 0.5 to 8.0), p = 0.33 indicative of past or persistent C. pneumoniae infection. Similarly, no difference in high IgG or IgA antibody levels (IgG titre ≥ 1:512 or IgA titre ≥ 1:64) between the two groups, indicative of recent C. pneumoniae infection, was found, OR 0.4 (95%CI 0.1 to 2.1), p = 0.27. For C-reactive protein (CRP), the mean difference between 18 matched pairs (case – control) was – 3.33 mg/L. There was no significant difference between cases and controls when comparing log transformed values, OR 0.03 (95%CI 0.00 to 2.89), p = 0.13 or comparing CRP values above or below the median, OR 0.8 (95%CI 0.2 to 3.4), p = 0.71. For fibrinogen, the mean difference between pairs (case – control) was -0.07 g/L. There was no statistical difference between cases and controls when comparing log transformed values, OR 0.6 (95%CI 0.0 to 31.2), p = 0.79 or between fibrinogen values above and below the median, OR = 0.5 (95%CI 0.1 to 2.0), p = 0.50.
Conclusion
We found no evidence for a significant association between C. pneumoniae infection, inflammatory markers such as CRP and fibrinogen, and vascular dementia.
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Background
Vascular dementia is characterized by a loss of cognitive function and social adaptive functions in individuals with cerebrovascular disease [1,2]. Vascular dementia is the second most common cause of dementia and accounts for 10% to 15% of all cases [3]. The clinical presentation of this illness is variable, depending on the site and extent of the lesion or infarct [2]. The pathogenesis of vascular dementia has not been well defined [1,3]. Chronic inflammation and cytokine dysregulation may play a role [4] similar to that seen in Alzheimer's disease [5].
Recent data from serological and PCR studies support an association between Chlamydia pneumoniae and cerebrovascular disease. C. pneumoniae has been associated with stroke, transient cerebral ischemia, and atherosclerosis in the middle cerebral artery in both prospective and case-control studies [6-12]. Since stroke is an important precursor to vascular dementia, these data raise the possibility that C. pneumoniae infection may also be a risk factor for vascular dementia. To our knowledge, this potential relationship has not previously been assessed. We conducted a pilot case-control study to determine an association between serological evidence of C. pneumoniae infection and vascular dementia. We also sought to determine if the inflammatory markers, C-reactive protein (CRP) and fibrinogen were associated with this illness.
Methods
Study design
Patients with vascular dementia were enrolled from the Geriatric Clinic at Henderson Hospital, an outpatient clinic affiliated with a tertiary hospital in Hamilton, Ontario. The diagnosis of vascular dementia for participants enrolled was determined in accordance with criteria established by the Neuroepidemiology Branch of the National Institute of Neurological Disorders and Stroke and Association Internationale pour la Recherche et l'Enseignement en Neurosciences (NINDS-AIREN) International workshop [13]. This includes both physical and imaging evidence of strokes, and a temporal relationship between stroke and dementia [3]. Case-patients meeting any of the following criteria were excluded: 1) cognitive impairment due to acute cerebral trauma, hypoxic cerebral damage post cardiac arrest, vitamin deficiency states, central nervous system infection, cerebral neoplasia, significant endocrine or metabolic disease, mental retardation; 2) stroke within the last 6 weeks; 3) patients known, in the past 3 months, to have taken a 7 day or more course of antibiotics with activity against C. pneumoniae (erythromycin, clarithromycin, azithromycin, levofloxacin, trovafloxacin, doxycycline, or tetracycline).
The controls for this study were chosen from a list of all caregivers who attended the geriatric clinic at the time of the study, regardless of the diagnosis of their spouse or family member. For each case, one caregiver matched for age (within five years) and sex was selected. Caregivers were excluded if they had a diagnosis that included any of the following: dementia, stroke, or cognitive impairment as determined by a Standardized Mini-Mental Status Examination score of < 27 [14]. Enrolment was from July 1999 to October 2001. All eligible cases and controls who attended the clinic during the study period were approached for consent to participate in the study. Demographic data (age, sex), medical history, and smoking history were collected as well as blood samples for C. pneumoniae IgG and IgA antibodies, CRP, and fibrinogen.
This study was approved by the research ethics board at McMaster University. Signed consent was obtained for all participants (proxy consent was utilized for participants considered decisionally impaired).
Laboratory methods
For C. pneumoniae IgG and IgA antibody detection, all sera were titrated at two-fold dilutions from 1:16 to endpoint. Samples were analyzed by microimmunofluorescence (MIF), using a 16 hour incubation of serum and substrate at 4–8°C with the same batch of C. pneumoniae IgG/IgM MIF slides (LabSystems OY, Helsinki) (23). To prevent IgG interference, sera used for IgA detection were first treated with goat anti-human IgG antibodies (GullSorb; Gull Laboratories, Salt Lake City, UT, USA). CRP was measured using a high sensitivity automated rate nephelometric immunoassay (Dade Behring high-sensitivity CRP, BNII Nephelometer System, Marburc, DE). Fibrinogen was assayed using an automated STA fibrinogen assay (von Clauss method) on a Roche/Stago (Diagnostica Stago SA).
Analysis
The presence of elevated antibody levels, indicative of past or persistent C. pneumoniae infection, was defined as an IgG titre of 1:32 or greater and IgA of 1:16 or greater [15,16]. High antibody titres to C. pneumoniae, suggesting a more recent infection, was defined by IgG titres of 1:512 or greater or IgA titres of 1:64 or greater [16]. Because a skewed distribution was anticipated and a linear relationship with risk was not expected, CRP and fibrinogen were analyzed in two ways: using a log transformation of the values and dichotomizing at the median.
The association between vascular dementia and C. pneumoniae titres and inflammatory markers was estimated using a matched analysis. Conditional logistic regression analyses were performed for antibody levels, dichotomized and log transformed CRP, as well as dichotomized and log transformed fibrinogen.
A stratified logistic regression analysis was also conducted for C. pneumoniae titres and inflammatory markers (both log transformed and dichotomized at the median), stratifying by age and sex (the following age strata were used: ≤ 70 years, 71–80 years, 81–90 years). All analyses for C. pneumoniae titres and inflammatory markers were also performed with adjustment for current smoking status. Data analyses were performed with SPSS version 10 or Egret for Windows version 2.0.3.
The original protocol involved two concurrent case-control studies: one including 30 vascular dementia patients and 30 controls and the other with 30 Alzheimer disease patients and 30 controls. The analysis was to include the additional 30 Alzheimer disease controls in the vascular dementia analysis (giving a 1:2 case:control ratio). Assuming that one third of controls would have elevated C. pneumoniae titres, for an alpha of 0.05 and 80% power, matching 30 cases to 60 controls would allow for detection of an odds ratio of 3.8 or higher. As the study proceeded, it became apparent that enrolling the Alzheimer's patients was not feasible. We decided then to limit the analysis to a 1:1 case:control ratio focusing on 30 patients with vascular dementia.
Results
Participants
A total of 28 case-patients were enrolled: mean age 76.2 years (minimum to maximum: 56 to 90 years); 18 (64%) were male. Nine of the 28 cases had at least one comorbidity (including angina, coronary heart disease, vascular disease, liver disease and renal disease); 1 case had 3 or more comorbidities. Thirteen of the cases were current smokers.
Twenty of the 28 cases could be matched to caregiver-controls, for a total of 20 case-control pairs. Of these 20 caregiver-controls, 16 were unrelated to a case, and four were spouses of a case. However, none of these four were matched to their spouse. Four additional caregiver-controls were selected for the unmatched analyses, so that data on a total of 24 caregiver-controls was obtained.
Where there was incomplete data on antibody or inflammatory marker levels, those pairs were excluded from matched analyses. Incomplete information on cases and controls occurred when individuals consented to participate in the study and provided medical information but did not attend the out-patient clinic for the required blood collection. All individuals with complete data were included in the unmatched analyses.
C. pneumoniae serology
Univariate analysis of C. pneumoniae specific IgG antibodies showed no statistically significant difference in elevated antibody levels between matched case-patients and controls, odds ratio [OR] = 1.3 (95% confidence intervals [CI] 0.3 to 6.0), p = 0.71. When pairs were broken and stratified analyses were performed the difference was not statistically significant, OR = 1.8 (95%CI 0.4 to 8.3), p = 0.46. The analysis was also performed with adjustment for participants' current smoking status, OR = 1.8 (95%CI 0.2 to 12.2), p = 0.57 (see Table 1).
Table 1 Comparison of analyses used to assess for associations of C. pneumoniae specific serology and vascular dementia
Variable Conditional Stratified* Adjusted**
OR p n1 OR p n2 OR p n2
IgG response 1.5 0.66 15 1.8 0.46 48 1.7 0.50 48
IgA response 1.7 0.48 15 2.8 0.13 48 3.0 0.11 48
High titre response 0.4 0.27 15 0.6 0.40 48 0.5 0.34 48
1 Number of pairs included in the analysis
2 Number of individuals included in the analysis
* Stratified on age (≤ 70 years, 71–80 years, 81–90 years) and gender
**Stratified analysis with adjustment for current smoking status
Similarly, no statistically significant difference between elevated C. pneumoniae specific IgA antibodies was found between matched pairs, OR = 2.0 (95%CI 0.5 to 8.0), p = 0.33. The stratified analysis produced slightly higher odds ratio estimates, although not statistically significant. For the unadjusted analysis, OR = 2.8 (95%CI 0.7 to 10.4), p = 0.13 and for the adjusted analysis, OR = 2.7 (95%CI 0.5 to 14.2), p = 0.24 (see Table 1).
There was also no statistical difference in high antibody levels between matched pairs, OR = 0.4 (95%CI 0.1 to 2.1), p = 0.27 or in the stratified analysis, OR = 0.6 (95%CI 0.2 to 2.0), p = 0.40 for the unadjusted analysis and OR = 0.5 (95%CI 0.1 to 2.5), p = 0.41 for the adjusted analysis (see Table 1).
Inflammatory markers
A matched analysis of CRP performed on 18 of the 20 matched pairs (all pairs with complete data) revealed no significant difference between log-transformed values, OR of 0.03 (95%CI 0.00 to 2.89), p = 0.13. Similarly, there was no difference comparing matched cases and controls with CRP values above or below the median, OR = 0.8 (95%CI 0.2 to 3.4), p = 0.71. In the stratified analysis (Table 2), the log transformed CRP variable and the CRP variable dichotomized at the median were not statistically significant in both the unadjusted (OR = 0.5 (95%CI 0.1 to 3.6), p = 0.50 and OR = 2.2 (95%CI 0.7 to 7.2), p = 0.20, respectively) and the adjusted analysis (OR = 0.4 (95%CI 0.04 to 4.7), p = 0.49 and OR = 1.4 (95%CI 0.3 to 6.3), p = 0.64, respectively).
Table 2 Comparison of analyses used to assess for associations of inflammatory markers and vascular dementia
Variable Conditional Stratified* Adjusted**
OR p n1 OR p n2 OR p n2
LogCRP 0.0 0.13 18 0.5 0.50 49 0.5 0.52 49
CRP 0.8 0.71 18 2.2 0.20 49 2.2 0.19 49
LogFibrinogen 0.6 0.79 18 0.8 0.92 49 0.6 0.83 49
Fibrinogen 0.5 0.33 18 0.6 0.38 49 0.5 0.32 49
1 Number of pairs included in the analysis
2 Number of individuals included in the analysis
* Stratified on age (≤ 70 years, 71–80 years, 81–90 years) and gender
**Stratified analysis with adjustment for current smoking status
A matched analysis of fibrinogen performed on the same 18 pairs revealed no significant difference between the log-transformed values of the two groups, OR = 0.6 (95%CI 0.0 to 31.2), p = 0.79. Similarly, there was no difference when comparing the pairs on fibrinogen values above and below the median, OR = 0.5 (95%CI 0.1 to 2.0), p = 0.33. In the stratified analysis (Table 2), the log transformed fibrinogen variable and the fibrinogen variable dichotomized at the median were not statistically significant in both the unadjusted (OR = 0.8 (95%CI 0.0 to 71.6), p = 0.92 and OR = 0.6 (95%CI 0.2 to 2.0), p = 0.38, respectively) and the adjusted analysis (OR = 0.1 (95%CI 0.0 to 73.9), p = 0.43 and OR = 0.3 (95%CI 0.1 to 1.6), p = 0.17, respectively).
Discussion
In this case-control study, we found no significant association between elevated or high C. pneumoniae specific IgG or IgA antibodies and vascular dementia. To our knowledge, this is the first epidemiologic study to test for an association between vascular dementia and infection with C. pneumoniae.
We conducted this study on the basis of evidence linking C. pneumoniae to cardiovascular disease and stroke. There is an extensive literature supporting an association between C. pneumoniae and atherosclerosis [17-19]. Although the majority of these studies initially focused on coronary heart disease more recent evidence also supports an association with stroke [6-12,20]. However, the clinical importance of this association is uncertain.
Although no significant associations were noted, the relatively small sample size and the odds ratio estimates for elevated IgA and IgG antibodies do not definitively rule out an association. In fact, we powered this study to detect a minimally important association between antibodies and vascular dementia of 3.8. Given that the odds ratio 95% confidence interval of IgG is from 0.3 to 6.0, and 0.5 to 8.0 for IgA, our data do not rule out clinically important associations. The point estimates for elevated IgA and IgG antibodies (2.0 and 1.3, respectively) are similar to the recent meta-analysis odds ratio estimates for coronary heart disease of 1.25 (95% CI 1.03 to 1.53) and 1.15 (95% CI 0.97 to 1.36), respectively [21,22]. In both cases the odds ratio estimate for IgA titres is slightly higher than IgG titres, but not statistically different. The meaning of this difference is uncertain. Danesh et al [21] suggest that these differences are likely due to chance, selection biases, or selective emphasis on particular reports. In contrast, other studies have suggested that IgA titres are more strongly associated with disease outcomes because they are a better indicator of chronic C. pneumoniae infection [23,15,10].
Vascular dementia is the second most common cause of dementia, second only to Alzheimer's disease. It was previously believed that most cases of dementia were the outcome of one of these two distinct diseases. However, the clear division between them has recently been challenged. It is now widely believed that vascular risk factors are also associated with Alzheimer's disease and Alzheimer's and vascular dementia may share many common clinical and pathological characteristics [3,24-26]. A number of studies have examined the association between Alzheimer's disease and C. pneumoniae infection. In 1998 Balin et al [27] found an extremely high association between the presence of C. pneumoniae in post-mortem brain samples and late-onset Alzheimer's disease. However, more recent studies have not repeated these findings [28-31]. A recent randomized controlled clinical trial [16], based on the hypothesis that chronic C. pneumoniae infection contributes to Alzheimer's disease, found an improved long-term cognitive state in patients with mild to moderate Alzheimer's disease who had been treated with doxycycline and rifampin. However, the serological data did not suggest that this clinical effect was due to treatment of chronic C. pneumoniae infection. One study has looked for C. pneumoniae in brain samples of vascular dementia patients. This study, like the later AD studies, did not identify C. pneumoniae in any of the brain samples [32]. These results suggest that the presence of C. pneumoniae in the brains is not strongly associated with late-onset Alzheimer's disease or vascular dementia.
Inflammatory responses are also known to be associated with cardiovascular disease and have recently been implicated in dementia [33]. Elevated levels of serum C-reactive protein (CRP), a non-specific marker of inflammation, predict cardiovascular disease [34] and dementia [33], and have been associated with stroke patients [35]. Recently, an association between inflammatory markers alpha 1-antichymotrypsin, interleukin 6, and, to a lesser extent, C-reactive protein were associated with an increased risk of dementia [36]. In this study we did not find a significant difference in CRP levels between the cases and controls. This most likely was due to the limited power in the study and the limitations of measuring serum CRP. Although CRP was originally thought to be produced almost exclusively by hepatocytes, CRP is now known to be synthesized in brain cells and upregulated in Alzheimer tissue [37,38]. Consequently, localized increases in CRP may be associated with vascular dementia but not detected with serum measurements.
We found no significant association between increased fibrinogen levels and vascular dementia. Abnormalities of haemostasis are thought to be important in the pathogenesis of cardiovascular disease, ischaemic stroke, and vascular dementia. Within the pathways of coagulation and fibrinolysis, fibrinogen represents an important marker. Elevated levels of fibrinogen are associated with increased risks of cardiovascular disease and ischaemic stroke [39,40] but the results are less conclusive for vascular dementia [41,42]. Lowe and Haverkate [43] believe that because vascular dementia is only one phenotype of the systemic atherothrombosis disease, associations between haemostatic variables and any given phenotype should be interpreted with caution. To show a specific association with a single phenotype, a study would need an extremely large sample size to overcome the overlap in phenotypes and risk factors seen in atherothrombosis.
We acknowledge several limitations of this study. Because of the relatively small sample size, the analyses were adjusted for only a small number of potentially important covariates and the analysis of CRP and fibrinogen was restricted to above and below the median (while quartiles would have been more sensitive). To adjust for variables that were not used as matching criteria and to maximize the data collected a stratified analysis was also done. The additional stratified analysis adjusted for current smoking status. We adjusted for smoking status because there is a known strong association between smoking and C. pneumoniae titres; and between smoking and vascular dementia [44]. However, it may also be important to adjust for additional factors that may affect inflammatory markers. We also acknowledge that C. pneumoniae serology is an imperfect test of C. pneumoniae exposure and chronic infection. First, the high prevalence of C. pneumoniae exposure makes it difficult to detect true serological differences between cases and controls. Second, it is unclear what the appropriate serological cut-offs should be for identifying exposure versus chronic infection or recent infections. As a result, different groups have used different criteria making comparisons across studies more difficult. However, the importance of this inconsistency is unclear. In the meta-analysis reported by Danesh et al [21] no significant heterogeneity was found among the studies even though four different cut-off titres were used to determine seropositivity in the microimmunofluorescence assays. An alternative test, that may prove to be more reliable, involves the detection of C. pneumoniae DNA in peripheral blood mononuclear cells [45]. Another potential limitation is the choice of controls; because C. pneumoniae is infectious an increased exposure in the caregivers could potentially mask a statistically significant association between the patients and controls. There is also evidence that caregivers, because of stress, may have altered immune systems [46] which could interfere with their generation of antibodies and inflammatory markers [47-49].
Conclusions
In summary, a case-control study of vascular dementia patients suggests that there is no significant association between C. pneumoniae antibodies and vascular dementia. We found no evidence for a significant association between systemic inflammatory markers and vascular dementia. While this study can rule out a strong association, larger studies are necessary to determine if a weak association exists.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
ML, MS, WM, CG, JM, and MC conceived and designed the original study. SCC conducted the analysis of data and drafted the manuscript. SS and TS coordinated the study and collected data. JG conducted the serological testing. All authors offered critical input into the manuscript and all have read and approved the final version.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15476562 | PMC524508 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Oct 12; 4:15 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-15 | oa_comm |
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BMC BiolBMC Biology1741-7007BioMed Central London 1741-7007-2-221547391410.1186/1741-7007-2-22Research ArticleLow dose pramipexole is neuroprotective in the MPTP mouse model of Parkinson's disease, and downregulates the dopamine transporter via the D3 receptor Joyce Jeffrey N [email protected] Cheryl [email protected] Han [email protected] Sabine [email protected] Diane [email protected] Thomas H. Christopher Center for Parkinson's Disease Research, Sun Health Research Institute, 10515 West Santa Fe Dr., Sun City, AZ, 85352, USA2004 11 10 2004 2 22 22 17 6 2004 11 10 2004 Copyright © 2004 Joyce 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
Our aim was to determine if pramipexole, a D3 preferring agonist, effectively reduced dopamine neuron and fiber loss in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model when given at intraperitoneal doses corresponding to clinical doses. We also determined whether subchronic treatment with pramipexole regulates dopamine transporter function, thereby reducing intracellular transport of the active metabolite of MPTP, 1-methyl-4-phenylpyridinium (MPP+).
Methods
Ten 12-month old C57BL/6 mice were treated with MPTP (or saline) twice per day at 20 mg/kg s.c. (4 injections over 48 h). Mice were pretreated for 3 days and during the 2-day MPTP regimen with pramipexole (0.1 mg/kg/day) or saline. Stereological quantification of dopamine neuron number and optical density measurement of dopamine fiber loss were carried out at 1 week after treatment, using immunostaining for dopamine transporter (DAT) and tyrosine hydroxylase (TH). Additional wild-type (WT) and D3 receptor knockout (KO) mice were treated for 5 days with pramipexole (0.1 mg/kg/day) or vehicle. The kinetics of [3H]MPP+ and [3H]DA uptake (Vmax and Km) were determined 24 h later; and at 24 h and 14 days dopamine transporter density was measured by quantitative autoradiography.
Results
Pramipexole treatment completely antagonized the neurotoxic effects of MPTP, as measured by substantia nigra and ventral tegmental area TH-immunoreactive cell counts. MPTP- induced loss of striatal innervation, as measured by DAT-immunoreactivity, was partially prevented by pramipexole, but not with regard to TH-IR. Pramipexole also reduced DAT- immunoreactivity in non-MPTP treated mice. Subchronic treatment with pramipexole lowered the Vmax for [3H]DA and [3H]MPP+ uptake into striatal synaptosomes of WT mice. Pramipexole treatment lowered Vmax in WT but not D3 KO mice; however, D3 KO mice had lower Vmax for [3H]DA uptake. There was no change in DAT number in WT with pramipexole treatment or D3 KO mice at 24 h post-treatment, but there was a reduction in WT-pramipexole treated and not in D3 KO mice at 14 days post-treatment.
Conclusion
These results suggest that protection occurs at clinically suitable doses of pramipexole. Protection could be due to a reduced amount of MPP+ taken up into DA terminals via DAT. D3 receptor plays an important role in this regulation of transporter uptake and availability.
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Background
An interesting development in the use of dopamine (DA) agonists for treatment of Parkinson's disease (PD) is that some of them have proven to be neuroprotective in animal models of PD. Antiparkinsonian agents that are direct DA agonists, such as apomorphine [1], bromocriptine [2], and pramipexole [3], are neuroprotective against 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced damage to the DA system in mice. Administration of MPTP, which is converted to 1-methyl-4-phenylpyridinium (MPP+) and intracellularly transported into DAergic neurons [4], provides a good model for studying neuroprotection in PD. MPTP produces Parkinsonism in humans and in subhuman species through selective loss of DAergic neurons of the substantia nigra (SN) [5,6], and a number of related compounds to MPTP also produce nigral cell loss in primates [7]. MPTP causes apoptosis associated with PD [8-10] ;MPTP produces progressive cell death in humans for decades after the initial insult [11]. Hence, drugs that reduce the neurotoxicity of compounds like MPTP may be neuroprotective in PD. In fact, it is now hypothesized that direct DA agonists may slow the loss of DAergic terminal function upon long-term administration to PD patients [12-15].
Dopaminergic neurons are tonically inhibited by dendritic and terminal autoreceptors, operating in interaction with DA transporters (DAT) and pharmacologically of the D2 receptor subtype [16-19]. However, Zapata et al [20] have reported that the D3 preferring agonist (+)-PD 128907 regulates extracellular DA levels via interactions with D3 autoreceptors. If D3 preferring agonists are potent autoreceptor agonists, then hypothetically long-term changes in expression of DAT or the functional properties of DAT might occur following subchronic treatment. Since intracellular accumulation of MPP+ following systemic injection of MPTP requires DAT [4], then when DAT is downregulated by D3 preferring agonists, this could result in lower intracellular accumulation of MPP+ and reduced neurotoxicity to MPTP.
The D3 receptor preferring agonists, pramipexole and ropinirole, are the most potent of the DA agonists affording neuroprotection at 1 mg/kg for pramipexole against MPTP-induced neurodegeneration [3,21] and at 2 mg/kg for ropinirole against 6-OHDA lesions in rats [22]. Doses 10–30 times higher of DA agonists with low D3 receptor affinity such as apomorphine [1] and bromocriptine [2,23] are needed against MPTP-induced neurodegeneration. Because neuroprotection by pramipexole is most evident with concurrent treatment with MPTP and not with post-MPTP treatment [24], i.e. when autoreceptor contributions should be most pronounced, regulation of DAT may be important. In addition, while the lowest effective dose reported is 1.0 mg/kg for mice, this is significantly greater than a clinically relevant dose in humans (1.5 mg t.i.d., p.o.[25]). Based on information from Pharmacia Corporation, equivalent plasma levels obtained with 1.5 mg t.i.d., p.o. in humans could be produced with 0.1 to 0.5 mg/kg in the mice. We tested whether 0.1 mg/kg pramipexole would be neuroprotective in aging mice against MPTP-induced neurodegeneration to the DA system, and if this effect could be due to regulation of DAT function.
Results
Neurohistopathology
Male C57BL/6 mice of 8–10 months of age were pretreated with saline or pramipexole (0.1 mg/kg/day) followed by MPTP. At the end of the 7-day recovery period following the last injection of MPTP or vehicle were assessed for the degree of toxicity to the dopamine system by MPTP. MPTP produced a marked loss of tyrosine hydroxylase-immunoreactive (TH-IR) neurons in the substantia nigra (SN), but had less impact in the ventral tegmental area (VTA) (Figs 1 and 2), Unbiased stereological quantification of the number of Nissl-stained and TH-IR neurons in the SNpc and VTA was made in the midbrains of the treated groups. MPTP produced a 31% loss of TH-IR neurons in the SN and 17% loss in the VTA. Pramipexole administered once a day for 5 days (i.e. 3 days prior to and during the 2-day vehicle treatment) did not alter the total number of TH-IR neurons in the SN or VTA. Pramipexole administered for 3 days prior to and during the 2-day administration of MPTP completely prevented TH-IR neuron loss in the SN and VTA of MPTP treated mice. To confirm that TH-IR neurons were dead and not simply exhibiting reduced TH-IR, neurons in Nissl stained sections were counted. The results confirmed the data that D3-preferring agonists can protect against MPTP in vivo as well as against MPP+ in vitro [26,27].
Visualization of DA fibers with dopamine transporter immunoreactivity, DAT-IR (Fig 3), and TH-IR (Fig 4) demonstrated uniform staining of the caudate-putamen (CPu) and nucleus accumbens (Nac) in the saline/saline cases. In saline pretreated mice, MPTP reduced DAT-IR by 52% in the CPu (Figs 3 and 5A) and had a smaller but significant impact on DAT-IR labeling of DA fibers in the Nac. DAT-IR in the CPu in the pramipexole plus vehicle (PPX-SAL)-treated mice was reduced by 17%. Furthermore, in pramipexole and MPTP treated mice, a significant attenuation of the impact of MPTP in the CPu (-27% vs -52% loss) and Nac of MPTP treated mice was seen.
In contrast to DAT-IR, levels of TH-IR (Figs 4 and 5B) was not significantly reduced (~11%) by pramipexole in PPX-SAL group. MPTP produced a significant (34%) loss of TH-IR labeling of DA fibers in the CPu of WT mice and to a lesser degree in the Nac SAL-MPTP group. Pramipexole did not significantly attenuate the effects of MPTP on TH-IR labeling of DA fibers in the CPu of WT mice (Fig 5B).
[3H]MPP+ and [3H]dopamine uptake in mouse striatal postnuclear preparations
Since pramipexole treatment in non-MPTP treated mice altered levels of DAT-IR, it was important to identify if pramipexole treatment altered the kinetics of [3H]DA and [3H]MPP+ uptake through the DAT. Kinetics of [3H]DA uptake in mouse striata demonstrated a higher Vmax but a similar Km to that reported for rat fresh striatal postnuclear preparation [28]. Uptake was sodium dependent and the Km was similar due to inclusion of COMT inhibitor in the preparations [29]. C57BL/6 mice treated with pramipexole once a day for 5 days exhibited significant differences from saline treated mice (Fig 6). The Vmax (P = 0.0008) and the Km (P = 0.016) for [3H]DA uptake was significantly decreased in pramipexole treated mice. The Vmax (P < 0.001) and the Km (P = 0.001) for [3H]MPP+ uptake were also significantly decreased in pramipexole treated mice.
To determine if the reduction in Vmax and the Km for [3H]DA uptake by subchronic treatment with pramipexole was due to the D3 receptor, the values for Vmax and the Km for [3H]DA uptake was measured in WT (C57BL/6) and D3 KO littermate mice (bred according to our previous methods [30]), treated with vehicle or pramipexole for 5 days. ANOVA showed that there were group differences for Vmax values (F = 72.41, P < 0.0001) and for Km values (F = 9.78, P = 0.0007). Synaptosomes prepared from D3 KO mice had significantly lower Vmax (P = 0.001, by 59%) and Km (P = 0.01, by 69 %), values for [3H]DA uptake than WT mice (Table 1). WT mice treated with pramipexole significantly lowered Vmax (P = 0.001, by 65%) and Km (P = 0.05, by 50%) values for [3H]DA uptake compared with WT mice treated with saline. D3 KO mice treated with pramipexole had significantly lower Vmax (P = 0.001, by 57%) and Km (P = 0.051, by 77%) values for [3H]DA uptake than WT mice treated with saline, but D3 KO mice treated with pramipexole were not different from D3 KO mice treated with vehicle. WT mice treated with pramipexole were also not different from D3 KO mice treated with pramipexole.
Dopamine transporter (DAT) autoradiography
To determine if the reduction in Vmax and the Km for [3H]DA uptake by subchronic treatment with pramipexole at 24 h post-treatment was due to the D3 receptor regulation of the number of DAT sites, the density of DAT sites was measured in WT and D3 KO mice treated with vehicle or pramipexole for 5 days. ANOVA showed that there were no group differences (Table 1) at 24 h post-treatment, but by 14 days there was a significant reduction of DAT sites in WT mice treated with pramipexole (P < 0.01), but not in D3 KO mice (Fig 7). WT mice treated with pramipexole exhibited a 27% reduction in the CPu, a 31% reduction of [125I]RTI binding to DAT in the Nac, and a 11% reduction in the Nas compared to WT mice treated with vehicle.
Discussion
Antiparkinsonian agents that are direct DA agonists (e.g. apomorphine[1], bromocriptine [2,23], and pramipexole [3]) are almost completely neuroprotective against MPTP-induced loss of striatal DA in mice. MPTP administration to mice can produce varying degrees of effect on permanent damage to the nigrostriatal DA system depending on the schedule of MPTP administration and survival period after MPTP [31-36]. In addition, under some conditions dramatic recovery from the initial loss of striatal DA levels can occur without any intervention [33,34]. Therefore, studies employing measures of striatal DA levels as the sole criteria for impact of MPTP and protection by DA agonists do not offer convincing evidence of protection [1-3,23,37]. Three studies have employed both unbiased stereological counts of TH-IR neurons in the midbrain along with estimate of striatal DA content of animals administered MPTP [21,24] or intracerebroventricular 6-hydroxoydopamine [38] to produce permanent damage to the DA system with protection by pramipexole. In all 3 studies the impact of MPTP on striatal DA levels was reduced by 50% with pretreatment or concurrent plus post-MPTP treatment with pramipexole (1 to 3 mg/kg). Protection against midbrain TH-IR neurons was greater, even at 12 to 14 days post MPTP/6-OHDA treatment than against DA levels. In this study of a much lower dose of pramipexole (10 to 30 times lower) given 3 days prior to and during the 2-day MPTP treatment, at 1-week post-MPTP pramipexole treatment completely spared the substantial MPTP-induced loss of TH-IR neurons from the SNpc, but was less effective against the loss of DA fibers in the CPu (TH-IR and DAT-IR measures). Measurements of striatal DA [21,24] which show more protection by pramipexole than our estimates of DA fibers in the CPu (TH-IR and DAT-IR measures) may reflect upregulation of DA synthesis in the remaining fibers rather than protection against fiber loss. In addition, pramipexole treatment by itself reduced DAT-IR of DA fibers, suggesting that protection against MPTP might be greater than our immunocytochemical measures indicate.
The interesting observation that pramipexole down-regulated DAT-IR in saline treated mice suggests that DAT regulation plays a role in neuroprotective effects of pramipexole. D3 receptors are most concentrated in the brain on neurons in the nucleus accumbens, however DAergic neurons do express both D2 and D3 receptors [39,40], and both D2 and D3 receptors are functional autoreceptors [16-20,41,42]. Dopaminergic neurons are tonically inhibited by dendritic and terminal autoreceptors operating in interaction with DA transporters and pharmacologically of the D2 receptor type [16-19]. Zapata et al [20] have reported that the D3 preferring agonist (+)-PD 128907 does interact with D3 autoreceptors to regulate extracellular DA levels. Those data are consistent with acute treatment with D2/D3 agonists leading to increased Vmax [42,43], but less is known about subchronic treatment.
We observed that subchronic treatment with pramipexole reduced the Vmax, and Km, of [3H]DA and [3H]MPP+ uptake in mice at 24 h post-treatment. Interestingly, D3 receptor KO mice exhibited substantially lower Vmax and Km values of [3H]DA uptake than WT mice, and pramipexole did not further reduce Vmax and Km values in D3 receptor KO mice. However, the actual density of sites on the DA terminals was not reduced 24 h after pramipexole treatment in WT mice (or in D3 receptor KO mice), as determined by DAT autoradiography. The inability to modulate the number of DAT binding sites 12 h after termination of subchronic treatment with D2/D3 receptor agonists has previously been reported [44], suggesting that the initial alteration of Vmax and Km with pramipexole might be due to modification of the kinetics of DAT. However, pramipexole treatment reduced DAT-IR of DA fibers 7 days post-treatment, suggesting a reduction in DAT sites. Consistent with this, there was a reduction of [125I]RTI binding to DAT sites in pramipexole treated WT mice 14 days after pramipexole treatment, but not in D3 receptor KO mice. Thus, there might be both rapid and slower modifications in DAT function produced by D3/D2 agonist treatment, ultimately resulting in lower DAT number, and mediated by the D3 receptor.
It is known that DAT half-life in the striatum is decreased by D2/D3 receptor agonists, and increased by the dopamine D2/D3 receptor antagonist, but not by D1 agonists and antagonists [45]. Furthermore, the D2 agonist-induced change in DAT kinetics iss inhibited by the co-administration of an antagonist. The absence of DA receptors can also influence DAT function, as shown by dopamine D2 receptor-deficient mice, which exhibit decreased striatal DA uptake [19]. The present results can be compared with those reported by Saunders et al. [46] using hDAT-FLAG expressed in human embryonic kidney 293-EM4 cells, who showed by confocal microscopy and whole-cell current recordings that 2 μM d-amphetamine increased internalization of surface DAT within 1 h. Treatment with DA in HEK-hDAT cells also reduced Vmax, due to a diminished presence of DAT at the surface of synaptosomes [47]. Subchronic pramipexole might lead to redistribution DAT from the plasma membrane to endosomal compartments, and regulated, in part, by the D3 receptor. The initial change in Vmax, and Km, could be related to a more rapid turnover of DAT, and the longer-term reduction in Bmax to greater internalization and/or reduced synthesis. Thus, D3 preferring agonists might be potent autoreceptor agonists, and long-term changes in expression of the DAT or the functional properties of DAT could occur following subchronic treatment. This, in turn, could lead to reduced MPP+ (and other neurotoxins) uptake into DA neurons, and reduced toxicity in animal models of PD. Ramirez and associates [37] reported that the neuroprotective effects of pramipexole against MPTP-induced DA loss in mice was attenuated by the selective D3 antagonist A-437203. Furthermore, the neuroprotective effect of a low dose of pramipexole was attenuated in D3 transgenic knockout mice and protection by pramipexole was not further attenuated by treatment with a D3 antagonist. These in vivo data support an important role for the D3 receptor in the neuroprotective effects of DA agonists, and our data suggest that this, in part, is due to reduced MPP+ uptake into DA neurons.
Conclusions
We have identified that subchronic treatment with a clinically relevant dose of pramipexole beginning before initiation of MPTP treatment affords neuroprotection against DA neuron loss and, to a lesser extent, DA fiber loss. This might involve down-regulation of DAT and reduced MPP+ uptake into DA fibers. Since intracellular accumulation of MPP+ following systemic injection of MPTP requires DAT [4], then if DAT function and/or number are reduced by D3 preferring agonists this could result in lower intracellular accumulation of MPP+ and reduced neurotoxicity to MPTP. The importance of knowing the targets of pramipexole, and other D3 preferring agonists, in neuroprotection in animal models of PD cannot be understated, given the possibility that this protection could be extended to humans [12]. However, in vivo imaging of DAT as a tool for analyzing the neuroprotective effects of DA agonists could be difficult to interpret, since DAT might be regulated by DA agonists [25,48]. Our data are consistent with this hypothesis and suggest that multiple measures of DA fiber integrity are required to assess neuroprotection by agents [49,50].
Methods
All animals were treated in accordance with a protocol approved by the Sun Health Research Institute Animal Care and Use Committee.
MPTP Treatment
We bred C57BL/6 mice from breeding pairs obtained from Jackson Laboratories. Eighteen male C57BL/6 mice 8–10 months of age were used for the experiments and were handled for 1 week prior to treatment. They had free access to food and water, and were maintained in a 12 h light/dark cycle prior to treatment. Mice were divided into 4 groups: the first received only vehicle (0.9% saline, 0.1 ml/10 mg body wt) (SAL-SAL), the second received pramipexole plus vehicle (PPX-SAL), the third received vehicle plus MPTP (RBI, MA) (SAL-MPTP), and the fourth received pramipexole plus MPTP (PPX-MPT). For those receiving pramipexole (Pharmacia Corporation, Kalamazoo, MI), the drug was dissolved in 0.9% sterile saline and administered by i.p. injection (0.1 ml/10 mg body wt). A single daily dose of 0.1 mg/kg body weight of pramipexole was given for 5 days. Those not receiving pramipexole were given identical injections of saline., Following pramipexole or vehicle injections on days 4 and 5, mice were given injections of either MPTP (20 mg/kg; s.c.) or vehicle (saline) twice daily at 8 h intervals.
After 7-day recovery period following the last injection of MPTP or vehicle, animals were euthanized by intracardiac perfusion with 4% paraformaldehyde in 0.15 M phosphate buffer (pH 7.2) following overdose with pentobarbital 120 mg/kg body weight i.p. Brains were removed, postfixed in the perfusion fixative for 24 h at 4°C and transferred to 30% sucrose solution for additional 24 h incubation at 4°C. The tissue was frozen on dry-ice and sectioned in cryostat at 20 μm thickness. Sections were placed in cryoprotectant solution for long-term storage at -80°C.
Neurohistopathology
Every 10th section at the level of striatum was processed for visualization of DAT and tyrosine hydroxylase (TH), and every 5th that of the midbrain processed for the visualization of TH-positive cell bodies using the avidin-biotin procedure [26,51]. Immediately adjacent sections from the midbrain were stained for cresyl violet for detection of cells. The sections were washed in phosphate buffer to remove cryoprotectant, incubated with 5% goat serum for 30 min to block background staining and incubated with anti-DAT (Chemicon, CA) or anti-TH (Chemicon, CA) at 1:1000 dilution overnight at room temperature. Control sections were treated with identical solutions but with no primary antibody. Sections were rinsed and incubated with biotinylated secondary anti-rabbit antiserum at a 1:500 dilution (Vector, CA) for 90 min at room temperature. Sections were again rinsed, incubated in streptavidin-peroxidase complex (Vector, CA) at a 1:250 dilution for 2 h at room temperature. After more thorough rinsing, sections were processed for DAB with nickel enhancement. Sections were then rinsed in phosphate buffer, mounted on gelatin-coated slides, air-dried, dehydrated, and cleared in xylene and mounted with Permount.
Unbiased stereological quantification of Nissl-stained and TH-IR neurons in the SNpc and VTA was used to estimate cell number in the midbrain. The general routine at low magnification involved use of a sampling grid for the SNpc and VTA. At high magnification the computer-based imaging system randomly selected a region of the grid and clearly definable neurons was counted within the 3-dimensional block. This was repeated for every 10th region of each section. Estimation of total neuron number was based upon actual cell counts, tissue thickness, total area of designated region, and the total number of sections analyzed per animal. Group means and variances were calculated.
Our routine quantitative measurement of the optical density of regions of the striatum stained for TH-IR and DAT-IR [26,51] was employed. Using a Macintosh-based image analysis system with CCD camera and imaging software (BRAIN version 3.0, Drexel University) optical density measurements calibrated to an external standard (Kodak density step tablet) of the region of interest (ROI) and a control region (corpus callosum) of each section were made, the ratio of the ROI to control region was calculated, and the average for each animal determined. Group means and variances were estimated. Statistical analysis of group differences were assessed by ANOVA with pairwise comparisons performed using post-hoc t-tests and the Bonferroni correction.
Testing of whether pramipexole administration alters dopamine transporter function
Fourteen C57/Bl6 mice (25–30 g, 6 months) were divided into 2 groups: one group received pramipexole (0.1 mg/kg) once a day for 5 days and the other group received the vehicle (saline). An additional 10 WT and 10 D3 receptor knockout mice (25–30 g, 6 months), bred according to our previous methods [30], were treated similarly. Twenty-four hours after the last injection of pramipexole or vehicle the mice were euthanized using CO2 narcosis and the brains rapidly removed and snap-frozen in liquid nitrogen. Km and Vmax values for [3H]1-methyl-4-phenylpyridinium (MPP+) and [3H]DA uptake in synaptosomes derived from the striatum were deterimined by the method of Eshleman et al [28], with minor modifications. Comparison of mean values for the Km and Vmax of [3H]MPP+ and DA uptake were made by t-test (0.05 level of significance).
[3H]MPP+ and [3H]dopamine uptake in mouse striatal postnuclear preparations
Mouse striata were dissected and homogenized with a glass-Teflon homogenizer in ice-cold modified HEPES (1 ml). The sample was centrifuged at 1000 g, for 10 min at 4°C. The supernatant was collected and centrifuged at 14,000 g for 10 min at 4°C. The pellet was resuspended in 8 ml of HEPES buffer (HEPES 25 mM, NaCl 122 mM, CaCl 2.5 mM, MgSO4 1.2 mM, pargyline 10 uM, glucose 0.2%, ascorbic acid 0.02%, pH7.4). To the 8 ml sample, butaclamol was added at a final concentration of 100 nM, 4 ml of sample was than removed and placed in a separate 15 ml centrifuge tube (VWR, Pennsylvania) and Mazindol (Sigma, Missouri) was added at a final concentration of 40 uM. 50μl of the samples were added to borosilicate tubes (Fisher, Texas) and placed in a 25°C water bath with the drugs for a 10 min preincubation. The assay was initiated by adding 50μl concentrations of unlabeled MPP+ or DA ranging from 0–300 nM with [3H]MPP+ or [3H]DA at a final concentration of 20 nM. The samples were incubated at 25°C for 10 min. Specific uptake was defined as the difference in uptake observed in the absence and presence of mazindol (40μM). Uptake was terminated after 5 min by filtration through Whatman GF/C filters presoaked in HEPES buffer. Scintillation fluid was added to each filtered spot and radioactivity remaining on the filters was determined using a Wallac β-scintillation spectrometer. Each experiment involved triplicate determinations, and 6 independent experiments for each drug competition curve were performed.
Dopamine transporter (DAT) autoradiography
10 WT and 10 D3 receptor knockout mice (25–30 g, 6 months) were divided into 2 groups: one group received pramipexole (0.1 mg/kg) once a day for 5 days and the other group received the vehicle (0.9% saline). Twenty-four h after the last injection of pramipexole or vehicle, the mice were overdosed as above and the brains rapidly removed and frozen on dry- ice. An additional group of 10 WT and 10 D3 receptor knockout mice (25–30 g, 6 months) were similarly treated but their brains were processed 14 days after the last treatment. Autoradiography of DAT sites were quantified following labeling with [125I]RTI-55 (3ß-(4-iodophenyl)tropan-2 ß-carboxylic acid methyl ester) (Dupont, New England Nuclear, Boston, MA) in the presence of 100 nM paroxetine (Smith Klein Beecham BRL 29060A) to block the serotonin transporter, according to published methodology [30]. Specific binding was defined with 40μM benztropine (Sigma, St. Louis MO), and amounted to 95% of total binding. Sections were apposed to 3H-Hyperfilm for 18 h for DAT. Autoradiographs were analyzed using a computer-based image analysis system (AIS, Imaging Research Inc., Ontario Canada) that converts transmitted optical density to the amount of radioligand bound in pmol per microgram of protein.
List of abbreviations
D3 KO = D3 receptor knockout
DA = dopamine
DAT = dopamine transporter
MPP+ = 1-methyl-4-phenylpyridinium
MPTP = 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine
PD = Parkinson's disease
SN = substantia nigra
TH = tyrosine hydroxylase
VTA = ventral tegmental area
WT = wild type
Competing interests
The author of this manuscript received a grant from Pharmacia Corporation to partially support the costs of the research. This company is no longer in existence.
Authors' contributions
CW and HR carried out the MPTP treatment, performed the immunocytochemistry and participated in the statistical analysis. SB and DH carried out the uptake assays, performed the DAT autoradiography and participated in the statistical analysis. JNJ conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
This work was funded by Federal Grant NS40669, Arizona Alzheimer's Disease Research Center contract 4001(Arizona Parkinson's Disease Center), and Pharmacia Corporation (Kalamzaoo, MI).
Figures and Tables
Figure 1 Impact of pramipexole on MPTP-induced loss of TH-IR neurons. Low-power photomicrograph (20x) of the substantia nigra and ventral tegmental area of tissue sections stained for TH-IR from individual cases of 4 groups of mice: (A) Veh-Veh, (B) pramipexole -Veh, (C) Veh-MPTP (25 mg/kg sc., 4 times in 2 days at 8 h intervals), (D) pramipexole-MPTP. Note the marked depletion of TH-IR from the substantia nigra and considerably lesser impact in the ventral tegmental area following MPTP. Pramipexole pretreatment regimen provided complete protection (see D) from MPTP. Abbreviations: SNpc, substantia nigra pars compacta; VTA, ventral tegmental area.
Figure 2 Histogram of pramipexole's neuroprotective effects against MPTP induced cell loss. Effects of subchronic treatment with pramipexole on total TH-IR cell counts in the substantia nigra and ventral tegmental area of mice treated with or without MPTP. Pramipexole were administered 3 days prior to and during the 2-day treatment with MPTP. Animals survived for 7 days after the last MPTP injection. Data are mean ± standard error of the mean from 4–5 samples per group. * P < 0.05 vs all other groups
Figure 3 Impact of pramipexole on MPTP-induced loss of DAT-IR fibers. Low-power photomicrograph (20x) of the caudate-putamen and nucleus accumbens of tissue sections processed for DAT-IR from individual cases of 4 groups of mice: (A) Saline-Saline, (B) pramipexole – Saline, (C) Saline -MPTP (20 mg/kg sc., 4 times in 2 days at 8 h intervals), (D) pramipexole-MPTP. Note the marked depletion of DAT-IR from the caudate-putamen (C) and apparent protection by pramipexole (D). Abbreviation: CPu, caudate-putamen; NAS, nucleus accumbens shell; NAC, nucleus accumbens core.
Figure 4 Impact of pramipexole on MPTP-induced loss of TH-IR fibers. Low-power photomicrograph (20x) of the caudate-putamen and nucleus accumbens of tissue sections processed for TH-IR from individual cases of 4 groups of mice: (A) Saline-Saline, (B) pramipexole – Saline, (C) Saline -MPTP (20 mg/kg sc., 4 times in 2 days at 8 h intervals), (D) pramipexole-MPTP. Note the marked depletion of TH-IR from the caudate-putamen (C) and apparent protection by pramipexole (D). Abbreviation: PPX, pramipexole.
Figure 5 Histogram of pramipexole's neuroprotective effects against MPTP induced fiber loss. Effects of subchronic treatment with pramipexole on DAT-IR (A) and TH-IR (B) optical density in the caudate-putamen, nucleus accumbens core and shell of mice treated with or without MPTP. Animals survived for 7 days after the last MPTP injection. Data are mean ± standard error of the mean from 4–5 samples per group. # for P < 0.01, all groups vs SAL + MPTP group; $ for P < 0.01, for SAL-PPX vs SAL-SAL. Abbreviations: CPU, caudate-putamen; Nac Core, nucleus accumbens core; Nac Shell, nucleus accumbens shell; PPX-MPTP, pramipexole + MPTP treated group; SAL-MPTP, saline + MPTP treated group; PPX-SAL, pramipexole + saline treated group; SAL-SAL, saline + saline treated group.
Figure 6 Effects of pramipexole on the kinetics of [3H]DA and [3H]MPP+ uptake. Effects of subchronic treatment pramipexole or vehicle (saline) on [3H]DA uptake (A) and [3H]MPP+ uptake (B) in striatal synaptosomes from treated mice. Group data are plotted with the mean ± standard error of the mean from 6 samples per group. The dashed line for each of the means represents the calculated 95% confidence interval. The Vmax and Km were calculated for each group and paired comparisons by t-test revealed significant differences.
Figure 7 Effects of pramipexole on DAT density at 14 days post-treatment. Effects of subchronic treatment with pramipexole on density of [125I] RTI-55 labeled sites by autoradiography. Animals survived for 14 days after the last pramipexole injection. Data are mean ± standard deviation of the mean from 4–5 samples per group. * for P < 0.01, PPX-WT vs all groups. Abbreviations: CPU, caudate-putamen; Nac Core, nucleus accumbens core; Nac Shell, nucleus accumbens shell; PPX-KO, pramipexole treated D3 knockout mice; PPX-WT, pramipexole treated WT mice; SAL-KO, saline treated D3 knockout mice; SAL-WT, saline treated WT mice.
Table 1 Effects of genotype and drug on [3H]DA uptake and DAT density
[3H]DA uptake DAT density
Group N Vmax SD Km SD DAT SD
WT w Veh 4 516.1 ± 56.5 56.7 ± 18.6 10.6 ± 2.5
D3 KO w Veh 4 212.4 ± 18.2 10.8 ± 5.7 10.8 ± 1.5
WT w PPX 6 183.4 ± 34.3 28.4 ± 18.5 9.9 ± 3.0
D3 KO w PPX 6 223.6 ± 36.4 10.4 ± 10.9 9.4 ± 2.6
Wild-type and D3 KO mice were administered vehicle (0.9% saline) or pramipexole (PPX, 0.1 mg/kg I.P.) once a day for 5 days. Twenty-four hours after the last injection the brains were removed, the CPu dissected and fresh frozen ([3H]DA uptake) or brains frozen on dry-ice ([125I] RTI-55 autoradiography).
[3H]DA uptake Vmax reported as pmol/mg P/5 min and Km reported as nM. [125I] RTI-55 autoradiography for DAT density reported as fmol/μg P.
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| 15473914 | PMC524509 | CC BY | 2021-01-04 16:02:56 | no | BMC Biol. 2004 Oct 11; 2:22 | utf-8 | BMC Biol | 2,004 | 10.1186/1741-7007-2-22 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-391548815510.1186/1471-2121-5-39Research ArticleProduction of hemo- and immunoregulatory cytokines by erythroblast antigen+ and glycophorin A+ cells from human bone marrow Sennikov Sergey V [email protected] Tatyana V [email protected] Sergey V [email protected] Alexandr N [email protected] Igor B [email protected] Natalya J [email protected] Anton N [email protected] Mary I [email protected] Vladimir A [email protected] Laboratory of the Regulation of Immunopoiesis, Institute of Clinical Immunology SB RAMS, Yadrintsevskaya 14, Novosibirsk, 630099, Russia2 The Department of Haematology, Regional Haematological Center, Novosibirsk, Russia2004 18 10 2004 5 39 39 27 5 2004 18 10 2004 Copyright © 2004 Sennikov et al; licensee BioMed Central Ltd.2004Sennikov 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
Erythroid nuclear cells (ENC) of the bone marrow (BM) have not previously been considered as important producers of wide spectrum of haemo- and immunoregulatory cytokines. The aim of the current work was to confirm the production of the main hemo- and immunoregulatory cytokines in human ENC from BM.
Results
We used native human BM ENC in our experiments. We for the first time have shown, that the unstimulated erythroblasts (Gl A+ or AG-EB+) produced a wide spectrum of immunoregulatory cytokines. Human BM ENC produce cytokines such as interleukn (IL)-1β, IL-2, IL-4, IL-6, interferon (IFN)-γ, transforming growth factor (TGF)-β1, tumor necrosis factor (TNF)-α and IL-10. They can be sub-divided into glycophorin A positive (Gl A+) and erythroblast antigen positive (AG-EB+) cells. To study potential differences in cytokine expression between these subsets, ENC were isolated and purified using specific antibodies to Gl A and AG-EB and the separated cells were cultivated for 24 hours. The cytokine contents of the supernatant were measured by electrochemiluminescence immunoassay. Quantitative differences in TGF-β1 and TNF-α production were found between Gl A+ and AG-EB+ BM ENC. Furthermore, in vitro addition of erythropoietin (EPO) reduced IFN-γ and IL-2 production specifically by the AG-EB+ ENC. Thus, Gl A+ and AG-EB+ ENC produce IL-1β, IL-2, IL-4, IL-6, IFN-γ, TGF-β1 and TNF-α. Gl A+ ENC also produce IL-10.
Conclusion
Cytokine production by erythroid nuclear cells suggests that these cells might be involved in regulating the proliferation and differentiation of hematopoietic and immunocompetent cells in human BM.
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Background
Haematopoesis is regulated by lymphoid and non-lymphoid cells through a complex network of paracrine and autocrine mechanisms involving cytokines, growth factors and their receptors. However, although stromal [1-6], endothelial [7-10], megakaryocytic [11,12] and osteogenic cells [13-16] and lymphocytes are known to express cytokines, erythroid nuclear cells (ENC), the major cell population of the BM, are not considered as important producers of hemo- and immunoregulatory cytokines.
Experiments on ENC isolated from mouse spleen undergoing erythroid hyperplasia, and on cells separated from single erythroid colonies, have revealed that mRNAs for cytokines such as IL-1α, IL-1β, IL-4, IL-6, granulocyte-macrophage colony stimulating factor (GM-CSF), TGF-β1 and IFN-γ are present [17,18]. Production of GM-CSF and IFN-γ has also been reported [17]. Similarly, IL-2 and IL-3 mRNAs were found after erythroid cells were treated with erythropoietin (EPO) [17-20]. Human fetal liver erythroid cells have also been shown to produce such cytokines as IL-1, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and TGF-β1 [20]. Stopka and co-authors have demonstrated the ability of burst-forming unit erythroid cells (BFU-E) to express and produce EPO [21]. Also it has been shown that erythroid cells express and produce vascular-endothelial growth factor (VEGF)-A and placental growth factor (PlGF) both in vitro and in vivo. Production of these proteins varies during differentiation and is increased 100-fold by PlGF and 3-fold by VEGF-A [22]. Macrophage colony stimulating factor (M-CSF), fibroblast growth factor (FGF)-2, VEGF-A, hepatocyte (H)GF, insulin-like (I)GF-1, IL-1β, trombopoietin (TPO), TNF-α, IFN-γ, FAS-L, and macrophage inflammatory protein (MIP)-1α mRNAs are also expressed in BFU-E isolated from BM, and VEGF-A and TGF-β1 are produced [23,24]. All these data allow us to consider erythroid cells as cytokine producers involved in regulating hemo- and immunopoiesis.
The aim of the current work was to confirm previous observations by studying the production of the main hemo- and immunoregulatory cytokines, IL-1β, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and TGF-β1, in human ENC from BM. Furthermore, we wished to determine whether different subsets of ENC (AG-EB+ and Gl A+ erythroid cells from human BM) might account for altered expression levels. Expression of both these markers is specific to erythroid cells; they are absent from the membranes of other blood-forming cells. AG-EB is expressed in a proportion of BFU-E cells (up to 4% of AG-EB+ BFU-E cells) and on colony-forming units – erythroid (CFU-E) (up to 36% of AG-EB+ CFU-E cells) [25]. Its expression on erythroblasts is further increased (up to 90% in AG-EB+erythroblasts). Later in development, AG-EB expression decreases, and only 4% of reticulocytes carry AG-EB [25]. In contrast, Gl A is expressed in a minor subpopulation of the CFU-E cells (less than 4%). On the erythroblasts this expression is increased and reach up to 100% in reticulocytes [26,27]. Sheme of AG-EB and Gl A expression during erythroid maturation present in Table 1.
Table 1 Expression of AG-EB and Gl A during erythroid maturation. The percentage of positive cells detected by flow cytometric analysis [25, 26, 27].
% of positive cells
BFU-E CFU-E Proerythroblasts → erythroblasts reticulocytes
AG-EB 0 – 4 36 93–95 4
Gl A 0 0 – 4 Increase with maturation 100
In this article we show which human BM ENC produce the cytokines IL-1β, IL-2, IL-4, IL-6, IFN-γ, TGF-β1, TNF-α and IL-10.
Results
Erythroid cells were separated according to the presence of the surface markers AG-EB and Gl A. BM erythroid cells carrying AG-EB produce the cytokines IL-1β, IL-2, IL-4, TNF-α, IFN-γ and TGF-β1 (figure 1). Those carrying Gl A also produce these cytokines and in addition might be secrete IL-10 (figure 1). Interestingly, erythroid cells carrying AG-EB release more TGF-β1 (p < 0.05) than Gl A+ cells, whereas Gl A+ secrete more TNF-α (p < 0.05).
Figure 1 Cytokine production by BM erythroid nuclear cells carrying Erythroblast Antigen and Glycophorin A. □ – BM AG-EB+ erythroid nuclear cells; ■ – BM Gl A+ erythroid nuclear cells. All populations were cultivated for 24 h at concentrations of 106 cells per ml. Results present as (Mean ± SEM). n/d – not detected. * – P < 0.05.
To the best of our knowledge, this difference between BM AG-EB+ and Gl A+ ENC has not previously been reported. To verify that the cytokines were truly expressed by these erythroid cells, we isolated erythoid colonies by cultivating single BM cells in MethoCult H4433 medium. As shown in Table 2, the cytokines produced were comparable between ENC derived from erythroid colonies and ENC enriched from the BM.
Table 2 Concentration of cytokines in conditioned medium derived from erythroid cells. Concentration of cytokines (pg/ml) in conditioned medium derived from enriched population of BM erythroid cells (1 × 106/ml) and erythroid cells isolated from erythroid colonies (1 × 106/ml). Results were obtained from at least three independent experiments (Mean ± SEM).
Enriched erythroid nuclear cell population Erythroid nuclear cells derived from erythroid colonies
IL-1β 78.64 ± 44.84 10.8 ± 0.39
IL-2 255.36 ± 88 237.73 ± 237.73
IL-4 62.66 ± 45.28 738.26 ± 640.15
IL-6 741.08 ± 395.95 324 ± 56.14
IL-10 121.26 ± 53.22 73.4 ± 73.4
TNF-α 2.59 ± 1.41 0.0
IFN-γ 766.35 ± 319.45 752.88 ± 492.65
TGF-β1 524.88 ± 372.59 479.25 ± 267.75
EPO regulates the proliferation and differentiation of erythroid cells [28-30]. Therefore, we assessed the effect of EPO on our different ENC cell populations and found that the production of some hemo- and immunoregulatory molecules was altered (figure 2). For instance, EPO treatment of AG-EB+ cells led to a 99% decrease in IFN-γ production and a 50% decrease in IL-2 production, and the production of IL-1β and TNF-α was enhanced. However, EPO treatment had no discernible effect on the cytokine profile of Gl A+ cells.
Figure 2 Influence of EPO on cytokine production by BM AG-EB+erythroid nuclear cells. □ – BM AG-EB+ erythroid nuclear cells; ■ – BM AG-EB+ erythroid nuclear cells after treatment with EPO. BM AG-EB+ erythroid nuclear cells (106 cells/ml) were treated with EPO (2 U/ml) for 24 h. Results present as (Mean ± SEM). n/d – not detected. * – P < 0.05.
Discussion
We have shown that the native BM ENC can produce a wide spectrum of cytokines, which are capable of both stimulating and inhibiting erythropoiesis. Cytokine production has been confirmed using ENC isolated from erythroid colonies cloned from a single BM cell.
Earlier we demonstrated cytokine production by ENC derived from fetal tissues [19,20]. However, there was a great difference between the profiles of cytokines released by BM and fetal ENC.
TGF-β1 is known to inhibit the proliferation of erythroid precursors and to provoke or accelerate the terminal differentiation of mature erythroid cells [31-33]. The fetal AG-EB+ and Gl A+ erythroid cells produced TGF-β1 in concentrations less than 5 pg/ml and we suggested that this could be related to the increased proliferative activity of erythroid cells in the fetal liver [20]. We propose that the high level of TGF-β1 production by BM erythroid cells is probably related to the need to restrain erythroid cell proliferation in healthy adult individuals. This hypothesis were declared also in review [24].
It is notable that BM erythroid cells produce not only inhibitors but also high levels of stimulators of erythropoiesis such as IL-4 and IL-6, especially in the presence of EPO [34,35]. The production of these hemoregulatory molecules suggests that erythroid cells have a capacity for self-regulation. Interestingly, IL-2, which inhibits erythropoiesis [36], is produced by these cells in smaller quantities or, like IL-10, is not produced at all.
Besides the differences in production of some cytokines by BM erythroid cells, there are parallels with cytokine production from fetal erythroid cells. A similar situation can be observed when TNF-α production by BM erythroid cells is compared with those from fetal liver [20]. In both cases AG-EB+ cells produce significantly less TNF-α than Gl A+ (figure 1). TNF-α is a known inhibitor of precursor cell proliferation and an inducer of cell death in a proportion of these cells [37,38]. Therefore it is plausible that erythroid cells use this cytokine for autocrine self-regulation, by maintaining a negative feedback loop. If this is so, then relatively small increases in the number of more differentiated Gl A+ cells would lead to a decreased precursor proliferation rate, due to the elevated level of TNF-α. Negative feedback can also be maintained through IFN-γ, which is produced at the same level by both Gl A+ and AG-EB+ cells. However, IFN-γ initiates apoptosis only in BFU-E [39,40], while in CFU-E cells it increases the expression of Bcl-x and protects these cells from elimination. Thus, IFN-γ enhances the differentiation of erythroid precursors [41,42]. We found no statistical significant differences between AG-EB+ and Gl A+ cells in the production of IL-1β, IL-2, IL-4, IL-6 and IL-10.
The capacity of erythroid cells to change their cytokine expression profiles quantitatively and qualitatively under different conditions is an important indicator of their active role in the regulation of hemo- and immunopoiesis. Similar data were previously obtained in mouse cells and are presented here for human BM erythroid cells. Treatment with EPO leads to significant changes in IL-2 and IFN-γ production. Notably, IFN-γ and IL-2 secretion by AG-EB+ cells is lowered in the presence of EPO, while Gl A+ cells show no such change. This difference could be related to EPO-R expression in AG-EB+ cells, which are mainly CFU-E and erythroblasts [43-45]. Expression of this receptor ceases in more differentiated early erythroblasts. Indeed, Gl A+ cells do not express EPO-R, since late erythroblasts and more differentiated forms of erythroid cells are prevalent in this population [28,43,46]. The EPO-induced changes in cytokine production mainly concern inhibitors of erythroid precursor proliferation, such as IL-2 and IFN-γ. Thus, one mechanism by which erythroid cell proliferation is stimulated could be a decrease in proliferation inhibitor production by these cells as a result of EPO treatment.
Conclusion
Erythroid nuclear cells appear to be active producers of hemo- and immunoregulatory cytokines, involved in regulating the proliferation and differentiation of hematopoietic and immunocompetent cells in human BM. Changes in the cytokines produced by erythroid cells in response to EPO suggests the ability of these cells to respond to microenvironmental changes by altering the cytokine production profile.
Methods
BM cells
This work was approved by the local ethics committee. After obtaining informed consent, sternum BM samples were collected from 8 normal healthy volunteers. All samples used in this study exhibited normal myelograms. The enriched population of erythroid cells was isolated from BM mononuclear cells by depleting of adherent cells and granulocytes as described previously [19].
Isolation of erythroid cell population carrying a surface erythroid antigen and Glycophorin A
We used indirect panning to obtain cells expressing either the erythroid antigen (AG-EB) or Glycophorin A (Gl A) [19]. Monoclonal mouse anti-red blood cell Glycophorin A (MAS518, Harlan-Sera Lab, England), and monoclonal HAE-9 antibodies against AG-EB (kindly provided by Prof. Mechetner, Russia) were used [25]. Affinity isolated polyclonal rabbit anti-mouse immunoglobulin (Biosan, Russia) was used as a secondary reagent to coat Petri dishes during the panning procedure. To avoid non-specific binding to FcRs, cells were blocked with aggregated normal human IgG and then incubated with anti-Gl A or HAE-9 antibodies for 50 min at 4°C. Cells were transferred to Petri dishes covered with rabbit anti-mouse antibodies for panning for 50 min at 4°C. Subsequently, immobilised cells were collected, cultivated for 24 h in RPMI-1640 with 10% horse serum (Sigma, USA) with and without EPO [19,47]. Samples were collected after cultivation.
Cultivation of erythroid cells with EPO
1 × 106 cells were placed in culture medium containing 2 U/ml Epo (Boehringer Mannheim, Germany) for a 24-h incubation.
CFU-E
Cells from the enriched erythroid population were maintained as described above [19,20] at 2·105/ml and then cultivated in MethoCult H4433 medium (Stemcell Tech. Inc. Vancouver, B.C.) for 14 days. Colonies were visually characterised as CFU-E using an inverted microscope. After cultivation, the colonies were harvested and washed twice to remove methyl cellulose. The resulting cellular suspension (1 × 106/ml) was cultivated for 24 h in RPMI-1640 with 10% horse serum, then supernatants were collected. Samples were stored at -20°C.
Cells purity
Cell purity was assessed using Nocht-Maksimov staining of smears [48] and staining of haemoglobin with benzidin [49]. All cells (Gl A+, AG-EB+ and CFU-E cells) were characterised as erythroblasts and were haemoglobin positive.
Electrochemiluminescence (ECL) method for quantitative determination of cytokines
Quantitative determination of cytokines was performed by the electrochemiluminescent immunoassay [50-52] using an ORIGEN Analyzer (IGEN Inc., USA) according to the manufacturer's protocol. Calibration curves ranged from 10 to 10,000 pg/ml. Assay sensitivity was 2.8 pg/ml, 2 pg/ml, 2 pg/ml and 6 pg/ml for IL-1β, IL-2, IL-4 and IL-6, respectively. Assay sensitivities were 2 pg/ml, 1 pg/ml, 1 pg/ml and 2 pg/ml for IL-10, TNF-α, IFN-γ, and TGF-β1, respectively. The ruthenylated and biotinylated antibodies were diluted to working concentrations (μg/ml) 1:1, 1:1, 1:2 and 2:1 for IL-1β, IL-2, IL-4 and IL-6, respectively. The biotinylated and ruthenylated antibodies were diluted to working concentrations (μg/ml) 2:2, 1:1, 2:1 and 4:2 for IL-10, TNF-α, IFN-γ and TGF-β1, respectively.
For TGF-β1 detection, samples were treated with HCl to obtain intact and easily detectable proteins as described [53].
Antibodies
All polyclonal and monoclonal antibodies were purchased from R&D Systems (Abington, UK). The following antibodies were used: against recombinant IL-1β, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ, TGF-β1 polyclonal and monoclonal: anti-hIL-1β #AB-201-NA, anti-hIL-1β #MAB201, anti-hIL-2 #AB-202-NA, anti-hIL-2 #MAB202, anti-hIL-4 #AB-204-NA, anti-hIL-4 #MAB204, anti-hIL-6 #AB-206-NA, anti-hIL-6 #MAB206, anti-hIL-10 #AB-217-NA, anti-hIL-10 #MAB217, anti-hTNF-α #AB-210-NA, anti-hTNF-α #MAB210, anti-hIFN-γ #AB-285-NA, anti-hIFN-γ #MAB285, anti-hTGF-β1 #AF-101-NA, anti-hTGF-β1 #MAB240. No antibody used displayed cross-reactivity with other cytokines.
Cytokines
Recombinant cytokines were purchased from R&D Systems (Abington, UK): rhIL-1β #201-IL, rhIL-2 #202-IL, rhIL-4 #204-IL, rhIL-6 #206-IL, rhTNF-α #210-TA, rhIFN-γ #285-IF, rhTGF-β1 #240-B-002; and PeproTech, Inc. (Rocky Hill, NJ): rhIL-10 #200-10 was used for calibration curves. All the recombinant cytokines were diluted to 2 μg/ml in PBS, pH 7.4, supplemented with 0.05% NaN3 and 0.1% BSA (aliquots were kept at -70°C before use).
Statistical analysis
Results are presented as means ± SEM. Statistical significance was determined using the nonparametric Mann-Whitney U-test. P < 0.05 was considered to be significant.
Authors' contributions
IBK, NJD, ANZ and MIL carried out the selection of donors, sternum BM sampling and analysis of myelograms. TVI, ANS and SVK carried out all of the experiments and drafted the manuscript. SVS participated in design and coordination of the research and revision of the manuscript. ANS participated in the preparation and revision of the manuscript. VAK conceived of the study. All authors read and approved the final manuscript.
Acknowledgements
We thank IGEN Inc. for the equipment and reagents provided that made our research possible.
The authors express gratitude to regional public fund "Sodeystvie otechestvennoy medicine" for support in realisation of researches.
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| 15488155 | PMC524510 | CC BY | 2021-01-04 16:31:36 | no | BMC Cell Biol. 2004 Oct 18; 5:39 | utf-8 | BMC Cell Biol | 2,004 | 10.1186/1471-2121-5-39 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-381547655510.1186/1471-2148-4-38Research ArticleCharacterization of an endogenous retrovirus class in elephants and their relatives Greenwood Alex D [email protected] Claudia C [email protected] Ross DE [email protected] GSF-National Research Center for Environment and Health, Institute of Molecular Virology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany2 Department of Vertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, New York 10024-5192 USA3 GSF-National Research Centre for Environment and Health, Institute of Bioinformatics, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany2004 11 10 2004 4 38 38 30 7 2004 11 10 2004 Copyright © 2004 Greenwood et al; licensee BioMed Central Ltd.2004Greenwood 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
Endogenous retrovirus-like elements (ERV-Ls, primed with tRNA leucine) are a diverse group of reiterated sequences related to foamy viruses and widely distributed among mammals. As shown in previous investigations, in many primates and rodents this class of elements has remained transpositionally active, as reflected by increased copy number and high sequence diversity within and among taxa.
Results
Here we examine whether proviral-like sequences may be suitable molecular probes for investigating the phylogeny of groups known to have high element diversity. As a test we characterized ERV-Ls occurring in a sample of extant members of superorder Uranotheria (Asian and African elephants, manatees, and hyraxes). The ERV-L complement in this group is even more diverse than previously suspected, and there is sequence evidence for active expansion, particularly in elephantids. Many of the elements characterized have protein coding potential suggestive of activity.
Conclusions
In general, the evidence supports the hypothesis that the complement had a single origin within basal Uranotheria.
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Background
ERV-Ls are retroviral elements (retroelements) lacking the envelope gene (env) and exhibiting homology to the class of human endogenous retroviruses designated as HERV-L by [1]. Similar retroelements have been identified in several eutherian groups (see below), but their incidence in metatherians and monotremes is not known at present. They presumably arose from successful germ-line infection by foamy-like viruses, but when or how many times this might have occurred during the course of eutherian evolution is unknown. From the perspective of evolutionary biology it is of great interest that some classes of ERVs are known to retain original functions, including the capacity to produce infectious viral particles [2]. Others have gained novel regulatory functions in the mammalian genome [3]. Formation of the human placenta may depend on expression of a HERV-W element env gene [4]. Human immunodeficiency virus (HIV) shares specific functionally homologous sequences with endogenous retroviruses, suggesting the possibility that recombination with ERVs could change the properties of exogenous retroviruses [5]. Thus, ERVs may serve as a variable pool from which exogenous viruses may diversify. Exogenous retroviruses may have originated from ERVs and ERV-Ls in particular may represent an intermediate between retrotransposons and exogenous viruses [6].
Comparison of ERV-L polymerase (pol) gene sequences from 22 mammalian species revealed ERV-Ls that have expanded in copy number and remained active over long periods of time [1]. Phylogenetic analysis of these sequences demonstrated that primates and rodent ERV-L sequences are both diverse and, with few exceptions, monophyletic, whereas carnivore and ungulate ERV-L sequences were polyphyletic. The phylogenetic picture reflects the particularly robust expansion of the primate and rodent ERV-L complement. Importantly, the primates and rodents were the only groups that included ERV-L sequences with protein coding potential and therefore potential transpositional activity. These points suggests that, if the history of active expansion of retroelements within a group can be deciphered, it might be possible to use this information in the same way that parasite data are conventionally used [7], to perform tests of host phylogenetic relationships that are at least logically independent of other data sources. In this connection, the superorder Uranotheria is of particular interest.
Uranotheria [8] is the most recent nomen for a constellation of relationships that has, in fact, been supported by the majority of ungulate specialists throughout the past century. Simpson [9] grouped proboscideans, hyraxes, embrithopods and sirenians under the group-name Paenungulata, but was not certain of its monophyly. Most other authorities have supported this clade, albeit with some variation in content, in the years since Simpson's [9] publication (e.g., [10-14]). McKenna and Bell [8] divided Uranotheria into three major groups, Hyracoidea, Embrithopoda, and Tethytheria. The last is further subdivided into Sirenia and Behemota; behemotans consist of Proboscidea and Desmostylia. Only Hyracoidea, Sirenia, and Proboscidea possess living members.
Morphologically, there is considerable evidence that supports the association of Proboscidea and Sirenia as sister taxa to the exclusion of Hyracoidea [10], and little that appears to contradict it. Fischer and Tassy [15] take the position that alleged hyracoid morphological resemblances to tethytheres are either convergences or misconstrued, on the argument that hyraxes are in fact perissodactyls or closely allied to them. This aspect of the Fischer-Tassy hypothesis is not supported by molecular data [14,16]. On the other hand, it must also be admitted that sequence data have not provided especially strong support for Tethytheria (and, by extension, the monophyly of Uranotheria) [17]. In the most recent exercise in this arena, Asher et al [18] were able to recover Tethytheria under certain conditions when fossil and morphological data were combined with sequence information, but not when sequence data were used alone.
To investigate whether ERV-L and other retroelements may be useful in resolving phylogenetic questions involving uranotheres at multiple taxonomic levels, we utilized an ERV-L polymerase gene (pol) fragment using degenerate primers tested in other mammalian orders. Extending our previous work [19], we found that ERV-L sequence diversity was high in all members of this group and that phylogenetic analysis of our data to a limited extent supported Uranotheria as a distinct clade when sequences that lack coding potential are used. By contrast, sequences that are potentially active form separate monophyletic groups, indicating a more recent origin. Thus, it appears that ancient ERVs reflect the phylogeny of their host like classic genes and more recently active ERVs will tend to be more similar to one another as opposed to their host.
Results
Among-clone comparisons
A ~330 bp PCR product was amplified for African elephant, Asian elephant, manatee, and rock hyrax. The products were cloned and 10 clones sequenced for each product. Of the 40 sequences thus developed, only one Asian elephant sequence had no homology to ERV-Ls and was removed from analysis (not shown). No identical sequences were shared among taxonomic groups. All nine Asian elephant and all 10 manatee clones were unique. However, one Asian elephant clone, designated Max3 (accession number AY394573), was a recombination product of clone Max2 (accession number AY394572) and clone Max6 (accession number AY394576). Whether this represents a PCR artifact or is a genomic recombination event is not known. However, it is not expected that recombinational PCR would be observed in modern undamaged DNA [20].
Among the African elephant runs, four clones differed at 0–1 positions. As PCR errors probably account for these minor differences we assume only 6 unique ERV-Ls were discovered for this individual. Similarly, the hyrax sample yielded 3 groups comprised of 2 identical sequences, while two other sequences differed at 5 positions. Thus, 5 unique ERV-Ls were also obtained for Procavia.
Recovered sequences were compared to a mouse element with full coding potential in the gag and pol genes (MuERV-L, GenBank no. Y12713). Twelve clones were in frame with no stop codons. However, only 6 of the total 12 were unique (Figure 1). This is surprising, as 87 sequences from 22 mammalian species previously revealed only 7 sequences with coding potential [1]. Among the 39 sequences determined here, 6 unique sequences had coding potential among only 4 species. The observed sequence diversity and frequency of observed coding potential is consistent with active ERV-L expansion in these four species and consistent with results with a smaller internal fragment from the same groups (plus extinct woolly mammoth in the proboscidean sample) [19].
Figure 1 Endogenous retrovirus type L (ERV-L) phylogeny. Non-uranothere sequence designations taken from [1]. Uranothere designations are, Max (Elephas maximus), Lox (Loxodonta africana), Mana (Manatus trichechus), and Hyrax (Procavia capensis). Neighbor-joining tree of all uranothere sequences including representative ERV-L elements from other mammalian orders. "*" designates sequences with coding potential.
Phylogenetic analysis
A heuristic search of the entire uranothere ERV-L data set yielded 12 equally parsimonious trees, a strict consensus of which (Figure 2) showed poor recovery of accepted clades within Uranotheria and low bootstrap support at each node. While neighbor-joining analysis produces a tree with uranothere sequences as a monophyletic group, branch lengths in some cases were very short and bootstrap support under any method used was not statistically significant (Figure 1). On the assumption that sequences with potential RT activity may have been under different evolutionary constraints and may differ in their phylogenetic resolution, the sequences with no stop codons in the retrieved pol gene were analyzed separately from those with stop codons (Figure 3, 4 and 5). Those with potential RT activity grouped as distinct monophyletic groups, possibly reflecting their more recent activity and thus showing closer affinity to one another as opposed to other related ERV-L sequences (Figure 3). Those with stop codons showed a different picture with modest support for Uranotheria as one might expect for single or low copy sequences that have been transmitted vertically over time (Figure 4). Likelihood analysis of the data produced similar groupings, though with weak support, suggesting the associations found are not an artifact of the phylogenetic analysis methodology (Figure 5). However, ME analysis did not produce statistically significant resolution for any ERV group examined including non-uranothere ERVs (not shown). Poor resolution within Uranotheria with all analyses could be due to several factors, although the likeliest is different ages of individual element copies. This is not unlike the situation with various sequences recovered from primates and rodents: some sequences reconstruct accepted ordinal groupings, while others do not. For example, in the study by Bénit et al. [1], one New World monkey sequence (As2) grouped with two dog sequences and not with other primates. In other mammals, sequences (e.g., those retrieved from cow and horse) were dispersed in no evident pattern.
Figure 2 Maximum parsimony bootstrap consensus tree of all uranothere ERV-L sequences. Bootstrap values over 50% are shown. The scale bar indicates the number of steps.
Figure 3 Bootstrap consensus tree of Uranothere ERV-L sequences with coding potential only.
Figure 4 Bootstrap consensus tree of Uranothere ERV-L sequences without coding potential.
Figure 5 Quartet Puzzle maximum likelihood tree of sequences without coding potential. Puzzle support for each node is indicated.
Discussion
The most important finding resulting from this study is that elements that have undergone expansion–i.e. have remained transpositionally active–are the ones that are most likely to group monophyletically and those that have not tend to be consistent in their higher-level taxonomic distribution (in this case, at the superordinal level). Thus, there are different elements within the same family demonstrating different evolutionary trajectories. It is reasonable to suppose that for some groups, such as rodents, primates, and uranotheres, continued expansion of active ERV-L elements was tolerated by the host. By contrast, in other ungulate and carnivore lineages active elements were not inherited or were silenced early during their evolution and ERV-L expansion did not occur. Older elements tend to evolve as typical orthologous sequences. An advantage of the great diversity of elements is that with a single PCR, cloning, and determination of multiple clone sequences one retrieves multiple independent sequences with which to do phylogenetic analysis. In these regards, the uranothere evidence is consistent with results previously reported for primates and rodents in which older elements and elements that have undergone bursts of transposition were found coexisting. In each of these groups there is now good sequence evidence for retroelements that have retained coding capacity, which is of some interest since HERV-L pol (for example) is known to be expressed in specific tissue types [21]. This indicates that, in addition to potential transpositional activity, retroelements other than syncytin may have acquired biological functions important for their hosts.
Conclusions
In terms of our general results, elephants and the manatee were found to contain the most diverse sequences, while hyrax showed comparatively less diversity. This finding is consistent with results from our previous study involving extinct elephantids [19]. Elements that have been investigated in other ungulates (bovids and suids) do not yield monophyletic groupings and are represented by low copy numbers [1], suggesting that expansion did not occur (or has not recently occurred) in these taxa. Although critical studies would have to be undertaken to demonstrate the matter conclusively, it appears that amplification and diversification of ERV-L elements were independent events in primates, rodents and uranotheres.
Although this study shows that the value of ERV-Ls for the narrow purpose of phylogenetic reconstruction is limited at higher taxonomic levels, ERV-L is only one class among many different groups of ERVs in mammalian genomes. Some regions of ERVs have been used successfully to reconstruct phylogenies at lower taxonomic levels [22]. Additional transposable elements could serve as phylogenetic markers in a manner similar to ERV-L in the present study, while providing multiple independent sequences to test ordinal level phylogenies.
Methods
Samples
African elephant (Loxodonta africana) DNA was supplied by N. Georgiadis of the M'Pala Research Centre, Kenya. Asian elephant (Elephas maximus) blood was provided by J. Hektor of the Tierpark Hellabrunn, Munich. Manatee (Trichechus manatus) blood was provided by D. Murphy of the Lowry State Park, Florida. Hyrax (Procavia capensis) muscle was provided by G. Amato of the Bronx Zoo, New York.
DNA extractions
One ml of blood or approximately one gram of tissue was incubated in 1–2 ml 10 mM Tris-/Cl (pH 7.5), 10 mM EDTA (pH 8.0), 50 mM NaCl, 2% SDS, and 0.6 mg/ml Proteinase K overnight at 37 C, extracted with phenol and chloroform, and subsequently concentrated with 50 ul Millipore Ultrafee MC 30,000 NMWL columns or precipitated in 2.5 volumes ethanol and 1% NH4 Oac.
PCR, cloning, and sequencing
PCR primers for the ERV-L pol gene are described in [1]. Three μl of extract was added to 50 μl PCR containing standard buffer supplied by Boehringer Mannheim and 30 PCR cycles performed. PCR products were cloned using the pGEM-T cloning system (Promega). After heat shock into bacteria, ampicillin and blue/white selection, colonies were picked with a sterile pipette tip and added to 30 μl PCR reactions where M13 forward and reverse primers were used to amplify inserts for 25 cycles using the same buffer system described for ERV-L amplifications and as described in [19]. Five μl of the colony PCR products were visualized on ethidium-stained gels. Insert positive PCR reactions were purified with QIAquick columns and sequenced with T7 and SP6 primers using an ABI 377 sequencer.
Phylogenetic analysis
Alignment
Representatives of each clade determined by Bénit et al. [1] were included in an alignment with the elephant, manatee, and hyrax sequences determined. HERV-L, X89211; MERV-L, Y12713; NWM (AS2), AJ233633; Lemur CM8, AJ233645; horse1, AJ233650; horse24, AJ233654; horse26, AJ233655; horse27, AJ233656; pig1, AJ233661; cow1, AJ233662; cow2, AJ233663; dog1, AJ233665; rabbit4, AJ233627. Alignments were performed using ClustalX [23] and adjusted where necessary. Elephant, manatee and hyrax sequences have been deposited in GenBank (accession numbers AY394571-AY394609)
Phylogenetic methods
Maximum parsimony and neighbor joining analysis was performed using PAUP 4.0b [24]. Heuristic searching including all uranothere ERV-L sequences yielded 12 maximum parsimony trees (MPTs). 100 bootstrap replicates were performed to test MPT robustness, the strict consensus of which is shown in Figure 2. A bootstrap consensus tree using only sequences with no stop codons yielded 2 trees. The strict consensus of these trees following 1000 bootstrap replicates is shown in Figure 3. A consensus tree of 1000 bootstrap replicates, this time excluding sequences with coding potential, is shown in Figure 4. Maximum likelihood was performed using quartet puzzle in PAUP 4.0b after determining the evolution model as HKY +G using Modeltest 3.5 [25]. 10,000 puzzling steps were employed to determine the tree topology. Minimum evolution trees were generated using the program Mega2 [26].
Authors' contributions
ADG oversaw the molecular genetic studies, coordinated the study, participated in the sequence alignment and phylogenetic studies and co-drafted the manuscript. CCE participated in the phylogenetic analysis. RDEM participated in the design and coordination of the study and co-drafted the manuscript.
Acknowledgments
The authors wish to thank F. Lee and P. Marx (Tulane University and Aaron Diamond Aids Research Center in New York City) for technical assistance. P. Wynne helped execute the figures. We also thank N. Georgiadis of the M'Pala Research Center (Kenya) and J. Hektor of Tierpark Hellabrunn (Munich) for African and Asian elephant samples respectively. We wish to thank D. Murphy of the Lowry State Park Zoo (Florida) for the manatee specimen and G. Amato of the Bronx Zoo (New York) for the hyrax specimen used in this study. D. Ho (ADARC) is thanked for his support of this project. The work was supported by grants from the National Science Foundation, the Irene Diamond Fund, and the Adler Fund.
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| 15476555 | PMC524511 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Oct 11; 4:38 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-38 | oa_comm |
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BMC GenetBMC Genetics1471-2156BioMed Central London 1471-2156-5-291545857610.1186/1471-2156-5-29Research ArticleOn modeling locus heterogeneity using mixture distributions Lin Shili [email protected] Swati [email protected] Department of Statistics, The Ohio State University, Columbus, OH 43210, USA2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA2004 30 9 2004 5 29 29 28 6 2004 30 9 2004 Copyright © 2004 Lin and Biswas; licensee BioMed Central Ltd.2004Lin and Biswas; 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
Locus heterogeneity poses a major difficulty in mapping genes that influence complex genetic traits. A widely used approach to deal with this problem involves modeling linkage data in terms of finite mixture distributions. In its simplest setup, also known as the admixture approach, a single parameter is used to model the probability that the disease-causing gene of a family is linked to a reference marker. This parameter is usually interpreted as the overall proportion of linked families.
Results
In this article, we address two issues regarding the admixture approach. First, we tackle the question of whether the single parameter of linked proportion is well defined in general. By formulating the likelihood under a classification scheme based on distributions, we show that such a parameter is meaningful only when a certain well-characterized condition is met. Second, we study a condition given in the literature for validating the admixture approach. A counter example is constructed to illustrate that the condition does not necessarily lead to valid estimates.
Conclusions
Estimators from the admixture approach may be inconsistent. This holds even if a condition given in the literature to validate the approach is satisfied.
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Background
Mapping disease genes influencing complex genetic traits is far more difficult than mapping genes underlying Mendelian traits. One of the difficulties is due to locus heterogeneity, in which the disease in different families (or in different individuals within a family) is caused by different loci or non-hereditary factors. Finite mixture distributions have been proposed to model linkage data in the presence of locus heterogeneity [1], also known as admixture modeling. It is currently a widely used approach for testing heterogeneity and/or linkage [2]. In its simplest setup with two parameters, each independent data unit (usually a single family) is assumed to be either linked or unlinked. Here a family is said to be linked if its disease-causing gene is linked to a reference marker. In the two-parameter setup, all linked families are assumed to have the same linkage parameter, the recombination fraction θ. The heterogeneity is modeled by a single parameter (α) that denotes the probability that a family is linked. This parameter is interpreted by many researchers as the overall proportion of linked families.
Despite the popularity of the admixture approach, a number of authors have pointed out limitations with the approach, albeit reaching different conclusions on its practical values. For example, one of the assumptions of the admixture approach is that there is only inter-family, but no intra-family, heterogeneity. Goldin [3] and Durner et al. [4] carried out simulation studies to show that violation of this assumption does not necessarily lead to a loss of power for detecting linkage. Another inherent assumption of admixture approach is that the genetic models at the linked and unlinked loci are the same. Vieland and Logue [5] showed that violation of this assumption leads to asymptotically biased parameter estimates. They showed that in this situation, the parameter α does not even measure the proportion of linked families within the sample, contrary to popular belief. Similar conclusions were obtained in the simulation study by Pal and Greenberg [6], in which the authors simulated data under various two-locus heterogeneity models, genetic parameters, ascertainment schemes, and phenocopy frequencies to see their effect on α. Nevertheless, these studies as well as some other simulation studies (see [7] and references therein) support the use of admixture approach as a robust tool for testing linkage in the presence of heterogeneity.
Other researchers take a different view, pointing to potentially severe biases of the estimates obtained from the admixture approach (e.g., [8,9]). Whittemore and Halpern [9] provided comprehensive discussion on the genetic assumptions underlying the admixture approach. One of the foci was on whether the admixture parameter (α) is meaningful when certain assumptions are violated. Janssen et al. [8] showed, through examples, that estimates of α and θ can be severely biased when the distribution of informativeness in the linked families is not roughly the same as that in the unlinked families, under some measure of informativeness for linkage studies. They used a measure called Effective Number of Informative Meioses (EFNIM) to assess the informativeness of a family. It is based on the Expected LOD (ELOD) score of a family. They argued that, if the Linked and Unlinked Families are nearly Equally Distributed (LUFED) in terms of EFNIM, then the admixture procedure should provide satisfactory, i.e., nearly unbiased, results.
In this article, we consider the same problems discussed in Janssen et al. [8] and Whittemore and Halpern [9], but from a more statistical perspective in terms of formulating the likelihood based on finite mixture distributions. In the usual formulation of finite mixture modeling for statistical inference, the data in the sample are independent and identically distributed (iid). The distribution of each of these data points is a finite mixture of several component distributions, with known or unknown mixing proportions, called weights [10,11]. In linkage analysis with family data, unless all families follow the same distributions both under linkage and no linkage (i.e., all families having the same component distributions), the usual admixture approach does not reflect the correct likelihood function (or, equivalently, the heterogeneity LOD function). In most genetic studies using family data, this condition is violated as data usually come from families of various structures, sizes and complexities. That is, the families are not iid; they are independent but not necessarily identically distributed. Therefore, the admixture modeling with a single heterogeneity parameter is under parameterized. So, a natural question is what is its implication on the estimators of the parameters. Are they consistent? Under what condition does consistency hold? We investigate these questions by formulating a more general likelihood that assigns a separate heterogeneity parameter to each class of families following the same distribution. Then using this formulation, we show that the estimators from the admixture formulation are consistent only if a certain condition holds. Furthermore, we study the argument by Janssen et al. [8] on the LUFED condition for obtaining nearly unbiased estimates. We show that LUFED is a necessary condition for obtaining consistent estimates. However, as we illustrate through counter examples, this condition is not sufficient to guarantee consistency. In fact, for certain data satisfying LUFED, the asymptotic estimates can be far from their true values.
Results
Formulation of expected log-likelihood
Suppose that data from independent families in the sample are classified into T types such that families in the same type follow the same distribution. That is, families are classified into the same type based on their "structures" that lead to the specification of their distributions. Here we use the term structure in a broad sense that includes not only the pedigree structure but also any other available information about the pedigree (such as knowledge of phase) that contributes to the distribution from which the family is generated. Note that structure does not include any phenotypic or genotypic data of a family. Families with the same structure but different phenotypic and/or genotypic data are simply different realizations from the same distribution and hence are iid, and are classified into the same type. In the most general setup, with g potential linkage scenarios, e.g., linked or unlinked (g = 2) in the simplest setting, the probability distribution, ft*, for a realization (yt) from type t, t = 1,…,T, can be expressed in terms of its component distributions, fti, i = 1,…,g [10]:
where the mixing parameters satisfy 0 ≤ αti ≤ 1 (i = 1,…,g), , and θ is the linkage parameter vector, including various recombination fractions.
To simplify our presentation without compromising our objectives, we assume that the disease under study is caused either by a locus linked to the reference marker with a recombination fraction θ or by a locus unlinked to the reference marker. Further, we assume that the genetic (trait) models at the linked and unlinked loci are the same. So, the linkage parameter vector is θ = {θ, 1/2}, and the probability distribution is simplified to
ft*(yt; {θ,1/2}) = αtft(yt; θ) + (1 - αt)ft(yt; 1/2),
where αt is the probability that a type t family is linked. Note that the two distributions under the mixture have the same parametric form but differ in their linkage parameter values, being θ or 1/2. Also, note that a single α parameter is used for all families in the same type.
Let a denote the true proportion of linked families in the entire sample. Let pt, t = 1,…, T, be the true proportion of type t families in the linked group. Thus p = (p1,…, pT) is the distribution of types among the linked families. Recall that all families in a given type have the same distribution, i.e., same likelihood for any value of the recombination fraction. Similarly, define q = (q1,…, qT) to be the distribution of family types among the unlinked group. Then the probability that a family is of type t is st = apt + (1 - a)qt, and the probability that a type t family is linked is thus . Furthermore, let r denote the true common recombination fraction for the linked families. The likelihood contribution for estimating the parameters {αt, θ} by a type t family with data yt is
Lt(αt, θ | yt) = αtft(yt; θ) + (1 - αt)ft(yt; 1/2). (1)
Then the expected contribution to the log-likelihood from a family of type t can be expressed as
Note that r and are the true underlying quantities, whereas θ and α = (α1,…, αT) are the parameters to be estimated from the data. Combining the expected log-likelihoods, ELLt, t = 1,…, T, we can form the total expected log-likelihood from all family types as follows:
Since the above formulation of likelihood is correct under the stated assumptions, it can be shown that ELL(α0, r) ≥ ELL(α, θ), for any (α, θ), following Stuart and Ord [12]. This implies that this formulation of likelihood is guaranteed to provide consistent estimators of (α, θ). Note that α is a vector of nuisance parameters; only θ is the true parameter of interest.
We note that the above likelihood, although similar to the one presented in Vieland and Logue [5], is different from it in two respects: First, their likelihood (and hence log of likelihood ratio, referred to as 2T-HLOD in their paper) allows for different trait models at the linked and unlinked loci (as they wanted to see the effect of incorrectly assuming same trait models at the two loci) while these two are assumed to be the same in (1). Second, 2T-HLOD involves different α parameters for families with different structures as well as different phenotypic data. So, families with the same structure have different α parameters if they have different phenotypic data. On the other hand, in (1) families with the same structure (and hence their data coming from the same distribution as elucidated at the beginning of this section) share a common α parameter. Since the issue addressed by Vieland and Logue [5] is different from ours and is based on different assumptions, the two likelihood formulations are not directly comparable.
The two-parameter setup
In this subsection, we show that the two-parameter setup leads to correct likelihood formulation only when p = q, that is, the distribution of family types among the linked families is the same as that among the unlinked families. In this setup, all αt's are set to be equal to a common parameter, α, the overall proportion of linked families. Hence, the total expected log-likelihood is with respect to the recombination fraction θ and a single proportion parameter α:
The expected log-likelihood ratio between the two sets of parameter values, (a, r) and (α, θ), is
where
When pt = qt for each t,
and by Jensen's inequality [13]
In other words, when p = q, ELL(a, r) ≥ ELL(α, θ), for any (α, θ), implying that = a and = r are the maximum likelihood estimates of α and θ. However, as shown in the examples in the next section, when p ≠ q, ELL(a, r) may be smaller than ELL(α, θ) for some parameter values (α, θ). That is, the maximum likelihood estimates of α and θ may not be consistent because the likelihood model is incorrectly specified (under-parameterized). In order for the two-parameter formulation to be correct, the following constraints should be placed on the makeup of the families:
which implies that p = q given the additional constraints that and . Notice that p = q is stronger than the LUFED condition. Equal distributions of types in the linked and unlinked families according to the classification scheme based on distributions of the data implies equal distributions of informativeness in the same two groups. Therefore, LUFED is a necessary condition for the admixture approach. We also note that p = q is not necessarily satisfied for every dataset (see Discussion).
In general, in a parameterization that demands an estimate for the overall proportion of linked families, α, the relationship between α and αt is as follows:
where ut and vt are also unknown parameters (with constraints ∑ut = 1 and ∑vt = 1) that need to be estimated. Note that ut and vt are the parameters to be estimated from the data and are not necessarily the same as pt and qt, respectively, as the latter are the true values of the former. We have shown earlier that the maximum likelihood estimates for αt exist. However, one cannot solve the equations in (3) to obtain a corresponding estimate for α. This is because there are only T + 2 equations (including the two constraints) but 2T + 1 unknown parameters, and thus not all parameters are identifiable when T ≥ 2. Hence, one may not be able to find a meaningful estimate of the overall proportion of linked families, contrary to popular desire for such an estimate. This is consistent with the finding of Whittemore and Halpern [9], although the conclusions are reached from two different perspectives (see Discussion).
Further investigation of LUFED condition
We have already shown that LUFED is a necessary condition for obtaining consistent estimators using the admixture approach. In this section, we investigate, through a contrived dataset, whether LUFED is also a sufficient condition for achieving satisfactory results, as contended by Janssen et al. [8]. We use ELOD, a popular measure of informativeness [2], for family type classification in terms of their informativeness for linkage studies. This measure is the basis for the EFNIM criterion of Janssen et al. ([14,8]); see these two references for detailed description of EFNIM.
In our contrived dataset, the linked group consists of two types of families: Phase Known (PK) double backcross families in proportion p1, and Phase Unknown (PU) families in proportion p2, where p1 + p2 = 1. The unlinked group is also composed of PK and PU families, but in proportions q1 and q2, respectively, where q1 + q2 = 1. We choose to work with the PK and PU families as the PK families were used by Janssen et al. [8] to evaluate the admixture approach, and more generally, these family types are frequently used to evaluate exact properties of linkage analysis methods [2]. We assume that there are m children in all PK families and m + 1 children in all PU families. As these two types of families have different distributions [2], we have two family types (T = 2) under the classification scheme according to distributions.
Now, let us consider the ELOD of each family. The ELOD of a PK family with m children is:
where, as before, θ is the parameter for recombination fraction while r is the true recombination fraction. Similarly, one can find the ELOD of a PU family with m + 1 children:
For r close to 0, it can be seen that ELODPK(θ) and ELODPU(θ) are both approximately m log10 2, when they are evaluated at θ = r [2]. Hence, both PK and PU families, irrespective of their linked or unlinked status, are of one common type according to the ELOD criterion for classification. Thus the data satisfy Janssen et al.'s [8] LUFED condition. Note that the ELOD criterion for classification (and hence the LUFED condition) is based on homogeneity LOD scores to evaluate the informativeness of a family for linkage study and thus does not involve α parameters.
It is worth noting that this dataset may serve as an example for understanding classification based on distributions and family realizations that make up a family type. The phase information as well as the number of offspring are part of the family structure, which is the basis of classification according to distributions. However, phenotypes and genotypes do not configure into this classification scheme. For example, the PK data type consists of families with several phenotypes, i.e., various combinations of recombinant/non-recombinant (m/0, m - 1/1,..., 1/m - 1, 0/m) offspring.
To illustrate our results, we consider two specific settings for the distributions of families in the linked and unlinked groups under the classification scheme according to distributions: (a) p = (0.9, 0.1), q = (0.1, 0.9); and (b) p = (0.3, 0.7), q = (0.4, 0.6). For each of these two settings, we take m = 3 children in PK families and m + 1 = 4 children in PU families. In both settings, the overall proportion of linked families, a, is 0.5, and the true (small) recombination fraction, r, is 0.02. So, as discussed above, the distribution of informativeness among the linked families, under the ELOD measure, is the same as that among the unlinked families. The two pictures in Figure 1 show the contour plots of the expected log-likelihood under the two settings, for the two-parameter admixture formulation as given in (2). We see that the values of the parameters (α, θ) where the maximum expected log-likelihood occurs are not the same as their true values. So the estimates are inconsistent. The extent of this inconsistency depends on several factors, including how close p is to q. As seen in Figure 1 (a), where p differs greatly from q, the parameter estimates are far away from the true values. On the other hand, in Figure 1 (b), where p is close to q, the estimates are not too far from the truth. In fact, the estimates are expected to converge to the true parameter values when the difference between p and q approaches 0.
Figure 1 Contour plots of the expected log-likelihood for the two-parameter model: (a) p1 = 0.9, p2 = 0.1, q1 = 0.1, q2 = 0.9; (b) p1 = 0.3, p2 = 0.7, q1 = 0.4, q2 = 0.6. The numbers in each plot are various levels of the expected log-likelihood. The expected log-likelihoods at the parameter combinations represented by a curve are the same.
Discussion
This article addresses two questions. Is the single parameter representing the overall proportion of linked families in the admixture approach well defined? Can one validate the admixture approach if the distributions of linkage information in the linked and unlinked families are roughly the same according to a certain measure of informativeness? A simple situation, where the disease is caused either by a linked or an unlinked locus following the same genetic model, suffices for our purpose. That is, although issues such as age-dependent penetrances, locus-dependent models, and intra-family heterogeneity are very important in analyzing data in the presence of locus heterogeneity, they are not within the scope of this article.
The first question of interest was addressed in Whittemore and Halpern [9], where they characterized the genetic conditions under which the parameter for overall linked proportion is meaningful. In contrast, we consider the same question from a more statistical perspective by characterizing statistical conditions under which the linked-proportion parameter is well defined and consistent estimate can be obtained. Our basic argument is built upon the realization that an implicit assumption in the usual admixture approach can be badly violated. The admixture approach assumes that, implicitly in the way its likelihood is formulated, all families follow the same distribution. However, data from different families may follow different distributions, defining various types of data. Our results show that, if the distributions of the family types, classified by the distributions of the data, are the same for both the linked and unlinked families, i.e., p = q, then the parameter is meaningful, and a consistent estimate exists. Otherwise, the desire for an estimate of such a parameter is ill-conceived. Note that the condition p = q may not be satisfied in practice, even asymptotically, because linked and unlinked groups may have other differences such as different fertility levels, age of disease onset, disease severity, etc, that may indirectly lead to different family structures (and hence different distributions) in the two groups. Although the problem considered in Vieland and Logue [5] is different from what we consider, as they focus on violation of different implicit assumptions of this approach, from a broader perspective, their as well as our conclusions demonstrate the inconsistency of estimators obtained from the admixture approach.
The second question stems from our curiosity on an argument made by Janssen et al. [8] for the admixture approach. We show, through counter examples with a contrived dataset, that the answer to the question is no under the ELOD measure for informativeness. The difference between the estimates and the true values of the parameters, even with infinite amount of data, can be large, if one would carry out the analysis under the admixture approach. This should serve as a warning against complacency when the LUFED condition is met. We do realize that, with phase known and phase unknown data, these examples can be quite extreme in human genetic studies, although such data arise frequently in experimental crosses. In situations where data from more general family structures are available, the effect of violation of the admixture approach assumptions may be much smaller.
The criteria for classification of families deserve further clarification because it is the heart of the problem. We discuss two criteria for classification, one according to the distributions of the data, and the other according to a measure of informativeness of the data for linkage studies (leading to the LUFED condition). The classification scheme based on the distribution criterion leads to a necessary and sufficient condition for validating the admixture approach. This result is a by-product of our theoretical development for finding an answer to the first question. The classification scheme based on the informativeness criterion, on the other hand, leads to the conclusion that LUFED is only a necessary, but not a sufficient, condition for the admixture approach, contrary to Janssen et al.'s [8] contention.
Finally, we note that although the general formulation of likelihood based on forming groups of families with the same distribution gives consistent estimators, its practical utility seems to be limited. This is mainly due to the difficulty of classifying families according to distributions, in most applications, except for simple situations such as those involving only the PK and PU families. The general likelihood formulation is used here as a vehicle to further the understanding of potential problems in using the two-parameter admixture approach. A practical solution that embeds the correct likelihood formulation in a Bayesian framework is being pursued in a separate study.
Authors' contributions
Both authors were involved in all aspects of this research project. Both authors read and approved the final manuscript.
Acknowledgments
This work was supported in part by NSF grants DMS-9971770 and DMS-0306800 to S.L.
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| 15458576 | PMC524512 | CC BY | 2021-01-04 16:30:33 | no | BMC Genet. 2004 Sep 30; 5:29 | utf-8 | BMC Genet | 2,004 | 10.1186/1471-2156-5-29 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-5-211546961010.1186/1471-2296-5-21Case ReportAn unusual case of chronic meningitis Boos Christopher [email protected] Cyrus [email protected] Anna [email protected] Matthew [email protected] Department of General Medicine, Portsmouth Hospitals NHS Trust, Milton Rd, Portsmouth, UK2 Department of Elderly Care Medicine, Portsmouth Hospitals NHS Trust, Milton Rd, Portsmouth, UK2004 6 10 2004 5 21 21 31 3 2004 6 10 2004 Copyright © 2004 Boos et al; licensee BioMed Central Ltd.2004Boos 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 meningitis is defined as symptoms and signs of meningeal inflammation and persisting cerebrospinal fluid abnormalities such as elevated protein level and pleocytosis for at least one month.
Case presentation
A 62-year-old woman, of unremarkable past medical history, was admitted to hospital for investigation of a four-week history of vomiting, malaise an associated hyponatraemia. She had a low-grade pyrexia with normal inflammatory markers. A CT brain was unremarkable and a contrast MRI brain revealed sub-acute infarction of the right frontal cortex but with no evidence of meningeal enhancement. Due to increasing confusion and patient clinical deterioration a lumbar puncture was performed at 17 days post admission. This revealed gram-negative coccobacilli in the CSF, which was identified as Neisseria meningitidis group B. The patient made a dramatic recovery with high-dose intravenous ceftriaxone antibiotic therapy for meningococcal meningitis.
Conclusions
1) Chronic bacterial meningitis may present highly atypically, particularly in the older adult. 2) There may be an absent or reduced febrile response, without a rise in inflammatory markers, despite a very unwell patient. 3) Early lumbar puncture is to be encouraged as it is essential to confirm the diagnosis.4) Despite a delayed diagnosis appropriate antibiotic therapy can still lead to a good outcome.
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Background
Bacterial Meningitis usually presents as an acute illness, predominantly affecting children and young adults. It typically presents with the classical clinical triad of fever, neck stiffness and an altered mental state. However, it may rarely present as a chronic illness, without the classic clinical features noted in acute meningitis.
Case presentation
A 62-year-old retired woman was admitted to hospital via her GP for investigation of a four-week history of vomiting and malaise associated with hyponatraemia. She was initially diagnosed as suffering from viral gastroenteritis. However, the vomiting had persisted and had become associated with a mild frontal headache. She had an unremarkable past medical history and was not taking any regular medication. She had never smoked and there was no recent antecedent foreign travel.
On examination she appeared clinically dehydrated but otherwise looked well, and was alert and orientated. She was apyrexial and had no rash, photophobia, neck stiffness or stigmata of endocarditis. She had a sinus tachycardia of 104/minute, with normal heart sounds, and a blood pressure of 130/76 mmHg. Chest, abdominal and neurological examinations were unremarkable. She had a plasma sodium of 127 mmol/L (135–145 mmol/L), potassium of 3.4 mmol/L (3.5–5.0 mmol/l), urea 4.8 mmol/L (3.0–6.5 mmol/L) and creatinine of 68 mmol/L (60–125 mmol/L). There was no biochemical evidence of the syndrome of inappropriate antidiuretic hormone (SIADH) production (serum osmolality 261 mmols/kg; urine osmolality 71 mmols/Kg; urine sodium < 10 mmol/L). Serum complement and plasma immunoglobulin levels were unremarkable with no evidence of immunosuppression. In addition, she had a normal full autoimmune profile and thyroid function. Random cortisol level was mildly elevated at 799 nmol/L (normal 140 – 700 nmol/L) consistent with a stress response.
Her initial white cell count (WCC) was mildly elevated at 13.0 × 109/L (normal 4–11 × 109/L) with a neutrophilia of 10 × 109/L (normal 2–7.5 × 109/L). Her ECG and chest X-ray were normal. Her C-reactive protein (CRP) was slightly elevated at 10 mg/L (normal <5 mg/L) and erythrocyte sedimentation rate (ESR) was normal at 5 mm/hour. Her Chest X-ray and electrocardiogram were normal. Initial microbiological investigations (blood cultures, urine microscopy and culture) were normal.
Initial management consisted of slow intravenous rehydration with normal saline and antiemetic therapy, which led to a mild symptomatic improvement. Upper gastrointestinal endoscopy revealed mild oesophagitis.
During the ensuing two weeks her laboratory investigations remained stable (CRP normal; ESR normal; sodium 127–131 mmol/L; WCC 11–13 × 109/L). However, on day 4 of admission she developed a low-grade pyrexia of 37.5°C, which persisted (<38°C). A CT scan of the head revealed periventricular patchy white matter changes but no features of raised intracranial pressure or space occupying lesion.
Unfortunately the patient had become slowly more lethargic, withdrawn, and depressed. By day 17 of admission, although alert, she was uncooperative with intermittent confusion. Her symptoms of intermittent nausea and vomiting with occasional frontal headache continued.
On day 18 she underwent a lumbar puncture (LP) as she still had a low-grade pyrexia (temperature 37.5°C) and neutrophilia of 9.3 × 109/L). In addition, her nausea and vomiting had failed to fully settle with supportive treatment. The LP results were as follows: cerebrospinal fluid (CSF) appearance was pale yellow and clear; protein = 5.69 g/L (0.15–0.4 g/L); CSF glucose 1.7 mmol/L versus plasma glucose 5.7 mmol/L (ratio = 30%, normal > 50%); CSF WCC = 106/mL (normal <5 WCC/mL) – 99% lymphocytes. Gram's stain revealed gram-negative coccobacilli; acid-fast bacilli were not seen. She was commenced on intravenous ceftriaxone.
Contrast MRI brain revealed sub-acute infarction of the right frontal cortex but with no evidence of meningeal enhancement. EEG demonstrated slow wave activity, which was consistent with a meningo-encephalitis.
Within 48 hours of intravenous antibiotics she was more alert, orientated, and sitting out of bed. CSF culture grew gram-negative cocci, which was identified as Neisseria meningitidis group B, type NT, subtype NT P1.16/nt. She underwent contact tracing and completed a 10-day course of intravenous ceftriaxone. She continued to make a slow but progressive recovery. After a period of rehabilitation and intense physiotherapy she was discharged home 40 days after admission, with mild residual gait ataxia.
Conclusions
This case report presents two important clinical concepts: firstly, the presentation of chronic meningitis and secondly, the clinical presentation of bacterial meningitis in the older adult (defined as > 60 years old). The diagnosis was delayed due to the highly atypical clinical presentation [1].
Chronic meningitis is defined as symptoms and signs of meningeal inflammation and persisting cerebrospinal fluid (CSF) abnormalities such as elevated protein level and pleocytosis for at least one month [2,3]. It affects less than 10% of meningitis sufferers and is linked to a large variety of both infective and non-infective causes [4]. However, whilst there are numerous published individual case reports on chronic meningitis, there is a definite paucity of large case series in the literature. The most common cause of chronic meningitis is Mycobacterium tuberculosis, which accounts for 40–60% of cases [3,5]. Other relatively frequent causes include malignancy (8–13%) and crytococcal infection 7–11%) [3,5]. In up to 33% of cases no underlying cause is identified [3,5]. Chronic meningococcal meningitis is rare and is limited to a few isolated case reports in the literature [6-8].
There are several distinguishing features that may help to differentiate chronic meningitis from adult acute bacterial meningitis (table 1). The classic triad of clinical features of meningitis (fever, neck stiffness, altered mental state), whilst seen in up to 85% of patients presenting with acute bacterial meningitis is far less commonly seen in chronic meningitis [3,5,9]. Focal neurological signs with cranial nerve palsies and abnormal CT brain findings are also far more commonly seen in chronic meningitis [5,10].
Table 1 Features distinguishing chronic meningitis (bacterial and non bacterial) compared with acute bacterial meningitis
Description Acute bacterial meningitis Chronic meningitis
Aetiology Variable
Neisseria meningitides 13–56% [10,17, 39]
Streptococcus pneumoniae 24–37% [10,17] Variable
TB- 40–60% [3,5]
Malignancy 8–13% [3,5]
Cryptococcus 7–11% [3,5]
Unknown 30–33% [3,5]
Clinical features
- Classic triad of fever, headache and neck stiffness 85% [9] 10% [4]
- Fever 78–91% [39] 44% [4]
- Headache 32–68% [39] 79% [4]
- Neck Stiffness 58–82% [39] 75% [5]
- Altered Mental state 52–82% [39] 41% [4]
- Focal neurology 23% [39] 32% [5]
- Papilloedema <1–4% [9,10] 30% [5]
- Cranial Nerve Palsies 4% [10] 24% [5]
Mortality Variable – aetiology dependent
19.7–25% overall [10,17]
37–44% ≥ 60 years old [1,10]
10–25% < 60 years old [10,16–20] Variable – aetiology dependent
29%- overall [5]
Elevated WCC, CRP and ESR Elevated Normal or only mildly elevated [5]
Hyponatraemia <10% >90% [5]
Cerebrospinal fluid analysis 10% – lymphocytic [9,17]
90% – neutrophilic [9,17]
Gram stain positive 57–90% [9,10,17] >90% lymphocytic [5]
<10% neutrophilic [5]
Gram stain positive <10% [5]
Abnormal CT 2.7 – 13% [10,40] 60% [5]
WCC, white cell count; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate.
Hyponatraemia (as in our patient), whilst very uncommon in acute bacterial meningitis, is seen in the vast majority of cases of chronic meningitis [5,11]. Although there was a persistent mild neutrophilia, both the CRP and ESR were normal throughout the course of the disease, which, whilst being highly unusual for acute meningitis, has been reported, in chronic meningitis [12,13].
Acute bacterial meningitis is usually a rapidly progressive and highly lethal disease in older adults [1]. Rapid diagnosis is vital as the prognosis worsens with treatment delay leading to a high rate of sustained neurological deficit in this age group [14,15]. Despite the widespread use of antibiotics the overall case mortality rate remains unchanged and is far higher (37–44%) in the older adult compared with that seen in younger adults (10–25%) with significant long-term morbidity (up to 70% of infected patients) in survivors [16-20].
Given the success of childhood immunization, and an increasingly aging population, the proportion of older adults presenting with bacterial meningitis is increasing [16]. There are several additional factors, which make the older adult more prone to bacterial meningitis. Older adults often have underlying acute and chronic diseases (e.g. diabetes, renal or hepatic failure) with immunosenescence (age related decline in immune function) [1,21]. This can lead to symptoms, which can be confused with those of meningitis and at the same time increase the propensity to infection [1,21]. The role of immunosenescence in predisposing patients to bacterial meningitis is not clearly defined, but appears to relate to defects in innate, specific cellular and humoral immunity leading to an attenuated immune response [1,22-24]. Persons who lack or are deficient of antibody-dependent, complement-mediated lysis (bacteriocidal activity) are most susceptible to meningococcal disease [25]. Our patient had an unremarkable past medical history with normal complement and immunoglobulin levels with no evidence of immunosuppresion [26-28].
The clinical presentation of bacterial meningitis is more variable in the older as compared with the younger adult, with fewer patients manifesting with the classic symptoms of fever, neck stiffness and altered mental state than among younger adults [1]. It has been suggested that 1 of 3 findings (fever, neck stiffness, altered mental state) is present in virtually all patients with meningitis and that the absence of these features virtually excludes meningitis with a high negative predictive value (table 1) [1]. Our patient had none of these features on presentation and had been unwell for four weeks prior to presentation, but did develop a mild fever (<38°C) and cognitive dysfunction during her inpatient stay. The blunted febrile response is well recognised in older adults in general [29].
Our patient's CSF showed a lymphocytosis, raised protein, and low glucose ratio, which are seen in only 10% of bacterial meningitis cases. This CSF profile would normally suggest infection with Listeria monocytogenes meningitis or alternative causes such as tuberculous and fungal infection [30,31].
Neisseria (N) meningitidis is a leading cause of bacterial meningitis in the Western World and tends to predominate in young adults [19,20,25]. N. meningitidis is a gram-negative, aerobic diplococcus. It is classified into serogroups (e.g. A,B,C etc) according to the immunological reactivity of their polysaccharides [25]. The most prevalent serogroups implicated in clinical meningococcal meningitis are serogroup B (62%, as in our patient) and the more virulent serogroup C (22%) [19,20]. The relatively reduced virulence of serogroup B may partly explain the chronicity of presentation and reduced inflammatory response seen in our patient. Serogroups B and C have a seasonal variation occurring more commonly in the first quarter of the year (our patient presented in February) [19]. Meningococcal meningitis is also more common among the following groups: persons of black race; lower socioeconomic classes; those exposed actively or passively to tobacco smoke; persons exposed to overcrowding and amongst binge drinkers [32-37].
This case highlights the diagnostic challenge associated with bacterial meningitis presenting in an older patient. The presentation was made even more difficult owing to the blunted febrile response, the lack of inflammatory response observed in laboratory tests and the chronicity of the patient's symptoms. The diagnosis required thorough investigation during the inpatient stay. Early lumbar puncture is to be encouraged as it is essential to confirm the diagnosis. Despite a delayed diagnosis appropriate antibiotic therapy can still lead to a good outcome.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MD generated the idea of writing the case report and was the consultant in charge of the patient. CD reviewed the case notes of the patient and wrote the original draft of the case presentation. CB significantly revised the original draft and added the conclusions, references and figures. AH offered considerable help with the manuscript revisions. All authors contributed to the final version of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank Dr Chester Choi for his considerable help in peer reviewing and offering constructive help with each draft of this manuscript.
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| 15469610 | PMC524513 | CC BY | 2021-01-04 16:29:00 | no | BMC Fam Pract. 2004 Oct 6; 5:21 | utf-8 | BMC Fam Pract | 2,004 | 10.1186/1471-2296-5-21 | oa_comm |
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Curr Control Trials Cardiovasc MedCurrent Controlled Trials in Cardiovascular Medicine1468-67081468-6694BioMed Central 1468-6708-5-91546178410.1186/1468-6708-5-9ResearchA novel approach to treatment of hypertension in diabetic patients – a multicenter, double-blind, randomized study comparing the efficacy of combination therapy of Eprosartan versus Ramipril with low-dose Hydrochlorothiazide and Moxonidine on blood pressure levels in patients with hypertension and associated diabetes mellitus type 2 – rationale and design [ISRCTN55725285] Pater Cornel [email protected] Deepak [email protected] Jean-Pascal [email protected] Joachim [email protected] Katrin [email protected] Department of Cardiovascular Clinical Development, Solvay Pharmaceuticals GmbH, Hannover, Germany2 The Royal Oldham Hospital/Manchester Royal Infirmary, UK3 Global Product Strategy Department, Solvay Pharmaceuticals GmbH, Hannover Germany2004 1 10 2004 5 1 9 9 21 7 2004 1 10 2004 Copyright © 2004 Pater et al; licensee BioMed Central Ltd.2004Pater 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.
Hypertension and diabetes mellitus are closely interrelated and coexist in as many as two-thirds of patients with type 2 diabetes. The consequent risk of such an association is an accelerated development of atherosclerotic cardiovascular disease and nephropathy complications.
In choosing an antihypertensive agent, effectiveness needs to be accompanied by favourable metabolic, cardioprotective, and nephroprotective properties. Given the multifactorial nature of hypertension, the approach that has gained widespread agreement is treatment with more than one agent. Agents with different mechanisms of action increase antihypertensive efficacy because of synergistic impacts on the cardiovascular system. Combination therapy allows the use of lower doses of each antihypertensive agent which accounts for the excellent tolerability of combination products.
The aim of the present study is to quantify the efficacy of combination therapy of Eprosartan 600 mg respectively Ramipril 5 mg with low-dose Hydrochlorothiazide and Moxonidine on blood pressure levels in patients with essential hypertension and associated diabetes mellitus type 2.
The use of monotherapy (Eprosartan or Ramipril) followed by addition of low-dose Hydrochlorothiazide as second agent and of Moxonidine as a third agent will be individualized to the severity of hypertension in the particular patient and to his/her degree of response to current treatment.
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Background
The clinical combination of hypertension and diabetes carries a particular poor prognosis [1-6]. Clinical studies done in individuals with type 2 diabetes and substudies obtained from clinical trials done in the general population have demonstrated that achievement of goal blood pressure (< 130/80 mm Hg) in this patient category is crucial in decreasing the premature morbidity and mortality [7]. Thus, management of subjects with type 2 diabetes and associated hypertension needs to be early and aggressive, and must use a global approach. Findings from large, international outcomes studies as well as guidelines and recommendation of prestigious international scientific bodies have made available consensus recommendations [8-13].
The challenge clinicians are facing is to tighten blood pressure control to less than 130/80 mmHg and to adjust initiation of therapy to the severity of hypertension in the individual patient.
This multicenter study will evaluate the efficacy and tolerability of monotherapy, double- and triple- antihypertensive combination therapies in a large spectrum of hypertension & diabetes patient population, as summarised in Table 1.
Table 1 Large spectrum of hypertension and diabetes patient population selected for the multicenter study that will evaluate the efficacy and tolerability of monotherapy and double and triple-antlhy pertensive combination therapies
GoalBP* Threshold Upper limit
for all patients regardless BP values for initiation of double-combination of BP values targeted
< 130/80 mmHg > 150/90 mmHg ≤ 179/109 mmHg
* The Goal BP defines the cut off point for responders/non-responders to any therapy.
Table 2 (see Additional file 1) specifies the treatment strategies to be employed in the study as adjusted to severity of hypertension in the particular patient and to his/her degree of response to that therapy.
The primary objectives of hypertension management in patients with diabetes are to reduce blood pressure levels to currently recommended target level and thus to reduce the risk of cardiovascular and renal complications without adversely impacting glycemic and lipid control.
Previous debate regarding the level of blood pressure reduction that optimizes cardiovascular risk reduction is currently settled. BP goal of < 130/85 mmHg promoted by the JNC-VI guidelines issued 1997 [10] were replaced in 2002 by a position paper of the American Diabetes Association (ADA) supporting a target blood pressure in hypertension & diabetes patients of < 130/80 mmHg [14]. This blood pressure-goal is also endorsed by the most recent JNC-7 guidelines [15] and two other American professional societies [16,17] as well as by the ESH/ESC [9] and formally by the ISH.
A widespread agreement, supported by the above mentioned organizations/societies is in place, regarding the principles governing the use of appropriate antihypertensive drug combinations to maximize hypotensive efficacy while minimizing side effects. Polypharmacy is common place and, with at least one third of patients requiring two or more agents simultaneously, a paradigm shift in the approach of initiating therapy is done by advocating use of two agents in subjects with more severe hypertension (BP in excess of 20/10 mmHg above goal). Low-dose thiazide diuretic is favored as one of the two starting agents.
In general, monotherapy is likely to be successful in mild hypertensive patients (grade 1 hypertension) without associated major risk factors for CHD. In contrast, patients with type 2 diabetes need more rigorous control of BP in an easier, simpler fashion, given the remarkable complexity of the multiple drug regimens needed to control their comorbid medical problems (e.g., diabetes, obesity, high cholesterol).
A large body of evidence derived from a multitude of international trials have demonstrated both the benefit of low-level, goal blood pressure, in terms of prevention of long-term complications and, the need for multiple drug combinations in order to achieve that goal [13,18-20]. Furthermore, in a computer-modelled cost-effectiveness analysis of the JNC-VI treatment goal (< 130/85 mmHg), lowering blood pressure to goal increases patients' life expectancy and decreases long-term cost [21]. Cost-effectiveness analysis in the context of the UKPDS study has also revealed that incremental cost of tight control (< 150.85 mmHg) versus less tight control (< 180/105 mmHg) was considered to be effective [22].
In the HOT study [13], which recruited grade 2 and 3 hypertensives after washout from previous agents, monotherapy was successful in only 25–40% of patients, according to the target diastolic blood pressure. In trials of diabetic patients, the vast majority were on at least two drugs, and, in two recent trials on diabetic nephropathy [23,24] an average of 2.5 to 3.0 non-study drugs were required in addition to the angiotensin receptor antagonist used in these studies (losartan/irbesartan).
Given the very poor BP control rate, i.e., 11% in patients with hypertension & diabetes, the use of combination therapy is an important therapeutic consideration, as it facilitates quicker and easier attainment of goal BP and should lead to a greater proportion of people with diabetes who achieve BP goal. Initiation of treatment by combination therapy was effectively tested in the VA study at the beginning of the antihypertensive treatment trial era [25,26] and recently in the PROGRESS study [27].
Methods
Patient Population
Subjects will be recruited in outpatient clinics/offices of general practitioners and internal medicine/cardiology specialists from the entire spectrum of patients having coexistent hypertension and diabetes mellitus type 2. The upper limit of blood pressure values targeted (≤ 179/109 mmHg) correspond grade 2 hypertension (according to current ESC/ISH and WHO guidelines).
Subjects to be recruited are supposed to have both entities (hypertension with BP values in the range ≥ 130/80 – ≤ 179/109 mmHg and diabetes mellitus type 2) diagnosed since previously, undergoing current antihypertensive treatment and, to be eligible for the study according to the specific inclusion/exclusion criteria described below.
The study consists of five distinct phases: Screening (S) (up to seven days), Placebo Run-in phase (two weeks), monotherapy (four weeks, double-blind fashion), double-combination treatment (Eprosartan/HCTZ respectively Ramipril/HCTZ for four weeks, HCTZ open labelled), triple-combination treatment (Eprosartan/HCTZ/Moxonidine versus Ramipril/HCTZ/Moxonidine with HCTZ and Moxonidine open labelled) and Follow-up (two to seven days). Patients allocated to monotherapy will participate in the study for a four weeks period while those starting with double combination therapy will receive medication for a maximum of 12 weeks.
The flowchart below captures the main events during the study conduct (Fig. 1).
Figure 1 Study Design. A multicenter, double-blind, randomized study comparing the efficacy of combination therapy of Eprosartan versus Ramipril with low-dose Hydrochlorothiazide and Moxonidine on blood pressure levels in patients with essential hypertension and associated diabetes mellitus type 2.
Randomization and Blinding
Randomization will be concealed.
A stratified randomization will be employed based on the following rules:
1. Subjects with blood pressure in range: BP ≥ 130/80 – ≤ 150/90 mm Hg will be randomly allocated to one of the monotherapy arms (Eprosartan or Ramipril). 30 subjects will be recruited for each arm.
2. Subjects with blood pressure in the range: BP > 150/90 – ≤ 179/109 mm Hg will be randomly allocated to double-combination therapy (Eprosartan/HCTZ or Ramipril/HCTZ); 190 subjects will be recruited for each arm.
The randomization lists will be provided by the Department of Clinical Supplies at Solvay Pharmaceuticals BV with the program ADLS. Patients will be allocated in equal numbers to each sequence. A fixed block size of patients will be used, and only complete blocks of study medication will be provided to the centers.
Within each center, randomization numbers will be used in ascending order and patients will be allocated to randomization code numbers in chronological order. The study will be unblinded when all CRFs are in house and the data on the database have been declared clean.
The following drugs are to be used in the study:
• Eprosartan 600 mg, once daily
• Ramipril 5 mg, once daily
• Hydrochlorothiazide 12.5 mg, once daily
• Moxonidine 0.4 mg, once daily
• Placebo tablets matching Eprosartan 600 mg and Ramipril 5 mg will be used in the monotherapy, in the double- and in the triple-combination therapy phases. Hydrochlorothiazide tablets will be open labelled in the double- and triple-combination therapy phases. Moxonidine tablets will open-labelled in the triple-combination therapy phase.
Eprosartan and Ramipril and the corresponding placebos will be packaged according to the double-dummy technique. Fig. 2 summarizes the treatment algorithm.
Figure 2 Treatment Algorithm
Monotherapy (Eprosartan vs. Ramipril)
Patients eligible for participation in the study by the assessment at V2 will be randomized on the basis of the severity of their hypertension and allocated to either monotherapy or to double-combination therapy (described below).
Patients with initial blood pressure values in the range ≥ 130/80 – ≤ 150/90 mmHg, will be randomly allocated to one of the monotherapy groups.
By the end of the first four-week phase an assessment will be made as to whether patients have reached or not the "goal" hypertension (< 130/80 mmHg).
Patients on monotherapy deemed to be responders at the end of four weeks treatment (i.e., have reached the "goal") will terminate their participation in the study and be followed up during the ensuing two to seven days after stopped monotherapy (procedure similar with Follow-up at end of study (V6).
Non-responders (according to above mentioned criteria, i. e., with BP still ≥ 130/80 mmHg), will receive double-combination therapy (Eprosartan/HCTZ respectively Ramipril/HCTZ) and will be followed-up for eight weeks (red-dotted line in the flowchart).
Double-combination Therapy (Eprosartan/HCTZ vs. Ramipril/HCTZ)
Patients with initial blood pressure values in the range > 150/90 – ≤ 179/109 mmHg, will be randomly allocated to double-combination therapy. By the end of this first four-week phase an assessment will be made as to whether patients have reached or not the "goal" hypertension (< 130/80 mmHg).
Responders will maintain double-combination therapy and will be followed-up for an eight week period (V3 toV5, dotted-line in the flowchart) and retain therapy unchanged.
Non-responders will receive triple-combination, as described below.
Triple-combination Therapy (Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine)
Non-responder patients after four weeks of double-combination therapy will receive triple-combination (Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine) and will be followed-up for an eight weeks period (V3 to V5).
After the first four weeks of monotherapy respectively double-combination therapy (V3), patients will be reassessed for compliance, adverse events and supplied with medication for the next eight weeks (except for the monotherapy patients who reached goal blood pressure and who will terminate the study).
During the eight weeks triple-combination therapy all patients will be reassessed for compliance and adverse events (visits V4, V5 and V6). A 12-lead ECG will be performed by S, V3 and V5 while safety laboratory parameters will be performed by S, V2 and finally by visit V5.
A Follow-up Visit will be performed on all patients with full physical examination, BP and pulse rate check within the ensuing two to seven days after study end (V6). Further follow-up and optimal treatment will be decided on a case-by-case basis by the physician in charge. Table 3 (see Additional file 2) displays a summary of the scheduled investigations, as planned for each particular visit.
Inclusion Criteria
1. Males and females aged 40 to 80 years of age. Women of childbearing age will be subject to pregnancy testing and will agree to maintain adequate hormonal contraception.
2. Eligible patients should have diagnosed essential hypertension (not controlled with current treatment, i.e., BP ≥ 130/80 – ≤ 179/109 mmHg) and diagnosed associated diabetes mellitus type 2, willing to accept withdrawal of any antihypertensive medication by the time of the Screening visit.
Exclusion Criteria
A multitude of exclusion criteria, carefully listed in the study protocol, can be summarised in three different groups:
1. Ineligibility based on hypertension grade 3 (BP ≥ 180/110 mmHg), any form of secondary hypertension or hypotension (SBP ≤ 90 mmHg).
2. Any form of organic heart disease requiring medical treatment that might have hypotensive effect, imply need for invasive investigation or surgery.
3. The patient is suffering from a severe concomitant illness related to any body organ or system, likely to affect outcome assessment. Likewise, ineligibility is declared for patient anticipated to have compliance problems, participants in another trial during the past 30 days, pregnancy and lactations and known hypersensitivity to ingredients of any of the employed agents (eprosartan, ramipril, hydrochlorothiazide, moxonidine). In addition, diabetes mellitus type 1 is exclusion criteria.
Study Outcomes
Prior and Concomitant Therapy
The study protocol calls for every patient to be treated optimally by the physician in charge and to receive comprehensive, individualized lifestyle change advice regarding relevant diet and physical activity. Visits are to be scheduled in the context of the study (at four weeks interval during ongoing treatment). Any antihypertensive medication should be withdrawn latest by Screening visit and will be prohibited during the whole period of ongoing study.
Ethics and Informed Consent
The study will be conducted in accordance with ICH GCP and the European Directive 2001/20/EC of the European Parliament and of the Council of 4 April 2001 (on the approximation of the laws, regulations and administrative provisions of the member states relating to implementation of good clinical practice in the conduct of clinical trials of medicinal products for human use) and on the basis of ethical principles laid down in the current revision of the Declaration of Helsinki (Edinburgh 2000). In addition, Solvay Pharmaceuticals GmbH policies and procedures should also be followed.
Written consent, involving provision of detailed information regarding the study objectives, design, scope of the intervention, risks and benefits, will be obtained for all patients before initiating any study procedures. Likewise, study documentation is to be subject to the scrutiny of local ethical committees in the two countries participating in the study.
Sample Size and Statistical Analysis
Efficacy
The primary objective is to demonstrate the superiority of combination therapy of Eprosartan/HCTZ (600/12.5 mg) versus Ramipril/HCTZ (5/12.5 mg) with the primary parameter of attention being the percentage of patients brought to goal blood pressure (<130/80 mmHg) at visit V3.
Null hypothesis: H0: PE+HCTZ = PR+HCTZ
Alternative hypothesis: H1: PE+HCTZ ≠ PR+HCTZ,
Where PE+HCTZ is the percentage of patients brought to goal blood pressure at visit 3 with Eprosartan/HCTZ and PR+HCTZ is the percentage of patients brought to goal blood pressure at visit V3 with Ramipril/HCTZ.
The primary parameter will be analyzed using the Cochran-Mantel-Haenszel test, controlling for center effects. Statistical significance will be assessed with a two-sided test at 0.05 α level. The confirmative analysis of the primary parameter will be performed on the intent-to-treat patient sample.
Secondary efficacy objectives:
• To compare the mean change in sitting systolic blood pressure (sitSBP) and sitting diastolic blood pressure (sitDBP) between Eprosartan/HCTZ and Ramipril/HCTZ (V3 vs. V2).
• To compare the percentage of patients brought to goal blood pressure by the triple-combination Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine (V5 vs. V3).
• To compare the mean change in sitSBP and sitDBP between triple-combination with Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine (V5 vs. V3).
• To compare the mean change in sitSBP and sitDBP between monotherapy with Eprosartan vs. Ramipril (V3 vs. V2)
• To compare the percentage of patients brought to goal blood pressure at visit V5 between Eprosartan/HCTZ and Ramipril/HCTZ in patients not at goal blood pressure after four weeks of monotherapy.
• To compare the mean change in sitSBP and sitDBP as well as the responder rate in patients non-responders (not at goal) after four weeks of monotherapy (switched to double combination therapy) (V5 vs.V3); and to compare the mean change in sitSBP and sitDBP as well as the responder rate maintenance in patients who reached goal blood pressure value at the end of first four weeks of double-combination therapy and successively entered an eight weeks follow-up period (V5 vs.V3).
Changes in blood pressure parameters will be assessed by analysis of covariance (ANCOVA). The model will include the intercept, treatment and center as fixed effects and the baseline value as covariate. For response rates, the treatment groups will be compared using the Cochran-Mantel-Haenszel test, controlling for center effects. Comparisons of the medication regimens will be reported along with 95% confidence intervals of the relative risk ratios. These analyses will be considered as exploratory.
Safety
All patients who receive at least one dose of double-blind medication will be assessed for clinical safety and tolerability. Evaluation of safety data will be based on comparisons of patient experience by treatment group. Clinical interpretation of safety will be based on reviews of standard displays of adverse events incidence, pulse rate data, and laboratory test values. Summary statistics of laboratory test values and incidence of adverse events according to treatment and time of onset will be presented.
Sample Size, Power and Level of Significance
A formal sample size estimation has been done for patients with blood pressure in the range: > 150/90 mmHg and ≤ 179/109 mmHg.
Assuming that 55% of the patients in the Eprosartan/HCTZ group would reach goal blood pressure as compared with only 40% in the Ramipril/HCTZ group, a 0.05% two-sided significance level with 80% power to detect the targeted 15% difference will imply the need for 346 patients supposed to complete the four-weeks double-combination therapy phase. Further 35 patients (10% of the total) will be recruited to account for drop-outs.
In addition, 60 subjects with BP ≥ 130/80 and ≤ 150/90 mmHg will be randomly allocated to either Eprosartan or Ramipril monotherapy group at visit V2. Inclusion of monotherapy phase with a relatively low number of patients (30 subjects per arm) is justified by the intention to therapeutically target the whole spectrum of patient population having coexistent diabetes mellitus type 2 and mild to moderate hypertension, in whom the agents tested are likely to be effective.
Blood Pressure Measurements
Office blood pressure will be determined by Riva-Rocci method with a mercury or a mercury calibrated sphygmomanometer throughout the study. All measurements will be made on the same arm supported at heart level, using the same cuff size and the same equipment. If the patient's arm circumference is > 32 cm, a large blood pressure cuff should be used. Diastolic blood pressure will be measured at the disappearance of Korotkoff sounds phaseV. Measurements should be taken by the same staff member at the particular visits.
For an individual patient blood pressure measurements should be performed at 24 hours after the last oral dose, at the same time (± 2 hour) in the morning, between 8 and 10 am. Blood pressure will be measured in the following sequence: after the patient sits quietly for at least 5 minutes, blood pressure will be measured twice at approximately 2-minutes interval. The average of these measurements will be recorded. If the difference between measurements is in excess of 5 mmHg a third reading will be performed and the average value recorded as mean sitting systolic and diastolic blood pressure.
Measurements should be performed by the same study assistant using the same device, in each of the centres involved in the study.
Discussion
The current evidence base is strongly in favour of combining drugs in order to achieve blood pressure goals, in particular in patients with coexistent hypertension & diabetes. Likewise, there is a widespread agreement in the scientific community as to the goal blood pressure to be achieved in these patients. Further, common sense in clinical practice dictates that combination therapies should be tailored to severity of hypertension in the individual patient and that, eventual associated risk factors/comorbidities should be accounted for in the process of treatment decision making.
Patients with high blood pressure and associated impaired glucose tolerance or overt diabetes mellitus type 2, as a group, are insulin resistant, [28] glucose intolerant [29-31], hyperinsulinemic [32-36], dyslipidemic [37-42] and with evidence of endothelial dysfunction [43,44]. Extensive epidemiological evidence indicates that diabetic individuals with hypertension have greatly increased risk of cardiovascular disease, renal insufficiency, and diabetic retinopathy [45-47].
For every 5 to 10 mmHg decrease in systolic blood pressure achieved with diuretics, ARBs, ACE inhibitors, beta-blockers or calcium channel blockers in patients with diabetes, there is a 20% to 30% relative risk reduction in cardiovascular events [48-53].
Agents belonging to the nine, most well-known different antihypertensive drug classes produce a similar reduction in systolic and diastolic blood pressure (10–15 and 5–10 mm Hg respectively). Differences in terms of magnitude of blood pressure lowering, as indicated by results from comparative efficacy studies, are usually small [54]. However, larger differences have been shown as to effects on hard endpoints (myocardial infarction, heart failure, stroke).
Comparisons between different agents in patients with hypertension & diabetes mellitus type 2, convincingly point to ACE inhibitors and ARBs as being the two classes of antihypertensive drugs that reduce the activity of the renin-angiotensin II system, and should be among the preferred first-step drugs for the treatment of these conditions [55]. Angiotensin II increases blood pressure by enhancing aldosterone synthesis, resulting in sodium retention and direct vasoconstriction. The first step in this pathway is inhibited by adrenergic blockers. The third and forth steps are inhibited by ACE inhibitors and ARBs, respectively [56].
Clinical trials carried out world-wide have shown that ACE inhibitors have renoprotective effects [57,58] and clear cardiovascular benefits [59-63]. Their main side effects are dry cough and angioedema.
In contrast, in placebo-controlled trials, the ARBs have demonstrated almost no side effects [64]. Both ACE inhibitors and ARBs have been shown to maintain quality of life of hypertensive patients equal to or better than other classes of antihypertensive drugs [65-68]. The only laboratory abnormality that may occur with agents from both classes is mild hyperkaliemia, especially in some elderly patients with type 2 diabetes who have hyporeninemic-hypoaldosteronism [69].
Use of low-dose thiazide diuretic (< 25 mg) as a second agent in treatment of patients with hypertension & diabetes is well-documented and widely recommended [21,70-73]. It has beneficial effects on both morbidity and mortality figures while, previous general concern on the negative impact of diuretics on the different lipid parameters is no longer justified as, all long-term studies with low-dose diuretics have not been shown to affect lipid profiles in a negative way [74-76]. Moreover, in studies of a year or more, diuretics have been shown to reduce cardiovascular risk in every trial to date [77-79].
Since drug combinations may be required for many years in the age-groups in which type 2 diabetes is most prevalent, there have been calls for the use of agents devoid of adverse effects on carbohydrate and lipid metabolism. It has been suggested that such effects may account for the shortfall in reduction of coronary heart disease observed in clinical trials of diuretics and β-blockers [80,81].
Moxonidine stimulates imidazoline-I1 receptors in the medulla, thereby reducing central sympathetic drive and attenuating peripheral vascular resistance. In addition, reduced sympathetic drive results in lower plasma concentrations of catecholamines and renin. Randomised comparative studies show that the efficacy of moxonidine as monotherapy is similar to that of other antihypertensive agents [82]. Moreover, selectivity for the I1 receptor greatly reduces the adverse affects attributable to costimulation of medullary α2-adrenoceptors [82] observed with the first generation of centrally acting agents, α-methyldopa and clonidine.
In clinical studies, moxonidine has been shown to have neutral or beneficial effects on lipid and carbohydrate metabolism [82,83]. A retrospective analysis suggested minor dose-dependent reductions in fasting plasma glucose in moxonidine-treated hypertensive patients.
On the available evidence, moxonidine seems to be a logical choice as component of combination treatment of patients with hypertension and associated diabetes mellitus type 2 or impaired glucose tolerance.
Conclusions
The poor blood pressure control in patients with hypertension & diabetes in everyday life lies, at least in part, in the emphasis in the evidence-based guidelines of the recent past towards advice on initial, single treatments as well as in their lack of clarity and transparency in recommending pre-specified blood pressure targets [10,84-86].
Previous consensual advice that combination treatments expose patients to the increased risk of adverse events has been replaced by good evidence to the contrary: use of several agents combined or of fixed-dose combinations treatments have the potential to bring patients to goal blood pressure and thereby to minimize long term risk of hypertension/diabetes-related complications [22-27,87].
Despite the apparent simplicity of the paradigm shift towards clear blood pressure goal and individualized therapy on the basis of hypertension severity (and addition of a third agent in case of uncontrolled BP with two agents), comparative data to guide clinical practice is still lacking and this applies also to the comparison of ARBs versus ACE inhibitors. The present study attempts to explore this area.
Competing interests
Pater C, Berrou JP, Luszick J and Beckman K are employees of Solvay Pharmaceuticals.
Authors' contributions
Study concept and design: Pater, Berrou, Luszick
Drafting of manuscript: Pater, Bhatnagar
Statistical expertise: Beckman
Supplementary Material
Additional File 1
Table 2 – Blood pressure-adjusted treatment stratification
Click here for file
Additional File 2
Table 3 – Investigations Schedule
Click here for file
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| 15461784 | PMC524514 | CC BY | 2021-01-04 16:47:31 | no | Curr Control Trials Cardiovasc Med. 2004 Oct 1; 5(1):9 | utf-8 | Curr Control Trials Cardiovasc Med | 2,004 | 10.1186/1468-6708-5-9 | oa_comm |
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Curr Control Trials Cardiovasc MedCurrent Controlled Trials in Cardiovascular Medicine1468-67081468-6694BioMed Central 1468-6708-5-91546178410.1186/1468-6708-5-9ResearchA novel approach to treatment of hypertension in diabetic patients – a multicenter, double-blind, randomized study comparing the efficacy of combination therapy of Eprosartan versus Ramipril with low-dose Hydrochlorothiazide and Moxonidine on blood pressure levels in patients with hypertension and associated diabetes mellitus type 2 – rationale and design [ISRCTN55725285] Pater Cornel [email protected] Deepak [email protected] Jean-Pascal [email protected] Joachim [email protected] Katrin [email protected] Department of Cardiovascular Clinical Development, Solvay Pharmaceuticals GmbH, Hannover, Germany2 The Royal Oldham Hospital/Manchester Royal Infirmary, UK3 Global Product Strategy Department, Solvay Pharmaceuticals GmbH, Hannover Germany2004 1 10 2004 5 1 9 9 21 7 2004 1 10 2004 Copyright © 2004 Pater et al; licensee BioMed Central Ltd.2004Pater 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.
Hypertension and diabetes mellitus are closely interrelated and coexist in as many as two-thirds of patients with type 2 diabetes. The consequent risk of such an association is an accelerated development of atherosclerotic cardiovascular disease and nephropathy complications.
In choosing an antihypertensive agent, effectiveness needs to be accompanied by favourable metabolic, cardioprotective, and nephroprotective properties. Given the multifactorial nature of hypertension, the approach that has gained widespread agreement is treatment with more than one agent. Agents with different mechanisms of action increase antihypertensive efficacy because of synergistic impacts on the cardiovascular system. Combination therapy allows the use of lower doses of each antihypertensive agent which accounts for the excellent tolerability of combination products.
The aim of the present study is to quantify the efficacy of combination therapy of Eprosartan 600 mg respectively Ramipril 5 mg with low-dose Hydrochlorothiazide and Moxonidine on blood pressure levels in patients with essential hypertension and associated diabetes mellitus type 2.
The use of monotherapy (Eprosartan or Ramipril) followed by addition of low-dose Hydrochlorothiazide as second agent and of Moxonidine as a third agent will be individualized to the severity of hypertension in the particular patient and to his/her degree of response to current treatment.
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Background
The clinical combination of hypertension and diabetes carries a particular poor prognosis [1-6]. Clinical studies done in individuals with type 2 diabetes and substudies obtained from clinical trials done in the general population have demonstrated that achievement of goal blood pressure (< 130/80 mm Hg) in this patient category is crucial in decreasing the premature morbidity and mortality [7]. Thus, management of subjects with type 2 diabetes and associated hypertension needs to be early and aggressive, and must use a global approach. Findings from large, international outcomes studies as well as guidelines and recommendation of prestigious international scientific bodies have made available consensus recommendations [8-13].
The challenge clinicians are facing is to tighten blood pressure control to less than 130/80 mmHg and to adjust initiation of therapy to the severity of hypertension in the individual patient.
This multicenter study will evaluate the efficacy and tolerability of monotherapy, double- and triple- antihypertensive combination therapies in a large spectrum of hypertension & diabetes patient population, as summarised in Table 1.
Table 1 Large spectrum of hypertension and diabetes patient population selected for the multicenter study that will evaluate the efficacy and tolerability of monotherapy and double and triple-antlhy pertensive combination therapies
GoalBP* Threshold Upper limit
for all patients regardless BP values for initiation of double-combination of BP values targeted
< 130/80 mmHg > 150/90 mmHg ≤ 179/109 mmHg
* The Goal BP defines the cut off point for responders/non-responders to any therapy.
Table 2 (see Additional file 1) specifies the treatment strategies to be employed in the study as adjusted to severity of hypertension in the particular patient and to his/her degree of response to that therapy.
The primary objectives of hypertension management in patients with diabetes are to reduce blood pressure levels to currently recommended target level and thus to reduce the risk of cardiovascular and renal complications without adversely impacting glycemic and lipid control.
Previous debate regarding the level of blood pressure reduction that optimizes cardiovascular risk reduction is currently settled. BP goal of < 130/85 mmHg promoted by the JNC-VI guidelines issued 1997 [10] were replaced in 2002 by a position paper of the American Diabetes Association (ADA) supporting a target blood pressure in hypertension & diabetes patients of < 130/80 mmHg [14]. This blood pressure-goal is also endorsed by the most recent JNC-7 guidelines [15] and two other American professional societies [16,17] as well as by the ESH/ESC [9] and formally by the ISH.
A widespread agreement, supported by the above mentioned organizations/societies is in place, regarding the principles governing the use of appropriate antihypertensive drug combinations to maximize hypotensive efficacy while minimizing side effects. Polypharmacy is common place and, with at least one third of patients requiring two or more agents simultaneously, a paradigm shift in the approach of initiating therapy is done by advocating use of two agents in subjects with more severe hypertension (BP in excess of 20/10 mmHg above goal). Low-dose thiazide diuretic is favored as one of the two starting agents.
In general, monotherapy is likely to be successful in mild hypertensive patients (grade 1 hypertension) without associated major risk factors for CHD. In contrast, patients with type 2 diabetes need more rigorous control of BP in an easier, simpler fashion, given the remarkable complexity of the multiple drug regimens needed to control their comorbid medical problems (e.g., diabetes, obesity, high cholesterol).
A large body of evidence derived from a multitude of international trials have demonstrated both the benefit of low-level, goal blood pressure, in terms of prevention of long-term complications and, the need for multiple drug combinations in order to achieve that goal [13,18-20]. Furthermore, in a computer-modelled cost-effectiveness analysis of the JNC-VI treatment goal (< 130/85 mmHg), lowering blood pressure to goal increases patients' life expectancy and decreases long-term cost [21]. Cost-effectiveness analysis in the context of the UKPDS study has also revealed that incremental cost of tight control (< 150.85 mmHg) versus less tight control (< 180/105 mmHg) was considered to be effective [22].
In the HOT study [13], which recruited grade 2 and 3 hypertensives after washout from previous agents, monotherapy was successful in only 25–40% of patients, according to the target diastolic blood pressure. In trials of diabetic patients, the vast majority were on at least two drugs, and, in two recent trials on diabetic nephropathy [23,24] an average of 2.5 to 3.0 non-study drugs were required in addition to the angiotensin receptor antagonist used in these studies (losartan/irbesartan).
Given the very poor BP control rate, i.e., 11% in patients with hypertension & diabetes, the use of combination therapy is an important therapeutic consideration, as it facilitates quicker and easier attainment of goal BP and should lead to a greater proportion of people with diabetes who achieve BP goal. Initiation of treatment by combination therapy was effectively tested in the VA study at the beginning of the antihypertensive treatment trial era [25,26] and recently in the PROGRESS study [27].
Methods
Patient Population
Subjects will be recruited in outpatient clinics/offices of general practitioners and internal medicine/cardiology specialists from the entire spectrum of patients having coexistent hypertension and diabetes mellitus type 2. The upper limit of blood pressure values targeted (≤ 179/109 mmHg) correspond grade 2 hypertension (according to current ESC/ISH and WHO guidelines).
Subjects to be recruited are supposed to have both entities (hypertension with BP values in the range ≥ 130/80 – ≤ 179/109 mmHg and diabetes mellitus type 2) diagnosed since previously, undergoing current antihypertensive treatment and, to be eligible for the study according to the specific inclusion/exclusion criteria described below.
The study consists of five distinct phases: Screening (S) (up to seven days), Placebo Run-in phase (two weeks), monotherapy (four weeks, double-blind fashion), double-combination treatment (Eprosartan/HCTZ respectively Ramipril/HCTZ for four weeks, HCTZ open labelled), triple-combination treatment (Eprosartan/HCTZ/Moxonidine versus Ramipril/HCTZ/Moxonidine with HCTZ and Moxonidine open labelled) and Follow-up (two to seven days). Patients allocated to monotherapy will participate in the study for a four weeks period while those starting with double combination therapy will receive medication for a maximum of 12 weeks.
The flowchart below captures the main events during the study conduct (Fig. 1).
Figure 1 Study Design. A multicenter, double-blind, randomized study comparing the efficacy of combination therapy of Eprosartan versus Ramipril with low-dose Hydrochlorothiazide and Moxonidine on blood pressure levels in patients with essential hypertension and associated diabetes mellitus type 2.
Randomization and Blinding
Randomization will be concealed.
A stratified randomization will be employed based on the following rules:
1. Subjects with blood pressure in range: BP ≥ 130/80 – ≤ 150/90 mm Hg will be randomly allocated to one of the monotherapy arms (Eprosartan or Ramipril). 30 subjects will be recruited for each arm.
2. Subjects with blood pressure in the range: BP > 150/90 – ≤ 179/109 mm Hg will be randomly allocated to double-combination therapy (Eprosartan/HCTZ or Ramipril/HCTZ); 190 subjects will be recruited for each arm.
The randomization lists will be provided by the Department of Clinical Supplies at Solvay Pharmaceuticals BV with the program ADLS. Patients will be allocated in equal numbers to each sequence. A fixed block size of patients will be used, and only complete blocks of study medication will be provided to the centers.
Within each center, randomization numbers will be used in ascending order and patients will be allocated to randomization code numbers in chronological order. The study will be unblinded when all CRFs are in house and the data on the database have been declared clean.
The following drugs are to be used in the study:
• Eprosartan 600 mg, once daily
• Ramipril 5 mg, once daily
• Hydrochlorothiazide 12.5 mg, once daily
• Moxonidine 0.4 mg, once daily
• Placebo tablets matching Eprosartan 600 mg and Ramipril 5 mg will be used in the monotherapy, in the double- and in the triple-combination therapy phases. Hydrochlorothiazide tablets will be open labelled in the double- and triple-combination therapy phases. Moxonidine tablets will open-labelled in the triple-combination therapy phase.
Eprosartan and Ramipril and the corresponding placebos will be packaged according to the double-dummy technique. Fig. 2 summarizes the treatment algorithm.
Figure 2 Treatment Algorithm
Monotherapy (Eprosartan vs. Ramipril)
Patients eligible for participation in the study by the assessment at V2 will be randomized on the basis of the severity of their hypertension and allocated to either monotherapy or to double-combination therapy (described below).
Patients with initial blood pressure values in the range ≥ 130/80 – ≤ 150/90 mmHg, will be randomly allocated to one of the monotherapy groups.
By the end of the first four-week phase an assessment will be made as to whether patients have reached or not the "goal" hypertension (< 130/80 mmHg).
Patients on monotherapy deemed to be responders at the end of four weeks treatment (i.e., have reached the "goal") will terminate their participation in the study and be followed up during the ensuing two to seven days after stopped monotherapy (procedure similar with Follow-up at end of study (V6).
Non-responders (according to above mentioned criteria, i. e., with BP still ≥ 130/80 mmHg), will receive double-combination therapy (Eprosartan/HCTZ respectively Ramipril/HCTZ) and will be followed-up for eight weeks (red-dotted line in the flowchart).
Double-combination Therapy (Eprosartan/HCTZ vs. Ramipril/HCTZ)
Patients with initial blood pressure values in the range > 150/90 – ≤ 179/109 mmHg, will be randomly allocated to double-combination therapy. By the end of this first four-week phase an assessment will be made as to whether patients have reached or not the "goal" hypertension (< 130/80 mmHg).
Responders will maintain double-combination therapy and will be followed-up for an eight week period (V3 toV5, dotted-line in the flowchart) and retain therapy unchanged.
Non-responders will receive triple-combination, as described below.
Triple-combination Therapy (Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine)
Non-responder patients after four weeks of double-combination therapy will receive triple-combination (Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine) and will be followed-up for an eight weeks period (V3 to V5).
After the first four weeks of monotherapy respectively double-combination therapy (V3), patients will be reassessed for compliance, adverse events and supplied with medication for the next eight weeks (except for the monotherapy patients who reached goal blood pressure and who will terminate the study).
During the eight weeks triple-combination therapy all patients will be reassessed for compliance and adverse events (visits V4, V5 and V6). A 12-lead ECG will be performed by S, V3 and V5 while safety laboratory parameters will be performed by S, V2 and finally by visit V5.
A Follow-up Visit will be performed on all patients with full physical examination, BP and pulse rate check within the ensuing two to seven days after study end (V6). Further follow-up and optimal treatment will be decided on a case-by-case basis by the physician in charge. Table 3 (see Additional file 2) displays a summary of the scheduled investigations, as planned for each particular visit.
Inclusion Criteria
1. Males and females aged 40 to 80 years of age. Women of childbearing age will be subject to pregnancy testing and will agree to maintain adequate hormonal contraception.
2. Eligible patients should have diagnosed essential hypertension (not controlled with current treatment, i.e., BP ≥ 130/80 – ≤ 179/109 mmHg) and diagnosed associated diabetes mellitus type 2, willing to accept withdrawal of any antihypertensive medication by the time of the Screening visit.
Exclusion Criteria
A multitude of exclusion criteria, carefully listed in the study protocol, can be summarised in three different groups:
1. Ineligibility based on hypertension grade 3 (BP ≥ 180/110 mmHg), any form of secondary hypertension or hypotension (SBP ≤ 90 mmHg).
2. Any form of organic heart disease requiring medical treatment that might have hypotensive effect, imply need for invasive investigation or surgery.
3. The patient is suffering from a severe concomitant illness related to any body organ or system, likely to affect outcome assessment. Likewise, ineligibility is declared for patient anticipated to have compliance problems, participants in another trial during the past 30 days, pregnancy and lactations and known hypersensitivity to ingredients of any of the employed agents (eprosartan, ramipril, hydrochlorothiazide, moxonidine). In addition, diabetes mellitus type 1 is exclusion criteria.
Study Outcomes
Prior and Concomitant Therapy
The study protocol calls for every patient to be treated optimally by the physician in charge and to receive comprehensive, individualized lifestyle change advice regarding relevant diet and physical activity. Visits are to be scheduled in the context of the study (at four weeks interval during ongoing treatment). Any antihypertensive medication should be withdrawn latest by Screening visit and will be prohibited during the whole period of ongoing study.
Ethics and Informed Consent
The study will be conducted in accordance with ICH GCP and the European Directive 2001/20/EC of the European Parliament and of the Council of 4 April 2001 (on the approximation of the laws, regulations and administrative provisions of the member states relating to implementation of good clinical practice in the conduct of clinical trials of medicinal products for human use) and on the basis of ethical principles laid down in the current revision of the Declaration of Helsinki (Edinburgh 2000). In addition, Solvay Pharmaceuticals GmbH policies and procedures should also be followed.
Written consent, involving provision of detailed information regarding the study objectives, design, scope of the intervention, risks and benefits, will be obtained for all patients before initiating any study procedures. Likewise, study documentation is to be subject to the scrutiny of local ethical committees in the two countries participating in the study.
Sample Size and Statistical Analysis
Efficacy
The primary objective is to demonstrate the superiority of combination therapy of Eprosartan/HCTZ (600/12.5 mg) versus Ramipril/HCTZ (5/12.5 mg) with the primary parameter of attention being the percentage of patients brought to goal blood pressure (<130/80 mmHg) at visit V3.
Null hypothesis: H0: PE+HCTZ = PR+HCTZ
Alternative hypothesis: H1: PE+HCTZ ≠ PR+HCTZ,
Where PE+HCTZ is the percentage of patients brought to goal blood pressure at visit 3 with Eprosartan/HCTZ and PR+HCTZ is the percentage of patients brought to goal blood pressure at visit V3 with Ramipril/HCTZ.
The primary parameter will be analyzed using the Cochran-Mantel-Haenszel test, controlling for center effects. Statistical significance will be assessed with a two-sided test at 0.05 α level. The confirmative analysis of the primary parameter will be performed on the intent-to-treat patient sample.
Secondary efficacy objectives:
• To compare the mean change in sitting systolic blood pressure (sitSBP) and sitting diastolic blood pressure (sitDBP) between Eprosartan/HCTZ and Ramipril/HCTZ (V3 vs. V2).
• To compare the percentage of patients brought to goal blood pressure by the triple-combination Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine (V5 vs. V3).
• To compare the mean change in sitSBP and sitDBP between triple-combination with Eprosartan/HCTZ/Moxonidine vs. Ramipril/HCTZ/Moxonidine (V5 vs. V3).
• To compare the mean change in sitSBP and sitDBP between monotherapy with Eprosartan vs. Ramipril (V3 vs. V2)
• To compare the percentage of patients brought to goal blood pressure at visit V5 between Eprosartan/HCTZ and Ramipril/HCTZ in patients not at goal blood pressure after four weeks of monotherapy.
• To compare the mean change in sitSBP and sitDBP as well as the responder rate in patients non-responders (not at goal) after four weeks of monotherapy (switched to double combination therapy) (V5 vs.V3); and to compare the mean change in sitSBP and sitDBP as well as the responder rate maintenance in patients who reached goal blood pressure value at the end of first four weeks of double-combination therapy and successively entered an eight weeks follow-up period (V5 vs.V3).
Changes in blood pressure parameters will be assessed by analysis of covariance (ANCOVA). The model will include the intercept, treatment and center as fixed effects and the baseline value as covariate. For response rates, the treatment groups will be compared using the Cochran-Mantel-Haenszel test, controlling for center effects. Comparisons of the medication regimens will be reported along with 95% confidence intervals of the relative risk ratios. These analyses will be considered as exploratory.
Safety
All patients who receive at least one dose of double-blind medication will be assessed for clinical safety and tolerability. Evaluation of safety data will be based on comparisons of patient experience by treatment group. Clinical interpretation of safety will be based on reviews of standard displays of adverse events incidence, pulse rate data, and laboratory test values. Summary statistics of laboratory test values and incidence of adverse events according to treatment and time of onset will be presented.
Sample Size, Power and Level of Significance
A formal sample size estimation has been done for patients with blood pressure in the range: > 150/90 mmHg and ≤ 179/109 mmHg.
Assuming that 55% of the patients in the Eprosartan/HCTZ group would reach goal blood pressure as compared with only 40% in the Ramipril/HCTZ group, a 0.05% two-sided significance level with 80% power to detect the targeted 15% difference will imply the need for 346 patients supposed to complete the four-weeks double-combination therapy phase. Further 35 patients (10% of the total) will be recruited to account for drop-outs.
In addition, 60 subjects with BP ≥ 130/80 and ≤ 150/90 mmHg will be randomly allocated to either Eprosartan or Ramipril monotherapy group at visit V2. Inclusion of monotherapy phase with a relatively low number of patients (30 subjects per arm) is justified by the intention to therapeutically target the whole spectrum of patient population having coexistent diabetes mellitus type 2 and mild to moderate hypertension, in whom the agents tested are likely to be effective.
Blood Pressure Measurements
Office blood pressure will be determined by Riva-Rocci method with a mercury or a mercury calibrated sphygmomanometer throughout the study. All measurements will be made on the same arm supported at heart level, using the same cuff size and the same equipment. If the patient's arm circumference is > 32 cm, a large blood pressure cuff should be used. Diastolic blood pressure will be measured at the disappearance of Korotkoff sounds phaseV. Measurements should be taken by the same staff member at the particular visits.
For an individual patient blood pressure measurements should be performed at 24 hours after the last oral dose, at the same time (± 2 hour) in the morning, between 8 and 10 am. Blood pressure will be measured in the following sequence: after the patient sits quietly for at least 5 minutes, blood pressure will be measured twice at approximately 2-minutes interval. The average of these measurements will be recorded. If the difference between measurements is in excess of 5 mmHg a third reading will be performed and the average value recorded as mean sitting systolic and diastolic blood pressure.
Measurements should be performed by the same study assistant using the same device, in each of the centres involved in the study.
Discussion
The current evidence base is strongly in favour of combining drugs in order to achieve blood pressure goals, in particular in patients with coexistent hypertension & diabetes. Likewise, there is a widespread agreement in the scientific community as to the goal blood pressure to be achieved in these patients. Further, common sense in clinical practice dictates that combination therapies should be tailored to severity of hypertension in the individual patient and that, eventual associated risk factors/comorbidities should be accounted for in the process of treatment decision making.
Patients with high blood pressure and associated impaired glucose tolerance or overt diabetes mellitus type 2, as a group, are insulin resistant, [28] glucose intolerant [29-31], hyperinsulinemic [32-36], dyslipidemic [37-42] and with evidence of endothelial dysfunction [43,44]. Extensive epidemiological evidence indicates that diabetic individuals with hypertension have greatly increased risk of cardiovascular disease, renal insufficiency, and diabetic retinopathy [45-47].
For every 5 to 10 mmHg decrease in systolic blood pressure achieved with diuretics, ARBs, ACE inhibitors, beta-blockers or calcium channel blockers in patients with diabetes, there is a 20% to 30% relative risk reduction in cardiovascular events [48-53].
Agents belonging to the nine, most well-known different antihypertensive drug classes produce a similar reduction in systolic and diastolic blood pressure (10–15 and 5–10 mm Hg respectively). Differences in terms of magnitude of blood pressure lowering, as indicated by results from comparative efficacy studies, are usually small [54]. However, larger differences have been shown as to effects on hard endpoints (myocardial infarction, heart failure, stroke).
Comparisons between different agents in patients with hypertension & diabetes mellitus type 2, convincingly point to ACE inhibitors and ARBs as being the two classes of antihypertensive drugs that reduce the activity of the renin-angiotensin II system, and should be among the preferred first-step drugs for the treatment of these conditions [55]. Angiotensin II increases blood pressure by enhancing aldosterone synthesis, resulting in sodium retention and direct vasoconstriction. The first step in this pathway is inhibited by adrenergic blockers. The third and forth steps are inhibited by ACE inhibitors and ARBs, respectively [56].
Clinical trials carried out world-wide have shown that ACE inhibitors have renoprotective effects [57,58] and clear cardiovascular benefits [59-63]. Their main side effects are dry cough and angioedema.
In contrast, in placebo-controlled trials, the ARBs have demonstrated almost no side effects [64]. Both ACE inhibitors and ARBs have been shown to maintain quality of life of hypertensive patients equal to or better than other classes of antihypertensive drugs [65-68]. The only laboratory abnormality that may occur with agents from both classes is mild hyperkaliemia, especially in some elderly patients with type 2 diabetes who have hyporeninemic-hypoaldosteronism [69].
Use of low-dose thiazide diuretic (< 25 mg) as a second agent in treatment of patients with hypertension & diabetes is well-documented and widely recommended [21,70-73]. It has beneficial effects on both morbidity and mortality figures while, previous general concern on the negative impact of diuretics on the different lipid parameters is no longer justified as, all long-term studies with low-dose diuretics have not been shown to affect lipid profiles in a negative way [74-76]. Moreover, in studies of a year or more, diuretics have been shown to reduce cardiovascular risk in every trial to date [77-79].
Since drug combinations may be required for many years in the age-groups in which type 2 diabetes is most prevalent, there have been calls for the use of agents devoid of adverse effects on carbohydrate and lipid metabolism. It has been suggested that such effects may account for the shortfall in reduction of coronary heart disease observed in clinical trials of diuretics and β-blockers [80,81].
Moxonidine stimulates imidazoline-I1 receptors in the medulla, thereby reducing central sympathetic drive and attenuating peripheral vascular resistance. In addition, reduced sympathetic drive results in lower plasma concentrations of catecholamines and renin. Randomised comparative studies show that the efficacy of moxonidine as monotherapy is similar to that of other antihypertensive agents [82]. Moreover, selectivity for the I1 receptor greatly reduces the adverse affects attributable to costimulation of medullary α2-adrenoceptors [82] observed with the first generation of centrally acting agents, α-methyldopa and clonidine.
In clinical studies, moxonidine has been shown to have neutral or beneficial effects on lipid and carbohydrate metabolism [82,83]. A retrospective analysis suggested minor dose-dependent reductions in fasting plasma glucose in moxonidine-treated hypertensive patients.
On the available evidence, moxonidine seems to be a logical choice as component of combination treatment of patients with hypertension and associated diabetes mellitus type 2 or impaired glucose tolerance.
Conclusions
The poor blood pressure control in patients with hypertension & diabetes in everyday life lies, at least in part, in the emphasis in the evidence-based guidelines of the recent past towards advice on initial, single treatments as well as in their lack of clarity and transparency in recommending pre-specified blood pressure targets [10,84-86].
Previous consensual advice that combination treatments expose patients to the increased risk of adverse events has been replaced by good evidence to the contrary: use of several agents combined or of fixed-dose combinations treatments have the potential to bring patients to goal blood pressure and thereby to minimize long term risk of hypertension/diabetes-related complications [22-27,87].
Despite the apparent simplicity of the paradigm shift towards clear blood pressure goal and individualized therapy on the basis of hypertension severity (and addition of a third agent in case of uncontrolled BP with two agents), comparative data to guide clinical practice is still lacking and this applies also to the comparison of ARBs versus ACE inhibitors. The present study attempts to explore this area.
Competing interests
Pater C, Berrou JP, Luszick J and Beckman K are employees of Solvay Pharmaceuticals.
Authors' contributions
Study concept and design: Pater, Berrou, Luszick
Drafting of manuscript: Pater, Bhatnagar
Statistical expertise: Beckman
Supplementary Material
Additional File 1
Table 2 – Blood pressure-adjusted treatment stratification
Click here for file
Additional File 2
Table 3 – Investigations Schedule
Click here for file
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| 15471550 | PMC524515 | CC BY | 2021-01-04 16:05:46 | no | Microb Cell Fact. 2004 Oct 7; 3:12 | latin-1 | Microb Cell Fact | 2,004 | 10.1186/1475-2859-3-12 | oa_comm |
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Nutr JNutrition Journal1475-2891BioMed Central London 1475-2891-3-181546267710.1186/1475-2891-3-18ResearchEffect of smoking on vitamin A, vitamin E, and other trace elements in patients with cardiovascular disease in Bangladesh: a cross-sectional study Bashar Sam K [email protected] Amal K [email protected] North South University, Dhaka, Bangladesh2 Department of Community Health Sciences, The University of Southern Mississippi, Hattiesburg, MS, USA2004 5 10 2004 3 18 18 26 8 2004 5 10 2004 Copyright © 2004 Bashar and Mitra; licensee BioMed Central Ltd.2004Bashar and Mitra; 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
Data regarding the impact of cigarette smoking on trace elements are scarce and inconsistent. In this study, we evaluated the effect of smoking on serum concentrations of trace elements among adult males with heart disease.
Methods
This cross-sectional study included 100 adults hospitalized with heart disease in Bangladesh. The major variables of interest included mean serum concentrations of trace elements and proportion of subjects with bacterial growth on throat swab culture.
Results
Smokers had significantly lower serum concentrations of retinol, alpha-tocopherol, selenium, and zinc and increased concentrations of copper. Throat swab cultures were more often positive for Streptococcus β-hemolyticus in smokers than controls.
Conclusions
Smoking decreases serum concentrations of trace elements. Smoking control programs are needed in Bangladesh to improve health and nutrition of the people who are already nutritionally deficient.
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Introduction
Smoking is a widely accepted practice in Bangladeshi men and is associated with socialising, sharing, and male identity [1]. According to an earlier cross-sectional study, approximately 50% of males and 3% of females are tobacco smokers in Bangladesh [2]. Although smoking is a recognized risk factor for several diseases including emphysema, chronic bronchitis, cardiovascular diseases, and cancer [3-5], very little is known about the nutritional consequences of smoking. In animal models, administration of benzo(a)pyrene, a constituent present in cigarette smoke induced vitamin A depletion [6]. Vitamin A deficiency per se causes emphysema. Some other trace elements, such as iron, zinc, and vitamin E were found to be deficient among healthy smokers compared to non-smokers. However, the available data are inconsistent regarding the effect of smoking on trace elements. In this study, we documented the effect of different doses of smoking on trace elements among hospitalized patients with heart disease in Bangladesh.
Methods and Materials
A cross-sectional study was conducted among 100 male patients admitted to the National Institute of Cardiovascular Disease (NICVD), Dhaka, Bangladesh from January through December 1998, after obtaining informed consent from the participants. The study protocol was reviewed and approved by the Human Subjects Ethical Committee of the NICVD. The patients who had a history of smoking 10 or more cigarettes per day were considered smokers, and those who never smoked were controls. All the patients, including controls, were admitted with heart disease. The study did not include females in this study because smoking is not a norm among females in that society. All the smokers and the first 20 of the non-smokers who met the selection criteria were eligible for the study. This study included only heavy smokers who smoked at least 10 sticks per day, and excluded mild or casual smokers to leave a buffer zone of comparison between smokers and non-smokers. Patients were stratified according to their smoking status as follows: Control (n = 20), non-smokers; Grade I (n = 20), 10 – 15 sticks/day; Grade II (n = 30), 16 – 20 sticks/day; Grade III (n = 30), 21 and more sticks/day.
Body weight and height were measured at admission. Weight (kg) was measured to the nearest 0.1 kg, with participants wearing light clothing and no shoes and using a beam balance with non-detachable weights. Height (cm) was measured with a stadiometer to the nearest 0.5 cm. Body mass index (BMI) was calculated using the following formula: weight (kg) / height (m)2. Five ml of venous blood was obtained from each patient at admission in a test-tube, which was wrapped with aluminum foil to avoid degradation of vitamin A in light. Serum samples were separated by centrifugation, and kept stored at -20°C until further analysis. For the analyses of trace elements, serum samples were stored at separate ion-free vials. Throat swab cultures were detected for the growth of bacteria.
Laboratory Methods
Serum retinol (vitamin A) and serum α-tocopherol (vitamin E) concentrations were determined by high performance liquid chromatography (HPLC) according to Bieri et al. [7]. In brief, serum retinol and α-tocopherol were extracted with hexane after deproteinization with absolute ethanol containing retinyl acetate and α-tocopherol acetate (Sigma Chemical Co., St Louis, MO, USA) as internal standards for retinol and α-tocopherol respectively. Retinol and α-tocopherol were separated by HPLC (model PU 4010; Pye-Unicam) on a reverse-phase C18 column using methanol-water (97.5:2.5, v/v) as the mobile phase. Coefficient of variation (CV) values of ten replicates from a pooled serum sample for retinol and α-tocopherol were 2.3 and 3.3% respectively.
Serum zinc was measured by flame atomic absorption spectrophotometry (AAS, Analyst 800, Perkin-Elmer, Norwalk, CT, USA) using a modification of the method described by Kirgbright [8]. Serial replication of aliquots from a pooled serum sample and quality control sera were used to check the precision and accuracy of the analytical methods. The within-run CV for zinc in a pooled serum sample was between 2.2 and 4%, based on six to seven samples in each of the five runs. Serum concentrations of copper and selenium were measured by the AAS method mentioned above.
Statistical Methods
Data were analyzed using SPSS for windows, version 11.0 (SPSS Inc., Chicago, IL). Descriptive statistics of the major variables of interest were calculated to determine the distribution of the data. All the variables except serum iron concentrations were normally distributed. Variables between smokers and non-smokers were compared by Student t-test for continuous data and by Chi-square test for categorical data. A probability level of 5% was considered statistically significant.
Results
Of the 110 patients enrolled, 10 dropped out; eight had incomplete data, one withdrew early, and one left the hospital without notice. The mean ± SD age of the study patients was 43.67 ± 6.79 y (range, 28 – 57 y). The groups did not differ significantly in terms of age and anthropometric measurements (Table 1).
Table 1 Baseline characteristics of the study subject
Control (Non-smoker) n = 20 Smoker All n = 100
Grade I 10–15 sticks/d n = 20 Grade II 16–20 sticks/d n = 30 Grade III 21+ sticks/d n = 30
Age (year) 41.60 ± 8.30 44.40 ± 7.52 43.57 ± 5.85 44.67 ± 6.06 43.67 ± 6.79
Weight (kg) 60.95 ± 3.32 63.25 ± 3.40 62.50 ± 3.64 62.90 ± 2.81 62.46 ± 3.34
Height (m) 1.64 ± 0.03 1.65 ± 0.03 1.65 ± 0.03 1.63 ± 0.04 1.65 ± 0.03
BMI 22.32 ± 1.35 23.11 ± 1.31 22.90 ± 1.63 23.17 ± 1.10 22.91 ± 1.38
Values are mean ± SD.
Serum retinol concentrations were below normal (0.70 μmol/L) among all smokers and the majority (60%) of the controls. Table 2 shows that the smokers who smoke 16 sticks or more cigarettes per day had significantly lower concentrations of serum retinol compared with controls. Percentage decrease of alpha-tocopherol was most striking of all the trace elements. Zinc concentrations did not change among grade I and grade II smokers but decreased among grade III smokers compared with controls. Concentrations of copper increased but selenium decreased among smokers than controls.
Table 2 Effect of smoking on serum concentrations of retinol, alpha-tocopherol and other trace elements
Trace element (μmol/L) Control (Non-smoker) n = 20 Smoker
Grade I 10–15 sticks/d n = 20 Grade II 16–20 sticks/d n = 30 Grade III 21+ sticks/d n = 30
Retinol .72 ± .06 .66 ± .12 .45 ± .09* .28 ± .07*
Alpha-tocopherol 13.5 ± 1.6 10.0 ± .7** 8.6 ± 1.9*** 7.9 ± 1.2***
Copper .44 ± .25 .46 ± .16 .61 ± .19* .62 ± .27*
Selenium .013 ± .001 .004 ± .001* .003 ± .001* .003 ± .001*
Zinc .46 ± .23 .46 ± .15 .46 ± .20 .42 ± .15*
Values are mean ± SD. *P < .05; **P < .01; ***P < .001 compared with the control.
A significantly higher proportion of smokers compared with controls had bacterial growth on their throat cultures, mostly due to Streptococcus β-hemolyticus (Table 3).
Table 3 Effect of smoking on bacterial growth on throat swab culture
Organism Control (Non-smoker) n = 20 Smoker
Grade I 10–15 sticks/d n = 20 Grade II 16–20 sticks/d n = 30 Grade III 21+ sticks/d n = 30
Streptococcus β hemolytica 2.8 70.5 71.4 72.5
Aerobacter aerogenes 12.2 25.4 26.7 27.0
No growth 85.0 4.1 1.9 1.0
Values are percentages. P < .001 for all values of smokers compared with the control.
Discussion
In this study, adult male smokers with heart disease had significantly decreased serum concentrations of retinol, alpha-tocopherol, and selenium, and increased concentrations of copper, compared to non-smokers. Depression of trace elements in blood was more with increasing doses of smoking.
In a study in Turkey, plasma selenium, copper, zinc and iron concentrations, and the activities of related erythrocyte antioxidative enzymes copper-zinc superoxide dismutase (Cu-Zn SOD), catalase, and glutathione peroxidase (GSH-Px) were measured in tobacco smokers and compared with those of nonsmokers [9]. Plasma thiocyanate levels were measured as an index of smoking status. While plasma copper concentration and erythrocyte Cu-Zn SOD activity were significantly higher, plasma selenium concentration and erythrocyte GSH-Px activities were significantly lower in tobacco smokers than in nonsmokers. We did not measure antioxidative enzyme levels in blood; however, our study had consistency with earlier findings of decreased serum concentrations of selenium and increased concentrations of copper among smokers. Kocyigit et al. [9] did not observe any significant effect of smoking on zinc or iron status. Our observation of a significantly depressed zinc status only among heavy smokers (those who smoked 21 or more sticks per day) compared to non-smokers was consistent with findings of Uz et al. in Turkey [10].
Several studies documented that smoking may increase oxidative stress and impair oxidant defense system [11]. Serum selenium glutathione peroxidase, glutathione reductase, and extracellular superoxide dismutase activities were found lower in smokers than in non-smokers. Serum ascorbic acid and folate concentrations were lower in smokers than in non-smokers, whereas serum thiobarbituric acid-reactive substances (TBARS) were higher. However, Kim et al. (2003) did not observe any effect of smoking on serum copper, iron, and magnesium concentrations [11].
In a later study, Kim et al. (2004) further evaluated the influence of short– and long-term cigarette smoking on blood antioxidant status among Korean teenage girls (aged 14 to 18 y) and adult males (aged 36 to 51 y) [12]. Extracellular superoxide dismutase activities and concentrations of serum vitamin C and folate were lower in both short-term and long-term smokers. Serum copper concentrations were higher only among long-term smokers compared to non-smokers. In our study, we observed increased serum concentrations of copper among grade II and grade III smokers (those who smoked 16 or more sticks per day) but not among grade I smokers (those who smoked 10–15 sticks per day), as compared to non-smokers. Both the studies suggest that probably an increasing dose of smoking modify serum copper status more compared to those who smoke less or do not smoke at all. However, cigarette smoking, irrespective of dose or duration, had negative effects on antioxidant status in the Korean study [12].
Increasing evidence suggests that smoking is a causal factor for coronary heart disease and stroke. In a prospective study in Japan [4], 19,782 men and 21,500 women aged 40 to 59 years who were free of prior diagnosis of stroke, coronary heart disease, or cancer and reported their smoking status were followed. During a 461,761 person-year follow-up, relative risks (95% CIs) for current smokers compared with never-smokers were 1.27 (1.05 to 1.54) for total stroke, 0.72 (0.49 to 1.07) for intraparenchymal hemorrhage, 3.60 (1.62 to 8.01) for subarachnoid hemorrhage, and 1.66 (1.25 to 2.20) for ischemic stroke.
One of the limitations of our study is that it is difficult to establish any causal association of heart disease and deficiency of trace elements or increased isolation of Streptococcus β-hemolyticus among our study subjects, because it is a cross-sectional study. However, epidemiologic evidence has suggested a modifying role for antioxidant micronutrients, including tocopherols and carotenoids, in atherosclerosis and heart disease. In an experimental study, Handelman et al. (1996) exposed freshly obtained human plasma to the gas phase of cigarette smoke to assess its effects on tocopherols, carotenoids, and retinol. Exposure to cigarette smoke led to the depletion of most of the lipophilic antioxidants in human plasma [13].
In addition to the impact on health, tobacco smoking represents a major economic burden for impoverished Bangladeshis. Average male cigarette smokers spend more than twice as much on cigarettes as per capita expenditure on clothing, housing, health and education combined. The typical poor smoker could easily add over 500 calories to the diet of one or two children with the daily tobacco expenditure [14]. It may be noted that most of the study subjects were undernourished, as indicated by an average BMI of 23. Strong tobacco control measures are needed in the context of Bangladesh to decrease tobacco expenditures and thus significantly increase resources and improve health and nutrition of the people.
Conclusion
This study demonstrated that increasing amount of cigarette smoking negatively impact serum concentrations of retinol, alpha-tocopherol, selenium, and zinc. Cigarette smoking may act as an important adjunct to the deficiency of those trace elements in a population who are already nutritionally compromised.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SKB participated in the design of the study and collected the samples. AKM performed the statistical analysis and drafted the manuscript.
Acknowledgements
The authors are thankful to the staff including Nurun Nahar Sultana of the Department of Biochemistry, University of Dhaka, Bangladesh for their contribution in data collection and the biochemical analysis of blood samples. Written consent was obtained from the patient or their relative for publication of study.
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| 15462677 | PMC524516 | CC BY | 2021-01-04 16:39:29 | no | Nutr J. 2004 Oct 5; 3:18 | utf-8 | Nutr J | 2,004 | 10.1186/1475-2891-3-18 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-51550712610.1186/1743-422X-1-5ResearchPersistent expression of chemokine and chemokine receptor RNAs at primary and latent sites of herpes simplex virus 1 infection Cook W James [email protected] Martha F [email protected] Russell M [email protected] Timothy J [email protected] Holly A [email protected] Donald M [email protected] David M [email protected] Millennium Pharmaceuticals Inc., Cambridge, MA 02139, USA2 GlycoFi, Inc., 21 Lafayette Street, Suite 200, Lebanon, NH 03766, USA3 Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School, Boston, MA 02115, USA4 Department of Microbiology and Molecular Genetics, Harvard Medical School, Boston, MA 02115, USA2004 23 9 2004 1 5 5 25 5 2004 28 5 2004 Copyright © 2004 Cook et al; licensee BioMed Central Ltd.2004Cook 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.
Inflammatory cytokines and infiltrating T cells are readily detected in herpes simplex virus (HSV) infected mouse cornea and trigeminal ganglia (TG) during the acute phase of infection, and certain cytokines continue to be expressed at lower levels in infected TG during the subsequent latent phase. Recent results have shown that HSV infection activates Toll-like receptor signaling. Thus, we hypothesized that chemokines may be broadly expressed at both primary sites and latent sites of HSV infection for prolonged periods of time. Real-time reverse transcriptase-polymrease chain reaction (RT-PCR) to quantify expression levels of transcripts encoding chemokines and their receptors in cornea and TG following corneal infection. RNAs encoding the inflammatory-type chemokine receptors CCR1, CCR2, CCR5, and CXCR3, which are highly expressed on activated T cells, macrophages and most immature dendritic cells (DC), and the more broadly expressed CCR7, were highly expressed and strongly induced in infected cornea and TG at 3 and 10 days postinfection (dpi). Elevated levels of these RNAs persisted in both cornea and TG during the latent phase at 30 dpi. RNAs for the broadly expressed CXCR4 receptor was induced at 30 dpi but less so at 3 and 10 dpi in both cornea and TG. Transcripts for CCR3 and CCR6, receptors that are not highly expressed on activated T cells or macrophages, also appeared to be induced during acute and latent phases; however, their very low expression levels were near the limit of our detection. RNAs encoding the CCR1 and CCR5 chemokine ligands MIP-1α, MIP-1β and RANTES, and the CCR2 ligand MCP-1 were also strongly induced and persisted in cornea and TG during the latent phase. These and other recent results argue that HSV antigens or DNA can stimulate expression of chemokines, perhaps through activation of Toll-like receptors, for long periods of time at both primary and latent sites of HSV infection. These chemokines recruit activated T cells and other immune cells, including DC, that express chemokine receptors to primary and secondary sites of infection. Prolonged activation of chemokine expression could provide mechanistic explanations for certain aspects of HSV biology and pathogenesis.
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Introduction
Acute viral infections are usually cleared from the primary site of infection by the host immune response [1], but some viruses can persist at other sites in a latent form. Herpes simplex virus (HSV), for example, causes a primary infection at a mucosal site, which is cleared within 7–10 days by the host immune response. HSV, nevertheless, enters sensory neurons and establishes a latent infection within those cells. In a mouse corneal model of HSV-1 infection, infectious virus is detected in corneal secretions and tissue for approximately 7 days [2]. Similarly, infectious virus is detected in trigeminal ganglion (TG) tissue for up to approximately 10 days [2]. Latent infection is established by 30 days postinfection (dpi) because no infectious virus can be detected in homogenates of TG tissue at that time. HSV DNA, however, is readily detected in latently infected TG for at least 150 dpi [3-5]. Viral gene expression is greatly attenuated during latent infection because the only abundant viral gene product detected is the latency-associated transcript or LAT [6]. Nevertheless, low levels of lytic transcripts can be detected in ganglia latently infected with HSV [5]. Evidence of viral protein expression is provided by the continued T cell infiltration [7,8], elevated levels of interferon γ (IFN-γ) and TNF-α transcripts and numbers of IL-6 expressing cells in the ganglia, [3,9-11]. Expression of IFN-γ and TNF-α transcripts persists in TG latently infected with HSV strains unable to replicate in neurons, indicating that neither HSV replication nor ability to reactivate are required for persistent cytokine gene expression [3]. While CD4+ T cells appear to be important in immunized mice for protection against challenge virus infection [12], CD8+ T cells appear to be important for establishment of latent infection in mice [7]; and CD8+ T cells specific for HSV persist in TG for long periods of time [8]. Thus, there is evidence for long-term immune surveillance in the ganglion during latent infection by HSV.
Chemokines are critical for recruiting inflammatory cells to infected tissues. Chemokine specificity is due in large part to the cell-specific expression of their respective receptors (reviewed in [13-15]. Inflammatory-type receptors including CCR1, CCR2, CCR5, and CXCR3 are expressed by activated T cells, macrophages, natural killer (NK) cells, and immature (i.e. potent for antigen capture but not antigen presentation) dendritic cells (DC), while homostatic-type receptors including CCR7 and CXCR4 are highly expressed by resting T and B cells and mature (i.e., antigen-presenting) DC (Table 1). In addition, receptors including CCR2, CCR5 and CXCR3 are expressed on cells (e.g. Th1 cells) specific for infection-induced inflammation, while others including CCR3 and CXCR4 are on cells (e.g., Th2 T cells) associated with allergic inflammation. Certain receptors are expressed by specific subsets of a given cell type. For example, CCR6 is highly expressed on Langerhans-like (CD34+) DC that migrate to skin, but not on monocyte-derived DC that migrate to non-skin tissues (reviewed in [14]. Acute viral infection in the mouse corneal model system is known to induce the expression of cytokines and chemokines in corneal tissue. Thomas et al. [16] observed the induction of transcripts encoding N51/KC, macrophage inflammatory protein-1 β (MIP-1β), MIP-2 and monocyte chemotactic protein 1 (MCP-1) and the cytokines IL-1, IL-6, IL-12, and TNF-α. Similarly, Tumpey et al. [17] showed induction of MIP-2, MIP-1α, and MCP-1 chemokines in the cornea during acute infection. Infection of mouse fibroblast cells by HSV induces expression of IL-6 [18], and infection of macrophages by HSV induces RANTES expression directly [19]. Infection of other cell types may induce expression of other cytokines and chemokines. Less is known about chemokine expression during HSV latent infection phase. Halford et al. [10] observed RANTES RNA expression, in addition to RNAs for IL-2, TNF-α, IFN-γ, and IL-10, during latent infection.
Table 1 Expression of Chemokine Receptors, Chemokines and Cytokines in Leukocyte Populations
Chemokine receptors Cell type expression Chemokine ligand Proposed primary function(s)
CCR1 T cells, macrophages, immature dendritic cells (DC), natural killer cells (NK) RANTES; MIP-1α; MCP-3, and 4; HCC-1, 2, and 4 Migration of DC to sites of inflammation Recruitment of T cells, macrophages and NK
CCR2 T cells, natural killer cells (NK), macrophages, immature DC MCP-1, 3, and 4 Migration of effector T cells (Th1) Migration of DC progenitors to sites of inflammation
CCR3 eosinophils, basophils, T cells eotaxin-1 and 2; RANTES; MCP-2, 3, and 4; HCC-2 Recruitment of eosinophils
CCR5 T cells (Th1, Tc1), macrophages, immature DC RANTES; MIP-1α and 1β Migration of effector T cells (Th1) Migration of DC to sites of inflammation Recruitment of macrophages
CCR6 immature DC (CD34+/Langerhans-like), T cells MIP-3α Migration of DC to skin
CCR7 T cells, B cells, mature DC SLC, ELC Migration of naïve T cells to lymph nodes Migration of memory T cells to lymphoid tissue
Migration of B cells Migration of DC to lymphoid tissues
CXCR3 T cells (Th1, Tc1) IP-10, MIG, ITAC Migration of effector T cells (Th1)
CXCR4 T cells, macrophages, DC, B cells, others including neurons SDF-1 Migration of effector T cells (Th2)
Migration of B cells
Migration of hematopoietic progenitors
Chemokines Receptor
MIP-1α T cells, NK, macrophages, others CCR1, CCR5 Chemoattract macrophages, T cells, NK, and others
MIP-1β T cells, NK, macrophages, others CCR5, CCR1 (weak) Chemoattract macrophages, T cells, and others
RANTES T cells, NK CCR1, CCR5, CCR3 (weak) Chemoattract T cells and others
MCP-1 macrophages, others CCR2 Chemoattract macrophages, T cells, NK, and others
Eotaxin-1 epithelial cells, NK, macrophages, others CCR3 Chemoattract eosinophils
Cytokines Receptor
IFN-γ T cells, NK IFN-γR Activation of antiviral response
TNF-α macrophages, NK, others TNF-R Broad activation of antiviral and inflammatory response
Recent studies have shown that HSV infection activates Toll-like signaling and chemokine synthesis [20,21]. Thus, we hypothesized that HSV infection might induce prolonged expression of a broad range of chemokines at sites of acute and latent infection. Real-time quantitative RT-PCR methods have facilitated studies of immune cell RNA expression in mouse models [22,23]. We report here the use of real-time RT-PCR to monitor RNA expression of selected chemokine receptors and their chemokine ligands during HSV infection of mouse corneal and TG tissue. Our data show that RNA encoding inflammatory-type chemokine receptors and their ligands persists in infected corneas and TG long after infectious virus can be detected, suggesting prolonged chemokine production and subsequent homing of inflammatory immune cells to these tissues. Strikingly, the data demonstrate the persistent expression of chemokines and chemokine receptor genes in the apparent absence of detectable viral productive infection transcripts in infected corneas.
Results
Development of TaqMan® RT-PCR assays to measure viral and host gene expression during acute and latent infection
To monitor RNA expression of viral and host genes during HSV infection of mice, we developed TaqMan® RT-PCR assays for the quantification of transcripts from the HSV tk and ICP0 genes and from mouse genes encoding selected chemokine receptors and their ligands. In the real-time PCR assay detailed in Materials and Methods, RNA isolated from corneal and ganglionic tissue was used for synthesis of cDNA. Primers and Taqman® probes for the viral or cellular genes (Table 2) were used in real-time PCR assays to measure the concentration of cDNA for each transcript.
Table 2 Primer and Probe Sequences
Forward Primer Reverse Primer Probe*
HSV
tk CGAGACAATCGCGAACATCTAC CCCCGGCCGATATCTCA CCACACAACACCGCCTCGACCA
ICP0 CTGCGCTGCGACACCTT CAATTGCATCCAGGTTTTCATG TGCATGCACCGCTTCTGCATCC
Chemokine receptor
CCR1 GGGTGAACGGTTCTGGAAGTAC CAGCCATTTTGCCAGTGGTA ACATGCCTTTGAAACAGCTGCCGAA
CCR2 ATGAGTAACTGTGTGATTGACAAGCA GCAGCAGTGTGTCATTCCAAGA CTCTGTCACCTGCATGGCCTGGTCT
CCR3 ACCAGCTGTGAGCAGAGTAAACAT CACAGCAGTGGGTGTAGGCA CACCTCAGTCACCTGCATGGCCA
CCR5 ACTGCTGCCTAAACCCTGTCA GTTTTCGGAAGAACACTGAGAGATAA TCCGGAACTTCTCTCCAACAAAGGCA
CCR6 TTGGTGCAGGCCCAGAAC GAACACGAGAACCACAGCGAT CCAAGAGGCACAGAGCCATCCGA
CCR7 CTGCTACCTCATTATCATCCGTACCT TGATCACCTTGATGGCCTTGT CTCCAGGCACGCAACTTTGAGCG
CXCR3 TGTAGTTGGGCTAGCTCGAACTT ACCTGGATATATGCTGAGCTGTCA GCATCCTGGCAGCAAAGTTACGGG
CXCR4 CTCCAAGGGCCACCAGAA GGCAAAGAAAGCTAGGATGAGG CGCAAGGCCCTCAAGACGACAGTC
Chemokine
MIP-1α TCATCGTTGACTATTTTGAAACCAG GCCGGTTTCTCTTAGTCAGGAA AGCCTTTGCTCCCAGCCAGGTGTC
MIP-1β AGGGTTCTCAGCACCAATGG GCTGCCGGGAGGTGTAAGA CTCTGACCCTCCCACTTCCTGCTGTTT
RANTES CTGTCATCGCTTGCTCTAGTCCTA CGGATGGAGATGCCGATTT ATCCCCTACTCCCACTCCGGTCCTG
MCP-1 GCTGGGTTCAGTTTCCTTAAGC CCTAGTCTTTAGCTGTGAGACCTTCTG AGGCCTCGCTGCTCCACATCCA
Eotaxin-1 CCTAAGACGTGCTCTGAGGGAAT TCCCATCTGGAACTACATGAAGC TCAGCACCAGTCGCCCAAGGACT
Cytokine
IFN-γ TGAGTATTGCCAAGTTTGAGGTCA GTGGACCACTCGGATGAGCT CCACAGGTCCAGCGCCAAGCA
TNF-α ACAAGGCTGCCCCGACTAC CGCAGAGAGGAGGTTGACTT CCTCACCCACACCGTCAGCCG
* all probes FAM-5' and 3'-TAMRA
To characterize the range over which the HSV tk and ICP0 real-time PCR assays were accurate and linear, we tested 10-fold dilutions of purified HSV genomic DNA (kind gift of Jean Pesola) starting from 5.5 × 104 copies for tk and ICP0 gene levels. The HSV tk and ICP0 primer/probe sets gave linear amplification curves over 4 logs of template concentrations until the limit of detection within the linear range was reached at 55 DNA copies for tk and 550 copies for ICP0 (not shown). At these limits of detection, the threshold cycle (CT) value, which indicated the PCR cycle at which a significant increase in amplification was first detected, was 39.2 for tk at 55 DNA copies and 36.5 for ICP0 at 550 DNA copies.
Using 2-fold dilutions of uninfected mouse TG cDNA, we observed that the primer/probe sets for host genes listed in Table 2 including GAPDH gave linear amplification curves over at least 3 and up to 7 dilutions. In all cases, CT values changed by about 1 cycle for every 2-fold change in template concentration as expected (not shown). Thus our assays matched well with previously described TaqMan® assays [22-24] for linearity and sensitivity.
Following corneal inoculation of mice with HSV or virus diluent (mock), we collected corneas and TG during acute (3 and 10 dpi) and latent (30 dpi) phases. To monitor viral gene expression in infected mice, we tested tissue samples for tk and ICP0 gene transcripts. In infected corneal tissue, HSV tk and ICP0 transcripts were readily detected at 3, but not at 10 or 30 dpi where CT values = 40 (indicating no measurable RNA) (Fig. 1). Thus we could not detect lytic transcripts in infected corneas beyond the acute phase using this assay.
Figure 1 HSV tk and ICP0 RNA expression in mock and HSV-infected cornea and TG. RNA isolated from tissues harvested at 3, 10, or 30 days postinfection (d) was subjected to TaqMan RT-PCR analysis using HSV tk primers/probe (A) and HSV ICP0 primers/probe (B) as described in Materials and Methods. Mouse GAPDH RNA was measured in multiplex reactions, and used to calculate relative expression using the formula Rel Exp= 2-(ΔΔCT) × 1000 as described in Materials and Methods. Shown below the plots are relative expression values and the CT value measured for tk (A) and ICPO (B) in each sample. The ICP0 signal detected at 10 and 30 dpi in HSV-infected TG is likely due to LAT RNA as described in the text. Results shown are for one experiment (Experiment #1) in which the number of individual mouse tissues pooled were 10 for cornea and 6 for TG. Similar results were obtained in two additional experiments (Experiment #2 and Experiment #3), except for variation in detection of tk RNA in infected TG at 30 dpi as described in the text.
In infected TG, tk RNA peaked at 3 dpi then dropped precipitously (200-fold) to low but readily detectable levels by 10 dpi. At 30 dpi, we detected very low or undetectable tk RNA expression in infected TG. In the experiment shown in Fig. 1A, we measured a CT value of 38.2 for tk expression in infected TG at 30 dpi, resulting in a relative expression value of 0.0002. In an independent experiment, we measured a CT of 38.1 for tk RNA in 30 dpi TG; however, a CT value of 40 was measured in two additional experiments (not shown). CT values for all reactions without RT were 40, indicating no DNA contamination. Thus, while tk expression in latent TG was at the limit of detection for our assay, our ability to detect tk expression in some but not all latent TG was consistent with previous reports in which very sensitive RT-PCR assays were used to detect tk (and ICP0) gene transcripts in some but not all TG during latent infection [5,25]. In those previous reports, an assay that included a radioactive Southern blotting step subsequent to RT-PCR could detect single copies of tk nucleic acid per PCR reaction. Our present assay for tk transcripts is at least 50-fold less sensitive than that used by Kramer and Coen [5].
ICP0 RNA levels were similar to tk in that they peaked at 3 dpi in cornea and TG (Fig. 1B). However, because our ICP0 probe/primer set overlaps latency-associated transcript minor (LAT) – coding sequences, the signal detected at 10 and 30 dpi in TG but not cornea may be due to minor LAT read-through RNAs. RT-PCR analysis of LAT transcripts from the TGs at 30 dpi was consistent with latent virus in infected TG (unpublished results).
Chemokine and chemokine receptor expression in infected cornea and ganglia
We next used TaqMan® RT-PCR to monitor expression of a selected series of mostly T cell and macrophage-specific chemokine receptors and chemokines in mock and HSV-infected cornea and TG. We chose chemokine receptors CCR1, CCR2, CCR5, and CXCR3, which are expressed by activated T cells, macrophages, NK cells, and immature DC that would be part of the immune infiltration in response to HSV infection, and their ligands MIP-1α, MIP-1β, RANTES, and MCP-1. For comparison, we included CCR3 which is primarily expressed on granulocytes, the CCR3 ligand eotaxin-1, CCR6 which is primarily expressed on resting T cells and immature Langerhans-like (i.e., skin homing) DCs, CCR7 which is primarily expressed on resting T and B cells and mature DCs that home back to lymphoid tissues, and CXCR4 which is broadly expressed on many immune and non-immune cell types (Table 1). We also tested the chemokine-inducing cytokines IFN-γ and TNF-α, whose RNA and protein have previously been shown to be expressed during both acute and latent phases of HSV infection [3,9-11].
i. Chemokine and chemokine receptor expression in infected cornea
Epithelial cells of the cornea are the initial sites of replication following infection but infectious virus and viral mRNAs are not detectable past 7–10 dpi [26]. We harvested RNA from mock and HSV-infected cornea at 3, 10, and 30 dpi, and tested for chemokine receptor and chemokine RNA expression in parallel. As expected for tissues supporting active replication or having recently cleared virus, chemokine receptors CCR1, CCR2, CCR5, CCR7, CXCR3 and CXCR4, but not CCR3 or CCR6, were highly expressed and strongly induced (i.e., >3-fold) at 3 and 10 dpi (Fig. 2 and Table 3). Chemokines MIP-1α, MIP-1β, RANTES, and MCP-1, but not eotaxin-1, were also highly expressed and strongly induced in infected cornea at 3 and 10 dpi. IFN-γ and TNF-α were also induced in infected cornea as previously reported [16]. Surprisingly, induction of all host RNAs tested persisted into latent phase at 30 dpi in infected corneas. For example, CCR1, CCR2, and CCR5 exhibited similar induction and similar or only slightly reduced expression levels at 30 dpi as compared to earlier time points. Relative expression and induction of CCR7 and CXCR4 in infected cornea appeared to be biphasic in that values were high at 3, lower at 10, and higher again at 30 dpi. These results suggested that continued presentation of HSV antigens stimulates chemokine production and subsequent homing of effector cells to cornea despite the apparent clearance of infectious virus.
Figure 2 Relative levels of chemokine and chemokine receptor RNA expression in mock and HSV-infected cornea. Corneas were harvested at 3 (A), 10 (B), or 30 (C) days postinfection, and relative levels of expression were determined by TaqMan RT-PCR analysis as described in Fig. 1 and Materials and Methods. Results shown are the average of relative expression values determined using cDNA from two independent experiments, with each cDNA subjected to 2 or 3 separate measurements. Dashed bars represent ranges of individual values. Each cDNA was synthesized from RNA isolated from pooled corneas (5 mice) as described in Fig. 1 and Materials and Methods. The induction ratios (HSV+ vs. mock) for individual genes are tabulated in Table 3.
Table 3 Induction Ratio (HSV+/Mock) of Transcripts for Chemokine Receptors, Chemokines and Cytokines in Cornea and Trigeminal Ganglia (TG)
Corneaa TGb
Gene 3d 10d 30d 3d 10d 30d
CCR1 11 (9.2–12) 18 (13–23) 20 (10–26) 5 (2.2–7.1) 15 (9.0–19) 4 (1.7–7.0)
CCR2 14 (9.2–19) 22 (11–32) 14 (8.1–24) 3 (1.5–4.2) 15 (11–19) 3 (1.3–4.4)
CCR3 2 (1.0–5.0) 3 (2.0–5.0) 3 (2.5–3.3) 2 (0.5–5.0) 8 (2.2–20) 3 (1.8–4.7)
CCR5 12 (12.3–12.5) 11 (8.0–14) 20 (8.9–36) 9 (4.8–11) 57 (22–110) 9 (7.0–10)
CCR6 3 (2.4–3.0) 2 (1.0–2.5) 5 (1.5–8.5) 3 (0.3–11) 3 (1.0–5.0) 14 (1.0–40)
CCR7 24 (8.0–40) 5 (3.0–6.5) 17 (13–21) 13 (9.0–17) 19 (17–20) 7 (2.0–11)
CXCR3 10 (5.0–18) 15 (8.0–23) 5 (2.8–6.5) 2 (1.0–4.0) 104 (54–160) 36 (14–59)
CXCR4 11 (4.8–14) 3 (1.7–4.0) 45 (33–74) 0.6 (0.4–0.9) 4 (2.9–6.2) 3 (2.3–3.7)
MIP-1α 69 (33–106) 394 (263–1700) 34 (16–53) 232 (80–471) 126 (80–168) 25 (13–45)
MIP-1β 53 (39–67) 285 (261–310) 16 (11–21) 282 (10–595) 230 (202–245) 31 (24–37)
RANTES 55 (36–73) 43 (38–48) 16 (12–18) 64 (61–66) 304 (302–306) 31 (12–50)
MCP-1 54 (52–55) 64 (55–74) 12 (7.5–20) 153 (113–194) 22 (16–27) 3 (1.6–4.2)
Eotaxin-1 3 (1.9–3.5) 1 (0.6–1.3) 3 (1.0–5.4) 5 (3.3–9.1) 2 (1.2–2.8) 1.5 (0.7–2.3)
IFNγ Inf.c Inf. Inf. Inf. Inf. Inf.
TNF-α 3 (2.9–3.0) 3 (2.6–3.8) 7 (3.9–12) Inf. Inf. Inf.
a Induction ratios were calculated as relative expression in HSV-infected/relative expression in mock-infected cornea. Each value is the average of induction ratios (2 or 3 separate measurements per cDNA sample) from two independent experiments. Ranges of individual ratios are in parentheses.
b Induction ratios were calculated for HSV- vs. mock-infected TG as in footnote a. Each value is the average of induction ratios (2 or 3 separate measurements per cDNA sample) from three independent experiments, with ranges in parentheses.
c Inf., infinite due to relative expression = 0 in all or most mock-infected samples.
ii. Chemokine and chemokine receptor expression in infected ganglia
In infected TG, transcripts from the genes encoding receptors CCR1, CCR2, CCR5, CCR7, and CXCR3 were induced by HSV infection during both acute (3 and 10 dpi) and latent (30 dpi) phases (Fig. 3 and Table 3). Peak induction of these RNAs was at 10 dpi during the clearance phase. CXCR4 was induced at 10 and 30 dpi but not at 3 dpi. While we measured induction of CCR3 and CCR6 at 10 and 30 dpi, their very low expression was at the limit of our detection (i.e., relative expression values < 0.5) as also seen in corneas. RNAs for the MIP-1α, MIP-1β, RANTES, and MCP-1 chemokines were also strongly induced at each timepoint, particularly at 3 dpi. Eotaxin-1 was induced at 3 dpi, but much less so at 10 and 30 dpi. As seen previously [3] cytokines IFN-γ and TNF-α were strongly induced at 3 and 10 dpi, but much less so at 30 dpi.
Figure 3 Relative levels of chemokine and chemokine receptor RNA expression in mock and HSV-infected TG. TG were harvested at 3 (A), 10 (B), or 30 (C) days postinfection, and RNA levels were determined by TaqMan RT-PCR analysis as described in Fig. 1, Fig. 2 and Materials and Methods. Results shown are the average of relative expression values determined using cDNA from three independent experiments, with each cDNA subjected to 2 or 3 separate measurements. Dashed bars represent ranges of individual values as described in Fig. 2. The induction ratios (HSV+ vs. mock) for individual genes are tabulated in Table 3.
A striking finding in this analysis was the persistent expression of inflammatory cell RNAs during the latent phase of TG infection when detectable production of infectious virus has ceased. To determine if induction of these RNAs persisted past 30 dpi, we monitored expression of a limited number of transcipts from in TG collected at 45, 62, and 90 dpi. In previous studies [3-5], HSV genomic DNA was maintained at constant levels (~104 copies per TG) for up to 150 dpi in infected TG, indicating that latent virus persists well beyond 90 dpi in this mouse model. Induction of all RNAs in our panel persisted for at least 62 dpi; furthermore, all but CCR3 and eotaxin-1 were also induced at 90 dpi (Table 4). Thus chemokine receptor and ligand expression persisted long into the latent phase in infected TG.
Table 4 Induction Ratio (HSV+/Mock) of Transcripts for Chemokine Receptors and Chemokines in Trigeminal Ganglia (TG) at Late Times Post-Infection
Induction Ratioa
Gene 45d 62d 90d
CCR2 3 (3.2–3.3) 5 (1.3–8.8) 2 (2.2–2.4)
CCR3 8 (5.0–12) 3 (1.0–4.4) 0.7 (0.4–1.0)
CCR5 5 (5.1–5.7) 7 (4.9–9.0) 5 (2.9–6.5)
CXCR3 17 (10–24) 68 (25–111) 20 (11–28)
MIP-1α 10 (7.0–13) 35 (4.0–67) 4 (1.0–7.0)
Eotaxin-1 3 (1.5–3.9) 2 (1.1–3.1) 1.5 (0.8–2.3)
a Induction ratios were calculated as relative expression in HSV-infected/relative expression in mock-infected cornea as described in Table 3. Each value is the average induction ratio (2 separate measurements per cDNA sample) from one experiment. Ranges of individual ratios are in parentheses.
Discussion
Recent studies have shown that HSV infection induces Toll-like signaling and chemokine synthesis. Thus, we hypothesized that HSV infection might induce a broad range of chemokines at sites of primary and latent infection. In agreement with and extending previous studies [3,9-11], we have found evidence for persistent expression of chemokines and trafficking of inflammatory cells including activated T cells to acutely infected corneal tissue and to latently infected trigeminal ganglia. We also observed prolonged expression of chemokine and chemokine receptor gene transcripts in corneal tissue, the primary site of HSV-1 infection in this model system, long after infectious virus has been cleared. Microarray analysis of host gene expression has also demonstrated long-term alterations of host gene expression during latent infection by HSV, including alterations in expression of CXCR6 mRNA in TG [27]. These results argue for long-term persistence or expression of viral antigens or immunogens and stimulation of expression of these chemokines, even at the primary site of infection, the cornea. Recent results [28] have shown similar elevated chemokine expression in lung tissue after clearance of murine gamma herpesvirus 68. It will be of interest to determine how widespread this effect is among different virus infections or whether it is unique to viruses that persist in the host, such as the herpesviruses.
Potential mechanisms for elevated expression of chemokines and chemokine receptors after viral clearance
Low level expression of viral lytic transcripts in TG during latent infection has been documented [5], which could result in low level expression of viral proteins. Recent results have shown that HSV-1 can activate Toll-like receptor 2 to stimulate chemokine expression and secretion and to activate NF-κB regulated promoters [20]. Lund et al. [21] showed that infectious HSV-2 and also purified HSV-2 DNA activates signaling through DC-expressed Toll-like receptor 9, resulting in the induction of IFN-α secretion. Toll-like receptor activation by HSV-2 DNA raises the intriguing possibility that HSV DNA alone is at least partially responsible for TLR-dependent induction of chemokine expression in latent TG. Among the transcripts that we studied, we detected persistent expression of transcripts for MIP-1α, MIP-1β, and RANTES, whose expression is activated by Toll-like receptors [29]. Expression of MIP-1α and MIP-1β could recruit NK cells, which express CCR5, and immature dendritic cells, which express CCR1 and CCR5, into the site of infection. Thus, elevated expression of at least some of the chemokines could be due to Toll-like receptor activation. It is also possible that other chemokines that were not assayed in this or previous studies are also induced during latent HSV infection via Toll-like receptor dependent mechanisms. Elevated expression of chemokine receptors is likely due to the chemokine-induced trafficking of inflammatory cells to the site of infection or, in the case of 30 days postinfection or latent infection, the site of viral antigen persistence.
Although we have not examined expression of IP-10, a chemokine also induced by Toll-like receptor signaling [29], we did examine the expression of transcripts for CXCR3, its receptor on activated T cells. Levels of both are elevated during latent infection in TG. Thus, stimulation of expression of this chemokine could attract activated T cells to the latently infected TG, providing a mechanism for the persistent presence of HSV-specific CD8+ T cells in latently infected TG [8].
Implications of persistent chemokine expression
Long-term inflammatory responses in neural tissue could induce pathology due to damage to neuronal cells. A number of neurological diseases have been associated with HSV infection [30], and these could be associated with these long-term inflammatory responses. In addition, the possibility of other types of specific pathological effects is raised.
Role of HSV in coronary heart disease
Recent data have shown an association between HSV-1 seropositivity and myocardial infarction and coronary heart disease in older adults [31]. These authors hypothesized that HSV-1 reactivation from autonomic nerves that innervate the coronary arteries could cause infection of endothelial cells, endothelial injury, and the initiation of an acute thrombotic event. Similarly, based on our work, HSV infection might induce expression of MCP-1 and IL-8, which are known to cause adhesion of monocytes to vascular endothelium [32], an early step in the development of atherosclerotic lesions in mouse models (reviewed in Gerszten et al. [32]. Therefore, the induction and prolonged expression of these chemokines by HSV infection could play a role in the pathogenesis of coronary heart disease.
Role of HSV in HIV transmission
Considerable evidence has accumulated for the role of genital herpes infections in promoting the transmission of human immunodeficiency virus (reviewed in [33]. Although we examined HSV-1 in these studies, HSV-2 shares many biological properties with HSV-1. Thus, it is conceivable that genital herpes infections could similarly induce the expression of chemokines in the genital mucosae and the trafficking of dendritic cells and CD4+ T cells to that site. In addition to the break in the genital epithelium provided by the genital lesion, the recruitment of dendritic cells and CD4+ T cells to sites of HSV infection would provide cells to transport HIV to lymph nodes and the primary host cell, respectively, and increase the potential for HIV infection.
Implications for HSV biology and vaccine design
Recent studies on the persistence of CD8+ T cells in latently infected ganglia have concluded that these cells play a role in maintaining the latent infection [8]. The results presented here raise the possibility that the presence of CD8+ T cells in latently infected TG's could be the result of chemokine expression. Thus, further studies are needed to establish the causal relationship between the presence of CD8+ T cells in latently infected ganglia and maintenance of latent infection.
Various HSV strains, including replication-defective mutants and amplicon vectors which do not establish neuronal latency efficiently, have been shown to induce durable immune responses [12,34,35]. These results suggest that the basis for the durable immune responses may be the persistence of antigen or continued antigen expression at sites of primary infection. Further studies are needed to determine the source of this antigen and the mechanism of the induction of chemokine expression at primary and latent sites of HSV infection.
Materials and Methods
Viruses, infection of mice, and tissue collection
HSV-1 KOS was propagated and titered on Vero cell monolayers as described previously [36]. Seven-week-old HSD:ICR mice (Harlan, Sprague, Dawley) were anesthetized and infected with 2 × 106 pfu of virus or mock infected with virus diluent via corneal scarification as described [2]. At specific days post infection (dpi), cornea and TG were collected and flash-frozen on dry ice with minimal elapsed time post sacrifice [5]. Cornea and TG from each time and treatment group were pooled prior to isolation of RNA. A total of four infections were performed: in Exp. #1 cornea and TG were collected at 3, 10, and 30 dpi; in Exp. #2 TG were collected at 3, 10, and 30 dpi; in Exp. #3 TG were collected at 3, 10, 45, 62, and 90 dpi; and in Exp. #4 cornea and TG were collected at 30 dpi.
Preparation of RNA and cDNA, and real-time quantitative RT-PCR
Total RNA was purified from tissues using RNA STAT-60 (Tel-Test, Friendswood, TX), followed by secondary purification and DNAse I treatment using RNeasy columns (Qiagen). cDNA was synthesized using the Omniscript Reverse Transcriptase Kit (Qiagen) for Exp. #1 or TaqMan® Reverse Transcription Reagents (Perkin Elmer) for Exps. #2, #3, and #4 following the manufacturers' suggested protocols. Design of the PCR primers and TaqMan® probes for mouse chemokine and chemokine receptors was done using Primer Express (Applied Biosystems) software. Primer and probe sequences are listed in Table 2. Primers and the VIC-labeled TaqMan® probes for the housekeeping control genes rodent GAPDH and 18S rRNA were purchased from Applied Biosystems. Real-time quantitative RT-PCR assays were performed with reagents recommended by the manufacturer (Applied Biosystems) using an ABI PRISM 7700 Sequence Detection System instrument. Briefly, 0.5 μL (approximately 300 pg) of cDNA was added to 25μL reactions containing 12.5 μL of PCR Universal Mix (Applied Biosystems), 600 nM F primer, 600 nM R primer, 200 nM FAM-labeled TaqMan probe, 200 nM rodent GAPDH F primer, 200 nM rodent GAPDH R primer, and 100 nM rodent GAPDH TaqMan® probe. The number of PCR cycles needed for FAM or VIC fluorescence to cross a threshold where a statistically significant increase in change in fluorescence (CT=threshold cycle) was measured using Applied Biosystems software. Relative RNA expression was determined using the formula Rel Exp= 2-(ΔΔCt) × 1000 where ΔΔ CT= (CT gene of interest-CT rodent GAPDH in experimental sample)-(CT gene of interest-CT rodent GAPDH in a no-template control sample) (the ΔΔ CT method, Taqman® Bulletin #2: Relative Quantitation of Gene Expression, Applied Biosystems, updated 2001, ). To assure that GAPDH RNA levels were not affected by HSV infection and thus a good control, we repeated most analyses using 18S rRNA as an internal control. In all cases tested, induction measurements (HSV+/mock) were indistinguishable whether 18S or GAPDH were used (not shown). Control reactions lacking RT were used to test for the presence of contaminating HSV or mouse DNA, and in all cases either no or low (relative to when RT was present) levels of amplification were measured (not shown). Purified HSV-1 genomic DNA was kindly provided by Jean Pesola.
Competing interests
The author(s) declare that they have no competing interests.
Authors' Contributions
W. Cook, R. Walker and T. Burwell performed the RT-PCR analyses of chemokine transcripts. M. Kramer and H. Holman performed the animal infections and provided tissues for transcript analysis. D. Coen and D. Knipe participated in the design of experiments, oversight of the conduct of the experiments, and in the interpretation of the results.
Acknowledgments
This research was supported by NIH grant P01 NS35138 and a grant from Millennium Pharmaceuticals to DMC and DMK.
We thank numerous colleagues at Millennium Pharmaceuticals, particularly Laura Rudolph-Owen, Michael Donovan, and Jose-Carlos Gutierrez, and members of the Knipe and Coen laboratories. We thank Ming Chen for help with Experiment #1.
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| 15507126 | PMC524517 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Sep 23; 1:5 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-5 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-251546960610.1186/1476-4598-3-25Short CommunicationGenetic alterations and in vivo tumorigenicity of neurospheres derived from an adult glioblastoma Tunici Patrizia [email protected] Lorena [email protected] Elena [email protected] Bianca [email protected] Laura [email protected] Giovanni [email protected] Gabriella [email protected] Gaetano [email protected] Istituto Nazionale Neurologico Besta, Dept. Experimental Neurology, Milano, Italy2 Istituto Nazionale Tumori, Dept. Experimental Oncology, Milano, Italy3 Istituto Nazionale Neurologico Besta, Dept. Clinical Neurosciences, Milano, Italy4 Istituto Nazionale Neurologico Besta, Dept. Neurosurgery; Milano, Italy2004 6 10 2004 3 25 25 30 8 2004 6 10 2004 Copyright © 2004 Tunici et al; licensee BioMed Central Ltd.2004Tunici 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.
Pediatric brain tumors may originate from cells endowed with neural stem/precursor cell properties, growing in vitro as neurospheres. We have found that these cells can also be present in adult brain tumors and form highly infiltrating gliomas in the brain of immunodeficient mice. Neurospheres were grown from three adult brain tumors and two pediatric gliomas. Differentiation of the neurospheres from one adult glioblastoma decreased nestin expression and increased that of glial and neuronal markers. Loss of heterozygosity of 10q and 9p was present in the original glioblastoma, in the neurospheres and in tumors grown into mice, suggesting that PTEN and CDKN2A alterations are key genetic events in tumor initiating cells with neural precursor properties.
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Recent data have proposed that brain tumors contain a "core" of stem cells providing them with the potential to grow aggressively, escaping the effects of radiotherapy and chemotherapy [1,2]. These cancer stem cells were isolated from medulloblastomas or gliomas and grew in vitro as neurospheres, suspended clonal aggregates containing cells with different levels of commitment [3].
Such observations, derived from pediatric tumors only, did not include data on the in vivo tumorigenicity of cancer stem cells. We have found that neurospheres from an adult glioblastoma (GBM) have the potential to express glial and/or neuronal markers and form highly infiltrating gliomas into the brain of immune-deficient mice.
The neurospheres were derived from three adult brain tumors and two pediatric malignant gliomas (BT1–BT5, see Additional file 1). The neurospheres of BT1, a glioblastoma multiforme (GBM) were studied by flow-cytometry and immunohistochemistry. Under differentiating conditions (EGF-bFGF-LIF withdrawal and FBS addition) nestin expression decreased and BT1 neurospheres expressed high levels of neuronal and astrocytic markers. Remarkably, most of the cells expressed both such markers, suggesting the altered function of a complete differentiation program (see Additional file 2).
To test their neoplastic potential we injected BT1 and BT2 (a central neurocytoma) neurospheres into nude mice. All the mice injected intracerebrally (i.c.) with BT1 neurospheres, but none of those injected subcutaneously (s.c.), developed brain tumors that were lethal after 3, 5 and 6 months, respectively. After 4 months, however, none of the mice injected with BT2 neurospheres developed a tumor. Adherent cells from the same two patients were also injected i.c. and s.c. into nude mice. Two of three mice injected i.c. with BT1 adherent cells, but none of those injected with BT2 cells, developed a brain tumor that were lethal 4 and 5 months after injection, respectively.
All the brain tumors in nude mice appeared as large, infiltrating gliomas (Fig. 1A-B) with features of a grade II-III oligoastrocytoma (Fig 1D-E). Both the primary tumor (Fig 1F), and the tumors in nude mice (Fig 1G-H) expressed nestin.
Figure 1 Histological analysis of BT1 and BT1-derived tumors in nude mice. BT1 neurospheres (1 × 10e5) were stereotactically injected into the left hemisphere of nude mice (Charles River Italia, Calco, Italy; n = 3) or subcutaneously (n = 3). Nude mice were also injected with 1 × 10e5 BT1 adherent cells into the brain (n = 3) or subcutaneously (n = 3). Cells from BT2 were injected with similar procedures into nude mice. Control mice (n = 3) were injected with 1 × 10e5 neural stem/progenitor cells obtained from C57BL6J mice with previously described methods [11]. Fig 1A-B shows the GFAP staining in brown of coronal sections of the tumor derived from neurospheres (1A) or from adherent cells (1B). The right part on the figures correspond to the left hemisphere, were cells were injected. Fig. 1C-E show H-E staining of the primary tumor with features of a glioblastoma multiforme (1C) and of a tumor in mouse brain derived from neurospheres, showing an area with a prevailing aspect of oligodendroglioma (1D) or adherent cells, exhibiting anaplastic changes (1E). Fig. 1F-H show nestin staining of the primary tumor (1F) and of a tumor in mouse brain derived from neurospheres (1G) or adherent cells (1H).
The five chromosomal regions showing frequent allelic imbalance in gliomas (1p, 9p, 10q, 17p and 19q) were investigated on six specimens obtained from BT1 surgery. No allelic loss was detected in specimen 1 (S1; frontal area); S2 and S3 (fronto-temporal area) showed LOH on chromosome 10q; S4 and S5 (temporal area) had LOH on 10q and 9p (Fig 2). Neurospheres were derived from S6 (temporal) and their analysis showed the same alterations of S5, i.e. LOH on 10q and 9p (Fig. 2). Adherent cells deriving from S6 did not show any detectable LOH and no alteration was found on 1p, 17p and 19q. In the primary tumor the allelic imbalance was partial, in neurospheres, on the contrary, it was complete. Interestingly, not only tumors deriving from BT1 neurospheres but also the tumor from adherent cells showed LOH on 9p and 10q (Fig 2).
Figure 2 Genetic analysis on BT1, BT1-neurospheres and adherent cells and BT1-tumors in nude mice. DNA was extracted from frozen tissues, cell cultures or lymphocytes, using standard protocols. Primers, microsatellite markers and PCR conditions for LOH analysis were described before [12]. We also investigated markers 9S157 and 9S171 flanking the CDKN2A gene on 9p21. Before doing microsatellite analysis on mouse tumors we confirmed that PCR primers did not hybridize on mouse DNA. For cytogenetic analysis cells were harvested with 0.1 μg/ml Colcemid (Karyomax Colcemid, Life Technologies) overnight. Hypotonic treatment, fixation and GTG banding of metaphase chromosomes were performed with standard methods. The karyotypes were described in accordance with ISCN guidelines Spectral karyotyping was performed on metaphase cells according to the manufacturer's instructions (ASI, Carlsbad, CA) and to published procedures [13]. Spectral images were acquired and analyzed with an SD200 Spectral Bio-imaging System (ASI Ltd., MigdalHaemek, Israel) and a charged-coupled device camera (Hamamatsu, Bridgewater, NJ) connected to a Zeiss Axioskop 2 microscope (Carl Zeiss, Canada) and analyzed by the use of SKYVIEW (version 1.6.1; ASI) software. The upper panel shows the results of LOH analysis on 9p and 10q of the different samples outlined on the left. The lower panel illustrates a representative spectral karyotype of neurospheres obtained with the simultaneous hybridization of 24 combinatorially labeled chromosome painting probes. Karyotype display of chromosome banding (inverted DAPI) and SKY analysis (chromosomes were assigned a pseudo-color according to the measured spectrum) are shown. The number (7) next to the marker chromosome (der(3)) indicates the origin of inserted material.
Cytogenetic analysis of BT1 neurospheres showed a pseudo-diploid karyotype with monosomy of chromosomes 9, 10, 18, trisomy of chromosomes 19 and 20 and presence of three marker chromosomes. A pseudo-tetraploid clone was also present, resulting from duplication of the pseudo-diploid clone and with the same numerical and structural abnormalities (Fig. 2). The G-banding karyotype of BT1 adherent cells resulted 46, XY. SKY analysis confirmed the numerical changes (monosomies and trisomies) shown by G-banding and allowed to unravel the nature of a the marker chromosomes as a der(3)ins(3;7)(3pter→3q11::7q11→7q22::3q11→3qter).
Three observations are provided by the follow-up of nude mice injected with BT1 cells. First, tumors only developed into the brain and not subcutaneously. Thus, in BT1 the cancer "stem" cells required to be in their niche, i.e. the brain, to develop tumors and the evolution of these tumors resembled closely that of "real" gliomas. The phenotype of such gliomas, however, appeared less aggressive than in the original tumor, possibly because the cancer "stem" cells were conditioned by in vitro passaging and by growth in the brain of immune-deficient mice.
Second, the tumors obtained from neurospheres were completely different from those obtained from established cell lines like U87, 9L, C6 or F98: they grew slower, were highly infiltrating and showed a morphological pattern resembling that of an anaplastic, mixed glioma, but without necrotic areas and palisade cells typical of a GBM (compare Fig. 1C with 1D-E). LOH studies demonstrated the loss of a region chromosome 10q where PTEN is located. PTEN is a critical tumor suppressor gene in GBM but has also an important role in the regulation of neural stem cell proliferation [4-6]. Its loss can therefore be a central event in the neoplastic derangement of brain cancer "stem" cells. We also found combined 9p LOH associated to 10qLOH in S4–5 and in the neurospheres, but not in S2–3, suggesting that 9p LOH is secondary to that on 10q. LOH on 9p suggests the alteration of the important tumor suppressor gene CDKN2A, encoding p16 and p14(ARF). p16 expression is absent or defective in glioblastomas [7,8] and p16 has an important role in the terminal differentiation of neural precursor cells [9]. Furthermore, p16 is the main target through which Bmi1 regulates neural stem cell differentiation and self-renewal [10].
Third, LOH on 10q and 9p were present not only in the original tumor and in neurospheres but also in neurosphere-derived gliomas in nude mice. Remarkably, even if adherent cells had a normal karyotype and no allelic imbalance, the derived tumors did show 10q and 9p LOH. This suggests that few adherent cells with these genetic abnormalities escaped our analysis and underwent a positive selection in vivo. These results, therefore, point to PTEN and CDKN2A alterations as critical events in tumor initiating cells, a definition synonymous of cancer stem cells.
The identification of neurospheres from adult brain tumors, and specifically from an adult GBM, is strengthening the case for the importance of cancer "stem" cells in the genesis of these malignancies. A thorough genetic dissection of such cells on a larger scale should give new insights for the therapeutic targeting of these cancer "queen-bee" cells.
Supplementary Material
Additional File 1
Additional file 1 (Tunici et al-Additional file 1.doc) contains Methods with references, comments on in vitro data and the legend to the additional file 2.
Click here for file
Additional File 2
Additional file 2 (Tunici et al-Additional file 2.ppt) contains figures of brain tumor neurospheres, and flow cytometry and immunohistochemical data for their characterization.
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Acknowledgements
We thank Ettore Salsano for collaboration with real-time PCR experiments, Luigi Poliani for helpful suggestions and Francesca Inverardi for help with morphological analysis. This study was partially supported by grants to GF from the Associazione Italiana per la Ricerca sul Cancro and from the Istituto Superiore di Sanita'.
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| 15469606 | PMC524518 | CC BY | 2021-01-04 16:36:33 | no | Mol Cancer. 2004 Oct 6; 3:25 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-25 | oa_comm |
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J NanobiotechnologyJournal of Nanobiotechnology1477-3155BioMed Central London 1477-3155-2-101546178510.1186/1477-3155-2-10ResearchSurface structure, model and mechanism of an insect integument adapted to be damaged easily Boevé Jean-Luc [email protected] Véronique [email protected] Tanguy [email protected] Philippe [email protected] Sergio [email protected] Department of Entomology, IRSNB-KBIN, Royal Belgian Institute of Natural Sciences, Rue Vautier 29, B-1000 Bruxelles, Belgium2 Present address: Unité d'écologie et de biogéographie, Croix du Sud, 4–5, B-1348 Louvain-la-Neuve, Belgium3 Unité de modélisation des structures et des matériaux, CP 194/5, Université Libre de Bruxelles, Avenue Roosevelt 50, B-1050 Bruxelles, Belgium4 Institut für Zoologie, Stephanstrasse 24, Justus-Liebig-Universität Giessen, D-35390 Giessen, Germany5 Present address: Institut für Forstzoologie und Waldschutz, Georg-August Universität Göttingen, Büsgenweg 3, D-37077 Göttingen, Germany2004 1 10 2004 2 10 10 28 5 2004 1 10 2004 Copyright © 2004 Boevé et al; licensee BioMed Central Ltd.2004Boevé 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
Several sawfly larvae of the Tenthredinidae (Hymenoptera) are called easy bleeders because their whole body integument, except the head capsule, disrupts very easily at a given spot, under a slight mechanical stress at this spot. The exuding haemolymph droplet acts as a feeding deterrent towards invertebrate predators. The present study aimed to describe the cuticle surface, to consider it from a mechanistic point of view, and to discuss potential consequences of the integument surface in the predator-prey relationships.
Results
The integument surface of sawfly larvae was investigated by light microscopy (LM) and scanning electron microscopy (SEM) which revealed that the cuticle of easy bleeders was densely covered by what we call "spider-like" microstructures. Such microstructures were not detected in non-easy bleeders. A model by finite elements of the cuticle layer was developed to get an insight into the potential function of the microstructures during easy bleeding. Cuticle parameters (i.e., size of the microstructures and thickness of the epi-versus procuticle) were measured on integument sections and used in the model. A shear force applied on the modelled cuticle surface led to higher stress values when microstructures were present, as compared to a plan surface. Furthermore, by measuring the diameter of a water droplet deposited on sawfly larvae, the integument of several sawfly species was determined as hydrophobic (e.g., more than Teflon®), which was related to the sawfly larvae's ability to bleed easily.
Conclusion
Easy bleeders show spider-like microstructures on their cuticle surface. It is suggested that these microstructures may facilitate integument disruption as well as render the integument hydrophobic. This latter property would allow the exuding haemolymph to be maintained as a droplet at the integument surface.
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Background
The integument of insects is very often involved in defence strategies towards predators and pathogenic agents [1,2]. Generally it constitutes the first contact point in the interaction between an insect and such natural enemies. It often offers an efficient protection as a physical barrier due to its hardness, for instance, in adult beetles. At the opposite extreme, a low mechanical strength of the integument can be implicated in insect defence strategies as well. One example of this is the phenomenon of reflex bleeding that is known in several insect orders. The integument presents a few localized weak points which can disrupt when the insect under disturbance will increase its internal hydraulic pressure, provoking the release of a droplet of distasteful haemolymph [e.g., [3]]. The phenomenon of easy bleeding is another type of adaptation used in defence, by where the whole body integument, except the head capsule, can disrupt easily at a given spot when this spot is subjected to mechanical stress [see definition in [4]]. The phenomenon occurs in the larvae of some species belonging to sawflies (Hymenoptera, Symphyta, Tenthredinidae). Species that show easy bleeding notably belong to genera such as Aneugmenus, Athalia, Monophadnus, Phymatocera and Rhadinoceraea. Recently, the mechanical strength of dissected pieces of larval integument was measured in a calibrated manner. The force needed to damage the integument can vary in more than one order of magnitude from one species to another [4]. Easy bleeding differs from reflex bleeding in that, first, almost the whole body integument is potentially involved in the phenomenon, and second, an external force is necessary to exhibit the phenomenon [4]. As soon as the integument of an easy bleeder is damaged, a haemolymph droplet exudes and can remain as such during several minutes.
An ecological implication of easy bleeding is that the emission of a haemolymph droplet will deter an attacking predator from killing and feeding on an easy bleeder. Indeed, the haemolymph is feeding deterrent towards foraging ants and wasps [4-8]. Birds are other important predators of sawfly larvae [9], but to which easy bleeding seems less clearly effective [10]. Thus the ecological function of easy bleeding is demonstrated as a chemically mediated defence strategy directed especially towards foraging invertebrate predators.
However, integument disruption remains puzzling from a morphological and mechanistic point of view. The present study is based on a comparative analysis of the larval integument surface in several sawfly species, which comprise easy bleeders as well as non-easy bleeders. We wanted to describe the geometry and to approach the mechanical properties of the integument surface, and to consider proximate, ecological implications.
Results
Microstructures covering the cuticle surface
The larvae of sawfly species observed by SEM showed above surface microstructures of their cuticle and which are described below. These microstructures were strikingly more complexly structured in easy bleeders than in non-easy bleeders (Fig. 1, 2) and this differing occurrence among sawfly species was significant (P = 0.0001, Fisher exact probability test, N = 24 species; Table 1).
Figure 1 Cuticle surfaces of sawfly larvae by SEM. Easy bleeders are A. rosae (a, b) and M. monticola (d). Non-easy bleeders are C. septentrionalis (c), H. australis (e), N. miliaris (f), P. parvula (g) and G. hercyniae (h). The dorso-lateral part of the abdomens is shown. Detailed view showing spider-like microstructures (b). Views showing blister-like swellings (c, e to g) or setae (h).
Figure 2 Cuticle surfaces of sawfly larvae by SEM and related integument sections by LM. Non-easy bleeder is S. multifasciata (a, e). Easy bleeders are P. aterrima (b, f), A. padi (c, g), R. nodicornis (d) and R. bensoni (h). Views by SEM (a to d) show blister-like swellings (a) or spider-like microstructures (b to d). Views by LM (e to h) showing that, above a cellular layer, the cuticle comprises a procuticle, in blue, whereas the epicuticle, in red (e, g), is not observed in some species (f, h).
Table 1 Easy bleeding, cuticle microstructures and hydrophobic property in sawfly larvae
Species Easy bleeding1 Microstructures2 Droplet3
2 μl Diameter3
4 μl
TENTHREDINIDAE
Allantiinae
Athalia rosae (L.) EB + ? ? or 2.1 ± 0.0
Blennocampinae
Eurhadinoceraea ventralis (Panzer) EB - · ·
Monophadnus monticola (Hartig) EB + · ·
Monophadnus spinolae (Klug) EB - · ·
Phymatocera aterrima (Klug) EB + ? or 1.5 ± 0.0 ? or 2.0 ± 0.0
Rhadinoceraea bensoni Beneš EB + · ·
Rhadinoceraea micans (Klug) EB + ? ?
Rhadinoceraea nodicornis Konow EB + ? or 1.6 ± 0.1 ? or 2.0 ± 0.1
Tomostethus nigritus (Fabricius) N-EB - · ·
Nematinae
Craesus alniastri (Sharfenberg) N-EB - 1.6 ± 0.0 2.1 ± 0.0
Craesus septentrionalis (L.) N-EB - 1.7 ± 0.0 2.2 ± 0.1
Hemichroa australis (Serville) N-EB* - 1.6 ± 0.1 2.2 ± 0.1
Hemichroa crocea (Geoffr.) N-EB - · ·
Hoplocampa testudinea (Klug) N-EB - · ·
Nematus melanocephalus Hartig · - · ·
Nematus miliaris (Panzer) N-EB* - · ·
Nematus pavidus Serville N-EB* - · ·
Pristiphora laricis (Hartig) N-EB - · ·
Pristiphora testacea (Jurine) N-EB - 1.9 ± 0.0 2.6 ± 0.0
Pseudodineura parvula (Klug) · - · ·
Selandriinae
Aneugmenus padi (L.) EB + 1.7 ± 0.2 2.2 ± 0.2
Strongylogaster mixta (Klug) N-EB - 1.7 ± 0.1 2.1 ± 0.1
Strongylogaster multifasciata (Geoffr.) N-EB - 1.6 ± 0.1 2.1 ± 0.0
Tenthredininae
Tenthredo scrophulariae L. N-EB* - · ·
ARGIDAE
Arge sp. N-EB - · ·
DIPRIONIDAE
Gilpinia hercyniae (Hartig) N-EB - 1.6 ± 0.0 2.0 ± 0.0
1 Species was an easy bleeder (EB), or a non-easy bleeder (N-EB). Data from Boevé & Schaffner [4], except data from U Schaffner & JLB, unpublished results (*).
2 Spider-like microstructures were present (+) or absent (-) by observations of the cuticle surface by SEM and/or of cuticle sections by LM.
3 Cuticle was either too hydrophobic so that adherence of water droplet was impossible (?) or the diameter (mean ± SD, in mm) of a 2 and 4 μl droplet on the cuticle was measured. (·) Not tested.
In easy bleeders, the cuticle is covered with irregularly shaped wart-like microstructures (verrucose). Their density is approximately of 15 units per 0.01 mm2. They possess fine ridges (carinulate) in a radiated way (Fig. 1b, 2b,2d), hence the term "spider-like". The fine ridges (i.e., the "legs" of the "spider") more or less imbricate in between those from adjacent microstructures, and their width is approximately of 0.5 to 1.5 μm. The form of the microstructure is generally circular (diameter excluding ridges: 10 μm in A. padi), but can be elongated (length: 35 μm in A. rosae). The ridges can be reduced (e.g., in A. padi). The height of microstructures was measured on LM views and reaches 23 μm (in P. aterrima). For further measurements by LM, see Table 2.
Table 2 Model input and output with force applied on cuticle of non-easy bleeders (A) and easy bleeders (B)
A
M1/1 M1/2 M1/10 Hc Pl Pt Sm Ts
W1 110 110 110 110 110 110 110 110
H1 20 20 20 7 5 8 8 11
H2 10 10 10 6 5 3 5 5
E1 500 500 500 500 500 500 500 500
E2 500 1000 5000 5000 5000 5000 5000 5000
F 1z
Max 0.206 0.170 1.001 2.910 4.520 2.651 2.906 2.201
Min -0.835 -1.003 -1.604 -4.167 -6.240 -6.482 -4.633 -3.772
F 1x
Max 1.553 1.614 1.794 2.545 2.956 3.337 2.714 2.569
Min -0.363 -0.383 -0.447 -0.717 -0.860 -0.864 -0.764 -0.710
B
M2 Ar Mm Pa Rb Rn
W1 110 70 33.5 60 50 60
H1 15 8 8 15 10 15
H3 15 8 14 23 10 10
D1 28 23 9 20 15 20
D2 20 11 1 2 10 10
S1 20 5 6 8 8 8
P 1 3.306 80 100 4 4
N μstr 1 1 5 1 1 1
F 1z
Max 0.665 1.696 0.623 4.291 1.430 0.770
Min -0.793 -2.560 -0.972 -7.286 -1.412 -2.134
F 1x
Max 4.788 7.554 35.820 174.600 12.840 7.632
Min -1.476 -2.241 -1.782 -9.267 -3.805 -2.317
Model of non-easy bleeders was based on parameter values measured on LM and SEM views from H. crocea and N. pavidus together (M1/1, M1/2, M1/10), and from H. crocea (Hc), P. luridiventris (Pl), P. testacea (Pt), S. multifasciata (Sm) and T. scrophulariae (Ts). Different relative values of Young's modulus for procuticle (E1) and epicuticle (E2) were used in M1/1, M1/2, and M1/10.
Model of easy bleeders was based on parameter values measured on LM and SEM views from P. aterrima and R. micans together (M2), and from A. rosae (Ar), M. monticola (Mm), P. aterrima (Pa), R. bensoni (Rb) and R. nodicornis (Rn). Parameter values, in μm, introduced in the model: width of the model sample (W1), height of procuticle layer (H1), height of epicuticle layer (H2), height of microstructure (H3), diameter at base of microstructure (D1), diameter at top of microstructure (D2), shortest distance between microstructures (S1). Number of microstructures set under pressure (N μstr). Pressure applied per microstructure (P).
Stress values, obtained with a normal force (F 1z) or shear force (F 1x), are given as extreme values in traction (Max) and compression (Min).
Compared to easy bleeders, the cuticle surface of non-easy bleeders was much smoother. It only shows blister-like swellings (pustulate) which have a diameter of 3–4 μm (e.g., in T. nigritus, H. australis, Nematus, Fig. 1e,1f), 6–7 μm (e.g., in S. multifasciata, Fig. 2a) up to 12 μm (in H. testudinea). In some genera such as Nematus and Craesus, each swelling shows a very small prickle (echinulate). Several swellings are sometimes aligned and then can be joined, several together, to form a low ridge of approximately 35 μm long (in C. septentrionalis).
Although E. ventralis and M. spinolae are easy bleeders, no spider-like microstructures were detected. Instead, small ridges with one or a few prickles, and small spines were observed, respectively. The larvae (alive) of these two species as well as T. scrophulariae are covered with a layer of waxy powder. Setae were observed instead of microstructures in the outgroup species G. hercyniae (Fig. 1h).
Modelling the mechanical behaviour of the cuticle
The aim of modelling was to compare the repartition of stresses of two cuticle configurations, as found in non-easy bleeders (M1) versus easy bleeders (M2), when a same loading is applied (Fig. 3a,3b). The maximum stress value (in compression and traction) is an indicator of possible initiation of crack or damage (see Methods).
Figure 3 Models of the cuticle of sawfly larvae. Model representing a non-easy bleeder (a, c to e, g) and an easy bleeder (b, f, h). View in perspective showing five microstructures (b) and the location of the applied force (a, b). Maximal stress distribution in a section through the cuticle (c to h). The ratio of Young's modulus for the procuticle to the one of the epicuticle is assumed to be 1/1 (c), 1/2 (d) and 1/10 (e). The applied force is normal (c to f) or sheared (g, h). The maximal value corresponds to the maximal stress of the principal stress 1 and the minimal value to the minimal stress of the principal stress 2. Only the distribution of principal stress 1 is shown, while the maximal value is given in Table 2. Degrees of freedom = 120,553 (a, c to e, g), 40,701 (b, f, h).
In Table 2, M1/1, M1/2 and M1/10 show the influence of the Young's modulus of the epicuticle, relative to the procuticle, on the distribution of stresses in a cuticle patch. When the Young's modulus of the epicuticle was increased, keeping the one of the procuticle constant, the stresses concentrated in the epicuticle and the maximal values increased (Fig. 3c to 3e). This occurred with both load cases (i.e., normal and shear force).
With a normal force, M1 and M2 resulted in stress values which were in the same range of magnitude (Table 2, Fig. 3c to 3f). Thus from this load case it cannot be deduced that one integument configuration will be damaged more easily than the other. In contrast, stress values obtained with a shear force were approximately three times higher in M2 than M1 (Table 2; Fig. 3g,3h). This suggests that an integument with microstructures is more constrained and will more easily reach the yield stress corresponding to the damage of the cuticle. This conclusion was corroborated by considering several single sawfly species. The maximal stress value in compression as well as in traction was always more extreme in five species of easy bleeders than in five species of non-easy bleeders (Table 2, see shear force). Note that for the easy bleeders M. monticola and P. aterrima, the apical part of the microstructure was extremely minute. All stresses were concentrated in the tip of the microstructure, which lead to non-physical deflections. The obtained values for these two species are probably irrelevant in a comparison with other species.
Hydrophobic property of cuticle surfaces
It was difficult or even impossible to deposit a water droplet on the larval body of some sawflies (Table 1). When the pipette tip was brought close to the integument, almost within physical contact, the droplet was pushed aside against the tip border. By then retrieving the pipette, the droplet was again on its tip, not on the integument. This could happen for some of the individuals tested per species (Table 1).
In species where the integument was less hydrophobic, the diameter of the droplet on it ranged from 1.5 to 1.9 mm (2 μl droplet) and from 2.0 to 2.6 mm (4 μl). Considering this latter droplet size, a small diameter (2.0 mm) or an immeasurable diameter (see above) was associated with sawfly species which are easy bleeders, whereas a larger droplet diameter (> 2.0 mm) was associated with non-easy bleeders (P = 0.045, Fisher exact probability test, N = 12 species, Table 1). Thus easy bleeders possess a hydrophobic and non-easy bleeders a rather hydrophilic integument.
On inert surfaces the diameter of 2 and 4 μl droplets was constantly as follows: immeasurable (see above) and 2.2 mm on Teflon®, 1.8 and 2.3 mm on Parafilm®, 1.8 and 2.3 mm on polystyrene and 2.6 and 3.3 mm on glass, respectively. Thus even Teflon®, that is considered as highly hydrophobic, led to a 4 μl droplet diameter which was comparable to the one obtained on the (hydrophilic) integument of non-easy bleeders. The 2 μl droplet was apparently light enough to impede its adhesion on the Teflon® surface, but not the larger droplet size tested.
Discussion
Several sawfly larvae showed a characteristic cuticle surface with spider-like microstructures and this was associated with a low mechanical strength of their integument. For instance, these microstructures were present in the easy bleeder Aneugmenus padi and absent in the non-easy bleeders Strongylogaster spp. Both genera are closely related since they belong to the same subfamily and have the same host plant [11]. It is likely that the occurrence of microstructures cannot be interpreted simply in terms of a systematic arrangement of species and that they are related to the phenomenon itself of easy bleeding. The larval abdomen of several Dolerus (Tenthredinidae, Selandriinae) species presents "meshes of microsculpture not sharply defined", with a dimension ranging from 20 to 40 μm, and these microstructures are often fused [12]. They may constitute an intermediate state between those observed by us on easy bleeders and non-easy bleeders, but being more physically comparable to those of non-easy bleeders by the absence of spider-like microstructures. Particular microstructures are also observed at the cuticle surface of other arthropods than sawflies, such as in nymphs of bugs and ticks [13] and in adults of flies and dragonflies [14,15]. Their function is to allow by stretching an increase of body volume during feeding, to ally flexibility with mechanical stability during highly repeated movements, etc., but their possible role in promoting a mechanical damage of the integument was not envisaged so far [16].
The question arises to know whether in sawfly larvae able to bleed easily the microstructures are directly involved in integument disruption. We compared cuticle models of non-easy bleeders versus easy bleeders and applied a unit force on it. Compared to the real-life, the model was simplified by considering a linear elastic behaviour of the cuticle (i.e., the stresses are proportional to the strains – Hooke's law), because we do not know the exact physical properties of the cuticle. Nevertheless, a comparison of geometrical parameters from easy bleeders versus non-easy bleeders revealed that by applying a shear force the cuticle stresses both in compression and tension were higher in the presence of microstructures (Table 2). This suggests that microstructures may directly contribute in the damage of the integument. Yet, the breaking line of a damaged integument goes between the microstructures (SA, personal observation on the easy bleeder P. aterrima). This biological observation is in agreement with our model results. The regions subject to high stresses are not restricted to the zone of the microstructure, but extend deeper into the cuticle mass (Fig. 3h). From this trend we may extrapolate that if the shear force is enhanced, the microstructure will not break off from the rest of the cuticle, but the fracture line will start at the base of a microstructure and continue throughout the whole cuticle thickness. In other words, the integument will disrupt. This conclusion becomes even more relevant in the realistic situation where an attacking predator applies a more or less oblique force on the cuticle. Beside physical aspects, chemical ones also contribute in the mechanical properties of an integument [16-20]. One of these properties, visco-elasticity, is determined in the abdominal integument of the bug Rhodnius by the matrix protein(s) of the procuticle with a reinforcing effect of chitin microfibriles. Differing chitin and protein patterns are observed in the cuticle when easy bleeders are compared to non-easy bleeders (M. Spindler-Barth & SA, unpublished results). Ongoing research aims to investigate these physiological aspects as well as the healing process, and to link them with the phenomenon of easy bleeding.
The cuticle surface of easy bleeders was highly hydrophobic (Table 1) as compared to a well-known hydrophobic material such as Teflon®. There is a trend for the integument of easy bleeders (e.g., P. aterrima, Rhadinoceraea spp., A. padi) to appear as mat, in contrast to the brilliant aspect in non-easy bleeders (e.g., Strongylogaster spp., Craesus spp.) (JLB, personal observations). We believe that the hydrophobic property is ecologically relevant during predator-prey interactions. When a predator, typically an insect with biting-chewing mandibles [10], bites into the integument of a sawfly larva at a given spot, the best for the larva is to keep the deterrent haemolymph spatially concentrated at this spot. A counter-example is that some insects are known to have morphological devices of the integument surface or wetting agents included in their defensive secretion, which help the secretion to spread out [21,22]. But such secretions are typically volatile and the defence consists of keeping the aggressor at a distance. The morphological devices and wetting agents modulate the evaporation of the secretion and, thereby, the effectiveness of a defence that acts by olfactory cues. In the case of easy bleeding, deterrent compounds dissolved in the haemolymph need to contact the mouthparts of an aggressor, acting by gustatory cues. Moreover, easy bleeders should not spread out their hemolymph since they would lose this valuable liquid. Remaining as a droplet and in contact with the larval haemocel, the droplet can be sucked back by the larva into its body within a few minutes, providing that the larva is not more disturbed [4].
A parallel can be drawn between the integument surface of easy bleeders and the one of several plant leaves. The lotus leaf led recently to the so-called Lotus-effect® [23]. Particular physico-chemical properties of the leaf allow a self-cleaning by rain. This effect relies on a micro-structured surface and a coating of waxy crystals. Both characteristics contribute in rendering the surface hydrophobic [23,24]. The optimal configuration and size of the structures is a coarse structure of 10 to 50 μm and a finer one of 0.2 to 5 μm [25]. This corresponds well to the case of the spider-like microstructures as found on the cuticle of easy bleeders. In the insect both these coarse and finer structures are provided by the spider-like structures (Results), whereas in the plant each scale of structures is due to microstructures and waxy crystals, respectively [26]. There are no waxy crystals on the body surface of easy bleeders. A fine layer of waxy powder covers only some species of easy bleeders as well as non-easy bleeders (see Results). Such a waxy powder consists mainly of hexacosan-1-ol in Eriocampa ovata [27], a non-easy bleeder [4] not studied in the present work. It is likely that in a majority of easy bleeders the hydrophobic property relies especially or solely on the geometry of the cuticle surface, by the occurrence of microstructures.
Conclusions
We suppose at least two types of functions in the occurrence of spider-like microstructures, which we observed specifically on the body surface of easy bleeders. Firstly the damage provoked by a biting predator could be facilitated. Secondly the integument of easy bleeders could be rendered hydrophobic, which helps stop the emitted haemolymph droplet from spreading out.
Methods
Insects
All sawfly larvae (see Table 1) were collected in the field (Belgium, Germany, Switzerland), except A. rosae and G. hercyniae that came from indoor populations. The larvae were identified according to Lorenz & Kraus [11]. The full-grown larval stage was used.
Observations by SEM and LM
Fixed larvae stored in ethanol were dried, coated with gold, and examined with a Philips XL-30 ESEM. Specimens were placed to observe the dorsal and lateral part of the abdomen. The terminology used in describing the cuticle surface refers to Harris [28].
Series of 7 μm thin cross sections were obtained from larvae by using classical histological techniques. They were deparaffinized in xylene and rehydrated in several decreasing ethanol to water solutions, then stained by the Azan trichrome method [29] and observed by LM.
Model by finite elements
General mechanical assumptions
The general rigorous mechanical behaviour of the cuticle is complex. As a first attempt to understand the property of easy bleeding, it was assumed that a damage of the integument is due to excessive stress under static loading. Since the cuticle of a larva is also geometrically complex in three dimensions, no simplified laws, for instance, derived from the strength of material could be used. The analysis was therefore performed on solid configuration, discretized by a standard finite element method [30]. It was assumed that the stress-strain law is linear and isotropic (Hooke's law) and that the displacements and strains are small. The geometrical dimensions of the cuticle are very small, at the microscale. It is known that for such a configuration, the assumption of continuum may not be valid [31]. But, it is also known for standard materials such as metals that the strength is generally underestimated with continuum assumption. This is the reason why the analysis performed in this paper was purely qualitative and based on a comparison of the stress between the geometry encountered in easy bleeders and non-easy bleeders. Most of the results were interpreted on the principal stress: for 3D mechanical configurations, three directions always exist for which the stress (and strain for isotropic laws) is maximum or minimum. According to these directions, the shear stress is zero. It was then supposed that the maximum stress values (in traction or compression) cause the initiation of the cuticle damage.
Finite element modelling
The finite element analysis was performed using the general mechanical purpose software SAMCEF® version 9.1. It was assumed that the integument is made of the repetition of reproducible patches in both x, y directions. Thus, only one patch has to be modelled by the appropriate boundary conditions representing this repetition (i.e., the displacements on each boundary are blocked in the direction normal to this boundary). The geometry was discretized with 3D solid linear finite elements (prisms or bricks). The patch used to model non-easy bleeders was composed of two layers, procuticle and epicuticle, of different properties in height and Young's modulus. It is supposed that generally the epicuticle of insects is pliant but not extensible, stronger in compression than tension, and that the epicuticle is less elastic than the procuticle [2]. The patch used for easy bleeders contained five microstructures and was homogenous. Indeed, generally no epicuticle is clearly detected in the cuticle of easy bleeders observed by LM (e.g., Fig. 2f), an exception being A. padi (Fig. 2g).
Loading
The loading was always divided into two load cases: a force perpendicular to the patch surface (normal force) and parallel to it (shear force).
In a real situation, the mandibles of an attacking predator, typically a small arthropod, apply the loading [10]. The diameter of a mandible's tip was measured on workers of the ant Myrmica rubra and reached 20 μm as the smallest value. In the model, the shape of the contact point made by the mandible was a disc (radius = 10 μm) on which the force was applied. This force was applied either on the upper centre of the epicuticle for non-easy bleeders (Fig. 3a) or on the top of the central microstructure when considering easy bleeders (Fig. 3b). As the radius of the upper part of the microstructure changed from one species to another, being generally lower than 10 μm, the applied surface force was adapted to obtain a same resultant force for each configuration.
The insect body contains a liquid, haemolymph. The patch, therefore, was modelled by applying a surface force equilibrated with the loading of the predator.
Hydrophobic property
This property of the integument was estimated by a simple method that allowed the use of insects alive. The first step was to notice whether a 2 and 4 μl droplet of charcoal filtered water could adhere, gently depositing it with a 1–10 μl pipette on the thoracic or abdominal integument of a sawfly larva that was resting on a leaf of its host plant. If an adherence was possible, the diameter of the deposited droplet was measured under a stereomicroscope with micrometer. Six full-grown larvae were tested per species. As control the following substrates were tested in the same manner: Teflon®, Parafilm®, polystyrene and glass. On these biological and inert surfaces, the droplet reaction (i.e., adherence capability and droplet diameter) was considered to express the hydrophobic or hydrophilic property of the surface.
Authors' contributions
JLB collected and identified the insects, performed the tests on the hydrophobic property, measured the cuticle parameters for the model on LM views, and wrote the manuscript, except the parts about this model in Results and Methods. VD obtained most SEM views. TM and PB performed the model by finite elements and wrote the two related parts in the manuscript. SA maintained indoor populations of two sawfly species and carried out and photographed the integument sections used in LM and, thereby, in the model.
Acknowledgements
We thank Johan Billen (Katholieke Universiteit Leuven) for his help in obtaining preliminary LM views, and Julien Cillis (IRSNB-KBIN) for his technical assistance in SEM. Previous versions of the manuscript were kindly reviewed by Travis Turner, Caroline Müller and three anonymous referees. The research performed by JLB and SA was supported by the European Community's Improving Human Potential Programme under contract HPRN-CT-1999-00054, INCHECO.
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| 15461785 | PMC524519 | CC BY | 2021-01-04 16:37:48 | no | J Nanobiotechnology. 2004 Oct 1; 2:10 | utf-8 | J Nanobiotechnology | 2,004 | 10.1186/1477-3155-2-10 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-331549407610.1186/1742-4690-1-33ResearchThe connection domain in reverse transcriptase facilitates the in vivo annealing of tRNALys3 to HIV-1 genomic RNA Cen Shan [email protected] Meijuan [email protected] Lawrence [email protected] Lady Davis Institute for Medical Research and McGill AIDS Centre, Jewish General Hospital, Montreal, Quebec, Canada H3T 1E22 Department of Medicine, McGill University, Montreal, Quebec, Canada H3T 1E23 Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada H3T 1E22004 19 10 2004 1 33 33 23 8 2004 19 10 2004 Copyright © 2004 Cen et al; licensee BioMed Central Ltd.2004Cen 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.
The primer tRNA for reverse transcription in HIV-1, tRNALys3, is selectively packaged into the virus during its assembly, and annealed to the viral genomic RNA. The ribonucleoprotein complex that is involved in the packaging and annealing of tRNALys into HIV-1 consists of Gag, GagPol, tRNALys, lysyl-tRNA synthetase (LysRS), and viral genomic RNA. Gag targets tRNALys for viral packaging through Gag's interaction with LysRS, a tRNALys-binding protein, while reverse transcriptase (RT) sequences within GagPol (the thumb domain) bind to tRNALys. The further annealing of tRNALys3 to viral RNA requires nucleocapsid (NC) sequences in Gag, but not the NC sequences GagPol. In this report, we further show that while the RT connection domain in GagPol is not required for tRNALys3 packaging into the virus, it is required for tRNALys3 annealing to the viral RNA genome.
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Background
During assembly of HIV-1, the major tRNALys isoacceptors in mammalian cells, tRNALys1,2 and tRNALys3, are selectively incorporated into the virus [1]. tRNALys3 is the primer for initiating minus-strand cDNA synthesis, and its annealing to the 18 nucleotide primer binding site (PBS) region in the 5' part of the viral genome via the 3' 18 nucleotides in tRNALys3 complementary to the PBS, is a key step in viral replication [2]. Other regions upstream and downstream of the PBS may also anneal with additional sequences in the tRNA [3,4].
Both tRNALys3 and sites of annealing in viral RNA contain double stranded regions which may require denaturation for annealing to proceed efficiently. Nucleocapsid protein (NC) has been shown to facilitate tRNALys3 annealing both in vitro [5,6] and in vivo [7], primarily through basic amino acids flanking the first zinc finger. While NC may destabilize viral RNA secondary structure, it has been demonstrated by several groups that nucleocapsid protein does not unwind the secondary structure of tRNA in vitro, and that the protein only has very subtle tertiary structural and helix destabilization effects on tRNALys3 alone [8-11].
Although processed nucleocapsid proteins have been shown to facilitate tRNALys3 annealing to genomic RNA in vitro, the annealing of primer tRNA onto the genomic RNA within HIV-1, murine leukemia virus, and avian retrovirus occurs independently of precursor protein processing [12-14]. However, while, tRNALys3 is annealed efficiently in protease-negative HIV-1 (about 80% that found in wild-type virions), optimal placement on the viral genome to achieve efficient initiation of reverse transcription requires exposure of the viral genome to mature nucleocapsid protein [15]. In these protease-negative viruses, mutations in NC sequences within Gag inhibit tRNALys3 annealing, while mutations in NC sequences within GagPol do not, indicating the importance of Gag NC sequences in the annealing [16]. In vitro, Gag has been reported to facilitate tRNALys3 annealing to viral RNA as efficiently as mature NC [17].
Nevertheless, we will present evidence in this report that GagPol still plays an important role in tRNALys3 annealing onto the viral RNA, independent of its role in the packaging of tRNALys3 into the virion. We present data herein indicating that the RT connection domain, while non-essential for tRNALys3 incorporation into virions, is required for tRNALys3 annealing to the viral RNA genome
Results
The RT connection domain within GagPol is not required for tRNALys incorporation into virions, but is required for the annealing of tRNALys3 to the viral genome.
293T cells were transfected with protease-negative HIV-1 proviral DNA coding for either full length, protease-negative, GagPol (BH10.P-) or C-terminally deleted GagPol species. The different constructs are shown in Figure 1A, and are named according to the number of amino acids deleted from the C terminus of GagPol. Figure 1B shows Western blots of lysates of the viruses produced from the different transfections, probed with anti-CA, and shows that all forms of GagPol deletion mutants tested here are incorporated into the virion. Total viral RNA was isolated from these virions, and dot blots of this RNA were annealed with probes specific for either viral genomic RNA or tRNALys3, to determine the tRNALys3/genomic RNA in each viral variant. These results are shown graphically in Figure 1C, and support our previous results using COS7 cells [18], which indicate that tRNALys incorporation into virions is not dramatically affected until GagPol sequences including the thumb domain of RT are deleted (Δ581 and Δ715).
Figure 1 The incorporation of GagPol and tRNALys3into wild-type and mutant HIV-1. A. Schematic showing the deletions made in the Pol region of GagPol. Δ# designates the number of amino acid residues deleted from the C terminus of GagPol, and solid black lines represent the sequences not deleted. The RT sequence is divided into its known structural domains. The mutation D25G inactivates the viral protease. B. Western blots of viral lysates, probed with both anti-CA and anti-RT as previously described [18]. C. Incorporation of tRNALys3 into wild-type and mutant virions. Dot blots of viral RNA were hybridized with probes specific for tRNALys3 or genomic RNA, and the tRNALys3:genomic RNA ratios, normalized to BH10.P- were determined by phosphorimaging. The values are the means +/- standard deviations of experiments performed three or more times.
To measure the amount of tRNALys3 annealed in vivo to the viral RNA genome, total viral RNA was used as the source of primer/template in an in vitro reverse transcription reaction, using exogenous HIV-1 RT, dCTP, dTTP, α-32P-dGTP, and ddATP. This assay measures the amount of extendable tRNALys3 placed onto the viral genome. It is not known if all annealed tRNALys3 is extendable. Since the sequence of the first six dNTP's incorporated is CTGCTA, annealed primer tRNALys3 will be extended by 6 bases, and the extended tRNALys3 can be resolved and detected by one dimensional polyacrylamide gel electrophoresis (1D PAGE). These results are shown in Figure 2A, and presented graphically in Figure 2B. The left side of panel A shows that there is a linear increase in the reverse transcription signal over an almost 10 fold change in the amount of BH10.P- viral genomic RNA used in the reaction. The data in the right side of panel A indicate that C-terminal deletions of GagPol extending into the connection domain result in an 85% or greater decrease in the initiation of reverse transcription. Thus, the data in Figures 1 and 2 indicate that deletions extending into the RT connection domain do not significantly effect tRNALys incorporation, but do severely reduce the ability of tRNALys3 to be functionally annealed to the viral RNA genome.
Figure 2 tRNALys3 annealing to viral genomic RNA. A. Total viral RNA was used as the source of primer tRNALys3/viral RNA template in an in vitro reverse transcription reaction as described in Methods. Six base extended tRNALys3 was resolved by 1D PAGE and quantitated by phosphorimaging. Each reaction used an equal amount of viral genomic RNA, as determined by hybridization with a genomic RNA-specific probe. B. Graphic presentation of 6 base-extended tRNALys3:genomic RNA ratios, normalized to BH10P-. The values are the means +/- standard deviations of experiments performed three or more times.
Rescue of tRNALys3 annealing by GagPol
As shown in Figure 3, this annealing defect can be rescued by coexpression of full-length GagPol. 293T cells were transfected with plasmids coding for BH10P-, Δ467, or Δ486, or cotransfected with either Δ467 or Δ486 and a plasmid coding for full-length GagPol. Western blots of cell lysates probed with anti-RT or anti-β-actin are shown in panel A, while Western blots of lysates of virus produced from these cells and probed with anti-RT and anti-CA are shown in panel B. These data indicate that both full length GagPol and the truncated GagPol are incorporated into the viruses with similar efficiencies. As previously indicated in Figure 1C, the mutant virions incorporate approximately 80–85% of the tRNALys3 as BH10P-, but cotransfection of mutant DNA with DNA coding for GagPol gives a small increase in tRNALys3 packaged to over 90% of BH10P- (Figure 3C).
Figure 3 Rescue by GagPol of tRNALys3 annealing in mutant virions. COS7 cells were transfected with either BH10P-, Δ467.P-, or Δ486.P-, and were also cotransfected with one of these plasmids and a plasmid coding for full-length GagPol (hGagPolΔFSΔPR). A. Western blots of cell lysates, probed with anti-RT or anti-β-actin. B. Western blots of viral lysates, probed with anti-RT and anti-CA. C. Incorporation of tRNALys3 into wild-type and mutant virions. Dot blots of viral RNA were hybridized with probes specific for tRNALys3 or genomic RNA, and the tRNALys3:genomic RNA ratios were determined by phosphorimaging. The values are the means +/- standard deviations of experiments performed three or more times. D,E. tRNALys3 annealing in wild-type and mutant virions. tRNALys3 annealing was measured as described in the Figure 2 legend. The values shown in E are the means +/- standard deviations of experiments performed three or more times.
As shown in panels D and E, cotransfection with GagPol also moderately rescues tRNALys3 annealing in these mutant virions. Using equal amounts of total viral RNA as the source of primer/template in the in vitro RT assay, the ability of primer tRNALys3 to be extended 6 deoxynucleotides is shown in panel D, which shows the extended 6 base product resolved by 1D PAGE. Quantitation of these bands by phosphorimaging is presented graphically in panel E. As previously shown (Figure 2), tRNALys3 annealing is reduced to 12–15% that of BH10P-, but can be increased 4–5 fold by the additional presence of full-length GagPol. The fact that tRNALys3 annealing is only rescued by GagPol to approximately 50–55% the level of that obtained when only wild-type GagPol is present may reflect the fact that in these rescue experiments, the viral population contains approximately equal amounts of wild-type and mutant GagPol (Figure 3B).
Attempts were also made to rescue tRNALys3 annealing using mature RT fused to Vpr [19], but unlike full-length GagPol, the Vpr-RT was unable to rescue tRNALys3 annealing in the mutant virions (data not shown).
Discussion
In vitro studies of the interaction between purified RT and tRNALys3 have indicated an interaction between the RT thumb domain and the tRNA [20-22]. In vivo studies also indicate an important role of the RT thumb domain in GagPol in tRNALys3 viral packaging. tRNALys3 incorporation into HIV-1 is not affected by deletion of the IN domain in GagPol, nor by further deletion of the RNaseH and connection domains in RT, but is severely inhibited by further deletion of the thumb domain as well [18]. Thus tRNALys3 interacts with the RT thumb domain during incorporation into virions, and Gag nucleocapsid plays a role in promoting tRNALys3 annealing to viral RNA [5-7], presumably through a denaturation of annealing RNA sequences.
What then is the role the RT connection domain sequence in GagPol in facilitating tRNALys3 annealing? One possibility, suggested by in vitro studies, is that RT plays a direct role in tRNALys3 annealing. Early work indicated that the in vitro annealing of primer tRNATrp to AMV genomic RNA was promoted by the addition of AMV reverse transcriptase [23]. In a later work, in which it was demonstrated that HIV-1 RT interacted with the D arm and TΨC loop of tRNALys3, HIV-1 RT was also shown facilitate the in vitro annealing of tRNALys3 to the PBS sequence [24]. These in vitro works suggest that RT alone can directly promote tRNALys3 annealing to viral RNA. Whether the RT sequences in GagPol can function similarly in vivo is not known.
Alternatively, the RT connection domain may undergo interactions with Gag that may result in placing the tRNALys3 bound to the thumb domain in RT closer to either NC in Gag or to the genomic RNA that is bound to Gag NC. Recent work has indicated that that Pol sequences alone can bind to Gag p6 through the RT sequences in Pol [25]. Pol protein alone is sufficient for obtaining both tRNALys incorporation into the virus and tRNALys3 annealing to the viral genome at levels approximately 35% those achieved using full-length GagPol. Thus, in addition to the interactions which probably occur between Gag and homologous sequences in the Gag part of GagPol, the interaction of RT sequences in GagPol with Gag p6 could place the RT-bound tRNALys3 closer to Gag NC sequences and viral RNA in the packaging complex. It remains to be determined which sequences within RT bind to Gag p6, but if it were those of the connection domain, this could explain how these sequences could promote tRNALys3 annealing through altering the configuration of GagPol.
Thus, two separate RT domains (thumb and connection) appear to be involved, respectively, in the viral incorporation of tRNALys3, and its annealing to HIV-1 RNA. One also finds two separate domains in Gag involved in these same processes. Evidence has been presented supporting the role of lysyl-tRNA synthetase (LysRS) in targeting tRNALys for viral incorporation, through a specific interaction of Gag capsid sequence with LysRS in a tRNALys/LysRS complex [26], while other evidence shows that Gag nucleocapsid sequence is involved in tRNALys3 annealing [6,16,17]. It is not known if LysRS plays any direct role in tRNALys3 annealing, and LysRS may be required to dissociate from tRNALys3 so as to free this tRNA for annealing to the viral RNA.
Methods
Plasmid construction
BH10 and BH10P- are protease-positive and protease-negative strains of HIV-1, respectively [18]. All deletions mutants used here were derived from BH10.P-, and their construction has been previously described [18]. hGagPolΔFSΔPR was a gift from Y. Huang and G. Nabel [27]. It was constructed by deleting 5 thymidines in the frame shift site, and codes for GagPol. The codons have optimized for mammalian cell codon usage, which results in more efficient translation and protein production, and also makes nuclear export of these mRNAs Rev-independent through modification of the INS [27,28]. hGag-PolΔFSΔPR contain an inactive protease due to an R42G mutation in the active site.
Production of wild type and mutant HIV-1 virus
Transfection of COS7 cells with wild type and proviral DNA was performed using the calcium phosphate method as previously described [29]. Briefly, virus were isolated from the cell culture medium 63 hours post-transfection. The supernatant was first centrifuged in a Beckman GS-6R rotor at 3000 rpm for 30 minutes, and the virus were then pelleted from the resulting supernatant by centrifuging in a Beckman Ti45 rotor at 35,000 rpm for one hour. The viral pellet was then purified by centrifugation at 26,500 rpm for 1 hour through 15% sucrose onto a 65% sucrose cushion, using a Beckman SW41 rotor.
Protein Analysis
Viral particles were washed with 1X TNE and cellular or viral proteins were extracted with 1X RIPA buffer (10 mM Tris pH 7.4; 100 mM NaCI; 1% DOC; 0.1% SDS; 1%NP40; 2 mg/ml Aprotinin; 2 mg/ml Leupeptin; 1 mg/mlPepstatin A; 100 mg/ml PMSF). Western analysis was performed using 300 mg cellular protein or 10 μg viral protein, as determined by the Bradford assay [30]. The cellular and viral lysates were resolved by SDS-1D PAGE, followed by blotting onto nitrocellulose membranes (Gelman Sciences). Detection of protein on Western blots utilized monoclonal antibodies or antisera specifically reactive with viral capsid (mouse antibody, Intracel), viral reverse transcriptase (rabbit antibody), or β-actin (mouse antibody, Sigma Aldrich). Western blots were analyzed by enhanced chemiluminescence (ECL kit, Amersham Life Sciences) using goat anti-mouse or donkey anti-rabbit (Amersham Life Sciences) as a secondary antibody, and quantitated using UN-SCAN-IT gelTM automated digitizing system. The sizes of the detected protein bands were estimated using pre-stained high molecular weight protein markers (GIBCO/BRL).
RNA Isolation and Analysis
Total viral RNA was extracted from viral pellets by the guanidinium isothiocyanate procedure [31], and dissolved in 5 mM Tris buffer, pH 7.5. To measure the incorporation of tRNALys3 into virions, hybridization to dot-blots of viral RNA was carried out with DNA probes complementary to tRNALys3 [1] or to genomic RNA [16]. To measure the amount of tRNALys3 annealed to genomic RNA, tRNALys3-primed initiation of reverse transcription was measured using total viral RNA as the source of primer tRNA/template in an in vitro HIV-1 reverse transcription reaction, as previously described [32]. The sequence of the first 6 deoxynucleoside triphosphates incorporated is CTGCTA, and in the presence of dCTP, dGTP, dTTP, and ddATP, tRNALys3 is extended by 6 bases, and this product can be resolved by 1D PAGE, and quantitated by phosphorimaging, as previously described [15].
Authors' contributions
SC carried out the molecular genetic studies, assisted by MJ. LK conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by a grant from the Canadian Institutes for Health Research. We thank Y. Huang and G. Nabel for the gift of plasmid hGagPolΔFSΔPR.
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| 15494076 | PMC524520 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Oct 19; 1:33 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-33 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-201547155110.1186/1476-7120-2-20Case ReportIntracardiac echocardiography to guide transseptal catheterization for radiofrequency catheter ablation of left-sided accessory pathways: two case reports Citro Rodolfo [email protected] Valentino [email protected] Alessandro [email protected] Michele [email protected] Michele [email protected] Giovanni [email protected] Department of Cardiology, "San Luca" Hospital, Vallo della Lucania (Salerno), Italy2 Ecocardiography Laboratory, " Portuense" Hospital, Rome, Italy3 Boston Scientific Europe Genoa, Italy2004 8 10 2004 2 20 20 3 6 2004 8 10 2004 Copyright © 2004 Citro et al; licensee BioMed Central Ltd.2004Citro 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.
Intracardiac echocardiography (ICE) is a useful tool for guiding transseptal puncture during electrophysiological mapping and ablation procedures. Left-sided accessory pathways (LSAP) can be ablated by using two different modalities: retrograde approach through the aortic valve and transseptal approach with puncture of the fossa ovalis. We shall report two cases of LSAP where transcatheter radiofrequency ablation (TCRFA) was firstly attempted via transaortic approach with ineffective results. Subsequently, a transseptal approach under ICE guidance has been performed. During atrial septal puncture ICE was able to locate the needle tip position precisely and provided a clear visualization of the "tenting effect" on the fossa ovalis. ICE allowed a better mapping of the mitral ring and a more effective catheter ablation manipulation and tip contact which resulted in a persistent and complete ablation of the accessory pathway with a shorter time of fluoroscopic exposure. ICE-guided transseptal approach might be a promising modality for TCRFA of LSAP.
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Background
Trans-catheter radiofrequency ablation (TCRFA) has become the treatment of choice for patients suffering from refractory to medical treatment supraventricular tachycardias [1,2]. During percutaneous ablation procedures, catheter location is usually monitored by using fluoroscopy together with the analysis of intracardiac electrograms in order to clarify the mechanisms underlining the arrhythmia and to both locate its origin and record its circuit. This technique may be adequate for several standard ablative procedures, but it has still some limitations concerning the treatment of more complex tachycardia forms.
Left-sided accessory pathway (LSAP) may be ablated using two different modalities: conventional approach through the aortic valve, or transseptal puncture of the fossa ovalis. By using the traditional approach, the left atrium is reached by a retrograde way through the left ventricle and crossing the mitral valve [3,4] whereas, with the transseptal puncture, the mitral ring is reached by an anterograde approach. For this reason, this approach has been considered an alternative and complementary technique for the transvenous introduction of catheters into the left cavities of the heart [5-8]. However, transseptal puncture under fluoroscopic guidance alone, might be hampered by some acute and potentially lethal complications that may be challenging even for expert electrophysiologists [9].
With the technological and miniaturization advances of low frequency transducers capable of enhanced tissue penetration, intracardiac echocardiography (ICE) has become feasible and potentially useful for guiding transseptal puncture and ablation procedures, especially when location of specific anatomic landmarks appears to be more crucial [10,11].
In this manuscript, two cases of ICE-guided catheter ablation of LSAP via transseptal approach have been described.
Case Presentation
Case 1
A 24 year-old woman with Wolf-Parkinson-White (WPW) syndrome and recurrent episodes of sustained supraventricular tachycardia, refractory to medical therapy, was referred to our Department in order to attempt TCRFA procedure. The electrophysiological study was performed off therapy using three diagnostic catheters (one decapolar and two quadripolars) which have been positioned in the coronary sinus (CS1 → CS5 = distal → proximal coronary sinus), His bundle region, and high right atrium/right ventricular apex. The clinical arrhythmia was diagnosed as an orthodromic atrio-ventricular re-entrant tachycardia (AVRT) due to a LSAP that was repetitively induced by both right atrial and ventricular programmed electrical stimulation. The length of the tachycardia cycle ranged from 310 to 300 msec. A well-localized accessory pathway insertion was detected in the lateral left free wall with atrial-ventricular (AV) and ventricular-atrial (VA) intervals of fusion noticeable only in CS2 (Fig 1). During programmed atrial stimulation and AVRT, the transaortic approach was initially attempted by using a 7F-4 mm bidirectional ablation catheter (Blazer HTD, Boston Scientific). Although very short VA and AV intervals (<50 msec without isoelectric line between the two potentials) and satisfactory mean temperature (54–56 C° for 20–30 sec with less than 30 Watts for each radiofrequency application) were obtained, repeated radiofrequency erogations resulted in a transient interruption of the anomalous connection, with recurrence of retrograde conduction along the accessory pathway and atrioventricular re-entrant tachycardia inducibility after a period of 20–30 min. Therefore, by using a mechanical single element ultrasound 9F-9 MHz catheter, an ICE-guided transseptal approach was attempted in order to cranially map the mitral ring (Ultra ICE Boston Scientific/CVIS San José, CA USA). This approach has provided a 360° two-dimensional imaging on a transverse plane perpendicular to the transducer with a radial field of view of approximately 5 cm in depth. ICE catheter was introduced via femoral vein through a 10F long sheath and advanced into the high right atrium. Careful handling of the catheter, along the interatrial septum, provided optimal view of the fossa ovalis that could be easily recognized as a thin area compared to the surrounding atrial structures. The standard approach to transseptal catheterization using a long sheath and dilator over a Brockenbrough needle, was used [5]. The tip of the needle-dilator-sheath apparatus was positioned facing the middle portion of the fossa ovalis. Careful up and down movements of ICE catheter allowed a clear visualization of the needle as well as its contact with the septal wall causing the typical "tenting" effect (Fig 2), which allowed precise location of the puncture of the needle.
Figure 1 Orthodromic AV re-entrant tachycardia. Orthodromic AV re-entrant tachycardia induction with programmed atrial stimulation. Notice VA fusion on CS2 (lateral portion of the mitral ring; see arrow).
Figure 2 Brockenbrough needle and the fossa ovalis. Brockenbrough needle in right atrium approaching the fossa ovalis (right panel); typical "tenting" of the fossa (see arrow) just before septal puncture (left panel). Note the left atrial free wall close to the interatrial septum. (FO = fossa ovalis; LA = left atrium; RA = right atrium).
Accessory pathway was completely and definitively ablated with this approach after two erogations lasting each 40 sec. Although the AV and VA intervals that had been selected to deliver radiofrequency energy were similar to those previously recorded, the only difference was a better AV ratio (Fig 3). Such findings might be related to the cranial approach of the atrioventricular ring which has allowed a more stable catheter position on endocardium surface and a lack of lateral sliding, resulting in a higher mean temperature (60° ± 3° vs 54° ± 2°). Radiation exposure was shorter with the transseptal approach (11 vs 19 min) when compared to the retrograde approach.
Figure 3 Ablation of the accessory pathway. Radiofrequency erogation during atrial pacing with maximal pre-excitation. Notice AV fusion (major arrow) followed by AV split (two minor arrows) on the ABLd recording, corresponding to lateral mitral annulus.
Case 2
A 61 year-old man was referred to our Department for a TCRFA procedure due to a recurrent sustained supraventricular tachycardia refractory to antiarrhytmic drugs. The electrophysiological study has been performed by using the previously described protocol. An atrioventricular re-entrant tachycardia, due to an overt LSAP, was repetitively induced by atrial and ventricular programmed electrical stimulation. The length of the tachycardia cycle ranged from 340 to 320 msec. The accessory pathway insertion resulted well-localized in the left lateral mitral ring during AVRT and atrial pacing with maximal pre-excitation (Fig 4A,4B). The trans-aortic approach was firstly attempted by using a 7F- 4 mm bidirectional ablation catheter (Blazer HTD, Boston Scientific). Despite an optimal catheter tip temperature for a reasonable period of time (five erogations reaching a mean temperature of 55° lasting 30–40 sec), only a temporary interruption of the anomalous pathway conduction was obtained. Therefore, ICE-guided transseptal approach was performed as previously described (see additional file: Movie 1 transseptal puncture.avi). The approach resulted in a persistent and complete ablation of the accessory pathway after two successful radiofrequency erogations which have been delivered when the VA interval resulted fused and its ratio was 1,5:1. In this case, ICE allowed a more complete mapping of the mitral ring and a confirmation of effective catheter ablation tip contact. As in the previously described case, the superior approach to the left AV ring resulted in a better manipulation of the ablation catheter in addition to a reduction in fluoroscopic time (10 vs 21 min).
Figure 4 Left sided accessory pathway. The shortest AV and VA are recorded in CS2 (see arrows) during programmed atrial stimulation (A) and atrio-ventricular orthodromic re-entrant tachycardia (B) respectively suggesting a left sided posterolateral accessory AV connection.
Conclusion
Intracardiac echocardiography and catheter ablation procedures
During a standard ICE examination, sequential pull-back of the probe from the superior vena cava through the right atrium to the inferior vena cava allows a detailed identification of important structures such as: the crista terminalis; the right atrial appendage; the caval and the coronary sinus orifices; the fossa ovalis; the ascending aorta and its root; the pulmonary artery; the right ventricle and all of the cardiac valves [12-14]. Thus, this technique has the potential to provide a direct visualization of the endocardium and to precisely locate the ablation catheter which can be identified by the highly specific fan-shaped echocardiographic artefact of the large tip of the ablation electrode [11,12].
ICE is a well-recognized tool to guide ablation procedures. Compared to fluoroscopy, which does not provide definition of endocardial structures, ICE gives a highly accurate evaluation of firm and stable tissue contact. This results in a reduced radiofrequency power output. In addition, it is possible to visualize the lesion site and formation, such as swelling and crater formation. Moreover, possible complications such as occurrence of microbubbles, clot formation and pericardial effusion may be promptly detected through this ultrasound technique [10,15].
A recently developed phased-array intracardiac echocardiography device provides two-dimensional and Doppler images of the heart [10]. Recently, new strategies for ablation of atrial fibrillation (linear atrial ablation and focal ablation of triggers) have been proposed. Phased-Array intracardiac echocardiography enables a direct visualization of the pulmonary veins and allows the assessment of the Doppler flow velocity for all of the pulmonary veins [16]. This technique has been proved to be helpful in monitoring pulmonary vein isolation in patients with atrial fibrillation by improving the outcome and decreasing the incidence of complications such as pulmonary vein stenosis [17].
Intracardiac echocardiography and transseptal catheterization
Transseptal catheterization is usually performed under fluoroscopic guidance [5]. However, safe transseptal puncture requires detailed visualization of the fossa ovalis that cannot be obtained through fluoroscopy. Thus, it remains a difficult procedure especially for patients which present anatomic abnormalities such as atrial and aortic root dilatation, musculoskeletal disorders and lipomatous hypertrophy of the atrial septum [18]. Furthermore, some complications of the transseptal catheterization due to accidental puncture of adjacent structures [6], such as atrial or aortic perforations, and pericardial tamponade, although rare, can be severe and life threatening. Moreover, in hypovolemic states, the left atrial free wall may be closed to the atrial septum. This potentially dangerous condition can be promptly recognized through the ICE technique, suggesting fluid administration in order to restore an adequate blood volume and to prevent left atrial free wall puncture [19].
The ability to visualize the anatomy of atrial septum and the localization of the fossa ovalis, may greatly enhance both the safety and the efficacy of the transseptal catheterization without any additional morbidity to the procedure [19,20]. As in the above-described cases, ICE allows a continuous monitoring of this procedure showing the tip of the sheath and Brockenbrough needle placed against the middle of the fossa ovalis immediately prior to the puncture [19,20].
Previous reports suggested the use of both transthoracic and transesophageal echocardiography, but these techniques present some limitations [21,22]. The former fails to display, in detail the fossa ovalis, the latter requires sedation which limits communication with the patient during the procedure and increases the risk of hypoventilation.
Intracardiac echocardiography and left-sided accessory pathway
LSAP represent the majority (59%) of all accessory pathway locations [23]. They can be ablated via transaortic approach. However, severe complications have been reported, including aortic dissection, lesion of the aortic valve, late endocarditis, peripheral and cerebral thromboembolic events [3,4]. Some authors, using the transseptal approach, have reported both a decreased procedural duration and radiation exposure, with higher success rate compared to the transaortic technique [7,24]. Two factors have been taken into account: 1) several left accessory pathways exhibit a broad or oblique insertion on the AV ring, so that they require a multisite radiofrequency energy delivery on both the atrial and ventricular sides in order to be ablated; 2) with the transaortic approach left atrio-ventricular ring mapping becomes a cumbersome procedure due to the retrograde crossing of the ablation catheter through the aortic and the mitral valves. In such a situation, the ablation catheter should be carefully manipulated in order to avoid entrapment into the mitral valve apparatus [4].
The use of ICE during ablation procedures in order to treat LSAP has been previously reported [25]. The observations pointed out by our case report have shown that ICE provides a precise localization of the needle tip position, a clear visualization of the fossa ovalis, a more complete mapping of the mitral ring and a more effective catheter ablation manipulation as well as tip contact. All of these advantages resulted in a persistent and complete ablation of the LSAP with a shorter time in fluoroscopic exposure in opposition to the transaortic approach. It has also been taken into account that a standard TCRFA procedure involves a radiation burden of about 17–25 mSv which appears to be equivalent to what is usually absorbed from natural radiation exposure in during a time period of 10 years and to about 1000 chest X-ray [26]. The combined use of ICE allows to reduce to a half the time of fluoroscopic exposure that induces a significant reduction in radiation load and consequent long term oncogenic risk both for patients and physicians [27].
In conclusion, ICE-guided transseptal approach might be a promising modality for TCRFA of LSAP. However, this report needs to be confirmed by further studies.
List of abbreviations
AV= atrial-ventricular;
AVRNT= atrio-ventricular re-entrant tachycardia;
CS= coronary sinus;
ICE = intracardiac echocardiography;
LSAP= left sided accessory pathway;
TCRFA= transcatheter radiofrequency ablation;
VA= ventricular-atrial;
WPW= Wolf-Parkinson-White.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
RC carried out intracardiac echocardiography and drafted the manuscript, VD carried out electrophysiological study, AS drafted the manuscript, MS carried out electrophysiological study, MS drafted the revision of the manuscript, GG drafted the manuscript.
All authors have read and approved the final manuscript.
Supplementary Material
additional file: Movie 1 transseptal puncture.avi
Brockenbrough needle immediately after the puncture of the fossa ovalis can be clearly visualized.
Click here for file
Acknowledgments
We are indebted to Antonio Isabella, Antonio Elia, Antonietta Sacco and Alessandro Laurito for their technical assistance. The study publication was partially funded by an unrestricted educational grant of Boston Scientific.
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-351549623310.1186/1479-5876-2-35ReviewTranslation research: from accurate diagnosis to appropriate treatment Webb Craig P [email protected] Harvey I [email protected] Principal Investigator, Laboratory of Tumor Metastasis and Angiogenesis and Director, The Multiple Myeloma Research Laboratory, 333 Bostwick Avenue, Van Andel Research Institute, Grand Rapids, Michigan 49503, United States2 Thoracic Oncology, Karmanos Cancer Institute/Wayne State University, Harper University Hospital, 3990 John R, Suite 2102, Detroit, Michigan 48201, United States2004 21 10 2004 2 35 35 17 8 2004 21 10 2004 Copyright © 2004 Webb and Pass; licensee BioMed Central Ltd.2004Webb and Pass; 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 focuses on the various aspects of translational research, where research on human subjects can ultimately enhance the diagnosis and treatment of future patients. While we will use specific examples relating to the asbestos related cancer mesothelioma, it should be stressed that the general approach outlined throughout this review is readily applicable to other diseases with an underlying molecular basis. Through the integration of molecular-based technologies, systematic tissue procurement and medical informatics, we now have the ability to identify clinically applicable "genotype"-"phenotype" associations across cohorts of patients that can rapidly be translated into useful diagnostic and treatment strategies. This review will touch on the various steps in the translational pipeline, and highlight some of the most essential elements as well as possible roadblocks that can impact success of the program. Critical issues with regard to Institutional Review Board (IRB) and Health Insurance Portability and Accountability Act (HIPAA) compliance, data standardization, sample procurement, quality control (QC), quality assurance (QA), data analysis, preclinical models and clinical trials are addressed. The various facets of the translational pipeline have been incorporated into a fully integrated computational system, appropriately named Dx2Tx. This system readily allows for the identification of new diagnostic tests, the discovery of biomarkers and drugable targets, and prediction of optimal treatments based upon the underlying molecular basis of the disease.
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The Ultimate Goal
Our systematic approach to translational medicine has been designed to achieve a vision shared by numerous "ambitious" investigators who are focused on applying bench-side discoveries to the clinical setting (Figure 1). We share a vision of future medicine, in which it will be possible to predict the likelihood (risk) of a clinical event during the course of an individual's lifetime, accurately diagnose the event in its earliest manifestation, and treat accordingly based upon the diagnosis. We believe that this will become reality through the combination of medical informatics and the multiplex "omics" technologies (that we broadly characterize as genomics and proteomics throughout this review) now at our disposal. Clearly, there are certain bioethical, political and fiscal roadblocks which need to be considered as we progress towards this goal, not limited to patient privacy, regulatory issues, health care reimbursement and ownership of intellectual property. In this review however, we will focus on the science and logistics relating to implementation of a successful translational research program.
Figure 1 The goal of our translational research effort – From Diagnosis to Treatment (Dx2Tx). We have developed a systems approach to track pertinent clinical events within the lifespan of an individual subject. In the future, it may be possible to predict the risk of a clinical event in advance, accurately diagnose the event in its earliest manifestations, and treat based upon the underlying molecular/clinical traits. We believe the integration of medical informatics with cutting edge molecular technologies such as genomics and proteomics will expedite this transition to molecular-based medicine.
While a degree of project specificity must be incorporated into the design of any research effort, many of the components of translational research are shared across apparently disparate disease areas (Figure 2). Although many facets of a research proposal (i.e. attaining funding and IRB approval) require specification of the particular hypothesis under evaluation, we also make every effort to collect samples (such as blood, urine and tissue) together with as much clinical data (both historical and longitudinal) as possible while maintaining patient confidentiality, privacy and safety at the forefront. In addition to testing the pre-conceived clinical hypothesis (for example, does an event have an underlying molecular basis), this approach readily allows for the discovery of additional genotype-phenotype patterns (hypotheses) that can be subsequently cross-validated in additional subjects and samples (i.e. this event does have an underlying molecular basis). This somewhat blinded yet methodical approach clearly requires a database and supporting analytical software that are intricately linked, such that analytical results can become a new query against the database content (Figure 3). While these aspects of the Dx2Tx system will be discussed in more detail throughout this review, it is essential to stress the importance of collecting and archiving standardized clinical data throughout the course of a patient's lifespan. It is difficult to identify statistically significant correlations within non-standardized datasets. Well documented clinical data is equally as important as clinical samples, a fact often overlooked in many research studies reported to date.
Figure 2 Systematic overview of translational research. Commencing with human research subjects, we can transition through specimen and data collection, data analysis, preclinical models and ultimately clinical trails. The various facets of this pipeline will be discussed throughout this review.
Figure 3 The Dx2Tx integrated solution stores and analyzes clinical (and preclinical), experimental and molecular data from a variety of disparate sources. For more information see
Effective translational research requires a multi-disciplinary and team approach. Our initial concept meetings include HIPAA/IRB advisors, research nurses, surgeons (and/or other health care providers such as oncologists), pathologists and members of diagnostic service labs, information technologists (both biological and medical disciplines) and statisticians in addition to the principal investigators. Each member of the translational research team is critical to the overall productivity and success of the pipeline. In acknowledgement of the effectiveness of this approach, we would advise other establishments to involve key personnel across the various disciplines early. We also believe that smaller dedicated teams can often be more efficient and less bureaucratic than oversized consortium.
IRB, HIPAA, Scheduling and Consenting
The development of research protocols on human subjects that involve specimen and/or data collection must be approved by the investigational review boards (IRB). The IRB governs patient safety and risk for the hospitals and/or universities/institutions. In accordance with federal regulations, one of the requirements states that each human subject must be thoroughly informed about the research to be undertaken on their sample and/or data during a consenting process. The specificity of the consent should be such that it outlines the research protocol, study procedures and risks and benefits and meets the federal, state and local requirements, so that the subject can make an informed decision regarding participation in the study. In retrospect, a well informed patient can help facilitate the procurement of clinical data and samples in an efficient and longitudinal manner.
While the typical testing of a hypothesis requires the collection of only a subset of the available clinical data (for example tumor stage, survival time), the emerging field of medical informatics and electronic charts allows for the potential collection of vast quantities of standardized clinical data that potentially harbors invaluable information with regard to additional "traits" or "phenotypes". As such, we currently collect upward of 9000 potential data fields per patient within our specific IRB-approved protocols, all of which can be populated during the normal course of patient care and stored within either electronic or paper records for each subject. In addition, each patient receives a standardized self-reporting questionnaire addressing histories (such as habits, family history, medication history, etc). Any protected fields are restricted through a security portal, which can be considered a suitable clinical gatekeeper (such as research nurse or electronic portal) (Figure 2). This security portal allows the flow of clinical data to the laboratory researcher as defined in the IRB-approved protocol, and also links each subject/sample with an anonymous ID. Patient data that is de-identified in this fashion is not subject to HIPAA regulations. It should be noted that some protected health information (such as zip code) may represent valuable information to the research team (for example when addressing potential local or regional causes of disease incidence). Patients are informed and provide written authorization for the inclusion of such potential identifiers during the consenting process; these are then collected and archived in secure locations. For example, we routinely include current home and work 5-digit ZIP codes and date of birth in our IRB-approved protocols. The latter is used to normalize patients based upon age of each clinical event, where date of birth represents time zero in a patient's life span. As such, every recorded event in a patient's life is tagged to a date and time, such that events can be readily interrelated. For more information on HIPPA and sharing of PHI for research purposes, see .
In addition to IRB and HIPAA issues, patient recruitment can also be a major hurdle. The teamwork approach is critical here, together with patient/physician outreach and screening efforts as necessary. Our experience is the vast majority of patients are enthusiastic about inclusion in research studies so long as they are informed of the opportunity, particularly since we are careful to follow standard-of-care. For the most part, we are finding a willingness to participate for the hope of betterment for the whole, whether it is potentially helping their own families or others with their disease. At this level, the physician is the central figure and is uniquely positioned to introduce the protocol and support patient participation in the study. For studies where low participation is expected (predominantly due to low incidence), we typically include an IRB-approved public educational effort (for both physicians and potential patients) that maximizes recruitment. Our clinical research nurses work closely with the physician(s) and their office scheduling staff for notification of potential candidates in advance wherever possible. This team approach with the physician advocating the effort allows the greatest accrual particularly when there are several geographic sites for meeting potential protocol volunteers. On site consenting of high risk or suspected individuals within the outpatient clinic and hospital settings where these patients are typically evaluated should to be considered to enhance accrual. For patients with known disease, the best environment for accrual occurs in a multidisciplinary setting in which the key physician works closely with the medical oncologist, radiation oncologist, nurses, physician assistants, residents and research staff. This is particularly important for longitudinal sampling of patients on treatment protocols to minimize the loss of follow-up data. The logistics of a coordinated approach may differ between a university medical school setting and a community based practice due to the competing obligations of a busy private practice. The multidisciplinary approach is optimal in a single setting in which patients can be seen by the various disciplines, thereby, reducing the extent of patient relocation between specialty offices and allowing for centralized specimen and data collection. Nonetheless, in community-based practice, we have introduced standardized mechanisms allowing us to consent patients and collect clinical samples and data from multiple sites.
Clinical Data Acquisition
There are now abundant examples where clinical researchers are using the revolutionary "omics" technologies to demonstrate a clear association between the molecular make-up ("genotype") of clinical samples and well-defined clinical characteristics ("phenotype"). Near-complete genotypes can now be obtained through multiplex technologies such as sequencing, mutational screening (such as single nucleotide polymorphism (SNP) analysis) and gene/protein expression profiling. The absence of well annotated clinical information (such as medications, response to treatments, histories, environmental exposures, toxicities, etc), however, is evident from most studies reported to date. Rather than taking the reductionist approach at the time of data collection, our group instead chooses to gather all information relating to an individual's medical history as well as follow-up data as it becomes available and as approved by the IRB. It is relatively trivial to reduce the clinical data pool retrospectively during analysis as deemed necessary. Our Dx2Tx system date/time stamps individual events for a given subject/sample, such that multiple events can be temporally associated. The first step is to gather the information in its rawest form from a standardized source. This could be as complicated as a centralized medical informatics database (such as the Oracle-based clinical informatics system used by Spectrum Health Hospitals (Grand Rapids, Michigan) housing rich longitudinal clinical data that can be expressed in Health Level 7 standards for data portability), or it could be as uncomplicated as a locally maintained excel spreadsheet or access database. Regardless of the source, we attempt to collect as much information regarding a subject's history, diagnosis, treatment, and response assessment as possible. As highlighted above, the critical elements are reliability of the source data and standardization. While we do perform statistical analysis on parsed text from open string comments, we attempt to force objective measurements (such as integers, floating numbers, text pull downs, binary data, etc) wherever feasible. In the absence of a centralized clinical database utilizing standardized clinical nomenclature and data, the responsibility falls on the clinical members of the team to interpret clinical data within isolated databases and/or paper charts. Clearly, the navigation from paper to electronic medical records, and the generation of middleware that can link disparate databases, will greatly alleviate this burden that rapidly becomes a major rate-limiting step in the translational pipeline. Coupled with voice recognition and electronic data recording with inbuilt QC check-points, standardized digital data entry should become the norm in the not so distant future.
Clinical data permitted under protocol should be, where possible, prospectively accumulated. The accuracy of the data, particularly those parameters such as the pathologic characteristics, clinical staging, dates of intervention, dates of intermediate endpoints (such as disease progression or death), must be unquestionable in order to identify clinical and molecular correlations among diverse datasets. The accuracy of these data will depend to a large extent upon the frequency of follow-up for parameters such as disease progression, and the degree to which the patients' status is investigated longitudinally. Patients on clinical trials, where the procedures and follow-up are specified as part of the protocol, will have the most robust and standardized data since interval radiographic and physical examinations and/or other clinical procedures and methods of data collection must be adhered to in order to avoid protocol violation. Moreover, the approach of the physician(s) who follows the patient and the availability of an existing mechanism that readily allows the retrieval of prospective data (i.e. a database) will greatly influence the accuracy of the data. Within oncology, staging issues are some of the more difficult to resolve, especially if inadequate staging is performed in the operating room (OR) or if one must rely entirely on clinical or radiographic data. For the majority of organ systems, the oncological staging system is specified in the AJCC Staging Handbook, but it is the responsibility of the investigator to know whether the pathologic and intraoperative details necessary for accurate staging were adequately performed. These include but are not limited to accurate size measurement of the primary tumor, careful intraoperative description of abutment or invasion of nearby structures, verification of negative margins, and extent of lymph node involvement. The patients used in the examples below were all part of either Phase II or Phase III mesothelioma protocols, with computerized tomographic surveillance after surgery every 3–4 months until death. As such, the accuracy of the clinical data was optimized. One surgeon performed the operations, obtained aggressive intraoperative staging in all cases, and took the responsibility for collecting samples and data from all participants in the study. This approach led to the development of a consistent standard of care philosophy for the surgical management of mesothelioma.
Our Dx2Tx system performs detailed statistical analysis on both clinical and molecular data, thus leaving little room for error when it comes to standardized data collection and entry. Physicians are uniquely positioned to organize the various aspects of data collection. This endeavor begins with the design of a clinical protocol for the procurement of specimens and the collection of data from a patient population. In addition to identifying the experimental group of patients (i.e. those with a condition or trait of interest), it is important to ensure the inclusion of the proper control populations wherever possible. With respect to our mesothelioma protocols, the correct controls for early diagnostic strategies include age-matched individuals exposed to similar doses of asbestos in order to compare with equivalent individuals diagnosed with the established disease. The recruitment of these control subjects ideally begins in parallel with the recruitment of the experimental group. In the absence of the correct control group, the investigator must identify cohorts of individuals within existing cooperative group mechanisms, SPORES, or the Early Detection Research Network (EDRN). In order to circumvent this problem, the Karmanos Cancer Institute has established a National Center for Vermiculite and Asbestos Related Cancers through a cooperative clinical trial with the Center for Environmental Medicine in Southeast Michigan. This center, under informed consent, evaluates individuals who have been exposed to asbestos in the workplace or at home, and depending on history and pulmonary function data, these individuals undergo computerized tomographic scanning to establish a baseline radiographic evaluation of asbestos exposure and risk. These individuals also give written permission for periodic sample collection including blood and urine for studies of marker assessment for asbestos related malignancies.
A major focus of our research is to identify the molecular causes of differential therapeutic response across patient populations (pharmacogenetics). It is generally appreciated that patients and their disease show significant variations in response to a given treatment [1]. It is therefore critical to develop standardized approaches for routinely monitoring adverse responses (for example using common toxicity criteria) as well as disease response (for example using the Response Evaluation Criteria in Solid Tumors, or RECIST criteria). While various reporting schema are in place under specific clinical protocols, we are working on a more systematic approach; the collection of raw clinical data that can be compiled to assess clinical response and toxicity. Accordingly, if the raw data is collected, the response of all patients undergoing defined treatments can be assessed in a longitudinal fashion. The compiled response and toxicity data can then be analyzed retrospectively with other clinical and molecular attributes as outlined below.
Specimen Collection and Archiving
We have introduced standard procurement procedures for various physiological samples including tissue, urine and blood, all of which are now routinely collected under our IRB-approved protocols. These have been optimized for both ease and reliability of clinical collection as well as maintaining the integrity of the sample for subsequent histopathological and molecular analysis. DNA from peripheral blood mononuclear lymphocytes (PBML) obtained from whole blood can be screened by SNP analysis, to identify possible genetic markers of a clinical event [2-4]. Once validated, these SNP markers could serve as a genetic test to predict the risk of the event in prospective subjects. Plasma or serum (we prefer non-clotted plasma) and urine can be screened for proteomic markers of a clinical incident, and used in future screening for diagnostic purposes [5]. Per the approved protocol, tissue can be subjected to a variety of DNA/RNA/Protein technologies, and important diagnostic and therapeutic insight gained at the molecular level [6]. It should be noted, that wherever possible, adjacent uninvolved "normal" tissue free of disease should also be collected for comparison with the corresponding diseased tissue. The example in this review will focus on some published works [7] identifying diagnostic and prognostic biomarkers in the tumor tissue of patients with mesothelioma utilizing Affymetrix GeneChips for assessing gene expression within the tissue. In this case, we collected normal mesothelium of the peritoneum or the pleura in conjunction with mesothelioma tumor tissue.
The precise flow of the clinical sample from the patient to the researcher will depend largely on the disease in question and pre-existing departmental procedures. For example, in a study of pancreatic cancer, urine is provided by the patient prior to the procedure, since we have found that intra-procedural catheterization can result in blood contamination that effects subsequent proteomic analysis. Blood is drawn from an IV access for collection of plasma and PBML, while surgically resected tissue is snap frozen on site (see below). However, a multiple myeloma research protocol requires the collection of bone marrow aspirates from both inpatient and outpatient clinics, which are placed into tissue culture media and rapidly transported on ice to the Flow Cytometry Molecular Diagnostic Laboratory for immunophenotyping and cytometric sorting of plasma cells. The sorted fraction is then archived in freezing media for subsequent DNA, RNA and/or protein analysis. Irrespective of procedure, it is important to maintain a log book that tracks the flow of a sample from the subject to freeze. Procurement time is particularly important for RNA and some protein analysis, since biomolecular degradation can be a significant factor.
Prior to collection, our procurement team (lab personnel and research nurses) pre-label collection tubes and containers with anonymous identifier tags that are freezer safe. Several kits that include equipment for blood draws, urine collection and tissue procurement are preassembled and made available to the research staff to ensure rapid response for obtaining specimens. We have an on-call list of several members for the research team who can reach the pick-up point within 30 minutes of receiving a collection call. Through a coordinated multidisciplinary approach, consenting and scheduling is typically obtained well in advance in either the outpatient or inpatient setting.
At the Karmanos Institute, we have found that the easiest way to insure proper specimen harvest from patients with solid tumors is for the surgeon to divide the specimen directly in the OR and supervise its collection and distribution to the laboratory for archiving. However at the Van Andel Research Institute, we require oversight from the Hospital Department of Pathology to release surgically resected samples within the OR to the research team. The critical issue is to ensure suitable material is harvested in the correct fashion to allow accurate histopathological diagnosis. In no way should the research effort impede this quality-of-care. As a safety margin, we hold all frozen tissue for a period of time until an official diagnosis has been reported. In addition, wherever possible we embed samples in OCT freezing media, and generate hematoxylin/eosin (H/E)-stained histopathology slides for each research sample. This not only provides high quality histopathology slides for possible diagnostic back-up, but also allows us to correlate our molecular findings with the histology of the same working sample. In addition to entering pertinent data regarding the official diagnostic pathology report, we have also developed standardized report templates within Dx2Tx that accompany each research sample, in which the pathological image is associated with the corresponding data addressing critical variables such as relative amount of each cell type, stage, morphology etc. As with all data entered into Dx2Tx, these metadata are directly amenable to subsequent statistical analysis (see below).
At the time of resection, solid tissue samples are immediately frozen in either liquid nitrogen or, for sites with no supply, a dry ice-chilled isopentane bath. Samples are transported in aluminum foil, and stored at -80°C until further processing. It should also be pointed out that many of our protocols involve the collection of tissue biopsies in addition to surgical resections. Since it is physically difficult to split a biopsy (although this is done for example with bone marrow core biopsies), we typically acquire additional samples beyond what are required for accurate pathological diagnosis. If these additional samples are required by the research team, it is important to disclose the supplementary procedures to the patient in the informed consent. While obviously smaller in size compared to surgical resections, we procure these specimens as fresh tissue as described above. It is important to note, some of our molecular technologies allow for the use of as few as 500 cells for complete multiplex analysis. Since biopsy material will doubtless be the predominant source of tissue for future molecular-based diagnostics, it is important to develop protocols to address smaller sample size and possible mis-sampling issues. In addition, as a result of not restricting samples to excess surgical specimens, biopsies can rapidly increase sample accrual from larger cohorts of patients.
Urine and blood samples are transported on wet ice to the research labs, while frozen tissue is transferred on dry ice. Upon arrival in the laboratory, urine is typically divided into suitably sized aliquots and frozen at -80°C. Blood is fractionated into plasma or serum (again aliquoted and frozen), while PBML are isolated by centrifugation and Ficoll/Hipaque density gradient centrifugation and cryogenically frozen. Fresh frozen tissue specimens are blocked in OCT if possible and stored at -80°C until further use. All specimens are associated with the donating subject within Dx2Tx, and archived in a simple grid system within dedicated and alarmed -80°C freezers. The location and use of each aliquot is tracked within Dx2Tx to readily allow for subsequent retrieval.
Sample Procurement – The "Omics"
The tissue collection protocols outlined above should be evaluated for compatibility for the molecular analysis to be performed. We typically perform SNP analysis on DNA isolated from the PBML fraction of whole blood, gene expression (mRNA) analysis on tissue, and proteomic analysis of blood plasma/serum, urine and tissue. The detailed discussion of each of these molecular protocols is beyond the scope of this review (see [6] for a good starting point of reference). There are important differences between the various platforms available that can result in some disparities in the results generated [8-10]. As such, it is essential to document and ideally database protocols, deviations from protocols (version control), and in general, all experimental variables that could potentially confound the results (for example see Minimum Information About a Microarray Experiment ). We enter these variables into Dx2Tx in association with the corresponding experimental procedure such that they can be analyzed together with the derived molecular and clinical data. In this fashion, important QC and QA parameters can be interrelated with molecular and clinical data. Experiments that do not meet certain QC/QA criteria can be excluded from the analysis if the data suggest that the variable(s) is a major confounder. In this fashion, Dx2Tx serves as an electronic notebook, the entries into which can be routinely recorded and statistically analyzed. This feature will be particularly important as potential applications advance towards clinical utility, for example through ensuring compliance with the FDA and accrediting agencies such as JACHO, CMS and CAP, and while maintaining the necessary state, federal, and insurance applications required to provide clinical diagnostic services.
Data Analysis
For so many investigators, analysis represents the "black box" of the translational pipeline. It is reasonable to state that statistical analysis of the data is one of the most important steps in the translational process. In addition to determining the sample size and inclusion/exclusion criteria necessary to test a hypothesis, it is essential to utilize the correct tests of statistical significance during both discovery and application phases of the translational pipeline. This review will not detail all possible permutations of biostatistics, but we will touch briefly on certain statistical concepts. The average gene expression profiling experiment can generate in excess of 30,000 individual data points per clinical sample, while current high-throughput SNP-chips can generate >100,000 discrete attributes per analysis. Similarly, proteomic experiments generate vast amounts of coupled data that includes both fractionation (such as 2-D gels or 2-D liquid chromatography) and detection (such as mass spectrometry) elements. Coupled with extensive clinical content, this collectively presents significant challenges for data storage and retrieval, as well as statistical analysis. The most frequent frustration of new users of the "omics" technologies remains "What are the data telling me?"
Before proceeding with specific examples, it is important to grasp the concepts of normalization and data massaging or "filtering" [11]. To compare data across multiple samples, data is typically normalized or scaled such that results from one experiment are directly comparable to those of another. There are a number of methods to normalization across experiments including the use of a common reference sample or internal controls such as housekeeper genes/proteins that do not alter across experiments. Various forms of mathematical normalization can then be used (for example mean centering) to scale the results across experiments. As discussed above, through the tracking of protocols and minimizing experimental variation, one can reduce the degree of scaling required to directly compare data across different experiments. The normalization routines, as with pre-filtering of data, should be performed during an analysis session and not prior to data entry, since the different normalization and filtering routines can significantly effect the analytical results. As such, it is important to track the normalization and filtering criteria employed during analysis, such that results from sessions using different methods can be compared and contrasted.
One problem in dealing with vast amounts of clinical and/or multiplexed molecular data from a relatively small sample population is that many of the observed correlations could (not necessarily do) occur by random chance. One method investigators use to minimize the probability of this so called "false discovery" is to reduce the complexity of the data through successive rounds of filtering. Depending on the application, we do use some filtering under certain circumstances. However, as with normalization, this is done during data analysis and not prior to database entry. In this fashion, we can filter and analyze the data "on the fly", allowing the user to evaluate the effect of filtering the raw data. For example, if we were looking for a specific set of genes that are found highly expressed in mesothelioma relative to other tumor types, we may eliminate genes in mesothelioma samples below a certain expression (intensity) threshold, and those above a certain level in other tumor types. This would maximize the likelihood of identifying sensitive and specific mesothelioma genes, and minimize the gene pool and hence probability of false discovery. We also use fold-change filters depending on the application. For example, we may only be interested in diagnostic biomarkers that display at least a 4-fold increase in mesothelioma tumor tissue relative to other tumor types and normal mesothelium. In addition, genes with certain characteristics (such as those that encode only secreted or transmembrane proteins [12] can be filtered (included or excluded) within Dx2Tx. Thus, while it is important to note that these filters are not tests of statistical significance, they nonetheless can be used in the context of biological logic, to maximize the likelihood of identifying clinically valuable data within multiplexed data. The logic flow depicted in Figure 4 shows the effect of successive rounds of filtering the data from mesothelioma samples with the intent of identifying possible biomarkers that may be detected in blood or pleural effusions. This logic-based informatics approach to identifying possible disease biomarkers in physiological fluids can lead to candidate proteins that can be specifically detected in the corresponding blood/urine samples by other methods. We have shown the utility of this predictive approach in parallel with proteomic analysis of plasma in some experimental models of cancer. While this approach is yet to be validated in the human disease, we believe that this represents an excellent supplement to existing biomarker discovery programs that is readily implemented through simple data filters.
Figure 4 Sequential filtering of Affymetrix gene expression data to identify potential plasma biomarkers of mesothelioma. The key aspects of a disease biomarker include sensitivity and specificity. These can be partially addressed through logic-based filters within Dx2Tx.
There are an abundant number of methods that can be employed during analysis of multiplex data to determine statistical significance. In the absence of becoming a quasi-expert in statistics, we highly recommend that the assistance of any number of statisticians is sort. The key is to understand why certain statistical tests are used under a variety of circumstances, and what the limitations of each test may be. We typically use similarity-based tests (such as hierarchical clustering, principle component analysis, multidimensional scaling, etc) to identify relationships between variables (clinical, experimental and molecular) within large datasets [13,14]. These metrics identify the degree of similarity (or difference) between various attributes, and are extremely powerful when attempting to discover inter-sample relationships based upon their molecular and/or clinical features. For example, unsupervised hierarchical clustering can be used to cluster the X attributes (such as mesothelioma samples) based upon the values of the Y-attributes (such as relative gene expression) as shown in Figure 5. In addition, the Y attributes can be clustered, based upon their similarity across the X attributes, providing a 2-dimensional clustergram displaying overall relationships (Figure 6). The co-clustering of samples is essentially a raw form of a molecular diagnostic application since samples with similar genotypes cluster based upon biological similarity (phenotypes). The co-clustering of genes and/or clinical data is also a potentially powerful application. For example, genes/proteins with similar functionality are often co-regulated at the level of their expression, and hence typically "co-cluster" on a gene expression clustergram. This concept becomes particularly powerful when attempting to predict the function of unknown genes based upon their overall correlation with a gene of known functionality [15-17]. In the case of clinical data, inter-relating clinical and/or environmental events can also be performed. Hence, features that correlate (such as increased stage of disease and poor outcome) are typically adjacent on the clustergram (Figure 7). Coupled with the extensive collection of standardized clinical data highlighted throughout this review, this feature alone may have significant impact in the mining of clinical data in disciplines such as epidemiology.
Figure 5 A two-color clustergram generated after hierarchical clustering of gene expression data (Affymetrix U95A) across 21 tumor samples collected from patients with mesothelioma. Clustering has been performed in only the X axis, such that samples are grouped based upon similarity in overall gene expression (the identified sample sub-groups are color coded). The gene expression data has been mean centered, such that degrees of red and green indicate relatively high and low expression of the corresponding gene respectively, while black represents the mean value across samples. In this fashion, the relative expression of many genes can be readily visualized across several samples simultaneously, and the relationships between samples observed.
Figure 6 Hierarchical clustering of Affymetrix gene expression data as described in the legend to figure 5, with the exception that genes are also clustered based upon similarity in expression across the samples. In this fashion, correlations between samples and genes can be simultaneously observed.
Figure 7 Clustering of mesothelioma tumor samples (X attribute) by clinical data (Y attribute) reveals possible epidemiological relationships between the various clinical features. Some well known relationships (such as stage of disease, lymph node status, death), as well as some less established patterns (such as a correlation between high platelet count and recurrence) are readily observed in this mode of operation. The degree of the clinical event is represented on a mean centered scale, such that red and green indicate relatively high and low extents respectively. A subset of samples has been selected based upon the extent of a single trait, in this case prolonged survival time (blue).
In addition to correlating clinical and molecular data individually, these data types can be merged and viewed simultaneously within the same clustergram. In this fashion, possible associations between clinical, experimental and molecular features can be readily identified. For example, as shown in Figure 8, there appears to be an association between T-stage and a number of genes known to be involved regulation of the cell-cycle. Classification of gene/protein function (so called "annotation") can provide important information with regards to the underlying molecular cause(s) of a clinical event. In addition to utilizing the publicly available annotations (for example see Gene Ontology ), we also map results to molecular pathways using detailed pathway mapping software now available. Taking this approach, the coordinated expression of genes/proteins can be seen to map to specific molecular networks, therefore providing important information as to which pathways maybe activated or in-activated in association with a clinical event. For example, when all the genes differentially expressed in recurrent mesotheliomas relative to non-recurring tumors at a defined statistical significance (p < 0.001) are mapped using the MetaCore™ software (MetaCore™ ), a clear signaling pathway associated with cell proliferation is identified that appears to be hyper-activated in aggressive mesothelioma tumors (Figure 9). As discussed below, in addition to providing clear diagnostic value, this information is particularly useful in the design of treatment strategies that may target key points within the identified molecular network.
Figure 8 Clustering of samples based upon integrated clinical, experimental and molecular attributes. In this sense, molecular-clinical (genotype-phenotype) associations can be readily observed. As described in the legend to Figure 7, the extent of the clinical/molecular attribute is represented on the same normalized scale, such that red and green represent relatively high and low values respectively.
Figure 9 Mapping molecular correlates of aggressive mesothelioma to highly curated molecular pathways can identify the underlying molecular mechanisms of the disease. This information could be used for diagnosis, as well identification of the key steps that may represent intervention points in the treatment of the disease. The red arrow indicates a predicted therapeutic target (EGFR). Pathway mapping was generated using MetaCore™ (GeneGo, Inc., St. Joseph, MI). For more information on this pathway mapping tool, see .
Clinical Diagnostics
With respect to identifying patterns (hypotheses) within the complex clinical and molecular datasets that could be translated into clinical diagnostic applications, we typically begin with unsupervised clustering techniques such as hierarchical clustering as shown above in Figures 5, 6, 7. In this fashion, sample similarity with respect to clinical, experimental and/or molecular attributes can be assessed. Dx2Tx extends these analyses to identify clinical and/or experimental variables that statistically correlate with defined sample sub-groups. During this step of hypothesis generation, Dx2Tx runs back into the database housing all of the standardized clinical and experimental data and identifies correlates of the selected sub-groups. This is a highly powerful utility when operating in unsupervised mode, and requires an intricate link between data analysis and database content. Unsupervised clustering may for example identify the degree of molecular similarity across a cohort of patient samples, which could identify several clearly delineated groups at the genotype level. Running in hypothesis generation mode, Dx2Tx then identifies statistically significant correlates of these groups, and assigns clinical/experimental features to each. When Hypothesis Generator was executed on the 2 sample subgroups highlighted in Figure 5, the clinical features time to recurrence (p = 0.003), T-stage (p = 0.004), survival time (p = 0.0002) and platelet count (p = 0.005) were returned as significant correlates of these sub-groups. Thus, while these samples may have been initially collected in the context of a different user-defined hypothesis, through the collection of standardized clinical data in addition to the generation of multiplexed molecular data, Dx2Tx was able to identify statistically significant patterns (hypotheses) within the data in an unbiased fashion. The user can of course decide which hypothesis to pursue. In this example, our ability to potentially utilize gene expression profiling to predict survival time of mesothelioma patients following surgery based upon gene expression within the tumor would have obvious prognostic value. Therefore, the next step would be to test this hypothesis and determine the accuracy of a possible diagnostic test.
Once a hypothesis has been generated, Dx2Tx identifies samples against which the hypothesis can be tested. Certain inclusion and exclusion eligibility criteria can be considered and used to filter the content of the database to identify subjects/samples/experiments with certain characteristics. Dx2Tx also allows samples to be selected based upon the extent of any attribute(s) (Figure 7). For example, the investigator may be primarily interested in only a subset of the sample population that displayed the greatest and least extensive toxicity to a given drug. This is assisted through the selection of the trait of interest and setting the extent of the trait (i.e. by defining standard deviations from the population mean). This feature may be particularly important in retrospective analysis of large clinical trial cohorts, since the outliers for a given trait can be identified prior to sample procurement.
Once the sample population is selected, we test the hypothesis across the series of selected samples in a 2-step process. A subset of the samples (typically defined as a training set) are selected (either logically or at random) from each subgroup (for example disease versus control) to develop a discrimination algorithm that identifies statistical correlates of the feature in question. It is worthwhile to note that Dx2Tx identifies clinical, experimental and molecular correlates of the selected feature(s), thereby integrating both clinical and molecular data into the potential diagnostic algorithm. The user can exclude any attribute from the input to the training algorithm. In a second cross-validation test, the trained algorithm is applied to the remainder of the samples (in retrospective mode of operation, with known outcome), to determine if the test could have accurately predicted the nature of the remaining samples. The outcome of the test is plotted using a receiver operator characteristic (ROC) curve to determine the accuracy of the test. The ROC curve is a way to visualize and quantify the effectiveness of a procedure by graphing the true positive rate (Y axis) against the false positive rate (X-axis). The area under the curve (AUC) provides an approximation of the accuracy of the test. A procedure with no effectiveness (AUC = 50%) would show a random 1:1 line, indicating that for every true positive, the procedure also generated a false positive. Generally, an AUC of 90–100% is considered excellent, while an AUC of 80–90% is good. The ideal diagnostic test would of course identify all true positives before encountering a false positive (AUC = 100%).
In this working example, the hypothesis generated from analysis of unsupervised clustering of gene expression data from mesothelioma tumors is that survival time of patients can be predicted based upon the underlying genomic signatures of the tumor. Thus, patients with the shortest and longest survival time following surgery were placed into two groups. Each group was then randomly divided into 2 additional groups, the training set and the test set. The discriminating clinical and molecular features are first identified using a standard t-statistic for numerical data and chi squared for binary (including text) data. This test statistic is then used in a weighted voting metric [18]. Data are first converted to a respective z score in order to normalize data of different types to a similar scale. A more refined statistical package, which will more rigorously integrate the binary and non-binary data, is currently in the process of being implemented into the Dx2Tx solution. In this fashion, the experimental, molecular and clinical attributes that statistically correlate with survival time are first identified. In this example, no experimental variables (i.e. those which may denote a variation in experimental protocol or quality) were identified that correlated with patient survival time. The clinical parameters platelet count and T-stage were identified as clinical correlates of survival time and therefore included into the training algorithm. In addition, 157 genes were identified, the expression of which correlated with survival time (p < 0.05). Each attribute (platelet count, stage, and the 157 individual genes) was then weighted based upon the calculated t-statistic within the training group. A discrimination score (the sum of the t-statistic multiplied by the normalized z-score for each attribute) was then calculated for each sample within the training groups and a threshold decision point (the value at which a sample is classified as neither group 1 or 2) is set halfway between the means of the two test groups. Alternatively, a user can set the threshold in order to maximize either sensitivity or specificity of the assay, or set it to a value which would demarcate an acceptable test failure rate. In this fashion, the end-user can set the decision point of the classification algorithm on the side of false positives or false negatives based upon the clinical consequence of the test result. For example, if a positive test results in administration of a poorly tolerated treatment, the physician would typically error on the side of false negatives. At this time, a discrimination score is calculated for the remaining test samples, compared to the threshold decision point, and assigned a classification. The predicted classification is then compared to the actual outcome. While complicated, Dx2Tx performs this cross-validation metric in a matter of seconds. In this working example, the ROC plot generated from the prediction of the prognosis of patients with mesothelioma suggests that this particular diagnostic test is approximately 90% accurate at determining the 6 month survival of patients following surgery as determined by the area under the ROC curve (Figure 10). Once validated, the classification algorithm is stored within Dx2Tx, such that it can be applied to any future sample. Thus, through the capture of standardized clinical, experimental and molecular data, hypotheses can be rapidly generated and tested, and further developed into potentially useful diagnostic applications. At this point, the focus may shift from retrospective analysis to prospective studies.
Figure 10 A Receiver Operating Characteristic (ROC) curve showing the performance of an integrated clinical and molecular diagnostic test for predicting prognosis of patients with mesothelioma. The Area under the curve (indicative of the tests accuracy) is approximately 90%.
From Diagnosis to Treatment
In addition to the identification of potential diagnostic applications, we have a major focus on identifying new treatment targets, and/or improving therapeutic strategies involving existing treatments. Patient variation, with respect to treatment response (efficacy and toxicity), is a well documented phenomenon [1]. Through the capturing of clinical data and pertinent samples across a large patient population that exhibits variable treatment response, retrospective statistical analysis of the integrated clinical, experimental and molecular data could reveal the underlying causes of this variation. For example, DNA polymorphisms in some isoforms of the cytochrome p450 enzymes have been associated with the variation in the rates of metabolism of many pharmaceutical drugs across a sample population [19]. As a result, a specific test now exists that could be used to better determine the optimal dose of some pharmaceutical treatments (Roche Release of AmpliChip ). These so called "companion diagnostics", which could accompany therapeutic agents and assist in treatment decisions, are also being developed for specific agents that display varying degrees of efficacy and toxicity across sample populations. With the accurate capture of longitudinal clinical data including toxicity and response assessment, associations between clinical response and molecular features of either the patient and/or the disease tissue should be readily identifiable within complex retrospective datasets. For example, we have identified a genomic signature within plasma cells isolated from multiple myeloma patients that correlates with tumor response to the drug melphalan. This genomic signature is currently being applied to additional patient samples using cross-validation statistics as outlined above, to determine the accuracy of this possible companion diagnostic application. As with most single agent treatment regimens, drug resistance in the area of oncology represents a significant problem in the treatment of the disease. Therefore, pre-treatment tests could conceivably identify the patients who would benefit the most and least from treatment.
Our research also includes the discovery of possible early surrogate markers of therapeutic index. This ideally requires the collection of clinical specimens and data both pre and post treatment. In conjunction with the treatment of cell lines in culture and molecular analysis of livers and kidneys from treated mice, early biomarkers of efficacy and toxicity have been identified in association with several treatment regimens. If these biomarkers of response can be validated in retrospective patients, they could be written into future clinical studies to provide an early indication of therapeutic effect. These biomarkers may ultimately be used as surrogate markers to determine discontinuation or modification of protocols to maximize therapeutic index in prospective trials.
There are currently a number of drugs in development, in clinical trials or that have recently received FDA approval that target specific molecular aberrations [20-23]. Unlike cytotoxic chemotherapies, molecularly targeted therapeutics often display a high degree of specificity against the selected target. Based upon the specificity of these drugs to defined proteins, it is envisioned that molecular-based diagnostics will naturally accompany these agents to identify the patient sub-population who will benefit from treatment. Because of the inherent genomic instability of cancer, combinations of these molecularly targeted drugs will almost certainly be required to ultimately treat the disease. Indeed, mathematical models of adaptive microevolution of the cancer cell suggest that a multi-modality treatment strategy that targets at least five individual molecular targets simultaneously will be required to minimize the chance of a single cell within the tumor acquiring resistance to each agent [24]. As part of our research effort, we are attempting to identify the optimal multi-modality targeting strategies to treat specific tumor types based upon their molecular makeup. For these reasons, we have incorporated a drug-target database that is regularly updated to include molecularly targeted agents as they are publicly disclosed. During analysis, we can filter the datasets to only include genes/proteins against which drugs have already been developed. For example, we can substitute the drug-target list as a filter in place of the known secreted or plasma membrane proteins discussed in relation to Figure 4. When performing this function on the mesothelioma dataset, epidermal growth factor receptor (EGFR) inhibitors in combination with eniluracil and topoisomerase II inhibitors are identified as a possible combination treatment for mesothelioma based upon relatively high expression of their molecular targets EGFR, Dihydropyrimidine dehydrogenase and topoisomerase II respectively. Such hypotheses obviously require testing in a relevant preclinical model of the disease (see below). Nonetheless, because the corresponding drugs have already been developed, this is a readily testable hypothesis assuming the investigator can gain access to the therapeutic agent. As discussed below, however, we do not believe that expression levels of the molecular target alone are necessarily sufficient to predict drug efficacy.
The FDA approval process of the EGFR inhibitor Iressa has received a great deal of attention [25]. Recently, it was shown that lung carcinomas from a subset of patients that possess activating mutations in the EGFR are most responsive to Iressa [26,27]. This raises an important concept we have been pursuing for some time; namely that diagnostic tests need to address activity of the target(s) rather than merely expression levels. Since an active molecule can often shut down its own expression, while conversely, hypo-active targets may consequently be over-expressed, this biological phenomenon of negative feedback often results in inverse expression-activity relationships (CPW, unpublished observations). Hence, in combination with gene silencing technologies (such as RNA interference) that mimic a selective drugs action, we are identifying the down-stream genomic and proteomic consequences of target gene disruption for the purpose of identifying biomarkers that could be used to assess target activity. We believe that rather than using expression levels of the target molecule alone as a rudimentary diagnostic test, this more sophisticated approach may yield greater success in attempting to predict the optimal treatment strategies based upon a molecular profile.
In addition to pursuing existing drug targets, we are also attempting to identify novel targets that may warrant future drug discovery efforts. We typically attempt to validate only candidate genes/proteins that have or are predicted to have "drugable" characteristics. To determine whether a potential target is drugable, we are currently using some relatively simple criteria based upon the classes of drug targets that have been actively pursued by pharmaceutical/biotech companies to date. For example, we have annotated the publicly available drug targets described above using gene ontology and literature mining tools within Dx2Tx [9], and identified several recurring features of these targets (such as kinases, phosphatases, G-protein coupled receptors, etc). Any gene/protein identified with the same annotation is "drugable" by simple association. We also identify genes/proteins that co-cluster with genes/proteins with drugable features, since as described above co-expressed genes/proteins often share similar functionality. Future developments will include sequence, domain and structural-based predictions of drugable characteristics, but it remains to be seen if this selective approach will lead to accelerated clinical application in the future. Nonetheless, using the combined content and analytical power of Dx2Tx we can sequentially filter data in an attempt to identify specific targets for the disease in question and condense our target candidates further to identify those with the greatest potential for drug development.
Preclinical Models of Disease
The majority of the preceding discussion has focused on the identification of molecular correlates of disease, and identifying those that may represent diagnostic biomarkers and/or treatment targets for intervention. A large proportion of our translational research effort is dedicated to functional validation, where through various means, the expression and/or activity of the target are modified and the functional consequences addressed. While determining the function of a diagnostic biomarker is not necessary, the functional consequence of target gene/protein disruption is essential when establishing the true therapeutic value of a potential target. An ideal therapeutic target would be one that is causative of the disease, and inhibitors against which thus cause disease regression. The following discussion will focus on a high-throughput means by which we assess the functional significance of target gene/protein disruption in murine models of various human malignancies.
There are a variety of approaches one can take to interfere with gene/protein expression and/or function with the purpose of demonstrating a definitive role in a biological process [28]. These include the use of pharmacological agents, antibodies and/or interfering mutants. However, these approaches require reagent access and/or some in-depth knowledge about the gene/protein in question. These approaches are also relatively low throughput and expensive, and can result in a significant bottleneck effect as potential targets are identified during the translational research effort. Antisense technologies represent an excellent approach to determine gene function, and are readily integrated into gene/protein discovery programs due to the wealth of gene/protein sequence information now available [29,30]. In this regard, the field of RNA interference has emerged as a highly specific and relatively simple way to disrupt genes [31]. RNA interference (RNAi) is a conserved phenomenon, whereby long double stranded RNA (dsRNA) is processed into small 21–23 nucleotide dsRNA fragments, termed short interfering RNA (siRNA) [32]. These fragments then target and degrade highly homologous RNA gene transcripts, thereby inhibiting gene expression in a sequence-specific manner. RNAi is thought to function to maintain genomic stability, regulate cellular gene expression, and defend cells against viral infection [33,34].
We have developed an avian retroviral vector that can deliver siRNA to cells expressing the viral receptor (TVA)
[35]. TVA expression can be directed to specific cells in vitro through exogenous transfection/infection, or in vivo through transgenic technology [36]. This system was developed to allow us to target the delivery of gene-specific siRNA to tumor and/or endothelial cells in vivo, such that the effect that target gene disruption has on tumor growth, metastasis and angiogenesis can be directly assessed. As discussed above, we typically target genes/proteins that we predict to have "drugable" characteristics, and in a sense this retroviral-siRNA approach mimics the optimal molecularly-targeted drug due to its targeted delivery and exceptional degree of gene specificity. In addition, these avian retroviruses do not replicate within mammalian cells and as such cells can be infected multiple times with vectors targeting multiple genes. This system therefore allows us to investigate the functional consequences of combinational targeting strategies. Because of the particular cloning strategies we have incorporated, it takes only six weeks from the discovery of a potential target gene to the point of assessing functional consequences of gene disruption in a relevant preclinical model of the disease. Because of the efficiency of the system, we are able to assess multiple targets simultaneously. For example, we are currently evaluating 19 targets in various combinations that have been predicted to display optimal efficacy in murine models of mesothelioma, pancreatic cancer, colorectal cancer and multiple myeloma. Thus, by introducing a systematic approach to target validation, we have limited the bottleneck between target discovery and functional validation.
Improved murine models that depict specific features of the human disease in question are essential to validate the in vivo significance of experimental findings in a relevant preclinical setting. We particularly focus on the development of orthotopic xenograft models, in which human tumor cells are implanted into their corresponding site of origin within an immune compromised mouse. In this fashion, human tumors form in the tissue site of origin that more closely resemble the human counterpart with respect to biological behavior. These orthotopic models better recapitulate the various stages of tumor progression and more accurately reflect responsiveness to various therapeutic intervention strategies [37]. Coupled with in vitro and in vivo delivery of siRNA against multiple target genes, these preclinical models readily allow us to evaluate the requirement of proposed target genes in tumor progression. Through these studies, we are beginning to identify the optimal combination targeting strategies that may lead to the eventual treatment of aggressive cancers.
Moving from Retrospective Analysis to Prospective Clinical Trials
In the vast majority of cases in which molecular data is being correlated with clinical events, a hypothesis-driven inquiry is tested against archived clinically documented specimens. The majority of these trials are asking either a classification question (i.e. what profile sets this tumor apart from other tumors), a prognostication pattern (what group of genomic/proteomic patterns will predict time to progression or time to death), early detection (how is this tumor different from its "cell of origin" at the earliest time point recognizable such that a genomic/proteomic pattern could predict the development of malignancy in a high risk population), or response to therapy (is there a de novo set of genomic/proteomic parameters which predict either response or resistance to a given therapy). For classification phenomena, the investigator must currently rely on pathologic differences to stratify tumors into clusters which, upon genomic/proteomic analysis, have consistent and congruent distinguishing features. This is probably the easiest of analyses and relies mainly on established architectural and morphologic differences between tumors instead of clinical behavior and endpoints. Nevertheless, as with all discovery test-sets, validation prospectively must be performed in order to test the accuracy of the classification algorithms. This requires prospective application of the algorithm to blinded samples, and comparing the predicted outcome with the actual observed pathology.
For the early detection, prognostication, and prediction of therapeutic response, one must link potential diagnostic tests to newly developing trials to allow validation of the established hypothesis. Before undertaking such an effort, however, the investigator must realize that the data were typically derived from a set of patients within a particular treatment regimen. For example, the patients presented in this article were characterized as having specimen procurement prior to definitive cytoreductive surgery followed by adjuvant therapy, and therefore a prospective trial which conforms to this treatment regimen should ideally be designed to avoid confounding variables. For the validation of diagnostics that predict time to recurrence or survival following treatment, a phase II trial of surgery and/or postoperative adjuvant therapy could be designed in which samples are harvested at the time of surgery. The diagnostic test would then be performed on the clinical specimen to obtain a prediction of patient outcome, and the patients' clinical course followed to see if there is prospective validation of the test. These analyses, however, must be performed in patient groups not weighted towards either high or low risk patients, and indeed, some stratification of clinical parameters should be specified at the outset in order to make reasonable comparisons. This is especially true if the molecular data is to be validated as part of a Phase III randomized trial comparing two treatment regimens after surgery. As the content of Dx2Tx expands to include clinical trial data, analysis can be performed on only those patients that conform to specified clinical parameters. Therefore, with the complete and standardized collection of clinical data from a large population of patients receiving various treatment regimens both on and off of protocol, established hypotheses can be further tested on additional retrospective subjects; in a sense a virtual trial that begins to more closely resemble the carefully controlled prospective clinical trial.
The ideal clinical trials addressing the validity of molecular data to predict a clinical parameter are those involving patients with no prior diagnosis or treatments, who subsequently receive a single therapeutic regimen. Preferably, both pre- and post-treatment specimens are obtained from these patients. Such a pure trial could, for example, test whether a predefined genomic/proteomic pattern could predict a poor outcome despite favorable clinical parameters. Trials of patients receiving initial chemotherapy could also help to define the fidelity of the omics technologies for the prediction of chemosensitivity. These hypotheses may have been derived from a retrospective set of patients receiving the same chemotherapy or targeted therapy for which outcomes were known, or from the in vitro discovery of genomic/proteomic patterns defined in cell lines treated with the corresponding agent(s).
Early detection clinical trials using genomic and/or proteomic technologies will typically take the longest to validate. After the discovery of proteins as candidate markers from analyses of serum, urine, or other fluids in patients with early stage malignancies compared to the appropriate high risk group, cohorts of high risk individuals (e.g. asbestos exposed or tobacco smokers) must be identified who are willing to have collections of the appropriate specimens longitudinally at given intervals. Moreover, in initial trials of these markers, the investigator must define at what time to disclose the value of the measured biomarker. One possibility would be to allow the prospective clinical trial to reach a certain endpoint (i.e. a certain number of cancers occur in the cohort which are detected by the standard of care radiographic or physical examination) and then reveal the results of the marker to see whether there is predictive value. Another way would be to define the value of the marker at follow-up intervals during the course of the trial and, in the face of radiographic or physical findings, initiate an invasive workup to "find" the predicted cancer. It is generally agreed that, although a two stage validation effort takes longer (i.e. blinded values for the marker in question until completion of the study followed by a study with invasive or semi-invasive investigations based on fluctuations or absolute levels of the marker), such a model is currently preferred.
With respect to novel treatment strategies predicted from retrospective analysis and/or through the preclinical studies defined above, the next logical step is to attempt to validate the retrospective data which initially pointed the investigators to these potential treatment options. If the new therapy has never been used in humans before, it is important to define the maximum tolerated dose of the treatment. This is defined as the dose at which, in a Phase I clinical trial, a defined fraction (typically one third) of the patient cohort develop dose limiting toxicity. Once the dose or treatment strategy is found to be acceptable, a Phase II trial is designed to provide a measure of the activity of the agent(s) and to begin to define whether there is any benefit of the regimen in certain patient cohorts. The use of tools such as Dx2Tx could be invaluable in the future tracking and analysis of such trials, especially if samples are prospectively harvested at various times pre and post treatment. For example, in the Phase I/II trial, genomic/proteomic correlates of clinical toxicity could be readily identified by melding the clinical data with "omic" data from the patients who have undue toxicity. Molecular pathways that intuitively result in toxicity could be identified which could possibly be abrogated with another agent. With respect to determination of therapeutic efficacy, our present means of defining "clinical correlates of response" is essentially a "best guess" or subjective prediction of what clinical markers may indicate that the new agent is yielding a therapeutic effect. A more objective measure of downstream events resulting from an efficacious agent would result from analysis of the molecular data in tandem with clinical information for the responders compared to the non-responders. Dx2Tx would be able to then define the most important markers of response, both clinical and molecular, which could then be validated in a Phase III trial assessing agent efficacy.
End Use
It is unknown when an integrated clinical/molecular evaluation of the suspected or afflicted cancer patient will be of use to the end user, the practicing physician. Certainly, many of the same arguments that are used for and against "genetic testing" in other diseases may be used for such a global approach to oncology. There is no doubt however, the ability to define clinical behavior in a more efficacious and predictive manner, other than the archaic prognostic indicators which we use today, will help clinicians initiate and/or alter treatment course earlier and guide clinicians toward informed discussions with their patients in order to make treatment and/or surveillance decisions. If indeed the fidelity of combinatorial molecular and clinical medicine proves to be satisfactory, we may then be able to spare patients unnecessary treatment interventions which are currently doomed to failure. Moreover, the medical oncologist will be able to choose the correct cytotoxic and/or targeted therapy based on a global clinical-molecular snap-shot, which should translate into more favorable health economic policies and patient outcomes.
Conclusions
Throughout this review, we have highlighted the importance of designing research protocols involving human subjects that permit the collection of not only clinical specimens, but also extensive standardized clinical data in a longitudinal fashion. Through the merging of clinical and molecular data, non-biased patterns can be discovered that could translate into novel diagnostic and/or treatment opportunities. We have introduced a methodical approach for archiving and mining these seemingly disparate data sources that we believe can accelerate the translational research discovery pipeline. While there are clearly improvements to be made in the systematic collection of accurate and nationally standardized clinical data, we believe the integration of medical informatics and the molecular technologies can convert the once visionary concept of molecular-based medicine into a present reality.
Competing Interests
CW – A provisional patent has been filed on the Dx2Tx system described in this article.
HP – This author declares that he has no competing interests.
Acknowledgements
We would like to thank Linda Pool, Debbie Ritz-Holland, Tara Ackerman, Michael Dobb, Jerry Callahan and Jeremy Miller for their critical review of this article. We would also like to express our deepest gratitude the numerous members of our multidisciplinary translational research teams for their dedication and continued support. The generation of the expression data presented in this review was partially funded by a VA merit review to HIP. The development of Dx2Tx was supported by the Van Andel Foundation.
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| 15496233 | PMC524522 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Oct 21; 2:35 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-35 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-291548259810.1186/1476-4598-3-29ResearchActivation of the hedgehog pathway in advanced prostate cancer Sheng Tao [email protected] Chengxin [email protected] Xiaoli [email protected] Sumin [email protected] Nonggao [email protected] Kai [email protected] Frank [email protected] Zoran [email protected] Jingwu [email protected] Sealy Centers for Cancer Cell Biology and Environmental Health, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, Texas, 77555-1048, USA2 Department of Dermatology, Xijing hospital, Xi'an 710032, China3 UCSF Cancer Center, 2340 Sutter Street, San Francisco, CA 94115, USA4 Department of Pathology, Creighton University Medical Center, 601 N 30th St. Omaha, NE 68131, USA2004 13 10 2004 3 29 29 22 9 2004 13 10 2004 Copyright © 2004 Sheng et al; licensee BioMed Central Ltd.2004Sheng 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 hedgehog pathway plays a critical role in the development of prostate. However, the role of the hedgehog pathway in prostate cancer is not clear. Prostate cancer is the second most prevalent cause of cancer death in American men. Therefore, identification of novel therapeutic targets for prostate cancer has significant clinical implications.
Results
Here we report that activation of the hedgehog pathway occurs frequently in advanced human prostate cancer. We find that high levels of hedgehog target genes, PTCH1 and hedgehog-interacting protein (HIP), are detected in over 70% of prostate tumors with Gleason scores 8–10, but in only 22% of tumors with Gleason scores 3–6. Furthermore, four available metastatic tumors all have high expression of PTCH1 and HIP. To identify the mechanism of the hedgehog signaling activation, we examine expression of Su(Fu) protein, a negative regulator of the hedgehog pathway. We find that Su(Fu) protein is undetectable in 11 of 27 PTCH1 positive tumors, two of them contain somatic loss-of-function mutations of Su(Fu). Furthermore, expression of sonic hedgehog protein is detected in majority of PTCH1 positive tumors (24 out of 27). High levels of hedgehog target genes are also detected in four prostate cancer cell lines (TSU, DU145, LN-Cap and PC3). We demonstrate that inhibition of hedgehog signaling by smoothened antagonist, cyclopamine, suppresses hedgehog signaling, down-regulates cell invasiveness and induces apoptosis. In addition, cancer cells expressing Gli1 under the CMV promoter are resistant to cyclopamine-mediated apoptosis. All these data suggest a significant role of the hedgehog pathway for cellular functions of prostate cancer cells.
Conclusion
Our data indicate that activation of the hedgehog pathway, through loss of Su(Fu) or overexpression of sonic hedgehog, may involve tumor progression and metastases of prostate cancer. Thus, targeted inhibition of hedgehog signaling may have significant implications of prostate cancer therapeutics.
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Background
The hedgehog (Hh) pathway plays a critical role in embryonic development and tissue polarity [1]. Secreted Hh molecules bind to the receptor patched (PTC-PTCH1, PTCH2), thereby alleviating PTC-mediated suppression of smoothened (SMO), a putative seven-transmembrane protein. SMO signaling triggers a cascade of intracellular events, leading to activation of the pathway through GLI-dependent transcription [2]. The hedgehog receptor PTCH1 is also a target gene of this pathway, which forms a negative feedback mechanism to maintain the pathway activity at an appropriate level in a given cell. Activation of Hh signaling through loss-of-function somatic mutations of PTCH1 in human basal cell carcinomas (BCCs) disrupts this feedback regulation, leading to uncontrolled SMO signaling. Activating mutations of SMO in BCCs, on the other hand, are resistant to PTCH1-mediated inhibition, leading to an outcome similar to PTCH1 inactivation [3-6]. More recently, abnormal activation of the sonic hedgehog pathway, through over-expression of sonic hedgehog, has been implicated in the development of subsets of medulloblastomas, small cell lung cancer and gastrointestinal tract (GI) cancers [7-10].
Development of prostate requires hedgehog signaling. Although the initial formation of prostate buds does not require sonic hedgehog signaling (shh), shh is critical for maintaining appropriate prostate growth, proliferation and tissue polarity [11-14]. In the adult prostate, however, the activity of the hedgehog pathway is quite low. It remains to be tested whether this hedgehog pathway is activated during development of prostate cancer, the second most prevalent cause of cancer death in American men. Activation of the hedgehog pathway is often indicated by elevated levels of PTCH1 and HIP. In addition to PTCH1 mutation, SMO activation and hedgehog over-expression, loss of Su(Fu) can result in activation of the hedgehog pathway. In the human, the Su(Fu) gene is localized at chromosome 10q24, a region with LOH in several types of cancer including prostate cancer, lung cancer, breast cancer and medulloblastomas [15,16]. As a negative regulator of the hedgehog pathway, Su(Fu) inhibits the function of Gli molecules, leading to inactivation of this pathway [17-19]. Su(Fu) is also reported to affect beta-catenin function [20]. In addition, over-expression of sonic hedgehog is shown to be involved in the development of GI cancers [9,10]. Here we report our findings that activation of the hedgehog pathway occurs frequently in advanced prostate cancers, possibly through loss of Su(Fu) protein or over-expression of sonic hedgehog.
Results
Elevated expression of hedgehog target genes in prostate cancer specimens
As an important regulator of tissue polarity, active hedgehog signaling is required for ductual morphogenesis and proliferation during prostate development [11-14]. The adult prostate, on the other hand, does not contain active hedgehog signaling. Because hedgehog signaling is an important regulator for epithelial-mesenchymal interaction, an event critical during prostate cancer development, we examined whether the hedgehog-signaling pathway is activated in prostate cancer.
Activation of hedgehog signaling causes elevated expression of target genes PTCH1 and HIP. Thus, increased protein expression of PTCH1 and HIP indicates activation of the hedgehog pathway. Using PTCH1 antibodies [10], we examined 59 prostate cancer samples for hedgehog signaling activation (see Table 1, Additional file 1 for details). We first tested the specificity of the PTCH1 antibodies in MEF cells. Ptch1 null MEF cells have no active Ptch1 gene, thus should not have positive staining with PTCH1 antibodies. Indeed, no staining was seen in Ptch1 null MEF cells (Fig. 1A). After transfection of PTCH1 expressing plasmid, transfected cells showed positive staining (Fig. 1A), indicating that the PTCH1 antibodies are specific to PTCH1. Furthermore, PTCH1 immunohistostining was abolished after addition of the specific peptide, from which the antibodies were raised (Fig. 1B,1c). We found that percentage of PTCH1 positive staining tumors increased in high grade tumors (Table 1, Additional file 1). In prostate cancers with Gleason scores 3–6, 4 out of 18 specimens were positive for PTCH1 (22%), whereas 16 out of 22 undifferentiated carcinomas (Gleason Scores of 8–10) expressed PTCH1 (73%, see Table 1, Additional file 1), suggesting that the hedgehog pathway is frequently activated in advanced prostate cancer. To confirm this data, we found that all four available metastatic prostate cancer specimens were all positive for PTCH1 staining.
Figure 1 Detection of PTCH1 expression in prostate cancers. Protein expression of PTCH1 was detected by immunostaining. PTCH1 antibodies (Santa Cruz Biotechnology Cat# 9149) were tested in Ptch1-/- null MEF cells (A). While Ptch1-/- null MEF cells had no positive fluorescent staining with PTCH1 antibodies, transfection of PTCH1 expressing plasmid lead to positive staining (green, indicated by an arrow, 400×). Immunohistochemistry of prostate cancer specimens with PTCH1 gave negative (B-a, 200×) or positive (Red in B-b, 200×) signals. When PTCH1 antibodies were pre-incubated with the very peptide for raising the antibodies, no positive signals could be observed (B-c).
To further confirm our data, we detected HIP protein expression, another marker of the hedgehog signaling activation. After transfection of HIP expressing plasmid into 293 cells, HIP antibodies recognize a single band around 75 KD (Fig. 3A), and an endogenous HIP protein with a similar size was also detected in two cancer tissues, in which hedgehog signaling is known to be activated (Fig. 3B and data not shown here). In contrast, the matched normal tissue did not express detectable HIP. Thus, HIP expression appears to be a good marker for hedgehog signaling activation. Immunohistostaining with HIP antibodies in prostate cancer specimens revealed a similar pattern to prostate specific antigen (PSA) and PTCH1 (Fig. 3C and Table 1, Additional file 1), further confirming that hedgehog pathway is activated in advanced prostate cancers. Thus the hedgehog pathway appears to be frequently activated in advanced or metastatic prostate cancers.
Figure 3 Detection of HIP in human cancer specimens. By Western blotting, HIP antibodies (R&D systems Cat# AF1568) recognized one band between 75 and100 KD (A). Expression of endogenous HIP was detected in two GI cancer tissues, which were known to contain activated hedgehog signaling (data not shown here), but not in the matched normal tissue (B). Immunohistostaining of HIP I prostate cancer showed a similar pattern to PSA (C, 200×)
Altered expression of Su(Fu) and Shh in prostate cancer specimens
There are several mechanisms by which the hedgehog pathway in these prostate tumors can be activated, including loss of Su(Fu) or over-expression of hedgehog [6-10]. The Su(Fu) gene is localized at 10q24, a region with a frequent LOH in prostate cancer [15,16,18]. Mutations of Su(Fu) have been reported in other human cancers [6]. To test whether loss of Su(Fu) function is responsible for hedgehog signaling activation, we examined expression of Su(Fu) protein in these prostate cancer specimens. The antibodies of Su(Fu) recognize a single band at 52-kD in Western blotting analyses (Fig. 4A), which was reduced following treatment with Su(Fu) SiRNA (Fig. 4B), indicating the specificity of the antibodies. Furthermore, addition of the peptide, from which the antibodies were raised, prevented the antibody binding, further confirming the specificity of our Su(Fu) antibodies (data not shown). Of the 16 PTCH1 positive prostate cancer specimens with Gleason scores 8–10, 9 have no detectable Su(Fu) protein (Fig. 4C,4D,4E and Table 1, Additional file 1). In total, 11 of 27 PTCH1 positive prostate cancer specimens have no detectable Su(Fu) protein. Prostate cancers with low Gleason scores, however, frequently have detectable Su(Fu) protein (see Table 1, Additional file 1), suggesting that loss of Su(Fu) protein may be associated with prostate cancer progression.
Figure 4 Detection of Su(Fu) in prostate cancer specimens. Su(Fu) antibodies (Santa Cruz Biotechnology Cat# 10933) recognized only one single band (54-Kd) in D283 cells (A). Following treatment of a specific SiRNA of Su(Fu), the endogenous Su(Fu) band was greatly reduced (B). Immunohistostaining with Su(Fu) antibodies in prostate cancer specimens revealed positive (C, in red, 200×), negative (D, 200×) or weak staining (E, red, 200×).
To confirm the immunohistochemistry data, we performed immunoblotting analyses using several dissected TURP (Transurethral resection of the prostate) specimens in which tumor portion can be as high as 70% of the tissue mass. Prostatectomy specimens (most of our tumors), however, often contain a small percentage (5–10%) of tumor tissue and are therefore not suitable for Western blotting or real-time PCR analyses. As shown in Fig. 5A, two tumors (PC48 and PC51) had no detectable Su(Fu) protein, which are consistent with our immunohistostaining, suggesting loss of Su(Fu) may be responsible for hedgehog pathway activation in these tumors. The matched normal tissues, however, retained expression of Su(Fu), indicating that alteration of Su(Fu) is a somatic event. Sequence analyses of these two tumors revealed genetic mutations in Su(Fu), which are predicted to create STOP codons in the coding sequence (Fig. 5B and Table 1, Additional file 1). In PC48, a homozygous deletion of A1315 was detected, which results in a STOP codon at +1318 bp (Fig. 5B). In PC51, we detected two types of mutations, one with a deletion of C255, which results in a STOP codon at +294 bp whereas another with a deletion of C198, create a STOP codon (Picture not shown here, see Table 1, Additional file 1). These mutations were confirmed with 6 independent clones from two separate experiments, which exclude the possibility of PCR errors. No mutations were detected from the matched benign tissues, indicating the somatic nature of the mutations. Real-time PCR analyses indicated that target genes of the hedgehog pathway, PTCH1 and Gli1, were all elevated in these tumors (Fig. 5C), confirming activation of the hedgehog pathway in these tumors. Thus, Su(Fu) inactivation appears to contribute to activation of hedgehog signaling in these prostate tumors.
Figure 5 Inactivation of Su(Fu) in prostate cancer. Two TURP (Transurethral resection of the prostate) tumors with loss of Su(Fu) expression were confirmed by Western blotting (A). One mutation of Su(Fu) found in prostate cancer PC48 is shown in B, which is predicted to create a STOP codon in the Su(Fu) coding sequence +1318. The levels of Gli1 and PTCH1 transcripts in prostate tissues were detected by real-time PCR (see methods for details) (C). Tumor tissues had higher levels of the target gene transcripts.
For tumors with high level of PTCH1 expression, but no changes in Su(Fu) protein expression, we examined expression of sonic hedgehog. It is reported that expression of hedgehog may be responsible for hedgehog signaling activation in lung cancer and GI cancers. Immunohistostaining with sonic hedgehog antibodies indicate that sonic hedgehog is highly expressed in 24 of 27 advanced prostate tumors with elevated expression of PTCH1 and HIP (see Fig. 2 and Table 1, Additional file 1). Thus, activation of the hedgehog pathway, as indicated by elevated PTCH1 and HIP expression, is associated with loss of Su(Fu) expression or elevated hedgehog expression.
Figure 2 Co-expression of PTCH1, PSA and Shh in prostate cancer specimens. Immunohistochemistry of prostate cancer specimens with PSA was used to confirm the cancer region. Positive staining was in red. Positive staining patterns of PTCH1 and Shh antibodies (Santa Cruz Biotechnology Cat# 9024) were similar to that of PSA staining. PC23 (A-C) was from tumors with Gleason score 7 (200×). PC38 (D-F) was a tumor from Gleason score 10 (400×) (see Table 1, Additional file 1 for details).
The role for activated hedgehog signaling for cellular functions of prostate cancer
To demonstrate the role of hedgehog pathway in prostate cancer, we screen five available cell lines for the expression of Gli1, PTCH1 and HIP. TSU, LNCap, Du145 and PC3 are prostate cancer cell lines whereas RWPE-1 is a prostate epithelial cell line. We found that the hedgehog target genes were significantly elevated in all cancer cell lines (Fig. 6A). Thus, we predicted that inhibition of the hedgehog pathway by smoothened antagonist, cyclopamine, would suppress cell proliferation and cell invasiveness.
Figure 6 Cellular functions of the hedgehog pathway in prostate cancer cells. Expression of hedgehog target genes, PTCH1 and Gli1, were detected by real-time PCR (A). DNA synthesis was detected by BrdU labeling (B). Over 1000 cells were counted under fluorescent microscope for the percentage of BrdU positive cells, and the experiment was repeated twice (C).
Following treatment with 5 μM cycloapmine in PC3 cells, expression of hedgehog target genes were dramatically inhibited (data not shown here), which was accompanied with a significant reduction of BrdU positive cells (see Fig. 6B for details). This effect is specific because addition of tomotidine, a non specific compound with a similar structure to cycloapmine, had no effects on either target gene expression or DNA synthesis (indicated by BrdU labeling in Fig. 6B and 6C). The prostate epithelial RWPE-1 cells which have no activated hedgehog signaling, on the other hand, were not sensitive to cyclopamine (data not shown here), indicating that cyclopamine specifically affects cells with elevated hedgehog signaling. LN-CAP, Du145 and TSU cells, like PC3 cells were also sensitive to cyclopamine treatment (Fig. 6C).
Prostate cancer progression is accompanied by increased cell invasiveness. Because the hedgehog signaling activation occurs frequently in advanced prostate cancer, we examined if inhibition of the hedgehog signaling can reduce cell invasiveness. Using BD Bio-coat cell invasion chambers, we found that treatment of cyclopamine in PC3 cells reduced the percentage of invasive cells by 70% (Fig. 7A). Similar data were also observed in Du145, LN-CAP and TSU cells (Fig. 7B). Under the same condition, RWPE-1 cells were not very invasive. Thus, hedgehog signaling activation regulates both cell proliferation as well as cell invasiveness of prostate cancer cells.
Figure 7 Effects of cyclopamine on cell invasiveness of prostate cancer cells. Cell invasion assay of prostate cancer cells was performed using BD Bio-coat cell invasion chambers (A). The rate of cell invasion was calculated by dividing cell numbers penetrated the matrigels by the number of cell in the control chambers (without matrigels) (B).
It has been shown that cyclopamine induced apoptosis in cancer cells with activated hedgehog signaling [21]. We have shown that Gli1 down-regulation is necessary for cyclopamine-mediated apoptosis in basal cell carcinoma cells [21]. To test the significant role of Gli1, the down-stream effector and the target gene of the hedgehog pathway, in cyclopamine-mediated apoptosis, we first transfected Gli1 expressing plasmid in to PC3 cells, and then treated the cells with 5 μM cyclopamine for 36 h. Since Gli1 is expressed under the control of the CMV promoter, we predicted that ectopic Gli1-expressing cells should be resistant to apoptosis, which is detected by TUNEL staining. As shown in Fig. 8, we found that all Gli1 positive cells (n = 500) were TUNEL negative, supporting our hypothesis that down-regulation of Gli1 may be an important mechanism by which cyclopamine mediates apoptosis in prostate cancer cells with activated hedgehog signaling.
Figure 8 Cyclopamine induces apoptosis in prostate cancer cells. Cyclopamine-mediated apoptosis in prostate cancer cells was analyzed by TUNEL assay. TUNEL positive cells were indicated by arrowheads. Cells with expression of Gli1 under the CMV promoter (indicated by the arrows) did not undergo apoptosis (n = 500).
All these data indicate that the hedgehog pathway is activated in advanced prostate cancers, as indicated by high expression of PTCH1 and HIP. Our results also indicate that hedgehog signaling is required for cell proliferation and cell invasion of prostate cancer cells. Thus, targeted inhibition of the hedgehog pathway may be effective in future prostate cancer therapeutics.
Discussion
Hedgehog signaling pathway regulates cell proliferation, tissue polarity and cell differentiation during normal development. Abnormal signaling of this pathway has been reported in a variety of human cancers, including basal cell carcinomas, medulloblastomas, small cell lung cancer and GI cancers [3,4,6-10,22,23]. Our findings in this report indicate a role of the sonic hedgehog pathway in prostate cancer. We detected a high expression of hedgehog target genes, PTCH1 and HIP, in advanced or metastatic prostate cancers. In contrast, only 22% of prostate tumors with Gleason scores 3–6 have elevated expression of PTCH1 and HIP. While our manuscript is being reviewed, three independent groups have recently reported similar results [24-26]. Thus, the hedgehog signaling pathway is frequently activated in advanced or metastatic prostate cancers.
Alterations of genes in the hedgehog pathway in prostate cancer
In our studies, we found that some prostate tumors had no detectable Su(Fu) protein expression while others contained high levels of Shh protein expression. We further identified inactivated mutations of Su(Fu) in two prostate cancers. In addition to inactivated mutations in the coding region, Su(Fu) may be inactivated through promoter methylation. The heterogeneous nature of prostate cancer makes it difficult to screen prostate cancer specimens for Su(Fu) mutations since the tumor content is often less than 5% of the specimens. Future improvement can be achieved using microdissection techniques for collecting pure population of tumor cells in gene mutation analysis.
Since all available prostate cancer cell lines express Su(Fu) at a high level, the role of Su(Fu) on cellular functions of prostate cancer cannot be investigated in these cells. It appears that over-expression of sonic hedgehog may be responsible for hedgehog signaling activation in these cell lines [our unpublished data and [24-26]]. After screening over 30 human cancer cell lines, we identified non-prostate cancer cell line with elevated hedgehog target genes and no detectable Su(Fu) expression (data not shown here). The growth suppression effects of Su(Fu) was demonstrated in this cell line, in which Su(Fu) expression down-regulated hedgehog target genes, inhibited DNA synthesis and cell growth (data not shown here). Thus, inactivation of Su(Fu) can contribute to active hedgehog signaling in prostate cancer.
Su(Fu) is reported to affect β-catenin signaling [27,28]. We analyzed expression of β-catenin and E-cadherin in our prostate cancer array and detected cytoplasmic distribution of E-cadherin and β-catenin only in PC51 (data not shown), indicating that Su(Fu) may be able to affect both the wnt pathway and the hedgehog pathway in prostate cancer.
In addition to Su(Fu) inactivation, over-expression of Shh is another mechanism by which the hedgehog pathway is activated in cancer [7-10]. We noticed that sonic hedgehog expression varies from tumor to tumor, which may be resulted from the heterogeneity of prostate cancer. Our immunohistostaining also revealed that three tumors (PC14, PC20 and PC36) expressed PTCH1 and HIP at high levels, but had no alterations of Shh and Su(Fu). This could be due to elevated expression of indian hedgehog, or even alterations of other components of the pathway (such as Rab23 or Fused).
Once hedgehog pathway is activated, the target gene expression will be up-regulated. Thus, analysis of target gene expression using immunohistochemistry will be an effective way to detect hedgehog pathway activation in prostate cancer. Currently, PTCH1, Gli1 and HIP are good markers for the hedgehog pathway.
Perspectives on prostate cancer therapy
Our findings not only provide novel basic understanding of prostate cancer, but also allow us to design new ways to treat prostate cancer. With a specific SMO antagonist, cyclopamine, it will be possible in the future to treat prostate cancers, which have over-expressed sonic hedgehog. However, as a downstream molecule, tumors with Su(Fu) inactivation may not respond to cyclopamine treatment. Therefore, additional small molecule inhibitors appear to be necessary to treat Su(Fu) inactivated prostate cancer. One possibility is to use Gli1 SiRNA since we have indicated that down-regulation of Gli1 may be an important mechanism by which inhibition of the hedgehog pathway by cyclopamine induces apoptosis (Fig. 8). Sanchez et al also indicated that Gli1 SiRNA down-regulated DNA synthesis in prostate cancer cells [24].
Conclusion
Taken together, our findings suggest that activation of the hedgehog pathway involves prostate cancer progression. There might be several mechanisms by which the hedgehog pathway is activated in advanced prostate cancers, including loss of Su(Fu) protein expression, over-expression of sonic hedgehog or other alterations. We demonstrate that activation of the hedgehog pathway is associated with DNA synthesis and cell invasiveness in prostate cancer cells. Inhibition of the hedgehog pathway, on the other hand, causes apoptosis possibly through down-regulation of Gli1. Our studies predict that targeted inhibition of the hedgehog pathway may be an effective way to prevent prostate cancer progression.
Materials and methods
Tissue Microarray of Prostate Cancer
A total of 55 paraffin-embedded tissue blocks from patients with prostate cancer were obtained from UTMB Surgical pathology with approval from the Institutional Review Board (IRB). Pathological reports, H#E staining of each specimen were reviewed to determine the nature of the disease and the Gleason scores. Of 55 specimens, 18 were from tumors with Gleason scores 3–6, 15 with Gleason score 7 and 22 with Gleason scores 8–10. The tumor area was first identified before tissue microarray (1.5 mm in diameter for specimens) was assembled with Beecher's Tissue arrayer-I® according to manufacturer's instruction .
Immunohistochemistry and Western blotting
A standard avidin-biotin immunostaining technique was performed using a kit from Vector laboratories using specific antibodies to Su(Fu) (Santa Cruz Biotechnology Cat# 10933), PTCH1 (Santa Cruz Biotechnology Cat# 6149), HIP (R&D systems Cat# AF1568) and Shh (Santa Cruz Biotechnology Cat# 9024) and PSA (Vector laboratories). Positive staining was in red or brown. The specificity of antibodies was tested using the very peptide used for raising the antibodies, which abolished the specific staining. Hematoxylin was used for counterstaining (in blue). Protein was analyzed by Western analysis with appropriate antibodies [Su(Fu) antibodies were from Santa Cruz, beta-actin antibody was purchased from Sigma). The signals were visualized with the enhanced chemiluminescence detection system (Amersham).
Cell lines and Cell invasion assay
Cell lines (RWPE-1, Du145, PC3, LN-CAP were purchase from ATCC and cultured according to the suggested conditions. TSU was kindly provided by Dr. Allen Gao. Cell invasion assay was performed with BD Bio-coat cell invasion chambers according to manufacturer's instruction (BD Bioscience, Inc., Franklin Lakes, NJ), with triplicates for each sample and the experiment was repeated three times with the similar results. Cell were treated with 5 μM cyclopamine (or tomatidine) before (for 12 h) and during cell invasion assay (for 24 h). The rate of cell invasion was calculated by dividing cell numbers penetrated the matrigels by the number of cell in the control chambers (without matrigels).
RT-PCR and sequencing analysis
Total RNA was isolated using Trizol® reagent (Invitrogen), and RT-PCR was performed using Promega's RT-PCR system according to the manufacturer's protocol. Two pairs of Su(Fu) primers were used (the first set with the forward primer 5'-cctacgcaccccgatggcg-3" and the reverse primer 5'-agccaaaaccactacctcca-3'; the second set with the forward primer 5'-tccaggttaccgctatcgtc-3' ad the reverse primer 5'-tagtgtagcggactgtcg-3'). PCR products were first purified using Qiagen's Gel Extraction Kit. Due to existence of possible Su(Fu) splicing isoforms in humans, Su(Fu) genetic mutations were screened after the PCR products were cloned into TOPO® TA cloning vectors (Invitrogen). Several independent clones (from three experiments) of each PCR product were selected for sequencing analysis in UTMB sequencing facility. All mutations were confirmed by at least six independent clones.
Real-time PCR We used Applied Biosystems' assays-by-demand 20× assay mix of primers and TaqMan probes (FAM™ dye-labeled) for the target genes (human Gli and PTCH1, the sequences have been patented by Applied Biosystems, Foster City, CA) and pre-developed 18S rRNA (VIC™-dye labled probe) TaqMan® assay reagent (P/N 4319413E) for an internal control. The primers are designed to span exon-exon junctions so as not to detect genomic DNA and the primers and probe sequences were searched against the Celera database to confirm specificity. To obtain the relative quantitation of gene expression, a validation experiment was performed to test the efficiency of the target amplification and the efficiency of the reference amplification. All absolute values of the slope of log input amount vs. ΔCT were <0.1. Separate tubes (singleplex) one-step RT-PCR was performed with 20 ng RNA for both target genes and endogenous control. The reagent we used was TaqMan one-step RT-PCR master mix reagent kit (P/N 4309169). The cycling parameters for one-step RT-PCR was: reverse transcription 48°C for 30 min, AmpliTaq activation 95°C for 10 min, denaturation 95°C for 15 sec and annealing/extension 60°C for 1 min (repeat 40 times) on ABI7000. Triplicate CT values were analyzed in Microsoft Excel using the comparative CT(ΔΔCT) method as described by the manufacturer(Applied Biosystems, Foster City, CA). The amount of target (2-ΔΔCT) was obtained by normalization to an endogenous reference (18sRNA) and relative to a calibrator.
BrdU labeling and TUNEL assay
BrdU labeling was performed using an in situ cell proliferation kit (Roche Molecular Biochemicals) [22]. Cells were treated with 5μM cyclopamine (or tomatidine) for 12 h before BrdU labeling (1 h at 37°C). The percentage of BrdU positive cells was obtained by counting over 1000 cells under microscope, and the experiment was repeated twice with similar results. TUNEL assay was performed using an in situ cell death kit (Roche Molecular Biochemicals) [21,29]. Cells were treated with 5 μM cyclopamine (or tomatidine) for 36 h before TUNEL assay).
List of abbreviations
PSA – prostate specific antigen; HIP – hedgehog-interacting protein; Su(Fu) – suppressor of fused; PTCH1 – human homologue of patched 1; Shh – sonic hedgehog; SMO – smoothened, BCC – basal cell carcinoma.
Authors' contributions
Tao Sheng contributed to Figures 6, 7, 8, cellular functions of the hedgehog pathway in prostate cancer cells. Chegxin Li contributed to primary tumor protein expression, particularly on Su(Fu) expression. Xiaoli Zhang contributed to mutation analyses of Su(Fu) in prostate cancer and real-time PCR analyses. Sumin Chi contributed to HIP antibody test (Fig. 3A and 3B). Nonggao He contributed to HIP antibody staining (Fig. 3C). Kai Chen contributed to PTCH1 antibody test (Fig. 1A). Frank McCormick involved in the initial project discussion. Zoran Gatalica contributed to prostate cancer histology and Gleason scores of the tumors.
Supplementary Material
Additional File 1
Table 1 Prostate cancer specimens and protein expression. Prostate cancer specimens and expression of several hedgehog signaling proteins are summarized in this table (A). A total of 55 specimens were used in this study. The Gleason scores and protein expression of Shh, PTCH1 and Su(Fu) are shown (B).
Click here for file
Acknowledgements
we thank Dr. Huiping Guo for technical support of real-time PCR. J.X. was supported by grants from a NIH R01 grant, a DOD grant and the Sealy Foundation for biomedical Sciences.
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| 15482598 | PMC524523 | CC BY | 2021-01-04 16:36:33 | no | Mol Cancer. 2004 Oct 13; 3:29 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-29 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1441546961210.1186/1471-2105-5-144Research ArticleOverdispersed logistic regression for SAGE: Modelling multiple groups and covariates Baggerly Keith A [email protected] Li [email protected] Jeffrey S [email protected] C Marcelo [email protected] Department of Biostatistics and Applied Mathematics, UT M. D. Anderson Cancer Center, Houston, TX, USA2 Department of Statistics, Rice University, Houston, TX, USA3 Department of Carcinogenesis, UT M. D. Anderson Cancer Center, Houston, TX, USA2004 6 10 2004 5 144 144 25 2 2004 6 10 2004 Copyright © 2004 Baggerly 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
Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed.
Results
We describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages.
Conclusions
The described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling.
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Background
The nature of SAGE
The Serial Analysis of Gene Expression (SAGE) methodology introduced by Velculescu et al. [1] is a sequencing-based approach to the measurement of gene expression.
Briefly, mRNA transcripts are converted to cDNA and then processed so as to isolate a specific subsequence; starting from the poly-A tail, the subsequence is the 10 (normal SAGE) or 14 (long SAGE) bp immediately preceding the first occurrence of a cleavage site for a common restriction enzyme. Ideally, this subsequence, or "tag" is sufficiently specific to uniquely identify the mRNA from which it was derived. Tags are sampled, concatenated and sequenced, and a table consisting of the tag sequences and their frequency of occurrence is assembled. The complete table derived from a given biological sample is referred to as a SAGE "library". As most tags are sparse within the entire sample, most libraries contain numbers of tags in the tens of thousands to allow the expression levels to be estimated. Due to the current costs of sequencing, however, the total number of libraries assembled for a given experiment is typically small: often in the single digits and occasionally in the tens.
While the type of information, gene expression, being investigated in a SAGE experiment is the same as that in a cDNA or oligonucleotide microarray experiment, there are some qualitative differences in the approaches. First, SAGE uses sequencing as opposed to competitive hybridization. Second, while the expression value reported for an array experiment is a measure of fluourescence and is loosely continuous, SAGE supplies data on gene expression in the form of counts, potentially allowing for a different type of "quantitative" comparison. Third, SAGE is an "open" technology in that it can provide information about all of the genes in the sample. Microarrays, by contrast, are "closed" in that we will only get information about the genes that have been printed on the array.
Mathematically, the information pertaining to the abundance of a particular tag in a sample is summarized in two numbers: Y, the number of counts of that tag in the library, and n, the total number of tags in the library. In analyzing SAGE data across a series of libraries, interest typically centers on assessing how the underlying true level of gene expression is changing as we move from one library to the next.
Mathematical formulation of the differential expression problem
When surveyed across a series of libraries, the sufficient statistics containing all of the information about the change in expression for a single tag are the set of counts {Yi} and the set of library sizes {ni}, where the subscript i denotes the specific library. Unless otherwise specified, we will restrict our assessment of differential expression to the case of a single tag. This approach is common to all of the procedures described below. In a real analysis the chosen test is applied to all tags individually and a list of those tags showing differential expression is reported. Different tests will provide altered assessments of significance for individual tags, and hence the list provided will depend on the test employed.
In most problems of interest, there is also covariate information Xi describing properties of library i. The most common case involves comparing two groups of libraries, such as cancer and control. In this case the information Xi simply defines which group library i belongs to. If there are more than two groups, Xi can have more levels or can even be vector valued, but as before interest centers on assessing how and whether the expected proportion changes with X.
Much work has been done on the problem of comparing expression between two groups. Most of the approaches [2-9] deal with comparing one library with another. Of these, [2,6,7] extend their consideration to the case of two groups of libraries by pooling the libraries within a group, effectively reducing the sufficient statistics to the summed counts
This approach, while it captures the count nature of the data, loses information in that variation of the proportions within a group is ignored. As noted by both Man et al. [9] and Ruijter et al. [10], most of the above tests give equivalent results in terms of assessing significant differences. By contrast, the two-sample t-test used to compare two groups of samples in [11] reduces the sufficient statistics to the set of proportions {pi} = {Yi/ni}, capturing the variation between members of a group but losing track of the inherent count sampling nature and variability of the data. The two-sample t-test results can be dramatically different from the pooled test results, as they focus on two different types of variation. The effects of these two approaches on a single group of four libraries are shown in Table 1. Pooling reduces the data to the summed counts at the right, and focusing on proportions reduces the data to the proportions on the bottom. In both cases, this reduction results in a loss of information. When pooling is used, we can't tell that one of the group proportions was large and the other small, indicating instability. When proportions are used, we can't tell that one library was much smaller than the other, so that proportion should be "trusted less" than the other.
Baggerly et al. [12] proposed a beta-binomial hierarchical model for SAGE data in an attempt to simultaneously model both types of variation. This model leads to a test statistic called a weighted two-sample t-test, tw. Computing the value of this test statistic requires all 8 of the numbers in the main body of Table 1; there is no reduction of the sufficient statistics. This test statistic exhibits different behaviors depending on which type of variation is larger for a given tag. When the within-library sampling variation is much larger than the between-library variation, tw gives results close to those supplied by pooling tests, which focus on within-library variation. Conversely, when the between-library variation is much larger than the within-library variation, tw gives results very similar to those of a two-sample t-test, which focuses on between-library variation. The tw model also allows the relative contributions of the two types of variation to be assessed. Baggerly et al. [12] found that for high-count tags, between-library heterogeneity is the much larger source of variation and that pooling methods which do not allow for heterogeneity are biased towards finding high count tags to be significantly different. This can potentially lead to large fractions of false positives, as becomes apparent when the results for several different tags are plotted.
Extensions to multiple groups
While cases with more than two groups have been described in the literature [2,13-15], the means of analysis is currently something of a hybrid. Methods explicitly attacking the multi-library problem have been proposed [16,17], but the most common approach at present [13,15] seems to involve coupling hierarchical clustering of the data with pairwise tests for differential expression [2] between one group and another. This hybrid approach can indirectly capture both types of variability, with the hierarchical clustering focused on the variation between proportions within a group, and the pairwise test focusing on sampling variation. Clustering has other benefits for clarifying thought apart from assessing differential expression, and we definitely recommend it for exploring the structure of the data. However, clustering tends not to provide a numerical summary, so combining the clustering results with those of the pairwise comparisons can be something of an art. An additional drawback is that the pairwise comparisons may miss useful information about variability by focusing only on a subset of the libraries available. For the purposes of assessing differential expression we believe more efficient tests are available.
Our approach: Overdispersed logistic regression
We seek to construct a method that takes the count nature of the data into account, deals with multiple groups simultaneously, and allows for variability in the proportions beyond that due to sampling alone. Fortunately, this is not the first time such a problem has arisen.
The problem of assessing differential expression for multiple groups corresponds to the classical statistical problem of the analysis of variance (ANOVA). When the values of interest are continuous (e.g., microarray log ratios), the test statistics become F-tests, higher-dimensional generalizations of the two-sample t-test. When the data are counts (SAGE data), and sampling variability needs to be dealt with, the ANOVA test can be adapted to give logistic or Poisson ANOVA. The multi-library test for differential expression proposed by Stekel et al. [17] corresponds to Poisson ANOVA, but without allowance for overdispersion. ANOVA deals with the extension from two to a larger number of distinct groups, but this can be viewed as a special case of the situation where the covariate information is continuous. One common way of modelling the dependence of proportions upon covariates is through logistic or Poisson regression, both of which are special cases of generalized linear models [18,19]. Such models incorporate the form of the sampling variability directly. For example, the logistic model for proportions,
defines both the function of the data that is to be modeled in terms of the covariates (the logit of the proportions) and the precision of each of the measurements. The maximum likelihood estimates of the parameters of this model can be found through iteratively reweighted least squares (IRLS).
Excess variation within a level, or overdispersion, can be introduced into a logistic regression framework in a number of ways. The most common and most widely implemented approach is to replace the binomial likelihood function being maximized above with a "quasi-likelihood" which differs from the initial formulation solely through the introduction of a scale term, , into the variance equation, so that
V(Yi) = nipi(1 - pi).
This approach has the advantage that it inflates the variance of each of the observations by a like amount, so that the estimated values will be the same – just the associated standard errors will be inflated. Logistic regression with quasi-likelihood overdispersion is implemented in a wide variety of statistical packages, including S-PLUS, R, GLIM, and SAS. Another method of introducing overdispersion is to assume a hierarchical model in which the proportions at a given level of the covariate are drawn from a nondegenerate distribution, and the distribution of the observed counts is binomial conditional on the value of the drawn proportion. When a beta distribution is assumed for the proportions, the final unconditional distribution of the observed counts is beta-binomial. This is the model suggested by Baggerly et al. [12] for modelling overdispersion in SAGE data, and is also the model used by Crowder [20] in generalizing ANOVA to deal with proportions subject to overdispersion. It can be shown (eg, Collett [18] p.201) that the variance of beta-binomial counts is of the form
V(Yi) = nipi(1 - pi)[1 + (ni - 1)φ],
which is equivalent to the quasi-likelihood formulation when all of the library sizes ni are the same. While approximate equality may suffice, even this assumption may be questionable for SAGE data, particularly if some of the libraries are drawn from experiments conducted at different times. Williams [21] shows how IRLS can be adapted to deal with this type of overdispersion, and notes that estimation involves φ only and need not assume further structure from the beta distribution, making the procedure slightly more general. This form of overdispersion is implemented in R as part of the dispmod package.
In the logistic regression framework, assessing differential expression reduces to a case of deciding whether a set of regression coefficients is different from zero. This can lead to slightly different inferences than models such as t-statistics applied to the proportions, in that approximate normality is assumed to hold for the β values rather than the proportions themselves. When we have worked with a beta model for the pi's, we have been led to choices of parameters which yield quite skewed distributions, suggesting that the logit scale may be more appropriate. Working with the β values has the additional advantage that confidence intervals are naturally interpreted in terms of fold changes.
Results
Comparing two groups
We begin by comparing the counts of the tag ATTTGAGAAG in 8 colon libraries initially described in Zhang et al. [2]. These 8 libraries include two normal colon (NC1 and NC2), two primary tumors (TU98 and TU102) and four cell lines (CACO2, HCT116, RKO, and SW837). For now, we focus on comparing normal colon with all tumors, primary or cell line. Counts of the tag and the corresponding library sizes are given in Table 2. A χ2 test applied to the pooled counts from each of the two groups yields a test statistic of 444.27; the 95% cutoff for the null distribution is 3.84, with values above this being deemed "significant". The two-sample t-test applied to the two groups of proportions yields 1.60; the 95% cutoffs for the null t6 distribution are ± 2.45, so this test suggests that the difference is not significant, showing the possibility of stark disagreements between tests focusing on different portions of the variability. The tw statistic proposed by Baggerly et al. [12], which incorporates both types of variance, yields a test statistic of 1.60. The null distribution of this test statistic in this case is approximately a t6 distribution, and the qualitative results are far closer to those of the t-test than those of the pooled tests, reflecting the relative dominance of patient heterogeneity in driving the total variation for this tag. We note in passing that this disagreement between the two types of tests is not an isolated incident. When we surveyed all of the tags in this group of libraries we found 10 tags with |t| < 2 and χ2 > 200, and 48 tags with |t| < 2 and χ2 > 50. In Baggerly et al. [12] it was found that most high-count tags appeared significantly different when a pooled test was used and not significant when a t-test was tried, and that in this case the t-test was more likely correct.
The results from three logistic regression model fits to the data are shown in Table 3. In the first model there is no allowance for overdispersion, in the second the quasi-likelihood approach to overdispersion is employed, and in the third the hierarchical approach to overdispersion is used. Here, the values of the covariate X are 0 or 1 as the library is in the first or second group, respectively. In models 1 and 2, the fitted proportions are = e-4.66/(l + e-4.66) = 0.94% and = e-4.66 - 0.89/(l + e-4.66 - 0.89) = 0.39% for the first and second groups, respectively, and the proportions are only slightly altered in model 3. We note that the estimated coefficient values are exactly the same for the first two models, and this is true for these two approaches in general. Fitting the model with no allowance for overdispersion gives a z-value of β1/s.e. (β1) = -20.42, which is definitely significant. Note that the square of this value is of the same order as the value found by the χ2 test. The Pearson residuals from this model, , however, show a problem. If the model fits well, these should be approximately distributed as a standard normal, with extreme values from a set of 8 observations around 3 or 4 in magnitude. The actual values, -14.6 and 19.0, are far too extreme. When the model is fit with allowance made for overdispersion, the point estimate of the dispersion parameter is = 187.57; this value should be close to 1 if there is no overdispersion. With this allowance made the t-value of -1.49 is no longer significant. This t-value can be found from the first z-value (-20.42) by dividing by = 13.70. Similarly scaling the residuals yields values far more commensurate with a standard normal. We note that due to the differences in the models employed, the presumed distributions of the test statistics have changed. If we assume that the standard logistic model with no overdispersion holds, the test statistic has an approximately normal distribution. This is because the number of total successes is driving the binomial distributions to approximate normality. When we shift to a model where we presume the existence of overdispersion, the test statistic now has a t distribution. This is because our estimate of the variance is now strongly dependent on the precision with which we can estimate the overdispersion parameter, and this precision depends on the number of libraries, not the number of successes. Fitting this model with the hierarchical type of overdispersion, model 3 in Table 3, yields slightly different answers but the size of is still not significant. The difference in values from those found before is due to the fact that in this model the amount of overdispersion attributed to each proportion changes slightly with library size, thus altering the weights used in the regression model. The point estimate for the hierarchical dispersion parameter φ is = 3.399e - 03, so the multipliers for the binomial variances are
1 + ({ni} - 1) =
(169.62, 165.78, 141.62, 190.32,
207.26, 190.12, 175.35, 208.84).
averaging these gives 181.11 which is close to the value found for the quasi-likelihood dispersion parameter. We note that the differences in coefficient values for models 2 and 3 are largely cosmetic, but the differences in significance between model 1 and the others are not. Choosing to account for overdispersion is more important than the precise model used to achieve this.
We note that the overdispersed logistic regression approaches give t-values about -1.49, whereas the two-sample t-test and the modified version tw suggested by Baggerly et al. [12] both give t-values of about -1.6 (as noted earlier, agreement between t and tw suggests that for this tag, the between-library variation is much larger than the within-library variation). There are two reasons for this difference. First, the t statistic works on the proportion scale, and logistic regression works on the β scale, which is roughly the log proportion scale. Second, the tw statistic used here,
does not assume that the overdispersion factor is the same in the two groups being compared; the variance estimate is not pooled. The latter difference is actually the more important for this contrast, particularly as the variance estimate from the first group of size 2 is very unstable. This effect is not always subtle; if we consider instead the tag GCGAAACCCT, with counts given in Table 2, the two-sample t test and the weighted tw test both give -1.57, and the logistic regression t value is -4.16.
Of the two answers, we tend to prefer the one given by the logistic regression fit, for two reasons. First, when we have fit the parameters of the beta distributions for the proportions directly, we have found the distributions to be quite skewed. As such we find it better to assume rough normality on the β coefficient scale. Second, when the number of libraries in a group is quite small, which will often be the case with SAGE data, we prefer the pooled estimate of the variance. This preference is due in large part to its greater stability through the use of more degrees of freedom. It is possible to explicitly incorporate levels of overdispersion that change with the covariates in logistic regression, but we have not pursued this here.
Comparing three or more groups
Above, we treated the colon libraries as if they came from two groups, but it is more natural to view them as coming from three: normal samples, primary tumors, and cell lines. When we have data from multiple groups, there are two different ways in which this changes the nature of the problem. First, if we are only interested in comparing two of the groups, it is often nonetheless worthwhile to incorporate the data from the other groups into the model. The reason for this is that when overdispersion is driving the variance, the significance of our results depends strongly on the precision with which we can estimate the overdispersion parameter. The libraries in the groups not directly involved in the comparison of interest can still supply information about the overdispersion parameter and increase the degrees of freedom of the associated t-test. Second, by examining the fitted proportions for all groups, the relative sizes of the transitions can be assessed.
We begin by looking at the results for a single tag flagged as interesting in the paper by Zhang et al. [2], namely TGCTGCCTGT, where we presume that the contrast of most interest is between normal colon and primary tumors. The counts for this tag and the corresponding library sizes are given in Table 2.
We first attempt to compare the levels in normal colon and primary tumor while ignoring the cell lines (ie, using just four libraries), and then using a model incorporating all three groups. The results using logistic regression with hierarchical overdispersion are shown in Table 4.
In the model with only two groups, we have a single covariate vector x1 = (0,0,1,1) denoting which of the two groups the library belongs to. This model produces an overdispersion estimate of = 8.938e - 05, for inflation factors of
1 + ({ni} - 1) = (5.43, 5.33, 4.70, 5.98).
The fact that these factors are significantly larger than one suggests that the within-group heterogeneity is the dominant component of the variance not explained by the model. In the model with three groups, we cannot use a single covariate vector x1, as this is not suited to indicating 3 or more groups in an unordered fashion (using 0, 1, and 2 for the three groups respectively would force an ordering by saying that primary tumors are intermediate betwixt normal samples and cell lines). In general, if we have k groups, we need to use k - 1 covariate vectors. Here, we use x1 = (0, 0, 1, 1, 0, 0, 0, 0) and x2 = (0, 0, 0, 0, 1, 1, 1, 1). The set of all 0s (x1 = 0, x2 = 0) corresponds to the first group, here normal colon, and the other groups are defined by which one of the other covariates is nonzero: Group 2 (primaries), (x1 = 1, x2 = 0), Group 3 (cell lines), (x1 = 0, x2 = 1) As we are still focused on the difference between normal colon and primary tumors, for which the logit values are β0 and β0 + β1 respectively, the main interest remains on whether β1 is significantly different from zero, and the predicted logit for the cell line group, β0 + β2, does not enter the problem directly. Fitting this model produces an overdispersion estimate of = 1.160e - 04, for inflation factors of
1 + ({ni} - 1) =
(6.76, 6.62, 5.80, 7.46,
8.04, 7.45, 6.95, 8.09).
In neither case (considering two groups or three) does the contrast between normal colon and primary tumor, represented as the magnitude of , appear significant once allowance is made for overdispersion, but there is an interesting point to note. Even though the point estimate of overdispersion increases when the cell lines are included, and the value of the t-statistic (/s.e()) associated with the difference declines, the associated p-value indicates an increase in significance. Without using the cell lines, we have just 4 libraries, and after estimating the mean proportions in each group just 2 degrees of freedom for estimating φ. When we use the cell lines, we have 8 libraries and 5 degrees of freedom for estimating φ. Thus, the degrees of freedom in the t-tests shift from 2 to 5. The t2 distribution has very wide cutoffs, and the t5 is much closer to normal. In general, the inclusion of related groups can improve estimation by increasing the precision of our estimate of overdispersion.
In fitting the model with three groups, of course, we have also gained the ability to look at other contrasts. For example, we can look at normal colon versus cell lines, for which the logits are β0 and β0 + β2 respectively, by checking the significance of . Likewise, we can look at the difference between primary tumors and cell lines, for which the logits are β0 + β1 and β0 + β2 ,by testing the significance of the difference . While this significance is not listed in the table directly, we can compute the standard error of this contrast, s.e. , divide the estimate by its standard error to get a t-statistic with the degrees of freedom listed (here 5), and compute a p-value accordingly.
It is also possible to perform an omnibus test of whether there exists any significant difference among the groups, which is logistic ANOVA for proportions. The regular ANOVA test looks at the amount of variance explained by the terms of interest in the model and compares this to the amount of residual variance. Adjusting for the degrees of freedom in each group gives an F-test. When dealing with generalized linear models, the quantity -2 * log (likelihood ratio), known as the deviance, plays a role analogous to the variance in ANOVA and thus we can speak of the analysis of deviance. The analysis of deviance is complicated by the inclusion of overdispersion in the model, requiring a multi-step approach in which several different models are fit in succession. These models are listed in Table 5. First, the model is fit using all available covariates and the overdispersion parameter is estimated. Here, the available covariates are x1 and x2, and fitting the full model with both present, β0 + β1 + β2, gives = 1.160e - 04 as noted above. Second, submodels are fit with the value for overdispersion fed in as fixed. In this case, the submodels are β0 + β2, using x2 as the only covariate, β0 + β1, using x1 as the only covariate, and β0, using no covariates and simply fitting a single proportion to all of the data. The results are shown in Table 5, from which the significance of a given model can be assessed by comparing the scaled reduction in deviance with the scaled residual deviance to the appropriate F distribution. Here, for example, testing whether the overall model including β1 and β2 explains things significantly better than just fitting the same proportion throughout (β0) reduces to
indicating that the overall difference between groups is not significant at the 5% level. It may be noted that the submodel including just β1 in addition to the constant appears to explain very little; this is due to the way in which we have chosen the entries of X, so that including β1 isolates the effect of the primary tumor group, but excluding β2 still combines the normal colon group with the cell line group for the contrast. This latter grouping blurs the normal colon vs primary tumor distinction found to be a bit larger earlier.
Incorporating other covariates
It is possible to use the logistic regression approach to partition the variance amongst multiple effects of interest. For example, in the above section we considered a case with colon libraries taken from both primary tumors and cell lines. Such data is also available for other organs, eg pancreas. If we are interested in identifying consistent differences between primary tumors and cell lines, it would be natural to use libraries from both organ types. However, if these were then compared as two groups, primary vs cell lines, the differences would be difficult to isolate due to the large differences between tissue types within both the primary and cell line groups. The solution is to fit a model with two covariates, with x1 being 0 or 1 as the sample is colon or pancreatic, respectively, and x2 being 0 or 1 as the sample is a primary tumor or a cell line, respectively. Inference reduces to testing the significance of β2, with the scale of natural variation being assessed only after the effects of the change in tissue type, β1, have been factored out.
In the above example, we allowed for the effect of one other effect, tissue type. In principle, multiple factors can be allowed for through the inclusion of other covariates. Likewise, though the two covariates in the above example were both "factors" having a finite number of unordered levels, it is possible to include continuous covariates in the modelling process as well.
To illustrate this, we give two hypothetical examples using the counts for the GCGAAACCCT tag from Table 2. In the first example, we posit that we are trying to assess the differences between normal tissue and primary tumors, that the first 4 libraries come from normal colon and primary tumors as indicated, and that the remaining 4 libraries come not from cell lines but rather from normal tissue (libraries 5 and 6) and primary tumor (libraries 7 and 8) from some other organ. As noted above, this leads to a scenario where we want to fit a model with two covariates: x1 = (0, 0, 1, 1, 0, 0, 1, 1), indicating whether the library is normal (0) or primary tumor (1), and x2 = (0, 0, 0, 0, 1, 1, 1, 1), indicating the organ from which the library was derived. In the second example, we posit that in addition to the above information, we have access to the levels of a biomarker potentially predictive of survival. These levels are supplied as the values of a third covariate vector, x3 = (0.89, 0.35, 0.66, 0.23, 0.30, 0.54, 0.90, 0.90). The values for x3 were generated as random draws from a uniform distribution. In terms of fitting the models, the mechanics are similar to those presented earlier. The model fits are presented in Table 6.
When logistic regression breaks
The logistic regression fitting procedure can break down, or exhibit lack of convergence. Typically this means that all of the proportions in one of the groups are zero or one; only the former is realistic in the context of SAGE data. This is natural, in that the maximum likelihood point estimate for the group proportion is 0, and inference for β involves the fold change to the proportion in the second group, leading to division by zero. When the proportions are this small, the binomial variability dominates the heterogeneity and the values are completely noninformative with respect to the estimation of overdispersion.
We propose a fix that is iterative in nature in that it requires the logistic fitting routine to be run three times. To illustrate this procedure, we will use the data from tag ATTTGAGAAG in Table 2, with the first two tag counts, those from group 1, set to zero.
The first run of the fitting procedure serves to estimate the overdispersion parameter. This fit uses just the groups that have nonzero counts, omitting the problematic group(s). Here, this involves fitting a single proportion to the six libraries in group 2. The fitted proportion is 0.40%, and the overdispersion estimate is = 3.71e - 03.
The second run of the fitting procedure takes the overdispersion parameter as given, and fits the data after replacing the zero proportions in a group with the same small nonzero proportion, giving us a hopefully conservative estimate of the fold change. This type of replacement is commonly used, and is most often justified via the assumption of a vague prior distribution for the proportions, with the point estimate being derived as the posterior mean or mode. A common assumption for a prior in dealing with proportions is the uniform distribution. The posterior mean after 0 successes are observed out of ni trials is 1/(ni + 1); with multiple trials, it is 1/((∑ni) + 1). This is the value we use. This value is actually quite conservative here, for two reasons. First, the uniform distribution places far too much chance on the possibility of proportions greater than a few percent, which will never be observed with SAGE data. Restricting the distribution to be uniform over the range [0, 0.02] should be more than adequate. Second, the presence of overdispersion means that pooling the samples underemphasizes the evidence of a small proportion being supplied by the zero variance of the observed proportions. While we could pursue a more optimal proportion, we choose in this case to simply use the simplistic bound noted above. Here, as the library sizes in the first group are 49610 and 48479, the proportion is 1/(49610 + 48479) and the faked counts are 0.506 = 49610/(49610 + 48479) and 0.494, respectively. Some reformatted results from this fit are shown in Table 7 (Model 1).
The results for this fit are ridiculously "insignificant". The problem lies in the fact that the use of a t-value (a Wald test) relies on the approximate normality of the likelihood function in the vicinity of the maximum, and this shape assumption breaks down severely if the number of counts in one group is small. Tests based on changes in the scaled deviance, corresponding to likelihood ratio tests, are better.
The third run of the fitting procedure fits a simpler submodel, in this case a single proportion for all eight libraries, using the same overdispersion estimate so as to measure the change in deviance. The results of this fit are shown in Table 7 (Model 2). The analysis of deviance test for significance gives
Here, we cannot conclude (given the level of overdispersion) that the difference is real. Note that the degrees of freedom used in the denominator is 5; this follows from the fact that only 6 libraries were used to estimate the overdispersion parameter, and one of those 6 degrees of freedom was needed to estimate the proportion.
In general, when any of the groups has very small counts, checking the change in deviance is a good idea.
Discussion
Logistic regression with overdispersion addresses three issues with SAGE data: simultaneously modelling multiple types of variance, dealing with multiple groups at once, and allowing for the incorporation of covariates. This procedure is widely implemented in available software. Further, and most importantly, viewing SAGE data in the logistic regression setting supplies the framework for thinking of models that describe such data.
Dealing with multiple types of variance yields significance estimates we believe to be superior to those derived from pooled counts or from t-tests. The regression setting carries with it other benefits, such as a well-developed body of work regarding model checking, residual analysis, and detection of outliers. For example, the influence of any given library tag count on the overall analysis can be assessed, and methods can be made more robust by bounding these functions so that no single library drives the results.
There are some areas in which we can identify difficulties and see room for improvement.
First, the model that we are using for the error may be improved. For SAGE data, the proportion associated with a specific tag is rarely on the order of a percent, so
logit(pi) ≈ log(pi)
and we can speak of working with the log rather than the logit transform if we prefer. Assuming variance stability on the log scale then leads to the lognormal distribution often assumed in dealing with microarray data. Assuming a lognormal distribution is equivalent to introducing overdispersion in yet another way, namely as a random effect acting on the β scale. Here, the true proportion for library i is of the form
logit(pi) = β0 + β1xi + εi,
where εi is a normal random variable with mean 0 and variance . The model described here is a special case of a generalized linear mixed model (GLMM), where "mixed" refers to the fact that we have both fixed effects of interest, the changes with the covariates, and random shocks whose variance needs to be estimated and allowed for. Williams [21] suggests how this model might be fit using a Taylor-series type expansion, again invoking IRLS. However, as noted in Collett [18], p.272, "This approach is not entirely satisfactory for fitting such models to binary data, since the estimates can be biased in certain circumstances. Moreover, the deviance calculated for such models is often very approximate and cannot be recommended for general use in comparing alternative models." There are maximum-likelihood based approaches for fitting GLMMs available in SAS and S-PLUS, but there are known problems with fitting mixed effects models to binary data with small numbers of clusters or libraries. One way of addressing this issue more precisely is via simulation (for example via BUGS [22]). We are exploring these different error models now.
Second, the approach developed above works on one tag at a time. In doing so, it is not exploiting to the fullest the unique features of SAGE data. Examples of such exploitation include correcting for sequencing errors by looking at neighbors where sequence similarity is used to define a neighborhood network, and borrowing strength across genes by using common estimation of parameters such as φ over like groups. Work on these issues is ongoing (eg, Colinge and Feger [23], N. Blades (2002), unpublished dissertation, Johns Hopkins) and we think these features could be usefully combined with the approach presented here.
Methods
Data
The data used here were initially described in Zhang et al. [2]. The actual numerical libraries used were downloaded from the SAGE Genie web resource introduced by Boon et al. [24,25]. These libraries have had the linker tags removed.
Overdispersed logistic regression
Only a cursory description of the approach is given here; more detailed treatments are given in Collett [18] and McCullagh and Nelder [19], among others.
We want to fit the observed proportions, pi = Yi/ni, as a function of the covariates Xi. The first step in this process is to specify what form the relationship will take. If the relationship is linear, so that pi = β0 + β1Xi + ε, then we can potentially get fitted proportions outside of the interval [0,1], so we typically choose to fit a transformed version of the pis as being linear in the covariates. A common choice for proportions is the logistic transformation, logit(pi) = log(pi/1 - pi) = β0 + β1Xi + ε. This particular choice is suggested by the form of the likelihood function for binomial data (see McCullagh and Nelder [19], p.28–32), and we shall take it as assumed here, save to note that while the logit can range over all real values, the corresponding proportions are all between 0 and 1. At this point we are fitting a straight line to a transformed version of the data; this is akin to standard linear regression which is fit by minimizing the sum of squared deviations between the observations and their fitted values: the method of least squares. Now, the default assumption in least squares is that all of the observations are known with equal precision, and hence receive equal weight. This is not the case here, as the variance of a proportion is V(pi) = pi(1 - pi)/ni, so that the precision with which an observation is known depends on both the value of that observation and on the size of the total ni from which the proportion was derived. In the case where the observations are known with differing precisions, then the standard adjustment is to fit a weighted version of least squares, minimizing a weighted sum of the squared differences between the observations and their fitted values, where the weights are inversely proportional to the variances of the observations. Thus, at the first step we fit a logistic curve using weighted least squares where the weights are inversely proportional to the variances associated with our initial estimates of the proportions, (Yi + 0.5)/(ni + 1). After this first fit, we now have predicted values for each of the observations, and these predicted values in turn suggest new values for the variances and hence the weights. Thus, the second step is to refit the data using the new weights. This process is iterated (iteratively reweighted least squares, IRLS) until the changes in the predicted values from one fit to the next are small enough that the procedure is said to have converged.
Even after the process has converged, it is often the case that the sizes of the squared deviations will be substantially larger than might be expected if the variances were of exactly the form given above. In this case, the data are said to exhibit overdispersion relative to the postulated model, and we seek to estimate the scale of the overdispersion. We deal with the quasi-likelihood case of overdispersion here, where the variance is really of the form V(pi) = nipi(l - pi), for > 1. The added mechanics for computing the hierarchical form are somewhat involved and we refer the reader to Williams [21] for details. Using the quasi-likelihood model for overdispersion, the actual parameters of the best fitting model will not change, as the weights used in the weighted least squares routine are all proportional to the inverses of the variances, and scaling all of the variances by the same factor leaves the relative sizes of the weights unchanged. What does change is the presumed precision associated with these parameters; the variances of the parameters will likewise be multiplied by , and significance tests need to be adjusted accordingly. In order to estimate , we return to the weighted squared deviations between observations and predictions noted above. Ideally, the sum of the squared weighted residuals will have a chi-squared distribution with k - p degrees of freedom, where k is the total number of libraries and p is the number of β terms being estimated. As the mean of a chi-squared distribution is equal to its degrees of freedom, we get our initial estimate of by dividing the sum of squared weighted residuals by the posited degrees of freedom:
Given the estimated value of , the test statistics are scaled by and the significances recomputed. In the cases below, we outline the procedure and couple the descriptions with scripts for the freeware package R. In each case, the approach begins by loading the data corresponding to the tag counts Yi and the library sizes ni, which are used to supply the observed proportions. The main distinction between the cases resides in how the covariate X values are defined. All of the models assume the presence of a constant vector X0 of all ones; this produces the corresponding estimate for β0. Our discussion will likewise treat this covariate as present in all modelling steps.
Annotated R code
# Source code for models used in the paper
# "Overdispersed Logistic Regression for
# SAGE: Modelling Multiple Groups and
# Covariates", by Baggerly et al.
##########################################
# First, we deal with the case of two
# groups, and introduce the methods for
# fitting the logistic regression models.
##########################################
if(0){
# Load the tag counts for ATTTGAGAAG (y)
# from the 8 libraries in Zhang et al.
# [2], the associated library sizes (n)
# and the covariate vector indicating
# which of two groups the librares
# belong to, normal or cancer (x).
y <- c(320, 600, 312, 549,
246, 65, 41, 52);
n <- c(49610, 48479, 41371, 55700,
60682, 55641, 51294, 61148);
x <- c(0, 0, 1, 1, 1, 1, 1, 1);
# Now fit a standard logistic regression
# model to the data, with no allowance
# for overdispersion. This is done
# through a call to the generalized
# linear model (glm) routine. help(glm)
# provides more information about the
# nature of the arguments here.
fit1 <- glm(cbind(y, n-y) ~x,
family=binomial);
# check the results
summary(fit1);
# Next, we refit the model while
# allowing for overdispersion of the
# quasilikelihood type; all variances
# are inflated by a common factor. This
# call differs from the first only in
# the definition of the glm "family" to
# be used.
fit2 <- glm(cbind(y, n-y) ~x,
family=quasibinomial);
# check the results
summary(fit2);
# Ideally, the sum of the squared
# Pearson residuals should have a chi-
# squared distribution, with mean equal
# to its degrees of freedom. Dividing
# the sum by the degrees of freedom
# gives our initial estimate of the
# overdispersion parameter.
varQL <- sum(residuals(fit2,
"pearson")^2)/fit2$df.residual;
# Finally, we refit the model using the
# overdispersion method suggested by
# Williams [21], where the variances are
# inflated by factors that are slightly
# different depending on the underlying
# library sizes. This routine is
# implemented in the R package "dispmod"
# which is available at
#
library("dispmod");
fit3 <- glm.binomial.disp(fit1);
# check the results
summary(fit3);
phi <- fit3$dispersion;
# Note that the reported p-values from
# this fit are incorrect. This is due to
# the assumption that the test-stats
# have normal distributions, even though
# we have had to estimate the
# overdispersion parameter. When we have
# to perform this estimation, the
# correct test is a t-test, with a
# number of degrees of freedom
# corresponding to the number of
# libraries less the number of estimated
# parameters. As the number of libraries
# is typically not large, this can
# create a large difference.
sumfit3 <- summary(fit3);
t.values <- summary(
fit3)$coefficients [,"z value"];
p.values <- 2 * pt(-abs(t.values),
fit3$df.residual);
}
##########################################
# Next, we deal with three groups
##########################################
if(0){
# We begin by focusing on gains
# available when multiple groups are
# present, even if the other groups are
# not directly part of the contrast of
# interest, due to the additional
# information that the added groups can
# provide about the scale of the
# overdispersion.
# Here, we use the data from the tag
# TGCTGCCTGT, and this time we note that
# there are 3 groups of libraries:
# normals (libraries 1–2), primary
# tumors (libraries 3–4), and cell lines
# (libraries 5–8). If we are interested
# in the contrast between normals and
# primary tumors, we can fit this using
# only the data from those two groups,
# or using the data from all three.
# First, fit the model as if there were
# just two groups present.
y <- c(0, 1, 1, 15);
n <- c(49610, 48479, 41371, 55700);
x <- c(0, 0, 1, 1);
fit1 <- glm(cbind(y, n-y) ~x,
family=binomial);
fit2 <- glm.binomial.disp(fit1);
# get the correct p-values
fit2.t.values <- summary(
fit2)$coefficients [,"z value"];
fit2.p.values <- 2 * pt(-abs(
fit2.t.values), fit2$df.residual);
# Next, fit the model assuming that
# there are three groups. In this case,
# we cannot use a single covariate
# vector x, as this is not suited to
# indicating 3 or more groups in an
# unordered fashion (using 0, 1, and 2
# for the three groups respectively
# would force an ordering by saying that
# primary tumors are intermediate
# betwixt normal samples and cell lines)
# In general, if we have k groups, we
# need to use k-1 covariate vectors.
# Here, we use
# x1 <- c(0, 0, 1, 1, 0, 0, 0, 0);
# x2 <- c(0, 0, 0, 0, 1, 1, 1, 1);
# The set of all 0s (x1 = 0, x2 = 0)
# corresponds to the first group, here
# the normals, and the other groups are
# defined by which one of the other
# covariates is nonzero:
# Group 2 (primaries), (x1 = 1, x2 = 0),
# Group 3 (cell lines), (x1 = 0, x2 = 1)
y <- c(0, 1, 1, 15, 9, 1, 12, 27);
n <- c(49610, 48479, 41371, 55700,
60682, 55641, 51294, 61148);
x1 <- c(0, 0, 1, 1, 0, 0, 0, 0);
x2 <- c(0, 0, 0, 0, 1, 1, 1, 1);
fit3 <- glm(cbind(y, n-y) ~x1 + x2,
family=binomial);
fit4 <- glm.binomial.disp(fit3);
# get the correct p-values
fit4.t.values <- summary(
fit4)$coefficients [,"z value"];
fit4.p.values <- 2*pt(-abs(
fit4.t.values), fit4$df.residual);
# The above approach has fit the model
# with all of the covariates available,
# but in order to perform an analysis of
# deviance we want to fit various
# submodels using the same estimate of
# overdispersion as found here. In this
# case, there are 3 submodels:
fit5 <- glm(cbind(y, n-y) ~x1,
family=binomial,
weights = fit4$disp.weights);
fit6 <- glm(cbind(y, n-y) ~x2,
family=binomial,
weights = fit4$disp.weights);
fit7 <- glm(cbind(y, n-y) ~1,
family=binomial,
weights = fit4$disp.weights);
# alternatively, the anova function can
# be used, but this only considers the
# submodels obtained by adding terms
# sequentially. Thus, we get the
# deviances for beta_0 (the null model),
# beta_0 + beta_1 (adding the x1
# covariate only), and beta_0 + beta_1 +
# beta_2 (adding the x2 covariate to
# what we already have.
fit4.anodev <- anova(fit4);
}
##########################################
# Next, we deal with the case of other
# covariates, possibly continuous.
##########################################
if(0){
# Here, we are using the counts from the
# GCGAAACCCT tag, but we are treating
# the 8 libraries as coming from tissue
# type 1 (libraries 1–4) and tissue type
# 2 (libraries 5–8), with normal tissue
# of both types (libraries 1–2, 5–6) and
# primary tumor of both types (libraries
# 3–4, 7–8). In this hypothetical
# example, we are able to partition the
# changes into effects associated with
# normal/primary differences (x1) or
# tissue 1/tissue 2 differences (x2).
y <- c(167, 566, 64, 98, 33, 47, 40, 27);
n <- c(49610, 48479, 41371, 55700,
60682, 55641, 51294, 61148);
x1 <- c(0, 0, 1, 1, 0, 0, 1, 1);
x2 <- c(0, 0, 0, 0, 1, 1, 1, 1);
fit1 <- glm(cbind(y, n-y) ~x1 + x2,
family=binomial);
fit2 <- glm.binomial.disp(fit1);
# get the correct p-values
fit2.t.values <- summary(
fit2)$coefficients [,"z value"];
fit2.p.values <- 2*pt(-abs(
fit2.t.values), fit2$df.residual);
# Next, again using the tag as above, we
# posit that we also have access to the
# levels of a biomarker potentially
# predictive of survival, supplied as
# the levels of another covariate x3.
# The values supplied here were
# generated as random draws from a
# uniform (0,1) distribution
x3 <- c(0.89, 0.35, 0.66, 0.23,
0.30, 0.54, 0.90, 0.90);
fit3 <- glm(cbind(y, n-y) ~x1 + x2 + x3,
family=binomial);
fit4 <- glm.binomial.disp(fit3);
# get the correct p-values
fit4.t.values <- summary(
fit4)$coefficients [,"z value"];
fit4.p.values <- 2*pt(-abs(
fit4.t.values), fit2$df.residual);
}
Authors' contributions
KAB, LD and JSM developed the main ideas and the methodology; LD did most of the coding. CMA supplied SAGE data and provided practical feedback on aspects of earlier approaches found to be wanting, thus guiding further development.
Acknowledgements
The authors gratefully acknowledge support from NIH-NCI Grant 1U19 CA84978-1A1. Li Deng is also supported by a training fellowship from the W.M. Keck Foundation to the Gulf Coast Consortia through the Keck Center for Computational Biology.
Figures and Tables
Table 1 Methods of summarizing data. The effects of pooling and reduction to proportions on a single tag measured across four libraries. Pooling reduces the data to the summed counts at the right, and focusing on proportions reduces the data to the proportions on the bottom. In both cases, information is lost.
Summed Counts
Tag Count Y1 Y2 Y3 Y4
Library Size n1 n2 n3 n4
Proportions Y1/n1 Y2/n2 Y3/n3 Y4/n4
Table 2 Tag counts from sample SAGE libraries. Counts and proportions of tags ATTTGAGAAG, TGCTGCCTGT and GCGAAACCCT in 8 colon libraries from Zhang et al. [2]; two normal colon (NC), two primary tumors (TU) and four cell lines.
Group Normal Colon Primary Tumor Cell Lines
Library NC1 NC2 TU98 TU102 CACO2 HCT116 RKO SW837
ATTTGAGAAG 320 600 312 549 246 65 41 52
TGCTGCCTGT 0 1 1 15 9 1 12 27
GCGAAACCCT 167 566 64 98 33 47 40 27
Library Size 49610 48479 41371 55700 60682 55641 51294 61148
Propn ATT..(%) 0.65 1.24 0.75 0.99 0.41 0.12 0.08 0.09
Table 3 Logistic regression models for two groups. Logistic regression fits contrasting normal colon with cancer samples for tag ATTTGAGAAG from Table 2. The first model makes no allowance for overdispersion, and the latter two introduce it in different ways. The introduction of overdispersion is important as it dramatically affects the results, but the choice of overdispersion method is less crucial.
Model 1: No overdispersion V(Yi) = nipi(1 - pi)
Coefficients Estimate (s.e) z-value p-value
β0 -4.660 0.033 -140.68 < 2e-16
β1 -0.888 0.043 -20.41 < 2e-16
Model 2: Quasilikelihood V(Yi) = nipi(1 - pi) = 187.6
Coefficients Estimate (s.e) t-value p-value
β0 -4.660 0.454 -10.261 5e - 05
β1 -0.888 0.595 -1.489 0.187
Model 3: Hierarchical V(Yi) = nipi(1 - pi) [1 + (ni - 1)φ] = 3.4e - 03
Coefficients Estimate (s.e) t-value p-value
β0 -4.656 0.428 -10.874 3.6e - 05
β1 -0.850 0.570 -1.492 0.186
Table 4 Expanding contrasts from two to three groups. Logistic regression models testing the significance of a difference between normal colon and primary tumor (β1) for tag TGCTGCCTGT from Table 2. In the first model, only data from the four libraries directly involved are used. In the second model, data from the four cell line libraries are also included, providing a more stable estimate of the overdispersion parameter φ.
Model 1: Two Groups = 8.938e - 05 df = 2
Coefficients Estimate (s.e) t-value p-value
β0 -11.484 2.309 -4.973 0.038
β1 2.681 2.388 1.123 0.378
Model 2: Three Groups = 1.160e - 04 df = 5
Coefficients Estimate (s.e) t-value p-value
β0 -11.484 2.574 -4.462 0.007
β1 2.676 2.661 1.005 0.361
β2 3.020 2.604 1.159 0.299
Table 5 Analysis of deviance. Deviance table for various submodels fit to the data for tag TGCTGCCTGT given in Table 2. All of these models use the value for overdispersion found for the most extensive model, = 1.160e - 04.
Terms Fitted Deviance d.f.
β0 9.7433 7
β0 + β1 9.7418 6
β0 + β2 7.9826 6
β0 + β1 + β2 5.7866 5
Table 6 Incorporating covariates into the model. Models treating the fitting of counts for tag GCGAAACCCT from Table 2, with the cell lines hypothetically allocated to normal tissue B (libraries 5 and 6) and cancer tissue B (libraries 7 and 8). This division is made to illustrate how the effects of two differences, normal vs cancer and tissue A vs tissue B (β1 and β2 respectively) can be partitioned according to their importance. In Model 2, we have further introduced a continuous covariate (β3) corresponding to the levels of a biomarker to show how that can be figured in as well.
Model 1: Hypothetical Cov. = 1.224e - 03 df = 5
Coefficients Estimate (s.e) t-value p-value
β0 -4.928 0.291 -16.921 1.318e - 05
β1 -1.293 0.593 -2.181 0.0810
β2 -1.956 0.738 -2.650 0.0454
Model 2: Hypothetical Biom. = 1.254e - 03 df = 4
Coefficients Estimate (s.e) t-value p-value
β0 -4.167 0.608 -6.851 1.012e-03
β1 -1.423 0.611 -2.328 0.0674
β2 -2.031 0.752 -2.700 0.0428
β3 -1.365 1.028 -1.328 0.2417
Table 7 Fitting nested deviance models. Fitting nested models to the data in order to get deviance scores. The difference in deviance between models is a better indicator of the significance of the associated effect (β1) when the logistic regression fits are near the boundary of the space, giving proportions close to zero.
Model 1: Full Model Deviance = 5.0742
Coefficients Estimate (s.e) t-value p-value
β0 -11.494 13.518 -0.06 0.9519
β1 5.987 13.524 -0.03 0.9750
Model 2: Null Model Deviance = 8.7541
Coefficients Estimate (s.e) t-value p-value
β0 -5.794 0.392 -14.772 6.05e-06
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SAGE Genie
| 15469612 | PMC524524 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Oct 6; 5:144 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-144 | oa_comm |
==== Front
PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1553469210.1371/journal.pbio.0020391Research ArticleBioinformatics/Computational BiologyCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyHomo (Human)Widespread A-to-I RNA Editing of Alu-Containing mRNAs in the Human Transcriptome A-to-I RNA Editing of Repetitive ElementsAthanasiadis Alekos
1
2
Rich Alexander
2
Maas Stefan [email protected]
1
1Department of Biological Sciences, Lehigh UniversityBethlehem, PennsylvaniaUnited States of America2Department of Biology, Massachusetts Institute of TechnologyCambridge, MassachusettsUnited States of America12 2004 9 11 2004 9 11 2004 2 12 e39113 7 2004 13 9 2004 Copyright: © 2004 Athanasiadis et al.2004This 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.
Repetitive Editing and RNA Splicing
RNA editing by adenosine deamination generates RNA and protein diversity through the posttranscriptional modification of single nucleotides in RNA sequences. Few mammalian A-to-I edited genes have been identified despite evidence that many more should exist. Here we identify intramolecular pairs of Alu elements as a major target for editing in the human transcriptome. An experimental demonstration in 43 genes was extended by a broader computational analysis of more than 100,000 human mRNAs. We find that 1,445 human mRNAs (1.4%) are subject to RNA editing at more than 14,500 sites, and our data further suggest that the vast majority of pre-mRNAs (greater than 85%) are targeted in introns by the editing machinery. The editing levels of Alu-containing mRNAs correlate with distance and homology between inverted repeats and vary in different tissues. Alu-mediated RNA duplexes targeted by RNA editing are formed intramolecularly, whereas editing due to intermolecular base-pairing appears to be negligible. We present evidence that these editing events can lead to the posttranscriptional creation or elimination of splice signals affecting alternatively spliced Alu-derived exons. The analysis suggests that modification of repetitive elements is a predominant activity for RNA editing with significant implications for cellular gene expression.
A computational analysis of human RNA has identified 1,445 transcripts are edited mainly within non-coding Alu repeats, with the potential effect of regulating alternative splicing
==== Body
Introduction
On the molecular level, the complexity of higher organisms is based on the number of different gene products available for structural, enzymatic, and regulatory functions. Posttranscriptional and/or posttranslational mechanisms have an important role in generating RNA and protein diversity (Baltimore 2001). One posttranscriptional processing pathway present in higher eukaryotes is RNA editing by adenosine deamination involving modification of individual adenosine bases to inosine in RNA by adenosine deaminase acting on RNA (ADARs; reviewed in Bass 2002; Schaub and Keller 2002; Maas et al. 2003). Since inosine acts as guanosine during translation, A-to-I conversion in coding sequences leads to amino acid changes and often entails changes in protein function (Seeburg et al. 1998; Bass 2002; Schmauss and Howe 2002). The power of RNA editing in generating protein diversity lies in the fact that usually both the edited and unedited versions of the RNA and/or protein coexist in the same cell, and the ratio between the unedited and multiple edited variants can be regulated in a cell type-specific or time-dependent manner. Crucial functional properties of neurotransmitter receptors are regulated by A-to-I editing in the central nervous system (Seeburg et al. 1998; Schmauss and Howe 2002), and inactivation of editing enzymes in mice (Higuchi et al. 2000) and in the fruit fly (Palladino et al. 2000) have resulted in profound neurological phenotypes. In addition to amino acid changes, A-to-I RNA editing can theoretically lead to the alteration of transcriptional start and stop codons, as well as that of RNA splice sites. In only one case though has the creation of a splice acceptor site through intronic RNA editing been described (Rueter et al. 1999).
Currently it is not known if the recoding of mRNAs at single codon positions is the main function of A-to-I RNA editing or if other types of editing events with as yet unknown roles in the regulation of gene expression are more widespread. The recently reported embryonic lethality in mice with ADAR1 deficiency indicates that additional substrates for this enzyme exist that function during early embryonic development (Wang et al. 2000, 2004; Hartner et al. 2004). Furthermore, a role for ADAR1 in the immune system is widely accepted, as one of its isoforms is interferon induced (Patterson and Samuel 1995) and upregulated in immune cells during chronic inflammation (Yang et al. 2003). The ablation of editing enzymes in Caenorhabditis elegans resulted in transgene silencing, suggesting that the RNA editing and RNA interference (RNAi) pathways intersect (Knight and Bass 2002). This notion was recently confirmed by findings that the behavioral phenotype of ADAR-deficient worms could be rescued by inactivation of the RNAi pathway (Tonkin and Bass 2003). Since both RNAi and RNA editing target double-stranded RNA (dsRNA) molecules, RNA editing could suppress gene silencing by preventing the formation of small interfering RNAs (siRNAs).
A recurring theme of edited sequences is the involvement of an imperfectly dsRNA foldback structure (Higuchi et al. 1993). The importance of base-paired RNA elements for site-selective editing to occur is also mirrored in the presence of dsRNA binding domains in ADAR enzymes (Bass 2002). At present, though, it is not possible to predict if and to what extent a given RNA molecule is a substrate for A-to-I RNA editing in vivo.
Despite recent progress in identifying additional genes that undergo RNA editing (Morse and Bass 1997; Morse et al. 2002; Hoopengardner et al. 2003), the total number of currently known A-to-I edited genes in mammals is still small (Bass 2002). However, the activity of the mammalian editing machinery, as measured by inosine content in mRNA fractions (Paul and Bass 1998), is much higher than expected based on the current number of known substrates. Furthermore, ADARs are ubiquitously expressed in mammalian tissues, but almost all ADAR targets identified to date reside in the brain (Bass 2002; Maas et al. 2003). This discrepancy between signs that A-to-I editing is omnipresent and the scarcity of identified targets has puzzled researchers in the field for some time, wondering where all the edited transcripts are.
In this study we identify a minimum of 1,445 edited human mRNAs present in existing databases. Clusters of adenosine-to-guanosine (AtoG) discrepancies in these cDNAs are the result of RNA editing involving intramolecular pairs of inverted Alu repeat sequences, repetitive elements that represent approximately 10% of the human genome and are concentrated in and around genes (Batzer and Deininger 2002).
We also characterize functional consequences of the observed editing events and the factors that determine editing levels in Alu repeats and their modification patterns. The prevalence of Alu elements in primate genes, together with our experimental and computational analysis, suggests that the vast majority of primary human gene transcripts (greater than 85% of RNAs with average structure) are subject to A-to-I RNA editing. We show how editing might influence the alternative splicing of exonized Alu elements and discuss the implications of this extensive modification of mRNAs bearing repetitive elements for the regulation of gene expression.
Results/Discussion
Clusters of AtoG Discrepancies between Genomic and cDNA Sequences Are Due to A-to-I RNA Editing and They Are Located in Alu Repeat Elements
A hallmark of an A-to-I RNA editing event is an AtoG transition when comparing genomic and cDNA sequences of the affected gene since inosine base-pairs with cytosine and therefore is replaced by guanosine during reverse transcription and PCR amplification. However, AtoG discrepancies between genomic and cDNA sequences can also be due to single-nucleotide polymorphisms (SNPs) or errors in databases. Therefore the search for edited sequences on a genome-wide basis is not feasible solely based on this single feature. However, in some cases of editing, not a single, but a cluster of AtoG discrepancies between genomic and cDNA sequences is evident within a stretch of a few hundred nucleotides (Patton et al. 1997; Morse et al. 2002; Rosenthal and Bezanilla 2002). Therefore, we decided to inquire whether clusters of AtoG transitions seen in cDNA/genomic DNA (gDNA) sequence comparisons might represent bona fide editing events, since multiple base changes, all being of the AtoG type, are not likely accounted for by cosegregating SNPs or sequencing errors.
In an initial screen for candidate genes, we used the Human Unidentified Gene-Encoded (HUGE) database of ca. 3,000 human cDNAs derived from the Kazusa cDNA sequencing project (Kikuno et al. 2002). Several examples of cDNA sequences were found that within a window of 200–300 nt differ at several positions from the genomic sequence, such that the cDNA harbors a G where the genomic counterpart specifies an A. AtoG differences that coincide with an annotated A/G SNP were filtered out. Table 1 shows a list of all 26 genes from the HUGE database showing greater than two AtoG transitions in the exonic regions. Remarkably, we found that in all cases except one (KIAA0001) the location of the AtoG cluster coincides with the position of an Alu repeat element in the cDNA. As with Alu elements, most AtoG transition clusters are localized in 5′-UTR and 3′-UTR sequences and few in coding regions.
Table 1 A/G Discrepancy Clusters in Human Brain cDNAs
aNot all cDNA regions with A/G discrepancies were analyzed
bSNPs according to National Center for Biotechnology Information SNP database
cClone hh15303
Alu elements are short interspersed elements found in all primates, which are approximately 300 nt in length (Batzer and Deininger 2002). There are about 1.4 million copies of Alus from several closely related subfamilies present in the human genome, comprising approximately 10% of its mass (Lander et al. 2001). The enrichment of Alu repeats in gene-rich regions of the genome (Chen et al. 2002) makes their prevalence in transcribed sequences even more pronounced. Their high CpG dinucleotide content renders Alu sequences targets for methylation and implicates them in the regulation of gene expression (Rubin et al. 1994). Clusters of A/G discrepancies that mapped to Alu repeats had been noted before in the HUGE database (Kikuno et al. 2002). Furthermore, of ten newly identified editing targets in C. elegans (Morse and Bass 1999; Morse et al. 2002) and 19 in human brain (Morse et al. 2002), most were located in repeat elements. These findings suggested that repetitive elements, such as Alus, might be frequent targets for A-to-I RNA editing.
In order to better understand the connection of Alu's with the observed AtoG clusters, we analyzed experimentally the cDNAs from all 25 candidate genes for RNA editing in human brain. Total RNA and gDNA were isolated from the same human brain specimen to eliminate false positives from unmapped A/G SNPs. For all 25 genes in vivo RNA editing was detected by single-run sequencing of gene-specific RT-PCR products, and for five of them the editing efficiency was quantitatively evaluated through repeated experiments. Extents of editing ranged from less than 2% to 90% at individual sites (Table 1; Figures 1–3).
Figure 1 RNA Editing of an Alternatively Spliced Alu-Exon in a G-Protein Coupled Receptor
(A) Schematic representation of LUSTR (GPR107, KIAA1624) gene structure around edited exon 15a. The AluSx repeat element in intron 15 and the exonic, inversely oriented AluJo are predicted to form an intramolecular foldback structure as depicted below (MFold software). TM, exonic regions predicted to encode transmembrane domains; *, editing sites.
(B) Editing analysis of exon 15a (sequence in capital letters) and flanking regions. The two major editing sites predicted to change amino acids (H/R and Q/R) are indicated. Editing levels in brain (filled column) and lung (open column) are shown above each edited nucleotide. The splice acceptor site subject to editing is underlined.
Figure 3 RNA Editing of Alternative Exon 22a in Inhibitor BTKI and the 5′-UTR of KIAA1497
(A) The alternatively spliced exon 22a and surrounding region of the BTKI (KIAA1417) gene with two Alu elements and its computer-predicted foldback structure.
(B) Editing analysis of the AluSx- element with the exonic sequence in capital letters and edited A's in bold. The alternative splice acceptor site is underlined with a dashed line; the additional alternative consensus splice acceptor site, which undergoes editing, is underlined with a solid line.
(C) Gene architecture and Alu foldback structure of KIAA1497. The brain-derived cDNA of KIAA1497, also known as LRRN1; (Taguchi et al. 1996), has a total of 15 nonpolymorphic AtoG discrepancies to gDNA, 14 being located within the 5′-UTR of the gene and one within the coding region. We analyzed PCR products covering all 14 potential editing sites in the 5′-UTR for editing in cDNA from human brain and could confirm in vivo editing to an extend clearly above the detection limit of our method for most of these positions and also at additional adenosines (data not shown). ORF, open reading frame; *, editing sites.
Figure 2 RNA Editing of KIAA500 Alu Inverted Repeat
KIAA0500 is a cDNA of 6,577 nt in length cloned from human brain (AB007969) with a predicted open reading frame of 213 amino acids. Four AtoG discrepancies were present within the coding region of which two lead to an amino acid change (Q/R and S/G, respectively).
(A) Structure of the KIAA500 mRNA with location of Alu elements indicated and the predicted RNA secondary structure according to the MFOLD algorithm. Large open box indicates predicted open reading frame. *, editing sites.
(B) Editing analysis of an exonic Alu element in KIAA0500. Editing sites predicted to change amino acids are indicated. Our analysis revealed a significant percentage of editing (%G) at the nucleotide positions 3518 (27% ± 3%), 3522 (20% ± 3%) and 3625 (6% ± 1%) and additional editing sites with less than 5% editing, whereas parallel analysis of human gDNA confirmed the presence of adenosine at these positions. Editing levels in brain (filled column) and lung (open column, where detectable) are shown above each edited nucleotide.
Intramolecular Pairs of Oppositely Oriented Alus Are Responsible for Alu Element Editing
Since a prerequisite for A-to-I RNA editing is the presence of a partially base-paired RNA foldback structure (Higuchi et al. 1993; Bass 2002), the observed modifications in Alu repeats might be the result of two oppositely oriented, base-pairing repeat elements located within the same RNA molecule. For each of the 25 genes with edited, exonic Alu elements we find such oppositely oriented Alu repeats in the same pre-mRNA, many of which are located in intronic sequences. To determine if the predicted Alu pairs and the calculated foldback structures (Figures 1A, 3, and 4) actually form in vivo, we analyzed experimentally the predicted Alu partners from the pre-mRNA for four of the identified editing targets (LUSTR, KIAA0500, Bruton's tyrosine kinase [BTKI], and KIAA1497). In each case we found that the closest, oppositely oriented Alu repeat undergoes A-to-I RNA editing as well.
Figure 4 Alu-Mediated RNA Editing in p53, SIRT2, NFκB, and Paraplegin Pre-mRNAs
Schematic presentation of the gene structures from (A) P53, (B) SIRT2, (C) NFκB, and (D) SPG7. Edited repeat elements are marked by asterisks. RNA folds appear as calculated with MFOLD. The AluJb− in p53 is located in the 3′-UTR (A); all others are intronic. *, editing sites.
Because of the abundance of Alu elements in human pre-mRNAs, most primary transcripts contain one or more pairs of oppositely oriented Alus. If a majority of them is indeed subject to A-to-I RNA editing in vivo, it should be possible to predict RNA edited genes by identifying inverted pairs of Alu repeats in pre-mRNA transcripts. As a proof of principle, the analysis was extended to four arbitrary chosen genes (p53, SIRT2, NFκB, and paraplegin (SPG7) containing pairs of Alu repeats as seen schematically in Figure 4B–4E. In all four cases, editing in the Alu elements that are predicted to form a dsRNA foldback structure is readily detectable.
Many primary gene transcripts allow several energetically favorable foldback structures to be predicted for a given Alu that involve different combinations of Alu pairs. Do all these alternative Alu-pair foldback structures exist in vivo and are therefore subject to RNA editing? To address this question we examined the editing pattern of the G-protein-coupled receptor 81 (GPR81; identified through a computational search as described below). GPR81 contains four Alu elements, one sense and three antisense oriented, in the 3.6-kb pre-mRNA and was selected based on Alu repeat configuration and transcript length. If the alternative foldback structures depicted in Figure 5 coexist in vivo, all four Alu elements should show signs of editing with the level of editing indicating how prominent each of the alternative structures is. According to the analysis of GPR81 pre-mRNA, all three configurations form in vivo with variant II possibly being the dominant one since AluSp and AluJo show the highest levels of editing (Figure 5).
Figure 5 Editing of Alternative Foldback Structures of GPR81 Pre-mRNA
(A) The position and orientation of all four Alu elements in GPR81 pre-mRNA is indicated. Three alternative Alu pairings (I–III) are predicted and experimental editing analysis indicates that all three do form in vivo. ORF, open reading frame; *, editing sites.
(B) Editing analysis of AluSp+ in GPR81. Percentages of editing in human brain are indicated. The exonic sequence appears in capitals. The edited AT dinucleotide that becomes a splice donor site is underlined.
These results suggest that Alu elements in human mRNAs are subject to RNA editing by ADARs because of foldback structures formed between two oppositely oriented Alus present within the same primary transcript.
Editing of Alus Is Tissue Dependent and It Alters Codons and Pre-mRNA Splice Sites of Alternatively Spliced Alu Exons
Exonic Alu repeat elements are predominantly located in the 5′- and 3′-UTRs of mRNAs, and as a result, most cases of Alu editing occur in noncoding regions. However, some editing events predict amino acid changes (Table 1). Among the identified genes for which we performed a detailed, quantitative editing analysis several unique and recurring features emerge regarding the locations and functional implications of the editing events.
The LUSTR1 cDNA codes for a G-protein-coupled seven-transmembrane receptor (also termed GPR107 or KIAA1624), with three AtoG discrepancies located within an alternatively spliced AluJo-derived exon that leads to the in-frame insertion of 29 amino acids between transmembrane regions V and VI of the protein (see Figure 1A). The experimental analysis revealed a total of ten editing sites within this Alu element (see Figure 1B), including two major sites that lead to amino acid changes (H/R and Q/R sites). Interestingly, editing levels at all positions were significantly different in human brain (19%–58%) compared to lung (less than 5%), suggesting a tissue-specific regulation of editing (see Figure 1B).
Analyzing the RNA editing pattern of LUSTR1 pre-mRNA revealed additional intronic editing sites, one of which represents the splice acceptor adenosine (AG to IG) in intron 15 (22% edited in brain; see Figure 1B). Editing at this position is predicted to lead to the exclusion of the Alu exon, indicating that the alternative splicing of exon 15a might be coregulated by RNA editing of its splice junction. This is to our knowledge the first documented example where A-to-I RNA editing acts to destroy a pre-mRNA splice signal.
A picture similar to LUSTR1 emerges from analysis of the gene for human inhibitor of BTKI (also termed KIAA1417; Liu et al. 2001; Strausberg et al. 2002). Again, an alternatively spliced Alu exon (located between constitutive exons 22 and 23) is affected. This time two AluSx elements are positioned in opposite orientation at the start and end of the exon (see Figure 3). Inclusion of the exon using the splice acceptor site provided by AluSx− leads to the premature termination of translation within this exon with all editing sites located in the 3′-UTR. Editing levels at 20 sites throughout the Alu element range from less than 5% to 31% in human brain, whereas cDNAs isolated from human lung again displayed few editing sites with low editing levels of less than 5%. A splice site is also subject to editing in BTKI, this time affecting an additional alternative splice acceptor site within AluSx−. On the pre-mRNA level this position is edited to 15%. However, in transcripts that use the weak upstream splice acceptor site (underlined with a dashed line in Figure 3B; as in the HUGE database clone hh15303), the additional alternative splice site (underlined with a solid line in Figure 3B) is highly edited, raising the possibility that edited BTKI pre-mRNA preferentially follows the alternative splicing pathway (data not shown).
The analysis of GPR81 revealed another case of Alu exon alternative splicing and, surprisingly, a new mechanism showing how RNA editing might affect RNA processing. Within the AluSp+ element located in the 3′-UTR of GPR81 transcripts a splice donor site (AT to IT) is generated in 57% of primary transcripts by RNA editing. This is predicted to give rise to alternatively spliced mRNA products represented by GenBank entry AF385431 (see Figure 5B). This is, to our knowledge the first reported case of potential splice donor site creation by RNA editing. It is possible that here the Alu element was inserted into the 3′-UTR exon of the GPR81 gene and has evolved into a state where it is a single mutation away from initiating the birth of a novel intron. Posttranscriptionally RNA editing provides the final base change to create the new splice site. This scenario is supported by the fact that in mice the GPR81 gene lacks introns.
It is intriguing that we find cases where editing in alternatively spliced Alu exons, or within adjacent splice sites, interferes with or counteracts exon formation of an Alu repeat. It suggests that RNA editing might be more generally involved in the regulation of Alu exonization. Recently, it has been shown that more than 5% of the alternatively spliced exons in the human genome are Alu derived (Sorek et al. 2002). Exonization of Alu repeats occurs via activating mutations in mostly antisense-oriented, intronic Alus generating a novel splice acceptor site (Mitchell et al. 1991; Lev-Maor et al. 2003), and it has been speculated that exonization of transposable elements in general is a major mechanism for the generation of novel exons (Kreahling and Graveley 2004). A large number of intronic Alu elements are just a single mutation away from being exonized (Lev-Maor et al. 2003; Kreahling and Graveley 2004), and in some cases the constitutive splicing of an intronic Alu has been shown to cause a genetic disorder (Mitchell et al. 1991; Knebelmann et al. 1995; Vervoort et al. 1998). In this context RNA editing may partially counteract genomic mutations that lead to the incorporation of deleterious novel exons while maintaining their potential to form exons with beneficial functions through further mutation.
Furthermore, RNA editing in Alus might be involved also in the generation of novel introns as seems to be the case in GPR81. Statistically, however, the exonization of intronic Alus would be much more frequent than the intronization of exonic Alus because of the abundance of Alus in introns.
A Transcriptome Wide Screen for Edited Alu Repeats
The results presented above show that clusters of AtoG mismatches in cDNA/gDNA sequence comparisons represent an effective way to identify authentic editing cases with a low rate of false positives. Since all clusters of AtoG discrepancies mapped to repeat elements, we wondered how prevalent the editing of Alu or other repeat elements is in the human transcriptome. Therefore, we devised a database search procedure to identify pairs of inverted repetitive elements in human mRNAs exhibiting AtoG transitions.
Initially, a limited search was carried out for closely spaced (less than 2 kb) inverted pairs of human Alu, MIR, and L1 repeat elements that overlap with exonic sequences and for which an mRNA sequence can be found in GenBank entries. This search, involving about one-third of all repeat elements in the human genome, identified 71 mRNAs with exonic repetitive-element pairs (51 Alu, six L1, six MER, and eight MIR). From those mRNAs, 27 displayed clusters of AtoG changes, all in Alu elements. Fourteen of these genes were chosen for experimental analysis, and all 14 proved to be subject to A-to-I RNA editing (Table 2). Since these initial results indicated a high prevalence of editing in Alu elements, we decided to carry out a comprehensive search involving all elements present in cDNA sequences.
Table 2 Edited Alu Exons from Computational Screen for Alu Element Foldback Structures
aSNPs according to National Center for Biotechnology Information SNP database
bNot all cDNA regions with A/G discrepancies were analyzed
ND, not determined
We analyzed the total of 103,723 human mRNA sequences (from the University of California, Santa Cruz [UCSC] Genome database [Kent et al. 2002], July 2003 assembly) for overlaps with repetitive elements of the L1, Alu, MaLR, and MIR families. For Alus, 17,406 mRNAs (16.8%) contained a total of 31,666 complete or partial repetitive-element sequences. Comparing the cDNA sequences with their genomic counterpart revealed that the number of AtoG discrepancies within Alu repeats is more than seven times higher than the average number of the other transitions (23,204 versus 3,271 [the average for GtoA, CtoT, and TtoC transitions]). In fact, the number of AtoG mismatches is higher than all other eleven types of nucleotide discrepancies combined (Figure 6A).
Figure 6 Mismatch Bias in Exonic Repetitive-Element Sequences
(A) Plot of the nature and number of mismatches within Alu and L1 sequences present in human cDNAs. For reasons of comparison the L1 mismatch numbers have been multiplied by 2.9 so that the non-AtoG mismatch count for Alu and L1 is identical. Transition mismatches AtoG, GtoA, CtoT, and TtoC are displayed together for comparison.
(B) Plotted are the total number of Alu sequences found in human cDNAs (first column) and the number of elements harboring AtoG and GtoA mismatches (second and last column). The third column indicates the high confidence set of edited elements (α = 0.000001).
While the finding that non-AtoG transitions (GtoA, CtoT, and TtoC) are approximately three times more frequent than transversions is in line with results from previous studies analyzing gDNA sequences (Lander et al. 2001; Venter et al. 2001), there is no explanation for the observed excess of AtoG mismatches relative to other base transitions. Alu sequences carry 22–23 CpG dinucleotides, which are known to show high mutation rates because of C-methylation, and as a consequence, these positions should display an elevated frequency of SNPs. Nevertheless, an elevated number of SNPs would lead to a rise in AtoG as well as GtoA mismatches when comparing a representative population of cDNAs with the corresponding genomic sequence. Thus, we concluded that the excess exclusively in the number of AtoG discrepancies in Alus over other base changes may reflect cases of bona fide A-to-I editing at the RNA level.
We then devised a statistical approach to distinguish repetitive elements that show AtoG mismatches due to sequencing errors and SNPs from those that have undergone A-to-I RNA editing. The method was based on the observation above that Alu elements subject to RNA editing undergo multiple base modifications that result in a cluster of AtoG discrepancies (5–30) between cDNA and gDNA. The probability that a cluster of several AtoG discrepancies is due to sequencing errors or SNPs (in the absence of an increased number of other nucleotide discrepancies indicating low-quality sequence data) is negligible. Thus the number of clustered AtoG changes can be used to distinguish genuinely edited elements from elements with aberrant or non-editing-related base changes. For each Alu element with AtoG discrepancies, we computed the χ2 test comparing the observed number of AtoG discrepancies with the expected number, based on the number of non-AtoG mismatches present in the same sequence. Elements with a χ2 higher than the critical value for α = 0.00001 (corresponding to a probability of one in 100,000 that the observed AtoG transitions are due to SNPs or sequencing errors) were selected as “edited” and will be called so throughout the rest of the manuscript. Using this approach we found that out of those 17,406 mRNAs with one or more exonic Alu elements, 1,445 (8.0%) mRNAs are edited within one or more of the Alu sequences (for a full list of edited mRNAs see Table S1). When looking at all the 31,666 Alu elements present within these 17,406 RNAs, we find that 1,925 (6.1%) Alu elements are “edited,” while another 4,574 Alu elements (14.4%) show AtoG discrepancies but fail to pass our probability cutoff (Figure 6B). Thus, the total of 6,499 elements (or 20.5%) represents the upper limit of potentially edited Alus in our sample (Figure 6B). The total number of Alus with GtoA discrepancies in the same sequence sample is 2,002, and we considered this value to reflect base changes that are due to SNPs and sequencing errors. Assuming a similar number for random AtoG and GtoA mismatches, we can subtract this number from the total count of potentially edited Alus, obtaining 4,497 cases (14.2%) as the approximate number of actually edited elements.
In order to validate our screening approach, we performed an identical analysis for GtoA, CtoT, and TtoC mismatches. Compared to the 1,925 AtoG-edited Alu elements in mRNA, we found 12 GtoA, 11 CtoT, and 11 TtoC cases of “editing.” These cases may represent false positives and thus set the error level of our screen to less than 0.6%.
These results suggest that out of the 103,723 human mRNAs at least 1.4% are A-to-I edited within an exonic Alu element. Apart from Alu repeats, many more low- and high-frequency repeats exist in the human genome (Venter et al. 2001) and might give rise to RNA foldback structures that result in exonic A-to-I editing. Therefore, the total number of mRNAs edited in exonic repeat sequences is probably higher than the value obtained from our analysis of Alu elements.
Most Alu repeats are located in introns, and it is there where the bulk of RNA editing is expected to occur. The average number of Alu repeats per gene is 12.4 estimated for Chromosomes 21 and 22 (Grover et al. 2003). This value is comparable to the 19.3 Alus/gene estimated from our data (2,003,976/103,723: total number of Alus (nonunique) in mRNA boundaries/number of mRNAs) for the whole genome. Considering that based on our analysis 14.2% of exonic Alus are edited, and assuming similar editing rates for intronic Alus, we can estimate that the probability of an average pre-mRNA to be edited is approximately 1–0.85819.3 or 94.7% (85.0% with the 12.4 Alu/gene estimate). While this value is an approximation, assuming that all genes have similar structures, and does not take into account editing in other repeat elements, it does reflect the magnitude of repetitive-element editing.
Distance, Conservation, and Tissue Localization Influence Which Pairs of Alu Elements Are Edited
To gain insight into the factors that determine which Alus are subject to RNA editing, and under what circumstances, the identified set of 1,925 high-confidence cases of editing in Alu elements (contained in the 1,445 mRNAs listed in Table S1) was used for further computational analysis.
It was assumed that the observed editing is the result of RNA foldback structures formed between intramolecular inverted Alu repeats, as we have demonstrated for all the experimentally analyzed cases. If this hypothesis is correct, then the distance between an Alu and its closest inverted pairing element should be a critical determinant for how likely it is that a given element will be targeted by the RNA editing machinery. To test this hypothesis the closest inverted Alu was identified within the same gene for all 31,666 Alu elements. A properly oriented element was found for 19,231 of those Alu elements, and a plot was made showing the percentage of edited Alus as a function of the distance between elements (Figure 7A). The highest level of editing (approximately 16%) was found for Alu pairs 300–400 nt apart, which corresponds to slightly more than the size of a full-length Alu repeat. Editing levels subside with increasing distance as the probability for base-pairing between the two Alu elements apparently decreases. Alu pairs with distances below 300 nt indicate partial Alu elements, and the observed decrease in editing levels is likely because of the smaller, less energetically stable foldback structures. These results suggest that the optimal configuration of an Alu-pair stem loop involves two full-length Alus forming the stem separated by a short (10–50 bp) intervening loop sequence. Interestingly, as the distance increases we ultimately arrive at a low-level plateau of approximately 1% editing without any further drop in editing levels. RNA editing in trans caused by base-pairing Alus located in separate RNA molecules might be responsible for this “background” editing. A-to-I editing in trans does occur on pre-annealed RNA duplexes in vitro (Bass and Weintraub 1987; Nishikura et al. 1991) and could occur also in vivo if such intermolecular RNA duplexes form. In Xenopus one case of potential trans editing has been described, involving RNA duplexes formed between sense and antisense transcripts of bFGF (Saccomanno and Bass 1999).
Figure 7 Factors Determining Alu Targeting Probability
(A) Percentage of edited elements classified in bins according to the distance separating the element and its closest inverted Alu partner.
(B) Percentage of edited Alu elements clustered according to divergence from their corresponding Alu-subfamily consensus.
(C) Percentage of edited Alu elements in each Alu subfamily.
In (A), (B), and (C) the numbers at the bottom of the bars show the sample size in each bin. (D) Percentage of edited elements according to the tissue from which the RNA was isolated. Error bars show 95% confidence levels.
The distance dependence of the extent of editing clearly suggests that the formation of Alu–Alu stem loop structures predominantly results from intramolecular Alu inverted repeats with an upper limit of approximately 1% editing that could be due to intermolecular Alu pairings. To our knowledge, these results describe for the first time the distance relationship of long-range RNA folding interactions in vivo and how their stability is influenced by distance.
More important, considering the high frequency of Alu elements in primate RNA sequences and the low levels of potential intermolecular editing observed, we conclude that intermolecular duplexes between complementary RNA sequences do not form in the nucleus at a significant rate. This raises the question of how the regulation of thousands of human messages proposed by Yelin and colleagues involving antisense transcripts works (Yelin et al. 2003). It might also explain why cases of editing involving endogenous sense/antisense RNA duplexes have not been reported despite evidence for extensive antisense transcription.
The editing of RNAs by ADARs has been shown to be dependent on the double-stranded character of the substrates, such that editing levels and promiscuity increase with the extent of the base-paired region (Bass 2002). The human Alu family is composed of several subfamilies of different genetic ages, and their consensus sequences contain diagnostic changes distinguishing one subfamily from another. The extent of base-pairing between two oppositely oriented Alu elements, and in turn the extent of A-to-I editing, depends on their sequence homology, and it is expected that highly diverged elements would form less stable foldback structures. The relationship between observed editing level in our set of 31,666 Alu repeats and the sequence divergence of each Alu repeat from the consensus of its respective subfamily is shown in Figure 7B. A decrease in editing levels is seen with an increase in diversity, suggesting that an Alu element with lower sequence homology to most other Alu repeats has a lower probability of forming a suitable editing substrate. Unexpectedly, we also observed a drop in editing levels for Alu elements with low divergence from their subfamily consensus sequence. This trend may be caused by the distribution of Alu divergence. Within the human genome the majority of Alu elements have diverged by 10%–15% from their subfamily consensus (Stenger et al. 2001). Therefore, Alu elements with lower than average divergence have a lower likelihood of encountering another element of similar divergence, resulting in low editing levels for this subset. We obtained similar results when we compared the editing levels of Alu elements with the sum of the divergence of the edited Alu and its closest inverted Alu element (data not shown). In agreement with these conclusions, we find that the most populated Alu subfamily (AluSx) and the subfamilies closely related to AluSx sequences (AluSq, Sc, and Sg) show the highest levels of editing (Figure 7C).
The pool of mRNAs used in this study represents a heterogeneous collection of sequences from different tissues and cell types. In analyzing the editing of Alu elements as a function of tissue origin (Figure 7D), significant differences in editing levels were found. The highest editing activities were seen in brain tissues, in trachea and thymus. These results are in accordance with prior studies that have measured the overall activity of RNA editing enzymes in selected mammalian tissues as judged by the amount of inosine detectable in the poly(A)+ fraction of RNA (Paul and Bass 1998). The two human enzymes with A-to-I RNA editing activity (ADAR1 and ADAR2) display a different but overlapping activity profile on known substrates, and their expression is highest in brain (ADAR2) and in cells of the immune system (ADAR1; Bass 2002). Furthermore, ADAR1 was found to be induced during inflammation leading to high activity in blood cells and thymus (Yang et al. 2003). These findings are also in agreement with our experimental results, which show much higher editing levels in brain-derived RNAs than in the same mRNA isolated from lung tissue (see Figures 1–3).
The pool of edited Alu elements was analyzed for other features that might influence editing levels, such as the position of the edited Alu within the mRNA (3′-UTR, 5′-UTR, and coding region) or its orientation in relation to the mRNA (sense, antisense). No significant correlation of Alu editing was detected with any of these features (data not shown).
Editing of Alu Repeats Shows Sequence and Structure Preferences
The availability of such a large collection of A-to-I edited sequences resulting from this analysis allowed us to examine the modification pattern of edited Alu elements for potential editing hot spots or base preferences. To this end we first aligned all 141 edited Alu sequences (greater than 260 bp) in RNAs originating from Chromosome 1 and mapped the edited sites on their consensus sequence (Figure 8). Interestingly, certain adenosines are targeted in greater than 30% of the edited RNAs while other adenosines do not show any evidence of editing. The four editing hot spots all map to TA dinucleotides that are located in conserved Alu regions (greater than 80% identity), suggesting that they are base-paired in the average foldback structure. This confirms the previously proposed T/A 5′-neighbor preference for both ADARs (Polson and Bass 1994). Surprisingly, most of the 22 CpGs of the consensus sequence coincide with the location of high-frequency editing events. CpGs are known to be targeted by cytosine DNA methylation, which results in a high mutation rate, turning CpGs into either CA or TG dinucleotides (Batzer and Deininger 2002). Since less than 50% of the transcripts carry a CA or TG (edited in reverse complement) at these CpG consensus sites, the editing efficiency (edited adenosines/total adenosines) at these positions is comparable to that at the hot spots (Figure 8A, arrows).
Figure 8 Sequence and Structure Preferences of Editing in Alus
(A) The consensus sequence of 141 edited full-length Alu elements present within human Chromosome 1 transcripts with the number of editing events indicated for each sequence position (bars). Insertions and deletions present in fewer than five elements are not shown in the alignment for clarity. Bases conserved in more than 80% of the sequences are boxed. For the lesser conserved consensus positions the next most frequent base is listed below. Consensus CpG dinucleotides are in bold. Arrows indicate “high-efficiency” positions where more than 20% of adenosines present appear to be edited. Note the overlap of these positions with CpGs. Major features of Alu sequences, such as the A-Box and B-Box of Pol III and the Alu polyA sequence are labeled.
(B) A typical Alu foldback structure and its major features as discussed in the text. Arrows indicate TA hot-spot positions. The magnifications show the two typical configurations of editing sites found in Alu pairs: mismatched A/C bulges (i) and A/U base pairs (ii).
As a result of the high CpG mutation rate, frequently the Alu foldback structure of the unedited RNA is predicted to carry A–C mismatches at these positions. Editing at these sites restores the CpG repeat (CA→CI) on the RNA level and converts the A–C mismatch to an I–C base pair. Energy calculations for several predicted Alu pairs show, surprisingly, that the stability of the foldback structure is not diminished by editing but often increased because of the high frequency of I/C pair formation (data not shown). It is therefore unlikely that in the case of Alu foldback structures, RNA editing serves to resolve RNA secondary structures that interfere with the processes of splicing or translation of these RNAs, as suggested previously (Morse et al. 2002). Two typical configurations of editing sites observed in Alu elements are depicted in the magnifications of Figure 8B where either A–U pairs are turned into I–U wobble pairs in conserved regions of the sequence (ii), or A–C mismatches are converted into I–C pairs within nonconserved Alu regions (i).
While the above analysis shows the qualitative features of the editing sites in Alus, determination of cis preferences was carried out by extracting 14,774 pentanucleotide sequences with the edited adenosine as the middle base and estimating the frequency of each base at positions −2, −1, 1, and 2 relative to the editing site. To correct for Alu sequence bias we performed the same analysis for a randomly chosen adenosine for each edited adenosine in our sample. We then subtracted those frequencies to obtain unbiased editing preferences (Figure 9). The presence of large, unpaired poly(A)+ tails in Alus obscures our analysis for adenosines surrounding edited A's but is informative regarding other base preferences. Position −1 shows a strong preference for C and T and aversion for G in agreement with previous studies (Bass 2002). Interestingly, we observe preferences for G in position +1 and for C or G at positions −2 and +2, which have not been described before. This preference pattern appears not to be linked to any Alu-specific structural feature and therefore possibly reflects the editing enzyme cis preferences. We also identify a preference for an editing site to be preceded or followed by another editing site (Figure 9). This data-rich assessment of sequence preferences for edited sites might be useful in an ab initio identification of new editing sites. Taken together our results identify loose RNA duplexes carrying A–C mismatches or A/U-rich regions, as favored editing targets. The high incidence of “corrective” editing at mutated CpG consensus positions in Alus raises the possibility that posttranscriptional restoration of CpG repeats in Alu primary transcripts by RNA editing contributed to the surprising retention of CpGs in Alus during evolution (Batzer and Deininger 2002). This might constitute an important consequence of A-to-I editing in view of the role of CpG islands in the regulation of gene expression.
Figure 9
Cis Preferences of Editing Sites in Alus
Tables (i) and (ii) show the frequency of A, G, C, T, or an A/G editing site at positions −2, −1, 1, and 2 relative to each of the 14,774 AtoG mismatch sites found within the high confidence group of Alu elements (i) and in relation to a randomly chosen adenosine from each of the those sequences for each AtoG mismatch (ii). Table (iii) shows relative editing preferences after bias removal by subtracting table (ii) from Table (i). (iv) Graphical representation of Table (iii).
Potential Functional Implications of RNA Editing in Repetitive Elements
Considering the available data on in vitro editing activities of ADARs on dsRNA molecules of different sequences and structures, it is not surprising that highly base-paired RNA foldback structures such as the ones induced by Alu inverted repeats are substrates for the editing enzymes. However, it is remarkable and maybe surprising that these predicted structures are edited in vivo at significant levels. This indicates that many of these structures do form in vivo and are readily accessible for ADARs in the nucleus.
Alu elements are ideal for the formation of editable RNA structures because of their large numbers, size, and degree of conservation. We find no evidence for a sequence or otherwise specific interaction of the editing machinery with Alu sequences. Thus, other repetitive elements able to form similar structures should also be targets of A-to-I editing. Our data suggest, however, that editing levels in all other major repeat-element families that dominate the human genome (LINE, LTR, and other short interspersed elements) are very low compared to editing levels seen in Alu repeats (see Figure 6A and unpublished data). The selectivity for Alus might be explained based on the distribution features of each repetitive-element family: For example full-length L1 repeats are approximately 6 kb in length, and as a consequence, most of the time they have low chance of having a base-pairing sequence in proximity. MIR repeats, although found in significant numbers, which potentially could form foldback structures, have a low average level of conservation (30%–40% divergence) and so may be inadequately double stranded to be a substrate. MaLR elements of the LTR superfamily are present in numbers such that the average distance between an inverted pair is very high (approximately 10 kb). However, our analysis suggests that all repetitive elements might become targets of RNA editing at different stages in evolution. Young repetitive elements in their expansionary phase of evolution display features that we identify as important for being editing targets. Based on these observations it will not be surprising if repeat elements that show low levels of editing in humans are major targets in other organisms.
For mRNA fractions, we estimated the inosine content due to Alu editing as follows: In 103,724 mRNAs we found 23,204 AtoG mismatches, while the same sequence sample has an average for the other transitions of 3,271. Assuming an average mRNA size of 4 kb, the ratio of inosine in the sample is estimated to be one inosine every 20,814 nucleotides (103,724 × 4,000/[23,204–3271]) generated by editing in Alu sequences. This estimation for Alu editing is in the range of one inosine in 17,000 nt (brain), one in 33,000 nt (lung, heart), to one inosine in 150,000 nt (skeletal muscle) as was experimentally determined by Bass and colleagues in the polyA-fraction of rat RNAs (Paul and Bass 1998). Since the rat genome lacks Alus, the total amount of inosine generated in human mRNAs may be much higher than in rats, unless a class of edited sequences in rats exists with a similar prevalence to Alus in humans. In any case, our data imply that most of the inosine detected in mRNA transcripts can be explained by the widespread A-to-I editing of repetitive elements. Repeat-element editing might therefore point toward an important housekeeping function for RNA editing. In contrast, the well-studied examples of editing that lead to single nucleotide and codon changes in mRNA might be less frequent cases of editing events.
While a significant amount of editing occurs in mRNAs that contain repetitive elements in their exons, our results predict that the bulk of A-to-I editing takes place in intronic sequences missing from cDNA databases. This is suggested by the experimental results regarding the LUSTR, GPR81, p53, SIRT2, NFκB, and paraplegin genes, for which intronic data was available (see Figures 1A, 4, and 5A). This extensive editing of repetitive elements in pre-mRNAs creates an enormous pool for the generation of gain-of-function mutations. The involvement of editing in creating or destroying splicing sites of alternatively spliced Alu exons, along with internal editing of those exons, suggests an intriguing new mechanism for accelerated evolution. We are now in a position to analyze the extent to which this process occurs within the human transcriptome. Such a role in “stimulating” evolution, however, is unlikely to be related to the “daily” function of A-to-I RNA editing.
It has been shown that hyperedited, inosine-containing RNAs are retained in the nucleus by a protein complex containing the inosine binding protein p54 (Zhang and Carmichael 2001). In view of the widespread editing of Alus this offers an intriguing mechanism to preclude aberrantly spliced mRNAs and, more generally, repetitive-element-containing RNAs from exiting the nucleus. This model, though, suggests that intronic RNA editing occurs frequently in other organisms and in other repetitive-element types as well, something that remains to be shown.
A connection between A-to-I RNA editing and RNAi has recently been suggested through studies in C. elegans where inactivation of the editing machinery leads to transgene silencing (Knight and Bass 2002), and subsequent inactivation of the RNAi pathway restored transgene expression (Tonkin and Bass 2003). Furthermore, retrotransposon LTR sequences were shown to induce natural RNAi due to RNA duplex formation (Sijen and Plasterk 2003). The RNAi machinery has been implicated in gene silencing in two independent modalities: at the RNA level through degradation of mRNAs and at the chromatin structure level through induction of methylation (Dykxhoorn et al. 2003; Ekwall 2004). Both silencing pathways might be affected by editing of repetitive-element foldback structures. Silencing of RNAs containing such inverted repeats might be prevented through their modification by RNA editing and their subsequent nuclear retention (Zhang and Carmichael 2001) or by rendering those RNAs inadequate substrates of the RNAi machinery. It is possible that the observed embryonic lethality and apoptosis in A-to-I editing-deficient mice (Wang et al. 2000, 2004; Hartner et al. 2004) is related to the breakdown of this control mechanism leading to the posttranscriptional silencing of essential genes.
The work presented here has been based on the analysis of cellular mRNAs that contain Alu repeat elements. However, the underlying principles probably also apply to Alu RNAs generated from transcriptionally active Alu elements. Alu elements do not encode transcription termination signals (Deininger 1989), and thus read-through transcription from transposition-competent Alu repeats can result in intramolecular Alu pairs, leading to the editing of a sequence that subsequently becomes retrotranscribed. Editing of primary transcripts of repetitive elements may have an important role in the control of their proliferation and a dedicated analysis of such transcripts for editing events represents an important future direction.
A recent study by Levanon et al. (2004) reported a computational approach for the identification of heavily edited genes in the human transcriptome and found that editing mostly occurs in Alu repeat elements (greater than 92% of the substrates identified), giving us the opportunity to compare the two approaches and datasets. The computational strategy used by Levanon et al. (2004) differs substantially from ours both in the sequence dataset employed and in the methodology applied. The use of expressed sequence tags (ESTs; in contrast to our use of mRNA sequences) offers a much larger primary dataset for analysis; however, single-pass sequences have a higher error rate (Liang et al. 2000), and EST databases are biased toward sequences near 3′-termini of mRNAs (Liang et al. 2000). Levanon et al. (2004) selected candidate sequences for editing by identifying inverted repeats followed by the evaluation of AtoG mismatch rates, whereas we directly evaluated the AtoG mismatch content in repetitive elements irrespective of the presence of a nearby pairing sequence. The approach of Levanon et al. (2004) allows the discovery of edited inverted repeats that do not belong to any of the repetitive-element families (although the previously known brain substrates were missed), but it does not identify cases where a base-pairing sequence is not evident because of truncated cDNA and EST sequences and incomplete knowledge of gene boundaries. We found that for approximately one-third of the edited Alu elements a pairing Alu cannot be located within the gene boundaries as determined by known mRNAs, although in most of the cases it can be identified at the genome level. A comparison of the edited gene/mRNA datasets of the two studies shows a 34.5% overlap when gene names and symbols are compared. It should be noted, though, that editing of the same gene might reflect editing at different sites or within different Alu elements of the same gene.
The two approaches are overlapping as well as complementary. Taken together, they have probably uncovered the most significant part of the heavily edited exonic sequences for which sequence data are available. From our analysis we estimate an additional approximately 4,000 edited Alu elements besides the 1,925 Alus that we have selected as a very high confidence set. Thus, it is important to note that the heavily edited sequences represent the tip of an iceberg with many more mRNAs in the human transcriptome being edited at single or a small number of positions.
Materials and Methods
RNA editing analysis
Human brain samples were provided by the Harvard Brain Tissue Resource Center, Belmont, Massachusetts, United States; human lung cDNA was from Clontech (Palo Alto, California, United States). Total RNA isolation and reverse transcription have been described previously (Ausubel et al. 1995; Maas et al. 2001). Gene-specific PCR was performed as described earlier (Maas et al. 2001), and a list of oligonucleotide primer sequences used in this study is available on request. RNA editing analysis was done by direct sequencing of gene-specific, gel-purified RT-PCR products as described (Maas et al. 2001), using an automated ABI310 (Applied Biosystems, Foster City, California, United States) capillary electrophoresis sequencer. Human gDNA used for gene-specific PCR was isolated from the same tissues according to standard protocols (Ausubel et al. 1995).
Computational procedures
For analysis of the pool of human cDNA sequences we developed a program named Procedures for Repetitive Element Foldback Analysis (PREFA). We used the set of cDNA sequences from the UCSC database (July 2003) comprising 103,723 sequences (after removal of duplicate entries). The set of repetitive elements (for Alus 1,163,041 unique elements) and related information of the human genome (created with RepeatMasker based on the Repbase [Jurka 2000] release of June 2002) was obtained from the same source. For each examined repetitive-element family we first selected the subset overlapping partially or fully with genes. For Alus the number is 2,003,976, including duplicates, or 572,107 unique sequences. From this subset we then selected those overlapping with exons (31,666).
The RNA and genomic sequence for each element was extracted and compared base by base for mismatches. A small number of cases with very high non-AtoG mismatches (greater than 20/element) were discarded as misaligned or erroneous. From the repetitive elements showing at least a single AtoG change we selected those where mismatch distribution cannot be accounted for by SNPs and sequence errors using the following procedure:
The overall expected ratio of AtoG discrepancies relative to the total number of mismatches was calculated from the whole sample, assuming the expected AtoG mismatches to be approximately equal to the average of the rest of the transitions:
The expected probability for an AtoG mismatch at a single position in a given element was calculated from the total number of mismatches found in the element in cases where other mismatches were present (2) or from the whole sample where only AtoG mismatches were found (3): Here nAtoG and nOther is the total number of AtoG and non-AtoG mismatches found for this element:
Given the probability p for an AtoG mismatch to occur at any given position, the expected values for the number of AtoG were calculated:
A χ2 test was calculated for each element and those with a χ2 value exceeding the critical value (for α = 0.000001) were selected as edited, and these values correspond to approximately more than five AtoG changes in the absence of any other change in the approximately 300 bp of an Alu).
For each element in the high-confidence set the closest inverted element was identified among the elements present in the same gene boundaries. The distance separating the pair was calculated from the location of the first base of each element, according to the genomic sequence numbering and irrespective of their orientation. The divergence of each element was derived from the corresponding entry in the UCSC annotation database (ChrN_rmsk) representing mismatches per hundred bases. Tissue of origin of the RNAs was also derived from the UCSC mRNA annotation. For RNAs described to originate from multiple tissues, the corresponding RNAs were included in the count for each of those tissues. RNAs originating from a specific subregion of a tissue, such as subareas of the brain, were counted within the subregion but not in the whole-tissue set of RNAs.
Alignment of the Chromosome 1-derived Alu sequences was performed with the MegAlign program of the DNASTAR (Madison, Wisconsin, United States) package (Lasergene) using the CLUSTAL algorithm (Jeanmougin et al. 1998). Further manual adjustments were necessary owing to the presence of simple repeats in Alu sequences. Analysis of the alignment and base counts surrounding the editing sites were done with PREFA.
Supporting Information
Table S1 Database of Computationally Identified Editing Targets
The database lists the GenBank accession numbers, gene names, gene product description, chromosome location, and type of Alu element and location within the mRNA sequence, the identity of the most likely pairing Alu elements within the same gene, and the distance in base pairs (bp) between the pairing Alus. The positions of all predicted editing sites within the individual sequences can be viewed by pasting the accession number into the USCS genome browser (Kent et al. 2002) at http://genome.ucsc.edu/cgi-bin/hgGateway and following the link to mRNA/Genomic alignment. We found that six cDNAs map on two chromosomes (AB095924, AK021666, AK055562, AK092837, AK094425, and BC039501); details are given for the most plausible assignment. We have observed that in the 43 cases that we experimentally analyzed, usually additional editing sites were identified when directly sequencing gene-specific PCR products.
(276 KB XLS).
Click here for additional data file.
Accession Numbers
The GenBank ((http://www.ncbi.nlm.nih.gov/Genbank) accession numbers for the genetic sequences discussed in this paper are LUSTR (AB046844), KIAA0500 (AB007969), BTKI (AB037838), KIAA1497 (AB040930), and GPR81 (BC0067484).
The Entrez Gene (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene) ID numbers for ADAR1, p53, SIRT2, NFκB, and SPG7 are 103, 7157, 22933, 4790, and 6687, respectively.
We thank Jessica Rosenkrantz and Kieran Pechter for technical assistance and Chris Burge for useful discussions. AA was supported in part by Human Frontier Science Program.
Conflicts of interest. The authors have declared that no conflicts of interest exist.
Author contributions. AA and SM conceived and designed the experiments. AA and SM performed the experiments. AA and SM analyzed the data. AR contributed reagents/materials. AA, AR, and SM wrote the paper.
Academic Editor: Marv Wickens, University of Wisconsin
Citation: Athanasiadis A, Rich A, Maas S (2004) Widespread A-to-I RNA editing of Alu-containing mRNAs in the human transcriptome. PLoS Biol 2(12): e391.
Abbreviations
ADARadenosine deaminase acting on RNA
BTKIBruton's tyrosine kinase
dsRNAdouble-stranded RNA
ESTexpressed sequence tag
gDNAgenomic DNA
HUGEHuman Unidentified Gene-Encoded
PREFAProcedures for Repetitive Element Foldback Analysis
RNAiRNA interference
siRNAsmall interfering RNA
SNPsingle-nucleotide polymorphism
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| 15534692 | PMC526178 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 9; 2(12):e391 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020391 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1553469310.1371/journal.pbio.0020393Research ArticleBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyMus (Mouse)Use of a Dense Single Nucleotide Polymorphism Map for In Silico Mapping in the Mouse In Silico Mapping in MousePletcher Mathew T
1
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McClurg Philip
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Batalov Serge
1
Su Andrew I
1
Barnes S. Whitney
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Lagler Erica
1
Korstanje Ron
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Wang Xiaosong
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Nusskern Deborah
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Bogue Molly A
3
Mural Richard J
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Paigen Beverly
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Wiltshire Tim [email protected]
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1Genomics Institute of the Novartis Research Foundation, San DiegoCaliforniaUnited States of America2The Scripps Research Institute, San DiegoCaliforniaUnited States of America3The Jackson Laboratory, Bar HarborMaineUnited States of America4Celera Genomics, RockvilleMarylandUnited States of America12 2004 9 11 2004 9 11 2004 2 12 e39330 6 2004 15 9 2004 Copyright: © 2004 Pletcher et al.2004This 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.
Computational Mapping of Complex Traits in the Mouse
Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.
A large resource of genetic markers, typed in 48 strains of mice, combined with statistical techniques, allow "in silico" mapping of genetic regions involved in interesting traits in mice
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Introduction
The combined efforts of the public and private mouse genome sequencing consortiums have yielded important advances in understanding the structure and content of the genome (Mural et al. 2002; Waterston et al. 2002). Identification of new genes from the sequence data and placement of all genes, along with genetic markers, on a physical assembly has greatly aided in the search for phenotypically important genes in both quantitative trait loci (QTL) and mutagenesis-based mapping. The sequencing of four different strains of laboratory mice for the initial genome assemblies also produced a catalog of natural sequence variations that are present between these commonly used strains. Other smaller scale resequencing efforts have increased the breadth of this information by including additional strains (Lindblad-Toh et al. 2000; Grupe et al. 2001; Wade et al. 2002; Wiltshire et al. 2003).
The utility of this sequence-variation data is 2-fold. First, the single nucleotide polymorphisms (SNPs) identified by these sequencing projects provide denser coverage marker sets that are well suited for high-throughput genotyping systems. Currently, these benefits have only been available for crosses between the relatively few strains for which substantial polymorphism discovery has been undertaken.
Second, the distribution of SNPs between any two strains, or more precisely, the lack of SNPs between two mouse strains, indicates regions of their genomes that were inherited from a common ancestor. Phenotypic differences that are traditionally mapped in QTL studies are almost exclusively due to loci inherited from different ancestral progenitors rather than new mutations (Frazer et al. 2004). Thus, a detailed knowledge of where common ancestral regions lie between strain pairs would speed QTL mapping by elimination of shared regions from consideration as candidate loci (Wade et al. 2002). Additionally, it has been proposed that the actual haplotype structures marking these ancestral relationships can be determined and that the relationship of haplotype distribution among mouse strains and phenotypic variation could be used to directly map the genetic controls for the phenotypes (Grupe et al. 2001). However, three major factors have seriously curtailed the implementation of in silico mapping methods: a lack of the necessary SNP density and distribution along the genome for more than just a few strains; incomplete phenotype data for multiple strains, and lastly, the appropriate analysis tools for making genotype–phenotype associations. More recently uncertainties have also been expressed concerning the level of data that will be required to make in silico mapping a viable method. This is in part due to the emerging complexity of the haplotype structure in mouse and also to such issues as how many strains need to be phenotyped to be able to gain statistical power for in silico mapping (Darvasi 2001; Frazer et al. 2004; Yalcin et al. 2004).
To overcome the barriers to in silico mapping, 10,990 SNP assays have been typed against 48 mouse strains in this study. These assays provide an extensive polymorphic marker set enabling expansion of traditional mapping efforts to other strains. Wide-ranging phenotyping projects that have been coordinated by The Jackson Laboratory (http://www.jax.org/phenome) have collated multistrain phenotype data. We demonstrate that when using these datasets, in combination with new analysis methods, statistically significant associations between discrete genomic regions and biologically important phenotypes can be identified. Confirmation of these associations was obtained by comparison to data from traditional QTL mapping methods.
Results
SNP assays were designed based on sequence data from the Celera Mouse SNP Database and typed, in duplicate, against the genomic DNA of 48 strains of mice, including all 40 Mouse Phenome Project priority strains (see list of strains in Tables S1 and S2) (Bogue 2003). Twelve wild-derived inbred strains were included in the set. Two strains, SPRET/EiJ and SEG/Pas (Mus spretus), represent a different species of mouse from the other lines tested. Not surprisingly, fewer genotypes were obtained from these two because of the divergent genomes (Tables S1 and S2) of the distinct species, which led to a higher failure rate in the genotyping reactions. For the 36 non-wild-derived strains typed, 8,349 SNP assays produced at least 90% of the possible allelic data.
Previously, sufficient polymorphic markers have not been available for many strain–pair combinations. The development of this SNP panel provides a resource of polymorphic markers to enable traditional mapping projects between almost any strain–pair combination of the 48 strains. In mapping a phenotype, introduction of modifier genes can be a confounding influence, and selection of more closely related mapping partners can alleviate this problem. The SNP density in this set is sufficient that mapping can now be accomplished between strains that had previously been too closely related for sufficient markers to be found. For example, for C58L/J by C57BL/6J and C57BL/6J by C57BL/10J comparisons, over 2,000 and 400 polymorphic markers, respectively, are available. Although large gaps do exist in the coverage of these strain–pair combinations, markers are present on all chromosomes to allow for initial candidate region identification (Figure 1). Details of all allele calls and SNP assays are available in Dataset S1.
Figure 1 Visualization of the SNP Sets Allows for Mapping in Crosses That Minimize the Number of Potential Modifiers
When the distribution of the SNPs is plotted out genome-wide, the expected irregular clustering of SNPs mark regions where heterozygosity was continuing to segregate during the inbreeding of the C57 family. Likewise, there are regions that were successfully homozygosed before the split of C58/J from the rest of the family members. In all five strain comparisons, no SNPs were found in the distal 25 Mb of MMU19.
Just as in humans, a spectrum of phenotypic values can be observed among the inbred strains of mice. SNPs that occur between these strains may produce a specific functional change in a gene leading to this phenotypic variation but are more often simply markers for an ancestral haplotype. The goal of in silico mapping is to identify which haplotype patterns (genetic measure) track with the phenotypic outcome with the idea that these haplotypes contain causative mutations. For in silico mapping to be successful, a requirement is that the SNP data accurately represent this ancestral relationship of the mice at the genomic level. At a gross level, this was examined by comparing branches of the phylogenic tree generated from this SNP dataset with the known breeding histories of the strains used in this study (Beck et al. 2000). An inspection of the C57-related family of mice, derived from a tree built from the SNP data of all 48 strains (Figure S1), recapitulates the family's lineage in the phylogenic tree (Figure 2A).
Figure 2 Genome-Wide SNP Data Accurately Represent the Known Ancestries of the Genotyped Strains
(A) A tree, adapted from Beck et al. (2000), tracing the lineage of the C57 family of mice (upper tree) shows almost perfect correlation with a phylogenic tree based solely on SNP data (lower tree). The only difference in the two trees is the location of C57BLKS/J, which splits from C57BL/6J sooner in the phylogenic tree because of the genomic contributions of the non-C57 strain, DBA/2J. The maximum parsimony phylogenic tree of the strain relatedness was built using the pseudoalignment of the 10,990 SNP alleles for 48 strains with the Phylip package, version 3.6b.
(B) The DBA/2J contribution to C57BLKS/J can be visualized in its allelic patterns. The region from 104 Mb to 109 Mb on MMU9 shows the same SNP alleles for both C57BLKS/J and its other parental strain, C57BL/6J (a period represents identity with the C57BLKS/J allele). At 110 Mb, the pattern switches and every C57BLKS/J allele matches the DBA/2J content through 120 Mb. SNP marker names are positioned above the alleles with the first number representing the chromosome the marker is located on, the second number being the Mb position on the chromosome, and the third number being an approximate location within the Mb.
At a more detailed level the specific genomic contributions of mouse strains derived as hybrids of other common laboratory strains can be estimated. For example, DBA/2J is considered to have contributed approximately 16% of the genomic content to the C57BLKS/J mouse (Naggert et al. 1995). Comparisons of C57BL/6J, the other founder strain of C57BLKS/J, and DBA/2J-specific alleles to the SNP content of C57BLKS/J clearly defines these large regions of DBA/2J contribution as shown for Mus musculus Chromosome 9 (MMU9) (Figure 2B). Based on the SNP data, it can be estimated that 20% of the C57BLKS/J genome came from a DBA/2J origin, including almost all of MMUX. This type of analysis also indicates that at least one additional strain, possibly 129-like, contributed genomic content to C57BLKS/J in regions where the allelic pattern matches neither DBA nor C57BL/6J.
A lack of sufficient underlying SNP data to this point have prevented the thorough development and testing of an algorithm to carry out in silico mapping (Chesler et al. 2001; Darvasi 2001). Previously published methods were severely limited by the lack of SNP density and strain coverage leading to a method that utilized generalized genetic distances and resulted in lack of resolution in the analysis (Grupe et al. 2001; Smith et al. 2003). Based on the data above, this SNP set provides sufficient spacing and resolution to distinguish discrete ancestral patterns, allowing for subsequent in silico analyses to treat the genetic measure used in these calculations as categorical. Although the number of SNPs used here still does not allow the precise definition of haplotype blocks, the relatively even spacing of the SNPs every 300 kb does allow for an inference of ancestral relationships across 1-megabase (Mb) regions. For this reason, a sliding window of three SNPs is used to infer haplotypes at each locus. Strains showing the same pattern are grouped in the same inferred haplotype, as a single category, and any variations are considered to form distinct inferred haplotypes. All of the strain-distribution patterns created by this definition of inferred haplotype were compared across the genome to determine their uniqueness. Replication of the same strain-distribution pattern at multiple locations across the genome, or “mirror loci,” would result in regions that are all equally associated with the phenotype and produce false positives. No occurrences of mirror loci were found outside of a 5-Mb region of any three-SNP block.
With this in mind a logistic regression model followed by analysis of deviance was used to determine the association between a sliding window of three SNPs and phenotype scores of 1 or 0 for the presence or absence of three Mendelian traits: coat color traits of nonagouti and albino and retinal degeneration. All of the phenotypes were determined from phenotypic descriptions in The Jackson Laboratory mouse database (http://jaxmice.jax.org/jaxmice-cgi/jaxmicedb.cgi). Albino mice were excluded from the mapping of nonagouti because the nature of the phenotype prevents the ascertainment of agouti or nonagouti coat colors. In each case, the appropriate locus for the gene responsible for the particular trait was identified from this SNP collection with the most significant p-value (Figure 3A; Kwon et al. 1987; Bowes et al. 1990; Bultman et al. 1992). Interestingly, for in silico mapping of the nonagouti locus, a highly significant score was also obtained for a locus on MMU7 at 29.9 Mb. This is also the approximate location of the dark locus, an unidentified gene that also influences coat color (Silvers 1979).
Figure 3 In Silico Mapping Method Correctly Identifies Coat Color, Retinal Degeneration, and Sweet Preference Loci from SNP Data
(A) Presence or absence of the retinal degeneration, albino, or agouti phenotypes was given a numerical value of 1 or 0 for use in the mapping algorithm. In each case, the most significant p-value (indicated by an arrow) was obtained for the region that contains the gene known to produce these phenotypes. A closer inspection of the retinal degeneration mapping shows that the maximum linkage region indicated by the algorithm covered a 0.4-Mb region from 102.4 Mb to 102.8 Mb on MMU5.
(B) Tas1r3 is known to be a major control gene for the complex trait of preference for sweet tastes. Values for the sweet preference of 23 strains of mice produced a highly significant association with the one Mb region of MMU4 that contains Tas1r3.
The ability to properly identify causative genes for monogenic traits is a minimum requirement for a viable in silico mapping method, but to serve its intended purpose it must be able to point to controlling loci when multiple genes act in concert to contribute to a phenotype. To examine a quantitative trait, data from a two-bottle saccharin preference test for 23 strains of mice were analyzed with the Fisher permutation-based analysis of variance (ANOVA) statistical model. Briefly, at each three-SNP window, a modified F-statistic based on the true genotype–phenotype pairings is calculated (detailed in Materials and Methods). The significance of this test statistic is estimated by comparing to a distribution of 1 million random bootstrap samples of phenotypic values. A three-SNP window beginning with marker 04.155.136 obtained the lowest p-value of the genome scan (Figure 3B). This locus corresponds to the position of the gene, Tas1r3, identified by traditional QTL methods as a primary contributor to the variability of the sweet preference quantitative trait (Bachmanov et al. 2001; Max et al. 2001) Three other saccharin preference QTL were also found to overlap significant associations from this mapping (Table 1).
Table 1 Comparison of In Silico QTL with Experimentally Derived QTL
B6, C57BL/6J. For definitions of other abbreviations, see Abbreviations section in text
a References indicate source of QTL data
b The in silico mapping algorithm was run twice for each phenotype, and only SNP blocks that obtained a log p-value above the cutoff in both runs are shown here. When more than one marker within the same genomic region obtained a log p-value above the cutoff, only the marker with the most significant p-value is shown
c The median p-value from each algorithm run was averaged
d QTL regions were defined as the experimentally determined 95% CIs for the particular strain-cross referenced unless otherwise noted
e The 95% CIs were not available, so the QTL regions were defined as ± 10 cM from the position with the highest likelihood-of-odds score (peak), as it represents the approximate size of the available 95% CIs for this dataset and has been previously published in X. Wang and Paigen (2002) as the definition of HDL QTL size. One marker 10 cM away from the peak on either side was chosen, and their physical positions were retrieved from Celera Mouse Database
f Additional crosses support this QTL region
g MMUX has not been studied in mouse crosses used to detect HDL QTL
h QTL for gallbladder mucin content, an early step in gallstone formation
After validation with both monogenic traits and a quantitative trait, the same strategy was applied to map quantitative trait loci for the control of plasma high-density lipoprotein cholesterol (HDL) and gallstone development. The average HDL values from 10-wk-old mice fed on a normal chow diet were taken from the Mouse Phenome Database (Paigen et al. 2002; Bogue 2003). Because of the complexity of these traits, a conservative approach was used for strain selection. Data used for only 23 of the most related laboratory strains and two of the M. musculus domesticus strains because if a strain is from a unique lineage and contains unique haplotypes, it will not add any power to the analysis and risks increasing the level of noise (see Materials and Methods for a list of the 25 strains). Using a three-SNP window to analyze the 25 strains, there were no mirror loci present, and on average, 3.8 distinct inferred haplotypes were found at each locus.
Where multiple loci may be expected to be found, as is the case for multigenic traits, a significance threshold was defined. To determine the false positive rate of each p-value, a recently described method by Dudoit et al. (2004) was used. The generalized family-wise error rate (gFWER) method uses a bootstrap estimation of the null distribution to assign a significance cutoff. In the case of the HDL phenotype, a significance threshold associated with a false positive rate of less than 0.005 (p-value = 0.000506; −log[p] = 3.2958) was used (Figure S2). Nineteen three-SNP windows were identified as having significant association with the HDL phenotype, which collapsed into 11 distinct loci (Figure S3). To gauge the reliability of the in silico predictions, the results were compared to previously described QTL regions. Nine of these 11 loci fell within one of the regions identified by traditional two-strain crosses (Table 1). Of the two that were not found to match previously identified QTL, the in silico MMUX QTL would not be expected to be matched because MMUX has been excluded from consideration in prior HDL QTL work.
This same type of analysis was repeated for a phenotype that scored the formation of gallstones in 25 strains of male mice (Paigen et al. 2000). Eleven regions were produced that exceeded the gFWER false positive cutoff (p-value = 0.000398; −log[p] = 3.400117), and seven of these regions fell within the range of traditionally identified QTL for gallstone formation or mucin accumulation, which is considered a precursor to gallstone formation (Table 1; D. Q. Wang et al. 1997).
As well as identifying QTL, the inferred haplotype data from this SNP set also can be used to assist the narrowing of candidate regions, aiding in the selection of candidate genes. An association for a region overlapping an HDL QTL previously identified on MMU8 did not meet the stringent statistical cutoff set for the in silico method (X. Wang and B. Paigen 2002). The most significant p-values obtained for the MMU8 QTL region were consistently found between 89–94 Mb. Sample sequencing of the region confirmed, at a slightly higher resolution, the SNP pattern that generated the association. This sequencing also replicated an inferred haplotype break point in the BTBR strain that narrowed the region to 88.52–90.88 Mb. A candidate gene within this 2-Mb region, adenylate cyclase 7 (Adcy7), located at 89.55 Mb, is expressed in the liver and adipose tissue (http://symatlas.gnf.org) and functions by producing cyclic adenosine monophosphate (Watson et al. 1994). Cyclic adenosine monophosphate is known to be an important signaling component in the pathway to lipolysis (Cammisotto and Bukowiecki 2002). Homologous regions containing the rat and human ortholog of Adcy7 have also been identified as containing an HDL QTL (Bottger et al. 1996; Mahaney et al. 2003; Pajukanta et al. 2003).
Adcy7 was sequenced in strains representing the three inferred haplotypes identified for this locus in the SNP dataset. Twenty-eight SNPs were identified in the gene, three of which produced amino acid changes. Nineteen of these SNPs, including the three nonsynonymous changes, were typed against all 48 strains of mice (Figure 4A). One of the haplotypes showed a higher average HDL level than all the others (77.5 mg/dl + 20.3 versus 67.2 mg/dl + 24.3 and 57.9 mg/dl + 16.7). This haplotype also contained a SNP causing a C717Y change in exon 20. Among the 48 strains, the members of this haplotype are the only ones with a replacement of this cysteine, which is conserved in the rat, cow, and human versions of the gene (Figure 4B), making it a good candidate for being a gene that contributes to the variability of HDL levels in the blood (Abiola et al. 2003).
Figure 4 Analysis of Adcy7 Haplotypes Reveals Amino Acid Change Associated with HDL Phenotype
(A) Sequencing of Adcy7 in multiple strains revealed 28 SNPs distinguishing three distinct haplotype patterns. All strains were typed with markers selected to represent the three haplotypes. The strain distribution pattern predicted by the SNP data and the sample sequencing for this region was confirmed with NZB/BlNJ and BTBR T+ tf/J, I/LnJ and MA/MyJ, and C3H/HeJ, C57BL6/J, and C57L/J, each separating into unique haplotypes.
(B) The SNP represented by marker 08.089.597 resulted in a change from a cysteine to a tyrosine in the resulting protein (asterisk). This cysteine is conserved in orthologs of the gene in human, rat, and cow. It is also found at the beginning of a stretch of ten amino acids (indicated by black line) predicted to be one of the protein's ten transmembrane domains. Identical amino acids are black and conserved amino acid changes are gray.
Discussion
The SNP data here provide new resources for traditional mapping projects and enable development of inbred strain haplotype methods for QTL detection. The analyses presented here indicate that the inferred haplotype structures derived from this dataset provide sufficient estimation of genetic diversity/similarity to map Mendelian traits to within 1-Mb intervals. QTL can also be defined as inferred haplotype loci of several megabases in size. The analysis for QTL provides a rank order of significant phenotype/genotype associations, and using the gFWER method of controlling for multiple-testing error, the loci reported as statistically significant are very likely to be biologically relevant. This point is borne out by the high concordance between the in silico QTL and the traditionally determined QTL (Table 1). The traditionally determined HDL QTL identified in the mouse covers 42% of the genome and are in concordance with nine of ten in silico QTL—a significant result (p < 0.0025). This excludes the MMUX in silico QTL since they cannot be verified from current traditional QTL data. The false positive cutoff employed here is very restrictive and could be relaxed to find additional real associations, but the chances of including false positives would then increase. For the gallstones phenotype the concordance is not demonstrated to the same level; however, the top-ranked loci still show overlap with previously defined QTL. What about the loci that do not show overlap; are they still real? From a statistical analysis it is unlikely they are false positives. In these results, 25 strains are simultaneously combined, unlike standard QTL mapping using two-strain comparisons, and some phenotype–genotype associations may occur that have not been observed by classical methods. Contributions from diverse strains that have not normally been used in F2 crosses or available in RI lines can now be incorporated.
Even showing that this method does find significant associations, the question arises about its general utility and applicability. The methods of in silico mapping as described here should be viewed as a complement to, and not a substitute for, traditional methods for mapping QTL. Although we have demonstrated a robust approach to in silico mapping, it would certainly not be expected to find all QTL for a given phenotype. Major contributors to phenotypic variation will show up, but weaker contributors would be expected to be lost because of the limited power of 25 strains. It would also be expected to miss QTL resulting from recent strain-specific mutations or low-frequency haplotypes. Traditional QTL methods will still be required to identify the more subtle interactions, including those involving epistasis and modifying genes.
However, these methods would provide a useful starting point for a new phenotype that is being investigated, where often the first step for any QTL analysis is a strain survey to quantify the range of the phenotype. Additionally, if these analyses are overlaid with the results from a traditional two-strain QTL mapping, one of the major advantages to be gained from this approach is that associative loci are defined in terms of a few megabases instead of tens of centiMorgans.
The number and selection of strains and appropriate phenotype are also important considerations. Here we have limited our analysis of the complex traits, HDL and gallstones, to 25 strains—those that are best interrogated by this SNP set. While it is true that more strains have the potential to add greater statistical power to resolve QTL, this potential is limited by our ability to accurately represent the ancestral relationship of those additional strains. If we add more strains, but cannot accurately infer haplotype structures in those strains, we only add more noise to the analysis. The ability to detect all possible haplotypes in the utilized strains from the SNP data suffers from the availability of sequence data, currently from only four strains of mice. Because the source SNPs come from the sequencing of only four closely related strains, this current set is biased toward interrogating ancestry of M. m. domesticus. To be successful, phenotypes must have a low intrastrain variation but sufficient variance within the strain set selected. This however, is not a requirement restricted to in silico mapping.
The overall power of this method will only improve as the biases and limitations of the SNP panel are addressed and additional strains are genotyped and phenotyped. Unique strains would become more useful if all possible SNPs are known and the mapping is then done directly with the causative polymorphism or at least with a large unbiased set of SNPs. As resequencing of other mouse genomes progresses, the ability to correctly infer the complete number and structure of haplotypes will improve, and the number of QTL regions reaching statistically significant levels will increase.
Recently, two similar studies of haplotype structures across 5-Mb regions were published, although they produced differing conclusions on how their findings might affect in silico mapping efforts (Frazer et al. 2004; Yalcin et al. 2004). Yalcin et al. has suggested that the complex nature of mouse haplotype structure and the small size of many haplotypes in inbred strains will make in silico mapping methods untenable and will preclude the mapping of any meaningful genotype–phenotype association short of whole genome resequencing (Yalcin et al. 2004). This assessment would presumably hold true even for the well-defined Mendelian traits. The inferred haplotypes from a three-SNP window spanning on average 900 kb would not be able to reflect ancestral relationships, so the appropriate genotype–phenotype association could not be made no matter the strength of the allele in determining phenotype. Yet, clearly they can. The Frazer et al. (2004) study, which utilized more strains and produced significantly greater coverage of their 5-Mb region, estimates that the average ancestral segment length among classical inbred strains is in the order of 1.5 Mb in size, within the resolution of this work. In fact, the Yalcin et al. (2004) data show similar megabase-long ancestral relationships between strain pairs (for example, 5 Mb of near identity between A/J and C3H/J). This in silico approach concurs with the conclusions of Frazer et al. (2004). Despite the complexities of haplotype structures, the use of a large enough set of strains with a dense SNP map does allow for significant and real associations to be found.
This is not to suggest that fragmented small haplotypes are not common in the genome of the inbred mouse. This clearly does mean that there will be regions of the genome that will not be interrogated well by an in silico method. This approach is still limited by the density of this SNP map and can only be expected to visualize inferred haplotype patterns of approximately 1 Mb in size, and therefore smaller haplotype structures are hidden and potential phenotype–genotype associations will be missed. Here future, larger SNP sets that will allow more SNPs to infer haplotype will become important. However, this is the best resolved whole genome view of the diversity of the commonly used inbred strains to date.
The algorithms employed here provide a starting point for further development of in silico mapping. We have shown that they can be used to identify Mendelian traits and replicate classical QTL associations. Clearly, the next goals are to validate some of the previously unreported associations, and this work is ongoing.
Materials and Methods
SNP selection and detection
SNPs for use in genotyping were selected on a weighted basis from the Celera Mouse SNP Database containing data from the DBA/2J, A/J, C57BL/6J, 129S1/SvImJ, and 129X1/SvJ strains. Sufficient SNPs were selected for coverage of at least one SNP per 300 kb on average. The 129S1/SvImJ and 129X1/SvJ strains were considered as the same strain when their alleles agreed; preference was given first to SNPs where each allele of the SNP was represented by two strains. This was done to favor selection of SNPs representing ancestral inheritance, not recent strain-specific mutations, and to favor real SNPs as opposed to errors in sequence annotation. Additional selective value was incorporated based on whether the SNP was in a gene, how many sequencing runs supported the presence of the SNP, and the proximity of the SNP to previously selected SNPs. Additional SNPs used to characterize the Tas1r3 locus were gathered from sequence from multiple strains available in GenBank (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=nucleotide&cmd=search&term=tas1r3). All physical positions presented in the paper are from the Celera Mouse Genome Assembly R13.
Primers for PCR and single-base extension were designed by using the SpectroDESIGNER software package (Sequenom, San Diego, California, United States). Assay designs are available as Supporting Information. All SNP assays were named for their position in the genome in the following format: the chromosomal location, the Mb position on the chromosome, and the kb position with a period separating each number.
For SNP genotyping, genomic DNA from pedigreed mice (Mouse DNA Resources, The Jackson Laboratory, Bar Harbor, Maine, United States) was diluted to 10 ng/μl, and 1 μl of DNA was combined with 2.45 μl of water, 0.1μl of 25 mM dNTPs (Invitrogen, Carlsbad, California, United States), 0.03μl of 5 units/μl HotStar Taq (Qiagen, Valencia, California, United States), 0.625 μl of 10X HotStar PCR buffer containing 15 mM MgCl2, 0.5μl PCR primers mixed together at a concentration of 1.25 μM for multiplexed reactions, and 0.325 μl of 25 mM MgCl2. Reactions were heated at 95 °C for 15 min followed by 45 cycles at 95 °C for 20 s, 56 °C for 30 s, and 72 °C for 1 min and a final incubation at 72 °C for 3 min. After PCR amplification, remaining dNTPs were dephosphorylated by adding 1.5 μl of water, 0.17 μl of homogeneous mass extend reaction buffer (Sequenom), 0.3 units of shrimp alkaline phosphatase (Sequenom), and 0.03 μl of 10 units/μl exonuclease (USB Corporation, Cleveland, Ohio, United States). The reaction was placed at 37 °C for 20 min, and the enzyme was deactivated by incubating at 85 °C for 15 min. After shrimp alkaline phosphatase treatment, the genotyping reaction was combined with 0.76 μl of water, 0.2 μl of 10X Termination mix (Sequenom), 0.04 μl of 0.063 units/μl Thermosequenase (Sequenom), and 1μl of 10 mM extension primer. The MassEXTEND reaction was carried out at 94 °C for 2 min and then 99 cycles of 94 °C for 5 s, 52 °C for 5 s, and 72 °C for 5 s The reaction mix was desalted by adding 3 mg of a cationic resin, SpectroCLEAN (Sequenom), and resuspended in 30 μl of water. Completed genotyping reactions were spotted in nanoliter volumes onto a matrix arrayed into 384 elements on a silicon chip (Sequenom SpectroCHIP), and the allele-specific mass of the extension product was determined by matrix-assisted laser desorption ionization time-of-flight MS. Analysis of data was by automated allele calling from the SpectroTYPER software. All SNP data are available at NCBI dbSNP (http://www.ncbi.nih.gov/entrez/query.fcgi?db=snp) and The Jackson Laboratory Mouse Phenome Database (http://www.jax.org/phenome). Placement of the SNP data across the genome and major and minor allele distributions can be visualized using SNPview (http://snp.gnf.org).
Statistical modeling for in silico mapping
The use of a single marker is restrictive in the sense that it only allows a representation of the genome as diallelic. The use of windows of multiple markers enables the visualization of more complex genomic relationships between multiple strains. This more accurately models actual haplotype patterns than does a binary approach. In determining the size of the SNP window to use as a definition of inferred haplotype for purposes of the algorithm, sizes of two, three, four, and five SNPs were examined. A window of only two SNPs was still found to be too limiting. Windows of three, four, and five SNPs produced similar results, but as window size is increased biologically meaningful patterns become fragmented, creating more single-strain inferred haplotypes, resulting in an increase in noise. Singly represented haplotypes can never be informative in this analysis because the commonality of haplotypes is required to achieve significant association with a phenotype. Three SNP windows were also analyzed across the whole genome to identify mirror loci. This would be a locus that has exactly the same strain distribution pattern across all 25 strains used in an in silico run. There were no mirror loci, or 1-off, or 2-off mirror loci (with one or two strains not grouped identically) that occurred outside of a 5-Mb interval.
Defining the genetic measure as a categorical unit necessitated the use of an ANOVA-based model. The type of ANOVA to use was determined by the characteristics of the phenotypic values.
The phenotypes studied here fell into two categories: binary or continuous. The coat color phenotype was considered as binary, where phenotypic values were set to 1 and 0. The HDL phenotype is an example of a continuous phenotype since the phenotypic values are measured on a continuous scale. Two different statistical methods were employed based on this distinction.
When phenotypic values are binary, the appropriate statistical approach involves first fitting a binomial generalized linear model to the data. An analysis of deviance table is then computed for the fitted model. The R language function glm with the parameter family set to binomial was used. This was followed by an application of anova.glm with the parameter test set to Chisq.
For continuous phenotypic values, a log transformation was applied to reduce the effects of outliers in the phenotypic data. Next, an F-statistic weighted for the genotypic diversity of the strains within an inferred haplotype group was used. The weighted F-statistic had the following form:
where
and
where ng is the number of strains in a given inferred haplotype, μg is the mean of phenotypic values in a given inferred haplotype , μT is the mean of all phenotypic values, k is the number of inferred haplotypes, N is the total number of data values, and wg is the weight representing the genetic diversity of the inferred haplotype. The genetic diversity ratio (wg) between two strains is the number of SNPs genome-wide in which both strains have genetic information and they disagree, divided by the total number of SNPs in which they both have genetic information. The genetic diversity coefficient for an inferred haplotype in the weighted F-statistic is the average wg between all strain pairs contained in the inferred haplotype.
The weighted F-statistic calculated at each SNP window determines if at least one of the inferred haplotypes has an average phenotypic value significantly different from the other inferred haplotypes. To assess the significance of the computed value, the null distribution of the weighted F-statistic was simulated at each SNP window by taking a million bootstrap samples of the phenotypic values. As in the algorithm used for binary phenotypes, inferred haplotype patterns present in only one strain were not included in the calculation because they are not informative in elucidating shared ancestral blocks. From this distribution of a million random F-statistics, 200 bootstrap samples of size 1 million were computed. For each bootstrap sample, a p-value was computed by dividing the number of random F-statistics larger than the true F-statistic by the total number of random F-statistics (million). In this way 200 p-values were collected. The vertical heights reported in the bar graphs (see Figure S2) are the −log(p) transform of the median of these 200 p-values. A 95% confidence interval (CI) for the p-value at this window was also calculated from this bootstrap distribution.
To estimate the overall false positive rate for this type of calculation, calculating a significance threshold based on the family-wise error rate (FWER) has been proposed (Churchill and Doerge 1994). Others have noted that the traditional FWER calculation is too strict in the context of multiple testing and leads to a significant loss of power (Lander and Kruglyak 1995). Therefore, we employed a recently developed method of bootstrap estimation of common cutoffs based on the gFWER (Dudoit et al. 2004). Whereas the FWER method reports significance, using the most conservative criterion of only one false positive, the gFWER method controls for multiple testing while allowing for an acceptable false positive rate (in our case, α < 0.005).
The gFWER method to control for false positives as applied to in silico mapping is briefly described as follows. A null reference distribution was constructed using random bootstrap tests to determine a significance cutoff. Ten thousand bootstrap samples of phenotype values were randomly assigned to the true haplotype structure. For each random bootstrap sample, the nonparametric ANOVA approach outlined above was performed at each three-SNP window, with one difference. Whereas the initial true calculation reports the median of 200 bootstrap p-values, the gFWER method requires an estimate of the “supremum” (least upper bound) of expected values reported at each locus, so the most significant value is reported from the 200 bootstrap p-values (following Procedure 3 in Dudoit et al. 2004), ensuring a conservative false positive estimate. For each bootstrap sample, the genome-wide −log(p-value) corresponding to the (1 − α) percentile was added to the null distribution (as described in Procedure 5, Dudoit et al. 2004). Finally, after the 10,000 bootstraps are complete, the significance threshold is set as the (1 −α) percentile in the entire null reference distribution (computed from our 10,000 randomly bootstrapped iterations). While this threshold still represents a conservative estimate of the desired false positive rate, the gFWER has significantly more power than the traditional FWER calculation.
Using this method for calculation of false positives, it is not necessary to specify the marginal distribution of the test statistic at each window of SNPs. Estimations of false positives or power that assume some parametric form of test statistic's distribution are not reliable in this context. This distribution can alter radically at each SNP window. In this context, the statistical problem of calculating quantities like discovery power (that is, ultimately the type I and type II error) is further complicated. Nearly 11,000 hypothesis tests (one at each three-SNP window) are conducted in a single run of the algorithm. Therefore, equations that currently exist for the estimation of power for QTL mapping by traditional methods cannot be applied here because they assume that the test statistic has some previously defined parametric form. Code for the above described algorithms is available upon request.
For calculation of the significance of the number of in silico QTL that overlapped with previously identified QTL for the HDL phenotype, a binomial distribution was used
given p = probability of success of 0.42 (overlap with HDL QTL in previous literature).
Therefore q = probability of failure; the 0.0025 result is the probability of at least nine successes in ten trials. Only ten loci could be assessed for this result as no information is available for traditional HDL QTL present on the X chromosome.
For the mapping of the retinal degeneration traits, 37 strains were used. This represented all of the strains for which information existed in The Jackson Laboratory database minus the most divergent wild-derived strains for which inference of haplotype would be expected to be most inaccurate. These strains were A/J, AKR/J, BALB/cByJ, BUB/BnJ, C3H/HeJ, C57BL/10J, C57BL/6J, C57BLKS/J, C57BR/cdJ, C57 l/J, C58/J, CBA/J, CE/J, DBA/1J, DBA/2J, FVB/NJ, I/LnJ, KK/HlJ, LG/J, LP/J, MA/MyJ, NOD/LtJ, NON/LtJ, NZB/BlNJ, NZW/LacJ, PERA/EiJ, PL/J, RIIIS/J, SEA/GnJ, SJL/J, SM/J, ST/bJ, SWR/J, WSB/EiJ, ZALENDE/EiJ, 129S1/SvImJ, and 129X1/SvJ. Because of the added complexity of the coat color traits, mapping was restricted to the 25 most related strains for which coat color phenotype could clearly be determined. For the albino analysis, 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, C3H/HeJ, C57BL/10J, C57BL/6J, C57BLKS/J, C57BR/cdJ, C57 l/J, C58/J, CBA/J, DBA/1J, DBA/2J, I/LnJ, LP/J, MA/MyJ, NZB/BlNJ, NZW/LacJ, PERA/EiJ, PL/J, SEA/GnJ, SM/J, WSB/EiJ, and ZALENDE/EiJ strains were used. For the nonagouti mapping, the same strain set as the albino mapping was used except for the mice presenting the albino phenotype. The strains were 129S1/SvImJ, C3H/HeJ, C57BL/10J, C57BL/6J, C57BLKS/J, C57BR/cdJ, C57 l/J, C58/J, CBA/J, DBA/1J, DBA/2J, I/LnJ, LP/J, NZB/BlNJ, PERA/EiJ, SEA/GnJ, SM/J, WSB/EiJ, and ZALENDE/EiJ. Any mouse showing an agouti coat color was considered to be agouti for this analysis regardless of genotype at the agouti locus. Only limited phenotype data were available for saccharin preference, so again all strains with available data except the most divergent wild-derived strains for which inference of haplotype would be expected to be most inaccurate were used. These strains were A/J, AKR/J, BALB/cByJ, BUB/BnJ, C3H/HeJ, C57BL/6J, C57 L/J, CBA/J, CE/J, DBA/2J, FVB/NJ, I/LnJ, KK/HlJ, LP/J, NOD/LtJ, NZB/BlNJ, PL/J, RIIIS/J, SEA/GnJ, SJL/J, SM/J, ST/bJ, and SWR/J. For the mapping of the other complex traits, only the 25 strains with the closest ancestral relationship were used. These strains were 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, BTBR T+ tf/J, C3H/HeJ, C57BL/10J, C57BL/6J, C57BLKS/J, C57BR/cdJ, C57 l/J, C58/J, CBA/J, DBA/1J, DBA/2J, I/LnJ, LP/J, MA/MyJ, NZB/BlNJ, NZW/LacJ, PERA/EiJ, PL/J, SEA/GnJ, SM/J, and WSB/EiJ.
Supporting Information
Dataset S1 Complete Allele Call and Assay List
(16.1 MB XLS).
Click here for additional data file.
Figure S1 Phylogenic Tree of 48 Strains Generated from SNP Dataset
Ancestral relationships between strains can be seen within clusters of the tree such as the fact that BALB/cByJ is a progenitor strain to SEA/GnJ. The bias of the SNP set can also be viewed by the exaggerated distance between the C57 and 129 clusters and the DBA and A/J cluster. The wild-derived strains make up the outermost cluster, but the three M. m. domesticus strains show a much closer relationship than the other wild-derived strains to the common laboratory strains.
(2.2 MB EPS).
Click here for additional data file.
Figure S2 Duplicate In Silico Genome Scans for the HDL Phenotype
The log p-value at each three-SNP window was calculated and plotted along the x-axis. Because any log p-value below 3 will not reach significance, calculations are halted at any locus once obtaining a log p-value of 3 becomes impossible in order to increase computational throughput. As such all log p-values below 3 are reported at 3. The false positive cutoff established by the gFWER calculation is indicated by a horizontal red line. Every quantitative trait was run twice through the algorithm to ensure consistency of results.
(5.8 MB EPS).
Click here for additional data file.
Figure S3 Distribution of log p-Values from gFWER Calculation of Significance for HDL In Silico Analysis
To estimate an appropriate false positive cutoff, 10,000 genome scans are conducted on randomized datasets and the 99.5 percentile log p-value is reported from each run. The significance cutoff is indicated by the vertical red line.
(3.1 MB EPS).
Click here for additional data file.
Table S1 Frequency of Polymorphic Alleles between Strain Pairs
(44 KB XLS).
Click here for additional data file.
Table S2 Total Number of SNP Alleles between Strain Pairs
(34 KB XLS).
Click here for additional data file.
Accession Numbers
The Mouse Phenome Database (http://www.jax.org/phenome) accession numbers for the phenomes discussed in this paper are MPD:29 and MPD:99.
We would like to thank P. Merritt, C. Motta, N. Ziaee, and D. Stradley for invaluable work in genotyping analysis (Genomic Institute of the Novartis Research Foundation); G. Churchill (The Jackson Laboratory); E. Chesler (University of Tennessee Health Science Center); R. Mott (Wellcome Trust Centre); R. Glynne (Phenomix); and N. Schork (University of California, San Diego) for advice and criticism concerning the statistical methods; C. Fletcher (Genomic Institute of the Novartis Research Foundation) for his thoughtful comments and suggestions; S.C. Grubb (The Jackson Laboratory) for assistance with data submission; and Malcolm Lyons (University of New South Wales) for information on gallstone QTL. This study was supported by Novartis Grant SFP-1407.
Conflicts of interest. The authors have declared that no conflicts of interest exist.
Author contributions. MTP and TW conceived and designed the experiments. MTP, SWB, EL, and TW performed the experiments. MTP, PM, SB, AIS, XW, BP, and TW analyzed the data. MTP, PM, SB, AIS, RK, XW, DN, MAB, RM, BP, and TW contributed reagents/materials/analysis tools. MTP, AIS, and TW wrote the paper.
Academic Editor: Wayne Frankel, The Jackson Laboratory
Citation: Pletcher MT, McClurg P, Batalov S, Su AI, Barnes W, et al. (2004) Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse. PLoS Biol 2(12): e393.
Abbreviations
Adcy7
adenylate cyclase 7
ANOVAanalysis of variance
CIconfidence interval
gFWERgeneralized family-wise error rate
HDLhigh-density lipoprotein cholesterol
Mbmegabase
MMU
Mus musculus chromosome
QTLquantitative trait loci
RIrecombinant inbred
kbkilobase
SNPsingle nucleotide polymorphism
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| 15534693 | PMC526179 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 9; 2(12):e393 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020393 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1553469410.1371/journal.pbio.0020398Research ArticleBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyYeast and FungiConservation and Evolution of Cis-Regulatory Systems in Ascomycete Fungi Evolution of Fungal Cis-Regulatory SystemsGasch Audrey P [email protected]
1
¤Moses Alan M
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Chiang Derek Y
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Fraser Hunter B
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Berardini Mark
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Eisen Michael B [email protected]
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1Genome Sciences Department, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America2Graduate Group in Biophysics, University of CaliforniaBerkeley, CaliforniaUnited States of America3Department of Molecular and Cell Biology, University of CaliforniaBerkeley, CaliforniaUnited States of America4Life Science Division, Lawrence Berkeley National LaboratoryBerkeley, CaliforniaUnited States of America12 2004 9 11 2004 9 11 2004 2 12 e39831 3 2004 9 9 2004 Copyright: © 2004 Gasch et al.2004This 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.
Genes, Genomes, and the Road to Diversity: How Regulatory Networks Evolve
Relatively little is known about the mechanisms through which gene expression regulation evolves. To investigate this, we systematically explored the conservation of regulatory networks in fungi by examining the cis-regulatory elements that govern the expression of coregulated genes. We first identified groups of coregulated Saccharomyces cerevisiae genes enriched for genes with known upstream or downstream cis-regulatory sequences. Reasoning that many of these gene groups are coregulated in related species as well, we performed similar analyses on orthologs of coregulated S. cerevisiae genes in 13 other ascomycete species. We find that many species-specific gene groups are enriched for the same flanking regulatory sequences as those found in the orthologous gene groups from S. cerevisiae, indicating that those regulatory systems have been conserved in multiple ascomycete species. In addition to these clear cases of regulatory conservation, we find examples of cis-element evolution that suggest multiple modes of regulatory diversification, including alterations in transcription factor-binding specificity, incorporation of new gene targets into an existing regulatory system, and cooption of regulatory systems to control a different set of genes. We investigated one example in greater detail by measuring the in vitro activity of the S. cerevisiae transcription factor Rpn4p and its orthologs from Candida albicans and Neurospora crassa. Our results suggest that the DNA binding specificity of these proteins has coevolved with the sequences found upstream of the Rpn4p target genes and suggest that Rpn4p has a different function in N. crassa.
A systematic examination of the gene regulatory elements in ascomycete fungi reveals striking conservation along with some examples of the ways in which regulatory systems can evolve
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Introduction
The diversity of modern organisms reflects and arises from an underlying molecular diversity that is only beginning to be understood. In recent years, much focus has been given to the evolution of protein coding regions, under the assumption that diversification of protein function has driven the evolution of organismal form and function. Nevertheless, the relative dearth of species-specific genes, and the seeming abundance of functionally homologous proteins in many different genomes, suggest additional mechanisms of diversification. One mechanism likely to play a significant role is variation in gene expression (Monod and Jacob 1961; Wilson et al. 1974). Subtle alterations in the timing, location, and levels of protein synthesis can have considerable consequences at both the molecular and organismal level (Averof and Patel 1997; Gompel and Carroll 2003; Lee et al. 2003). Despite the likely importance of variation in gene expression, relatively little is known about the evolution of gene-expression regulation or how this evolution contributes to organismal diversification.
Much of a gene's expression pattern is dictated by flanking noncoding sequences that contain, among other things, binding sites recognized by sequence-specific nucleotide-binding proteins that modulate transcript abundance. A number of recent studies have examined the evolution of cis-regulatory elements in alignments of orthologous regulatory regions, consistently showing that these elements evolve at a slower rate than the nonfunctional DNA that surrounds them (Hardison et al. 1997; Loots et al. 2000; McGuire et al. 2000; Bergman and Kreitman 2001; Dermitzakis and Clark 2002; Rajewsky et al. 2002; Moses et al. 2003). Most of these studies have been limited to closely related species whose orthologous noncoding sequences can be aligned, such that the putativecis-regulatory elements can be identified and compared. Cis-regulatory elements can be conserved in more distantly related species, even when the orthologous regulatory regions are too divergent to be accurately aligned (Piano et al. 1999; Cliften et al. 2003; Romano and Wray 2003). However, without the guidance of multiple alignments, little has been gleaned about the patterns of evolution or the functional constraints that act on cis-regulatory elements over longer evolutionary timescales.
Recently, several methods have been developed to dissect the regulatory networks that function within an individual species. Myriad studies have shown that functional regulatory sequences can be identified in a set of coregulated genes on the basis of the enriched fraction of those genes that contain the sequence within their flanking regions (van Helden et al. 1998; Tavazoie et al. 1999; McGuire et al. 2000; Bussemaker et al. 2001; Sinha and Tompa 2002). Gene coregulation can be conserved in related species, and this conservation has been exploited for the computational prediction of cis-regulatory elements that are highly conserved (Gelfand et al. 2000; Qin et al. 2003; Wang and Stormo, 2003; Pritsker et al. 2004; Yu et al. 2004). We reasoned that we could extend this approach to examine the evolution of cis-regulatory networks across species, by analyzing the orthologs of genes coregulated in S. cerevisiae.
As a first step toward this goal, we have examined the simplest model of regulatory networks: the connection between groups of coregulated genes and the flanking cis-regulatory sequences that coordinate their expression. We characterized groups of coregulated S. cerevisiae genes and their orthologs in 13 additional ascomycete fungi (Figure 1) and assessed the enriched fraction of those genes that contain known and novel cis-regulatory sequences. Our results strongly suggest that many of the knowncis-regulatory systems from S. cerevisiae have been conserved over hundreds of millions of years of evolution (Berbee and Taylor 1993; Heckman et al. 2001). Based on these observations, we present a number of models for the mechanisms of cis-regulatory evolution.
Figure 1 Fungal Phylogeny
The phylogenetic tree shows the 14 different fungi analyzed in this study. The topology of the tree was based on Kurtzman and Robnett (2003), and the branch lengths represent the average of maximum-likelihood estimates of synonymous amino acid substitutions (obtained using the PAML package [Yang 1997]) for the 303 proteins that had orthologs assigned in all 14 of these genomes. The closely related saccharomycete species for which the orthologous upstream regions can be aligned are labeled in orange. The source of each genome sequence is also indicated to the right of each species.
Results
We began by systematically characterizing known cis-regulatory elements and their gene targets in the well-studied yeast S. cerevisiae. We compiled a catalog of known and predicted S. cerevisiae
cis-regulatory elements (Dataset S1) in two ways. First, we retrieved 80 known consensus transcription factor-binding sites from the literature, based in part on information summarized on the Yeast Proteasome Database (Costanzo et al. 2001) and the Saccharomyces Genome Database (Weng et al. 2003). The majority of these sequences have been experimentally defined. Six others were identified by virtue of their conservation in the 3′ untranslated regions of closely related Saccharomyces species (Kellis et al. 2003), and five downstream elements were computationally predicted from mRNA immunoprecipitation experiments (Gerber et al. 2004). In addition to these known consensus sequences, we used the program MEME (Bailey and Elkan 1994) to identify 597 upstream sequence motifs common to groups of predicted coregulated genes (see below). Genes that contained one or more instance of each of these sequences in the 1,000-bp upstream or 500-bp downstream regions were identified as described in Materials and Methods.
We next identified and manually annotated 264 partially redundant groups of genes that are predicted to be coregulated in S. cerevisiae, based on the genes' similarity in expression, physical association with the same transcription factor, or functional relationships (Dataset S2; see Materials and Methods for details). For each gene group, we systematically scored the enrichment of genes that contained each of the putative regulatory elements identified above, compared to all genes in the S. cerevisiae genome that contained that flanking sequence. Of the 80 consensus sequences, 41 were identified as significant by this criterion. Of these significant sequences, 34 were identified in the gene group known to be regulated by that element (Dataset S3), suggesting an upper limit of 17% false-positive identifications. Of the 597 MEME matrices we identified, only 43 were significantly enriched in the gene group that they were identified in (see matrices in Dataset S4). All but four of these matrices were very similar to the consensus element known to regulate those genes (see Materials and Methods for details). Therefore, out of 19,239 motif-gene group comparisons, we recovered 34 consensus sequences and four additional MEME matrices representing known cis-regulatory elements (thus 38 of 80 known elements) and four unannotated MEME matrices that may represent novel S. cerevisiae regulatory sequences, for a total of 42 S. cerevisiae
cis-elements in 35 unique gene groups.
Many of these S. cerevisiae regulatory elements were shown to be conserved in orthologous regulatory regions from four closely related saccharomyces species (Figure 1, orange species) (Cliften et al. 2003; Kellis et al. 2003). However, it was not known whether these elements are conserved in more distantly related species for which the intergenic regions cannot be aligned. To explore this possibility, we reasoned that many genes that are coregulated in S. cerevisiae should also be coregulated in other fungal species, and that functional cis-regulatory elements could be identified with the same methods applied to coregulated S. cerevisiae genes. Therefore, for each group of coregulated S. cerevisiae genes, we identified orthologs in each of 13 other fungal genomes using the method of Wall et al. (2003). This method identifies reciprocal BLAST hits between two genomes that span more than 80% of the protein lengths, thereby providing a more conservative list of putative orthologs than a simple BLAST method. The complete set of orthologs is available in Datasets S5–S13.
For each species-specific gene group, we scored the enrichment of genes that contain each of the 80 consensus sequences or examples of the MEME matrices discovered in the orthologous S. cerevisiae genes, as described above. This procedure was performed separately on each species, so that the identification of an enriched sequence in one species was independent of its identification in the other species. Therefore, when a given sequence was enriched in the orthologous gene groups from multiple genomes, we interpreted this to reflect the conservation of the cis-regulatory system represented by that element in the corresponding species. It is important to note that we have characterized this conservation at the level of regulatory networks, which does not necessarily imply that the individual elements upstream of each gene have been perfectly conserved (see Discussion).
Many S. cerevisiae Cis-Regulatory Systems Are Conserved in Other Fungi
The patterns of cis-sequence enrichment in gene groups from each species strongly suggest that many of the genes coregulated in S. cerevisiae are also coregulated in the other fungal species. Furthermore, these patterns suggest that the expression of those genes is likely to be governed by the same cis-regulatory systems. Figure 2 shows the enrichment measured for each S. cerevisiae cis-regulatory element in the gene group it is proposed to regulate (represented by each row of the figure) in the 14 fungal species (shown in each column in the figure). (All p-values are available in Datasets S14–S46.) All of the 42 elements were identified in the same gene groups from at least three of the four closely related saccharomycete species. The majority of these elements were identified in the orthologous genes from other hemiascomycete species as well: 31 (74%) were identified in S. castellii, 23 (56%) and 27 (64%) were found in the related species S. kluyveri and Kluyveromyces waltii, respectively, and 21 (50%) and 14 (33%) were found in Ashbya gossypii and Candida albicans, respectively. Outside of the hemiascomycete group, we identified three to four (7%–10%) of these elements in the euascomycete fungi and two (5%) in Schizosaccharomyces pombe. Notably, when an identical procedure was performed using randomized consensus sequences, zero sequences were enriched with p < 0.0002 in their respective gene group from any species (Figures S1 and S2).
Figure 2 Conservation ofCis-Sequence Enrichment in Specific Gene Groups
Gene groups from each of the 14 species that are enriched for genes whose flanking regions contain known or novelcis-sequences are represented by orange boxes. Each row represents a group of coexpressed S. cerevisiae genes and a singlecis-regulatory element known or predicted to control the genes' expression, as indicated to the left of the figure. Each column in the figure represents the orthologous gene groups in 14 different fungal species. An orange box indicates that the S. cerevisiae cis-regulatory sequence listed to the left of the diagram is enriched in the denoted S. cerevisiae genes or their orthologs in each fungal genome, according to the key at the bottom of the figure. The p-values for each group are available in Datasets S14–S46, and the number of orthologs in each gene group is available in Dataset S49. Somecis-regulatory elements did not meet our significance cutoff for enrichment but had been previously identified as conserved in related gene groups from the closely related saccharomycete species (Kellis et al. 2003), and these are denoted with a yellow box. A gray box indicates that the denoted sequence was not significantly enriched in that gene group, while a white box indicates that fewer than four orthologs were identified in the species. The rows are organized in decreasing order of the number of species in which the element was enriched.
The number of regulatory systems that could be found in each species roughly correlates with the species tree, in that morecis-regulatory elements were identified in species closely related to S. cerevisiae compared to the more distantly related fungi. This result could arise from the decreased accuracy of ortholog assignment in the distantly related species, which would hinder the identification of conserved regulatory systems. However, control experiments indicate that our ability to identify each regulatory element by enrichment is largely insensitive to noise in each gene group and to the ortholog assignment parameters (Figure S3 and unpublished data). These results therefore suggest that the number of regulatory systems conserved across species correlates with their divergence times.
A handful of these cis-regulatory systems are conserved in all or nearly all of the fungal genomes. For example, the group of G1-phase cell-cycle genes from all species was significantly enriched for genes containing the upstream Mlu1-cell cycle box (MCB) (McIntosh 1993). This sequence regulates the expression of the G1-phase genes from S. cerevisiae (Moll et al. 1992) as well as its distant relative Sch. pombe (Lowndes et al. 1992; Malhotra et al. 1993), strongly suggesting that the element has a similar role in the other fungi. Likewise, the Gcn4p binding site was identified in the amino acid-biosynthesis genes from all but Sch. pombe, consistent with the known involvement of Gcn4p-like transcription factors in the amino acid-starvation responses of S. cerevisiae, C. albicans, Neurospora crassa, and Aspergillus nidulans (Hinnebusch 1986; Ebbole et al. 1991; Tazebay et al. 1997; Tripathi et al. 2002). The expression of nitrogen-catabolism genes in C. albicans, N. crassa, and As. nidulans is thought to be governed by GATA-like factors (Kudla et al. 1990; Chiang et al. 1994; Marzluf 1997; Limjindaporn et al. 2003), as it is in S. cerevisiae (Magasanik and Kaiser 2002), consistent with our ability to detect upstream GATA-binding elements in the group of nitrogen catabolism genes from these species. In the majority of cases (approximately 80%) in which a givencis-regulatory element was identified by enrichment, we could also identify in that species an ortholog of its binding protein from S. cerevisiae. Therefore, the most parsimonious model is that gene-expression regulation through the identifiedcis-regulatory sequence is governed by the orthologous transcription factor in each species.
Novel Sequences Are Enriched in Coregulated Gene Groups from Other Fungi
In many cases, we were unable to detect significant enrichment of the S. cerevisiae upstream elements in the orthologous gene groups from other species, particularly in the more distantly related fungi. One possible explanation for this observation is that, although the genes are still coregulated in these species, thecis-regulatory mechanisms that control their expression have evolved. We therefore searched the upstream regions from each group of orthologous genes for novel sequence motifs, using the program MEME (Bailey and Elkan 1994) and selected matrices that were significantly enriched in the gene group in which they were identified (see Materials and Methods for details). As has been previously noted for this type of motif discovery (Tavazoie et al. 1999; McGuire et al. 2000), the majority of the identified motifs were not significantly enriched in the appropriate gene group and may represent background sequences that are not functional. Thus, a total of 53 matrices were identified as significant in at least one species based on this criterion (the complete list of matrices and enrichment p values are available in Datasets S47 and S48). Over half of these were similar to known S. cerevisiae elements shown in Figure 2 and were enriched in the orthologous S. cerevisiae genes. Of the remaining motifs, two recognizably similar matrices were identified in the same gene group from multiple species, suggesting that they represent conserved regulatory systems not present in S. cerevisiae. To further examine this possibility, we scored the enrichment of genes containing examples of the 53 matrices in the orthologous gene groups from all species.
This procedure identified 19 unique MEME matrices that were not identified in the S. cerevisiae genes and therefore may represent novelcis-regulatory elements in these fungi (Figure 3). More than a third of these elements were also enriched in the same gene group from other species, providing additional support for their functional relevance. For example, a number of upstream sequences identified in ribosomal-protein genes were enriched in the same gene group from four or five other species, but not from S. cerevisiae. Similarly, sequences identified upstream of tRNA synthetase genes and upstream of the proteasome genes were identified in the same genes from all of the euascomycete fungi (N. crassa, Magnaporthe grisea, and As. nidulans). In the case of the proteasome genes, MEME identified the same motif upstream of orthologous genes from the related euascomycete Histoplasma capsulatum, for which partial genome sequence is available ( http://www.genome.wustl.edu/projects/hcapsulatum/) (unpublished data). That these sequences were identified in the same gene groups from multiple euascomycetes (but not the other species) implies that they are clade-specific. Although future experiments will be required to elucidate the exact roles of these sequences, our observations suggest that the identified cis-sequences are functionally relevant and conserved across species.
Figure 3 Enrichment of Novel Sequences in Coregulated Genes from Other Species
Gene groups from each of the 14 species that are enriched for genes containing novel upstream sequences identified by MEME (see Materials and Methods for details) are shown, as described in Figure 2. Enrichment of genes that contain thecis-sequence listed to the left of the diagram is indicated by a purple box, according to the key at the bottom of the figure.
Cis-Regulatory Element Positions and Spacing Are Also Conserved across Species
The physical locations of many characterized S. cerevisiae
cis-regulatory elements are restricted to a narrow region upstream of their target genes (Mannhaupt et al. 1999; Tavazoie et al. 1999; McGuire et al. 2000; Lieb et al. 2001; Natarajan et al. 2001). This suggests that these elements must be positioned in the appropriate window of the upstream sequences, perhaps to promote proper interactions between the element's binding protein and other factors (such as nucleosomes or RNA polymerase subunits) (Workman and Kingston 1992; Vashee and Kodadek 1995; Fry et al. 1997; Fry and Farnham 1999; GuhaThakurta and Stormo 2001).
To characterize the upstream positions of cis-regulatory elements in S. cerevisiae, we compared the fraction of elements in 50-bp windows upstream of their target genes to the fraction of elements in the same 50-bp window upstream of all genes in the S. cerevisiae genome. (This model is required to overcome the nonrandom nucleotide distribution immediately upstream of genes in this and other species, as described in Materials and Methods.) We found that many of the S. cerevisiae cis-regulatory elements are nonrandomly distributed upstream of their target genes (Figure 4, blue boxes). Each element shows a different window of peak enrichment in S. cerevisiae. This likely reflects mechanistic differences between the regulatory systems that control the expression of each set of genes.
Figure 4 Distribution of Cis-Regulatory Elements Upstream of Coregulated Genes
The distribution of nine different sequences motifs (represented to the left of the figure by the consensus sequences and their known binding proteins) was measured in 50-bp windows within 1,000 bp upstream of the putative target genes (denoted to the right of the figure). Each colored box represents the frequency of an element in a 50-bp window upstream of the target genes compared to the element's frequency in the corresponding window of all upstream regions in each genome. Blue boxes represent sequences that matched the S. cerevisiae MEME matrices, while purple boxes represent sequences that matched the designated species-specific MEME matrices. Distributions that were significantly different from background in at least one 50-bp window (p < 0.01) were identified using the hypergeometric distribution (as described in Materials and Methods) and are denoted by an asterisk.
In the majority of cases, when a cis-regulatory system was conserved in another species, the corresponding element had a similar upstream distribution to that seen in S. cerevisiae, in that the distributions had the same window of peak enrichment (Figure 4). This is significant, as the underlying genomic distribution of many of these sequences is substantially different in each species, due in part to the different GC content of some of the genomes (unpublished data). For many regulatory systems, there was no correlation between the positions of individual elements in orthologous upstream regions from multiple species (although there were some exceptions; Figures S4 and S5). This indicates that the distributions of these elements have been conserved, even though the precise positions of individual elements have not (see Discussion). In addition to the conserved S. cerevisiae elements, many of the novelcis-sequences presented in Figure 3 also showed nonrandom distributions in the species in which they were identified (Figure 4, purple boxes). Thus, the positional distribution of cis-regulatory elements appears to be a general feature of cis-regulation in multiple ascomycete species.
In one case, the close spacing between twocis-regulatory elements was conserved across species. Chiang et al. (2003) previously reported that the distance between the Cbf1p- and Met31/32p-binding sites upstream of the methionine biosynthesis genes is closer than expected by chance. We found this feature to be conserved in other species as well. The Cbf1p and Met31/32p elements were independently identified upstream of the methionine genes from almost all of the hemiascomycetes (see Figure 2). In addition, the closer-than-expected spacing between these sequences was also conserved in these species (Figure 5). The spacing between elements was independent of the exact positions of the Cbf1p or Met31/32p sites in the saccharomycete species, indicated by permutation tests performed as previously described (p < 0.05; Chiang et al. 2003). Thus, the close spacing between these sites is not simply due to the conserved positioning of the individual elements in each orthologous upstream region, but likely resulted from an evolutionary constraint on the distance between these sequences (see Discussion).
Figure 5 Spatial Relationships betweenCis-Regulatory Elements
The mean spacing between the Cbf1p- and Met31/32p- binding sites within 500 bp upstream of the methionine biosynthesis genes (m) and of all of the genes in each genome (g) was calculated for the species indicated. The error bars represent twice the standard error, indicating the range of the estimated means with 95% confidence. The values below each plot indicate the number of binding-site pairs used in each calculation.
Evolution of the Proteasome Cis-Regulatory Element in S. cerevisiae and C. albicans
We were particularly interested in exploring patterns of cis-element evolution across fungi. One interesting example is the case of Rpn4p, a nonclassical Cys2-His2 zinc-finger protein known to regulate proteasome gene expression in S. cerevisiae (Mannhaupt et al. 1999; Xie and Varshavsky 2001). For the group of S. cerevisiae proteasome genes, the enrichment of genes containing the known Rpn4p binding site was highly significant (GGTGGCAA; p < 6 × 10–41). The same consensus sequence was also enriched in the orthologous upstream regions of all of the hemiascomycete fungi, but not in the upstream regions retrieved from fungi outside of the hemiascomycete group. We noticed that, in addition to the Rpn4p consensus site, a number of related hexameric sequences were also highly enriched in the orthologous upstream regions from C. albicans (unpublished data). This hinted at the possibility that a slightly different set of regulatory sequences governs the expression of the C. albicans proteasome genes.
To further explore this possibility, we compared sequences found upstream of the proteasome genes from S. cerevisiae and C. albicans. To identify these sequences in an unbiased way, we first generated a species-independent “meta-matrix” based on a limited subset of the proteasome upstream regions from both species (see Materials and Methods for details). We then identified all examples of the meta-matrix upstream of the proteasome genes from S. cerevisiae and C. albicans, partitioned the sequences according to their species, and calculated two species-specific position-weight matrices (Figure 6). These matrices were statistically different at the second, third, and ninth positions (p < 0.01; see Materials and Methods for details) and indicated that the C. albicans matrix had less basepair specificity at these positions.
Figure 6 Position-Weight Matrices Representing Proteasome Cis-Regulatory Elements
Sequences within 500 bp upstream of the S. cerevisiae or C. albicans proteasome genes that matched the species-independent meta-matrix were identified as described. The identified sequences were used to generate sequence logos (Crooks et al. 2004) to represent the set of cis-sequences from S. cerevisiae (left) or from C. albicans (right). The height of each letter represents the frequency of that base in that position of the matrix. Positions in the matrices that are statistically different (see Materials and Methods for details) are indicated with an asterisk.
The matrices are useful because they summarize the set of related sequences that are common to the upstream regions in each group, but a more direct assessment of these elements is to inspect the sequences directly. Sequences upstream of the S. cerevisiae and C. albicans proteasome genes that matched the “meta-matrix” described above were combined and organized by sequence similarity, using a hierarchical clustering method described in Materials and Methods. The sequences could be classified into three general categories (Figure S6). The first category consisted of related sequences that were found in both S. cerevisiae and C. albicans proteasome upstream regions, the second was composed of sequences found almost exclusively upstream of S. cerevisiae genes, and the third was composed of elements found only upstream of the C. albicans proteasome genes. Manual inspection of the proteasome-gene upstream regions supported these classifications: There were zero instances of the S. cerevisiae-specific 10-mer GGTGGCAAAW upstream of any C. albicans proteasome genes, although nearly 75% of the S. cerevisiae proteasome genes contained this upstream sequence. Similarly, zero instances of the C. albicans-specific 10-mer GRAGGCAAAA were found upstream of S. cerevisiae proteasome genes, whereas 25% of the C. albicans genes contained the element. These observations suggest that S. cerevisiae and C. albicans use different sequences to govern the expression of the proteasome genes.
Sc_Rpn4p and Ca_Rpn4p Have Different In Vitro Binding Specificities
Two mutually exclusive possibilities could explain the differences in the upstream sequences found in S. cerevisiae and C. albicans proteasome genes. One model is that the species-specific differences in thesecis-sequences reflect differences in the binding specificity of S. cerevisiae Rpn4p and its ortholog in C. albicans. Alternatively, the two transcription factors may bind with the same specificity, indicating that some other feature(s) contributed to the differences in these sets of sequences. Examination of the nucleotide frequencies in each genome ruled out the possibility that the differences in cis-sequences arose simply by drift in the underlying genomic base composition (unpublished data). To further distinguish between the above models, we cloned and purified S. cerevisiae Rpn4p (Sc_Rpn4p) and the orthologous protein from C. albicans (Ca_Rpn4p) and measured their binding properties in vitro. The interaction of each protein with three different DNA sequences (each representing one of the three classes of upstream sequences described above) was measured using the Biacore 3000 affinity system, which measures biomolecular interactions between proteins and DNA (see Materials and Methods for details). Briefly, double-stranded DNA fragments containing the relevant sequences were immobilized onto a solid surface, and real-time protein-DNA interactions were measured as each protein was passed over the immobilized DNAs and allowed to bind (reviewed in Malmqvist 1999).
The results of these in vitro binding experiments revealed that Sc_Rpn4p and its ortholog Ca_Rpn4p have different DNA-binding specificities. Figure 7 shows the binding of Sc_Rpn4p and Ca_Rpn4p to the S. cerevisiae-specific Sequence A (GGTGGCAAAA), the C. albicans-specific Sequence B (GAAGGCAAAA), and Sequence C (AGTGGCAACA), which represents sequences found in both species. Sc_ Rpn4p bound preferentially to Sequence A and, to a lesser extent, to Sequence C; however, the binding of Sc_Rpn4p to Sequence B was barely detectable (Figure 7A). Ca_Rpn4p also bound preferentially to Sequence A, but in contrast to Sc_Rpn4p, this protein bound nearly indistinguishably to Sequence B and Sequence C in vitro (Figure 7B).
Figure 7 In Vitro DNA-Binding Profiles of Rpn4p Proteins
Profiles of 50 nM Sc_Rpn4p (A), Ca_Rpn4p (B), Hybrid_Rpn4p (C), and Nc_Rpn4p (D) binding to Sequence A (S. cerevisiae-specific; red curve), Sequence B (C. albicans-specific; blue curve), and Sequence C (hybrid; black curve) are shown. Protein was injected into the Biacore system at time = 0 for a duration of 90 sec, after which time buffer was injected and the protein dissociated from the Biacore chip. The scale of each binding profile was adjusted such that the binding levels to Sequence A are comparable for all species.
In all cases, the DNA binding was specific, as competitor fragments that were similar to the Sc_Rpn4p consensus sequence, but not a dissimilar control fragment, were effective inhibitors of binding when preincubated with the protein (Figure 8). This was true even for Sc_Rpn4p binding to Sequence B, despite the low levels of binding to this sequence. A fragment identical to the immobilized Sequence A was the best competitor for both Sc_Rpn4p and Ca_Rpn4p binding to all immobilized sequences, compared to competitor fragments with single basepair differences in either the first or ninth position of the element. This was surprising in the case of Ca_Rpn4p, since the lower basepair specificity in the ninth position of the C. albicans proteasome matrix (see Figure 6) predicted that sequence variation at this position would not significantly affect binding.
Figure 8 In Vitro Competition for DNA Binding
The maximum response units of binding were measured for Sc_Rpn4p (A), Ca_Rpn4p (B), or the hybrid protein (C) binding to Sequence A (left graphs), Sequence B (center graphs), and Sequence C (right graphs) in the absence (“mock”) or presence of a 1× or 5× molar excess of competitor fragments: Sequence G (with a core sequence of CTGCATTTGG), Sequence D (GGTGGCAAAA), Sequence E (AGTGGCAAAA), and Sequence F (GGTGGCAACA). Each histogram shows the maximum response units of binding, relative to the maximum response units measured for that protein binding to the Sequence A in the absence of competitor. Replicate experiments were performed for each mock reaction and the 5:1 competition experiments for Sc_Rpn4p protein. The range of replicate measurements was very narrow and is indicated by the error bars.
A reasonable expectation is that amino acid differences in the DNA-binding domains of each protein account for the differences in their specificity, perhaps by promoting subtly different contacts between each protein and its DNA substrate. While this is not an obligate explanation, we found it to be the case: A hybrid protein that consisted of the amino-terminal portion of Sc_Rpn4p fused to the carboxyl-terminal DNA-binding domain of Ca_Rpn4p (see Materials and Methods for details) was able to bind Sequence B indistinguishably from Sequence C, as did the native Ca_Rpn4p (see Figure 7C). Again, the binding was specific, since the expected sequences, but not the negative control, were able to compete for binding (Figure 8). These results reveal that amino acid differences between the Sc_Rpn4p and Ca_Rpn4p DNA-binding domains account for the altered specificity of these proteins.
Nc_Rpn4p Has the Same In Vitro Specificity as Ca_Rpn4p
Although we could not identify Rpn4p-like elements upstream of the majority of proteasome genes from the other fungi, we did identify a different sequence, GGAGCT, upstream of the proteasome genes from the euascomycete fungi. Because each of these fungi has an ortholog of Rpn4p, we cloned Nc_Rpn4p as a representative and characterized its binding to the novel sequence and to Sequence A, B, and C described above (see Materials and Methods for details). Nc_Rpn4p did not bind detectably to the GGAGCT sequence in vitro, similar to its orthologs Sc_Rpn4p and Ca_Rpn4p that did not bind this sequence (unpublished data). In contrast, Nc_Rpn4p bound to the Rpn4p-like elements with a binding profile similar to Ca_Rpn4p: Nc_Rpn4p bound maximally to Sequence A and bound nearly identically to Sequence B and Sequence C on the Biacore chip (see Figure 7D). Since the majority of proteasome genes from the euascomycete fungi do not contain these sequences, these results suggest that Nc_Rpn4p does not regulate proteasome gene expression.
Discussion
The ascomycete fungi represent nearly 75% of all fungal species, and their diversity is evident by their unique morphologies, life styles, environmental interactions, and niches (Ainsworth et al. 2001). This diversity has been shaped by over a billion years of evolution (Berbee and Taylor 1993; Heckman et al. 2001) and has almost certainly been affected by variation in gene expression. To explore the evolution of gene-expression regulation in these fungi, we have examined the cis-regulatory networks of 14 ascomycete species whose genomes have been sequenced, using a framework that is not dependent on multiple alignments of orthologous regulatory regions. We have identified probable cis-acting sequences in each of these species by applying motif search and discovery methods to the flanking regions of orthologs of coregulated S. cerevisiae genes. Our ability to identify such sequences in the same gene groups from multiple species strongly suggests that the coregulation of those genes has been conserved. Examples from our analysis indicate that in many cases the genes' coregulation is governed by a conserved regulatory system, while other examples suggest that some regulatory networks have evolved. These examples provide insights into the functional constraints that underlie the evolution of gene-expression regulation, as summarized below.
Conservation of Cis-Regulatory Systems
Our results indicate that a large number of cis-regulatory networks that function in S. cerevisiae are conserved in other ascomycete species. This is expected for the closely related species, since conserved regulatory elements can be readily identified in alignments of orthologous regulatory regions (Cliften et al. 2003; Kellis et al. 2003). However, we show here that many of the cis-regulatory systems represented by these elements are conserved over much longer evolutionary time frames, beyond those for which orthologous noncoding regions can be aligned. For example, 50%–75% of the regulatory systems identified in S. cerevisiae are also found in S. kluyveri and S. castellii, which are diverged enough from S. cerevisiae that much of the gene synteny is lost and most orthologous intergenic regions cannot be aligned (Cliften et al. 2003). Over a third of these regulatory systems were identified in C. albicans, which is estimated to have diverged from S. cerevisiae over 200 million years ago, and a small number of regulatory networks have been conserved since the origin of the Ascomycetes some 500 million to a billion years ago (Berbee and Taylor 1993; Heckman et al. 2001). It is likely that we have underestimated the number of conserved regulatory networks, partly because of statistical limitations of our method. Nonetheless, these data indicate that regulatory networks can be conserved over very long periods of evolution.
Despite the widespread conservation of cis-regulatory networks, it is important to note that this does not necessarily imply that the individual cis-elements have remained perfectly conserved. For example, while we could identify the same cis-sequences in orthologous gene groups, the positions of the individual elements in orthologous upstream regions in many cases appear to have changed (see Figure S4). Evolution of cis-element position has been observed in closely related drosophilids, mammals, and other species (Ludwig and Kreitman 1995; Ludwig et al. 1998; Piano et al. 1999; Dermitzakis and Clark 2002; Scemama et al. 2002; Dermitzakis et al. 2003) and is proposed to occur by two general mechanisms (reviewed in Wray et al. 2003). The first is binding-site turnover, whereby the appearance of a new cis-element elsewhere in a promoter can compensate for the loss of a functional element in the same regulatory region. Simulation studies show that cis-element turnover occurs frequently over short evolutionary time scales and is likely to play an important role in gene-expression regulation (Stone and Wray 2001; Dermitzakis et al. 2003). Alternatively, small insertions and deletions in a regulatory region can permute the cis-element's position without changing the element's sequence (Ludwig and Kreitman 1995; Piano et al. 1999; Ruvinsky and Ruvkun 2003). Thus, regulatory regions appear to be relatively plastic in their organization. Despite this plasticity, however, a gene's expression pattern and the regulatory system governing its expression can remain intact even though the gene's flanking regulatory region has undergone reorganization (Piano et al. 1999; Ludwig et al. 2000; Scemama et al. 2002; Hinman et al. 2003; Romano and Wray 2003; Ruvinsky and Ruvkun 2003). This indicates that some combination of purifying selection and drift (Ludwig et al. 2000) can act to maintain the appropriate regulatory connections to conserve the gene's expression pattern.
Although the positions of many of the individual cis-elements have evolved in these species, we found that the distribution of elements upstream of their gene targets was often similar across species. This suggests that there has been constraint on the region in which the elements are positioned, without pressure to maintain the exact positions of individual elements. One explanation for this model is that mechanistic features of these regulatory systems are also conserved across species (Wray et al. 2003). For example, the restricted location of cis-regulatory elements may promote interactions between the cognate binding protein and other regulatory proteins. Therefore, selective pressure may act to maintain these interactions through the relative positions of the underlying binding sites. This model may also explain the conserved close spacing between Cbf1p and Met31/32p elements in methionine biosynthesis genes from the hemiascomycete fungi. These transcription factors are proposed to act cooperatively in S. cerevisiae to recruit additional transcriptional regulators (Blaiseau and Thomas 1998). That the spacing between the Cbf1p and Met31/32p elements is closer than expected in other species as well suggests that the cooperative interaction between the factors has been conserved across the Hemiascomycetes.
Evolution of Cis-Regulatory Networks
In addition to the clear cases of network conservation discussed above, we also found evidence for the evolution of cis-regulatory systems. Our ability to identify novel sequences enriched in orthologs of coregulated S. cerevisiae genes implies that, although the genes are still coregulated in those species, the systems governing their expression have changed. This indicates that the regulatory regions of those genes coevolved to contain the same cis-sequences.
We were interested in identifying global predictors of the relative rates of cis-regulatory network evolution, but these factors remain enigmatic. Unlike the evolutionary rates of protein coding regions, for which essential proteins typically evolve at a slower rate (Wilson et al. 1977; Hirsh and Fraser 2001; Krylov et al. 2003; H. B. F., personal communication), we found no evidence for a retarded rate of evolution/loss of the cis-regulatory systems of essential genes (unpublished data). For example, the proteasome subunits and the ribosomal proteins are among the most highly conserved proteins, and the genes that encode them are expressed with similar patterns in S. cerevisiae, C. albicans, and Sch. pombe (Gasch et al. 2000; Chen et al. 2003; Enjalbert et al. 2003). Nonetheless, we identified different upstream sequences for these groups in the different species we analyzed, suggesting that the regulation of the genes' expression has evolved even though their expression patterns have not. This is consistent with previous observations of developmentally regulated genes in higher organisms, whose temporal and spatial expression can be conserved across taxa despite divergence in their regulation (Takahashi et al. 1999; True and Haag 2001; Scemama et al. 2002; Hinman et al. 2003; Romano and Wray 2003; Ruvinsky and Ruvkun 2003; Wang et al. 2004). In contrast, we observed that proteins involved in mating have a high rate of evolution, yet we could identify the Ste12p binding site (Fields and Herskowitz 1985) upstream of mating genes in nearly all of the hemiascomycetes. Consistently, orthologs of Ste12p are known to be required for mating in distantly related fungi that mate through significantly different processes (Lengeler et al. 2000; Vallim et al. 2000; Young et al. 2000; Chang et al. 2001). Since mating may be triggered by similar environmental cues (Lengeler et al. 2000), evolutionary pressure may have conserved the regulatory system that mediates this process (to the extent of our observations), even though the mating proteins have evolved.
Although we could not find global correlates with the patterns of cis-regulatory network evolution, a number of individual examples from our analysis are consistent with specific models of network evolution. These examples are discussed below.
Addition of Gene Targets into an Existing Regulatory Network
Sequences that match cis-regulatory elements can readily appear in noncoding DNA through drift. In the same way that this process can promote binding site turnover within a given regulatory region, it can create de novo elements in the regulatory regions of random genes, giving rise to novel targets of that regulatory system (Stone and Wray 2001; Rockman and Wray 2002). The addition of novel targets into cis-regulatory systems may have occurred in the case of E2F-like transcription factors. In S. cerevisiae, the related MCB (ACGCG) and Swi4-Swi6 cell-cycle box, or SCB (CGCGAAA) regulatory elements are found upstream of G1-phase cell-cycle genes, similar to the E2F element found in these genes in worms, flies, humans, and plants (Lowndes et al. 1992; Malhotra et al. 1993; DeGregori 2002; Ren et al. 2002; De Veylder et al. 2003; Rustici et al. 2004). What is striking about the conservation of this network is that cell-cycle progression is markedly different in these organisms: The hemiascomycete fungi replicate by budding, unlike the filamentous fungi in the euascomycete group, the fission yeast Sch. pombe, and the other higher eukaryotes. While some of the genes regulated by these elements are well conserved across organisms (namely, the DNA replication proteins), genes whose products are involved in budding are also expressed in G1 phase and regulated by these elements in S. cerevisiae (Spellman et al. 1998; Iyer et al. 2001) and likely in its budding cousins as well. Because these genes are not conserved outside the hemiascomycete clade, and since it is unlikely that budding represents the ancestral mode of replication, this suggests that genes involved in budding were assumed into an existing cis-regulatory network in these yeasts.
Coevolution of an Existing Regulatory Network
Mutation of a cis-regulatory element can be compensated by the stabilizing effects of binding site turnover (Ludwig et al. 2000), as discussed above, but it could also be overcome by corresponding changes in its DNA-binding protein, such that the interaction between the two is maintained. Parallel changes in DNA element and protein sequence can occur to conserve the overall regulatory network (i.e., the same binding protein regulating the same set of genes), despite evolution of their molecular interaction. We found slightly different sets of sequences enriched upstream of the proteasome genes from S. cerevisiae versus C. albicans, and these differences corresponded with the different binding specificities of Sc_Rpn4p and Ca_Rpn4p in vitro. This result is consistent with the model that the binding specificity of Sc_Rpn4p and Ca_Rpn4p coevolved with the elements found upstream of the proteasome genes in each species.
Neither Ca_Rpn4p nor the hybrid protein functioned in an in vivo reporter system (unpublished data); however, Sc_Rpn4p could transcribe a reporter gene to higher levels if Sequence A was present in its promoter compared to when Sequence B or a minimal promoter was placed upstream of the reporter gene (see Figure S7). These results are consistent with the hypothesis that Sc_Rpn4p ineffectively initiates transcription from the C. albicans-specific element. Since Ca_Rpn4p and Nc_Rpn4p both bind significantly to Sequence B, it is likely that this was also true of the proteins' common ancestor and that Sc_Rpn4p largely lost the ability to bind productively to this sequence.
The altered specificity of Sc_Rpn4p is due to amino acid differences in its DNA-binding domain, since the hybrid Rpn4p (containing the Ca_Rpn4p DNA binding domain) bound to Sequence B as well as it did to Sequence C (see Figure 7C). Determining which residues are responsible for the altered activity is a difficult task, however, since all of the residues known to participate in zinc coordination and DNA contact (Rhodes et al. 1996; Wolfe et al. 1999; Wolfe et al. 2000; Pabo et al. 2001; Benos et al. 2002) are perfectly conserved between these orthologs (Figure 9). One obvious difference in the orthologous proteins is the spacing between the cysteine and histidine pair in the second zinc finger, which is proposed to contact the first half of the DNA-binding site (Wolfe et al. 2000; Pabo et al. 2001) wherein the base-specificity differences reside. Sc_Rpn4p, Ca_Rpn4p, and the euascomycete Rpn4p orthologs all vary in amino acid length and identity in this region, which implicated the region as relevant to the specificity differences. However, a mutant Sc_Rpn4p that contained the Nc_Rpn4p sequence in this region (see Figure 9) had the same binding specificity as the wild-type Sc_Rpn4p (albeit with less activity; unpublished data), indicating that this region alone is not sufficient to explain the differences in binding profiles.
Figure 9 Sequence Alignment of the DNA-Binding Domain of Rpn4p and Its Orthologs
Clustal W was used to identify a multiple alignment between S. cerevisiae Rpn4p and its orthologs in the other fungi; the alignment over the DNA binding domain is shown. No ortholog was identified by our method in S. kluyveri, apparently due to poor sequence coverage in that region (unpublished data). The conserved cysteine and histidine residues of the two C2H2 zinc-finger domains are highlighted in yellow, and the domain in each finger that is predicted to contact the DNA is indicated with a gray bar. The region of sequence variation between the hemiascomycete and euascomycete Rpn4p proteins is indicated with a box.
Cooption of a Regulatory System to Govern a Different Set of Genes
An extreme example of the previously discussed modes of evolution is the complete alteration of a regulatory system's target genes (True and Carroll 2002). This may have occurred for the Rpn4p regulatory system sometime after the divergence of the euascomycete and hemiascomycete fungi. Our data suggest that, while Sc_Rpn4p and Ca_Rpn4p control proteasome-gene expression in these species, the euascomycete orthologs of this transcription factor probably do not. Nc_Rpn4p did not bind the novel sequence we identified upstream of euascomycete proteasome genes, and reciprocally the majority of these genes did not contain examples of the Rpn4p binding site. One possibility is that Nc_Rpn4p and its orthologs regulate a different set of genes in the euascomycete clade. Preliminary investigation of orthologous euascomycete genes that contain examples of the Ca_Rpn4p matrix (used as a surrogate for the Nc_Rpn4p binding matrix) did not reveal any obvious relationships in the genes' functional annotations or striking similarities in their patterns of expression (T. Kasuga, personal communication). Interestingly, however, the orthologs of RPN4 in all three euascomycete species contained upstream Rpn4p elements, raising the possibility that this gene is autoregulated at the level of expression in these fungi. Future experiments will test the function of this factor in N. crassa as well as the role of the novel sequence in mediating proteasome gene expression.
The converse of this situation is that the regulatory regions of coregulated genes must coevolve, such that they all contain the same regulatory elements recognized by the new system. This apparently occurs despite strong constraint on the genes' expression patterns. For example, most proteasome subunits are essential and required in proper stoichiometric amounts (Russell et al. 1999; Kruger et al. 2001). Nonetheless, we found different cis-sequences upstream of the proteasome genes from the hemiascomycete and euascomycete fungi. Another example can been seen in the ribosomal protein genes, which must also be expressed to the same relative levels (Warner 1999; Zhao et al. 2003). In all species, we could find elements upstream of the ribosomal proteins, but different cis-sequences were identified in subsets of these species (see Figures 2 and 3). How the regulatory systems that control the genes' expression evolve is unclear. This process may involve an intermediate stage in which the genes' expression is controlled by two distinct, but partially redundant, regulatory systems (True and Haag 2001; True and Carroll 2002). Differential loss of one system in two diverged species would render the orthologous genes coregulated by different regulatory systems. This model for regulatory system “turnover” is in direct analogy to the case of binding site turnover, in which partially redundant cis-elements that are created by drift coexist in a regulatory region before they are differentially lost in the diverged species (Ludwig et al. 2000; Stone and Wray 2001).
Conclusions and Future Directions
We have provided a framework for studying cis-regulatory evolution without relying on alignments of intergenic regions. The evolutionary dynamics of transcriptional regulation is evident from the examples we have presented. We expect that as more complete fungal genomes emerge, particularly for fungi with intermediate evolutionary relationships, important gaps in the existing phylogeny will be filled. These key species may provide a window into intermediate stages of cis-element evolution, allowing us to further delineate the patterns of and constraints on the evolution of cis-regulation.
Materials and Methods
Genome sequences
Genome sequence and open reading frame (ORF) annotations for the saccharomycete species were obtained from P. Cliften, M. Kellis, and the Saccharomyces Genome Database (Goffeau et al. 1996; Cliften et al. 2003; Kellis et al. 2003). Sequences for other genomes were downloaded from the published or listed Web sites as follows. K. waltii (Kellis et al. 2004), A. gossypii (Dietrich et al. 2004), C. albicans (Assembly 6; http://www-sequence.stanford.edu/group/candida/) (Jones et al. 2004), N. crassa (Release 3; Galagan et al. 2003), M. grisea (Release 2; http://www-genome.wi.mit.edu/annotation/fungi/magnaporthe/) , As. nidulans (Release 3.1; http://www.broad.mit.edu/annotation/fungi/aspergillus/) , and Sch. pombe (Wood et al. 2002). A conservative list of putative ORFs from S. kudriavzevii,
S. castellii, and S. kluyveri was generated, taking all ORFs of more than 100 amino acids as putative genes. ORFs orthologous to S. cerevisiae genes were identified as described below; some intron-containing S. cerevisiae genes that may also contain introns in these species (namely ribosomal protein genes) were identified by tBLASTn and manually added to the list of orthologs for these species.
Orthologs between S. cerevisiae and S. paradoxus, S. mikatae, and S. bayanus (Kellis et al. 2003) were downloaded from the Saccharomyces Genome Database ( http://www.yeastgenome.org/) . All other orthologs to S. cerevisiae genes were assigned using the method of Wall et al. (Wall et al. 2003) using a BLAST e-value cutoff of 10-5 and the requirement for fewer than 20% gapped positions in the Clustal W alignments. The number of orthologs assigned in each species is listed in Table 1, and the complete results are available in Datasets S5–S12.
Table 1 Orthologs Assigned to S. cerevisiae Genes
aGene numbers reported in genome sequence publication or on the source website; see Materials and Methods for references
bOrthologs for S. paradoxus, S. mikatae, and S. bayanus were identified by Kellis et al. (2003)
All other orthologs were identified as described in Materials and Methods
nd, not determined
S. cerevisiae
gene clusters
Groups of known or putatively coregulated genes were identified in three ways. First, we used hierarchical (Eisen et al. 1998) and fuzzy k-means (Gasch and Eisen 2002) clustering to organize publicly available yeast gene expression data (DeRisi et al. 1997; Spellman et al. 1998; Gasch et al. 2000; Lyons et al. 2000; Ogawa et al. 2000; Primig et al. 2000; Gasch et al. 2001; Yoshimoto et al. 2002), taking gene clusters that were correlated by more than about 0.7 or with a membership of 0.08 or greater (Gasch and Eisen 2002). Second, we identified genes or transcripts whose flanking regions are physically bound by the same DNA or RNA binding proteins, as indicated by immunoprecipitation experiments (Simon et al. 1993; Iyer et al. 2001; Lieb et al. 2001; Simon et al. 2001; Lee et al. 2002; Gerber et al. 2004): For the DNA immunoprecipitation experiments, genes were ranked according to the published binding p values, and a sliding p value (between 10-2 and 10-4) was applied such that at least 20 genes were selected in each group. Transcripts that are bound by RNA binding proteins were taken from (Gerber et al. 2004). Finally, genes with the same functional annotations (Weng et al. 2003), and genes known to be coregulated by various transcription factors (Gasch et al. 2000; Lyons et al. 2000; Ogawa et al. 2000; Shakoury-Elizeh et al. 2004), were grouped together. In all, we identified 264 partially redundant groups of S. cerevisiae genes that are likely to be coregulated. These gene groups ranged in size from four to 570 genes, with a median size of 17 genes per group. The complete gene groups are available in Dataset S2.
Motif identification and enrichment
We compiled from the literature a list of 80 known transcription factor-binding sites, represented by IUPAC consensus sequences (Dataset S1) (Costanzo et al. 2001; Weng et al. 2003). Unless otherwise noted, we searched 1,000 bp upstream or 500 bp downstream of the genes from each group in each fungal genome for sequences that matched the consensus binding sites, by doing string comparisons on both strands using PERL scripts. For each group of genes identified above, we scored the enrichment of genes whose flanking regions (either 500 bp upstream, 1,000 bp upstream, or 500 bp downstream) contain one or more example of each cis-regulatory element, using the hypergeometric distribution
where M is the number of genes that contain the motif in a group of i selected genes, relative to N genes that contain the motif in a genome of l genes. A p ≤ 0.0002 (approximately 0.01/80 tests) was deemed statistically significant for the consensus sequences, although if the sequence was enriched in the known group of target genes, we relaxed the cutoff to p = 0.01. A cutoff of p ≤ 2 × 10–5 was applied to sequences that matched the MEME matrices. For the Mig1p and GATA binding sequences, which are sufficiently short and occur frequently in each genome, we also scored the enrichment of genes whose upstream region contained two or more examples of the known binding sites.
For each group of genes, we also ran the motif-finding algorithm MEME (Bailey and Elkan 1994) on the upstream regions of S. cerevisiae genes or their orthologs in each species, using a two-component mixture model both with and without a motif-width specification of 8 bp. Unless otherwise noted, we used 500 bp upstream (for the hemiascomycetes) or 1,000 bp upstream (for the euascomycetes and Sch. pombe) of the genes in each group. Thus, for each group of coregulated genes, we performed 14 MEME analyses (each identifying three matrices) on the upstream regions of the genes from a given species. Matrices that matched known S. cerevisiae regulatory elements were identified by manual and automated comparisons, similar to that previous described (Hughes et al. 2000). A position-weight matrix was calculated for each motif on the basis of n motif examples MEME identified by counting the number of occurrences of each base at each position in n motifs, adding one pseudocount, and dividing by n + 4. A log-likelihood score S was calculated for each motif example as follows.
In this formula, p is each position in the motif, b is the base {GACT} and X is a matrix of indicator variables representing the sequence, where Xpb = 1 if the sequence has base b at position p, and zero otherwise. The probabilities of bases in the motif according to the position-weight matrix are represented by f motif, and the probabilities of bases in the genomic background are represented by f background (see below). The score S′ was assigned to each matrix, equal to 0.75× the average S of the motif examples, using the base frequency from each genome as the background model (G/C = 0.2 and A/T = 0.3 for all species except N. crassa, where G/C/A/T = 0.25). This score was used as a cutoff to identify genomic examples of the matrix.
To identify genes whose upstream regions contained examples of each motif, we calculated the log-likelihood S of each 8-bp sequence within the 1,000 bp upstream region of each gene. The background model was based on the genomic nucleotide frequency in the 50 bp upstream window corresponding to the position of the sequence being assessed. We used this model to overcome the species-specific positional nucleotide biases immediately upstream of coding sequences (A. M. M., A. P. G., D. Y. C., and M. B. E., unpublished data). A sequence was considered a match to the matrix if S > S ′. The enrichment of genes that contained each motif was scored using the hypergeometric distribution, as described above. A p ≤ 1 × 10–5 (0.01 divided by the number of matrices tested in each species) was considered statistically significant. Out of the MEME matrices trained on the non-S. cerevisiae species, 53 were enriched in the gene group in which they were identified. Of these elements, 28 were similar to S. cerevisiae elements shown in Figure 2 and were enriched in the S. cerevisiae genes. An additional six matrices were redundantly identified in nearly identical gene groups (namely, Fhl1p targets and ribosomal protein genes) from the same species, and two elements were very similar and identified in the same gene group from As. nidulans andM. grisea. Thus, in all, 19 novel elements were identified. The complete list of matrices is available in Dataset S47.
Positional distribution and spacing of cis-sequences
Genes that contained sequences that matched theS. cerevisiae position-weight matrices were identified as described above. We then calculated the frequency of each sequence in 50-bp windows upstream of the potential target genes and compared it to the frequency of that element in the corresponding upstream window for all of the genes in that genome. To identify distributions that were statistically different from the background, we identified 50-bp windows that contained a disproportionate number of the cis-sequences in the target upstream regions compared to the background, using the hypergeometric distribution presented above, where i was the total number of elements identified upstream of the genes in each group, M was the number of those elements that fell within a given 50-bp window, l was the total number of elements upstream of all of the genes in that genome and N was the number of those elements that fell within the same 50-bp window. We considered an element's distribution to be significant if there was at least one 50-bp window with p ≤ 0.01; only 5%–10% of the elements had distributions that met this criterion in gene groups other than their putative target genes. We calculated the correlation between element positions in S. cerevisiae and each of the other species by taking all possible pairwise combinations of a cis-element's positions in a given S. cerevisiae upstream region and in the orthologous region from other species and plotting these values for each group of coregulated genes (example scatter plots shown in Figures S4 and S5).
Genes that contained sequences that matched the S. cerevisiae Cbf1p and Met31/32p position-weight matrices were identified in each species as described above. The average spacing between Cbf1p and Met31/32p binding sites within the 500 bp-upstream regions of the methionine biosynthesis genes and of all of the genes in each genome was measured by calculating the distance between all pair-wise combinations of the two motifs in each upstream region and taking the average spacing for the respective group of genes.
Rpn4p matrix comparisons
To compare the upstream sequences identified in proteasome genes fromS. cerevisiae and C. albicans, and to ensure that the identified sequences were not obtained by sampling bias, we performed the following permutation analysis. We ran MEME on the entire set of upstream regions of 26 proteasome genes with orthologs in both species, using the conservative one-per-sequence model. This produced a “meta-matrix” that identified exactly one putative binding site from each gene, leaving us with a set of exactly 52. We calculated the likelihood-ratio statistic, testing the hypothesis that the sequences were drawn from a single multinomial, or from multinomials estimated separately for each species. In order to test the significance of this statistic, we randomly divided the data into two equal-sized groups 10,000 times, recalculated the statistic, and found that matrix positions 2, 3, and 9 had values ofp < 0.001. The results were similar when the test was performed on all cis-sequences that matched the meta-matrix: These sequences were identified in both species using the S. cerevisiae background model (which identified a list of sequences that was nearly identical to that generated when the C. albicans background model was used to identify motifs from each species).
This set of elements was organized by sequence similarity as follows. Each basepair was represented by a four-dimensional binary vector of indicator variables: G = 1,0,0,0; A = 0,1,0,0; C = 0,0,1,0; T = 0,0,0,1. Each basepair in each 10-mer sequence was replaced by the corresponding vector of indicator variables, translating the 10-mer sequence into a 40-dimensional binary vector. The sequences were organized by hierarchically clustering the binary vectors that represented them, using the program Cluster (Eisen et al. 1998). The organized sequences were visualized using the program TreeView ( available at http://rana.lbl.gov) as shown in Figure S6.
Cloning and culture growth
The S. cerevisiae
RPN4 ORF and its orthologs in C. albicans
(orf6.4920) and N. crassa (NCU01640.1) were cloned by PCR from genomic DNA (S. cerevisiae strain S288C, C. albicans strain NIH 3147 [#10231D; American Type Culture Collection, Manassas, Virginia, United States], and N. crassa Mauriceville strain) using Bio-X-act DNA polymerase (BioLine, Boston, Massachusetts, United States). Primers that exactly spanned each ORF (excluding the first ATG) and introduced XmaI and NcoI sites at the 5′ and 3′ ends, respectively, of each PCR product, were used to amplify each ORF. The digested products were cloned into pCAL-n (Stratagene, La Jolla, California, United States) to add an amino-terminal calmodulin-binding protein tag to each protein. In addition, a hybrid protein was generated from the amino-terminal portion of Sc_ RPN4 (corresponding to nucleotide position 4–1,247) and the DNA binding-domain from C. albicans
orf6.4920 (position 1,235–1,611), guided by Clustal W (Thompson et al. 1994; Chenna et al. 2003) alignments of the proteins. The orf6.4920 fragment was amplified by PCR, generating an EcoRI site in the amino end of the fragment. The digested fragment was ligated to a natural EcoRI site inSc_RPN4 (present in a region of high sequence conservation between the proteins), and the hybrid was cloned into pCAL-n as described above. The wild-type amino acid sequences of Sc_Rpn4p, Ca_Rpn4p, and the hybrid clones were verified by DNA sequencing. (The Mauriceville Rpn4p ortholog had five amino acid differences compared to the published sequence from strain 74A. Because we recovered the identical sequence from multiple independent PCRs, we take this to be the wild-type Nc_Rpn4p for this strain.) Each plasmid was used to transform BL21DE3-RIL E. coli cells (Stratagene).
Yeast overexpression plasmids were constructed by PCR amplification of Sc_RPN4, Ca_RPN4, or Hybrid_RPN from the above plasmids and cloned into the GAL-inducible expression plasmid pRS-TAP (provided by D. Nix) by homologous recombination and gapped plasmid repair. Reporter constructs were generated by cloning 40-bp fragments that contained either one or five copies of Sequence A or Sequence B upstream of the HIS3 minimal promoter in pDC204 (provided by D. Y. C.). Yeast strain BY4741 (MATa
his3Δ1 leu2Δ0 met15Δ0 ura3Δ0, provided by M. Kobor) was transformed with each overexpression construct and each reporter construct. Liquid cultures were grown to mid-log phase and washed three times with synthetic-dropout medium lacking histidine and glucose. Serial culture dilutions were spotted onto solid SC medium lacking uracil, leucine, and histidine, with 2% galactose, and containing 0–15 mM 3-amino triazole (Sigma, St. Louis, Missouri, United States). Photos were taken after growth for 3 d at 30 °C.
Protein purification and Biacore measurements
The proteins were purified from bacteria by affinity purification. 250 ml of LB medium containing 50 ng/ml carbenicillin (Sigma) was inoculated with 8 ml of saturated cultures and grown at 37 °C to OD600 of approximately 1.0. The cells were induced with 0.3 mM IPTG (Sigma) at 30 °C for 1 h, collected by centrifugation at 4 °C, and flash-frozen in liquid nitrogen. The cells were resuspended in ice-cold 8V calcium binding buffer (50 mM Tris-Cl [pH 7.5], 150 mM NaCl, 1 mM magnesium acetate, 1 mM imidazole, 2 mM calcium chloride, and 1 mM PMSF) and lysed on ice by sonication. The lysate was cleared by centrifugation, and the soluble extract was loaded onto 0.5 ml of calmodulin resin (Stratagene) in a 2-ml column (BioRad, Hercules, California, United States) at 4 °C. The column was washed with 8V calcium binding buffer followed by 8V binding buffer adjusted to 0.5 M NaCl. The resin was eluted with elution buffer (50 mM Tris-Cl [pH 7.5], 0.5 M NaCl, and 2 mM EGTA), and the eluates were flash-frozen and stored at –80 °C.
The interaction of each purified protein with three predicted Rpn4p binding sites was measured using a Biacore 3000 system (Biacore, Piscataway, New Jersey, United States). Complementary 40-nucleotide oligonucleotides were designed, with one oligonucleotide containing a 5′ biotinylated group (Qiagen, Valencia, California, United States). Each of the three sequences contained a different 10-bp core flanked by the same 15 bp that flanked a natural Rpn4p site from the C. albicans orf6.8078 gene: Sequence A (
GCGTGCCAGATAATCGGTGGCAAAACGGAAGAAAAAGTGA); Sequence B (
GCGTGCCAGATAATCGAAGGCAAAACGGAAGAAAAAGTGA); and Sequence C (
GCGTGCCAGATAATCAGTGGCAACACGGAAGAAAAAGTGA). (The flanking sequence did not noticeably contribute to the binding properties, as a 40-bp fragment consisting of the natural Rpn4p site and flanking sequence from the S. cerevisiae gene PUP2 performed nearly indistinguishably from Sequence A in competition experiments [unpublished data].) The HPLC-purified oligonucleotides were combined at a ratio of 2:1 unbiotinylated:biotinylated oligonucleotides in 10 mM Tris-Cl (pH 7.4), 1 mM EDTA, and 50 mM NaCl, heated to 95 °C for 10 min, and incubated at room temperature overnight. Each double-stranded, biotinylated sequence was bound to one flow cell of an SA sensor chip (Biacore) in HBS buffer (10 mM HEPES [pH 7.4], 150 mM NaCl, 3 mM EDTA, and 0.005% P20) at a flow rate of 10 μl/min. Each cell was coated with roughly the same DNA (approximately 46–56 response units) according to the manufacturer's instructions. The fourth flow cell was not coated with DNA and served as a control. A single cell on a second SA chip was coated in the same way with double-stranded, biotinylated Sequence I (
ACTTGTTCCCGCTCGCTGGAGCTCCTCCAACGACACGGGC), representing an instance of the GGAGCT site and flanking sequence from the N. crassa proteasome gene NCU06712.1.
Protein was diluted to 10–100 nM in ice-cold HBS buffer and maintained on ice until injection into the Biacore system. Proteins were passed through four flow cells at a flow rate of 10 μl/min for 90 s at room temperature, then HBS buffer was flowed over the chip at 10 μl/min for 180 s. The protein was desorbed by flowing 0.5% SDS over the chip for 30 s followed by HBS. The kinetics of binding were examined using the Biacore software, and the fit of each calculation was acceptable according to the manufacturer's instructions.
Double-stranded competitor DNA was generated by mixing equimolar amounts of complementary 30-nucleotide fragments, heating to 95 °C for 10 min, and allowing the mixture to cool to room temperature overnight. The DNAs were quantified before and after annealing by replicate absorbance measurements. Four different competitor fragments were used: Sequence D (
CCAGATAATCGGTGGCAAAACGGAAGAAAA), Sequence E (
CCAGATAATCAGTGGCAAAACGGAAGAAAA), and Sequence F (
CCAGATAATCGGTGGCAACACGGAAGAAAA); the fourth sequence, Sequence G, (
CCAGATAATCCTGCATTTGGCGGAAGAAAA) was chosen as the worst-scoring sequence to the Sc_Rpn4p position-weight matrix and served as a negative control. Each fragment was mixed with 50 nM protein at a 1:1 or 5:1 molar ratio (or buffer was added for mock experiments), incubated for 50 min on ice, then injected into the Biacore system, as described above. The maximum response units of each protein binding to the three sequences on the chip were measured using the Biacore software.
Supporting Information
Dataset S1 List of S. cerevisiae Consensus Transcription Factor Binding Sites
(2 KB TXT).
Click here for additional data file.
Dataset S2 List of S. cerevisiae Genes in the 264 Gene Groups Identified
(339 KB XLS).
Click here for additional data file.
Dataset S3 Transcription Factor/Motif vs. Gene Group Relationships Used to Score Enrichment in Figure 2
(22 KB XLS).
Click here for additional data file.
Dataset S4
S. cerevisiae Matrices Identified by MEME That Matched Known S. cerevisiae Binding Sites
(47 KB XLS).
Click here for additional data file.
Dataset S5
S. cerevisiae–S. castellii Orthologs
(90 KB TXT).
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Dataset S6
S. cerevisiae–S. kluyveri Orthologs
(64 KB TXT).
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Dataset S7
S. cerevisiae–K. waltii Orthologs
(69 KB TXT).
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Dataset S8
S. cerevisiae–A. gossypii Orthologs
(53 KB TXT).
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Dataset S9
S. cerevisiae–C. albicans Orthologs
(74 KB TXT).
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Dataset S10
S. cerevisiae–N. crassa Orthologs
(70 KB TXT).
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Dataset S11
S. cerevisiae–M. grisea Orthologs
(63 KB TXT).
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Dataset S12
S. cerevisiae–As. nidulans Orthologs
(64 KB TXT).
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Dataset S13
S. cerevisiae–Sch. pombe Orthologs
(54 KB TXT).
Click here for additional data file.
Dataset S14 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. paradoxus Genes
(433 KB XLS).
Click here for additional data file.
Dataset S15 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. paradoxus Genes
(433 KB XLS).
Click here for additional data file.
Dataset S16 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. mikatae Genes
(433 KB XLS).
Click here for additional data file.
Dataset S17 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. mikatae Genes
(433 KB XLS).
Click here for additional data file.
Dataset S18 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. bayanus Genes
(433 KB XLS).
Click here for additional data file.
Dataset S19 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. bayanus Genes
(433 KB XLS).
Click here for additional data file.
Dataset S20 Probability of Enrichment of Genes Containing Two or More copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. castellii Genes
(416 KB XLS).
Click here for additional data file.
Dataset S21 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. castellii Genes
(359 KB XLS).
Click here for additional data file.
Dataset S22 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. kluyveri Genes
(411 KB XLS).
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Dataset S23 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of S. kluyveri Genes
(414 KB XLS).
Click here for additional data file.
Dataset S24 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of K. waltii Genes
(411 KB XLS).
Click here for additional data file.
Dataset S25 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of K. waltii Genes
(411 KB XLS).
Click here for additional data file.
Dataset S26 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of K. waltii Genes
(416 KB XLS).
Click here for additional data file.
Dataset S27 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of A. gossypii Genes
(398 KB XLS).
Click here for additional data file.
Dataset S28 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of A. gossypii Genes
(406 KB XLS).
Click here for additional data file.
Dataset S29 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of A. gossypii Genes
(405 KB XLS).
Click here for additional data file.
Dataset S30 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of C. albicans Genes
(404 KB XLS).
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Dataset S31 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of C. albicans Genes
(404 KB XLS).
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Dataset S32 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of C. albicans Genes
(389 KB XLS).
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Dataset S33 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 2,000 bp Upstream of N. crassa Genes
(399 KB XLS).
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Dataset S34 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of N. crassa Genes
(410 KB XLS).
Click here for additional data file.
Dataset S35 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of N. crassa Genes
(383 KB XLS).
Click here for additional data file.
Dataset S36 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of N. crassa Genes
(384 KB XLS).
Click here for additional data file.
Dataset S37 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of M. grisea Genes
(381 KB XLS).
Click here for additional data file.
Dataset S38 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of M. grisea Genes
(405 KB XLS).
Click here for additional data file.
Dataset S39 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of M. grisea Genes
(378 KB XLS).
Click here for additional data file.
Dataset S40 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of As. nidulans Genes
(403 KB XLS).
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Dataset S41 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of As. nidulans Genes
(408 KB XLS).
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Dataset S42 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of As. nidulans Genes
(402 KB XLS).
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Dataset S43 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 2,000 bp Upstream of Sch. pombe Genes
(382 KB XLS).
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Dataset S44 Probability of Enrichment of Genes Containing Two or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of Sch. pombe Genes
(399 KB XLS).
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Dataset S45 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 1,000 bp Upstream of Sch. pombe Genes
(372 KB XLS).
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Dataset S46 Probability of Enrichment of Genes Containing One or More Copies of S. cerevisiae Consensus Elements within 500 bp Upstream of Sch. pombe Genes
(379 KB XLS).
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Dataset S47 Significant MEME Matrices Trained on 500-bp or 1,000-bp Upstream Regions of Genes from Non-S. cerevisiae Species
(42 KB TXT).
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Dataset S48 The p-Values of Enrichment Measured for Species-Specific MEME Matrices
(11 KB TXT).
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Dataset S49 The Number of Orthologs Identified in Each Species in Each Gene Group
(19 KB XLS).
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Figure S1 The Enrichment Measured for Randomized Consensus Sequences in Target Gene Group Is Not Statistically Significant
Consensus sequences identified by enrichment in Figure 2 were randomized, and the enrichment of the randomized sequence in the denoted gene group was scored. An orange box indicates that the corresponding gene group was enriched for genes containing the randomized sequence, according to the key at the bottom of the figure. Notably, none of the randomized sequences was enriched with p < 2 × 10–4 in the denoted gene group from any species.
(1.1 MB TIF).
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Figure S2 Significant Enrichment Measured for Randomized Upstream Sequences in Random Gene Groups Is Not Consistent across Species
Fifteen of the randomized sequences shown in Figure S1 were enriched below the cutoff of p < 2 × 10–4 in any gene group. However, the enrichment was not consistent across species. Only two randomized sequences were enriched in the same gene group from two species, although the enrichment pattern did not correlate with the species tree. Thus, randomized sequences are enriched with different characteristics than the functional consensus sequences shown in Figure 2
(898 KB TIF).
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Figure S3 The Enrichment Measured for S. cerevisiae Consensus Sequences Is Tolerant of Noise in Each Gene Group
Our ability to detect conserved cis-regulatory elements in other species requires identification of orthologs of the coregulated S. cerevisiae genes. We wondered how our enrichment-based method would be affected if incorrect orthologs were assigned to individual S. cerevisiae genes, thereby producing “noise” in the gene groups. To test the sensitivity of our method to this type of noise, we performed the following gene replacement control: For each group of S. cerevisiae genes, we performed 100 trials in which 0%–100% of the genes in each group were randomly selected and replaced with random S. cerevisiae genes. The number of trials in which the p of enrichment was below our cutoff of p < 2 × 10–4 was scored with an orange box, according to the key shown at the bottom of the figure. Nearly all of the cis-elements could be identified in their respective gene groups despite some amount of “noise” in the gene group.
(864 KB TIF).
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Figure S4 Correlation between Rpn4 Element Positions in S. cerevisiae Upstream Regions and Orthologous Regions from Other Species
Positions of Rpn4p elements upstream of each S. cerevisiae proteasome gene (x axis) were plotted against the positions of Rpn4p elements upstream of the orthologous proteasome gene from each of the other species (y axis). The linear fit is shown in the upper right corner of each plot.
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Figure S5 Correlation between MCB Element Positions in S. cerevisiae Upstream Regions and Orthologous Regions from Other Species
Positions of MCB elements upstream of S. cerevisiae G1-phase genes (x axis) were plotted against the positions of MCB elements upstream of the orthologous G1-phase gene from each of the other species (y axis). The linear fit is shown in the upper right corner of each plot.
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Figure S6 Position-Weight Matrices and Cis-Sequences Found Upstream of Proteasome Genes
Sequences within 500 bp upstream of the S. cerevisiae or C. albicans proteasome genes that matched the species-independent meta-matrix were identified as described.
(A) The identified sequences were used to generate sequence logos (Crooks et al. 2004) to represent the set of cis-sequences from S. cerevisiae (top) or from C. albicans (bottom). The height of each letter represents the frequency of that base in that position of the matrix. Positions in the matrices that are statistically different (see Materials and Methods for details) are indicated with an asterisk.
(B) Examples of the species-independent meta-matrix found upstream of S. cerevisiae proteasome genes (shown in red) and C. albicans proteasome genes (shown in blue) were pooled and organized by a hierarchical clustering method, as described in Materials and Methods. The sequences found upstream of S. cerevisiae genes only (red bar), C. albicans genes only (blue bar), or both the S. cerevisiae and C. albicans proteasome genes (black bar) are indicated, along with the consensus sequence representing each denoted group.
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Figure S7 3-Amino-Triazole Resistance Due to Sc_Rpn4p Activity
S. cerevisiae cells harboring a HIS3 reporter gene with either a minimal promoter (left), minimal promoter + Sequence A (middle), or minimal promoter + Sequence B (right), and overexpressing Sc_Rpn4p from a galactose-inducible promoter, were grown on 0 mM, 1 mM, 5 mM, or 15 mM His3p inhibitor 3-amino-triazole. Two serial dilutions of each strain were plated for each drug concentration. The level of drug resistance is indicative of the level of HIS3 expression (Guthrie and Fink 2002).
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We wish to thank the sequencing projects that have made their data publicly accessible, and in particular we thank Paul Cliften and Manolis Kellis for advance access to the data. Betty Gilbert and John Taylor provided genomic DNA for N. crassa strain, Takao Kasuga and Louise Glass provided N. crassa microarray data, and Dennis Wall provided assistance in the ortholog assignments, for which we are grateful. We also wish to thank Michael Kobor, Joe DeRisi, David Nix, and DYC for yeast strains and plasmids; Gary Stormo, Dan Pollard, Justin Fay, and the members of the Eisen lab for helpful suggestions and critical reading of the manuscript; Marv Wickens for insightful advice on searching for 3′UTR elements; and Eric Kelley for much computer help. APG was supported by a National Science Foundation postdoctoral fellowship in Biological Informatics, MB was supported by National Institutes of Health SBDR grant #5P01CA092584-03, and MBE is a Pew Scholar in the Biomedical Sciences. This work was carried out under the United States Department of Energy contract ED-AC03-76SF00098.
Conflicts of interest. The authors have declared that no conflicts of interest exist.
Author contributions. APG, AMM, and MBE conceived and designed the experiments. APG and AMM performed the experiments. APG and AMM analyzed the data. APG, AMM, DYC, HBF, and MB contributed reagents/materials/analysis tools. APG and AMM wrote the paper.
Academic Editor: Andy Clark, Cornell University
¤Current address: Laboratory of Genetics and Genome Center, University of Wisconsin, Madison, Wisconsin, United States of America
Citation: Gasch AP, Moses AM, Chiang DY, Fraser HB, Berardini M, et al. (2004) Conservation and evolution ofcis-regulatory systems in ascomycete fungi. PLoS Biol 2(12): e398.
Abbreviation
MCBMlu1 cell-cycle box
ORFopen reading frame
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020425SynopsisBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyMus (Mouse)Computational Mapping of Complex Traits in the Mouse Synopsis12 2004 9 11 2004 9 11 2004 2 12 e425Copyright: © 2004 Public Library of Science.2004This 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.
Use of a Dense Single Nucleotide Polymorphism Map for In Silico Mapping in the Mouse
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The past few years have seen an explosion of data on the mouse genome, widely hailed as a guidebook to the genetic origins of human disease. Scientists are particularly interested in charting the location of SNPs—single nucleotide polymorphisms—throughout the genome. SNPs are DNA sequence variations, or mutations, that change a single nucleotide in the genome, replacing a cytosine (C) base, say, with thymine (T). Many SNPs, even those found within genes, have no functional effect. Others, however, can increase risk of specific diseases or alter a person's response to pathogens and drugs. Whether or not they are involved directly in a disease, SNPs are attractive markers for population studies aimed at identifying the multiple genes underlying complex diseases like diabetes and cancer.
Extensive data exist on multiple inbred strains of mice linking their genetic makeup (genotype) to physical traits (phenotype), and scientists have used these data to guide investigations of gene function and disease. Many data have been gathered by crossing mouse strains and painstakingly analyzing their progeny, to tease out the relative contributions different genes make to pathogenesis. But these efforts take time. Investigators would greatly benefit from high-throughput methods to scan the mouse genome and flag markers for candidate disease genes. In 2001, Andrew Grupe et al. introduced an “in silico” (computational) approach to do that very thing. The method scanned mouse SNP data to home in on chromosomal areas regulating complex traits and reduce the time needed to analyze mouse disease models from “many months” to “milliseconds.”
For a number of reasons, however, it wasn't clear whether in silico mapping could deliver on its promise. For one thing, the density of SNP maps was sufficient to provide meaningful markers for only a few mouse strains, and phenotype information was lacking for many strains. In a new study, Tim Wiltshire and colleagues have addressed these limitations by mapping nearly 11,000 SNP probes to 48 mouse strains. They have also been able to use this dataset for in silico mapping to predict genomic regions with functionally important phenotypes.
Wiltshire and colleagues first show that their method can predict the genomic location of a Mendelian trait (controlled by a single gene), in this case coat color, which the authors acknowledge is a “minimum requirement for a viable in silico mapping method.” They go on to map complex “quantitative” traits (controlled by differential contributions from multiple genes at what are called quantitative trait loci, or QTLs)—gallstone development and plasma levels of high-density lipoprotein cholesterol—and find that their predictions fall in line with loci identified by traditional mouse disease studies. Noting a high correlation between QTLs predicted in silico and those identified experimentally, the authors argue that loci predicted using this method are “very likely to be biologically relevant.”
In silico mapping for mouse genetics
Wiltshire and colleagues are careful to point out that in silico mapping is meant to complement, not replace, traditional gene mapping models. After all, computers are no match for living organisms in modeling the subtleties inherent in biological reactions. But this approach is a good starting point for identifying significant genomic areas in a new strain. And as new strains are genotyped and phenotyped, and refinements are made to the SNP database, the robustness of this method should only get better.
| 0 | PMC526181 | CC BY | 2021-01-05 08:21:16 | no | PLoS Biol. 2004 Dec 9; 2(12):e425 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020425 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020426SynopsisCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyHomo (Human)Repetitive Editing and RNA Splicing Synopsis12 2004 9 11 2004 9 11 2004 2 12 e426Copyright: © 2004 Public Library of Science.2004This 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.
Widespread A-to-I RNA Editing of Alu-Containing mRNAs in the Human Transcriptome
==== Body
The so-called “central dogma” of biology—DNA makes RNA makes protein—is a simple statement that subsumes a wealth of complexity. In particular, the past decade has shown that after RNA is made, it is run through a gauntlet of processes that strip it of introns and splice its exons, add a cap and tail, and even chemically modify one or more bases along the way. This last possibility includes deamination—removal of an NH2 group—from adenosine, converting it to inosine. During translation, the ribosome reads an inosine as a guanosine; thus, an A-to-I edit in RNA can even cause an amino acid change in the resulting protein. A new study in this issue by Stefan Maas and colleagues shows that A-to-I editing is remarkably widespread among human genes, and commonly targets a ubiquitous repetitive sequence, the Alu repeat.
A-to-I editing has been recognized for several years, but the known targets have been few, far fewer than the number predicted by measuring the inosine content in messenger RNA. To identify more targets, Alekos Athanasiadis, Alexander Rich, and Maas compared genomic sequences to cDNA sequences. Adenosines are unchanged in genomic DNA, while in complementary DNA (cDNA), which is derived from reverse transcribing mRNA, any adenosines that were converted to inosines during RNA editing show up as guanosines. Thus, A-to-G discrepancies revealed candidate editing sites. To reduce the number of false positives, the researchers confined their search to regions with multiple A-to-G discrepancies. In an initial screen of 3,000 cDNAs, they found 26 A-to-I edited genes. In all but one case, the editing occurred in an Alu sequence.
Sequence and structure preferences of editing in Alu repeats
There are approximately 1.4 million Alu sequences in the human genome, each about 300 base-pairs in length, which together comprise about 10% of the entire genome. Not all of them occur in genes, but those that do are typically found in transcribed but untranslated regions (introns), either upstream (3′) or downstream (5′) of the translated region. In many genes, they are found in pairs, ordered head to head or tail to tail, separated by a short intervening sequence. Once transcribed, the Alu sequences can pair up, forming a stretch of double-stranded RNA that makes an ideal target for the A-to-I RNA editing machinery, called ADAR (adenosine deaminase acting on RNA).
A typical gene contains between one and two dozen Alu sequences. Based on this and the frequency of editing found when analyzing more than 100,000 mRNAs in the human transcriptome, Athanasiadis, Rich, and Maas estimate that the probability that any particular mRNA undergoes A-to-I editing is between 85% and 95%.
While the bulk of edited Alu sites are in introns, a small fraction of them are in exons. Here they can lead to alternative forms of the same protein, expressed in different cell types or at different times; this appears to be especially common in the nervous system. Alu editing can also convert introns to exons, and vice versa, through creation or destruction of splice sites. It is possible A-to-I editing may be used to reduce the creation of deleterious new exons, although more work will be needed to explore this possibility, as well as what role, if any, A-to-I editing plays in promoting new exon creation.
| 0 | PMC526182 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 9; 2(12):e426 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020426 | oa_comm |
==== Front
PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020426SynopsisCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyHomo (Human)Repetitive Editing and RNA Splicing Synopsis12 2004 9 11 2004 9 11 2004 2 12 e426Copyright: © 2004 Public Library of Science.2004This 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.
Widespread A-to-I RNA Editing of Alu-Containing mRNAs in the Human Transcriptome
==== Body
The so-called “central dogma” of biology—DNA makes RNA makes protein—is a simple statement that subsumes a wealth of complexity. In particular, the past decade has shown that after RNA is made, it is run through a gauntlet of processes that strip it of introns and splice its exons, add a cap and tail, and even chemically modify one or more bases along the way. This last possibility includes deamination—removal of an NH2 group—from adenosine, converting it to inosine. During translation, the ribosome reads an inosine as a guanosine; thus, an A-to-I edit in RNA can even cause an amino acid change in the resulting protein. A new study in this issue by Stefan Maas and colleagues shows that A-to-I editing is remarkably widespread among human genes, and commonly targets a ubiquitous repetitive sequence, the Alu repeat.
A-to-I editing has been recognized for several years, but the known targets have been few, far fewer than the number predicted by measuring the inosine content in messenger RNA. To identify more targets, Alekos Athanasiadis, Alexander Rich, and Maas compared genomic sequences to cDNA sequences. Adenosines are unchanged in genomic DNA, while in complementary DNA (cDNA), which is derived from reverse transcribing mRNA, any adenosines that were converted to inosines during RNA editing show up as guanosines. Thus, A-to-G discrepancies revealed candidate editing sites. To reduce the number of false positives, the researchers confined their search to regions with multiple A-to-G discrepancies. In an initial screen of 3,000 cDNAs, they found 26 A-to-I edited genes. In all but one case, the editing occurred in an Alu sequence.
Sequence and structure preferences of editing in Alu repeats
There are approximately 1.4 million Alu sequences in the human genome, each about 300 base-pairs in length, which together comprise about 10% of the entire genome. Not all of them occur in genes, but those that do are typically found in transcribed but untranslated regions (introns), either upstream (3′) or downstream (5′) of the translated region. In many genes, they are found in pairs, ordered head to head or tail to tail, separated by a short intervening sequence. Once transcribed, the Alu sequences can pair up, forming a stretch of double-stranded RNA that makes an ideal target for the A-to-I RNA editing machinery, called ADAR (adenosine deaminase acting on RNA).
A typical gene contains between one and two dozen Alu sequences. Based on this and the frequency of editing found when analyzing more than 100,000 mRNAs in the human transcriptome, Athanasiadis, Rich, and Maas estimate that the probability that any particular mRNA undergoes A-to-I editing is between 85% and 95%.
While the bulk of edited Alu sites are in introns, a small fraction of them are in exons. Here they can lead to alternative forms of the same protein, expressed in different cell types or at different times; this appears to be especially common in the nervous system. Alu editing can also convert introns to exons, and vice versa, through creation or destruction of splice sites. It is possible A-to-I editing may be used to reduce the creation of deleterious new exons, although more work will be needed to explore this possibility, as well as what role, if any, A-to-I editing plays in promoting new exon creation.
| 0 | PMC526183 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 9; 2(12):e441 | latin-1 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020441 | oa_comm |
==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-731545391410.1186/1471-2164-5-73Research ArticleMicroarrays for global expression constructed with a low redundancy set of 27,500 sequenced cDNAs representing an array of developmental stages and physiological conditions of the soybean plant Vodkin Lila O [email protected] Anupama [email protected] Robin [email protected] Steven J [email protected] Delkin Orlando [email protected] Reena [email protected] Gracia [email protected] Françoise [email protected] Mark [email protected]ömvik Martina V [email protected] Elizabeth [email protected] Christina [email protected] Ernest [email protected] John [email protected] Randy C [email protected] Alicia M [email protected] Joseph C [email protected] Virginia [email protected] Paul [email protected] George [email protected] Lei [email protected] Jose [email protected] Peter [email protected] Department of Crop Sciences, University of Illinois, Urbana, IL, 61801, USA2 Center for Computational Genomics and Bioinformatics, University of Minnesota, Minneapolis, MN, 55455, USA3 USDA/ARS, Department of Agronomy, Iowa State University, Ames, IA, 50011, USA4 Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA5 Department of Biology, Northern Arizona University, Flagstaff, AZ, 86011, USA6 Keck Center for Comparative and Functional Genomics, University of Illinois, Urbana, IL, 61801, USA7 Epicentre, 726 Post Road, Madison, WI, 53713, USA8 USDA/ARS, National Soybean Research Laboratory, University of Illinois, Urbana, IL, 61801, USA9 Food and Drug Administration, Rockeville, MD, 20850, USA10 The Institute for Genome Research, 9212 Medical Center Drive, Rockville, MD, 20850, USA11 Department of Plant Science, McGill University, 2111 Lakeshore, St. Anne-de-Bellevue, QC, H9X3V9, Canada12 Mathematics and Computer Science, Macalester College, St. Paul, MN, 55105, USA13 Department of Microbiology, School of Medicine, University of Nevada-Reno, Reno, NV, USA14 Biotechnology Resource Center, Cornell University, Ithaca, NY, 14853, USA2004 29 9 2004 5 73 73 12 4 2004 29 9 2004 Copyright © 2004 Vodkin et al; licensee BioMed Central Ltd.2004Vodkin 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
Microarrays are an important tool with which to examine coordinated gene expression. Soybean (Glycine max) is one of the most economically valuable crop species in the world food supply. In order to accelerate both gene discovery as well as hypothesis-driven research in soybean, global expression resources needed to be developed. The applications of microarray for determining patterns of expression in different tissues or during conditional treatments by dual labeling of the mRNAs are unlimited. In addition, discovery of the molecular basis of traits through examination of naturally occurring variation in hundreds of mutant lines could be enhanced by the construction and use of soybean cDNA microarrays.
Results
We report the construction and analysis of a low redundancy 'unigene' set of 27,513 clones that represent a variety of soybean cDNA libraries made from a wide array of source tissue and organ systems, developmental stages, and stress or pathogen-challenged plants.
The set was assembled from the 5' sequence data of the cDNA clones using cluster analysis programs. The selected clones were then physically reracked and sequenced at the 3' end. In order to increase gene discovery from immature cotyledon libraries that contain abundant mRNAs representing storage protein gene families, we utilized a high density filter normalization approach to preferentially select more weakly expressed cDNAs. All 27,513 cDNA inserts were amplified by polymerase chain reaction. The amplified products, along with some repetitively spotted control or 'choice' clones, were used to produce three 9,728-element microarrays that have been used to examine tissue specific gene expression and global expression in mutant isolines.
Conclusions
Global expression studies will be greatly aided by the availability of the sequence-validated and low redundancy cDNA sets described in this report. These cDNAs and ESTs represent a wide array of developmental stages and physiological conditions of the soybean plant. We also demonstrate that the quality of the data from the soybean cDNA microarrays is sufficiently reliable to examine isogenic lines that differ with respect to a mutant phenotype and thereby to define a small list of candidate genes potentially encoding or modulated by the mutant phenotype.
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Background
Genes of higher plants are expressed in a coordinated fashion during development of tissue and organ systems and in response to different environmental conditions. This regulation may be tightly linked for some sets of genes, for example, in a specific biochemical pathway. Expression of regulatory genes may modulate the expression of key genes or entire sets of genes in individual pathways. The investigation of single gene expression patterns as determined by RNA blotting or quantitative reverse transcriptase PCR have been used to understand how different temporal, developmental, and physiological processes affect gene expression. With recent advances in genomics, very large numbers of genes can now be simultaneously analyzed for their expression levels in a comparative fashion between two biological states using microarray or biochip technology. Several techniques for the 'global' analysis of gene expression have been described [1-4]. These include (a) high density expression arrays of cDNAs on conventional nylon filters with radioactive probing; (b) microarrays or 'chips' using fluorescent probes, and (c) serial analysis of gene expression (SAGE).
Methods for global expression analysis require either knowledge of the entire genome of an organism or accumulation of a large EST (expressed sequence tag) database for the organism. In soybean (Glycine max), more than 286,000 5' EST sequences have been generated and deposited in public databases [[5], and this report]. These 5'ESTs represent a collection of 80 cDNA libraries from different tissue and organ systems at various stages of development and under diverse physiological conditions. Collaborative, multidisciplinary research to enhance the development of plant genome resources and information that would be publicly available for gene expression, gene tagging, and mapping has been a priority in recent years in plants of agronomic importance, including soybean [6]. Here, we report the development, qualification, and use of 27,513 members of a low redundancy set of tentatively unique cDNAs or 'unigenes' in soybean. The 3' sequence of this set was determined and microarrays constructed. The public availability of the low redundancy clone set, sequence information, and microarrays reported here will greatly enhance gene discovery and genomic scale research in soybean and other legumes by the community of researchers. For example, we illustrate the use of the 5' and 3' sequence-verified cDNA microarrays to determine organ-specific expression and we demonstrate their potential to discover the molecular basis of specific mutations in closely related isogenic lines.
Results and discussion
Cluster analysis of 280,000 ESTs reveals 61,127 'unigenes' in soybean
Figure 1 illustrates an overview of the data generation and analysis used to create a low redundancy 'unigene' set and its use in construction of cDNA microarrays. The 5' EST sequence information was used as the raw material including the addition of over 30 new cDNA libraries and more than 160,000 5' sequences since an initial report [5]. In total now, over 80 libraries and over 280,000 5' EST have been generated from many tissue and organ systems of various stages of development, ranging from roots, shoots, leaves, stems, pods, cotyledons, germinating shoot tips, flower meristems, tissue culture derived embryos, and pathogen challenged tissues. These libraries, with the one exception of library Gm-r1030 described below, were non-normalized. Thus, the mRNAs that are more abundant in various tissue and organ systems will be more highly represented in the EST collection. To remove redundancy and identify unique sequences, the ESTs were assembled using the Phrap program [7] into contiguous regions (contigs) representing overlapping sequences based on EST sequence similarity. In this way, longer overlapping sequences of expressed genes are assembled. Identical sequences that represent redundant mRNAs of various sizes are also recognized and the number of sequences in a contig in a non-normalized cDNA library is a rough approximation of the relative abundance of that particular mRNA within that tissue.
Figure 1 Steps in the construction and documentation of cDNA microarrays using a low redundancy soybean 'unigene' set of 27,513 cDNA clones. See Methods for details. (a) NSF Plant Genome Program project "A Functional Genomics Program for Soybean" (NSF DBI #9872565); (b) Soybean Public EST project [5]; (c) Washington University Genome Center, St. Louis, MO; (d) Center for Computational Genomics and Bioinformatics, University of Minnesota, Minneapolis, MN [20]; (e) Genome Systems, St. Louis, MO, until its closure; (f) Keck Center for Comparative and Functional Genomics, University of Illinois, Urbana, IL [21] (g) Soybean Functional Genomics, Department of Crop Sciences, University of Illinois, Urbana, IL [30]; (h) databases maintained by the National Center for Biotechnology Information, Bethesda, MD [22].
The combined number of contigs and singletons (sequences that occur only once) resulting from a computer assembly of ESTs is an estimate of the number of unique genes in the organism. As the number of ESTs grows, the number of unique genes in the organism will continue to be refined. Our current contig analysis of the entire public EST collection for soybean of 286,868 sequences yields 61,127 'unigenes' of which 36,357 are contigs and 24,770 are singletons.
The finding of 61,127 soybean unigenes by EST cluster analysis agrees well with an independent contig and unigene assembly in the databases of The Institute for Genomic Research (TIGR) which shows 30,084 contigs and 37,601 singletons for a total of 67,826 tentatively unique sequences from among 334,730 sequences representing all publicly available sequences clustered in Release 11 [8]. Other large scale plant EST collections as analyzed by the TIGR gene indices [8] show 42,301 unigenes for Arabidopsis thaliana (of 247,429 ESTs), 36,976 for Medicago truncatula (of 189,919 ESTs), 31,012 for tomato (of 156,645 ESTs), 109,509 for wheat (of 494,195 ESTs) and 56,364 for maize (of 377,188 ESTs). The complete genome sequence for Arabidopsis has revealed an estimated 26,000 genes [9]. Of course, the unigene sets determined by EST clustering are only estimates of the number of unique genes in an organism and depend on the number of ESTs available, the technologies used to make the libraries, and the bioinformatic methods used to assign clusters. The soybean genome is approximately 1.2 × 109 bp which is about 7.5 times the size of the Arabidopsis genome and twice that of tomato, but less than half the size of the maize genome. Thus, it is not unexpected that soybean may have a larger number of unigene clusters than Arabidopsis or tomato, for example. Although soybean is not hexaploid in origin as is modern wheat, it is thought to be an ancient autotetraploid and many examples of duplicate loci exist in soybean.
Virtual subtraction using high density cDNA filter arrays increases gene discovery in immature cotyledon libraries that abundantly express storage protein gene transcripts
Certain tissues contain large amounts of specialized transcripts. Developing soybean cotyledons, for example, contain large amounts of RNA transcripts representing highly expressed storage protein genes. In order to select for some of the weakly expressed cDNA clones from a mid-maturation stage cotyledon library, we used a virtual subtraction approach using high densisty filters. A total of 18,000 bacterial clones containing cDNAs from library Gm-c1007 (immature cotyledons of 100–300 mg fresh weigh range from Williams variety) was printed in high density on nylon filters and probed with 33P-cDNA produced by reverse transcription of total mRNA isolated from immature soybean cotyledons. Figure 2A shows the highly complex hybridization pattern resulting from using total mRNA from immature cotyledons to probe the high density filter. The intensity of each dot represents the hybridization signal and the relative abundance of that cDNA in the message population. The phosphorimager pattern was quantified by image analysis software and 5000 of the lowest expressing clones were selected. The cDNA clones were physically reracked to a new set of 384 well plates to form the filter-normalized reracked library designated Gm-r1030. The 5' end of these clones were then sequenced at Washington University. Figure 2B shows that 1,528, or 85%, of the 1799 sequences within the filter-driven reracked library Gm-r1030 were novel and not found in any of the 931 sequenced clones from the Gm-c1007 source cDNA library. Thus, the filter normalization method was an effective way to identify cDNAs with low expression and increase gene discovery in libraries that contain large numbers of transcripts from highly expressed genes. The virtual subtraction method using high density cDNA filters compares favorably with other mRNA subtraction methods used to create normalized libraries during the cloning process [10].
Figure 2 Gene discovery is increased by selection of weakly expressed cDNAs clones from a cDNA library made from immature cotyledons. (A) Phosphorimager pattern: A high density membrane containing 18,432 double spotted colonies from the Gm-c1007 cDNA library made from immature cotyledons was hybridized with 33P-labeled cDNAs transcribed from mRNAs isolated from immature cotyledons. (B) Graphical representation of the new cDNAs selected by the normalization process using filter hybridization. Circles represent the 931 total sequences obtained from the non-normalized source cDNA library Gm-c1007 versus the 1799 sequences of Gm-r1030 that were selected as weakly expressed sequences from the filter hybridization experiments shown in part (A). The intersection of the circles represent sequences common to both sets. H = number of sequences with hits in the databases; N = number of sequences that did not have a hit in the databases; and T = total number of sequences.
Selection and 3' sequencing of 27,513 soybean cDNAs from the soybean unigene set to use in microarrays
High density cDNA arrays of bacterial cultures spotted on nylon membranes and probed with radioactively labeled transcripts are useful for gene discovery as illustrated in Figure 2 above, but they have limited use for quantifying the relative abundance of transcripts expressed in independent mRNA samples. An alternative method to the high density filters is microarray technology [2,3] in which PCR-amplified cDNA inserts, or oligonucleotides, are printed on glass slides and probed with mRNAs populations that have been separately labeled with different fluorescent probes.
To enable global expression studies, the ideal would be to have each gene represented at least once on an array. Toward this aim, we selected 27,513 of the cDNA clones by four successive clustering assemblies of the soybean cDNAs performed as the 5' EST data accumulated. Table 1 shows the four reracked sets of cDNA clones (Gm-r1021, Gm-r1070, Gm-r1083, and Gm-r1088) and the level of uniqueness within them as defined by Phrap and CAP3 analysis [7,11]. Thus, the clustering was conducted periodically as the number of 5' EST input sequences grew in size. After each clustering, the previously selected and reracked cDNAs were excluded from subsequent reracking lists. In order to develop a 'unigene' set for soybean, a single representative of each contig was chosen. To select a representative from each contig, we chose the cDNA clone corresponding to the EST that was found at the furthest 5' region of each contig. Thus, we are selecting the cDNA clones most likely to be near full-length.
Table 1 Comparison of the percent unique sequences as determined by either CAP3 or Phrap analysis for the 5' and 3' ESTs represented in each of the four successive reracked clone subsets that constitute the low redundancy soybean 'unigene' set
Rerack order & name Number cDNAs No. ESTs clustereda Cap3b Phrapb % Unique ESTsc Cap3 or Phrap
1. Gm-r1021 4,089 2,797, 5' 2,202 s 259 c 2,054 s 334 c 88.0% : 80.4%
1. Gm-r1021 4,089 2,797, 3' 1,836 s 413 c 1,682 s 505 c 85.4% : 78.2%
2. Gm-r1070 9,216 6,938 5' 5,566 s 620 c 5,116 s 831 c 89.2% : 78.0%
2. Gm-r1070 9,216 6,938 3' 4,284 s 1,124 c 3,900 s 1,340 c 85.7% : 75.5%
3. Gm-r1083 4,992 3,879 5' 3,426 s 200 c 3,289 s 260 c 93.5% : 79.7%
3. Gm-r1083 4,992 3,879 3' 2,474 s 599 c 2,256 s 723 c 91.5% : 76.8%
4. Gm-r1088 9,216 7,434 5' 6,295 s 521 c 5,909 s 745 c 91.7% : 89.5%
4. Gm-r1088 9,216 7,434 3' 4,719 s 1,173 c 4,152 s 1,513 c 79.3% : 76.2%
Entire set, 1–4 27,513 27,513 5d 21,873 s 2,402 c 18,663 s 3,966 c 88.2% : 81.2%
Entire set, 1–4 27,513 21,048 3' 11,959 s 4,156 c 8,341 s 5,641 c 73.0% : 63.3%
a Unless otherwise noted, the ESTs included in the cluster analysis represent only the cDNAs for which both the 5' and 3' sequences are known and for which the read length is over 200 bases.
b The number of singletons (s) and number of contigs (c) are shown.
c The % unique sequences is the number of singletons plus the number of contigs divided by the total number of ESTs.
d In this analysis, all 5' sequences were included even if the corresponding 3' sequence was not known.
EST clustering will overestimate the number of unique genes as some of the shorter ESTs will not overlap and thus are falsely counted as independent, unique sequences. However, the clustering analysis can also falsely lump non-identical members of gene families into the same contig based on conservation of sequence similarity in the coding region. The 3' sequencing is especially useful for resolving both of these issues as there is generally more variation in the 3' UTR in plant genes than in the coding region. For those reasons and as a quality control of the reracking process, we sequenced the 3' end of the reracked cDNAs. From the 27,513 total 3' sequencing attempts on the tentatively unique cDNAs represented in Table 1, a total of 22,088 sequences met the criteria of high quality sequence. The 3' sequencing was more problematic than the 5' sequencing due to termination of the sequencing reactions at some of the long polyA tails characteristic of soybean and many other plant cDNAs. An anchored primer was used to increase the success rate (see Methods). The average length of the 3' ESTs was 526 bases compared to the average 5' sequence read length of 474 for 280,094 ESTs.
Since the clustering analyses were performed at successive intervals as the EST collection grew in size, we repeated the Phrap contig analyses separately using only the input sequences of each cDNA rerack for which both a 5' and 3' EST were known. We also performed a CAP3 analysis [11]. Table 1 shows that CAP3 values for the 5' sequence yielded 88.0 to 93.5% unique sequences while the Phrap values were slightly lower at 78.0 to 89.5% unique sequences. Interestingly, the estimate of unique sequences using the 5' EST data did not change substantially from reracked library r1021 where only approximately 6800 ESTs were clustered through library r1088 where over 250,000 sequences were clustered. A separate cluster analysis of only the 27,513 input 5' sequences revealed 81.2 to 88.2% unique sequences by Phrap and CAP3 analyses, respectively.
The 3' ESTs were also separately subjected to CAP3 or Phrap analysis. The CAP3 values showed a slightly higher level of uniqueness (or lower level of redundancy) with 79.3 to 91.5% for CAP3 in the successive clustering analyses versus 75.5 to 78.2% unique sequences as determined by Phrap. An overall figure of 73.0% for the CAP3 analysis on the 21,048 total 3' sequences clustered was found versus 63.3% for Phrap.
The differences between the 5' and 3' levels of uniqueness (i.e., 88.2% versus 73.0% for the entire sets as determined by CAP3) can be explained by the nature of reverse transcriptase action. The reverse transcriptase was primed using an oligo dT primer and so the cDNAs will begin at the 3' end and will terminate randomly at variable sites as the enzyme progresses to the 5' end of the mRNA template. Thus, 5' ESTs often begin at variable sites. Therefore, even though two 5' ESTs may have originated from the same mRNA transcript, they will not cluster if they are non-overlapping and will be counted as two separate ESTs. The 3' soybean EST reads begin just after the poly A tail and produce longer average read lengths than the 5' soybean ESTs; thus, the 3' ESTs are more likely to form an overlapping contig if there is any redundancy among them.
The 5' and 3' sequence (where known) of each soybean unigene were queried against the non-redundant (nr) database with BLASTX [12]. Annotations were assigned to each 5'and 3' if the best match had an e value of ≤10-6. Table 2 shows a complete cross list of all identifiers for each member of the 27,513 soybean unigenes in the reracked libraries including the 5' and 3' annotations.
Table 2 Information contained in a comprehensive cross list of soybean unigene clone IDs. Shown are various identifiers and annotations for 27,513 reracked cDNAs used in microarray construction. The full list is provided with arrays and available upon request.
Cross List Identifiers (for each cDNA clone) Example (one of 27,543 cDNAs) Comments
Reracked Clone ID Gm-r1021-12 The individual cDNA clone ID in the 384-well destination plates after reracking or rearraying of the selected clones from the cDNA source library plates.
Reracked Plate ID Gm-r1021 #1 The 384-well reracked plate name in increments of 384 (ie., 1, 385, 769, etc.)
Reracked row_column position A12 Position of the clone in the 384-well reracked plate
Reracked 3' Keck Sequence ID GM210001A21A6 Sequence identifier assigned by the Keck Center for the 3' EST
Reracked 3' Genbank Accession AW348131 Genbank assigned accession number for the 3' EST
Reracked 3' Annotation glutathione S-transferase GST 22 [Glycine max] Top BLASTX hit for the 3' EST, at E10-6 or lower
Source Clone ID Gm-c1004-464 The individual cDNA clone ID in the 384-well source plate.
Source Plate ID Gm-c1004 #385 The 384-well source plate name in increments of 384 (ie., 1, 385, 769, etc.)
Source row_column position D8 Position of the clone in the 384-well source plate
Source WashU Sequence ID sa26h04.y1 Sequence identifier assigned by Washington University, 5' EST
Source 5' Genbank Accession AI442436 Genbank assigned accession number for the 5' EST
Source 5' Annotation glutathione S-transferase GST 22 [Glycine max] Top BLASTX hit for the 5' EST at E10-6 or lower
Source Library Gm-c1004 Name of the cDNA source library
Cultivar/Genotype Williams Specific information on the soybean variety or genotype
Tissue/Developmental Stage Entire roots of 8-day old seedlings Tissue/organ system/stage from which the cDNA library was constructed
Construction of microarrays representing the 27,513 soybean unigene cDNAs
The current 'unigene' collection (or tentatively unique sequences) represents low redundancy sets of cDNA clones. We have processed all of these cDNAs for microarrays, as outlined in the Methods section, into three sets of 9,216 cDNAs per array. As shown in Table 3, these include reracked libraries Gm-r1070 (a set of 9,216 cDNA clones from libraries of various developmental stages of immature cotyledons, flowers, pods, and seed coats); Gm-r1021 plus Gm-r1083 (a set of approximately 9,216 cDNA clones from 8-day old seedling roots, seedling roots inoculated with Bradyrhozobium japonicum, 2-month old roots, and whole seedlings); and Gm-r1088 (a collection of 9,216 cDNA clones from a number of libraries made from cotyledons and hypocotyls of germinating seedlings and leaves and other plant parts subjected to various pathogens or environmental stress conditions and also from tissue-culture derived somatic embryos). As an example, the Gm-r1070 set contains 3,938 tentatively unique cDNAs that are directly derived from two flower cDNA libraries (Gm-c1015 and Gm-c1016) that were sequenced deeply with over 14,000 5' ESTs obtained from these two libraries. In addition, a total of 2,639 cDNAs on the array are directly derived from source libraries made from the immature stages of cotyledon development and representing over 11,000 input sequences from the cotyledon stages of seed development.
Table 3 Soybean microarrays and low redundancy and low redudancy unigene sets built from the public EST collection
Microarrays and Reracked Unigene cDNA sets a Source cDNA Library a No. of cDNAs on array Soybean Variety Soybean tissues b
Set 1. Gm-r1070: 9216 cDNAs highly representative of developing seeds and flowers
Gm-r1070 Gm-c1016 2242 Williams 82 immature flowers
" Gm-c1015 1696 Williams 82 mature flowers
" Gm-c1008 869 Williams whole young pods (2 cm)
" Gm-c1029 589 Williams immature cotyledons from 25–50 mg fresh weight seed
" Gm-c1010 234 Williams immature cotyledons 100–200 mg seed fresh wt.
" Gm-c1011 88 Williams immature cotyledons 100–200 mg seed fresh wt.
" Gm-c1007 528 Williams immature cotyledons 100–300 mg seed fresh wt.
" Gm-c1030 1200 Williams immature cotyledons 100–300 mg seed fresh wt. low expressing cDNAs fromGm-c1007 filter hybridizations
" Gm-c1023 89 T157 immature seed coats from seed of 100–200 mg fresh wt.
" Gm-c1019 1681 Williams immature seed coats from seed of 200–300 mg fresh wt.
Set 2. Gm-r1021+Gm-r1083: 9216 cDNAs highly representative of roots
Gm-r1021(c) Gm-c1004 4224 Williams roots of 8-days old seedlings
Gm-r1083 Gm-c1009 1117 Williams roots, 2 month old plants
" Gm-c1028 3055 Supernod roots innoculated with B. japonicum
" Gm-c1013 820 Williams whole 2–3 week old seedlings
Set 3. Gm-r1088: 9216 cDNAs highly representative of seedlings, leaves, and stressed or pathogen challended tissues
Gm-r1088 Gm-c1019 426 Williams immature seed coats from seed of 200–300 mg fresh wt.
" Gm-c1023 929 T157 immature seed coats from seed of 100–200 mg fresh wt.
" Gm-c1027 2706 Williams cotyledons of 3- and 7-day-old seedlings
" Gm-c1036 613 Jack somatic embryos cultured on MSD 20 for 2 to 9 mo.
" Gm-c1075 304 Jack differentiating somatic embryos cultured on MSM6AC
" Gm-c1064 707 Williams epicotyl, 2 week old seedling, auxin treatment
" Gm-c1065 1309 Williams germinating shoot, cold stressed, 3 day old seedlings
" Gm-c1066 191 Williams leaf and shoot tip, salt stressed, 2 wk. old seedling
" Gm-c1067 438 Williams82 germinating shoot, 3 day old seedling, auxin treatment
" Gm-c1068 630 Williams82 leaf, drought stressed. 1 month old plants
" Gm-c1072 365 PI 567.374 leaves and shots from 2–3 week old seedlings induced for SDS symptoms
" Gm-c1073 324 Williams 82 leaves and shoots from 2–3 week old seedlings included for SDS symptoms
" Gm-c1074 274 Williams 82 9–11 day old seedlings induced for HR response by P. syringae carrying avrB gene
a More description of the reracked and source libraries are available in Genbank at
b Tissues were collected from plants grown in greehouse or growth chamber except for the immature and mature flowers which were collected from plants grown in the field.
c Since the Gm-r1021 reracked liabrary contains 4089 cDNAs, a total of 135 were repeated to obtain an even 9216 when combined with the Gm-r1083 cDNAs.
The cDNAs from the sequence-driven, reracked clone sets were amplified by PCR using the Qiagen-purified cDNA templates that were prepared for 3' sequencing (as opposed to amplification of the inserts directly from E. coli cultures containing the plasmid DNA). All 27,513 PCR reactions were performed with generic M13 forward and reverse primers using a robotic pipettor. Approximately 25% of the purified PCR cDNA inserts were subjected to agarose gel electrophoresis for quality control. Of these, the average insert size was estimated to be 1,340 bp for library Gm-r1021, 1,110 bp for library Gm-r1070, 1,259 for library Gm-r1083, and 1,269 bp for library Gm-r1088.
The 9,216 amplified inserts of each set were singly spotted onto glass slides as outlined in the Methods section. A set of 64 control or 'choice' clones was assembled by hand into one 96-well plate (designated Gm-b10BB) and printed eight times repetitively throughout each array. Thus, the total number of spots on the array is 9,728 consisting of 9,216 cDNAs from the unigene set plus 512 (64 cDNAs × 8 repeats) from the choice clones. The choice clones were selected for various reasons. Some represent constitutively expressed genes (such as ubiquitin and EF1). Some are cDNAs whose expression is restricted to a subset of specific plant tissues (such as Rubisco or seed storage proteins). Some are clones of enzymes representing commonly used antibiotic resistance markers in transgenic plants (as hygromycin or kanamycin resistance), and 32 are cDNAs that represent at least 13 different enzymes of the flavonoid pathway. The flavonoid pathway was chosen because the corresponding genes often respond to many biotic and abiotic stress conditions and it has been widely studied in plant systems.
Soybean microarrays have potential to reveal the molecular basis of a mutant phenotype
Figure 3 illustrates an example of the reliability of the soybean microarray approach using dual labeled RNA probes from two near isogenic lines. These data illustrate the potential to discover novel genes by analysis of contrasting probes from mutant and normal lines. In this experiment, we compared two isogenic soybean lines that differ only at the T locus. The T locus controls the color of the pigment in the trichome hairs on the stems, leaves, and pods of the plant and also modifies the composition of the flavonoids and the color of the seed coats. Total RNA extracted from developing seed coats of line XB22A (T/T genotype) was labeled with Cy3 and compared to RNA extracted from the same stage of developing seed coats from an isoline containing the spontaneous mutation 37609 (t*/t* genotype) that was labeled with Cy5. A replicate experiment with a dye swap was also performed. Hybridizations were performed to microarrays constructed with the low redundancy set Gm-r1070 representing cDNAs from seeds, seed coats, and flowers. Figure 3 shows both replicates before and after the flagging and normalization procedures conducted as described in the Methods section. As shown in Figure 3, the normalization procedure serves to compensate within slide differences between the Cy3 and Cy5 intensity levels to shift the majority of spots to the line of equivalent expression between the isogenic lines. Also, as shown in Figure 3, very few of the 9,728 cDNAs on the array were reproducibly found to be expressed differentially in the two genotypes at levels higher or lower level than two-fold. A group of 16 of these (encircled by a line) are overexpressed in the T/T line relative to the t*/t* line by approximately three-fold. These cDNAs correspond to the flavonoid 3' hydroxylase cDNAs that were repetitively spotted on the array as members of the 'choice' clone set. Table 4 shows the Cy5 and Cy3 intensities and the ratios of the two replicate slides for all cDNA spots that exceeded a two-fold difference after normalizations within each slide and between the replicate slides. Only 23 cDNAs were found to have values that meet the criteria of exceeding a two-fold differences in both of the replicate slides. Of these 16 were the repetitively spotted flavonoid 3' hydroxylase cDNAs. Only an additional 22 cDNAs (known as partial hits) had values exceeding two-fold levels in one but not both of the slides replicates. Thus, out of over 9200 cDNAs represented on the array, there are relatively few that show differential expression between the RNAs in the normal and mutant lines.
Figure 3 The scatter plots of the log values of expression data from two duplicate microarray slides before (left) and after flagging and normalization (right). RNAs were extracted from seed coats of the 50–75 mg per seed fresh weight range by standard methods [13]. Replicate 1 was hybridized with Cy5 labeled RNA from seed coats of the T/T genotype and Cy3 labeled RNA from seed coats of the isogenic t*/t* mutant line. Replicate 2 is a dye swap experiment in which the mRNA from the T/T genotype is labeled with Cy3 and the isogenic t*/t* line is labeled with Cy5. The lines in each graph indicate expression either two-fold higher or two-fold lower than equivalent levels of expression. The dots encircled by the box represent repeats of flavonoid 3' hydroxylase cDNAs on the array that are overexpressed in the RNA samples from seed coats of the T/T genotype.
Table 4 Differentially expressed cDNAs detected in dual labeling microarray experiments comparing isogenic lines of the T locus in soybean.
Clone ID Genbank Intensitiesa Expression Ratios Functional Annotationd
3' Accession Replicate 1 Replicate 2
XB22A (T/T) 37609 (t*/t*) XB22A (T/T) 37609 (t*/t*) XB22A/37609 (T/T) / (t*/t*)
Cy 5 Cy3 Cy3 Cy5 Rep 1b Rep 2b Aveb,c
Overexpressed in XB22A
Gm-b10BB-23 AF499730 28686 10847 38350 8194 2.645 4.680 3.520 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 26794 9746 34956 7839 2.749 4.459 3.512 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 23979 9018 33272 7626 2.659 4.363 3.440 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 26094 9231 23580 5264 2.827 4.479 3.427 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 26812 10670 35600 7963 2.513 4.471 3.350 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 25663 9746 32520 7685 2.633 4.232 3.338 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 24020 9468 34329 8241 2.537 4.166 3.295 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 24578 10218 35465 8158 2.405 4.347 3.267 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 24662 9850 26041 5957 2.504 4.371 3.208 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 22240 9526 31641 7643 2.335 4.140 3.138 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 24548 11084 35328 8233 2.215 4.291 3.100 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 19465 8343 30897 7912 2.333 3.905 3.098 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 26572 12004 37720 8844 2.214 4.265 3.084 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 21583 9921 31828 7410 2.175 4.295 3.082 Flavonoid-3' hydroxylase
Gm-b10BB-22 AF499731 21122 10431 37273 8852 2.025 4.211 3.028 Flavonoid-3' hydroxylase
Gm-b10BB-23 AF499730 26053 12897 37691 8164 2.020 4.617 3.027 Flavonoid-3' hydroxylase
Underexpressed in XB22A
Gm-r1070-484 BE819850 2843 6235 3931 8702 0.456 0.452 0.454 Bowman-Birk inhibitor
Gm-r1070-8083 BE823467 2471 5070 3498 8557 0.487 0.409 0.438 Ribonucleoprotein homolog
Gm-r1070-8006 BE823540 3353 7237 4145 12219 0.463 0.339 0.385 Trypsin inhibitor, Kunitz
Gm-r1070-9195 BE824378 1889 4384 2383 7562 0.431 0.315 0.358 No hits found
Gm-r1070-120 BE657237 3083 7548 3769 11877 0.408 0.317 0.353 Trypsin inhibitor, Kunitz
Gm-r1070-8909 BE824331 3771 10945 3756 13568 0.345 0.277 0.307 Beta conglycinin
Gm-r1070-9099 BE824364 1806 19569 1663 31399 0.092 0.053 0.068 Albumin precursor/leginsulin
(a) Intensities after background subtraction and global normalization between replicates and within each slide are shown.
(b) The mean ratios of the 16 flavonoid hydroxylase cDNAs are significant below the p = 0.0001 level in a t-test compared to 2.0 as the mean.
(c) The average ratio of both slides is calculated by as follows: (XB22A Rep 1 intensity + XB22A Rep2 intensity) / (37609 Rep 1 intensity + 37609 Rep 2 intensity)
(d) The matches for all of the functional annotations were to soybean (Glycine max) sequences except for the ribonucleoprotein homolog which was to Arabidopsis thaliana.
An examination of the ratios of the 16 repetitively spotted flavonoid 3' hydroxylase cDNAs using a t test showed that the mean ratio of the repeated cDNAs on replicate 1 (2.424) were statistically significant at a P value of 0.0001 when compared to an expected mean of a 2.0, or a two-fold expression difference. The low P values were also found for replicate 2 and for the mean value (3.245) of both replicates. Thus, the flavonoid 3' hydroxylase cDNAs are statistically significant outliers in the microarray analysis.
The microarray data presented here and showing that the cytoplasmic levels of the flavonoid 3' hydroxylase are higher in the T/T line agree very well with RNA blot data which showed that the flavonoid 3' hydroxylase gene has reduced expression in the seed coats of the t*/t* isoline compared to the T/T lines [13]. In addition to the RNA blot data showing differences in these mutant lines, we have definitively shown that the flavonoid 3' hydroxylase is encoded by the T locus by sequence data of other alleles of the locus and by genetic cosegregation data [13]. We do not know the reason for the change in the expression levels of the seven other cDNAs as shown in Table 4, most of them representing various seed or storage type proteins. While the T locus does determine the flavonoid and pigment compounds synthesized in various tissues including seed coats and trichomes, it is possible that the flavonoid compounds themselves modulate an additional effect on seed protein synthesis in the seed coats. Alternatively, the observed differences in the levels of these cDNAs could be due to an artifact during the dissection procedure. We know from Northern blots, that flavonoid 3' hydroxylase is highly expressed in the seed coats, but is not expressed in the cotyledons so any small amount of contaminating cotyledon cells due to imprecise dissection of the seed coats of one line versus the other could lead to observed differences in seed protein RNAs.
As this example in Figure 3 and Table 4 illustrates, the use of dual labeled mRNAs from near isogenic lines to probe microarrays is a powerful approach with which to obtain a small list of candidate genes from among the thousands examined by microarray analysis. In this example only eight functionally different cDNAs (or seven if the two trypsin inhibitor cDNAs are counted as one) of over 9200 cDNAs spotted on the array met the criteria of exceeding two-fold levels of expression in both replicates. If a cDNA is repetitively spotted on an array, as were the flavonoid 3' hydroxylase cDNAs, then the data are statistically significant. After identifying a short list of candidate genes, it is then feasible to test them by other methods (as RNA blotting, quantitative RT-PCR, RFLP or SNP analysis) in order to find an association of a particular cDNA with the mutant phenotype. Of course, if a particular mutation has a regulatory or epigenetic effect on a large number of downstream RNAs, or if a mutation does not affect the abundance of an mRNA, then the global expression approach may not be effective in identifying the primary nature of the mutant locus. For example, the standard recessive t allele at the T locus is the result of a premature stop codon and does not affect abundance of the flavonoid 3' hydroxylase mRNA to the same extent as does the t* mutation at that locus [13].
Tissue specific gene expression using the soybean microarrays
In contrast to the results with near isogenic lines of the T locus which showed that relatively few cDNAs showed differential expression between the two very closely related lines, the soybean microarrays reveal larger numbers of cDNAs showing differential mRNA abundance levels in different tissue types of the same plants. For illustration, Figure 4 shows one of the two replicates of a dual labeling experiment using the low redundancy set Gm-r1088 of 9,216 cDNAs. The Cy3 labeled probe in this experiment was RNA from roots of hydroponically grown soybean plants, and the Cy5 probe was RNA from leaves of the same plants. The upper ratio threshold is 2.0 and the lower threshold is 0.5.
Figure 4 One of the scatter plots of the log values of expression data from microarray slides hybridized with Cy3 labeled RNA from leaves and Cy5 labeled RNA from roots. Many cDNAs have differential expression above or below the two-fold level as indicated by the lines.
Table 5 lists a selection of the 300 clones with significantly elevated expression in leaves (ratios below the 0.5 threshold) and the 125 clones with significantly elevated expression in roots (ratios above the 2.0 threshold) that consistently varied more than two-fold in both of the replicated slides. Among the clones with elevated expression in roots are chalcone isomerase, putative aquaporins (tonoplast intrinsic protein), tubulin, an auxin-repressed protein, peroxidase, sucrose synthase and PEP carboxylase. These plants were not inoculated with Rhizobia and therefore only a few nodulin-related genes were observed to be markedly upregulated: nodulin-26, nod factor binding lectin-nucleotide phosphohydrolase (GS50, Accession # AF207687) and a MtN19 homolog.
Table 5 A selection of genes that are differentially expressed in leaves or in roots
Clone Identification GenBank accession no. Average Ratio1 Function2 Annotation (BLAST hit and organism)
Leaves up-regulated
Gm-b10BB-41 AI495218 0.103 en Rubisco (Glycine max)
Gm-r1088-7900 BU550654 0.294 en Light harvesting chlorophyll a/b binding protein (Arabidopsis thaliana)
Gm-r1088-8981 BU549821 0.261 en Photosystem I subunit (Oryza sativa)
Gm-r1088-3538 BU546899 0.365 en Thylakoid lumen protein (Arabidopsis thaliana)
Gm-b10BB-47 AW185639 0.171 en Plastocyanin precursor (Glycine max)
Gm-r1088-2905 BU546179 0.258 en Trehalose-6-phosphate phosphatase (Arabidopsis thaliana)
Gm-r1088-7106 BU548940 0.150 st Vegetative Storage Protein (Glycine max)
Gm-r1088-5827 BU548964 0.465 df Acidic chitinase (Glycine max)
Gm-r1088-6724 BU549206 0.217 cmg Putative calreticulin (Oryza sativa)
Gm-r1088-3756 BU546067 0.415 cmg Cytochrome P450 (Pyrus communis)
Gm-r1088-8229 BU550097 0.454 cmg Catalase (Glycine max)
Gm-r1088-5243 BU547961 0.143 cmg Putative serine carboxypeptidase II-3 precursor (Oryza sativa)
Gm-r1088-1433 BU545435 0.200 cmg H protein (Flaveria anomala)
Gm-b10BB-12 AI900038 0.211 cmg F3H (Flavanone-3-Hydroxylase) (Glycine max)
Gm-r1088-4994 BU547986 0.205 cmg Matrix metalloproteinase MMP2 (Glycine max)
Gm-r1088-2794 BU547254 0.138 cmg Putative lipoic acid synthase (LIP1) (Arabidopsis thaliana)
Gm-r1088-4578 BU547484 0.165 cmg Lipid transfer protein-like protein (Retama raetam)
Roots up-regulated
Gm-r1088-5321 BU547784 3.806 no Nodulin-26 (Glycine max)
Gm-r1088-7410 BU550525 2.304 no MtN19 homolog (Medicago truncatula)
Gm-r1088-6955 BU551008 4.630 no Similar to nodulins and lipase homolog (Arabidopsis thaliana)
Gm-r1088-6384 BU550458 4.431 to bZIP transcription factor (Arabidopsis thaliana)
Gm-b10BB-11 AI930858 3.008 df Chalcone isomerase (Glycine max)
Gm-r1088-6204 BU547671 2.541 cmg Putative aquaporin (tonoplast intrinsic protein) (Arabidopsis thaliana)
Gm-r1088-2818 BU546503 2.848 cmg Phosphoenolpyruvate carboxkinase (Flaveria trinervia)
Gm-r1088-1741 BU544616 2.724 cmg Similar to sucrose synthase (Pisum sativum)
Gm-b10BB-37 AW309104 6.228 cmg Proline-rich protein (Glycine max)
Gm-b10BB-38 AI442449 2.233 cmg DAD-1 (Defender Against apoptopic cell Death) (Glycine max)
Gm-r1088-5369 BU547794 8.372 cmg Ripening related protein (Glycine max)
Gm-r1088-7112 BU548943 6.661 cmg Germin-like protein (Phaseolus vulgaris)
Gm-r1088-5330 BU547868 4.974 cmg Pectinesterase (EC 3.1.1.11) precursor (Vigna radiata)
Gm-r1088-6104 BU549267 4.554 cmg Asparagine synthase (glutamine-hydrolyzing) (Glycine max)
Gm-r1088-741 BU544257 2.455 cmg Cationic peroxidase (Glycine max)
Gm-b10BB-45 AW318233 2.135 cmg Tubulin (b chain) (Glycine max)
Gm-r1088-5315 BU547781 4.334 u Specific tissue protein 1 (Cicer arietinum)
Gm-r1088-5332 BU547869 4.267 oth Auxin-repressed protein (Robinia pseudoacacia)
1 The average ratios of individual values from two slides after normalization and using a dye swap procedure.
2 en – energy; st – storage; to – transcription; cmg – cell growth and maintenance; u – unknown; oth – other; df – defense; no – nodulation related
The soybean proline-rich protein (SbPRP1 Accession # J05208) (represented on the array by AW309104, Gm-c1019-3688) is also among the root expressed clones. SbPRP1 has been shown to be expressed preferentially in the roots [14]. A gene of interest overexpressed in roots is the DAD-1 (Defender Against apoptotic cell Death). No Rubisco or photosynthesis related genes were observed to be over expressed in the roots as would be expected for the non-green tissues.
In leaves, genes typical for green tissues are upregulated as expected. These are the photosynthesis genes (eg. Rubisco, plastocyanin precursor, chlorophyll a/b binding protein type II, trehalose-6-phosphate phosphatase, photosystem subunits and proteins, thylakoid lumen protein, light-harvesting chlorophyll a/b binding protein), and the vegetative storage proteins. Ribosomal proteins, cytochrome P450, catalase, and chitinase were also noted as overexpressed in the leaves.
Our publicly available Gm-r1021 soybean unigene subset containing 4,098 cDNAs has also been used to examine differential gene expression in roots and shoots of older soybean plants [15]. We have previously utilized microarrays containing 9,216 clones of the Gm-r1070 set (representing many cDNAs from developing seeds, seed coats, flowers, and pods) to carry out a detailed analysis of induction of somatic embryos during culture of cotyledons on auxin-containing media [16]. The resulting transcript profiles were subjected to a cluster analysis and revealed the process of reprogramming of the cotyledons cells during the induction process. The 495 cDNAs (5.3% of the cDNAs on the array) that were differentially expressed were clustered into 11 sets using a non-hierarchical method (K-means) to reveal cDNAs with similar profiles in either the adaxial or the abaxial side of the embryos from 0 to 28 days in 7 day intervals. Among other conclusions, these global expression studies indicated that auxin induces dedifferentiation of the cotyledon and provokes a surge of cDNAs involved in cell division and oxidative burst.
Thus, the soybean cDNA arrays that we have developed from the unigene cDNA set can be used to reveal the underlying physiological and biochemical pathways potentially operative in specific tissues, developmental stages, or environmental treatments. Obviously, cDNA arrays from soybean or any other organism that are constructed with PCR inserts representing an average size of 1.1 kb will generally hybridize with any RNAs from gene family members that share greater than 85% homology. Thus, cDNA arrays will generally not distinguish expression from closely related duplicated sequences. Oligo arrays spotted with synthetic 70-mers or Affymetrix short oligo arrays have greater potential to separate the expression from close related duplicated sequences if the oligos are chosen from the 3' or 5' non-coding regions that carry more sequence variability than the protein coding regions.
Conclusions
Although microarray data is limited from soybean and most plants other than Arabidopsis, the construction of the 27,513 member low redundancy 'unigene' cDNAs for soybean reported in this paper will greatly stimulate this area. The number of slides containing all 27,513 of the cDNAs is being reduced to one, or at most two slides, and the slides are publicly available. Spotted PCR products with average size of over 1 kb are useful not only for soybean, but for other legume species as cross hybridization to the long probes will be substantial. The 3' sequencing reported here is particularly useful for differentiating gene family members and for future design of gene specific oligo arrays of either 70-mers spotted on glass slides or by Affymetrix technology using short oligos synthesized in situ. The cDNA or oligo-based microarrays add to the developing suite of genome analysis approaches in soybean [17]. A few of the unlimited applications include profiling expression from genes that respond to challenges by various pathogens and by environmental stresses as drought, heat, cold, flooding, and herbicide application. Also, by analysis of the near isogenic lines of the T locus as an example, we demonstrated the potential of soybean cDNA arrays to be used for discovery of genes responsible for uncharacterized mutations. Future expression profiling of mutant phenotypes or of genotypes that differ in protein or oil content and other quantitative traits will yield significant clues to the genes involved in those pathways and traits.
Methods
Contig assembly for unigene selection
Raw sequence files of the 5' soybean EST data from Washington University or 3' data from the University of Illinois Keck Center were produced from sequence traces using the Phred base calling program [18,19]. The sequences were trimmed for leading and trailing vector and linker sequences and artifact E. coli sequences were removed. Quality checks included determining the number of ambiguous 'N' base calls in a sequence and trimming the leading and trailing poor quality (high-N) sections to obtain the best subsequence where the number of Ns was 4% or less of the total bases. The EST sequences were clustered into contig sets based on sequence overlap using the program Phrap [7]. The processing and analysis results for each sequence are displayed on a set of World Wide Web pages [20]. The distribution of sequence lengths in each submission set are displayed in histograms. The base call and quality information for each sequence in a submission are displayed in artificial gel images of the sequences. Each sequence is displayed as the raw sequence before vector filtering and the cleaned sequence after vector filtering. A color-coded sequence quality graph shows the part of a sequence retained after trimming as well as the regions trimmed for low quality, polyA or polyT, and vector sequences. Blast reports for each sequence are displayed and can be searched collectively for words or phrases of interest. Contig sequences and images of the contig assemblies are displayed on linked web pages along with graphs describing the contig qualities [20].
Clone reracking and 3' sequencing
Soybean cDNA clones corresponding to the 5' most representative member of a contig or to a singleton were selected using Oracle database tables and SQL queries. The E.coli stocks representing those clones were reracked into new 384 well plates to form the sequence driven reracked libraries Gm-r1021 (4,089 cDNA clones), Gm-r1070 (9,216 cDNA clones) and, Gm-r1083 (4,992 cDNA clones). Initially, these were reracked from source 384-well plates to destination 384-well plates by Genome Systems (St. Louis, MO) using a Qbot and shipped on ice to the University of Illinois for extraction and 3' sequencing. Reracked library Gm-r1088 (9,216 cDNA clones) was reracked at the University of Ilinois Keck Center using a QPix robot, (Genetix, New Milton, Hampshire UK). Growth rates for the E.coli stocks were over 99.5%.
The cDNA libraries were all constructed in either pSPORT 1 (Invitrogen, Carlsbad, CA) or pBluesciptII SK (+) (Stratagene, La Jolla, CA) plasmid vectors in DH10B host cells. Each 384-well plate of a bacterial library was split into four 96-well, 2 ml block plates, each corresponding to a different quadrant (A1, A2, B1, B2) and grown overnight in 1 ml LB media with 100 μg/ml ampicillin. High quality DNA templates were purified using a QIAGEN BioRobot 9600 or BioRobot 8000 with QIAprep 96 Turbo miniprep kits (QIAGEN, Germantown MD). Dideoxy terminator sequencing reactions for the 3' ends of the soybean cDNA clones were conducted by the University of Illinois Keck Center for Comparative and Functional Genomics [21] using standard methods analyzed either on gel-based ABI 377 or capillary-based ABI3700 instruments. Inserts within each vector type can be sequenced from the 5' end using the M13 reverse primer and the 3' end using the M13 universal forward primer. However, for higher success rates at the 3' end, a degenerate primer consisting of [5'-TTTTTTTTTTTTTTTTTT(A/C/G)-3'] was employed in order to enhance the success of 3' sequencing reactions by eliminating the need to sequence through the poly A tail. The primer was synthesized and purified by HPLC (Qiagen Operon, Alameda CA) to remove shorter, incomplete primers. Using high quality Qiagen purified cDNA templates, the average 3' untrimmed read length was over 600 bases with a success rate of 80 to 85%. Original sequence trace files are available by ftp from the University of Illinois Keck Center [21]. The trimmed sequences were entered into Genbank [22]. The reracked 5' and 3' sequences were analyzed by both the CAP3 [11] and Phrap programs [7].
All cDNA clones of the low redundancy reracked 'unigene' sets are available to the public through Biogenetic Services, Inc., Brookings, SD, or the American Type Culture Collection, Manasas, VA.
Annotation of the unigene cDNAs using BLASTX
The 5' and 3' sequences of the 27,513 unigene cDNAs clones were annotated using BLASTX against the nonredundant (nr) protein database with cutoff E value of 10E-6. The top blast hit was used as the annotation for each of the 5' and 3' ESTs represented in the unigene sets printed on the microarrays.
In some cases, the protein family assignments were also made using the Metafam program based on a BLASTX analysis against a protein sequence database consisting of a non-redundant set of sequences from SwissProt & TrEMBL [23], PIR & NRL [24], GenPept [25], and Integrated Genomics, Inc. (Chicago, IL). Each of the protein sequences in this database is also placed in a protein family in the MetaFam database [26-28]. The results from each BLASTX report were parsed and placed in an Oracle 8i database. The strong protein sequence hits from BLASTX are matched up to the MetaFam protein families to which those protein sequences belong.
Amplification of cDNAs and preparation for use in microarray construction
All pipetting steps involved in amplifying the cDNAs by PCR, purification of the cDNAs, and assembling them into 384-well spotting plates were conducted with a Multimek TM 96 Automated pippetor (Beckman Instruments, CA) to reduce errors associated with manual pipetting.
Amplification
The same Qiagen plasmid DNA templates that were prepared for the 3' sequencing by a Qiagen robot at about 100+ ng/μl were also used for PCR amplification using Taq polymerase (Invitrogen, Carlsbad, CA), universal forward and reverse primers in 96 well plates using the MJ DNA Engine Tetrad (MJ Research, Waltham, MA). Four PCR reaction plates are prepared at a time, one from each quadrant of a 384-well library plate. A master mix consisting of final concentrations of 1X PCR buffer (20 mM Tris-HCl, pH 8.4, 50 mM KCl), 2 mM MgCl2, 0.25 mM each of dGTP, dATP, dTTP, dCTP, 1 μM of M13 universal primer, 1μM of M13 reverse primer, and 0.05 U/μl of Taq polymerase (Invitrogen, Carlsbad, CA, cat no. 18038-042) was prepared and 48 μl were aliquoted into each well of a 96 well PCR reaction plate (MJ Research MSP-9621). A 0.5 μl aliquot of an undiluted plasmid template DNA was aliquoted into the 48 μl of master mix. The plates were briefly centrifuged for 1 min at 1500 rpm and placed into an MJ PTC-200 DNA Engine for 1 min of denaturation at 94°C, and 28 cycles of 92°C for 30 sec, 56°C for 45 sec, and 72°C for 30 sec and a final extension of 72°C for 5 minutes. A typical yield from the PCR was about 30–100 ng/μl.
Purification
The PCR products were loaded into Millipore multiscreen plates (Millipore #MANU 03050) and were subjected to a vacuum applied at 15 psi for about 10 min until the wells were completely empty. Then 60 μl of sterile water were added to each well using the Multimek automated pipettor and the PCR products were washed. The purified products were eluted in sterile water, retrieved and then stored in 96 well plates at -20°C. A 1 μl aliquot of each well from 3 rows from each 96 well plate is run on a gel to check the quality of the PCR and purification of the cDNA. The yield after purification was between 30 and 40 μls with concentrations around 15–50 ng/μl.
Spotting plate assembly
The four quadrants were then reassembled into a 384-well spotting plate containing 6 μl per well: 4.5 μl of PCR product from the 96 well plates mixed with 1.5 μl of 4X Micro Spotting Solution Plus (MSP4X, Telechem, Sunnyvale, CA). Alternatively, in earlier prints, the spotting plates were assembled at a final concentration of 3X SSC, 0.01% N-lauroylsarcosine by mixing 3.5 μl of purified PCR product with 1.5 μl of 10X SSC, 0.033% Sarkosyl, pH 7.0 (1.5M NaCl, 0.15 M citric acid, trisodium salt, 1.12 mM N-lauroylsarcosine, Sigma L-9150).
Microarray construction
A set of 9,216 prepared cDNA inserts from 24, 384-well spotting plates were single spotted onto amine coated glass slides (1 in × 3 in, Telechem Superamine, SMM slides, Telechem International, Sunnyvale, CA) using a Cartesian PyxSys 5500 robot (Genomic Solutions, Ann Arbor, MI) equipped with 16 quill pins (ChipMaker II from Telechem International) and an environmental chamber. The cDNAs were printed at 55% ± 5% relative humidity setting within the chamber and in a room that was controlled for humidity to be between 45 and 60% using room dehumidifiers as needed. Control of humidity was critical for printing.
All arrays contained 32 grids of spots arranged in an 8 × 4 matrix. Each grid had 19 rows and 16 columns of spots for a total of 9,728 spots per array. A total of 9,216 spots were the cDNAs prepared from the 'unigene' set to form 18 of the 19 rows with 288 spots per grid. After all of the 9,216 cDNAs were printed, an additional row of 16 spots was printed as the first row of each grid for a total of 32 grids × 16 spots = 512 additional spots. These cDNAs were printed from the choice clone spotting plate designated Gm-b10BB which contained 64 hand-picked clones. Thus, the 64 hand-picked, choice clones were printed 8 times each, i.e., each clone was printed in twice in four separate grids. In addition, since the Gm-r1021 library contained only 4089 cDNAs, an additional 135 were repeated in order to obtain an even 9216 cDNAs for printing when combined with the Gm-r1083 unigene set.
The three microarray platforms were entered in the Gene Expression Omnibus database [29] with platform accession numbers GPL229 for Gm-r1070, GPL1013 for Gm-r1021+Gm-r1083, and GPL1012 for Gm-r1088. Complete tables of sequence identifiers and accession numbers for the unigene cDNAs printed on arrays as illustrated in Table 2 are available [30].
Construction of the 'choice' clone PCR plate for repetitive spotting
To construct the choice plate Gm-b10BB, 64 clones were chosen to be used as negative and positive controls for expression analysis in all microarray slides. These 64 clones were chosen to represent certain constitutively expressed genes, or other markers for particular tissues, and is also highly representative of key genes of the soybean flavonoid pathway.
The 64 clones were hand picked and grown over night in microfuge tubes containing 100 μl of YT media at 37°C, 250 rpm. The following day, microfuge tubes containing 200 μl of YT supplemented with 100 μg/ml ampicillin and 8% glycerol were inoculated with 5 μl from the previous culture and grown over night at 37°C and 250 rpm.
To create a 96 well plate of these E.coli stocks, 100μl of the previously grown culture were transferred to wells in columns A1 thru H8 and stored at -80°C. Wells in columns A9 thru H12 were left empty. A small database for the Gm-b10BB plate was prepared containing the name of each gene, its sequence, accession number, and the corresponding well in the Gm-b10BB plate. To make a replicate copy for sequencing, 100 μl of YT supplemented with 100 μg/ml ampicillin and 8% glycerol were inoculated with 5 μl of the -80°C E. coli stock and incubated overnight at 37°C and 250 rpm. Miniprep DNAs were isolated and sequenced at the University of Illinois Keck Center using a 5' M13 primer. The identity of each clone was confirmed by comparison of the sequences obtained from the Keck center with the sequences contained in our previously prepared database by using the Pairwise Blast tool available at the NCBI web page. All sequences showed >97% identity with the corresponding sequence in the database.
PCR amplification using the DNA miniprep plate as a source for templates was performed with the Mutimeck 96 automated pipetor (Beckman) as described above. All PCR products were purified and separated in 1% agarose gel to evaluate the purity of the amplified DNAs and determine their size. The purified and analyzed PCR products from the 96 well plate, Gm-b10BB, were used to assemble a 384 well spotting plate. The 384 well spotting plate contained 6 μl per well: 4.5 μl of PCR product from the 96 well plate aliquoted on each of the 4 quadrants and mixed with 1.5 μl of 4X Micro Spotting Solution Plus (MSP4X, Telechem, Sunnyvale, CA) after assembly.
Post-print processing
After all slides were printed, the cDNAs were UV-cross linked to the slide coating with 650 m Joule ultra violet light using a StrataLinker (Stratagene, La Jolla, CA). [Note: prior to cross-linking the spots were rehydrated if necessary. Rehydration was required for the slides printed with the SSC-Sarkosyl spotting solution but was not required for those printed with Telechem spotting solution. DNA spots were rehydrated by passing the slide over a gentle vapor of steam for a few seconds until spots glistened but did not coalesce and then were quick dried on a 70°C heating block]. To remove excess spotted DNA as well as to denature attached DNA to single strands, slides were treated with the following series of washes with agitation: 2 min with 200 mls of 0.2% SDS, two 1 min water rinses, 95°C water for 2 min, 0.2% SDS for 1 min, and finally two water rinses of 1 min each. Slides were subjected to low speed centrifugation for 2 min at 500 rpm to dry and were stored in a slide rack in a dust free container.
Plant material and RNA isolation and labelling
Seed coats and cotyledons were dissected from plants grown to maturity in soil in the greenhouse. Roots and leaves were collected from soybean plants grown for 11 days after germination in an aerated hydroponic solution with normal nutrient conditions. Total RNA was extracted using phenol-chloroform and lithium chloride precipitation methods [31,32]. RNA was further purified by use of RNeasy Mini or Maxi columns Qiagen, Valencia, CA) according to the manufacturer's instructions. Prior to labelling, the purified RNA was concentrated in a Speed Vac (Savant Instruments, Halbrook, NY) or by using YM-30 Microcon column (Millipore, Bedford, MA).
For each RNA probe, 50 to 60 μg of purified total RNA was labeled by reverse transcription in the presence of Cy3- or Cy5-dUTP [33]. Briefly: the RNA and 5μg oligo-dT 18–21 mer (Operon, Qiagen) were annealed in a 10μl volume at 70°C for 10 min and cooled on ice. A 20 μl cocktail containing 1X first strand reaction buffer, 10 mM DTT, 0.5 mM each of dATP, dCTP, dGTP, 0.2 mM dTTP, 100μM Cy3- or Cy5-dUTP (Amersham, Pharmacia) and 400 U of 200 U/μl SUPERSCRIPT™II (Invitrogen, Carlsbad, CA, cat no. #18064-014) was added to 10μl of the denatured RNA and oligo-dT mixture). The 30 μl reaction was incubated for 1 hr at 42°C, after which 200 additional units of SUPERSCRIPT™II were added and incubation was continued for another hour at 42°C. The reaction was then treated with RNAse A and RNAse H (0.5μg and 1.0 U respectively, Invitrogen, Carlsbad, CA) for 30 min at 37°C to degrade the RNA. The resulting Cy3 and Cy5-labeled cDNAs were paired and mixed together according to the intended experiment and unincorporated nucleotides were removed using a PCR cleaning kit (Qiagen, Valencia, CA). Cleaned probes were concentrated in a SpeedVac (Savant Instrument, Holbrook, NY) for approximately 5 min to a volume of less than 32 μl prior to being used in hybridization to one array.
Microarray hybridization reactions
The microarray slides were prehybridized by incubation in 5X SSC, 0.1% SDS, 1% BSA at 42°C for 45 to 60 min. For each slide, the labeled cDNA probe was brought to 30.5 μl with the addition of sterile water. A 1.5 μl aliquot of 10 μg/μl polyA was added and the probe was denatured at 95°C for 3 min. An equal amount (32 μl) of pre-warmed 2X hybridization buffer (50% formamide, 10X SSC, 0.2% SDS, [33] was added to the mixture and the probe was pipetted between the pre-hybridized slide and the cover slip (LifterSlip, Erie Scientific Company, Portsmouth, NH). The slide was placed in a hybridization chamber (Corning, New York, NY) and incubated overnight for 16–20 hrs at 42°C. The next day the cover slip was removed and the slide was washed once in 1X SSC, 0.2% SDS prewarmed to 42°C; once in 0.2X SSC, 0.2% SDS at room temperature; and once in 0.1X SSC at room temperature. The washes were conduced with gentle shaking at 100 rpm for 5 min. Slides were subjected to low speed centrifugation for 2 min at 500 rpm to dry.
Scanning, quantitation, and normalization
The hybridized slides were scanned with a ScanArray Express fluorescent microarray scanner (Perkin Elmer Life Sciences, Boston, MA) and their fluorescence quantified by ScanArray Express software or by GenePix Pro 3.0 (Axon Instruments, Union City, CA). A perl program was written for post analysis processing of the quantitated image files from the Scan Array Express or GenePix Pro3.0. Local background was subtracted from each spot intensity. Spots showing signal intensities below the 95th percentile of the background distribution in the Cy3 or Cy5 channel were filtered out. The ratio of Cy5 mean to Cy3 mean (r) was computed and used to adjust the Cy3 values to Cy3 X sqrt(r) and the Cy5 values to Cy5/sqrt(r). A between-replicate correction was made using an ANOVA model, which equalized average grid or slide intensities between replicates, for Cy3 and Cy5 separately. The ratio of the resulting adjusted intensities of Cy5 to Cy3 was computed for each spot. The coefficient of variation (standard deviation/mean) across replicates was calculated for each spot to evaluate repeatability of the hybridizations.
High density filter hybridization and selection of weakly expressing cDNAs
High density nylon filters containing 18,432 non-sequenced cDNA clones from the cDNA library Gm-c1007 made from immature cotyledons were spotted using a Qbot by Incyte Genomics. Before use, the filters were washed in0.5% SDS solution that was heated to 60°C, poured over the membrane, and gently agitated for five minutes. This will rid the filter of any residual debris and will result in a cleaner hybridization.
Radiolabelling of probe
Total mRNA from developing cotyledons was labeled with 33P-dATP in the following manner: RNA in 8 μl water (up to 5 μg, but generally 2 to 3 μg of mRNA) was combined with 4 μl Oligo dT (0.5 μg/ul, 70 μM, Sigma Lot 29H9065). The mixture was heat treated for 10 min at 70°C and chilled on ice before adding the following: 6 μl of 5X first strand buffer (BRL/Life Tech Cat. #18064-014); 1 μl DTT; 1.5 μl each of 10 mM dGTP, dCTP, and dTTP; 1.5 μl reverse transcriptase (200 units/μl, SuperScript II RT from BRL/Life Tech Cat. #18064-014); and 10 μl 33P dATP at 10 mCi/ (NEN, 33P Cat#612H04029). After incubation at 37°C for 90 min, the probe was purified by a passage through a Bio-Spin 30 Chromatography Column (Bio-Rad Cat. #732-6006), then stored at 4°C until ready to be denatured and added to the pre-hybridized filter).
Prehybridization
The filter was rolled and placed in a hybridization bottle containing 25 ml of pre hybridization solution without formamide [34] and was prehybridized for 3–4 hrs at 65°C in a rotor oven.
Hybridization
Adding the probe to the filter: Once the filter was pre-hybridized, the probe was denatured for 10 min at 95°C and then the entire radiolabeled probe was added directly to the prehybridization mixture (in the bottle with the filer). The hybridization was allowed to proceed for 12–18 hrs.
Washing
The filters were washed twice in the pre-warmed (50–55°C) low stringency wash solution (2XSSC, 0.5% SDS, 0.1% Na pyrophosphate) for 15 min each. The filters were then washed for about 2 hrs at 55°C in high stringency buffer (0.1XSSC, 0.5% SDS, O.1% sodium pyrophosphate) with gentle shaking.
Imaging
Filters were analyzed with a Typhoon 8600 variable mode imager (Amersham Pharmacia Biotech, Inc, Piscataway, NJ) and imaged with the software package Array Vision (Imaging Research Inc., St. Catharines, Ontario, Canada) to correlate spot intensity and filter position. Spots with very low intensity of 1 to 500 were selected at random in order to enrich for cDNAs representing mRNAs of low abundance. Theses clones were reracked into 384-well plates to form library Gm-r1030 and sent for 5' sequencing at the Washington University Genome Center.
Distribution of materials
Upon request, all novel materials described in this publication will be made available in a timely manner for noncommercial research purposes. The cDNA clones are available from the Biogenetic Services, Brookings, SD or the American Type Culture Collection, Manasas, VA. Microarrays are available on a cost recovery basis by contacting Lila Vodkin, University of Illinois.
List of abbreviations
PCR polymerase chain reaction
SSC standard saline citrate
SDS sodium dodecyl sulfate
Authors' contributions
LOV led the unigene and microarray development, coordinated the project, and drafted the manuscript; AK constructed multiple cDNA libraries included in the unigene cDNAs, performed over 18,000 PCR reactions, performed the library normalization by filter screening shown in Figure 1, the array hybridization reactions including that of Fig 4, and drafted protocols; RS led the informatics and sample tracking efforts for array printing, high throughput PCR, cDNA clone reracking, printed arrays, developed and drafted protocols for analysis of array data; SJC initiated the PCR and array hybridization protocols for the project and constructed several cDNA libraries; DOG performed 10,000 PCR reactions, printed arrays, and participated in clone rearraying; RP contributed to protocol development, PCR production, and gel analysis; GZ accumulated the 'choice clone" cDNAs and performed the isoline hybridization analysis shown in Figure 3; MS contributed to informatics and sample tracking and gel analysis of PCR products; MVS performed EST cluster analysis for development of the unigene set and drafted sections of the paper; ES and CS performed EST analysis and clustering for the unigene set; ER coordinated EST processing and informatics; JE constructed multiple cDNA libraries used in selecting the unigene set; RS led the public EST project for library construction and 5' sequencing; AR-H and JCP provided hydroponically grown plant material and RNAs for Figure 4 and constructed a cDNA library; VC and PC constructed multiple cDNA libraries; GG and LL performed annotations of the 27,500 unigene set; JP and PS led or performed the 3' sequencing of the 27,500 unigene clones.
Acknowledgments
We gratefully acknowledge Sarah Jones, Anne Marie Boone, Tara Knackstedt, and Ben Pleune for assistance with analysis of the PCR products. We thank Jigyasa Tuteja for critical reading of the manuscript. This work was supported by NSF grant DBI9872565 from the Plant Genome Program (to LOV, ER, RS, JP, PK) and grants from the North Central Soybean Research Program and United Soybean Board for the Public EST Project (to RS, PK, LOV, and ER).
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| 15453914 | PMC526184 | CC BY | 2021-01-04 16:32:42 | no | BMC Genomics. 2004 Sep 29; 5:73 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-73 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-201545857210.1186/1471-2210-4-20Research ArticlePeroxynitrite decomposition catalyst ameliorates renal damage and protein nitration in cisplatin-induced nephrotoxicity in rats Chirino Yolanda I [email protected]ández-Pando Rogelio [email protected]í José [email protected] Departamento de Biología, Facultad de Química, Edificio B, Segundo Piso, Lab 209, Ciudad Universitaria, UNAM, México D.F. México2 Departamento de Patología, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán" 14000, México, D.F. México2004 30 9 2004 4 20 20 22 7 2004 30 9 2004 Copyright © 2004 Chirino et al; licensee BioMed Central Ltd.2004Chirino 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
Oxidative stress is involved in cisplatin-nephrotoxicity. However, it has not completely established if reactive nitrogen species and nitrosative stress are involved in this experimental model. The purpose of this work was to study the role of peroxynitrite, a reactive nitrogen specie, in cisplatin-nephrotoxicity using the compound 5,10,15,20-tetrakis (4-sulfonatophenyl) porphyrinato iron (III) (FeTPPS), a soluble complex able to metabolize peroxynitrite.
Results
In rats treated with cisplatin (a single intraperitoneal dose of 7.5 mg/kg body weight), renal nitrosative stress was made evident by the increase in 3-nitrotyrosine on day 3. In addition, cisplatin-induced nephrotoxicity was evident by the histological damage of proximal tubular cells and by the increase in (a) serum creatinine, (b) blood urea nitrogen, and (c) urinary excretion of N-acetyl-β-D-glucosaminidase and total protein. Cisplatin-induced nitrosative stress and nephrotoxicity were attenuated by FeTPPS-treatment (15 mg/kg body weight, intraperitoneally, every 12 hours for 3 days).
Conclusions
Nitrosative stress is involved in cisplatin-induced nephrotoxicity in rats. Our data suggest that peroxynitrite is involved, at least in part, in cisplatin-induced nephrotoxicity and protein nitration.
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Background
Cisplatin (cis-dichlorodiammine-platinum II) is an effective antineoplastic agent in the treatment of various solid tumours [1] including cancers of the ovary, testis, bladder, head, neck, lung, cervix, and endometrium [2]. Nevertheless, its full clinical utility is limited due to some adverse side effects including acute renal failure. The major site of renal injury is the S3 segment of the proximal tubule, located in the outer stripe of the outer medulla of the kidney [1]. The production of reactive oxygen species (ROS) and oxidative stress in kidney have been implicated in the pathogenesis of cisplatin-induced renal injury [3]. It has been shown that superoxide anion (O2•-) [4], hydrogen peroxide (H2O2) [5], and hydroxyl radical (•OH) [6] are involved in cisplatin-induced nephrotoxicity. In addition, it has been found that renal lipid peroxidation [5,7] is increased and glutathione is decreased [8] in this experimental model. The involvement of oxidative stress is further supported by the fact that the antioxidants melatonin [9] and vitamins C and E [5,10] prevent cisplatin-induced nephrotoxicity. Interestingly, overexpression of heme oxygenase-1 ameliorates [11] and heme oxygenase-1 deficiency [12] aggravates renal damage induced by cisplatin, supporting additionally the involvement of oxidant stress in this experimental model.
On the other hand, the role of reactive nitrogen species (RNS) and nitrosative stress has been less explored in cisplatin-induced nephrotoxicity. In this context, it has been studied the role of nitric oxide (•NO) and nitric oxide synthase (NOS) [13-19]. It has been found that the renal content of total nitrate/nitrite is increased in cisplatin-treated rats [18,19] suggesting that •NO production is enhanced in these animals. Furthermore, the inhibition of NOS by L-NAME [14] or by aminoguanidine [13] decreased renal damage induced by cisplatin, suggesting that •NO is playing a toxic role in this experimental model. However, it is unknown if peroxynitrite (ONOO-), a RNS that is generated by the reaction of •NO and O2•-, is involved in the renal damage induced by cisplatin. It has been shown that ONOO-, which is not a free radical, is involved in the pathogenesis of many diseases [20-25]. ONOO- can react with different biomolecules including amino acids such as cysteine, methionine, tryptophan, and tyrosine leading to changes in protein structure and function [26]. ONOO- has been shown to cause lipid peroxidation, chemical cleavage of DNA, and reduction in cellular defenses by oxidation of thiol pools [27].
In this work, we studied if ONOO- is involved in the nephrotoxicity induced by cisplatin by using 5,10,15,20-tetrakis (4-sulfonatophenyl) porphyrinato iron (III) (FeTPPS). This compound is a water-soluble Fe (III) porphyrin complex that catalyzes rapid isomerization of ONOO- to nitrate (NO3-) under physiologically relevant conditions (pH 7.4, 37°C) [28]. The cytoprotective actions of FeTPPS have been characterized [29].
Results
Body weight and urinary volume
Body weight decreased 8.5% in cisplatin (Cis) group on day 3 and FeTPPS tended to prevent this decrease in Cis+FeTPPS group, however there was no significative difference between Cis and Cis+FeTPPS groups. Body weight was similar in control (Ct), FeTPPS, and Cis+FeTPPS groups. Urinary volume was not significative difference among the four groups along the study and on day of sacrifice (Table 1).
Table 1 Body weight and urinary volume in the 4 groups of rats studied on day 3.
Ct Cis FeTPPS Cis+ FeTPPS
Body weight (g) 235 ± 5a 215 ± 4b 238 ± 3a 231 ± 4a
Urinary volume (mL/24 h) 5.7 ± 1.4a 7.4 ± 0.8a 3.5 ± 1.1a 7.4 ± 1.6a
Values are mean ± SEM. n = 6. Groups with different letter are significantly different (P < 0.05).
Markers of glomerular and tubular damage
Serum creatinine increased 4.9 times and blood urea nitrogen (BUN) increased 5.5 times in Cis group compared to control one (Fig 1). FeTPPS prevented partially the increase in serum creatinine and BUN levels in Cis+FeTPPS group. Cisplatin increased urinary excretion of total protein (4.6 times) and N-acetyl-β-D-glucosaminidase (NAG) (9.6 times) (Fig 2A and 2B). The increase in both parameters was prevented by FeTPPS in Cis+FeTPPS group (Fig 2). Serum creatinine, BUN, and urinary excretion of total protein and NAG were similar in Ct and FeTPPS groups (Figs 1 and 2).
Figure 1 (A) Serum creatinine and (B) BUN on day 3 in the four groups of rats studied. Ct: control group, Cis: cisplatin group; FeTPPS: 5,10,15,20-tetrakis(4-sulfonatophenyl) porphyrinato iron (III) group, and Cis+FeTPPS: cisplatin+5,10,15,20-tetrakis(4'-sulfonatophenyl) porphyrinato iron (III) group. Data are mean ± SEM. n = 6. aP < 0.001 vs. Ct; bP < 0.001 vs. Ct, cP < 0.05 vs Cis (Panel A); aP < 0.001 vs. Ct; bP < 0.001 vs. Ct, cP < 0.001 vs Cis (Panel B). Serum creatinine and BUN increased in cisplatin group and FeTPPS prevented these increases in the Cis+FeTPPS group.
Figure 2 Urinary excretion of (A) total protein and (B) NAG on day 3 in the four groups of rats studied. Data are mean ± SEM. n = 5–6. aP < 0.001 vs. Ct, bP < 0.05 vs. Cis. Cisplatin-treated rats increased urinary excretion of total protein and NAG and these increases were prevented by FeTPPS administration in Cis+FeTPPS group.
Histological analysis
After three days of cisplatin-treatment, the epithelium from proximal convoluted tubules (tubules with small lumen area and taller epithelial cells) showed cytoplasmic vacuolization, intracellular edema and extensive damage which affected 87 ± 4% of their surface area (Fig 3B). The cisplatin toxic activity was higher in the straight portion of proximal convoluted tubules located in the inner area of the kidney cortex, where more than 90% of the epithelial surface suffered damage (Fig 4B). Interestingly, FeTPPS administration partially decreased the damaged area from 87 ± 4 to 44 ± 6% (p < 0.0001) in proximal convoluted tubules (Fig 3D) and from 93 ± 2 to 68 ± 10 (p < 0.0001) in the straight portion (Fig 4D). The administration of FeTPPS did not produce any histological alteration in the kidneys (Figs 3C and 4C). At the light microscopy level, glomeruli structure remained unchanged in all groups.
Figure 3 Representative histological abnormalities in the external cortical kidney area after three days of cisplatin administration and their partial prevention by FeTPPS. (A) Normal kidney histology from control rat. (B) After three days of cisplatin administration, many cortical convoluted tubules are revisted by necrotic epithelial cells (arrows) or vacuolated swell cells (arrow heads), glomeruli do not show apparent damage. (C) FeTPPS administration does not produce histological kidney abnormalities. (D) The administration of FeTPPS partially prevents the cytotoxic damage induced by cisplatin; arrows indicate middle cellular vacuolization of cortical convoluted tubules.
Figure 4 Representative histological abnormalities in the inner part of the cortical kidney after three days of cisplatin administration and their partial prevention by FeTPPS. (A) Normal kidney histology from control rat. (B) After three days of cisplatin administration, the straight portion of many cortical tubules are revisted by necrotic cells (arrows). (C) FeTPPS administration does not produce histological abnormalities. (D) The administration of FeTPPS partially prevents the cytotoxic damage induced by cisplatin; arrows indicate tubules with focal necrotic cells.
Immunohistochemical localization of 3-nitro-L-tyrosine (3-NT)
A strong 3-NT immunostaining was observed in the straight portion of the proximal convoluted tubules located in the inner area of the kidney cortex of cisplatin-treated rats (Fig 5B). Interestingly, in the Cis+FeTPPS group, FeTPPS administration partially prevented the cisplatin toxic damage in the epithelium from the proximal convoluted tubules and its straight portion respectively, in coexistence with an evident decrease of 3-NT immunostaining (Fig 5D).
Figure 5 Nitrotyrosine (3-NT) expression determined by immunohistochemistry in the inner part of the cortical kidney after three days of cisplatin administration and its partial prevention by FeTPPS. (A) There is no 3-NT immunostaining in the kidney of control rat. (B) In contrast, three days after cisplatin administration there is a strong 3-NT expression in the necrotic cells from the straight portion of the proximal convoluted tubules (arrows). (C) FeTPPS administration does not induce 3-NT expression. (D) The administration of FeTPPS strongly decreases 3-NT expression induced by cisplatin-treatment (Cis+FeTPPS group).
Discussion
Cisplatin in an effective chemotherapeutic agent for a wide variety of tumors, nevertheless, nephrotoxicity is the major complication of this antineoplasic treatment [1]. The mechanism by which cisplatin causes renal damage is unclear, however, it has been postulated that oxidative stress is involved in this process [2,3,13,30]. The protective effect of overexpression of Mn-SOD [4] or the in vivo administration of some antioxidants such as vitamins C and E [5,10], melatonin [9], or selenium [31] in cisplatin-induced nephrotoxicity as well as the protective effect of tiron (a cell permeable O2•- scavenger), pyruvate and catalase (H2O2 scavengers), and dimethylthiourea and thiourea (•OH scavengers) in renal proximal tubular epithelial cells (LLC-PK1 cells) treated with cisplatin also strongly support the role of ROS in cisplatin renal toxicity [30].
In contrast the role of •NO and RNS in cisplatin-induced nephrotoxicity has not been completely established. It has been shown that the renal content of nitrate/nitrite is increased in cisplatin-treated rats suggesting that •NO is increased in these animals [18,19]. In fact it has been shown that renal NOS activity is increased in cisplatin-treated rats [14]. In addition, the following two experiments suggest a toxic role of •NO on cisplatin-induced renal toxicity: (a) aminoguanidine, an inhibitor of inducible NOS, decreased nephrotoxicity and prevented kidney lipid peroxidation and reduction of antioxidant enzymes induced by cisplatin [13], and (b) the administration of N(G)-nitro-L-arginine methyl ester (L-NAME), an inhibitor of NOS, reduced renal and gastrointestinal toxicity along with a significant inhibition in lipid peroxidation induced by cisplatin [14]. In contrast, Mansour et al. [15] and Li et al. [16] have found that L-NAME administration resulted in no protection and Saad et al. [17] found that this NOS inhibitor aggravates cisplatin-induced renal dysfunction. These data may suggest that •NO is not playing a toxic role in cisplatin-induced nephrotoxicity. The above mentioned disagreement justify the performance of additional experiments to clarify the role of NOS and •NO in cisplatin-induced nephrotoxicity.
On the other hand, •NO is able to react with O2 •- to produce ONOO-, which is a powerful oxidant more, reactive than its precursors, and has been implicated in an increasing list of diseases: hyperlipidemia [32], Alzheimer [20], acute renal ischemia [25], neurotoxicity induced by methamphetamine [33], and diabetes [23]. The ONOO- decomposition catalyst FeTPPS is a water-soluble Fe (III) porphyrin complex able to block ONOO- toxicity [28,29] and to protect against toxic insults in several experimental models. In focal cerebral ischemia-reperfusion in rats, massive production of •NO and O2•- results in continuous formation of ONOO- even several hours after ischemia-reperfusion insult [34]. Significant reduction of 3-NT in brain sections and prominent neuroprotection was observed by FeTPPS (30 mg/kg) [34]. In a model of sepsis induced by injection of endotoxin (10 mg/kg) in rats, FeTPPS prevented the accumulation of ONOO- as measured by plasma rhodamine fluorescence and heart 3-NT staining [35]. Interestingly, FeTPPS improved endotoxin-induced myocardial contractile dysfunction, which was associated with reduced degradation of nuclear factor kappa B inhibitory protein I-kappa-B, plasma TNF-alpha levels, and microvascular endothelial cell-leukocyte activation [35].
In this work it was found that FeTPPS partially prevented the increase in BUN and serum creatinine (markers of glomerular damage) and urinary excretion of NAG and total protein (markers of tubular damage) induced by cisplatin-treatment. The increase in urinary NAG and total protein excretion could be associated with necrosis of the proximal convoluted tubules, the primary site of drug accumulation [1]. FeTPPS prevented these alterations induced by cisplatin. This may be secondary to the ability of FeTPPS to catalyze the decomposition of ONOO- which could be responsible, at least in part, of the alterations induced by cisplatin. This ameliorative effect of FeTPPS was associated with the decrease in 3-NT staining suggesting that ONOO- is involved in protein nitration in cisplatin-nephrotoxicity. It is known that another RNS such as N2O4, HONOO, •NO2 [36], and nitryl chloride (NO2Cl) [37], are involved in protein nitration. Nitryl chloride is formed by the reaction of NO2- and HOCl-derived myeloperoxidase [37].
Studies in animals have established that tubular injury plays a central role in the reduction of glomerular filtration rate in acute tubular necrosis. Two major tubular abnormalities could be involved in the decrease in glomerular fucntion in cisplatin-treated rats: obstruction and backleak of glomerular filtrate. The alteration in glomerular function can not be attributed to structural damage since glomeruli structure is normal in cisplatin-treated rats. The alterations in glomerular function in cisplatin-treated rats may also be secondary to ROS [38] which induce mesangial cells contraction, altering the filtration surface area and modifying the ultrafiltration coefficient, factors that decrease the glomerular filtration rate. In addition our data suggest that ONOO- may also be involved in the glomerular alterations in cisplatin-treated rats.
The increase in renal ONOO- induced by cisplatin may be secondary to the increase in •NO and O2•- production. In fact, there are evidences of the renal increase in •NO production in cisplatin nephrotoxicity [18,19] and O2•- generation in cisplatin-treated LLC-PK1 cells [30]. The O2•- increase in cisplatin-nephrotoxicity may be simply consequence of the mitochondrial dysfunction [39] and the decrease in superoxide dismutase activity [5].
Conclusions
Nitrosative stress is involved in cisplatin-induced nephrotoxicity in rats. The ameliorative effect of FeTPPS on cisplatin-induced nephrotoxicity in rats was associated with the decrease in protein nitration suggesting that ONOO- is involved in both protein nitration and nephrotoxicity in these animals.
Methods
Reagents
Cisplatin (catalogue # P-4394) was from Sigma-Aldrich (St. Louis MO, USA). FeTPPS (catalogue # 341492) was from (Calbiochem, San Diego, CA, USA). Rabbit anti-3-NT polyclonal antibodies (Catalogue # 06–284) were from Upstate (Lake Placid, NY, USA). Anti-rabbit Ig horseradish peroxidase antibodies (Catalogue # SAB-300) were purchased from Stressgen (Victoria BC, Canada). Commercial kits to measure creatinine and urea were from Spinreact (Girona, Spain). All other chemicals were reagent grade and commercially available.
Experimental design
Male Wistar rats (Harlan Teklad, Mexico City, Mexico) initially weighing 200–250 g were used. Experimental work was approved by DGAPA (IN227103) and followed the guidelines of Norma Oficial Mexicana (NOM-ECOL-087-1995). All animals had free access to water and commercial rodent diet (Harlan Teklad, catalogue 2018S), and were randomly divided in four groups (n = 6 rats/group) as follows: (1) CT, injected intraperitoneally (i.p.) with isotonic saline solution; (2) Cis, treated with a single dose of cisplatin (7.5 mg/Kg b.w./i.p.) [40]; (3) FeTPPS, treated with FeTPPS (15 mg/kg/i.p./12 h) [32] for 3 days; and (4) Cis+FeTPPS, treated with Cis and with FeTPPS. During the study rats were maintained with a 12-h light:dark cycle in stainless steel metabolic cages to collect urine. On day 3, the animals were sacrificed by decapitation and blood was collected to obtain serum and to measure creatinine and BUN. Total protein and NAG were measured in 24-h urine. The kidneys were removed to obtain cortex samples for histological and immunohistochemical studies.
Markers of glomerular and tubular damage
The markers of glomerular damage, creatinine and urea, were measured using commercial kits. BUN was obtained by correcting the urea value by a 2.14 factor [41]. As markers of tubular damage, we measured urinary excretion of NAG and total protein. NAG activity was measured using p-nitrophenyl-N-acetyl-β-D-glucosaminide as substrate and total protein was measured by a turbidimetric method [42].
Histological analysis
Thin slices of kidney tissue with cortex and medulla were fixed by immersion in buffered formalin (pH 7.4), dehydrated and embedded in paraffin [43]. Sections of 3 μm were stained with hematoxilin and eosin. The histological profile of twenty proximal tubules randomly selected per rat (6 rats per experimental group) was recorded using a Leica Qwin Image Analyzer (Cambridge, England). The percentage of tubular area with histopathological alterations like swelling, cytoplasmic vacuolization, desquamation or necrosis was obtained. The percentage of damaged area of Cis and Cis+FeTPPS groups was compared.
Immunohistochemical localization of 3-nitro-L-tyrosine (3-NT)
For immunohistochemistry, 3 μm sections were deparaffined with xylol and rehydrated with ethanol. Endogenous peroxidase was quenched/inhibited with 4.5% H2O2 in methanol by incubation for 1.5 h at room temperature. Nonspecific adsorption was minimized by leaving the sections in 3% bovine serum albumin in phosphate buffer saline for 30 min. Sections were incubated overnight with a 1:700 dilution of anti-3-NT antibody. After extensive washing with phosphate buffer saline, the sections were incubated with a 1:1000 dilution of a peroxidase conjugated anti-rabbit Ig antibody for 1 h, and finally incubated with hydrogen peroxide-diaminobenzidine for 10 s. Sections were counterstained with hematoxilin and observed under light microscopy. All the sections from the four studied groups were incubated under the same conditions with the same antibodies concentration, and in the same running, so the immunostaining was comparable among the different experimental groups [43].
Statistics
Results are expressed as the mean ± SEM. Data were analyzed by one-way ANOVA followed by Bonferroni's multiple comparisons. Non-paired t-test was used to compare the quantitative histological damage data using the software Prism 3.02 (GraphPad, San Diego, CA, USA). P ≤ 0.05 was considered statistically significant.
Authors' contributions
YICH performed animal experimentation, biochemical determinations, statistical analyses, light microscopy and immunohistochemical studies. RHP supported the light microscopy and immunohistochemical studies. JPCH conceived, designed and coordinated the study. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by DGAPA (IN227103 and IX203504).
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| 15458572 | PMC526185 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Sep 30; 4:20 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-20 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-711546182310.1186/1471-2407-4-71Research ArticleRadiotherapy fractionation for the palliation of uncomplicated painful bone metastases – an evidence-based practice guideline Wu Jackson Sai-Yiu [email protected] Rebecca KS [email protected] Nancy S [email protected] Mary [email protected] Andrea [email protected] Timothy [email protected] Supportive Care Guidelines Group of Cancer Care Ontario [email protected] Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada2 Department of Radiation Oncology and the Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada3 Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada4 Division of Radiation Oncology, Juravinski Cancer Centre and the Department of Medicine, McMaster University, Hamilton, Ontario, Canada2004 4 10 2004 4 71 71 3 5 2004 4 10 2004 Copyright © 2004 Wu 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 practice guideline was developed to provide recommendations to clinicians in Ontario on the preferred standard radiotherapy fractionation schedule for the treatment of painful bone metastases.
Methods
A systematic review and meta-analysis was performed and published elsewhere. The Supportive Care Guidelines Group, a multidisciplinary guideline development panel, formulated clinical recommendations based on their interpretation of the evidence. In addition to evidence from clinical trials, the panel also considered patient convenience and ease of administration of palliative radiotherapy. External review of the draft report by Ontario practitioners was obtained through a mailed survey, and final approval was obtained from the Practice Guidelines Coordinating Committee.
Results
Meta-analysis did not detect a significant difference in complete or overall pain relief between single treatment and multifraction palliative radiotherapy for bone metastases. Fifty-nine Ontario practitioners responded to the mailed survey (return rate 62%). Forty-two percent also returned written comments. Eighty-three percent of respondents agreed with the interpretation of the evidence and 75% agreed that the report should be approved as a practice guideline. Minor revisions were made based on feedback from the external reviewers and the Practice Guidelines Coordinating Committee. The Practice Guidelines Coordinating Committee approved the final practice guideline report.
Conclusion
For adult patients with single or multiple radiographically confirmed bone metastases of any histology corresponding to painful areas in previously non-irradiated areas without pathologic fractures or spinal cord/cauda equine compression, we conclude that:
• Where the treatment objective is pain relief, a single 8 Gy treatment, prescribed to the appropriate target volume, is recommended as the standard dose-fractionation schedule for the treatment of symptomatic and uncomplicated bone metastases.
Several factors frequently considered in clinical practice when applying this evidence such as the effect of primary histology, anatomical site of treatment, risk of pathological fracture, soft tissue disease and cord compression, use of antiemetics, and the role of retreatment are discussed as qualifying statements.
Our systematic review and meta-analysis provided high quality evidence for the key recommendation in this clinical practice guideline. Qualifying statements addressing factors that should be considered when applying this recommendation in clinical practice facilitate its clinical application. The rigorous development and approval process result in a final document that is strongly endorsed by practitioners as a practice guideline.
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Background
Radiotherapy is a well-recognized, effective modality in the palliative treatment of painful bone metastases. Bone metastases are a common manifestation of distant relapse from many types of malignant tumours, especially from cancers of the lung, breast, and prostate. With the advent of effective systemic therapies and improvements in supportive care, cancer patients are expected to live longer and may suffer from metastatic disease for a considerable length of time. Many patients with bone metastases suffer from compromised mobility and performance status.
The optimal dose-fractionation schedule for the treatment of bone metastases is unclear. Two surveys of Canadian patterns of practice found that various fractionation schedules are employed by radiation oncologists, ranging from a single large-dose fraction (e.g., 8 Gy) to a more prolonged course of 30 Gy/10 fractions over 2 weeks [1,2]. It has been suggested that the choice of fractionation is influenced not only by patient-related factors but also by physician education and attitudes, treatment toxicity, resource utilization, and departmental policy [3-7]. While different clinicians may associate "optimal" with different treatment goals, one could recommend that a "preferred" dose-fractionation is one that provides pain relief without undue toxicity and is least onerous to the patient.
During the past decade, significant clinical trial efforts have been devoted to comparing single large-dose radiation (8 Gy to 10 Gy) with multifraction regimens (five to ten fractions) [8-14]. The two largest trials were published in 1999 by the Bone Pain Trial Working Party [10] and the Dutch Bone Metastasis Study group [11]. Results of a Canadian study were presented at the Canadian Association of Radiation Oncologists (CARO) meeting in 2000 and reported in an abstract for the 2000 meeting of the American Society for Therapeutic Radiology and Oncology (ASTRO) [9]. Those randomized trials should provide substantial evidence to address the question of a "preferred" fractionation for the majority of patients with bone metastases.
This provincial guideline was initiated to summarize the evidence and to provide recommendations on the preferred standard radiotherapy fractionation schedule for the treatment of painful bone metastases.
Clinical practice guideline development
This practice guideline was developed by Cancer Care Ontario's Practice Guidelines Initiative (PGI), using the methods of the Practice Guidelines Development Cycle [15]. The practice guideline report is a convenient and up-to-date source of the best available evidence on the preferred dose-fractionation of radiotherapy for the treatment of uncomplicated painful bone metastases, developed through systematic reviews, evidence synthesis, and input from practitioners in Ontario. The report is intended to promote evidence-based practice. The PGI is editorially independent of Cancer Care Ontario and the Ontario Ministry of Health and Long-term Care. The PGI has a formal standardized process to ensure the currency of each guideline report. This process consists of the periodic review and evaluation of the scientific literature and, where appropriate, integration of this literature with the original guideline information.
Evidence was selected and summarized by four members of the Supportive Care Guidelines Group (SCGG) and methodologists. Members of the SCGG disclosed potential conflict of interest information, reviewed the analysis of the evidence, and prepared draft recommendations. The SCGG includes palliative care physicians, medical and radiation oncologists, psychiatrists, nurses, psychologists, a chaplain, an anesthetist, a surgeon, methodologists, and administrators. After reviewing the evidence and considering issues of patient convenience and resource utilization, the SCGG reached consensus on draft recommendations. The systematic review and meta-analysis, conducted as the initial step in formulating this practice guideline, has been described elsewhere [16].
External review by Ontario practitioners was obtained through a mailed survey consisting of items that address the quality of the draft practice guideline report and recommendations and whether the recommendations should serve as a practice guideline. The efficacy of the practitioner feedback survey process has been previously described [17]. Final approval of the original guideline report was obtained from the Practice Guidelines Coordinating Committee (PGCC).
Methods
External review – Ontario practitioner feedback
Practitioner feedback was obtained through a mailed survey of 95 radiation oncologists across Ontario. The survey consisted of items evaluating the methods, results, and interpretive summary used to inform the draft recommendation and whether the draft recommendation should be approved as a practice guideline. Written comments were invited. Follow-up reminders were sent at two weeks (post card) and four weeks (complete package mailed again). The SCGG reviewed the results of the survey.
Results of practitioner feedback
Fifty-nine of the 95 surveys were returned (62% return rate). Key results of the practitioner feedback survey are summarized in Table 1. The survey results indicated that 83% of respondents agreed with the interpretation of the evidence and 74% agreed with the draft recommendations as stated. Seventy-five percent of respondents agreed that the report should be approved as a practice guideline. Twenty-one respondents (42%) also provided written comments. The final recommendation was revised to reflect feedback from practitioners and currently applies to patients for whom the treatment objective is pain relief.
Practice guidelines coordinating committee approval process
The practice guideline report was circulated to PGCC members for review and approval. Eleven of the fourteen PGCC members completed and returned ballots. Ten PGCC members approved the practice guideline report as written, and one member approved the guideline and provided suggestions for consideration by the SCGG. Suggestions made were to reword the Target Population section and to clarify the second qualifying statement. The SCGG agreed with the suggestions and modified the guideline accordingly.
Discussion
The preferred radiotherapy dose-fractionation schedule for the palliation of uncomplicated painful bone metastases has been a controversial subject [18-21]. The goal of our systematic review was to enable guideline developers and practitioners to determine whether the available evidence supports the notion of a "standard" dose-fractionation. "Standard" refers to what is applicable to the majority of patients, with a preference for patient convenience and ease of administration without compromising treatment efficacy or morbidity. Our meta-analysis of all published randomized trials found no difference in pain relief between single fraction and multifraction treatments [16]. Based on this information, the authors of this practice guideline conclude that a single fraction at 8 Gy is the preferred standard dose-fractionation for patients with uncomplicated painful bone metastases. In applying this evidence into practice, however, the following clinical factors merit consideration:
1. How durable is the pain relief?
Available evidence does not support the notion that a more durable response can be achieved with higher dose-fractionation [11].
2. Is the recommendation appropriate when preventing pathological response is an important consideration?
There is a lack of firm evidence relating the fractionation schedule to the prevention of pathologic fracture because no study evaluated the risk of pathologic fracture prior to treatment. Although the pathologic fracture rate was significantly higher after single fraction radiotherapy than after multifraction in the Dutch study [11], the absolute difference was only 2%. The RTOG study, on the other hand, showed a higher fracture rate following high dose-fractionation (40 Gy) than low-dose treatment (20 Gy) in patients with a solitary metastasis [22]. Until CT (computed tomography)-based bone density measurements [12] are correlated with pathologic fractures, no evidence-based recommendation can be given. Patients at risk of pathologic fractures in long or weight-bearing bones should be assessed by an orthopaedic surgeon. Where radiotherapy is considered for tumour downsizing prior to an orthopaedic procedure or for such patients who are not surgical candidates, fractionated treatment (e.g., 20 Gy/5 fractions, 30 Gy/10 fractions) would be considered appropriate by many clinicians. A discussion of fracture risk assessment is beyond the scope of this review but has been published elsewhere [23-25].
3. Does the recommendation apply to all pathologies?
It should be noted that the published studies included a heterogeneous group of patients differing in histologies, performance status, severity of pain, extent of disease, and so forth. The fact that breast, prostate, and lung cancer patients constituted the majority of trial patients implies a greater confidence in reproducing treatment results for these patients in practice. However, the evidence does not provide sufficient materials to allow a recommendation based on treatment outcomes among subgroups of different primary tumours or other patient- and tumour-related factors.
4. Do treatment field size and anatomical location affect application of the recommendation?
The evidence reviewed does not specifically address the results of large-volume (i.e. wide-field, hemibody irradiation), single fraction treatment. Although average treatment volumes were not reported in any of the single fraction trials, a significant proportion of patients did receive treatments to the lumbar spine and pelvis [10,11,13][Kirkbride P: Personal communications. 2001]. Since treatment volume was not an inclusion or exclusion criterion among those studies, it is reasonable to assume that study patients represent the majority of treatment volumes delivered in an average department. No difference in nausea and vomiting was seen in the subgroup of 133 patients from the Bone Pain Trial Working Party study, who were asked to self-assess nausea/vomiting experience in the first 14 days following treatments [10]. Therefore, the evidence does not support the choice of fractionated treatment based on volume consideration. However, the use of prophylactic ondansetron was shown to significantly reduce vomiting episodes in the single fraction arm compared with the 20 Gy arm (no prophylactic ondansetron) in the Canadian Bone Mets study [Kirkbride P: Personal communications. 2001]. For treatment over the epigastrium or lumbar spine, or with larger treatment volumes in the pelvis, it is reasonable to use a prophylactic antiemetic, as one would for hemibody irradiation. Patients may also be instructed to use anti-diarrheal agents if enteritis is experienced.
5. Do age and life expectancy affect application of the recommendation?
The underlying concern for this group of patients is whether single large-dose radiation compromises subsequent tolerance to re-irradiation. Although no untoward late effects were reported by the single fraction studies with follow-up of one year or more [10,11], clinicians may be uneasy about the long-term effects of repeated radiation. Given the lack of evidence to the contrary, single fraction radiotherapy remains an appropriate treatment option in this subgroup.
6. Does the presence of soft tissue disease around bone metastases affect application of the recommendation?
With CT/MRI (magnetic resonance imaging) diagnostic investigations becoming more routinely available, and the introduction of the CT-simulator into many departments, the extent of metastatic disease is likely to be better evaluated than in the past. In cases where lytic disease is associated with a large soft-tissue mass (e.g., in the acetabulum and adjacent pelvic bone), the desired palliative endpoint may be tumour shrinkage as well as pain control. No evidence-based recommendation can be given for this scenario.
7. When should re-irradiation be considered?
Re-irradiation may be considered in three scenarios: 1) no pain relief or pain progression after initial radiotherapy, 2) partial response with initial radiotherapy and the hope of achieving further pain reduction with more radiotherapy, and 3) partial or complete response with initial radiotherapy but subsequent recurrence of pain. The response after re-irradiation may be different for each of these scenarios. Two published studies reported response rates to re-irradiation [26,27] with doses ranging from 4 Gy as a single dose to 30 Gy in 10 fractions over two weeks. The Dutch Bone Metastases Study [11] was re-analyzed to separate the response to initial treatment from the response to re-treatment. Van der Linden presented the results at the 2003 ASTRO meeting [28]. The majority of the 173 patients re-irradiated received single fraction 8 Gy as the initial treatment. Overall response rate to re-treatment was 63%. At present no clear guideline can be given regarding dose-fractionation of re-irradiation. A new intergroup randomized trial supported by the National Cancer Institute of Canada (NCIC) of single versus multiple fractions for re-irradiation opened in January 2004 and is expected to accrue patients from Canada, the United Kingdom, the Netherlands, and Australia [29].
8. How is radiation-induced emesis best managed when delivering spinal irradiation?
No increase in acute gastrointestinal morbidity was observed with single fraction treatment compared to multiple fractions. The Canadian study showed significantly fewer vomiting episodes with single fraction treatment after prophylactic ondansetron was used in cases with treatment fields over the abdomen or pelvis [Kirkbride P: Personal communications. 2001]. Antiemetic agents should be considered as a prophylaxis, given that 30% or more of patients experienced vomiting following single or multifraction treatment in the two studies that specifically collected patient-assessed nausea/vomiting data [9,10].
Conclusions
For patients where the treatment objective is pain relief, a single 8 Gy treatment, prescribed to the appropriate target volume, is recommended as the standard dose-fractionation schedule for the treatment of symptomatic and uncomplicated bone metastases. This recommendation applies to adult patients with single or multiple radiographically confirmed bone metastases of any histology corresponding to painful areas in previously non-irradiated areas without pathologic fractures or spinal cord/cauda equina compression. It does not apply to the management of malignant primary bone tumour. The following qualifying statements are provided to support the application of the recommendation in clinical practice:
• "Standard" refers to what is applicable to the majority of patients, with a preference for patient convenience and ease of administration and without compromising treatment efficacy or morbidity.
• The recommendation does not apply to lesions previously irradiated, or lesions causing cord compression or pathologic fractures, because such patients were mostly excluded from clinical trials examining fractionation schedules.
• Prophylactic antiemetic agents should be considered when a significant proportion of the gastrointestinal tract is in the irradiated volume.
• Patients and referring physicians should be advised that repeat irradiation to the treated area may be possible.
• There is insufficient evidence at this time to make a dose-fractionation recommendation for other treatment indications, such as long-term disease control for patients with solitary bone metastasis, prevention/treatment of cord compression, prevention/treatment of pathologic fractures, and treatment of soft tissue masses associated with bony disease.
This practice guideline incorporates recommendations based on a systematic review, comprehensive consideration of how the evidence may be applied to clinical practice, feedback from Ontario practitioners, and input from the Practice Guidelines Coordinating Committee prior to final approval. It is strongly endorsed by practitioners for whom it was developed.
List of Abbreviations Used
ASTRO, American Society for Therapeutic Radiology and Oncology; CARO, Canadian Association of Radiation Oncologists; CT, computed tomography; Gy, gray(s); met, metastasis(es); MRI, magnetic resonance imaging; NCIC, National Cancer Institute of Canada; PGCC, Practice Guidelines Coordinating Committee; PGI, Practice Guidelines Initiative; RTOG, Radiation Therapy Oncology Group; SCGG, Supportive Care Guidelines Group.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JW was the lead author responsible for designing and conducting the systematic review of the literature and the meta-analysis that informed the practice guideline, and for drafting and modifying the practice guideline report. JW was a member of the Supportive Care Guidelines Group and a Radiation Oncologist at the Juravinski Cancer Centre during the development of this guideline. RW reviewed all drafts of the guideline report and made major contributions to performing the meta-analysis that informed the practice guideline, and provided extensive input to the guideline as a radiation oncologist and methodologist. RW is co-Chair of the Supportive Care Guidelines Group. NL and MJ coordinated input from members of the SCGG, conducted literature searches, and drafted and edited the guideline report. MJ conducted duplicate data extraction and meta-analysis. NL updated the literature search, incorporated new data, conducted the practitioner feedback survey, and coordinated approval of the guideline by the Practice Guidelines Coordinating Committee. As members of the Supportive Care Guidelines Group, AB and TW provided substantial feedback on the guideline report at several points during its development, from both a radiation oncology and methodology perspective. Members of the SCGG provided feedback on all draft guideline reports.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The Supportive Care Guidelines Group would like to thank Drs Wu, Wong, Whelan, Bezjak, and Ms. Lloyd and Ms. Johnston for taking the lead in drafting and revising this practice guideline report. For a complete list of Supportive Care Guidelines Group members, please visit the Cancer Care Ontario Web site at .
Figures and Tables
Table 1 Practitioner responses to eight items on the practitioner feedback survey.
Item Number (% responders to survey*)
Strongly agree or agree Neither agree nor disagree Strongly disagree or disagree
The rationale for developing a clinical practice guideline, as stated in the "Choice of Topic" section of the report, is clear. 53 (98) 0 1 (2)
There is a need for a clinical practice guideline on this topic. 46 (85) 7 (13) 1 (2)
The literature search is relevant and complete. 51 (94) 3 (6) 0
The results of the trials described in the report are interpreted according to my understanding of the data. 44 (83) 5 (9) 4 (8)
The draft recommendations in this report are clear. 48 (91) 4 (8) 1 (2)
I agree with the draft recommendations as stated. 39 (74) 5 (9) 9 (17)
This report should be approved as a practice guideline. 39 (75) 5 (10) 8 (15)
If this report were to become a practice guideline, how likely would you be to make use of it in your own practice*? Very likely or likely Unsure Not at all likely or unlikely
42 (78) 6 (11) 6 (11)
* may not equal 100 percent due to rounding error
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| 15461823 | PMC526186 | CC BY | 2021-01-04 16:03:00 | no | BMC Cancer. 2004 Oct 4; 4:71 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-71 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-771547390310.1186/1471-2164-5-77Research ArticleIntegrating linkage and radiation hybrid mapping data for bovine chromosome 15 Snelling Warren M [email protected] Mathieu [email protected] John W [email protected] Timothy PL [email protected] Roger T [email protected] Gregory P [email protected] Gary L [email protected] Naoya [email protected] Akiko [email protected] Haruko [email protected] Yoshikazu [email protected] André [email protected] USDA, ARS, U.S. Meat Animal Research Center, Spur 18D, Clay Center, Nebraska 68933-0166, USA2 Biochemical Genetics and Cytogenetics Unit, Department of Animal Genetics, Laboratory of Genetics and Biochemistry, INRA-CRJ 78350 Jouy-en-Josas, France3 Shirakawa Institute of Animal Genetics, Livestock Technology Association of Japan, Fukushima, Japan2004 8 10 2004 5 77 77 16 6 2004 8 10 2004 Copyright © 2004 Snelling et al; licensee BioMed Central Ltd.2004Snelling 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
Bovine chromosome (BTA) 15 contains a quantitative trait loci (QTL) for meat tenderness, as well as several breaks in synteny with human chromosome (HSA) 11. Both linkage and radiation hybrid (RH) maps of BTA 15 are available, but the linkage map lacks gene-specific markers needed to identify genes underlying the QTL, and the gene-rich RH map lacks associations with marker genotypes needed to define the QTL. Integrating the maps will provide information to further explore the QTL as well as refine the comparative map between BTA 15 and HSA 11. A recently developed approach to integrating linkage and RH maps uses both linkage and RH data to resolve a consensus marker order, rather than aligning independently constructed maps. Automated map construction procedures employing this maximum-likelihood approach were developed to integrate BTA RH and linkage data, and establish comparative positions of BTA 15 markers with HSA 11 homologs.
Results
The integrated BTA 15 map represents 145 markers; 42 shared by both data sets, 36 unique to the linkage data and 67 unique to RH data. Sequence alignment yielded comparative positions for 77 bovine markers with homologs on HSA 11. The map covers approximately 32% of HSA 11 sequence in five segments of conserved synteny, another 15% of HSA 11 is shared with BTA 29. Bovine and human order are consistent in portions of the syntenic segments, but some rearrangement is apparent. Comparative positions of gene markers near the meat tenderness QTL indicate the region includes separate segments of HSA 11. The two microsatellite markers flanking the QTL peak are between defined syntenic segments.
Conclusions
Combining data to construct an integrated map not only consolidates information from different sources onto a single map, but information contributed from each data set increases the accuracy of the map. Comparison of bovine maps with well annotated human sequence can provide useful information about genes near mapped bovine markers, but bovine gene order may be different than human. Procedures to connect genetic and physical mapping data, build integrated maps for livestock species, and connect those maps to more fully annotated sequence can be automated, facilitating the maintenance of up-to-date maps, and providing a valuable tool to further explore genetic variation in livestock.
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Background
Genome maps for livestock species are necessary to identify genes affecting economically important production traits. Linkage maps, based primarily on highly polymorphic, anonymous microsatellite markers, have been important for identifying chromosomal regions influencing economically important traits in cattle [1-3]. Because a lack of recombination between closely linked markers limits resolution, and because cattle linkage maps [4,5] contain few genes, linkage maps are of limited value for ordering closely linked markers and identifying genes underlying quantitative trait loci (QTL). The radiation hybrid (RH) approach allows mapping monomorphic markers for genes and can provide a higher resolution for ordering close markers [6,7], but high breakage frequency RH data are less reliable than linkage data for ordering widely separated groups of markers [8]. Integrating linkage and RH data into a single map will refine marker order to facilitate genomic sequencing and will also increase the efficiency of identifying genes associated with QTL.
Integrated analysis of both linkage data and RH data allows each source of information to complement the other, providing coarse to intermediate scale maps of the bovine genome, populated with gene markers to facilitate discovery of positional candidate genes for QTL. These integrated maps will lack the fine scale of complete genome sequence, but represent a resource useful for gene identification through comparative mapping approaches, using more complete genome sequence and annotation from other organisms. Similarity between segments of bovine DNA and genomic sequence from other species may supplement integrated data to predict the location of unmapped genes in the bovine genome [9]. A comprehensive integrated map, containing all identified genes and markers, will simplify database queries and reduce ambiguity inherent in mining information from other mammals.
An integrated map can also provide a framework for assembling bovine genomic sequence as data becomes available. A well-ordered map of sequence-tagged-sites (STS) was essential for assembling the human sequence [10]. The National Institutes of Health (NIH) identified the bovine genome as high priority for sequencing [11], and sequencing is underway. One pivotal criterion to classifying the bovine genome as ready for sequencing was the availability of well-maintained genetic and physical maps; integrating these maps will provide additional support for sequence assembly.
Integration of linkage and RH maps has been reported for a number of species [11-14] and individual bovine chromosomes [15-17]. The general approach to integrated mapping has been to score several markers from linkage maps on an RH panel, then align the independent maps via common markers. Nadkarni [18] and White et al. [19] described procedures to synthesize information from multiple independent maps onto a single merged map. These approaches do not directly use data contributing to each map, but merge results of independent analyses. A fundamentally different approach is to merge independent data sets with common markers, so each data set contributes to constructing a single integrated map. Agarwala et al. [20] developed procedures for integrating RH maps where markers common to independent RH panels contributed to the solution of a comprehensive RH map. Schiex et al. [8], developed procedures and released CarthaGene software [21] to merge and solve integrated maps representing multiple linkage and RH data sets.
A large volume of data are being generated in cattle and other livestock species that is not rapidly reflected in current map representations. The result is a lack of truly up-to-date maps of any livestock species, as the maps may lag by months or years in their representation of existing data. It is not feasible to devote significant human resources to constantly maintain and update these maps, so it is critical that automated procedures be developed to free human map curators from many of the time-consuming, error-prone tasks experienced in the mapping process. Existing map construction software is automated to the extent that the likelihoods of many alternative marker orders can be evaluated with a single command, but the entire process of gathering and formatting raw data, constructing maps, examining results and publishing on the internet, or elsewhere, requires human intervention at several stages. Automated procedures will streamline the process in order to focus human effort on the critical stages of verifying raw data and examining the resulting maps.
Bovine chromosome (BTA) 15 provides an interesting example to study the integration of linkage and RH data, and comparison of the bovine to the human genome. A QTL for meat tenderness has been reported on bovine chromosome 15 [22,23]. Comparative mapping indicates that alternating segments of human chromosome (HSA) 11 are conserved on BTA 15 and BTA 29 [15,23,24]. We combined the available linkage and RH data to further examine BTA 15. An integrated linkage and RH map was constructed using CarthaGene software (version 0.99 [21]), and the comparative positions of DNA sequences shared by segments of HSA 11 and the integrated BTA 15 map were established. We also assessed the potential for automating integrated mapping procedures, anticipating a need to extend integration to the entire bovine genome in order to provide up-to-date maps.
Results and discussion
The low resolution of the bovine linkage map is indicated by multiple markers sharing the same map position, even when they may be separated by a substantial physical distance. Inclusion of RH data provides additional evidence by which markers that are inseparable only with linkage data can be ordered. The BTA 15 linkage map (Figure 1A; Additional file 1) shows 78 markers placed in 54 distinct positions, with ten positions representing a pair of markers and seven representing three markers. Marker separation on the higher resolution RH map is greater (Figure 1B; Additional file 1), with 109 markers mapped to 105 distinct positions. Projected onto a common scale, the integrated map represents 145 markers in 118 different positions (Figure 1C; Additional file 1). Eighteen positions contain two markers, at three positions three markers are represented, and one position is occupied by four markers.
Figure 1 Linear representations of bovine chromosome 15 (BTA15) linkage (A), radiation hybrid (RH; B) and integrated linkage/RH maps (C). Named markers are common to both linkage and RH data sets. Tick marks without a marker name represent markers unique to an individual data set. The linkage map was solved with CRIMAP, and the RH map solved using Carthagene diploid RH data. The integrated linkage/RH map was ordered using CarthaGene with backcross linkage data merged by order with RH data.
Integrated RH and linkage maps
Markers common to both the linkage and RH data sets provide a basis for integrating the data and constructing maps representing both types of data. Primer sequences associated with the RH and linkage markers indicated 42 common markers in the two data sets, with 36 markers unique to the linkage data and 67 unique to RH, for a total of 145 markers represented on the integrated linkage-RH map.
Four sets of markers with different primer sequences matching the same bovine sequence were identified. In two instances (MB064 and HBBMS matching Genbank accession AC130787; T608B5 and SP608B5 matching Genbank accession NM_001752), markers in the set were placed adjacent to each other by the map building routine. In the two other cases (FSHB, FSHBMS, and CSPS101 matching accession Genbank M83753; NCAM1MS and MB085 matching Genbank accession X16451), markers in the set were separated by several markers after initial map construction. In both cases, the map could be reordered so markers in each set were placed next to each other without decreasing likelihood of the map. The final integrated order includes these manual adjustments, so that in all cases of different markers matching the same sequence, the markers are adjacent on the map.
Comparison of the integrated map to independently solved linkage (Figure 2A) and RH (Figure 2B) maps indicates relatively good agreement between the maps. Product-moment correlations between independent (CRIMAP linkage map; CarthaGene diploid RH map) and integrated (CarthaGene backcross linkage data merged by order with diploid RH data) map positions were greater than 0.99 for both the linkage and RH maps. The final integrated map did suggest some rearrangement of both the linkage and RH maps. Solved using CRIMAP, the integrated map order of linkage markers was somewhat more likely than the order of the independent linkage map (lod score of 2.4 favors integrated order). This result suggests that the most likely order identified by the integrated mapping process had not been evaluated while using CRIMAP to construct the linkage map. Because of differences in speed, CarthaGene can feasibly evaluate many more orders than CRIMAP; even without integration with RH data, CarthaGene might be utilized to identify errors in marker order and refine linkage maps.
Figure 2 Comparison of independent bovine chromosome 15 (BTA15) linkage (A) and radiation hybird (RH; B) maps with the integrated BTA15 map. The independent linkage map was solved with CRIMAP, and the independent RH map solved using Carthagene diploid RH data. The integrated linkage/RH map was ordered using CarthaGene with backcross linkage data merged by order with RH data. Tick marks along each axis represent positions of markers on the respective linear map. Symbols indicate the intersection of the maps. Symbols forming a straight line indicate agreement between the maps, while deviations from a straight line indicate inconsistencies between the maps. Syntenic group segments are indicated by shading on the RH map (B).
Comparison of the integrated map to the RH map shows the markers remained in the five blocks identified by Gautier et al. [24], and the order of those blocks is the same for both maps (Figure 2B). Some markers were reordered within blocks of the RH map. As with the linkage map, the integrated map order was more likely than the original independent map order (lod score of 3.4 favors integrated order; both likelihoods solved using CarthaGene with a diploid RH model).
Comparative bovine and human map
Comparative map positions for 77 markers mapped to BTA15 were established using primersearch [26] to identify bovine DNA sequence associated with each marker, and subsequent BLASTN against HSA11 contig sequences. Positions of the bovine-human matches were between 4.16 Mbp and 135.59 Mbp on the HSA11 draft sequence (Build 31). Percentage identities of the matches ranged from 83% (475/570 bases) to 100% (1941/1941 bases), with a mean of 93% (449/475 bases). The syntenic group segments (S1, S2, S3, S4 and S4') identified by Gautier et al. [24] were retrieved in the comparison of the integrated BTA15 map with HSA11 (Figure 3). The integrated BTA15 map covers approximately 32% of HSA11. There are eight gaps in coverage containing between 4.2 and 25.6 Mbp of HSA11 sequence. Boundaries of the syntenic segments encompass 36% of the loci on HSA11 (Table 1), not counting the 76 loci within large internal gaps in S1 (7.8 Mbp) and S4 (8.9 Mbp). Some of these gaps in HSA11 coverage are syntenic with BTA29 [15,22,23]. Our current BTA29 linkage map places at least one marker in each of the previously identified segments shared by HSA11 and BTA29, accounting for another 15% of HSA11 sequence. Accounting for segments shared with BTA29 leaves 7 gaps containing from 4.9 to 16.1 Mbp of HSA11 sequence that has not been shown to be homologous to mapped regions of bovine chromosomes 15 and 29, although two of the gaps are located within syntenic segments S1 and S4.
Figure 3 Comparison of the integrated bovine chromosome 15 (BTA15) map with human chromosome 11 (HSA11) DNA sequence (Build 31). Tick marks along the HSA11 axis indicate positions of HSA11 sequence homologous to bovine sequence mapped to either BTA15 or BTA29. Tick marks along the BTA15 axis indicates positions of markers on BTA15. Shading marks regions shared by HSA11 and BTA29. Boxes indicate syntenic group segments.
Table 1 Loci and gene ontology (GO) annotation of human chromosome 11 (HSA11).
HSA11 position (Mb) Number of Loci Loci with GO Term Unique GO Terms
Segmenta Start End
S1 104.0 124.4 200 70 176
S1 gap 104.0 111.8 44 15 54
S2 3.9 18.3 228 52 135
S3 73.3 78.5 80 27 68
S4 30.8 47.6 101 31 86
S4 gap 35.9 44.8 32 6 19
S4' 58.6 60.9 63 12 31
All syntenic regions 672 192 344
internal gaps removed 596 171 310
Entire chromosome 0.0 1640 433 578
>QTL 16.3 20.3 53 16 46
<QTL 122.4 126.4 63 9 25
a Syntenic group segments S1, S2, S3, S4, S4' identified by Gautier et al. (2002). Gaps are relatively long segments within a syntenic group that do not contain sequence common to HSA11 and bovine chromosome 15 (BTA15). Segments designated >QTL and <QTL are 4 Mb segments of HSA11 centered around syntenic markers defining boundaries of S1 (<QTL) and S2 (>QTL), flanking the BTA15 meat tenderness QTL identified by Keele et al. (1998). All syntenic regions represents the union of S1, S2, S3, S4 and S4'. Entire chromosome includes all loci with a position established on HSA11 sequence.
Markers more recent [23,24] than the original description of the meat tenderness QTL [22] have resulted in some rearrangment of the BTA15 map, so position of the QTL must be shifted to current positions of markers defining the QTL region. The syntenic segment S1 contains several markers that were within the 95% confidence interval surrounding the QTL, but the two markers most closely flanking the QTL peak, HEL1 and BMS1782, could not be matched to HSA11 sequence and are between defined boundaries of syntenic group segments S1 and S2. Because this QTL region includes a break in bovine-human synteny, the ends of both syntenic segments should be examined to identify positional candidate loci influencing the tenderness QTL. Human loci, in two 4 Mbp segments surrounding the boundaries of S1 and S2 that flank the QTL peak, were identified and associated with gene ontology (GO; [27]) terms to further describe genes near the QTL. These two segments contain 116 loci (Table 1); 25 of these loci have GO annotation [28]) with terms representing various biological processes, cellular components and molecular functions (Figure 4). The GO annotation of loci in both syntenic segments near the QTL may guide further marker development to fine-map the QTL by associations between new markers and tenderness. Adding new markers to this region will also refine boundaries of S1 and S2, and position of the breakpoint between these two segments.
Figure 4 Gene ontology classification of loci on human chromosome 11 (HSA11) in regions near a quantative trait loci (QTL) for meat tenderness. Bovine markers flanking the QTL peak are between defined syntenic regions, so loci in two 4 Mbp regions of HSA 11 (16.3 to 20.3 Mbp; 122.4 to 126.4 Mbp) surrounding markers that define syntenic regions were identified and classified by gene ontology annotation.
Order is well conserved within syntenic group segments S1, S3, S4' and portions of S2 and S4. The most notable rearrangements within segments are an inversion of several markers in the center of S2, and inconsistent ordering within a subset of S4. The internal rearrangements within syntenic groups found here, pig-human rearrangements [29], and mouse-human rearrangements [30] suggest that precise ordering requires reliable data from the species of interest. Comparative information can be used to predict gene location in regions where within-species mapping data are not available [9] or the available data are ambiguous, and may guide marker development and fine-mapping efforts in specific regions [23,24]. Marker orders based on comparative data, however, should be used with caution. For each systenic segment of BTA 15, marker orders predicted from human order were less likely than the order identified from bovine data (Table 2).
Table 2 Comparison of integrated bovine chromosome 15 (BTA15) map where marker order is based on bovine data with alternative maps where segments are reordered according to order of human chromosome 11.
log10likelihoodb Total LOD
Map Linkage RH Total
Bovine data -790.2 -890.4 -1680.6
Syntenic segmentsa reordered
S1 -790.3 -896.5 -1686.8 -6.2
S2 -859.6 -916.4 -1776.0 -95.4
S3 -790.2 -900.0 -1690.2 -9.6
S4 -922.0 -924.6 -1846.6 -166.1
S4' -790.2 -892.1 -1682.3 -2.3
a syntenic group segments described by Gautier et al. (2002).
b likelihoods computed with backcross linkage data merged by order with diploid RH model data
Challenges for building high-resolution integrated maps and leveraging data from various sources, both within and across species, will be to determine regions where additional data may be informative and placing appropriate emphasis on the different sources of information at different levels of resolution. Linkage maps can provide the scaffold for ordering an entire chromosome, so linkage data may receive the greatest emphasis for initially determining a coarse order. Increased emphasis should be given to higher resolution RH and other physical mapping data to resolve order where placement of linkage markers is uncertain, and markers are too close to provide definitive order. Comparative sequence and mapping information from other species should be most useful to position markers within regions where physical data have insufficient resolution and within-species sequence data are not available. Using appropriate weights to combine genetic and physical mapping data, within-species sequence and comparative sequence data should allow the different data sources to complement each other, resulting in consensus maps supported by the combined sources of information.
Automation
Genome maps of livestock species need to represent current information in order to maximize utility of the maps. Positions of putative QTL may become misleading if QTL positions are not updated to reflect subtle rearrangements resulting from new mapping data. Genes associated with phenotypic variation will be more readily identified if available information to link mapping data to genes and their function is maintained. Continually updating the maps to depict relevant existing information will be facilitated by automation, but a number of issues must be addressed for implementation of automated procedures to be fruitful.
Access to dynamic sources of mapping data must be maintained, so that new information can be incorporated into the maps soon after it is generated. Information to connect data from various sources must be available to expedite integration. Map computation strategies deserve some attention, to minimize the delay between acquiring new data and appearance of those data in subsequent maps.
Procedures developed to integrate BTA15 linkage and RH data can be applied to available data for the entire bovine genome. The integration effort will be more valuable, however, if sources of data for the integrated map are periodically updated. Success of a comprehensive integration effort will also depend on information available to establish connections between the data sets. One alternative is to resolve marker nomenclature, perhaps by developing and maintaining a database of marker names and synonyms. A more straightforward, and easily automated, approach is to use primer sequences as universal identifiers to establish connections between mapping data sets. Database curation efforts to associate mapping records (animal genotypes and RH vectors) with primer pairs may be more worthwhile than attempts to resolve all possible names for a given marker.
Primer sequence can also be used to establish connections to sequence databases. Sequence similarity searches should reveal connections to STS sequences associated with markers; the process will also identify connections to other sequences, including more completely annotated and assembled sequence. Sequences identified in this process can be used to establish connections with human and other well annotated, assembled genomic sequence for comparative mapping. Similar associations between mapping and sequence data may be established using marker and locus names, provided that marker nomenclature can be resolved Sequence-based connections between mapping data sets, integrated maps, and genomic sequence may be more reliable and are more amenable to automation than attempts to connect sources using names and other information.
Connections between maps and annotated sequence can accelerate positional candidate gene discovery if the sequence annotation includes functional information. Harhay and Keele [31] used GO and GO-annotated human sequence to link livestock EST with function; mapping the EST can extend their procedures to relate map position to function. Connecting map positions to GO terms requires synchronizing several information sources, including livestock maps, human sequence annotated GO terms, and GO databases.
Placement of new markers on integrated maps must keep pace with new marker development, if integrated maps are to remain current with available mapping and sequence data, The basic concept of map construction, finding the most likely marker order out of all possible orders, is conceptually simple but computationally demanding, because the number of possible orders increases factorially with the number of markers. Evaluating all possible marker orders is not feasible when mapping data represents more than twenty or thirty markers on a chromosome. Cost and time constraints limit map construction to strategies that evaluate a sufficient number of possible orders to ensure that a reasonably good order is identified.
As bovine sequence data becomes available, methods to exploit that resource to refine both the integrated maps and sequence assemblies must be implemented. Advent of whole-genome sequence assemblies has not diminished the value of maps in sequenced species. Discrepancies between human maps and sequence assemblies have been noted [32,33], although concordance between a SNP linkage map and sequence assemblies increased in later assemblies [33]. A comprehensive linkage-RH map has been used to validate mouse sequence assemblies, revealing cases of significant inversions and translocations in sequence, as well as confirming sequence order in other regions where the sequence order disagrees with previous mouse RH maps [34]. An integrated linkage-RH map of the rat suggests some errors in the draft sequence, but more importantly, provides a mechanism to anchor QTL on the genomic sequence [35].
The strategies employed must be sufficiently flexible to allow manual manipulation of the resulting maps. Some evidence, such as STS markers sharing the same sequence, and ordering information from other species, is not easily represented in linkage and RH mapping data. In some cases of markers sharing the same sequence, markers can be forced to share the same position, or data from multiple markers can be combined to create a single haplotype representing multiple markers. Marker orders suggested by maps of other species may be compared with likely orders identified from within-species data. Incorporating information not directly represented in mapping data can require manually evaluating additional orders, and making some judgement about which results are most acceptable.
In exploratory analyses merging BTA15 linkage and RH data, simulated annealing and taboo search algorithms in CarthaGene were explored as methods of initially ordering the integrated map, before refinement with the polish and flips routines. Resulting maps were similar to the map presented, but required more than 24 hours to compute. The map presented was initially ordered by placing each marker against a pair of markers common to both data sets, and was constructed in less than four hours. Another approach involved initially placing markers against the set of all markers common to both the linkage and RH data sets, in the linkage map order. While map construction was somewhat faster using this approach, the resulting map was less likely than the map initiated from a pair of markers and showed greater disagreement with the linkage map.
Parallelization of the mapping algorithms can substantially increase the speed of map construction. Likelihoods of a number of alternative orders must be computed at several steps during the map building process. If these calculations are distributed across multiple processors, time required to compute all likelihoods and arrive at a final order will be reduced because computations are performed simultaneously. Increased parallelization should also increase the feasibility of implementing more thorough algorithms that examine a larger number of possible orders, therefore increasing the probability of identifying more likely maps.
Conclusions
Linkage and radiation hybrid maps are powerful tools to facilitate discovery of genomic regions and ultimately genes influencing livestock production traits. Combining linkage and RH data can provide more accurate, consolidated maps representing more information, especially if the maps are connected to well annotated genomic sequence. Automating map construction and comparative mapping procedures will expedite construction of whole-genome integrated maps and maintaining a comprehensive resource as new data becomes available. Success of automated procedures to connect data from various sources and construct integrated maps depends on information available to establish connections between data; sequence-based approaches to connect data are preferrable.
Methods
Data sets for integrated map construction
Linkage data for 78 markers in the BTA15 linkage group were obtained from the U.S. Meat Animal Research Center (MARC) reference population (224 animals; [4]). Radiation hybrid data for 109 markers were obtained from the ComRad project radiation hybrid panel (94 cell lines; [7,24]). These data include two newly developed microsatellite markers genotyped in the MARC families (Table 3), and seventeen previously unpublished markers with RH data(Table 4).
Table 3 Description of previously unpublished linkage markers placed on the integrated bovine chromosome 15 map.
Marker Name Forward Primer
Accession number
Reverse Primer
DIK2411 CTAACGCCCCTGAGACAGAC AB112806
GTGGCGTTAGTTGGTCCTTC
DIK2374 CCTGTTTGGGACACTCTCCT AB112803
GAATCTCTTCAATGCCGAATG
Table 4 Description of previously unpublished radiation hybrid markers placed on the integrated bovine chromosome 15 map.
Locus Symbol Gene Name Forward Primer
Accession Number
Reverse Primer
C11ORF15 chromosome 11 open reading frame 15 GCATCCTAGAACAGACTGGCT AW657178
GGAGGCAACCGGAACTCCAGT
DKK3 dickkopf (Xenopus laevis) homolog 3 CGAAGACCATTATCAGCCACA AW336328
CTCTGGATGCATACATGAAGGA
EIF4G2 eukaryotic translation initiation factor 4 gamma, 2 AGCTTGAGGCCTGCTCAGTCT AV602677
GTCCCAAAGGTGGCGTTTGA
FLJ11790 protocadherin 16 dachsous-like (Drosophila) CCCAGCTTCTCACCTTCACTA AW428073
GATATGGAGCTCGGTGTCGTCT
INPPL1 inositol polyphosphate phosphatase-like 1 CAGCTCAACTTGGAGCGGGAA BF705795
GAACCCCGCTCATAGCGGTAA
KIAA0750 hypothetical protein KIAA0750 GTGGGAAGCTGGCTATTGCA AW652984
GAAGATGAAAGCCACACCGCT
MRPL17 mitochondrial ribosomal protein L17 CACCTGTTGCAGAACTTGCTT BE899833
CCCAGCTTCCCGTAGTCAATA
PARVA parvin, alpha GCCGTATCCCTCAACTCCTTT BE477207
CTCAAGAGTCCCTGTTGAAGA
PSMA1 proteasome (prosome, macropain) subunit, alpha type, 1 GAATATGCAATGGAAGCTGTC AV602233
GCTGCAAGTTCTGACTGTGCT
RANBP7 RAN binding protein 7 GGGTGAAGAGATGAGGAAGAT BF45355
CTGATACTCATCAACAGGGTT
RNF21 tripartite motif-containing 34 GAAGAGAAACTCCTACTCTTCT AW447003
CTCCTGAGATCGTTCACAAAGA
ST5 suppression of tumorigenicity 5 CGCTGCTCTGGTCTATCACTT BF604586
ATTGCCAGCCCCTGGCAGGAA
STIM1 stromal interaction molecule 1 GCCCTCCAGGCTAGCCGAAAT BE756550
CACTGCCACCCCCATCCTGTT
TAF2H TAF10 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 30 kDa TGGTGTCCAGCACGCCTCTA AW315164
GTAGTAACCAGTCACTGCATCA
UVRAG UV radiation resistance associated gene GTACATTTTCAGCTGAGCACC BE590188
CGCGGTACACTCCTTTCTCAA
WEE1 wee1+ (S. pombe) homolog GATGGATGCGTTTATGCCATA AV598317
CGAACTACATGAGAATGTTGC
ZFP26 C3HC4-like zinc finger protein CTGCTAAAGTGGCTTCTGGC BF04414
GGTACAGACCACTCGTACAA
All bovine sequence information stored in GenBank was identified using the taxonomy ID field of the sequence file annotation and obtained from NCBI. Provisional sequence data consisting of tentative consensus clustering of bovine EST data was obtained from the Bos taurus gene index (BTGI; [36]) assembled by The Institute for Genonomics Research (TIGR, [37]). Other sources of sequence were the NCBI nt database (NT; [38]), and human chromosome 11 draft sequence contigs (Build 31;[38]).
Data integration
Connections between data sets are necessary for integrated analyses of those data to be meaningful. Because some marker names were ambiguous, connections between markers in the linkage and RH data were established using primer sequence. Markers with identical primers were considered to be the same, regardless of marker name. Primer sequence was also used to establish connections with human sequence. Primer pairs were matched against bovine sequence from GenBank, NT and BTGI databases using the EMBOSS [26]primersearch tool. The longest matching sequence having one or fewer mismatches and an amplimer less than 600 bp was selected for homology search against HSA11 contigs. The selected sequences were examined for gaps, and where gaps occurred, only the ungapped pieces matching a primer pair was used in the homology search. Connections between the individual sequences matching bovine markers and human sequence were then determined via BLASTN [39] with an expectation value of e-20, and default values for other parameters.
Connections between human position and functional GO annotation were extracted from the downloadable LocusLink database [38]. Procedures using the GO database [40] and perl API [41] were developed to classify specific GO terms into general categories described by higher level terms.
Integrated map construction
Observations for RH data are binary (0/1), indicating absence or presence of a particular marker in a cell line, where each cell line represents a relatively short segment of DNA on a chromosome. Physically close markers are more likely to be observed on the same cell line than distant markers. Linkage data includes pedigree information and marker genotypes, where individual genotypes represent alleles inherited from each parent. Alleles for physically close markers on a single chromosome are more likely to be inherited from the same grandparent; the likelihood of marker alleles with different grandparental origin appearing on the same chromosome increases with distance between markers. These chromosomes can be represented in a binary, RH-like format that can be merged with RH data using CarthaGene. Analagous to RH data representing presence or absence of a marker in a cell line, binary representation of linkage data indicates presence or absence of a maternal allele on an individual chromosome. The chrompic option of CRIMAP [42] was used to construct these individual chromosomes, using the most likely order identified by an automated linkage mapping routine. No distinction was made between definite phase-known maternal and paternal inheritance, and statistically predicted inheritance when phase could not be determined.
An interface to the CarthaGene shared library was developed using perl and the perl Inline modules [43] to automate map construction (see Additional file 2). This interface includes procedures to initially place markers on a map and refine map order, as well as a number of utility routines. A map construction script using this interface was also developed (see Additional file 3). The script to order markers on the integrated map starts by merging the binary backcross representation of linkage data with the haploid model RH data, assuming common marker order (dsmergor). Two markers shared by the linkage and RH data sets are identified, and all other markers are inserted, one at a time, into the most likely position using the CarthaGene buildfw procedure. Once all markers from both data sets are placed, the marker order is refined iteratively, cycling through polish and flips routines until likelihood does not improve. The polish procedure individually tests each marker in all alternative positions, and flips evaluates permutations of all sets of six adjacent markers.
After convergence using the map construction script, further evaluation of alternative orders was carried out with the backcross linkage data merged with a diploid model of the RH data, again assuming common marker order. Marker orders consistent with available sequence information were evaluated. Where primer paris for different markers matched the same bovine sequence, but the markers were separated by one or more other markers by the map construction routine, likelihoods of orders with the matching markers placed adjacent to each other were determined. Likelihoods of marker orders consistent with human sequence within each syntenic segment were also computed. The sequence-based orders were used in the final integrated map if they did not decrease likelihood of the map. Log-likelihoods of the final integrated map order were computed with the RH and linkage data sets for comparison to the independent maps, using CarthaGene for the RH map and CRIMAP for the linkage map.
The final integrated marker order was projected onto a common relative scale representing all markers. This was accomplished by merging the linkage data with RH data, modeled as backcross, using dsmergen. Marker order was set to the final integrated order, map distances computed, then scaled to range from zero to 100.
Computation
All computation was performed on a 10-node Linux cluster, each node configured with 2 AMD 1900+ CPUs and 3 Gb RAM. When practical, computation was parallelized using perl scripts and open source Grid Engine software [44] to distribute tasks to each node in the cluster. Steps that were parallelized included matching primers to sequence, and the Blast searches to align bovine with human sequence.
Authors' contributions
WMS developed perl scripts for automated analyses, conducted analyses to match markers from linkage and RH data sets, constructed the linkage and integrated maps, and drafted the manuscript. MG and AE developed RH markers and constructed the RH map. AE and JWK conceived the research, and contributed to planning analyses and evaluating results. RTS and TPLS assisted with evaluation of the integrated map. GPH conducted BLAST analyses and associated bovine sequence with human GO annotation. NI, AT, HT developed linkage markers. YS and GLB coordinated linkage map data collection. GLB curates the MARC linkage data and linkage maps.
Supplementary Material
Additional File 1
linkage, RH and integrated maps of BTA 15
Click here for file
Additional File 2
perl interface to CarthaGene, requires perl Inline::Tcl module
Click here for file
Additional File 3
perl script to construct integrated maps using CarthaGene, requires carthaPerl.pl interface to CarthaGene
Click here for file
Acknowledgments
Thanks to Randy Bradley and Phil Anderson for network support. Jim Wray for database development. Linda Flathman and Sandra Nejezchleb for marker genotyping at MARC, and Thomas Schiex for providing and supporting CarthaGene.
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| 15473903 | PMC526187 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Oct 8; 5:77 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-77 | oa_comm |
==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-801548815410.1186/1471-2164-5-80Research ArticleReconstruction of putative DNA virus from endogenous rice tungro bacilliform virus-like sequences in the rice genome: implications for integration and evolution Kunii Motoyuki [email protected] Masanori [email protected] Hironori [email protected] Ichiro [email protected] Yuji [email protected] Yoshio [email protected] Laboratory of Plant Breeding, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan2 Laboratory of Plant Pathology, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan2004 18 10 2004 5 80 80 11 6 2004 18 10 2004 Copyright © 2004 Kunii et al; licensee BioMed Central Ltd.2004Kunii 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
Plant genomes contain various kinds of repetitive sequences such as transposable elements, microsatellites, tandem repeats and virus-like sequences. Most of them, with the exception of virus-like sequences, do not allow us to trace their origins nor to follow the process of their integration into the host genome. Recent discoveries of virus-like sequences in plant genomes led us to set the objective of elucidating the origin of the repetitive sequences. Endogenous rice tungro bacilliform virus (RTBV)-like sequences (ERTBVs) have been found throughout the rice genome. Here, we reconstructed putative virus structures from RTBV-like sequences in the rice genome and characterized to understand evolutionary implication, integration manner and involvements of endogenous virus segments in the corresponding disease response.
Results
We have collected ERTBVs from the rice genomes. They contain rearranged structures and no intact ORFs. The identified ERTBV segments were shown to be phylogenetically divided into three clusters. For each phylogenetic cluster, we were able to make a consensus alignment for a circular virus-like structure carrying two complete ORFs. Comparisons of DNA and amino acid sequences suggested the closely relationship between ERTBV and RTBV. The Oryza AA-genome species vary in the ERTBV copy number. The species carrying low-copy-number of ERTBV segments have been reported to be extremely susceptible to RTBV. The DNA methylation state of the ERTBV sequences was correlated with their copy number in the genome.
Conclusions
These ERTBV segments are unlikely to have functional potential as a virus. However, these sequences facilitate to establish putative virus that provided information underlying virus integration and evolutionary relationship with existing virus. Comparison of ERTBV among the Oryza AA-genome species allowed us to speculate a possible role of endogenous virus segments against its related disease.
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Background
The virus-like sequences that have been found in plant genomes are divided into two groups of plant viruses, single-stranded DNA geminivirus and double-stranded DNA pararetroviruses. The geminivirus segments, including the viral replication origin and the adjacent AL1 gene, have been found in the genomes of tobacco and its related species [1,2]. Pararetrovirus-like sequences have been reported in the petunia [3,4] banana [5-7] and tobacco genomes [8-10]. Compared to the intact virus sequences, most of the endogenous virus-like sequences were rearranged in the host genomes. Their rearranged structures suggested that illegitimate recombination may have occurred when putative virus progenitors integrated [11]. The endogenous viruses for banana streak virus (BSV) [6], tobacco vein-clearing virus (TVCV) [9] and petunia vein clearing-virus (PVCV) [4] could be activated as episomal viruses under certain conditions in the host plant, and appeared to have pathogenic potential. The integrations of these viruses were shown to have been relatively recent events and the copy numbers of the endogenous virus sequences were found to be very low. On the other hand, for tobacco endogenous pararetroviruses (TEPRVs), it was estimated that there are about 1000 segments in the tobacco genome [8], but the intact virus has not been identified so far, suggesting that the integration of TEPRVs was not a recent event. The finding of such endogenous virus sequences raises questions concerning 1) the integration process giving rise to endogenous virus sequences, 2) possible differences in the evolutionary rate between the virus and endogenous virus and 3) resistance potential as a result of endogenous virus integration.
In the rice genome, pararetrovirus-like sequences that are similar to rice tungro bacilliform virus (RTBV) have also been found [12-14]. In South and Southeast Asia, rice tungro bacilliform virus, which is transmitted by green leafhoppers, causes one of the most serious diseases of rice with the assistance of rice tungro spherical virus (RTSV) [15]. Kobayashi and Ikeda [16] reported that African rice species, Oryza glaberrima and O. barthii, showed much severer systemic necrosis compared to the other rice species present in South and Southeast Asia after inoculation of both RTBV and RTSV.
Here, we have characterized RTBV-like sequences in the Japonica (cv. Nipponbare) genome. These sequences, denoted endogenous RTBV-like sequences (ERTBVs), were highly rearranged and dispersed throughout the rice genome. Sequences of the putative viruses for ERTBV were reconstructed from the dispersed segment in the genome. Copy numbers of ERTBV segments are shown to vary among AA-genome Oryza species. Asian species have more ERTBV segments than the species originated from the other regions where RTBV is not distributed. The results obtained advance our understanding of the manner of integration of authentic pararetrovirus into the host genome, evolutionary implication of the integrated virus and possible involvement of endogenous virus segments in the corresponding disease resistance.
Results
Identification of RTBV-like sequences in the rice genomes
Previously, we found a repetitive sequence near the rice waxy gene, which is partially homologous to the rice tungro bacilliform virus (RTBV) genome [12,17]. A probe of this segment hybridized to about 30 bands in EcoRI-digested fragments from both Japonica and Indica genomic DNAs [12]. In the present study, we collected 29 RTBV-like sequences from the rice genome databases for Japonica (cv. Nipponbare). The segments collected here had ample length for further analyses. The segments of ERTBV were distributed throughout the rice genome. Structural differences among the collected ERTBV segments appeared to be due to rearrangements of the segments including deletions, insertions, inversions and duplications. None of the segments had the same structure as the virus or seemed likely to be active as virus. However, the similarities among the ERTBV segments were scored as more than 80% (described in detail in below), so that each homologous part in the ERTBV segments was easily recognized. Most of the ERTBV segments are flanked by AT-repeated sequences (Table 1), which might be involved in the integration mechanism.
Table 1 Summary of the ERTBV structures in the rice genomes1.
Chr.2 Accession Number ERTBV position (length)3 Cluster4 alignment of gene5 AT-repeated sequence6
Intergenic region Region x ORF y (MP/CP/PR) ORF y (RT/RH) ORF z 5'-end 3'-end
[Japonica]
1 AP003338 31237-38745 (7509) C 32277-31237 38711-38217 38259-34814 34534-33094 33127-32278 34 (2) 153 (0)
38745-38712
2 AP006160a 128189-135399a C 128189-128420a 128421-128915a 128873-129447a 134248-132808a 132841-131989a 97 (0)a 12 (0)b
AP004842b 1-7610b(11686) 131988-131526a 7379-6885b 130212-131451a
135399-134914a 134913-134534a
7610-7380b 5595-3136b
6927-6352b
4 AL606628 47001-48868 (1868) B - - 47001-47054 47334-48768 48756-48868 16 (0) 45 (391)
AL606618 82165-88112 (5948) B - 88112-87631 87673-84239 83959-82532 82544-82165 13 (678) 156 (48)
AL606592 36282-40413 (4132) B 39437-40413 - 36282-36894 37174-38614 38602-39436 65 (0) 101 (17)
AL662971 104278-111739 (7462) C 105283-104278 111720-111226 111268-107823 107543-106102 106135-105284 65 (727) -
111739-111721
AL607003 63290-71598 (8309) A 63406-64469 64470-64882 64846-68248 68532-69972 63290-63405 52 (68) 151 (265)
70794-71598 69939-70793
5 AC107085 22244-29756 (7513) B 22436-22244 28859-28374 28416-24971 24691-23258 23270-22437 25 (0) 55 (64)
29756-28860
6 AP002542 33961-41438 (7478) B 39087-38121 38120-37636 37678-34233 39121-40541 40529-41362 216 (2) 252 (1)
41363-41438
AP006056 28996-33700 (4705) C 29562-28996 - 33700-32101 31821-30381 30414-29563 93 (10) 337 (87)
AP004750 97242-104472 (7231) A 98061-97242 104472-103986 104022-100607 100323-98883 98916-98062 39 (178) 52 (694)
7 AP006163 118513-126010 (7498) C 118513-118518 118519-119013 118971-122416 122696-124136 124103-124954 82 (502) 224 (129)
124955-126010
AP005719 363-7932 (7570) C 3278-2211 2210-1716 1758-363 5526-4096 4129-3279 65 (100) 33 (2)
7932-5806
AP004348 17568-24897 (7330) A 18513-17568 24897-24437 24473-21059 20775-19335 19368-18514 96 (100) 37 (20)
8 AP005164 171654-179236 (7583) C 173775-174838 174839-175333 175291-178760 171654-172956 172923-173774 52 (284) 274 (11)
179071-179236
AP003883 117668-125143 (7476) C 117668-117743 117744-118238 118196-121641 121921-123361 123328-124179 54 (612) 41 (16)
124180-125143
AP003914 22973-25201 (2229) A - - - 23074-24514 24481-25201 373 (14) 1136 (0)
AP005301 34330-41566 (7237) A 34330-34741 34742-35228 35192-38611 38895-40314 40281-41135 153 (14) 34 (23)
41136-41566
AP005159 88879-97300 (8422) B 88879-89828 89829-90315 90273-93716 93996-95435 95423-96256 179 (0) 34 (19)
96257-97300
9 AP005424 11998-18576 (6579) C 18576-17867 11998-12492 12450-15849 16129-17569 17536-17833 57 (10) 95 (225)
AP005860 50709-58281 (7573) A 50709-50806 50807-51295 51259-54579 54863-56304 56271-57124 218 (0) 8 (3)
57125-58281
10 AC119147 89355-102382 (13028) B 90235-89355 101319-100834 99260-96143 95863-94424 94436-93603 71 (0) 183 (68)
93602-93432 100876-100548
102382-101320
AC027660 24299-31769 (7471) A 29370-28257 28256-27768 27804-24390 31632-30192 30225-29371 - 28 (520)
AC069300 89901-96619 (6719) A 90209-89901 96619-96133 96169-92755 92471-91031 91064-90210 21 (162) 21 (688)
11 AC135957 69527-81251 (11725) A 73392-74286 81251-80765 69527-70846 71130-72570 72537-73391 368 (619) -
75398-74475 80801-77387 75444-75410
12 AL713945 45991-49426 (3436) B 47029-45991 - - 49291-47851 47863-47030 55 (1) 261 (9)
AL731743 14231-18608 (4378) B 17596-18608 14768-14283 14325-14231 15333-16774 16762-17595 - 44 (40)
14791-15053
AL928780 103187-110757 (7571) A 103187-103406 103407-103893 103857-107273 107665-109105 109072-109926 44 (25) 25 (32)
109927-110757
AL928749 6275-11538 (5264) A 7149-6275 - 11538-9695 9411-7971 8004-7150 479 (127) 624 (0)
[Indica]
4 AB124591 554-4677(4124) B 3709-4677 - 554-1166 1446-2886 2874-3708 43(0) 105(26)
ND AB124592 8867-14510(5644) C 10990-12051 12052-12546 12504-14510 8867-10171 10138-10989 64(273) unknown
ND AB124593 158-5914(5757) C 4214-5914 - 158-1677 1957-3397 3364-4213 157(0) 62(2)
1 The sequences were mined from Japonica database or investigated from phage clones isolated from Indica (IR36) genomic library.
2 Chromosome no. on which the ERTBV segment resides.
3 Nucleotide positions of ERTBV in the registered genomic clone. Parenthesis indicates a length of ERTBV sequence.
4 Clusters into which the individual ERTBV sequences were phylogenetically grouped.
5 The region belongs to the part of ERTBV, and their nucleotide positions in the clone registered in the database.
6 The numbers indicate repetition times of AT at both the ends of ERTBV, and parenthesis indicates distance (bp) from the ERTBV.
a and b These clones contain the identical ERTBV, but both the clones ended within the ERTBV sequence.
Assembling ERTBV segments
We sorted out the 29 ERTBV segments using the sequences homologous to the RT gene, which is encoded in the end of ORF 3. Similarity analyses based on the RT gene grouped them into three clusters (Figure 1 and Table 1), suggesting the presence of three independent ERTBV sequence families. The discontinuity of the three clusters allows us to predict independent integration events for each of the ERTBVs into certain rice species genome(s). We then attempted reconstruction of the complete ERTBVs from the three clusters present in the phylogenetic tree. Approximately 7.5-kb circular virus-like structures could be reconstituted by assembling common parts in individual ERTBVs. The assembled virus-like sequences, which are designated as ERTBV-A, -B and -C, encode potentially functional ORFs. The nucleotide similarities among ERTBVs range from 82% to 93%, and therefore the segments are clearly distinguished in any of the clusters. Of the four ORFs in RTBV, ORF 3 and 4 correspond to ORF y and z of ERTBVs, but ORF 2 is absent from ERTBVs (Figure 2A). Nucleotide sequence of ORF 1 showed a 49% of homology to Region x of ERTBVs, while we failed to find ORF from the Region x sequences (Figure 2A). Pararetroviruses form a circular double-stranded DNA genome (Figure 2B), therefore, it is reasonable to believe that the authentic ERTBV viruses had a similar structure. It seems that the integration of ERTBVs did not occur randomly since more than a half of the ERTBV ends connected with rice genomic DNA were in the putative intergenic region (IGR) (Figure 2B). In addition, the other common junctions are found in the middle of ORF y (Figure. 2B), which corresponds to the location of the discontinuities in the open circular form of RTBV [18]. These structures may indicate that the integration process occurred after the reverse transcription of the virus genome.
Figure 1 The phylogenetic tree based on reverse transcriptase (RT) gene of 29 ERTBV segments and RTBV. These ERTBV were collected from the rice genome database (Table 1). The nucleotide sequences were aligned using the CLUSTAL W program [35] from the DNA Data Bank Japan (DDBJ). The method detailed of construction of the tree was described in the text. These sequences were fallen into three clusters, ERTBV-A, -B and -C. Numbers above the nodes are bootstrap support based on 100 bootstrap replicates for all branches were resolved on the strict consensus tree.
Figure 2 Deduced virus form of ERTBV that was assembled from the rice genomic sequences. A: Comparison of the assembled ERTBV and RTBV. Percentages indicate the nucleotide similarity of each of the corresponding segments or ORFs between ERTBV and RTBV. ERTBV lacks ORF 1 and 2. The assembled sequences designated as ERTBV-A, -B and -C have 7526 bp, 7496 bp and 7499 bp in length, respectively. Their assembled sequences consist of intergenic region (IGR) (A: 1–1114; 1114 bp, B: 1–1066; 1066 bp: C: 1–1063; 1063 bp), Region x (A: 1115–1600; 486 bp, B: 1067–1552; 486 bp, C: 1064–1558; 495 bp), ORF y (A: 1570–6702; 5133 bp, B: 1519–6672; 5154 bp; C: 1525–6678; 5154 bp) and ORF z (A: 6702–7523; 822 bp, B: 6672–7493; 822 bp, C: 6678–7496; 819 bp). Identical organizations of ORFs and their orders were observed in their structures. The nucleotide sequence for Region x corresponds with ORF 1, but ATGs for initiation codon were not present. ORF 3 contains movement protein (MP), coat protein (CP), asparatic protease (PR) and RNase H (RT/RH) [19]. B: Schematic representation of the junction sites of the ERTBV segments adjoining the rice genomic sequence. The junctions are indicated by vertical bars on the circular virus form of ERTBV. The figure shows the number of junctions of all the examined segments referred to Table 1. The different colors in the circle correspond to the above-mentioned segments. The junctions are concentrated in the IGR, which contains the transcriptional initiation and terminal overhanging segments.
The longest ORF in RTBV, ORF 3 encodes the movement protein (MP), coat protein (CP), asparatic protease (PR), RT and RNase H (RH) in the single polycistronic mRNA [19]. ORF 3 mostly parallels ORF y in ERTBV-A, -B and -C. Similarities within these nucleotide sequences between the RTBV and ERTBVs were around 50%. The amino acid identity of the genes in ORF 3 ranged from 63% for RT/RH to 40% for PR genes. With respect to RT and RH genes in ORF 3, all characteristic motifs and invariant amino acids for individual genes were preserved in the corresponding ORF in each consensus ERTBV (Figure 3). Therefore, the putative genes in consensus ERTBVs potentially encode proteins comparable to those in RTBV.
Figure 3 Amino acids sequence alignments for RT and RH domains of RTBV and the assembled ERTBVs. The alignments were performed with the CLUSTAL W program and the conserved motifs are shown encompassed by yellow. The numbers after each motif indicate the numbers of the amino acids, which are not conserved. Conserved amino acid residues are marked with an asterisk. Invariant amino acids are highlighted in red above the sequences.
To evaluate the genetic relationship of ERTBVs and RTBV, RT amino acid sequences from the viruses belonging to Caulimoviridae, were compared. Using the PHYLIP package program [20], the phylogenetic tree was constructed for 14 kinds of viruses in Caulimoviridae (Figure 4). RTBV and ERTBV-A -B and -C were found to be most closely located in the RT peptides dendrogram among the Caulimoviridae viruses. These results strongly suggest that ERTBVs are virus in origin and are closely related with RTBV.
Figure 4 Neighbor-joining dendrogram of putative RT amino acid sequence relationships among species of the different genera of the Caulimoviridae family. The dendrogram was bootstrapped 100 times (percent scores shown at nodes) and rooted on a random sequence.
No sequences exactly matching RTBV per se were found in the databases for Japonica cv. Nipponbare. We conducted Southern hybridization analysis to see whether RTBV-homologous sequences are present in 14 lines from Oryza AA-genome species (Table 1). The hybridization patterns probed with RTBV sequence resulted in faint and indistinct bands (data not shown).
Periods of integration of ERTBV segments into the rice genome
To investigate the periods of integration of ERTBV, we attempted to obtain ERTBV sequences from an Indica variety (cv. IR36) using the RTBV-like sequence near the waxy locus as a probe. We thereby isolated three clones carrying ERTBV-homologous sequences (Table 1). Indica clone, AB124591, was found to have the same ERTBV sequence as that found in Nipponbare accession AL606592 (Table 1), indicating that the ERTBV integration events occurred before the Japonica-Indica differentiation. The other two Indica clones do not correspond with any ERTBV segments from Nipponbare.
Except for the sequences examined here, we could not find any other RTBV-like sequences in database searches using RTBV nor each of ERTBVs sequences as queries. Therefore, a fourth cluster of the RTBV-like sequences is unlikely to be present. The integration of ERTBV or its derived segments after the differentiation of Japonica and Indica cultivars thus seems to have not occurred.
Distribution of ERTBVs in the Oryza AA-genome species
To test whether other Oryza species contain ERTBV in their genomes, Southern blotting experiments were performed with 14 lines of the Oryza AA-genome species. A PCR fragment of the 7.4-kb ERTBV sequence from chromosome 10 was prepared as a probe, and Figure 5 shows the discrete bands and varying copy numbers revealed by the hybridization patterns. Each accession or cultivar of O. sativa and O. rufipogon (lanes 1–8) has about 50 bands, some of which showed common sizes among the lines. An Australian rice (O. meridionalis) and two O. longistaminata accessions showed middle-copy numbers (approximately 10–20) of the ERTBV segments. In O. glaberrima, O. barthii and O. glumaepatula accessions, only a few bands hybridized to ERTBV; this may also be caused by divergence of the ERTBV sequence and not only due to lower copy numbers of the sequence. Especially in These three species derived from Africa and Latin America were genetically distinct from Asian rice species, but another African rice species,O. longistaminata, was considered to have undergone introgression with Asian species [21]. Africa and Latin America are regionally isolated from rice tungro disease.
Figure 5 Southern blotting patterns for the genomic DNAs from 14 Oryza lines probed with 7.4-kb ERTBV fragment. The numbers above the blots correspond to the numbers of the materials (Table 2). The regions are the origins of the materials. Some accessions/lines of O. sativa, O. rufipogon and O. longistaminata are being used as the sources of resistance against tungro disease..
Methylation of ERTBV in Oryza AA-genome species
Since high-copy-number sequences generally tend to have a heavier methylation state than low-copy-number sequences, we investigated the methylation state of ERTBVs in several Oryza species with various copy numbers. The methylation states were analyzed by Southern blotting method using a methylation-sensitive enzyme, HpaII, and a partially methylation-sensitive isoschizomer, MspI. The 7.4-kb probe was employed to examine the methylation states of the ERTBV in the Oryza AA-genome species. The blotting patterns of O. sativa (Shimokita), O. rufipogon (W1954), O. longistaminata (W1034), O. meridionalis (W1625), O. barthii (W1592), O. glumaepatula (W1185) showed differences of methylation state depending on the ERTBV copy number (Figure 6). In the high-copy-number species, O. sativa and O. rufipogon, the MspI and HpaII digests of DNA clearly showed different digestion patterns, and in particular, most of the small bands in the MspI digests were not observed in the HpaII digests, indicating that these ERTBV sequences were considerably methylated. Although the results of blotting for the middle-copy-number species, O. longistaminata and O. meridionalis, also showed the presence of methylcytosine within their ERTBV sequences, some bands in the MspI digests were shared with the HpaII digests. Preferential digestion of DNA from the low-copy-number species, O. barthii and O. glumaepatula, compared to DNA from the other species was observed. The above results indicate that the copy number of the ERTBV sequences in the Oryza genomes is correlated with the methylation level. A chloroplast DNA fragment [22] was used as a control probe to confirm the completeness of digestions (data not shown).
Figure 6 Methylation state of ERTBV in the genomic DNAs from the Oryza AA-genome species. Each genomic DNA was digested with MspI (M) or HpaII (H) as described in Materials and Methods. Differential hybridization patterns between the MspI and HpaII digests observed in O. sativa and O. rufipogon show heavier methylation compared to the others, and O. longistaminata indicates possession of methylated ERTBV segments in the genome.
Discussion
Completion of ERTBV via integration of the virus
Our data suggest that the putative virus for ERTBVs has been integrated into the Oryza genomes at least three times. These putative viruses are thought to be closely related to RTBV and to form circular double-stranded DNA. Cleavage of circular DNA molecules or open circular forms should be required for integration of the infecting virus. One of the preferential cleavage sites of ERTBV was mapped within a putative promoter segment of the authentic ERTBV sequence. Integrated sequences of TPVL in tobacco [8] and hepatitis B (hepadna) virus in human liver [23] were also found to have preferred junctions with their host genomes at a similar region to that seen in ERTBV. Jakowitsch et al. [8] proposed that the open circular form of the virus during the process of replication was involved in integration, and the free ends of the virus DNA molecule might contribute to integration or recombination. The ends of the ERTBV segments likely correspond with IGR and the putative discontinuities of RTBV (Figure 2B), which possibly functions in transcription and replication initiation or in the priming of DNA strand synthesis [18]. This fact accords with Jakowitschs' idea that the preferential integration occurred while the virus was in the process of replication (Figure 7). In addition to the preferred sites for integration within the virus DNA, we found that 93% of the ERTBV ends were flanked by AT-repeated sequences. This high probability of the presence of AT-repeated sequences adjoining ERTBV led us to postulate that the AT tracks have facilitated or are associated with integration of the virus DNA into the host genome (Figure 7). Transgenes introduced by particle bombardment into the Arabidopsis genome were preferentially delivered to AT-rich scaffold/matrix-attached region (S/MAR)-like sequences [24,25]. SINE integration sites in the Brassica genome show strong affinity for S/MAR-like sequences [26]. Similar processes accounting for these two instances might also function in the case of integration of the virus for ERTBV. Our data alone, however, are unable to distinguish between whether ERTBV integrated into the AT-repeat sequences or whether ERTBV accompanied the AT-repeat sequences in their integration (Figure 7).
Figure 7 Two hypothetical processes for the integration of the putative virus for ERTBV into the rice genome. DNA strands after the reverse transcription step of the virus were targeted to AT-repeat sequences (left), or AT-repeat sequences became attached to the virus segments during the process of the reverse transcription and the complex was integrated into the rice genome (right).
Relationship between ERTBV and RTBV
Although the genomes of ERTBV and RTBV are structurally similar, particularly in the longest ORF, some regions are markedly different. Phylogenetically, both are apparently closely related viruses. One important question is whether or not ERTBV is a cognate of RTBV. ERTBVs existed in the Oryza AA-genome species before differentiation of Japonica and Indica. Considering this evolutionary period, two possible relationships of RTBV and ERTBV might be predicted: one is that the virus that was to become ERTBV was a direct progenitor of RTBV, and the other is that both were differentially branched from a common ancestral virus. Because the evolution of virus genomes is generally much faster than that of plant genomes, we cannot compare them equivalently. In fact, rapid evolution of RTBV was inferred from high level of genetic diversity of RTBV field isolates [27]. Even in a single field in the Philippines, more than one RTBV isolate could be observed, and the genotype changes year by year. Comparison of the sequences of several different isolates revealed evidence for incidences of nucleotide substitution, insertion/deletion and recombination occurred during differentiation of RTBV isolates [28]. Particularly, it was suggested that recombination had played a role in the evolution of RTBV [29]. If ERTBV is a direct progenitor of RTBV, recombination events might have contributed as a driving force to establish the present RTBV form. The longest ORF (ORF 3) of RTBV is functionally essential [30] and is thought to have been conserved since ERTBVs were present as viruses, but the other less homologous segments might have gradually undergone substitution by recombination. If RTBV was derived from the virus that became ERTBV, estimation of the nucleotide substitution rate of the genes common to RTBV and ERTBV would allow us to compare the evolutionary rates of a plant genome and a plant virus. By such a comparison, we estimated the virus evolution ranged from 30 to140 times faster than that of the host genome. The faster evolution of a virus was thus substantiated for the first time in plants if the virus for ERTBV was in fact the progenitor of RTBV. The different evolutionary rates are dependent on the virus genes; that is, the slow evolution might reflect the functional conservation of the gene.
In the other case, in which RTBV and the virus for ERTBV are phylogenetically located on different branches, the progeny of the virus corresponding to ERTBV might have vanished or may be hidden in a small population. Even though RTBV is not in the case a direct progeny of the virus corresponding to ERTBV, their structural similarity and parallel distribution lead us to consider their common ancestral origin.
Correlation between rice tungro disease and ERTBV
Plant virus-like sequences have been found in several plant genomes such as banana, tobacco and petunia [11,31]. So far, no association between virus disease and endogenous virus sequences has been reported [32]. Kobayashi and Ikeda [16] reported that O. glaberrima and O. barthii showed severe systemic necrosis within 4 weeks after infection of RTBV and rice tungro spherical virus (RTSV). O. longistaminata, which possesses ERTBV fragments in its genome, showed disease symptoms like those of the Asian species, and some of the accessions were utilized for obtaining the resistance gene [29]. Interestingly, this species originated from Africa where RTBV is not distributed. The fact led us to suppose that there is a correlation between presence or absence of ERTBV in the genome and the degree of RTBV susceptibility. The species, which have a low-copy-number of ERTBV tend to be vulnerable to the rice tungro disease caused by both RTBV and RTSV. The methylation of ERTBV appeared to be positively related in the copy number in the genome. Based on a study on the endogenous virus sequences (EPRV) in tobacco, Mette et al. [33] proposed a model in which methylation dependent on the copy number of endogenous virus sequences may induce episomal viral methylation through a homology-dependent process involving DNA-DNA or RNA-DNA interaction. The phenomenon observed here fits their model. The copy number of EPRV in the tobacco genome is 10 times as high as that of ERTBV in the rice genome. Our results demonstrated that about 50 copies of endogenous elements are sufficient to induce methylation in the genome. If we ever find the rice germ lines that have incorporated sequences more similar to those of RTBV, as well as ERTBV in germ lines, those would be exploited as valuable sources of stronger resistance against the rice tungro disease.
Conclusions
The rice genome contains more than 30 of RTBV-like sequence (ERTBVs) which were unlikely to have functional potential as a virus, while we were able to assemble putative virus forms from these sequences. The phylogenetic analysis showed that at least three times integrations of authentic ERTBVs occurred during Oryza speciation. ERTBV integrations likely occurred when the virus was in the replication process, and were preferentially targeted to AT-repeat sequences. The closely relationship between ERTBV and RTBV were proven by comparisons of the DNA and amino acid sequences. The Oryza AA-genome species originated from RTBV-distributed regions appeared to contain higher copy numbers of ERTBV segments. The methylation state of the ERTBV sequences was correlated with their copy number in the genome. The results obtained allowed us to speculate a possible relationship between RTBV disease resistance and the copy number and/or DNA methylation of ERTBV in the Oryza AA-genome species.
Methods
ERTBV sequences from Japonica
Sequences of ERTBV in Japonica (cv. Nipponbare) were mined with rice blast search queries [34] against the rice genome sequences that had been registered as of June 2003. Twenty-nine ERTBV sequences were found in the following Japonica genomic database (Table 1).
ERTBV sequences from Indica
We attempted to search for ERTBV sequences in the Indica (cv. 93–11) genome database, however, the homologous sequences found through the search had insufficient length for designation as ERTBV segments. Indica ERTBV sequences were isolated from the EMBL 3 genomic library constructed with IR36 strain (FL1041j), which was purchased from Clontech (Palo Alto, California). For screening, a 3.5-kb ERTBV fragment about 50-kb upstream of the waxy locus was used as probe [17]. Three clones carrying an ERTBV-containing segment of more than 4 kb were selected. Nucleotide analysis was performed with using a d-Rhodamine Terminator Cycle Sequencing Ready Reaction-Sequencing Kit (Applied Biosystems) and an ABI377 Automated DNA Sequencer (Applied Biosystems).
Construction of phylogenetic trees
The nucleotide sequences and amino acids sequences inferred from the reverse transcriptase (RT) gene were aligned using the CLUSTAL W program [35] from the DNA Data Bank Japan (DDBJ). We calculated the pairwise nucleotide divergence (K) between 30 independent ERTBV sequences (including RTBV) based on Kimura's two-parameter method [36] without taking synonymous and nonsynonymous changes into account. We constructed a neighbor-joining (NJ) tree based on these estimates [37]. The tree was drawn using PAUP*4.0 [38]. The consensus maximum parsimony and NJ trees based on amino acid sequences for RT in 14 viruses including ERTBV-A, -B and -C were calculated using programs from the PHYLIP package [20]. The minimum evolution tree was calculated by the implementation of PAUP software in the GCG package.
Southern hybridization
Total genomic DNA was isolated from 7 Oryza AA-genome species including 14 strains (Table 2). EcoRI-digested DNA was separated by 0.7% agarose gel electrophoresis. The resultant DNA was transferred to nylon membranes (Pall: Biodyne B), and hybridized using the Alk Phos Direct Southern hybridization kit (Amersham Life Science). For the probe, nearly the full length of the 7.4-kb ERTBV fragment (located on chromosome 10, BAC clone accession no. AC069300) was amplified by PCR with the primer combination of 5'GAACTACAACTAGATATGAACGGGGATA3'+5'CACAACTATTCTTAGTGCTGAATTCACTT3'. The membranes were washed twice under the standard Alk Phos conditions with 0.5 M NaCl at 42°C for 20 minutes. To test the methylation state of ERTBV, Southern blotting analysis was carried out using the C-methylation-sensitive enzyme HpaII and the partially sensitive enzyme MspI (isoschizomer of HpaII). The same probe mentioned above was prepared using a PCR-based labeling system with the PCR DIG labeling mix (Roche). To verify that complete digestion was achieved by the enzymes, a chloroplast DNA fragment, the 5.2-kb Sma-8 fragment of buckwheat [22], was used as a control probe.
Table 2 Plant materials used.
Sample Species Cultivar or accession Remarks
1 O. sativa Shimokita Japonica from Japan
2 O. sativa T65wx Near-isogenic line of Taichung 65 with wx from Kinoshitamochi (BC12)
3 O. sativa 221 Javanica type from Indonesia
4 O. sativa PTB10 Indica type from India
5 O. rufipogon W107 Annual type from India
6 O. rufipogon W120 Perennial type from India
7 O. rufipogon W1717 Perennial type from China (through IRRI)
8 O. rufipogon W1718 Perennial type from China (through IRRI)
9 O. glaberrima W025 From Guinea
10 O. barthii W1592 From Cameroon
11 O. glumaepatula W1185 From Surinam
12 O. meridionalis W1625 From Australia
13 O. longistaminata W1034 From Nigeria
14 O. longistaminata W1572 From Nigeria
Sequence data
The sequences containing Indica ERTBV have been deposited in DDBJ: accession nos. AB124591, AB124592 and AB124593. The reconstructed sequences for the authentic ERTBV viruses, ERTBV-A, -B and -C were deposited in DDBJ: accession nos. BR000029, BR000030 and BR000031, respectively.
Authors' contributions
MoKu and MaKa carried out the molecular genetic studies and participated in the sequence alignments. HN participated in the sequence alignment and performed the phylogenetic analysis. IU participated in the design of the study. YK conceived of the study and drafted the manuscript. YK and YS participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
We thank Dr. IL Ryong Choi (International Rice Research Institute, Philippines) Dr. Kaien Fujino (Hokkaido Univ.), and Ms. Kumi Saito (Hokkaido Univ.) for valuable comments. We are also grateful to Prof. Testo Mikami (Hokkaido Univ.) for providing laboratory facilities. This work was supported by Grants-in-aid for scientific research on priority area (A) Ministry of Education, Culture, Sports, Science and Technology.
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| 15488154 | PMC526188 | CC BY | 2021-01-04 16:32:42 | no | BMC Genomics. 2004 Oct 18; 5:80 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-80 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-211546961310.1186/1471-2210-4-21Research ArticleIn vitro evaluation of the potential role of sulfite radical in morphine-associated histamine release Gordon Emma M [email protected] Carolyn [email protected] Jeffrey [email protected] Division of Pediatric Pharmacology and Critical Care, Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio, USA2004 6 10 2004 4 21 21 26 8 2004 6 10 2004 Copyright © 2004 Gordon et al; licensee BioMed Central Ltd.2004Gordon 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
Intravenous morphine use is associated with elevated histamine release leading to bronchoconstriction, edema and hemodynamic instability in some patients. This study evaluated the possibility that sulfite, which is present as a preservative in many morphine preparations, might contribute to histamine release in vitro.
Results
The human mast cell line, HMC-1, was exposed to various morphine concentrations, in the absence of sulfite, under cell culture conditions. Clinically attained concentrations of morphine (0.018μg/ml and 0.45μg/ml) did not cause increased histamine release from mast cells. There was a significant increase in histamine release when the morphine concentration was increased by 1184-fold (668μg/ml morphine). Histamine release from mast cells exposed to morphine and/or sulfite required the presence of prostaglandin H synthetase. Histamine release in experiments using sulfite-containing morphine solutions was not statistically different from that observed in morphine-only solutions.
Conclusion
Sulfite in sulfite-containing morphine solutions, at concentrations seen clinically, is not responsible for histamine release in in vitro experiments of the human mast cell line, HMC-1. This does not preclude the fact that sulfite may lead to elevation of histamine levels in vivo.
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Background
Morphine is among the most common analgesic agents used in the intensive care setting. It is usually administered parenterally as an intravenous infusion. Despite its efficacy, its use is associated with a number of hazards including respiratory depression [1], tolerance [2], physiological dependence [2] and abstinence upon discontinuation of treatment [2]. Less frequent, but perhaps more profound, is the bronchoconstriction, edema and hemodynamic instability seen in some patients following intravenous bolus doses of the drug [3-7]. These effects have been attributed to histamine release from mast cells by morphine [3-7].
A number of clinical studies have demonstrated an increase in plasma histamine concentration following intravenous morphine administration [3-7], but these studies generally fail to provide details concerning the formulation of the morphine employed. Most of the morphine used in both clinical practice and for clinical investigations contains sodium bisulfite as a preservative [8]. Until the mid 1980's sulfite preservatives were generally considered universally safe; however, their status was changed after the receipt by the FDA of more than 250 reports of serious and life-threatening reactions related to sulfite exposure through both diet and therapeutic agents [9,10].
In the body, sulfites are generally oxidized by the mitochondrial enzyme sulfite oxidase and non-toxic metabolites are excreted in the urine [11]. A small amount of the sulfite, either ingested or injected may also be metabolically activated by a number of other enzyme systems including xanthine oxidase [12], peroxidase [11] and prostaglandin H synthetase [13]. As an electron acceptor in these reactions or through electron transfer from transition metals such as copper and iron, sulfite may accept an electron from the resulting hydroxyl, superoxide or sulfate radicals [14] leading to the formation of sulfite radical.
Sulfite radicals are chemically reactive and have been implicated in lipid [15], protein [16] and nucleic acid [17] oxidation as well as neuronal injury [18]. They may stimulate neutrophils to release oxygen free radicals and augment the free radical response to other neutrophil activators [19]. In addition, their generation has been shown to contribute to oxidative stress resulting in sulfite toxicity and impaired B-cell function.
The potential generation of free radicals from sulfite preservatives may confound our understanding the association of morphine with histamine release in man. A number of studies have demonstrated that, when exposed to free radicals, mast cells may release histamine [20-26]. It is possible; therefore, that the histamine released following morphine infusion may be the result of the formation of sulfite radicals from the preservative rather than the morphine itself. This study evaluated the possibility that sulfite and its activation might contribute to histamine release by mast cells in vitro.
Results
Mast cell histamine release
Total histamine content of mast cells was calculated by freezing and thawing suspensions for 3 cycles. Cell suspensions containing 500,000 cells/ml released 1.27μg/ml (0.21) histamine, corresponding to 2.53 pg (0.41) histamine per mast cell.
Mast cells stimulated with 0.25μg/ml calcium ionophore released 63% (7.2) of total histamine.
Effect of sulfite-free morphine concentration on histamine release
The effect of histamine release from mast cells treated with both clinically attained concentrations of morphine and concentrations above those seen clinically was determined.
To chemically defined reaction mixtures, morphine was added at varying concentrations (18 ng/ml, 450 ng/ml, 6.68μg/ml and 668μg/ml). Enzymes causing free radical activation were not present. The blank sample contained no morphine. Reaction mixtures were incubated in a water bath at 37°C for 60 minutes. Experiments were performed in triplicate. Histamine release from samples was determined following derivitization with o-phthaldialdehyde and mercaptoethanol using high-performance liquid chromatography as detailed below. Histamine release from reaction mixtures was expressed as a % of total histamine present in the cell pellet.
Histamine release from reaction mixtures containing morphine at all concentrations was not statistically different (p > 0.1) from histamine release from blank samples (Table 1).
Table 1 Histamine release from HMC-1 cells stimulated with varying concentrations of morphine with and without sulfite Histamine release expressed as a percentage of release of histamine in comparison to control (SD). As a control, the HMC-1 cells were allowed to freeze and thaw for 3 cycles to release the remaining histamine from cells. Each value represents the mean (SD) of triplicate determinations.
Morphine concentration (μg/ml) Histamine release (% total)* Sodium bisulfite
+ -
0 2.9 (2.6)
0.018 2.5 (0.75) 3.2 (1.1)
0.450 1.7 (0.4) 2.6 (0.4)
6.68 1.8 (0.6) 2.2 (1.3)
668 2.1 (0.06) 2.8 (0.2)
To verify the time dependence of histamine release, a morphine concentration of 668 μg/ml was tested. Release was detectable as early as 5 minutes after the addition of drug and appeared to plateau between 40 and 60 minutes (Figure 1).
Figure 1 Concentration-effect of morphine in the presence of prostaglandin Hsynthetase on histamine release from the mast cell line HMC-1 (mean of 3 experiments).
Effect of sulfite-containing morphine solutions on histamine release
To determine whether the addition of sodium bisulfite causes increased histamine release from mast cells, sodium bisulfite at a concentration of 0.1% of morphine concentration (the % of sulfite present in morphine most formulations) was added to the reaction mixtures described previously. Blank samples contained neither morphine nor sulfite. Specimens were incubated, dried, re-dissolved, derivitized and analyzed as above.
There was no statistical difference in histamine release at any concentration of morphine with sulfite compared with blank samples (Table 1).
Reaction mixtures containing 668μg/ml morphine with 0.1% sodium bisulfite were incubated for 5, 10, 20, 40 and 60 minutes yielded results identical to those observed in the absence of sodium bisulfite (results not shown).
Effect of prostaglandin H synthetase on histamine release from mast cell reaction mixtures
Prostaglandin H synthestase may catalyze free radical formation [20]. The first experiment investigated its effect on mast cell histamine release in the absence of sulfite and morphine. This ensured that any increase in histamine release in subsequent experiments was not due to the effect of the enzyme only.
Prostaglandin H synthetase was added to reaction mixtures at a concentration of 25 mU. In reaction mixtures containing no drug, the enzyme was added to mast cell samples containing neither morphine nor sulfite. Controls contained no enzyme.
Reaction mixtures containing the enzyme alone (no sulfite or morphine) produced similar histamine release compared to blank samples (3.2% (0.7) of total and 2.9% (2.6) of total respectively). The presence of prostaglandin H synthetase had no effect.
The effect of clinically attained concentrations of morphine in the presence of prostaglandin H synthetase on mast cell histamine release was investigated. In vivo studies show histamine release at these concentrations of morphine [3-7]. Morphine sulfate was added to the mast cell solution at clinically attained concentrations of 450 ng/ml (a typical peak in vivo concentration following an intravenous morphine bolus) and 18 ng/ml (a typical steady-state concentration during a 20 μg/kg/hour morphine infusion) [27]. Mast cell histamine release from higher concentrations of morphine (668μg/ml and 6.68μg/ml) was also investigated.
Blank samples contained water instead of morphine. All samples contained 25 mU of prostaglandin H synthetase. Specimens were incubated, dried, re-dissolved, derivitized and analyzed as described below. Histamine release values are shown in Table 2. There was no statistical difference between histamine release from morphine samples at both clinically-attained concentrations (18 ng/ml (p = 0.87) and 450 ng/ml (p = 0.08)) and histamine release from blank samples. Histamine release from HMC-1 cells incubated with morphine 668μg/ml and 6.68μg/ml were significantly increased compared with blank samples (p = 0.03 at both concentrations). Results are shown in table 2. Maximum histamine release (16.1% of total (6.3)) occurred at the highest concentration of morphine (668μg/ml) (Figure 1).
Table 2 Histamine release from HMC-1 cells stimulated with varying concentrations of morphine with and without sulfite in the presence of prostaglandin H synthetase. Histamine release expressed as a percentage of release of histamine in comparison to control (SD). As a control, the HMC-1 cells were allowed to freeze and thaw for 3 cycles to release the remaining histamine from cells. Each value represents the mean (SD) of triplicate determinations.
Morphine concentration (μg/ml) Histamine release (% total)* Sodium bisulfite
+ -
0 2.9 (2.6)
0.018 3.3 (2.9) 4.5 (1.4)
0.450 6.5 (0.9) 6.7 (1.8)
6.68 8 (0.3) 6.2 (1.5)
668 17.6 (7.8) 16.1 (6.3)
The effect of sodium bisulfite 0.1% in varying concentrations of morphine in the presence of prostaglandin H synthetase was investigated. This preservative is present in many morphine preparations and its effect on histamine release from mast cells is determined here. Morphine was added to the mast cell samples at concentrations of 668μg/ml, 6.68μg/ml, 450 ng/ml and 18 ng/ml. Sodium bisulfite was added to samples at a concentration of 0.1% of morphine concentration. Controls contained morphine only. Each sample also contained 25 mU of prostaglandin H synthetase. Specimens were incubated, dried, re-dissolved, derivitized and analyzed as described below. Reaction mixtures containing morphine 668μg/ml with 0.1% sodium bisulfite showed a statistically significant increase in histamine release compared with blank samples (p = 0.03). There was no statistical difference in histamine release between morphine only samples at all concentrations and samples containing morphine and 0.1% sodium bisulfite (Table 2) (morphine 668μg/ml (p = 0.8), morphine 6.68μg/ml (p = 0.13), morphine 450 ng/ml (p = 0.89) and morphine 18 ng/ml (p = 0.56). Sulfite did not have any additive effect on histamine release from mast cells in the presence of morphine and prostaglandin H synthetase.
Studies looking at plasma histamine levels following morphine administration in humans have shown peak histamine levels within 5 minutes of administration [3,4,6]. This experiment investigated whether maximum histamine release occurred at a similar time in vitro in the presence of the enzyme prostaglandin H synthetase. HMC-1 cells were incubated at 37°C for 5, 10, 20, 30 and 60 minutes with morphine 668μg/ml, 0.1% sodium bisulfite and 25 mU prostaglandin H synthetase. The cells were centrifuged and the supernatant was examined for the presence of histamine and expressed as a % of total histamine.
Histamine release was time dependent with a maximum effect after 1 hour of incubation (Figure 2).
Figure 2 Time response effects of morphine 668 μg/ml in the presence of 0.1% Na bisulfite and 25 mU prostaglandin H synthetase on histamine release in HMC-1 cells (mean of 3 experiments).
Interestingly, reaction mixtures containing a typical peak morphine concentration (450 ng/ml) showed a significant increase in histamine release in the presence of prostaglandin H synthetase compared with no enzyme. This was true for both sulfite-free (p = 0.003) and sulfite-containing (p = 0.009) morphine solutions. This suggests that even at clinically-attained morphine concentrations, it is not only possible to cause the metabolic activation of sulfite, but also that of morphine.
The effect of sulfite alone (without morphine) on histamine release from mast cells was investigated. To 4 mast cell reaction mixtures, varying concentrations of sulfite (0.018 ng/ml, 0.45 ng/ml, 0.007μg/ml and 0.67μg/ml) were added. These concentrations of sulfite correspond to the concentration of sulfite present in morphine solutions of 18 ng/ml, 450 ng/ml, 6.6μg/ml and 668μg/ml. To each sample 25 mU prostaglandin H synthetase was added. The highest concentration of sulfite (0.67μg/ml) was incubated for 10, 20, 40 and 60 minutes to assess time dependence. The reaction mixtures were incubated, centrifuged and analyzed as described below.
In the presence of prostaglandin H synthetase, sulfite resulted in histamine release from mast cells. There was a significant increase in histamine release (9.7% (0.65) of total) in the reaction mixture containing 0.67μg/ml sulfite (p = 0.01). There was no statistically significant difference in histamine release compared with blank samples at all other concentrations of sulfite (0.018 ng/ml sulfite (2.8% (0.3) of total, p = 0.91), 0.45 ng/ml sulfite (1.93% (0.35) of total, p = 0.53) and 0.007μg/ml sulfite (4.5% (0.7) of total, p = 0.36) (Table 3).
Table 3 Histamine release from HMC-1 cells stimulated with varying concentrations of sulfite-only solutions in the presence of prostaglandin H synthetase. Histamine release expressed as a percentage of release of histamine in comparison to control (SD). As a control, the HMC-1 cells were allowed to freeze and thaw for 3 cycles to release the remaining histamine from cells. Each value represents the mean (SD) of triplicate determinations.
Sulfite concentration (ng/ml) Histamine release (% total)
0.018 2.8 (0.3)
0.45 1.93 (0.35)
7 4.5 (0.7)
670 9.7 (0.65)
The time-dependent experiment showed histamine release of 8% of total (3.3) at 10 minutes incubation, 7.7% of total (2.2) at 20 minutes, 10.5% of total (6.1) at 40 minutes and 9.7% of total (0.65) at 60 minutes.
Discussion
Our experiments showed similar histamine release from the mast cell line HMC-1 stimulated with morphine with or without 0.1% sodium bisulfite. Maximum histamine release was 16.1% of total in morphine only samples and 17.6% of total in morphine with 0.1% sodium bisulfite samples. This percentage is similar to other studies of histamine release in the human mast cell line HMC-1, where maximum release was 20–30% [28-30]. In our study, the concentrations of morphine and sulfite required to achieve maximum histamine release were significantly higher than concentrations seen clinically. Reaction mixtures containing sulfite only also caused significant histamine release at high concentrations. Blank samples, containing neither morphine nor sulfite, did not cause a significant increase in histamine release (Table 2). These results suggest that at high concentrations, both sulfite and morphine could be responsible for elevated histamine release from mast cells. In addition to the formation of the sulfite radical from sulfite, it is possible that carbon-centered or nitrogen-centered free radicals are being generated from the metabolic activation of morphine [20], thereby inducing mast cell histamine release.
HMC-1 cells contain histamine and release their histamine content in response to various exogenous stimuli [28-30]. Our study showed no evidence of elevated histamine release from mast cells when stimulated with morphine and/or sulfite at concentrations seen clinically (both peak and steady-state). Although HMC-1 exhibits a phenotype similar to that of human mast cells [31], it is possible that immature cells (cell lines) are less sensitive than mature (primary) cells [28]. The model used is an artificial system and may not represent physiological conditions. In addition, maximum histamine release occurred after a one hour incubation. This is significantly different from the in vivo situation, where maximum release usually occurs within 5 minutes of morphine administration, and suggests that we were not able to simulate exactly the human environment where histamine release occurs. It is likely that although the sulfite radical is formed rapidly in vitro, it may take some time for its effects to become evident [32].
Metabolic activation of sodium bisulfite may occur through a variety of mechanisms. We investigated a number of enzyme systems and our final model used prostaglandin H synthetase. Other enzyme systems (horseradish peroxidase/ hydrogen peroxide and hypoxanthine/ xanthine oxidase) did release histamine in our model (results not shown), but they produced excess background interference in the chromatograms and made identification of peaks difficult. Activation of sulfite following morphine administration may not occur through this mechanism, however, and is another possible flaw in our aim to construct a model similar to the one that exists in vivo.
Morphine has been shown to cause significant histamine release in vivo, but it has not been investigated whether sulfite, in the form of the sulfite radical, is the cause of this release. Our study finds that sulfite in sulfite-containing morphine solutions, at clinically attained concentrations, is not responsible for histamine release in in vitro experiments of the mast cell line HMC-1, but this does not preclude the fact that sulfite may lead to marked elevation in histamine levels in vivo. The release of a significant amount of histamine with either sulfite or morphine at concentrations higher than those seen clinically suggests that both substances may be responsible for histamine release from mast cells. Further clinical research studies need to be performed to examine this possibility.
Conclusions
Our study finds that the sulfite in sulfite-containing morphine solutions, at concentrations seen clinically is not responsible for histamine release in in vitro experiments of the mast cell line, HMC-1, but this does not preclude the fact that sulfite may lead to marked elevation in histamine levels in vivo. Our experiments suggest that at high concentrations, both morphine and sulfite are responsible for histamine release from mast cells. Further in vivo research needs to be performed to examine this possibility.
Methods
Preparation of HMC-1 cells
Human leukemic mast cells, HMC-1 [33] were kindly provided by J. Butterfield, Rochester, Minnesota. This cell line shows many characteristics of immature mast cells. They have been shown to release histamine following stimulation [28-30] Cells were carried in 75 cm2 vented tissue culture flasks (BD Falcon, Franklin Lakes, NJ) containing Iscoves modified Dulbecco's medium (Sigma, St.Louis, MO) with 10% iron-supplemented calf serum (Hyclone, Logan, Utah) and monothioglycerol 1.2 mM (Sigma, St.Louis, MO). Incubations were conducted in a humidified atmosphere at 5% CO2 and 37°C. Cells were passed weekly and fed only when necessary (evidence of color change in media).
To pass cells, the cell suspension was placed in a 50 ml polypropylene centrifuge tube (BD Falcon, Franklin Lakes, NJ) and centrifuged at 250 × g at 4°C for 10 minutes. The culture medium was discarded and the cells were re-suspended in fresh medium. Cell density was maintained at 500,000 cells/ml.
Chemicals
Reagent grade O-phthaldialdehyde (OPA), mercaptoethanol, dihydrogen sodium phosphate, disodium EDTA, sodium bisulfite, morphine sulfate, hydrogen peroxide, xanthine oxidase, boric acid and phosphate buffered saline (PBS) were purchased from Sigma (St.Louis, MO, USA). Methanol (HPLC grade), acetonitrile (HPLC grade) and sodium hydroxide were from Fisher Scientific (Hampton, NH). Phosphate buffered saline (PBS) pH 7.4, contained 120 mmol/L NaCl, 2.7 mmol/L KCl and 10 mmol/L phosphate. Horseradish peroxidase was purchased from Cooper Biomedical. Prostaglandin H synthetase was purchased from Calbiochem (Darmstadt, Germany).
Determination of histamine release
Mast cell suspensions, containing approximately 500,000 mast cells were, aliquoted into 1 ml samples. These were rinsed 4 times with 4 ml of PBS, pH 7.4. Cells were then re-suspended in 1 ml PBS. Reaction mixtures were supplemented with morphine, sulfite and prostaglandin H synthase at this stage according to experimental design, unless otherwise specified. Reaction mixtures were incubated for 60 minutes in a water bath at 37°C. Reactions were stopped by centrifugation at 250 × g for 4 minutes at 4°C and placed on ice. Then 200μl of the supernatant was collected and dried under nitrogen at 40°C.
Histamine in the supernatant was measured using derivitization with OPA and mercaptoethanol (ME) as described [34]. Dried samples were re-dissolved in 100μl of derivitisation buffer (0.4 M boric acid adjusted to pH 9.5 with 1 M sodium hydroxide) and then derivitized with 20 μL of a freshly made 1:1 mixture of OPA (3.8 mM in methanol) and ME (2.5 ml/L in methanol) and analyzed within 30 minutes. The derivitized samples were used for chromatography.
The response of HMC-1 cells to the stimulant calcium ionophore was investigated to assess the releasability of HMC-1 cells. 0.25μg/ml calcium ionophore was added to mast cell suspensions. Reaction mixtures were incubated and analyzed as above.
Recovery of total histamine from cells
To extract the remaining histamine from the original sample, the mast cell pellet in suspension was frozen and thawed 3 times. It was then centrifuged at 250 × g for 4 minutes and 200μl of the supernatant was dried under nitrogen as described previously. Samples were re-dissolved, derivitized and analyzed as above. Histamine release during morphine incubations was expressed as a percentage of total histamine release.
Chromatography system
Histamine was measured by high-performance liquid chromatography using a Varian model 5500 chromatograph. The mobile phase was 0.1 M dihydrogen sodium phosphate and 1 mM disodium EDTA with 16% methanol and 14% acetonitrile adjusted to pH 6.4. The flow rate was 0.8 ml/minute.
Separations were performed using a 15 cm × 4.6 mm, Ace 5 C18 reversed-phase cartridge column (Advanced Chromatography technologies, UK). A Zorbax C8 guard column with a 5 micron, 4.6 × 12.5 mm analytical guard-column cartridge (Agilent technologies, Palo Alto, CA) was used. Fluorescence detection was used with the fluorimeter (Varian 9070) set at an excitation of 348 nm and an emission of 444 nm (determined by running individual excitation and emission spectra on the fluorometer).
Chromatographic conditions involved the use of a mobile phase gradient to optimize peak separation. A linear gradient starting at 90% mobile phase, 10% acetonitrile and ending at 80% mobile phase, 20% acetonitrile took place over 20 minutes. The column was washed by increasing the acetonitrile to 30% by 22 minutes and by 23 minutes the proportions were returned to the original values. The length of each run was 30 minutes.
Data analysis
Values are expressed as a mean +/- SD of 3 experiments. One-way analysis of variance was used to determine the significance of the difference between groups. Results were considered significantly different when p < 0.05.
Authors' contributions
EG participated in experiments and drafted the manuscript. JB conceived of the study and participated in its design. CM suggested the methodology of experiments. All authors read and approved the final manuscript.
Acknowledgements
The authors wish to thank Carolyn Myers, Ph.D. and Anita Pettigrew, MS for their assistance in assay development and Mary Thomas for her expert assistance in the preparation of the manuscript. This work was supported in part by a NIH Pediatric Pharmacology Research Units grant HD-31323-09.
==== Refs
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| 15469613 | PMC526189 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 6; 4:21 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-21 | oa_comm |
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BMC Emerg MedBMC Emergency Medicine1471-227XBioMed Central London 1471-227X-4-41547391310.1186/1471-227X-4-4Research ArticleA report of dangerously high carbon monoxide levels within the passenger compartment of a snow-obstructed vehicle Griffin Jill A [email protected] Stephen J [email protected] Benjamin [email protected] Howard A [email protected] Department of Emergency Medicine, Tufts University School of Medicine, Baystate Medical Center, 759 Chestnut Street, Springfield, MA, 01199 USA2004 10 10 2004 4 4 4 29 7 2004 10 10 2004 Copyright © 2004 Griffin et al; licensee BioMed Central Ltd.2004Griffin 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
We sought to determine how quickly carbon monoxide would accumulate in the passenger compartment of a snow-obstructed vehicle.
Methods
A 1992 sedan was buried in snow to the level of the undercarriage, the ignition was then engaged and carbon monoxide levels recorded at 2.5-minute intervals. The primary outcome was the time at which a lethal carbon monoxide level was detected. Six trials were conducted: windows closed; windows open one inch; windows open 6 inches; windows closed and tailpipe swept clear of snow; windows closed and one cubic foot of snow removed around tailpipe; windows closed and tailpipe completely cleared of snow to ground level in a path 12 inches wide.
Results
Lethal levels of carbon monoxide occurred within 2.5 minutes in the vehicle when the windows were closed, within 5 minutes when the widows were opened one inch, and within 7.5 minutes when the widows were opened six inches. Dangerously high levels of carbon monoxide were detected within the vehicle when the tailpipe had been swept clear of snow and when a one cubic foot area had been cleared around the tailpipe. When the tailpipe was completely unobstructed the carbon monoxide level was zero.
Conclusions
Lethal levels of carbon monoxide occurred within minutes in this snow-obstructed vehicle.
==== Body
Background
Carbon monoxide poisoning is the number one cause of toxin related death in the United States [1]. It has been estimated that this poison may be responsible for up to 1,500 accidental deaths and 10,000 medical visits annually in the United States [2].
One of the manners in which people die from carbon monoxide poisoning is hypoxia secondary to entrapment within a snow-obstructed vehicle. Recommendations for prevention of these poisonings has been well documented but not thoroughly studied. The Centers for Disease Control (CDC) recommends that "following heavy snowfall, the public should be reminded to inspect vehicles to ensure that exhaust pipes are cleared of snow before engines are started" [3]. Brian Horner, in the Wilderness Medicine Letter made the following recommendation "The windows should be rolled down one inch on each side to provide cross-ventilation. Check the exhaust tail-pipe frequently to see that it is free of drifting snow" [4]. Both the Federal Emergency Management Agency (FEMA) [5] and the National Oceanic and Atmospheric Administration (NOAA) [6] recommend keeping the tailpipe clear and partially opening a window to prevent carbon monoxide poisoning.
In reviewing case reports regarding snow emergencies, it appears that there have been situations where venting carbon monoxide through an open window may not have been sufficient to prevent dangerously high levels of carbon monoxide from accumulating within the passenger compartment [7]. This raises the question of whether current prevention guidelines are safe and accurate.
We performed a pilot study with a single vehicle to simulate a snow emergency whereby a person stranded in a vehicle during a snowstorm would perform a safety maneuver. Our hypothesis was that opening a window a few inches for ventilation or clearing a tailpipe of snow would be sufficient to keep carbon monoxide levels in a non-lethal range.
Methods
In 1995, a 1992 four-door sedan whose exhaust system had been inspected by the State of Massachusetts and found to be functioning normally was placed in a driveway and snow was shoveled around its base on all four sides until the car was obstructed by snow to the level of the bumpers. A Nighthawk, battery powered, digital readout, 80 mAmp carbon monoxide detector was attached to the front of the driver's seat headrest. The instrument had the ability to detect carbon monoxide levels in the range of zero to nine hundred ninety nine parts per million (0 – 999 ppm) [8]. The ignition was engaged and carbon monoxide levels were measured every two and a half minutes until either the maximum level of 999 ppm was detected or 10 minutes had passed.
Six trials were performed. Between each trial all doors and windows were opened, the carbon monoxide was allowed to exhaust from the vehicle until the detector registered zero parts per million.
Results
Dangerously high carbon monoxide concentrations were recorded in the passenger compartment within three minutes when the windows were closed, within five minutes when the front windows were open one inch and within 7.5 minutes when the windows were opened six inches (Table 1). With the windows closed and tailpipe swept clear, a carbon monoxide level of 751 ppm was recorded at 7.5 minutes. With the windows closed and the tail pipe cleared one cubic foot around, the highest CO level recorded was 299 ppm at 10 minutes. When the tailpipe was completely cleared (12 inches wide to ground level) no carbon monoxide was detected.
Table 1 Carbon monoxide levels in a snow-obstructed vehicle.
Time (minutes) Trial #1 Trial #2 Trial #3 Trial #4 Trial #5 Trial #6
0 0* 0 0 0 0 0
2.5 999 530 8 28 0 0
5 999 113 555 33 0
7.5 999 751 265 0
10 585 299 0
12.5 622 276 0
*Parts Per Million
Trial #1: Windows Closed; Trial #2: Windows Open one inch; Trial #3: Windows Open 6 inches; Trial #4: Windows Closed, Tailpipe brushed clear of snow; Trial #5: Windows Closed, Tailpipe brushed clear of snow in an area one cubic foot around tail-pipe; Trial #6: Windows Closed, Tailpipe brushed clear of snow in an area 12 inches wide, depth to ground level.
Discussion and conclusions
Carbon monoxide is an odorless, tasteless by-product of fossil fuel combustion [1]. When it accumulates undetected in the passenger compartment of a snow obstructed vehicle it can rapidly cause toxicity and death. Toxic exposures increase during winter months in the United States and heavy snowfalls that occur over a short periods of time represent a potentially hazardous situation for travelers and for those who must remove snow from vehicles that have been parked outside [9].
In this study, only opening a window was not enough to prevent accumulation of carbon monoxide within the passenger compartment of the vehicle. As the guidelines suggest [3-6], snow must also be cleared from around the tailpipe. However, in this study, the passenger compartment was not safe until the tailpipe had been brushed clear of snow in an area twelve inches wide down to ground level. The major limitations of this study are the small sample size, the fact that the snow used in the trial was shoveled (higher density) rather than naturally accumulating, and that trial #1 was performed on a cold engine. Since multiple vehicles were not tested and since the temperature of the engine, air, and snow was not determined, it would be difficult to generalize the findings of this single vehicle study. Cold engines emit significantly higher levels of carbon monoxide at startup [10], all vehicles emit different levels of carbon monoxide at startup, and snow density and environmental conditions may effect carbon monoxide accumulation. Further studies to validate the study findings using in multiple vehicles at multiple operating temperatures are recommended.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JG gathered data and prepared the manuscript. SP conceived of the project and gathered data. BO gathered data and presented the abstract at the Society for Academic Emergency Medicine. HS oversaw the project and provided statistical support.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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Cobb N Etzel RA Unintentional carbon monoxide-related deaths in the United States, 1979 through 1988 Jama 1991 266 659 663 1712865 10.1001/jama.266.5.659
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From the Centers for Disease Control and Prevention. Carbon monoxide poisonings associated with snow-obstructed vehicle exhaust systems--Philadelphia and New York City, January 1996 Jama 1996 275 426 427 8627950
Horner B Using Vehicles for Shelter in Cold Conditions Wilderness Medicine Letter 1997 14
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Rao R Touger M Gennis P Tyrrell J Roche J Gallagher EJ Epidemic of accidental carbon monoxide poisonings caused by snow-obstructed exhaust systems Ann Emerg Med 1997 29 290 292 9018198
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| 15473913 | PMC526190 | CC BY | 2021-01-04 16:31:01 | no | BMC Emerg Med. 2004 Oct 10; 4:4 | utf-8 | BMC Emerg Med | 2,004 | 10.1186/1471-227X-4-4 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-431549150110.1186/1471-2334-4-43Research ArticleEffect of granulocyte colony-stimulating factor in experimental methicillin resistant Staphylococcus aureus sepsis Alp Emine [email protected] Suveyda [email protected] Ozlem [email protected] Beyhan [email protected] Mehmet [email protected] Department of Infectious Diseases, Faculty of Medicine, Erciyes University, Kayseri, Turkey2 Department of Pathology, Faculty of Medicine, Erciyes University, Kayseri, Turkey2004 18 10 2004 4 43 43 21 4 2004 18 10 2004 Copyright © 2004 Alp 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
Methicillin resistant Staphylococcus aureus (MRSA) is the leading pathogenic cause of nosocomial infections, especially in bacteraemia and sepsis. The essential therapy for MRSA infection is glycopeptides. Therapeutic failure can be seen with this therapy and the mortality is still high. The aim of this study was to evaluate the additional effect of G-CSF on the traditional antibiotic treatment in an experimental MRSA sepsis.
Methods
Experimental sepsis was performed in mice by intraperitoneal injection of MRSA isolate. Inoculum dose was estimated as 6 × 109/ml. Mice were randomised for the study into four group; control group (not receive any therapy), G-CSF group (1000 ng/daily, subcutaneously for 3 d), antibiotic group (vancomycin 25 or 50 mg/kg intraperitoneally every 12 hours for 7 d), and vancomycin+G-CSF group (at the same concentrations and duration). Autopsy was done within one hour after mice died. If mice was still alive at the end of seventh day, they were sacrificed, and autopsy was done. In all groups, the effect of G-CSF therapy on the survival, the number of the MRSA colonies in the lung, liver, heart, spleen, and peritoneal cultures, the histopathology of the lung, liver, heart and spleen was investigated.
Results
One hundred and six mice were used. There were no significant differences in survival rates and bacterial eradication in G-CSF group compared with control group, and also in antibiotic +G-CSF group compared with antibiotic alone group. These parameters were all significantly different in antibiotic alone group compared with control group. Histopathologically, inflammation of the lung and liver were significantly reduced in vancomycin (25 mg/kg)+G-CSF and vancomycin (50 mg/kg)+G-CSF subgroups, respectively (p < 0.01). The histopathological inflammation of the other organs was not significantly different in antibiotic+G-CSF group compared with antibiotic group and, also G-CSF group compared with control group.
Conclusion
G-CSF treatment had no additional effect on survival and bacterial eradication in MRSA sepsis in nonneutropenic mice; and only a little effect on histopathology. G-CSF treatment is very expensive, likewise glycopeptides. The more interest in infection control measures, and prevent the spread of MRSA infections is more rational.
==== Body
Background
Staphylococcus aureus is an extremely virulent pathogen, and causes serious and deep-seated infections (e.g. endocarditis, osteomyelitis) [1]. In recent years, S. aureus is the leading pathogenic cause of both community-acquired and nosocomial infections, especially in bacteremia and sepsis [2-4]. Despite advances in antistaphylococcal drugs, S. aureus sepsis is one of the most important causes of death [5]. Furthermore, methicillin resistant strains are increasing in community and hospitals during the past decades, and many investigators proposed methicillin resistance as an independent predictor of adverse outcome [6,7]. The essential therapy for methicillin resistant S. aureus (MRSA) infections is glycopeptides (vancomycin or teicoplanin). However glycopeptides are intrinsically less effective against staphylococci than are antistaphyloccal β-lactams [1]. This may explain therapeutic failure and high mortality of MRSA infections.
Advances in the pathophysiology of sepsis and septic shock suggest new therapeutic agents and approaches. Granulocyte-colony stimulating factor (G-CSF), a potent stimulator of neutrophil counts and functions, is one of these new strategies in sepsis. Despite a number of studies in sepsis, especially in neonatal sepsis, clinical efficacy of G-CSF is still controversial [8].
The aim of this experimental study was to evaluate the role of G-CSF in the treatment of MRSA sepsis.
Methods
Animal care and use
Male BALB-C mice (8–10 weeks old, weighing 20–25 g) were obtained from Erciyes University Hakan Cetinsaya Experimental and Clinical Research Center. The Animal Care Committee of Erciyes University approved the experimental protocol used in this study. The animals were kept in a cage and allowed to feed and drink water.
Bacterial strain
The clinical MRSA isolate, obtained from a patient's blood and pleural effusion admitted in General Surgical Intensive Care Unit of Erciyes University with nosocomial sepsis, was used. The strain was subcultured on blood agar at 37°C over night. On the day of experiment, bacterial suspension was prepared by sodium chloride 0.9% solution and the concentration was adjusted by spectrophotometer. The bacterial account needed for experimental sepsis was determined by a fore study. The fore study was begun with the inoculum dose of 1 × 107/ml bacterial suspension and gradually increased until experimental sepsis developed. Experimental sepsis was defined as the growth of MRSA in two or more organs. The inoculum dose of this study was estimated as 6 × 109/mL.
Treatment
First dose of antibiotic was given at the sixth hour of bacterial inoculation. Vancomycin (DBL, 500 mg flacon), diluted in 5% dextrose, was given at 25 mg/kg or 50 mg/kg concentrations intraperitoneally every 12 hours for 7 days. rhG-CSF (Roche, Neupogen flk 30 mu/ 1 mL), diluted in 5% dextrose, was received 1000 ng/daily subcutaneously for 3 days [9].
Study design
Animals were randomised to four groups; control group, G-CSF group, vancomycin group, and vancomycin +G-CSF group. The last two groups were divided into two subgroups for different dosage of vancomycin (25 mg/kg or 50 mg/kg). Each group had at least 15 mice and were kept in different cage. In control group, only bacteria suspension was given and no treatment was received.
Bacterial suspension was given intraperitoneally to the mice, and when mice died, autopsy was done within one hour in aseptic conditions. If the mice was still alive at the end of seventh day, mice were sacrificed by servical dislocation and autopsy was done. The samples were taken from lung, liver, heart, spleen, and peritoneum for microbiological investigation, and from lung, liver, heart and spleen for histopathological examination. In each group, survival days were noted.
Samples were taken from the organs with swab by one rotation on its axis, and were cultured on blood agar over night. The colonies on the agar were counted. The colonies more than 300 cfu in a plate noted as >300 cfu.
The same pathologist examined tissue samples stained with hematoxylin and eosin. The degree of inflammation was graded by on a scale of 0 to ++++ (0, no inflammation; focal interstitial inflammation +; more diffuse interstitial inflammation ++; intense interstitial inflammation or microabscesses +++; more extensive abscess formation with tissue necrosis ++++) [10]. The pathologist was unaware about the groups.
Survival days, semiquantitative bacterial count and histopathologic findings in the tissues of the treatment groups were compared with the control group.
Statistics
Survival was assessed using a log-rank test. Fisher's extract test and chi-square test were used to compare the groups. Mann Whitney U test was used to compare the histopathology. A p value less than 0.05 was considered significant.
Results
This study included 110 mice. Three mice that developed intraabdominal hemorrhage, and one mouse that developed Esherichia coli sepsis were excluded. 106 mice were evaluated. Six mice in control group, six mice in G-CSF group, 15 mice in vancomycin 25 mg, 15 mice in vancomycin 25 mg+G-CSF group, 18 mice in vancomycin 50 mg group and 14 mice in vancomycin 50 mg+G-CSF group were sacrificed at the end of seventh day. The death day and the survival rate of the mice in the groups are shown by the survival curve in figure 1,2,3. The comparison of the groups between G-CSF and control, vancomycin (25 mg/kg) and vancomycin (25 mg/kg)+G-CSF, vancomycin (50 mg/kg) and vancomycin (50 mg/kg)+G-CSF showed no differences (p >0.05).
Culture and histopathology results are shown in Table 1. Antibiotic administration decreased bacterial count, and decreased inflammation rate in the organs. Culture and histopathological results in control and G-CSF group were not statistically different (p > 0.05). However, all these parameters were significantly different in antibiotic groups compared with control group (p < 0.01). Cultures of the organs in antibiotic groups were not statistically different from antibiotic+G-CSF group (p > 0.05). Only the inflammation degree in the lung and the liver were significantly reduced in vancomycin (25 mg/kg)+G-CSF group and vancomycin (50 mg/kg)+G-CSF group, respectively (p < 0.01). The inflammatory changes were not significantly reduced in the other organs in two groups (p > 0.05).
Discussion
In recent years, MRSA has become widespread around the world, and become highly endemic in some hospitals. In the United States, the proportion of MRSA isolates increased from 2.4% in 1975 to up to 55% in recent years [11,12]. Similarly, in Europe the resistance rates increased from 12.8% to 26.3% [12,13]. Also, it is extremely high (>60%) in some regions of the world [12]. The recent studies, conducted in our hospital, showed 66% methicillin resistance in nosocomial S. aureus strains, isolated from the bloodstream infections [2,14]. Unfortunately, community-acquired MRSA also increases during the past decades [15].
G-CSF is a cytokine that stimulates myeloid progenitor cell proliferation and increases the bone marrow storage pool and the number of circulating mature neutrophils, which are important component of the host defense [16]. Also, it enhances neutrophil activities (chemotaxis, phagocytosis, antibody-mediated cytotoxicity, etc.) [17]. An inappropriate endogenous G-CSF response may be associated with an adverse outcome to sepsis. Low serum G-CSF concentrations (0 to 125 pg/mL) on admission are supposed to be associated with fatal outcome in patients with bacterial infections [18]. Investigators proposed to use G-CSF in infections in which neutrophil number and function are important to resolution and survival, also in patients which may have reduced neutrophil numbers or function because of underlying disease or physiologic state. The low toxicity and the beneficial effect on survival in animal studies have led to several clinical trials of rhG-CSF as an adjuvant therapy in treatment of infection in nonneutropenic patients [19,20]. Its beneficial effect was shown in clinical studies in diabetic foot infections, wound infections, extensive burns and fungal infections [21-23]. Also neutrophils in sepsis demonstrate a number of functional abnormalities (e.g. reduced bacterial killing, superoxide production, and migration) [24] and it can be hypothesized that these abnormalities can be corrected with G-CSF.
The previous experimental therapeutic studies were mostly carried out in gram-negative sepsis, and in this experimental study, we investigated the effect of G-CSF in MRSA sepsis. The survival rates were not significantly different in G-CSF group compared with the control group, and also in antibiotic+G-CSF group compared with antibiotic alone group. Likewise, most of the other experimental studies in gram negative sepsis, showed that prophylactic rhG-CSF administration reduced endotoxemia and serum TNF-α levels and also improved cardiac function and survival, whereas therapeutic rhG-CSF (i.e. administered after the onset of infection) did not improve outcome and at very high dosages appeared harmful [25,26]. A recent multicenter, double-blind, randomised and placebo controlled study in patients hospitalised with pneumonia and severe sepsis, demonstrated that G-CSF had no beneficial effect in reducing mortality rates or complications from severe sepsis [27].
In this study, we did not measure the WBC count, so not have subgroups with neutropenia. In an experimental gram-negative sepsis in rabbits, Smith and colleguaes [28] showed the beneficial effect of therapeutic rhG-CSF only in early sepsis due to gram negative bacteria when complicated by leukopenia, no significant difference in nonneutropenic group.
Bacteria are rapidly cleared from blood and tissues following intravenous antibiotic therapy [10]. In our study, antibiotic received group had significantly low bacterial count in the organ cultures compared with the other groups, however G-CSF had no beneficial effect on the bacterial clearance.
The histopathologic findings of invasive S. aureus infections are leukocytic infiltration, focal pneumonitis, edema, microabscesses, etc. In an experimental study, significant pathologic changes during and after the elimination of bacteria from the blood and tissues were noted. Also, expression of cytokines (TNF, IL-1, IL-6) was observed in all of the infected tissues, and correlated tissue damage after clearance of bacteria from the blood and tissues[10]. However, in our study, G-CSF significantly reduced only the inflammation in the lung and liver in vancomycin +G-CSF subgroups (p < 0.01). This effect of G-CSF could not be explained. There was no significant effect of G-CSF on the other organs inflammation.
In conclusion, G-CSF treatment had no additional effect on survival and bacterial eradication in MRSA sepsis in nonneutropenic mice; and only a little effect on histopathology. Furthermore, G-CSF treatment is very expensive, likewise glycopeptides. Because of high mortality and morbidity rates and excess costs, more interest in infection control measures, and prevent the spread of MRSA infections is more rational.
Competing interests
This study was supported by Erciyes University Research Fund. There was no non-financial competing interest.
Authors' contributions
EA and SG were the primary researchers. OC and BK are the pathologist and examined tissue samples. MD was the director of the study. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Proportion of animals surviving after inoculation with MRSA in control and G-CSF groups.
Figure 2 Proportion of animals surviving after inoculation with MRSA in vancomycin 25 mg/kg and vancomycin 25 mg/kg+G-CSF groups
Figure 3 Proportion of animals surviving after inoculation with MRSA in vancomycin 50 mg/kg and vancomycin 50 mg/kg+G-CSF groups
Table 1 Cultures and histopathology of study groups
Control G-CSF Vanco – 25 mg Vanco 25 mg + G-CSF Vanco – 50 mg Vanco – 50 mg + G-CSF
n n n n n n
Mice 15 15 15 15 24 22
Cultures Lung
no growth - 1 15 15 18 14
<300 cfu - 3 - - - -
>300 cfu 15 11 - - 6 8
Liver
no growth - 1 15 15 17 14
<300 cfu - 2 - - 1 -
>300 cfu 15 12 - - 6 8
Heart
no growth - 1 15 15 18 14
<300 cfu - 3 - - - -
>300 cfu 15 11 - - 6 8
Spleen
no growth - - 13 15 13 12
<300 cfu - 1 - - 3 2
>300 cfu 15 14 2 - 8 8
Periton
no growth - 2 15 15 17 13
<300 cfu - 1 - - 1 1
>300 cfu 15 12 - - 6 8
Histopathology (inflammation degree) Lung
0 0 0 0 0 0 0
+ 2 1 5 1 7 9
++ 7 6 7 6 9 9
+++ 5 7 3 7 8 4
++++ 1 1 0 1 0 0
Liver
0 0 0 0 1 1 0
+ 1 4 7 5 12 5
++ 7 2 4 5 9 10
+++ 4 6 4 4 2 7
++++ 3 3 0 0 0 0
Heart
0 1 0 8 8 10 10
+ 9 11 7 6 12 7
++ 5 2 0 1 0 5
+++ 0 0 0 0 0 0
++++ 0 0 0 0 0 0
Spleen
0 0 1 0 0 1 1
+ 0 2 7 5 13 8
++ 5 4 7 6 7 8
+++ 4 6 1 4 2 5
++++ 6 2 0 0 1 0
Inflammation degree No inflammation 0
Focal interstitial inflammation +
More diffüse interstitial inflammation ++
Intense interstitial inflammation veya microabscesses +++
More extensive abscess formation with tissue necrosis ++++
==== Refs
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| 15491501 | PMC526191 | CC BY | 2021-01-04 16:03:31 | no | BMC Infect Dis. 2004 Oct 18; 4:43 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-43 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-291546182610.1186/1471-244X-4-29Research ArticlePsychosocial correlates with depressive symptoms six years after a first episode of psychosis as compared with findings from a general population sample Forsell Yvonne [email protected] Sonja [email protected] Johan [email protected] Dep. of Public Health, Division of Social Medicine, Karolinska Institutet, S-171 76 Stockholm, Sweden2 Stockholm Center of Public Health, Unit of Mental health, Stockholm, Sweden2004 5 10 2004 4 29 29 1 7 2004 5 10 2004 Copyright © 2004 Forsell et al; licensee BioMed Central Ltd.2004Forsell 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
Depression is frequently occurring during and after psychosis. The aim of this study was to analyze if the psychosocial characteristics associated with depression/depressive symptoms in the late phase of a first episode psychosis (FEP) population were different compared to persons from the general population.
Methods
A questionnaire was sent out to all individuals six years after their FEP and to a general population sample. Depressive symptoms were recorded using a self-rating scale, the Major Depression Inventory.
Results
Formerly FEP persons had a higher representation of depressive symptoms/depression, unemployment, financial problems and insufficient social network. Depressive symptoms/depression were found to be associated with psychosocial problems. An age and gender effect was found in the general population, but not in the FEP sample. When the psychosocial characteristics were taken into account there were no association between having had FEP and depressive symptoms.
Conclusions
The association between having been a FEP patient and depressive symptoms/depression disappeared when negative social aspects were taken into account.
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Background
Major Depression is the one of the most common psychiatric disorders and is frequently occurring in persons with psychotic disorders. Up to 25% of individuals with psychosis have this condition at some point during the course of their illness [1].
Insufficient social network, unemployment, living alone, financial problems and low social class are among the reported risk factors for depression in prospective studies [2-5]. Studies of characteristics associated with depression in psychosis have reported various results. Associations have been reported with negative as well as positive symptoms, medications and neuroleptic induced movement disorders [6-9]. In a study by Baynes et al, depressive symptoms were explored in a population of 120 patients with stable, chronic schizophrenia living in the community [10]. Patients who perceived themselves to have poor social support were more likely to be depressed. They proposed that a similar mechanism for the etiology of depression might exist in schizophrenia as in non-schizophrenic persons.
The most commonly used depression self-rating scales in population studies were developed before the introduction of DSM-III, e.g. the Beck Depression Inventory (BDI) and the Zung Self-rating Depression Scale (Zung-SDS) [11,12]. The Major Depression Inventory (MDI) used in the present study was developed by Bech et al based on the DSM-IV symptoms of Major Depression and ICD-10 moderate to severe depression [13,14]. The MDI includes symptom thresholds as well as duration criteria. The internal and external validity has been reported to be higher than for Zung-SDS [14]. In order to evaluate screening scales there is a need for a "golden standard" and several studies has used Schedules for Clinical Assessment in Neuropsychiatry (SCAN) [15,16]. SCAN incorporates the 10th edition of the Present State Examination and the reliability has been reported to be good [17]. The authors of the scale have reported the sensitivity to be 0.90 and the specificity 0.82 when validating the MDI versus SCAN in a clinical setting [18].
The aim of the present study was to analyze if the psychosocial characteristics associated with depressive symptoms in persons six year after a first episode of psychosis (FEP) differed from the associations found in a population sample.
Methods
Population from the Parachute project
The Parachute project is a Swedish multi center study of FEP. It includes all persons from the catchment area who for the first time sought psychiatric help for psychotic symptoms during 1996–1997. Persons who were 18–45 years old and without a dominating substance abuse or diagnosed brain disorder were included in the study. The catchment area covers approximately 20% of the Swedish population. The project integrates an epidemiological approach with intensive psychosocial and medical treatment of a cohort of first episode psychotic patients. It includes a large-scale system of "need adapted treatment" [19], which includes high degree of psychosocial support, lowest optimal antipsychotic medication, participation of families and treatment in normalized and integrated settings. The participating patients were followed for a period of six years with assessments of psychiatric, psychological, social and economic aspects. The project, which is a controlled study, is described in detail in a previous paper [20].
Of the 175 patients included in the project from the start 133 were followed during the complete six-year period. The questionnaire included in this study was sent out to the 133 persons six years after their first psychotic episode and 57.1% (76 persons) participated. Those who participated did not differ in age, gender, country of origin or psychiatric diagnosis from those who did not participate.
Population from the PART study
The PART study is a longitudinal population study of risk factors and social consequences of mental ill health in the Stockholm County. 19 744 Swedish citizens aged 20–64 years registered as living in the county of Stockholm were randomly selected in 1998–2000. This represents 1.8% of the population in this age group in this area. 10 442 persons (53%) participated in the study. The personal identification number (that all Swedish citizens have) of participants as well as non-participants were linked to the following official registers: income and wealth, sick leave, hospital discharge register (including diagnoses) and disability pension. Participation was related to female gender, higher age, higher income and education, being born in Sweden, and having no psychiatric diagnoses in the hospital discharge register or in the disability pension register. The odds ratios for associations between gender, income, country of origin, education and having a psychiatric diagnosis previously according to the registers, were similar among participants and non-participants (Lundberg et al, manuscript). Participants having had a diagnosis of psychotic disorder in the hospital discharge register were excluded from this study (n = 20).
Questionnaire
A questionnaire was sent out to the population included in the PART study. The questions included risk and protective factors for mental illness as well as psychiatric symptoms scales [21]. The same questionnaire was replicated in the follow up of the FEP group.
The following variables were used in the present study
• Demographic characteristics: age, gender and country of origin (Sweden/other).
• Financial problems included the availability to get 14 000 SEK (1 797 USD) within a week, if necessary.
• Working life: The persons were divided in the following two groups: employed/students and unemployed/disability pension/sick leave/early retirement pension.
• Social network: An instrument developed by Unden & Orth-Gomer [22] was used. This instrument is developed from ISSI, the Interview Schedule for Social Interaction [23] Two sub scales were used, AVAT-availability of attachment and AVSI-availability of social integration. The score was calculated according to the authors of the scale.
• Depressive symptoms: The Major depression Inventory was used strictly according to the authors of the scale [13,14]. The scale covers the ICD-10 as well as the DSM-IV symptoms of depression. It contains 12 items, but functionally it has 10 items since two of them contain sub items (restless/subdued and reduced/increased appetite). Each item gives a score from 0–5 based on the following answers: at no time, some of the time, slightly less than half of the time, slightly more than half of the time, most of the time and always. The MDI can be used as a scale for measuring the severity in which the total score is calculated giving a theoretical score from 0–50. A score of 26 is considered pathological according to the authors of the scale.
Statistical analysis
Simple factorial ANOVA'S were performed using being in the PART population or not as the dependent variable and age, gender and country of origin as covariates. Pearson's correlation was used to see if the demographic and psychosocial variables correlated with depressive symptoms. These analyses were performed separately in the PART and FEP populations. Additionally multiple regression analyses were performed with the scores on the Major Depression Inventory and a cut off score of 26 or more as the dependent variables. All variables were entered simultaneously. Being in the PART population or not was entered as a variable.
Results
The demographic and psychosocial characteristics of the two populations are presented and compared in table 1. Persons in the FEP population more often had unemployment/disability pension/sick leave/early retirement pension (F = 110.26, df 1, p < 0.001) and financial problems (F = 30.06, p < 0.001). Additionally fewer of them had a sufficient social network ((AVSI; F = 52.26, df 1, p < 0.001) and (AVAT; F = 39.18, df 1, p < 0.001)). They also had higher score on the Major Depression Inventory (F = 25.69, df 1, p < 0.001). The symptoms within the Major Depression Inventory were also analyzed separately. The only symptom that was equally distributed was sleep disturbances; all other symptoms were more common in the FEP population.
Table 1 Demographic and psychosocial variables in the FEP population and in the general population sample. The statistical analyses were controlled for age, gender and country of origin (Sweden/other).
General pop. sample, n = 10 425, %(n) FEP pop. n = 76, %(n)
Female gender 55.5(5 798) 50.0(38)
Not born in Sweden 10.7(1 120) 15.8(12)
Unemployed† 9.7(1 009) 38.2(29)*
Financial difficulties 14.9(1 553) 40.8(31)*
Mean (95%CI) Mean (95%CI)
Age 41.4 (41.2–41.7) 28.5 (26.8–30.3)
AVAT (availability of attachment) 17.7 (17.6–17.7) 16.0 (15.1–16.9)*
AVSI (availability of social integration) 16.2 (16.2–16.3) 12.4 (11.4–13.4)*
Major Depression Inventory 8.8 (8.6–9.0) 15.8 (12.7–18.9)*
†Including disability pension/early retirement pension/sick leave, *p < 0.001
When using a MDI cut-off score of 26, 25.8% (17 persons) had a score above the cut-off in the FEP population and 8.0% (783 persons) in the general population sample.
Table 2 presents the correlations between the demographic and psychosocial characteristics and the total score of the MDI. In the general population sample all demographic and psychosocial variables were found to be associated with the total score of the MDI. In the FEP population not born in Sweden and being younger were not associated. The gender associations were different in the two samples, while female gender was associated in the general population sample male gender was associated in the FEP population.
Table 2 Correlations between the demographic, psychosocial characteristics and total score of the Major Depression Inventory in the FEP population and in the general population sample.
General pop. sample N = 10 425 FEP pop. N = 76
Not born in Sweden 0.11*** 0.12
Age -0.13*** -0.01
Female gender 0.13*** -0.21*
Unemployment 0.14*** 0.23*
Financial problems 0.27*** 0.35**
AVAT (availability of attachment) -0.32*** -0.48***
AVSI (availability of social integration) -0.31*** -0.41***
*p < 0.1, **p < 0.01,***p < 0.001
Separate multiple regression analyses were performed in the two samples with the Major Depression Inventory total score as the dependent variable. In the general population sample the adjusted R square was 0.21 (SE 8.7) and in the FEP population 0.28 (SE 11.4). In addition an analysis was made were general population/FEP population was inserted as a variable. The result is presented in table 3, and shows that the correlation between being a person having had a FEP and higher scores on the Major Depression Inventory no longer was present when the other variables were taken into account. Adjusted R square for this analysis was 0.21 (SE 8.7). A similar regression was performed entering depressed/not depressed using a cut-off score of 26 and the result is also presented in table 3. Adjusted R square for this analysis was 0.12 (SE 25.5)
Table 3 Multiple regression analyses with total score on the Major Depression Inventory and a MDI cut off at 26 as the dependent variables.
MDI Score Beta Stand. MDI Cut-off Beta Stand.
FEP pop. vs. General pop. sample 0.00 0.00
Not born in Sweden 0.04* 0.04*
Age -0.16* -0.07*
Female gender 0.13* 0.08*
Unemployed 0.10* 0.09*
Financial problems 0.10* 0.09*
AVAT (availability of attachment) -0.15* -0.19*
AVSI (availability of social integration) -0.24* -0.09*
*p < 0.001
Discussion
The main finding of this study was that the association between having suffered a FEP and self-reported depressive symptoms/depression six years later disappeared when a negative social situation was taken into account.
Not surprisingly, persons with a previous FEP had a higher representation of unemployment, financial problems and insufficient social network, which have been reported in other studies [24-26]. In the general population there was an age and gender effect, females and younger age had an overrepresentation of depressive symptoms. This was not seen in the FEP follow up group where age had no effect and being a male was slightly over represented. This is in agreement with a study by Zisook et al [9]. Not born in Sweden was associated with depressive symptoms in the general population sample, but not in the FEP population. This could have been due to low numbers in the FEP population.
The non-participation rate was high in the general population and persons with severe psychiatric disorders most likely did not respond to the enquiry. However, the associations between gender, income, country of origin, education and having a previous psychiatric diagnosis was similar among participants and non-participants. The FEP group also had a high non-participation rate, although the distribution of age, gender, country of origin and psychiatric diagnoses were similar among participants and non-participants. The general population sample was an urban population while the FEP population was from areas all over Sweden, which might have affected the result.
The strengths of the study were that the FEP group was a total population, followed over six years and having received treatment according to a "need-adapted approach". Moreover, the instrument in use, the MDI has been reported to have a higher internal validity than Ham-D17 and Zung-SDS [13,14]. The sensitivity and specificity have been above 0.80 when the MDI was compared to clinical interviews using SCAN [18].
The results of this study fully agree with the goals of WHO [27]: Links need to be established between mental health services and various community agencies at the local level so that appropriate housing, income support, disability benefits, employment and other social service supports are mobilized on behalf of patients and in order that prevention and rehabilitation strategies can be more effectively implemented. Following these recommendations would most likely decrease the rates of depressive symptoms in former FEP person with a secondary positive effect on their quality of life in general.
Conclusions
Having had a first episode psychosis six years earlier had no association with depressive symptoms/depression when a negative social situation was taken into account.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
YF, SL and JC participated in the data collection. YF performed the statistical analysis. All three authors wrote and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Funding was provided by the Stockholm county and the Swedish Medical Research Council.
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| 15461826 | PMC526192 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Oct 5; 4:29 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-29 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-411545651810.1186/1471-2458-4-41Research ArticleThe impact of employee level and work stress on mental health and GP service use: an analysis of a sample of Australian government employees Parslow Ruth A [email protected] Anthony F [email protected] Helen [email protected] Dorothy H [email protected] Lyndall [email protected]' Souza Rennie M [email protected] Centre for Mental Health Research, (CMHR) Australian National University ACTON ACT 0200 Australia2 National Centre for Epidemiology and Population Health (NCEPH), Australian National University ACTON ACT 0200 Australia2004 30 9 2004 4 41 41 11 5 2004 30 9 2004 Copyright © 2004 Parslow et al; licensee BioMed Central Ltd.2004Parslow 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 sought to identify the extent to which employee level and work stressors were associated with mental health problems experienced by Australian government employees, and with their use of primary care services.
Methods
806 government employees aged between 40 and 44 years were surveyed as part of an epidemiological study conducted in Australia. Data collected from participants included sociodemographic attributes, physical health, psychological measures and work stressors relating to job control, job demands, job security and skills discretion at work. For 88% of these participants, information on visits made to general practitioners (GPs) for the six months before and after their survey interview was obtained from health insurance records.
Results
When work stress and personal factors were taken into account, men at more junior levels reported better mental health, more positive affect and used fewer GP services. Women at middle-management levels obtained less GP care than their more senior counterparts. Both men and women who reported higher levels of work stress were found to have poorer mental health and well-being. The impact of such stressors on GP service use, however, differed for men and women.
Conclusion
Measures of work stress and not employee level affect the mental health and well-being of government employees. For governments with responsibility for funding health care services, reducing work stress experienced by their own employees offers potential benefits by improving the health of their workforce and reducing outlays for such services.
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Background
In 1999, the World Health Organization reported that workers continued to suffer high levels of work-related injuries and deaths [1]. It also flagged, however, the increase in mental health problems reported by workers in industrialized countries as a result of their experiencing psychological stress and excessive job demands in the workplace [1]. The health consequences of such psychosocial aspects of the work environment have been examined in a range of settings across different countries. Much of this research has drawn on the model developed and refined by Karasek who proposed that work-related mental strain and associated psychiatric disorder result from combinations of, and interactions between, four different employment factors: heavy job demands, limited input to decision making processes, lack of skill discretion within the job and poor work-based social support [2,3]. Such factors, in particular those concerning decision making, skill discretion and social support have been found to be most problematic for those in lower grades of employment and to be less prevalent among employees in higher ranking positions [4-6].
The applicability of this model for the government sector is well supported by cross-sectional and longitudinal studies drawing on the Whitehall II study of a large cohort of 10,308 London-based government employees. Again, such studies have found that those in lower grades report that they have less job control, less variety in their work, and less job satisfaction [7]. Those reporting higher levels of such work stress have also been found to have greater risk of cardiovascular health problems [8] and poorer psychiatric health [5,9,10].
There has been little research undertaken on the health impact of job level and work stressors for government employees in Australia. An earlier study that explored the relationships between work stressors and blood pressure in Australian government employees, found chronic perceived work stress to be associated with blood pressure change [11]. The impact of job level and work stress on Australian government employees' mental health has not been previously explored.
We have been able to explore these issues using data collected from 806 government employees who participated in the PATH Through Life Project, a large community-based study being conducted by the Centre for Mental Health Research in Canberra, Australian Capital Territory (ACT). Survey participants provided information on sociodemographic measures, mental and physical health, employment level and work-related stress. For 88% of these participants, independently collected information on their use of general practitioner services was also available. These data have allowed us to examine the impact of employment level and work-related stress on Australian government employees' mental and physical health, their psychological well-being, and also their use of general practitioner care. We hypothesised that those working in lower level government positions would report higher levels of work-related stress, that they would be found to have more mental and physical health problems and that they would use higher numbers of primary medical services.
Methods
Subjects
The PATH Through Life Project is a longitudinal study of individuals living in the community with participants being drawn from three age groups: 20–24, 40–44 and 60–64 years. Those in the age group of interest for this study were aged from 40 to 44 years on 1 January 2000 and drawn from the Australian Electoral Rolls for Canberra in the Australian Capital Territory and adjacent town of Queanbeyan in New South Wales. Enrolment on these rolls is compulsory for all Australians aged 18 and over. Potential participants were drawn from a 10-year age range, the minimum range then released for research purposes by the Australian Electoral Commission. The number of potential participants found, and in the required age range, was 3919, of whom 2530 participated in the survey, giving a response rate of 64.4%.
Canberra is the national capital of Australia and many Australian Government entities are based in the ACT, including both houses of parliament, and the 16 major agencies that currently comprise the Australian Public Service. In this study, 806 respondents aged between 40 and 44 reported that they worked in office-based government administrative positions, developing and implementing government policy. As well as providing information on labour force status and the type of position held, respondents who worked in government positions were specifically asked to provide details on the level of the position they occupied. Five mutually exclusive employee categories were formed by grouping together those whose levels of employment were broadly comparable as follows. Employees who occupied positions at Australian Public Service (APS) Levels One to Four were grouped together as Junior employees and those at both the APS Levels 5 and 6 were classified as belonging to the Intermediate category of employees. Employees in the next two classifications of the APS (Executive Levels 1 and 2) were allocated to separate groups, Senior 1 and Senior 2. While those in Executive Level 1 positions develop policy and implement government programs, those at the Executive 2 level are primarily managers with direct responsibility for managing a number of employees from APS Level 1 to Executive Level 1 [12]. Finally, all respondents in the Senior Executive Service of the government were allocated to one category, Executive. The number of participants in each of these five categories of employees is given in Table 1, together with a short description of the positions included in each of those categories.
Table 1 Descriptions of government employee categories
Position level in Australian Public Service (APS) Description of positions covered by these levels Employee category Number, % of participants % female Mean years of education
APS Level 1
APS Level 2
APS Level 3
APS Level 4 Work is always supervised; can include: drafting correspondence, organising travel, filing, other routine clerical work. Junior 123 15.26 74.80 13.46
APS Level 5
APS Level 6 Work includes: supervising junior staff, liaising with external bodies, supporting project managers, drafting complex correspondence and policy papers, undertaking research. Inter-mediate 215 26.67 51.63 14.87
APS Level 7 Work includes: managing government programs and contracts, supervising staff, preparing high level policy advice, developing and implementing government policy Senior 1 220 27.30 37.73 15.69
APS Level 8 Work includes: managing a Section of staff, providing policy, financial, or administrative advice to government, representing department at external meetings. Senior 2 193 23.95 33.16 16.02
Senior
Executive
Service Responsible for: overall management of large numbers of staff; achieving government objectives through development and implementation of innovative and financially sound policy. Executive 55 6.82 30.91 16.58
Total 806 100.0
Mean measure 45.53 15.27
Measures
Survey participants completed a questionnaire that included socio-demographic characteristics and measures of physical and mental health, and well-being. Participants in the workforce were asked 22 questions relating to their work situation. These questions matched those used in the Whitehall II study [7,13]. Nine questions related to job control, and reflected the amount of authority the worker has over decision-making [9]. Four questions concerned the manageability of job demands; the extent to which the worker is faced with difficult time and workload pressures and conflicting demands. Finally six questions addressed skill discretion and related to the variety of tasks to be done and the breadth of skills needed to undertake those tasks. For each of these 19 questions, respondents could answer: often, sometimes, rarely or never. Responses were given values of 1 to 4 with the highest score allocated to the less stressful work circumstances: those in which the individual had more job control, more manageable job demands, and higher levels of skill discretion. Participants were also asked the number of hours they usually worked per week and their assessment of their employment security and future employment opportunities. Answers to the last two questions used four point Likert-type scales and, again, were coded to give higher scores to those who reported that they had a more secure position or could obtain another job relatively easily. The mean of these two scores was used as an overall measure of job security.
Socio-demographic measures used in these analyses included sex, age, years of education, level of household responsibilities, and experience of any of six life events during the previous six months. Since each of these factors has the potential to modify an individual's mental health independently of their work stress, we adjusted for these in our final analyses. Scores for level of household responsibility were drawn from participants' responses to questions concerning the extent to which, in their household, they were responsible for four areas: household tasks, childcare, financial management, and providing money. Comparable scores for participants who did not have children in their households were then derived by calculating the mean of their measures for household tasks, financial management and providing money, and adding this to their total household responsibility score. Scores for these measures could range from zero to 16 with higher sores representing more household responsibilities.
Health measures obtained from participants and used in these analyses included: scores on Goldberg's depression and anxiety scales [14] and state measures of positive and negative affect using the Positive and Negative Affect Scales (PANAS) [15]. Measures of self-rated health, mental and physical health were taken from respondents' answers to the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) [16]. The first of these is measured by a single question in which participants rate their health as excellent, very good, good, fair or poor with higher scores indicating poorer self-rated health. Records on participants' visits to general practitioners (GPs) were also obtained. In Australia, the costs of most health care visits made to medical practitioners by Australians with citizenship or residency status are subsidised, either partly or totally, through the Australian Government funded universal health insurance scheme, Medicare. Information on the number of such visits is collected by the Health Insurance Commission. These data are used for administrative purposes and identify general practitioner and specialist services, but not the health problems addressed during each visit. While these records cover most visits made to general practitioners, they will not include a small number of services, paid for by patients but not claimed against Medicare. All participants were asked if they would consent to the researchers being provided information on the number of visits they made to general practitioners for specific periods before and after their interview. 709 (88.0%) of the 806 participants consented to this request and information on the number of GP visits they made during the six months preceding and the six months following their PATH interview was obtained from the Health Insurance Commission.
Statistical analyses
Analyses of variance (ANOVAs) were first undertaken to examine the extent to which sociodemographic measures and work stress measures changed with level of employment. Similar analyses, conducted separately for men and women, then compared mean mental health measures across the five employee levels. Finally, regression analyses were used to examine the contribution of employee category and work attributes in explaining participants' health and health service measures, whilst controlling for the following possible modifying factors: participant's age, years of education, level of household responsibilities and life events experienced in the past six months. For these analyses, categorical variables identifying each of the five government employee categories were created and the first four of these included in the regression equations, taking the most senior category, Executive, as reference group. After initial testing indicated that two dependent variables, negative affect and use of GP services, were not normally distributed and more closely fitted the negative binomial and Poisson distributions respectively, analyses of these two measures used negative binomial and Poisson regressions respectively. Strength of associations between dependent variables and predictor variables were measured using R2 for linear regressions and Incidence Rate Ratios when the Poisson or negative binomial regression model was used. Incidence Rate Ratios (IRRs) are interpreted in a similar manner to odds ratios and represent the expected change in the dependent variable in response to one unit change in the predictor variable. The contribution of employee level and work stress measures in explaining variation in health measures was also obtained. This contribution was measured using change in R2 for linear regressions and the change in the Chi-square estimate of the fit of model for the negative binomial and the Poisson regression analyses. A final analysis examined the impact of employee category and work stressors on use of GP services, taking into account demographic, lifestyle and health measures. Analyses were undertaken using SPSS 11.5 and STATA 7 [17].
Results
Across the five categories of government employees, there was no significant difference in education level or in numbers of life events experienced in the past six months. Employees working at higher levels, however, reported lower levels of household responsibility and had more opportunities to develop and use different skills in their work, reported more job control and felt more secure in their current jobs. As expected, those in more senior positions also had less manageable job demands and worked longer hours.
Analyses were then performed, separately for men and women, to examine differences in measures of mental health, well-being and GP service use across the five government employee categories. Level of physical health, as measured by the SF-12, was also examined for comparative purposes. The only measure to differ significantly across employee categories was level of positive affect in the past four weeks (Table 3). For both men and women, those in higher categories recorded higher scores on this measure.
Table 3 Mean health scores by government employee category
Government employee category:
Junior Intermediate Senior I Senior 2 Executive P
Men
Self-rated health 2.48
(2.17–2.80) 2.31
(2.14–2.48) 2.26
(2.11–2.42) 2.25
(2.10–2.40) 2.08
(1.82–2.34) 0.42
SF-12 mental health 51.91
(49.60–54.22) 50.92
(49.17– 52.66) 49.35
(47.64– 51.07) 50.52
(49.13– 51.91) 49.71
(47.50– 51.92) 0.49
SF-12 physical health 52.91
(50.91–54.92) 51.60
(50.25–52.95) 52.56
(51.36–53.76) 52.83
(51.81–53.86) 54.65
(52.47-56.83) 0.18
Goldberg Depression Score 2.50
(1.61–3.39) 2.09
(1.64–2.53) 2.17
(1.75–2.59) 2.15
(1.80–2.50) 1.45
(1.01–1.88) 0.35
Goldberg Anxiety Score 3.30
(2.27–4.33) 3.12
(2.58–3.65) 3.18
(2.69–3.66) 3.65
(3.22–4.08) 3.13
(3.22–4.08) 0.54
Positive affect 31.53
(28.89–34.18) 30.25
(28.91–31.59) 31.65
(30.46–32.84) 32.87
(31.70–34.04) 34.29
(32.74–35.84) 0.01
Negative affect 16.93
(14.98–18.89) 16.25
(14.96–17.54) 16.31
(15.34–17.29) 16.31
(15.32–17.30) 15.45
(14.00–16.90) 0.88
GP services used 2.86
(1.54–4.17) 3.24
(2.39–4.08) 3.12
(2.45–3.79) 2.14
(1.97–2.85) 2.30
(1.37–3.23) 0.33
Women
Self-rated health 2.36
(2.17–2.55) 2.24
(2.09–2.39) 2.13
(1.94–2.32) 2.14
(1.90–2.38) 2.35
(1.81–2.90) 0.42
SF-12 mental health 47.98
(45.52–50.30) 48.12
(45.95–50.30) 48.40
(46.19–50.61) 51.11
(49.18–53.03) 52.60
(49.48–55.71) 0.91
SF-12 physical health 51.68
(50.12–53.24) 52.55
(51.10–53.99) 52.29
(50.58–54.01) 51.11
(49.18–53.03) 52.60
(49.48–55.74) 0.76
Goldberg Depression Score 2.76
(2.20–3.32) 2.58
(2.13–3.02) 2.35
(1.84–2.86) 2.02
(1.46–2.58) 2.24
(1.12–3.35) 0.39
Goldberg Anxiety Score 3.96
(3.37–4.54) 3.78
(3.28–4.29) 4.01
(3.39–4.63) 3.38
(2.70–4.05) 3.41
(2.21–4.62) 0.62
Positive affect 29.59
(28.14–31.03) 31.78
(30.63– 32.94) 32.05
(30.72– 33.37) 32.30
(30.68–33.91) 32.59
(30.30–34.87) 0.03
Negative affect 17.89
(16.12–19.66) 17.31
(16.05–18.57) 17.70
(16.16–19.24) 16.64
(14.76–18.52) 17.59
(14.70–20.48) 0.87
GP services used 4.21
(3.36–5.06) 5.11
(4.03–6.19) 4.47
(3.33–5.62) 3.15
(2.35–3.96) 4.73
(2.33–7.13) 0.16
We next used regression analyses to examine the impact of employee category and work stress on participants' measures of health and well-being and on their use of GP services. In preliminary testing, we found that the two measures – job control and skill discretion – both contributed significantly and independently to mental health measures, hence these measures were not combined but included separately in the analyses. The results of these analyses for men are in Table 4 and for women in Table 5. After controlling for socio-demographic and work stress measures, men in the lowest levels of employment reported significantly better mental health as measured by the SF-12 Mental Health score, higher levels of positive affect and used fewer GP services. Other measures of mental health, including self-rated health, and symptoms of anxiety and depression, did not vary significantly with employee level. Men with more manageable job demands reported better mental health, fewer depressive and anxiety symptoms and less negative affect. For men, there was a consistent association between less work stress and better health. Those with more job security or higher levels of skill discretion reported significantly better self-rated health, mental health, fewer depressive and anxiety symptoms, more positive and less negative affect and also used fewer GP services. However, those who worked fewer hours per week made more visits to GPs.
Table 4 Associations between health measures, and government employee category and work stressors – men
Health measure
Predictor variables: Self-rated health SF-12 Mental health SF-12 Physical health Goldberg Depression Goldberg Anxiety Positive Affect Negative Affect GP services obtained
Betac Betac Betac Betac Betac Betac Incident Rate Ratioa Incident Rate Ratioa
Government employee category
Junior -0.033 0.161* -0.014 0.029 -0.038 0.137* 1.011 0.492***
Intermediate -0.002 0.115 -0.183 0.096 0.021 -0.034 1.029 0.742
Senior 1 0.017 0.027 -0.151 0.141 0.040 -0.032 1.049 0.864
Senior 2 0.036 0.089 -0.120 0.112 0.078 -0.008 1.032 0.797
Work stress measures
Job control -0.060 0.031 0.019 -0.022 -0.016 0.082 0.966 0.808*
Manageable job demands 0.010 0.150** 0.037 -0.145** -0.245*** 0.019 0.879*** 1.018
Usual hours per week 0.066 -0.047 -0.013 0.103 0.050 0.009 1.002 0.982***
Job security -0.180*** 0.193*** 0.007 -0.171** -0.154** 0.125** 0.942* 0.868**
Skill discretion -0.171** 0.302*** 0.024 -0.247*** -0.237*** 0.398*** 0.792*** 0.766**
ΔR2 attributable to level & work stress 0.085*** 0.176*** 0.016 0.134*** 0.152*** 0.228*** 75.77b*** 91.20b***
All analyses controlling for age, education, household responsibilities, and life events in past 6 months
a Incidence Rate Ratio from negative binomial or Poisson regression
b Chi-square estimate of the improvement in fit of regression model due to employee level and work stress factors.
c Standardised Beta
* p < 0.05; ** p < 0.01; *** p < 0.001
Table 5 Associations between health measures, and government employee category and work stressors – women
Health measure
Predictor variables: Self-rated health SF-12 Mental health SF-12 Physical health Goldberg Depression Goldberg Anxiety Positive Affect Negative Affect GP services obtained
Betac Betac Betac Betac Betac Betac Incident Rate Ratioa Incident Rate Ratioa
Government employee category
Junior -0.178 0.044 -0.015 -0.041 0.057 -0.030 0.970 0.807
Intermediate -0.138 -0.022 0.000 0.044 0.113 0.013 1.014 1.015
Senior 1 -0.156 0.005 0.011 0.008 0.104 0.009 1.022 0.928
Senior 2 -0.073 0.066 -0.119 -0.043 0.003 0.008 0.914 0.737*
Work stress measures
Job control -0.049 0.120* 0.015 -0.099 -0.098 0.027 0.826*** 0.864*
Manageable job demands -0.123* 0.173** 0.141* -0.245*** -0.265*** 0.146* 0.852*** 1.213**
Usual hours per week 0.004 -0.086 0.058 0.043 -0.023 -0.010 1.002 1.001*
Job security -0.054 0.100 -0.076 -0.101 -0.095 0.159** 0.925 1.018
Skill discretion -0.157* 0.139*** 0.061 -0.310*** -0.111 0.273*** 0.967** 0.994
ΔR2 attributable to level & work stress 0.037* 0.083 0.033 0.150*** 0.093*** 0.121*** 68.60b*** 27.52b**
All analyses controlling for age, education, household responsibilities, and life events in past 6 months
a Incidence Rate Ratio from negative binomial or Poisson regression;
b Chi-square estimate of improvement in fit of the regression model due to employee level and work stress factors.
c Standardised Beta
* p < 0.05; ** p < 0.01; *** p < 0.001
For women, we found no effect of employee level on their measures of mental or physical health. However, employment level was associated with GP service use with those in middle management positions being less likely to have obtained GP care, compared with those at the executive level. Women's levels of mental health and well-being were better when they worked in a job that offered higher levels of skill discretion. Manageability of job demands impacted on all health measures considered while those who worked longer hours were more likely to have obtained GP services.
Finally, we explored the contributions of employment level and work stress factors in explaining participants' use of GP services, after including in the model SF-12 measures of mental and physical health that could also contribute to explaining such service use. Again, men and women were considered separately. Overall, controlling for mental and physical health in addition to sociodemographic measures had little impact on our findings. Men in lower employee categories continued to use significantly fewer GP services compared with their counterparts in executive positions while those with less job control and less job security again obtained more care. After controlling for mental and physical health, women in middle management levels again used fewer GP services compared with those at the executive level. Similarly, women with more manageable job demands and those working longer hours continued to be higher users of GP care.
Discussion
This paper has reported on the associations between categories of employee levels, work stressors and mental health measures of 806 government employees aged between 40 and 44 who participated in the PATH Through Life Project being conducted in Canberra, Australia's national capital.
Impact of employee level on work stressors, health and GP service use
Our first hypothesis, that government employees working at lower levels would report higher levels of work-related stress, is supported in the main by our research. Overall, participants at more senior levels reported that they had more control over aspects of their work, greater opportunities to do interesting work using a range of skills, more job security but also that they were subject to higher job demands. These results closely match those reported by Marmot and colleagues in their 1991 study of over 10,000 British civil servants aged between 35 and 55 [7].
We had hypothesized that those in lower level positions would have poorer physical and mental health compared with more senior staff. None of our results supports this hypothesis. Although both male and female participants in the higher grades reported better well-being as measured by higher positive affect, we found no mental health benefit associated with having a more senior position. Furthermore, for men, we found this result to be reversed when the analyses controlled for work stress factors. Men in lower level positions reported higher levels of positive affect and better mental health, as measured by the SF-12. Women's mental health and negative affect were not affected by their employee level both when this factor was considered alone and when work stress factors were taken into account. We were unable to replicate the finding by Marmot and colleagues that those in higher positions had significantly better physical health compared with their subordinates. This difference in our results might be explained by our having a smaller sample. However, it also indicates that while those working at lower levels are more likely to experience some types of work stressors, working at those lower levels, per se, is not automatically associated with poorer mental or physical health for Australian government employees. While these findings are unusual, they do align with some previous research undertaken in the UK showing that those working in lower grades had better mental health [18]. One possible interpretation of our results is that men and women of this age group who have continued to work at lower employee levels may be pursuing satisfying goals in other areas of their lives, for example, family, outside business, recreational pursuits. Of course, confirmation of such an interpretation would require more detailed information from participants concerning their working arrangements and life priorities.
We found government employee level affected men's use of GP services but the direction of this effect was the reverse of that hypothesised. After taking into account education, other personal measures and levels of work stress, men at lower levels of the public service used fewer GP services than their superiors. This aligns with the previous finding that men at lower level positions also had better mental health [18] and suggests that further research is needed on the health drawbacks for men of their rising through levels of government employment. We also found that women in middle-management levels were less likely to have obtained GP services compared with their more senior counterparts.
The impact of work stress on health and GP service use
Work stress factors experienced at all levels of employment played a more significant role in affecting participants' health and well-being. For male and female participants, those whose job demands were more manageable had significantly better mental health. This measure of work stress was particularly important for women, for whom manageability of demands was significantly associated with all health measures. Correspondingly, level of skill discretion had a greater impact on men's mental health. This last finding concurs with a recent Australian study of government employees which reported that, for men, having interesting work was an important reason for their staying in the government sector whereas women saw their relative job security as the advantage of this form of employment [19].
Since previous research has linked job insecurity and poorer mental health [20,21], we expected similar findings in this current study. Women with less job security had less positive and more negative affect. Men with less job security, on the other hand, scored significantly worse on all six mental health measures. This finding, with that of the Australian study reported above, suggests that, while women place value on security, this and other work attributes may play a less important role in affecting their overall mental health, compared with their male counterparts. Previous studies have also reported sex differences concerning the mental health consequences of job insecurity [22]. Job security has also previously been found to affect the physical health of men more than women [23,24]. However, the one measure of physical health used in this study was not directly affected by job insecurity for men or women.
A number of work stress factors contributed significantly to explaining men's and women's use of GP care. Women who used more services had less job control while men were more likely to visit a GP if they reported less skill discretion, job control or felt more insecure about their job. Research reported in the 1980's also found that factory workers with higher job insecurity used more health services [25]. This finding in our study of government employees suggests that job insecurity has an important effect on men's, but not women's, self-assessment of their levels of health and well-being.
Women who worked longer hours used slightly more GP services and also had more manageable job demands. Men who worked fewer hours, however, obtained more GP care. These findings indicate that time spent at work is unlikely to have deterred women from addressing their health care needs. For men, on the other hand, working fewer hours per week might have given them needed, additional opportunity to set aside time for a GP visit. In Australia, many GP surgeries are open during standard working hours and offer only limited access outside of these times [26].
Limitations
This study is limited by having access only to cross-sectional data, since the direction of relationships between psychological stress and work stressors cannot be confirmed. It may be that those with fewer symptoms of depression or anxiety, for example, also have more positive views about the current or potential benefits of their positions. A number of issues raised here may become clearer when data from the next wave of the PATH project are collected.
Conclusions
These findings have implications for governments in their role as major employers. Large organisations in the public and private sectors inevitably develop hierarchical systems of employment as efficient mechanisms for allocating work functions and responsibilities. This study suggests that employees' health is less affected by their position within this structural hierarchy and is more associated with various work stressors that can be experienced by individuals across all levels of employment. For a large employer, reducing the impact of work stress on its workforce may be beneficial, not only for individual employees, but also for the productivity of the organisation as a whole. These findings also have implications specific to the Australia setting where GP services are provided and subsidised through the universal health insurance system, Medicare. This component of health care is the financial responsibility of the Australian Government, which is also the employer of the majority of participants in this current study. Initiatives aimed at reducing work stress experienced by government employees, and correspondingly, the numbers of GPs services obtained by that workforce, might prove to be a judicious use of Australian Government resources. Such potential benefits may well apply to other governments that have responsibility for funding health care services.
Competing interests
The authors declare that they have no competing interests.
Abbreviations
ACT Australian Capital Territory
APS Australian Public Service
GP General practitioner
IRR Incidence rate ratio
PANAS Positive and Negative Affect Scale
SF-12 12-item Short Form Health Survey
SPSS Statistical Package for Social Scientists
Authors' contributions
RP contributed to the conception of this study, and was responsible for all analyses of data, interpretation of results and writing of successive drafts of the paper. AFJ was leader in designing and running the PATH Through Life Project, contributed to interpretation of findings and edited successive drafts of the paper. HC was co-leader in designing and running the PATH Through Life Project, contributed to interpretation of findings and edited successive drafts of the paper. DB, LS and RD contributed to the conception of the study and editing successive drafts of the paper. All authors read and approved the final version of the manuscript.
Table 2 Mean sociodemographic measures, work stress and working hours by government employee categories
Government employee categories
Junior Intermediate Senior 1 Senior 2 Executive P
Years of education 13.46 14.87 15.69 16.03 16.58 0.37
Number of life events in past 6 months 1.02 0.95 0.92 0.89 0.73 0.59
Level of household responsibility 9.90 9.80 9.66 9.57 8.42 0.01
Job control score* 2.91 3.12 3.25 3.29 3.35 <0.01
Manageable job demands score* 2.33 2.28 2.03 1.81 1.63 <0.01
Skill discretion score* 2.76 3.13 3.35 3.42 3.54 <0.01
Job security* 2.64 2.76 2.78 2.89 2.91 0.02
Number of hours worked per week 36.78 39.20 43.20 48.06 53.73 <0.01
* Individuals' responses were scored from 1 to 4. Higher scores were given to work circumstances in which the individual had more job control, more manageable job demands, higher skill discretion and greater job security.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Funding for this study was provided by A Unit Grant (No. 973302) and New Program Grant (No. 179805) from the National Health and Medical Research Council, and additional support from the Australian Rotary Health Research Fund. We wish to thank Bryan Rodgers, Patricia Jacomb, Karen Maxwell and the team of interviewers from the Centre for Mental Health Research for their assistance with this study.
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| 15456518 | PMC526193 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Sep 30; 4:41 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-41 | oa_comm |
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BMC SurgBMC Surgery1471-2482BioMed Central London 1471-2482-4-131546268010.1186/1471-2482-4-13Research ArticleElectroesophagogram in gastroesophageal reflux disease with a new theory on the pathogenesis of its electric changes Shafik Ahmed [email protected] Olfat [email protected] Ismail [email protected] Ali [email protected] Department of Surgery and Experimental Research, Faculty of Medicine, Cairo University, Cairo, Eygpt2 Department of Surgery, Faculty of Medicine, Menoufia University, Shebin El-Kom, Eygpt2004 5 10 2004 4 13 13 20 3 2004 5 10 2004 Copyright © 2004 Shafik et al; licensee BioMed Central Ltd.2004Shafik 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 view of the disturbed esophageal peristaltic activity and abnormal esophageal motility in gastroesophageal reflux disease, (GERD), we investigated the hypothesis that these changes result from a disordered myoelectric activity of the esophagus.
Methods
The electric activity of the esophagus (electroesophagogram, EEG) was studied in 27 patients with GERD (16 men, 11 women, mean age 42.6 ± 5.2 years) and 10 healthy volunteers as controls (6 men, 4 women, mean age 41.4 ± 4.9 years). According to the Feussner scoring system, 7 patients had a mild (score 1), 10 a moderate (score 2) and 10 a severe (score 3) stage of the disease. One electrode was applied to the upper third and a second to the lower third of the esophagus, and the electric activity was recorded. The test was repeated after the upper electrode had been moved to the mid-esophagus.
Results
The EEG of the healthy volunteers showed slow waves and exhibited the same frequency, amplitude and conduction velocity from the 2 electrodes of the individual subject, regardless of their location in the upper, middle or lower esophagus. Action potentials occurred randomly. In GERD patients, score 1 exhibited electric waves' variables similar to those of the healthy volunteers. In score 2, the waves recorded irregular rhythm and lower variables than the controls. Score 3 showed a "silent" EEG without waves.
Conclusion
The electric activity in GERD exhibited 3 different patterns depending on the stages of GERD. Score 1 exhibited a normal EEG which apparently denotes normal esophageal motility. Score 2 recorded irregular electric waves variables which are presumably indicative of decreased esophageal motility and reflux clearance. In score 3, a "silent" EEG was recorded with probably no acid clearance. It is postulated that the interstitial cells of Cajal which are the electric activity generators, are involved in the inflammatory process of GERD. Destruction of these cells appears to occur in grades that are in accordance with GERD scores. The EEG seems to have the potential to act as an investigative tool in the diagnosis of GERD stages.
Slow wavesaction potentialsacid refluxGERDgastroesophageal reflux disease
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Background
Reflux esophagitis is a multifactorial disease that entails the reflux of gastric contents into the esophageal lumen [1]. Esophagitis develops when noxious substances in the refluxate have sufficient time to get in contact with the esophageal mucosa and then prevail over structural and functional defenses [2] The mechanisms involved in the defense of the esophagus against the gastric acid pepsin comprise the antireflux barriers, luminal clearance and epithelial resistance [3-6]. The antireflux barriers and the luminal clearance mechanism by peristalsis represent motor components of the reflux disease. The non-motility elements include salivary and esophageal submucosal glands.
Abnormal esophageal motility results in an increased exposure of the esophageal mucosa to gastric contents. Progressing severity of the reflux disease is associated with failing primary peristalsis [7-9]. The failure rate in patients with mild and severe GERD is 25% and 36%, respectively [7]. The amplitude of peristaltic contractions in the esophagus is significantly lower in the esophagitis patients than in the controls [6-9], and is inversely related to esophagitis severity [7,10]. The duration of contractions was variable but the propagation velocity was unequivocally slower in esophagitis patients than in controls [7,8,11].
Previous studies have shown that the esophagus possesses electric activity presenting as slow waves (SWs) followed or superimposed by fast activity spikes or action potentials (APs) [12,13]. Action potentials were associated with a rise in the intraesophageal pressure. Balloon distension of the esophagus effected an increase in the electric activity proximally to the balloon and a decrease distally [12]. The caudad direction of the SWs and APs was evidenced when after esophageal myotomy the potentials appeared proximally but not distally to the cut in the experimental animal [12]. This suggested also the presence of a pacemaker in the cervical esophagus which might initiate the electric waves[12].In achalasia of the esophagus, three electroesophagographic patterns were identified: bradyesophagia, esophagoarrhythmia and "silent" electroesophagogram [13]. The three patterns seem to represent different stages in one pathologic process.
In view of the disturbed esophageal peristaltic activity and abnormal esophageal motility in GERD, we hypothesized that these findings result from a disordered myoelectric activity of the esophagus. This hypothesis was investigated in the current study.
Methods
Subjects
Twenty seven patients with GERD (16 men, 11 women, mean age 42.6 ± 5.2 SD years, range 36–48) were enrolled in the study. The diagnosis was confirmed by 24-hour pH monitoring, endoscopy and esophageal motility test. According to Feussner's scoring system [14], seven patients had a mild (score1), 10 a moderate (score 2) and 10 a severe (score 3) stage of the disease. Score 1 had mild heartburn with mild chest pain but no regurgitation, dysphagia, hiatus hernia or mucosal changes. Exposure time to pH was <4.4–8%. Score 2 had moderate heartburn, moderate chest pain, regurgitation after large meals, small hiatus hernia, and isolated erosive mucosal lesions. Score 3 had severe heartburn, severe chest pain, regurgitation, occasional dysphagia, hiatus hernia, and esophageal ulcers.
The study also included 10 healthy volunteers (6 men, 4 women, mean age 41.4 ± 4.9 SD years, range 35–50) who had no reflux esophagitis. They had no gastrointestinal complaints in the past or at the time of enrollment in the study.
Physical examination of both the patients and healthy volunteers had normal findings. The results of laboratory work comprising blood count, renal and hepatic function tests as well as electrocardiogram were unremarkable. The studied subjects gave an informed consent after having been fully informed about the nature of the study, the tests to be done and their role in the study. The study was approved by the Review Board and Ethics Committee of the Cairo University Faculty of Medicine.
Methods
The electric activity of the esophagus was recorded in the patients with GERD and the healthy volunteers. We used a monopolar silver-silver chloride electrodes of 0.8 mm diameter (Smith-Kline Beecham, Los Angeles, CA, USA) introduced through a 6F catheter (Rubber Industries Ltd., London, UK) with the electrode protruding by 1 cm from the catheter tip. The catheter was attached to the esophageal mucosa by negative pressure suction of 50 to 100 mmHg which was maintained during the test.
Two electrodes were introduced into the esophagus by means of an endoscope and fixed to the esophageal mucosa by suction; one electrode was applied to the upper third, and the second to the lower third of the esophagus, and the electric activity was recorded. The upper electrode was then transferred to the middle third of the esophagus and recordings of the electric activity from the middle and lower third electrodes were performed. Signals from the electrodes were fed into an AC amplifier with a frequency response within ± 3 dB from 0.016 Hz to 1 kHz; they were displayed on a recorder at a sensitivity of 1 mV/cm. A metal disc applied to the abdominal skin served as the indifferent electrode. A strain gauge respiration transducer was attached to the thoracic wall for respiratory artefacts. We allowed the esophagus a 30 minute period to adapt to the electrodes applied to its wall, before we started a 120-min recording session for each subject.
The results were analyzed statistically using the Student's t test. Differences assumed significance at p < 0.05 and values were given as the mean ± standard deviation (SD).
Results
No adverse side effects were encountered during or after the performance of the tests and all the subjects were evaluated. There was no difficulty in applying the electrodes to the esophageal mucosa. We found that a suction pressure of 50–60 mmHg was sufficient to keep the electrode fixed to the esophageal mucosa during the testing period in most of the subjects; in few cases we had to increase the suction pressure to 100 mm to keep the electrodes in position. Applying the aforementioned pressures, we encountered neither migration nor detachment of the electrodes during the entire test. We met no mucosal bleeding, tears or ulcers during application or after removal of the electrodes either.
Electroesophagogram in healthy subjects
Monophasic negatively deflected SWs were recorded from the 2 electrodes of each subject of all the studied individuals (fig. 1). They had an unvariable shape in all recordings from the same site. The frequency, amplitude and conduction velocity were constant in the individual subject. The SWs in each individual exhibited the same frequency, amplitude and regular rhythm from both electrodes (fig. 1), regardless of their location in the upper, middle or lower third of the esophagus. The mean and range of frequency, amplitude and conduction velocity of the 10 healthy volunteers are displayed in table 1. These values were reproducible from the electrodes in the upper, middle or lower third of the esophagus. Bursts of APs representing fast activity spikes were recorded (fig. 1). They followed or were superimposed over the SWs; they occurred randomly and their frequency was inconsistent in each subject.
Figure 1 Electroesophagogram of a healthy volunteer showing slow waves with regular rhythm and random action potentials.
Table 1 The frequency, amplitude and conduction velocity of the slow waves of the healthy volunteers and patients with gastroesophageal reflux disease (GERD)+
Slow waves
Frequency c/m Amplitude (mV) Velocity (cm/s)
Mean Range Mean Range Mean Range
Volunteers 5.2 ± 1.3 4 – 7 0.52 ± 0.1 0.4 – 0.6 4.8 ± 0.7 3.5 – 6.1
Score 1 GERD 4.8 ± 1.2 4 – 6• 0.49 ± 0.1• 0.35 – 0.6 4.6 ± 0.7• 4.1 – 5.9
Score 2 GERD Irregular
Score 3 GERD Absent waves
+ values were given as the mean ± SD
• p > 0.05
p values of the patients were compared to those of the healthy volunteers
Electroesophagogram in GERD patients
In score 1 GERD, the electric waves' variables were similar to those of the healthy volunteers in all the subjects (p > 0.05, fig 2). The SWs were monophasic and negatively deflected and had a regular rhythm (fig 2). The frequency, amplitude and conduction velocity are shown in table 1. Action potentials were randomly recorded following or superimposed over the SWs. This electromyographic pattern was similar from the 2 recording electrodes of the individual subject and was reproducible during the recording period.
Figure 2 Electroesophagogram of a patient with score 1 gastroesophageal reflux disease showing slow waves with regular rhythm and random action potentials.
Score 2 GERD patients exhibited a different electromyographic pattern. The SWs had an irregular rhythm with varying but lower frequency, amplitude and conduction velocity compared to the normal controls (fig. 3). The APs occurred randomly and were less frequent than in the normal recordings (fig. 3). The SWs and APs differed from one electrode to the other of the same subject and were variable during the recording period. In 8 score 3 GERD patients, the electrodes did not register electric waves; neither SWs nor APs were recorded. A "silent" electromyographic pattern was registered (fig. 4); this picture was reproducible during the recording period. In the remaining 2 patients; occasional SWs were recorded that were inconsistent and different from the 2 electrodes of the same patient (fig. 5); no APs were recorded at any time during the recording period (fig. 5).
Figure 3 Electroesophagogram of a patient with score 2 gastroesophageal reflux disease exhibiting slow waves with irregular rhythm and varying frequency, amplitude and conduction velocity from the same electrode and between the 2 electrodes of the same subject.
Figure 4 Electroesophagogram of a patient with score 3 gastroesophageal reflux disease recording no electric activity: a "silent" electroesophagogram.
Figure 5 Electroesophagogram of a patient with score 3 gastroesophageal reflux disease recording occasional slow waves with no action potentials.
Discussion
The current study has demonstrated that the esophagus possesses an electric activity in the form of regular SWs and APs. The waves in the healthy volunteers were reproducible with identical frequency, amplitude and conduction velocity in the same subject. Previous studies have shown that the APs were coupled with increased esophageal pressure, while the SWs were not [12,13], these findings presumably denote that the APs have a contractile activity [12,13].
Effective esophageal motility is a critical determinant for esophageal clearance of refluxed gastric contents [15]. A single normal peristaltic wave completely clears the entire barium bolus from the esophagus. If a peristaltic wave fails due to motile dysfunction, there is little or no volume clearance [16]. Increased exposure of the esophageal mucosa to gastric contents may result from abnormal esophageal motility. Thus, while a defective gastroesophageal barrier accounts for an increased number of gastroesophageal reflux episodes, abnormal esophageal peristalsis results in impaired esophageal acid clearance [17,18].
The relationship between esophageal peristalsis and gastroesophageal reflux has been studied using stationary and ambulatory prolonged esophageal manometry. Various motor-disorders have been detected in GERD including a significantly high rate of incomplete primary peristalsis, changes in esophageal motility and an increased number of non-transmitted contractions [6,19]. Contractions had a shorter duration and a slower propagation velocity [20].
Patients with abnormal GER but mild esophagitis, or none, had normal amplitude of contractions with increased prevalence of simultaneous contractions [16,21]. Meanwhile patients with severe esophagitis had reduced amplitude of contractions, slow propagation velocities and an increased rate of failed primary peristalsis [21]. The current study may shed some light on the mechanism of these motor changes in GERD.
The electric activity in GERD exhibited different patterns depending on the stage of the GERD. In score 1 GERD, an electroesophagram similar to that of healthy volunteers was recorded. This apparently denotes that the motile activity of the esophagus in score 1 is normal and that the esophageal peristaltic activity can probably clear the esophagus of the refluxed gastric contents. The irregular and diminished esophageal electric wave variables displayed in score 2 GERD are presumably indicative of diminished motile activity and peristalsis of the esophagus with a resulting inhibited reflux clearance rate. The failure of adequate esophageal clearance is probably responsible for the clinical manifestations and investigative results encountered in score 2 GERD. With progress of the condition to score 3, there is probably no motor or peristaltic esophageal activity as evidenced by the absence of the esophageal electric waves. In such case, we presume that there is no esophageal clearance.
We do not know the cause of the diminished esophageal electric activity in GERD. Is it due to the refluxed acid material or to the resulting esophageal inflammation? It may be argued that the refluxed acid content into the esophagus inhibits its motile and peristaltic activity. However, the current and earlier studies have demonstrated normal peristalsis in score 1 GERD in which acid reflux was manifest [16]. Furthermore, the current study showed normal EMG activity in this condition. Probably these findings negate the role of acid reflux as inhibitor of the peristaltic and electric activity in GERD.
What then could be the cause of the deranged electric activity and peristaltic movement in the more advanced stages of GERD?
A new theory of the pathogenesis of diminished electric activity in GERD
The electric waves seem to be generated from the interstitial cells of Cajal that are located at the level of the myenteric plexus and in the circular muscle layer of the esophageal wall [22-24]. They are considered as the generators of the spontaneous pacemaker activity in the smooth muscle layers of the gut [22-24] and are also involved in neurotransmission [25-27]. They mediate or transduce inputs from enteric motor nerves to the smooth muscle syncytium.
In the advanced stages of GERD it may be assumed that the inflammatory changes in the esophageal wall have involved the interstitial cells of Cajal. Destruction of these cells appears to occur in grades that are in accordance with the GERD socres. It seems that in score 1 GERD, the mucosal inflammatory changes of the esophagus, if present, have not involved the Cajal cells yet. With the more advanced stages of the disease as in scores 2 and 3, the Cajal cells are presumingly being gradually destroyed by the advancing esophageal inflammatory process. The diminished SW variables encountered in score 2 GERD seem to be due to partial involvement of Cajal cells in the inflammatory process; the cells are not yet completely destroyed and are still mediating electric and peristaltic activity. However the Cajal cells in score 3 GERD are believed to be extremely injured so that they cannot generate electric activity.
Diagnostic role of electroesophagogram in GERD
There are various methods for the diagnosis of GERD. They include pressure measurements using water-perfused manometry catheters, external transducers or intraluminal transducers, pH-metry and others [28,29]. However they might have disadvantages [30]. In view of the results of our above study, the introduction of the electroesophagogram as an investigative tool may be a valuable addition to the armamentarium of esophageal investigations of GERD.
Conclusion
The electric activity in GERD expressed 3 different patterns depending on the stage of GERD. In score 1 GERD, a normal electroesophagogram was recorded which would denote normal esophageal motile activity and acid clearance. Score 2 GERD exhibited irregular and diminished esophageal electric waves' variables which are presumably indicative of decreased esophageal motility and reflux clearance. In score 3, a "silent" electroesophagogram was recorded with probably no acid clearance. A new theory of the pathogenesis of diminished electric activity in GERD is put forward. It is postulated that the interstitial cells of Cajal which are considered as the generators of the pacemaker activity and electric waves, are involved in the inflammatory process of GERD. Destruction of these cells appears to occur in grades that are in accordance with GERD scores. The electroesophagogram may serve as an investigative tool in diagnosing the various GERD stages, especially if they can be recorded percutaneously.
List of abbreviations
gastroesophageal reflux disease = GERD
(electroesophagogram = EEG
slow waves = SWs
action potentials = APs
standard deviation = SD
Authors contributions
AS: Study design/ planning
OES: Data collection/entry, Data analysis/statistics, Literature analysis/search
IS: Data collection/entry, Data analysis/statistics, Literature analysis/search
AS: Data collection/entry, Preparation manuscript
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgment
Margot Yehia assisted in preparation of the manuscript.
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| 15462680 | PMC526194 | CC BY | 2021-01-04 16:28:03 | no | BMC Surg. 2004 Oct 5; 4:13 | utf-8 | BMC Surg | 2,004 | 10.1186/1471-2482-4-13 | oa_comm |
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BMC Clin PharmacolBMC Clinical Pharmacology1472-6904BioMed Central London 1472-6904-4-71546181810.1186/1472-6904-4-7Research ArticleDay-to-day variations during clinical drug monitoring of morphine, morphine-3-glucuronide and morphine-6-glucuronide serum concentrations in cancer patients. A prospective observational study Klepstad Pål [email protected] Priscilla [email protected] Jorunn [email protected] Stein [email protected] Petter C [email protected] Kolbjørn [email protected] Ola [email protected] Department of Circulation and Imaging, Faculty of Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway2 Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway3 Department of Clinical Pharmacology, St. Olavs University Hospital, Trondheim, Norway2004 4 10 2004 4 7 7 28 5 2004 4 10 2004 Copyright © 2004 Klepstad et al; licensee BioMed Central Ltd.2004Klepstad 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 feasibility of drug monitoring of serum concentrations of morphine, morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) during chronic morphine therapy is not established. One important factor relevant to drug monitoring is to what extent morphine, M6G and M3G serum concentrations fluctuate during stable morphine treatment.
Methods
We included twenty-nine patients admitted to a palliative care unit receiving oral morphine (n = 19) or continuous subcutaneous (sc) morphine infusions (n = 10). Serum concentrations of morphine, M6G and M3G were obtained at the same time on four consecutive days. If readmitted, the patients were followed for another trial period. Day-to-day variations in serum concentrations and ratios were determined by estimating the percent coefficient of variation (CV = (mean/SD) ×100).
Results
The patients' median morphine doses were 90 (range; 20–1460) mg/24 h and 135 (range; 30–440) mg/24 h during oral and sc administration, respectively. Intraindividual fluctuations of serum concentrations estimated by median coefficients of day-to-day variation were in the oral group for morphine 46%, for M6G 25% and for M3G 18%. The median coefficients of variation were lower in patients receiving continuous sc morphine infusions (morphine 10%, M6G 13%, M3G 9%).
Conclusion
These findings indicate that serum concentrations of morphine and morphine metabolites fluctuate. The fluctuations found in our study are not explained by changes in morphine doses, administration of other drugs or by time for collection of blood samples. As expected the day-to-day variation was lower in patients receiving continuous sc morphine infusions compared with patients receiving oral morphine.
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Background
Morphine is degraded in the liver to several metabolites of which morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) are biological active [1]. M6G is shown to contribute to the analgesia produced by morphine and may cause opioid related adverse effects such as sedation or nausea [2-5]. Due to first pass metabolism and slow accumulation of M6G in the brain the analgesic activity of M6G is most prominent during oral long-term treatment with morphine while single dose studies show less contribution from M6G to the analgesic effects from morphine [2,3,6]. M3G may in exceptional cases cause excitatory adverse effects such as delirium, myoclonus or allodynia [7]. Animal studies observed that M3G have an anti-nociceptive effect [8,9], but this effect was not reproduced in a study administering M3G to volunteers exposed for human experimental pain [10].
The most obvious determinants for serum concentrations of morphine, M6G and M3G are morphine doses, route of administration and renal function. However, a considerable variation of serum concentrations between patients remains after correcting for dose and route of administration [3,11-13].
Measurements of morphine, M3G and M6G serum concentrations can explain individual responses in patients where morphine treatment turns out to have unexpected effects and help physicians to determine changes in pain treatment. Physicians tend to believe that samples obtained for therapeutic drug monitoring during steady state conditions will be representative irrespective of which day the sample is collected. However, morphine, M6G and M3G serum concentrations may also have fluctuations not caused by changes in morphine doses, administration of other drugs or by time for collection of blood samples. This variation represents the day-to-day variability. In order to evaluate the clinical implications from morphine and metabolites serum concentrations measurements it is necessary to know if these serum concentrations have fluctuations not related to changes in drug administrations.
The day-to-day variability in serum concentrations of morphine, M6G and M3G are previously reported in a study of 8 cancer patients treated with subcutaneous (sc) morphine infusions. This study observed coefficients of variation (CV) ranging from 26%–56% for morphine, 20% to 51% for M6G and 20%–49% for M3G [12]. To our knowledge, the day-to-day variations of morphine, M6G and M3G serum concentrations obtained from consecutive days or during chronic oral administration of morphine are not previously reported.
Thus, the aim of this study is to investigate the day-to-day variations of morphine, M6G and M3G serum concentrations during stable chronic oral and sc morphine administration to cancer patients.
Methods
Patients
We included twenty-nine patients admitted during a nine-month period to the Palliative Care Unit at the University Hospital in Trondheim. The inclusion criteria were; verified malignant disease, expected survival time less than 6 months, scheduled morphine treatment started at least three days prior to inclusion, stable scheduled doses of morphine for a minimum of three days and age more than eighteen years. The exclusion criteria were; planned hospitalisation less than three days and lack of ability to communicate (e.g. dementia, deafness).
All patients gave their written informed consent before inclusion. The study was conducted according to the guidelines of the Helsinki declaration. The Regional Committee for Medical Research Ethics, Health Region IV, Norway, approved the study.
Study design
Inclusion
The patients were included in the study within three days after admission to the Palliative Care Unit. Each patient was followed for four days. Patients readmitted to the Palliative Care Unit were allowed to a new trial period identical to the first trial period. No patients were included in more than three trial periods.
The patients' age, gender, primary malignant diagnosis, presence of metastasis, and other medications were registered. Morphine treatment during the last 24 hours was registered with respect to route of administration, morphine formulation, scheduled dose and consumption of rescue morphine for breakthrough pain. The patients' functional status was assessed using the Karnofsky performance status score [15].
Blood samples
Blood samples were obtained each day during the trial period. The samples were obtained at the same time each day during the routine morning round for collecting blood samples.
Observations
In order to observe if the patients were studied during stable treatment conditions the scheduled morphine dose, rescue morphine consumption and route of administration were registered each study day. The use of other medications was also registered daily. Pain, nausea and sedation were assessed at day study two, three and four during the trial period using a 5 category verbal rating scale (VRS) score ranging from no to very severe. All symptoms were assessed for the last 24 h.
Analyses
The blood samples were placed in EDTA tubes until separated by centrifugation (3000 rpm, ten minutes) and stored at -85°C until analysed. All samples were analysed for serum concentrations of morphine, M6G and M3G applying liquid chromatography mass spectrometry [16]. The limits of detection were for morphine 0,35 nmol/l and for M6G and M3G 2,2 nmol/l. The analytical coefficients of variation obtained in quality control samples (CVAnalytical) were for morphine 3,0%, for M6G 5,5% and for M3G 7,0%. The analytical coefficients of variation were determined at 100 nmol/l for morphine and 1000 nmol/l for M6G and M3G. Serum values of creatinine concentrations, alanin aminotransferase activities (ALAT), aspartat aminotransferase activities (ASAT) and albumin concentrations were determined using standard analytical methods.
Statistical evaluation
Total use of morphine for each trial day was calculated by adding scheduled morphine doses and rescue morphine consumption. Samples obtained less than two hour after the administration of a morphine rescue dose were excluded from the analyses.
Day-to-day variations of morphine and its metabolites are presented as biological coeffecients of variation. This biological variation (CVBiological), expressed in terms of percent coefficient of variation, was calculated for each patient in each trial period using the equation [15,16]:
CVBiological = CVObserved - CVAnalytical
The observed coefficients of variation (CVObserved) for morphine, M6G and M3G, which represent the variation in serum concentrations for each patient during each trial period, were calculated using the equation [17,18]:
At least three observations were needed in order to calculate an observed coefficient of variation.
Statistical comparisons between the trial days and trial periods were performed using one-way analysis of variance tests. Due to multiple comparisons statistical significance was defined as p < 0,01.
The statistical software SPSS version 9.0 for Windows was used throughout the analyses.
Results
Patient characteristics
The patients (16 males and 13 women) median age at inclusion was 68 years (range; 39–89). The patients' Karnofsky performance status, primary tumor diagnoses and presence of metastases are shown in Table 1. The median serum creatinine concentrations at inclusion were 72 μmol/l (range; 45–121). The median values at inclusion of ASAT and ALAT were 31 IU/l (range; 7–154) and 17 IU/l (range; 5–65), respectively. No patient had clinical significant liver failure. The median serum albumin concentrations at inclusion were 32 g/l (range; 23–42).
Table 1 Patient characteristics
Karnofsky performance status (median (range)) 60 (40–80)
Cancer Diagnoses
Prostate 11
Colorectal 5
Kidney 3
Breast 3
Pancreatic 2
Lung 2
Gastric 1
Malignant melanoma 1
Leiomyosarcoma 1
Metastases
Liver 7
Bone 16
Other 16
Antidepressants 4
Neuroleptics 1
Benzodiazepines 6
Corticosteroids 13
Antiemetics 7
Ten patients used non-opioid analgesics (nine paracetamol, one acetylsalicylic acid). The patients used a median number of 5 (range; 1–10) non-pain medications. The numbers of patients using psychotropic drugs, antiemetics or corticosteroids are given in Table 1. All except one patients received laxatives, lactulose and bisacodyl, during the study period. All medications were stable during the study period. Similar pain, nausea and sedation scores were observed throughout the trial periods (Table 2). Twenty-seven patients had died at the time of manuscript preparation. The median survival time from inclusion was three months.
Table 2 Symptom scores All scores were obtained using a 5 category verbal rating scale score (scores; 1–5). All results are given as mean (SD). No significant differences in scores were observed between trial days.
Trial day 2 Trial day 3 Trial day 4
Pain 2.8 (0.7) 2.2 (1.1) 2.2 (1,1)
Nausea 1.7 (1.0) 1.7 (1.1) 1.8 (0.9)
Sedation 3.4 (1.2) 3.4 (1.0) 3.2 (1.1)
Of the nineteen patients receiving oral morphine sixteen patients completed one trial period, two patients completed two trial periods and one patient completed three trial periods. The corresponding numbers for the ten patients receiving sc morphine were four, five and one, respectively. Six patients were excluded during a study period. The reasons were; discharge from hospital (n = 2), opioid treatment changed to fentanyl patch (n = 1) and fatigue (n = 3). Opioid induced adverse effects caused no exclusions. Sixteen blood samples were not obtained due to circumstances related to the patients' or relatives' needs (e.g. visits from relatives at the time of a planned blood sample).
Morphine treatment
The median duration of morphine treatment before entering the study was 7 months (range; 0–29). The median morphine dose at inclusion for the patients receiving oral treatment (controlled-release morphine) was 90 mg/24 h (range; 20–1460). The median morphine dose for the patients receiving sc morphine infusions was 135 mg/24 h (range; 30–340). The morphine doses varied between the study days because the patients were allowed to use rescue morphine. This variation, however, was minor (Table 3 and 4).
Table 3 Serum concentrations for morphine and metabolites for the oral route The morphine doses (mg/24 h) vary because of variable doses of rescue morphine. A total of 23 trial periods in 19 patients were studied. All data are given as median and range.
Day 1 Day 2 Day 3 Day 4
Morphine dose (mg/24 h) 90 (20–1460) 80 (20 – 1700) 95 (30 – 1520) 90 (20 – 1580)
Serum morphine (nmol/l) 255 (46–2520) 59 (17–1437) 94 (12–1429) 77 (9–2296)
Serum M6G (nmol/l) 1156 (149–7874) 568 (66–7874) 516 (66–9678) 620 (80–8026)
Serum M3G (nmol/l) 6341 (1734–31997) 3696 (404–36887) 3226 (595–41452) 3778 (526–43043)
Table 4 Serum concentrations for morphine and metabolites for the subcutaneous route The morphine doses (mg/24 h) vary because of variable doses of rescue morphine. A total of 17 trial periods in 10 patients were studied. All data are given as median and range.
Day 1 Day 2 Day 3 Day 4
Morphine dose (mg/24 h) 135 (30–340) 163 (30 – 335) 164 (50 – 440) 150 (84 – 440)
Serum morphine (nmol/l) 240 (42–741) 254 (62–1297) 305 (106–1045) 373 (103–1222)
Serum M6G (nmol/l) 723 (78–1811) 674 (88–2867) 1009 (374–2023) 1225 (400–2339)
Serum M3G (nmol/l) 5350 (578–11784) 4490 (779–16312) 5631 (3028–8342) 6119 (2777–13715)
The diurnal distributions of rescue morphine administrations were recorded in order to assess the possible influence from rescue morphine on the serum concentration observations. Three blood samples were obtained during the two-hour interval following an administration of rescue morphine. The results from these samples were excluded from the analyses.
Morphine, M6G and M3G serum concentrations
The median serum concentrations of morphine during oral morphine treatment ranged from 59 to 255 nmol/l during the four study days. The median serum concentrations for M6G and M3G on each study day for patients receiving oral morphine are given in Table 3.
The median serum concentrations were more stable during sc morphine treatment compared with oral treatment. The median serum concentrations of morphine during sc morphine treatment ranged from 240 to 373 nmol/l during the study days. The median serum concentrations for M6G and M3G on each study day for patients receiving sc morphine are given in Table 4.
Day-to-day variation
The median biological coefficient of variation (CV) for morphine serum concentrations was 46% (range; 13–103) during oral morphine therapy and 10% (range; 0–36) during sc morphine infusions (Table 5). The median biological CV values for M6G were 25% (range; 1–72) during oral therapy and 13% (range; 2–40) during s.c. therapy. The corresponding results for M3G serum concentrations were 18% (range; 0–57) and 9% (range; 0–34), respectively (Table 5).
Table 5 Biological coefficients of variation (CV) of morphine, morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) serum concentrations during four consecutive days of oral or subcutaneous morphine treatment. All values are given as median and range.
Biological coefficient of variation %
Oral morphine Subcutaneous morphine
Morphine 46 (13–103) 10 (0–36)
M6G 25 (1–72) 13 (2–40)
M3G 18 (0–57) 9 (0–34)
Discussion
Intraindividual fluctuation of drug serum concentrations not explained by changes in doses, administration of other drugs or by time for collection of blood samples, is the day-to-day variation. Routine measurements of serum concentrations of morphine and metabolites are of questionable value because of the large variability of minimum effective serum concentration and the lack of a direct relationship between serum concentrations and adverse effects [19]. However, measurements of serum concentrations of morphine and metabolites are of importance in patients displaying unexpected opioid toxity [4,7] Physicians assessing results from serum drug concentrations determinations should be aware to what extent serum concentrations of drugs fluctuate during stable treatment conditions. Without this knowledge differences and changes in serum concentrations observations may be unduly interpreted.
The available data on day-to-day variability during chronic morphine treatment is sparse. Vermeire et al. reported day-to-day variations during morphine treatment in eight cancer patients receiving continuous sc morphine infusion for 1 to 23 weeks. The individual CV values observed in their study varied between 26% to 56% for morphine, 20% to 51% for M6G and 20% to 49% for M3G [14].
We observed less day-to-day variations of morphine and metabolites concentrations during sc morphine treatment (morphine 10%, M6G 13%, M3G 9%) compared to the fluctuations reported by Vermeire et al.. One explanation for this discrepancy is that Vermeire et al. obtained blood samples during treatment periods up to 23 weeks. This study design may overestimate day-to-day variability since patient related factors will vary more during long time intervals than between consecutive days.
To our knowledge this is the first study to assess the day-to-day variation of morphine and morphine metabolites serum concentrations during oral morphine therapy. The median observed CV values for serum concentrations of morphine, M6G and M3G during oral morphine therapy (morphine 46%, M6G 25%, M3G 18%) were higher than the CV values observed in patients receiving sc morphine treatment (morphine 10%, M6G 13%, M3G 9%). This observation was expected due to a more stable delivery rate and since absorption during sc administration is not influenced by food intake, gastric retention, malabsorption, vomiting or variable first-pass metabolism.
The results in our study, as in the study by Vermeire et al., represent day-to-day variability in cancer patients admitted to a palliative care unit. In this patient population pharmacological observations will be influenced by variations in food intake, gastric retention, malabsorption, effects from other drugs on gastric emptying, vomiting and drug interactions. In order to perform a study on day-to-day variation not suspect to these confounding factors patients or volunteers must be recruited into a controlled experimental environment. We believe that studies in controlled experimental environments and studies in patients with advanced cancer disease are complementary to each other. The first targets the pharmacokinetic phenomenon of day-to-day fluctuations, the second targets the fluctuations met during clinical real-life conditions.
We recognise some limitations in our study. First, blood samples were collected during four trial days. An extended trial period in order to obtain a larger number of samples from each patient gives a more precise estimate of day-to-day variation. However, due to ethical considerations, taking into account the strain on each patient from serial blood sampling, we chose to not extend the trial periods beyond four days. A second potential confounding factor is absorption peaks in serum concentrations caused by rescue doses of morphine. We chose to allow for rescue morphine because we wanted to observe the variability of serum concentrations as observed in a normal clinical setting in patients considered to be clinical stable in respect to pain treatment. We belive that the variability caused by serum concentration peaks is limited since samples obtained within a time interval of two hours after administration of a morphine rescue dose were excluded. However, it is important to recognize that in order to observe the exact pharmacological day-to-day variability of serum concentrations of morphine and morphine metabolites a design with a stable baseline morphine dose and a non-morphine alternative for breakthrough pain should be applied. Third, the use of rescue morphine implies that the daily morphine doses were not constant. However, the small changes in daily morphine doses can not explain the observed day-to-day variability.
In this study we assessed clinical symptoms related to opioid treatment in order to verify the stable intensities of symptoms during the study period. We did not attempt to explore the relationships between serum concentrations and clinical outcome measures. As a rule of thumb 25 patients are required per independent variable in order to give valid results in studies exploring the effects from factors predicting clinical observations [20]. Consequently, the size of this study was not sufficient to investigate the relationships between opioid serum concentrations and clinical symptoms.
Conclusions
Morphine, M6G and M3G serum concentrations vary considerably in samples obtained on consecutive days. Such variability is present during stable morphine doses and stable clinical symptoms. The day-to-day variability was lower in patients receiving continuous sc morphine infusions compared with patients receiving oral morphine. These findings indicate that results from blood samples taken in order to assess a patient's pharmacological morphine status should be interpreted with the understanding of that variability is partly caused by day-to-day variation.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
PK, PH and JM participated in the design of the study, running of the study and preparation of the manuscript. SK, PCB and OD participated in the design of the study and preparation of the manuscript. KZ was responsible for measurements of morphine and morphine metabolite serum concentrations.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank Bjørn Fougner for interviewing patients and Per Balstad for support in data management. The study was financial supported by grants from The Norwegian Cancer Society and the Norwegian Research Council.
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| 15461818 | PMC526195 | CC BY | 2021-01-04 16:27:54 | no | BMC Clin Pharmacol. 2004 Oct 4; 4:7 | utf-8 | BMC Clin Pharmacol | 2,004 | 10.1186/1472-6904-4-7 | oa_comm |
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BMC Clin PharmacolBMC Clinical Pharmacology1472-6904BioMed Central London 1472-6904-4-71546181810.1186/1472-6904-4-7Research ArticleDay-to-day variations during clinical drug monitoring of morphine, morphine-3-glucuronide and morphine-6-glucuronide serum concentrations in cancer patients. A prospective observational study Klepstad Pål [email protected] Priscilla [email protected] Jorunn [email protected] Stein [email protected] Petter C [email protected] Kolbjørn [email protected] Ola [email protected] Department of Circulation and Imaging, Faculty of Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway2 Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway3 Department of Clinical Pharmacology, St. Olavs University Hospital, Trondheim, Norway2004 4 10 2004 4 7 7 28 5 2004 4 10 2004 Copyright © 2004 Klepstad et al; licensee BioMed Central Ltd.2004Klepstad 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 feasibility of drug monitoring of serum concentrations of morphine, morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) during chronic morphine therapy is not established. One important factor relevant to drug monitoring is to what extent morphine, M6G and M3G serum concentrations fluctuate during stable morphine treatment.
Methods
We included twenty-nine patients admitted to a palliative care unit receiving oral morphine (n = 19) or continuous subcutaneous (sc) morphine infusions (n = 10). Serum concentrations of morphine, M6G and M3G were obtained at the same time on four consecutive days. If readmitted, the patients were followed for another trial period. Day-to-day variations in serum concentrations and ratios were determined by estimating the percent coefficient of variation (CV = (mean/SD) ×100).
Results
The patients' median morphine doses were 90 (range; 20–1460) mg/24 h and 135 (range; 30–440) mg/24 h during oral and sc administration, respectively. Intraindividual fluctuations of serum concentrations estimated by median coefficients of day-to-day variation were in the oral group for morphine 46%, for M6G 25% and for M3G 18%. The median coefficients of variation were lower in patients receiving continuous sc morphine infusions (morphine 10%, M6G 13%, M3G 9%).
Conclusion
These findings indicate that serum concentrations of morphine and morphine metabolites fluctuate. The fluctuations found in our study are not explained by changes in morphine doses, administration of other drugs or by time for collection of blood samples. As expected the day-to-day variation was lower in patients receiving continuous sc morphine infusions compared with patients receiving oral morphine.
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Background
Morphine is degraded in the liver to several metabolites of which morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) are biological active [1]. M6G is shown to contribute to the analgesia produced by morphine and may cause opioid related adverse effects such as sedation or nausea [2-5]. Due to first pass metabolism and slow accumulation of M6G in the brain the analgesic activity of M6G is most prominent during oral long-term treatment with morphine while single dose studies show less contribution from M6G to the analgesic effects from morphine [2,3,6]. M3G may in exceptional cases cause excitatory adverse effects such as delirium, myoclonus or allodynia [7]. Animal studies observed that M3G have an anti-nociceptive effect [8,9], but this effect was not reproduced in a study administering M3G to volunteers exposed for human experimental pain [10].
The most obvious determinants for serum concentrations of morphine, M6G and M3G are morphine doses, route of administration and renal function. However, a considerable variation of serum concentrations between patients remains after correcting for dose and route of administration [3,11-13].
Measurements of morphine, M3G and M6G serum concentrations can explain individual responses in patients where morphine treatment turns out to have unexpected effects and help physicians to determine changes in pain treatment. Physicians tend to believe that samples obtained for therapeutic drug monitoring during steady state conditions will be representative irrespective of which day the sample is collected. However, morphine, M6G and M3G serum concentrations may also have fluctuations not caused by changes in morphine doses, administration of other drugs or by time for collection of blood samples. This variation represents the day-to-day variability. In order to evaluate the clinical implications from morphine and metabolites serum concentrations measurements it is necessary to know if these serum concentrations have fluctuations not related to changes in drug administrations.
The day-to-day variability in serum concentrations of morphine, M6G and M3G are previously reported in a study of 8 cancer patients treated with subcutaneous (sc) morphine infusions. This study observed coefficients of variation (CV) ranging from 26%–56% for morphine, 20% to 51% for M6G and 20%–49% for M3G [12]. To our knowledge, the day-to-day variations of morphine, M6G and M3G serum concentrations obtained from consecutive days or during chronic oral administration of morphine are not previously reported.
Thus, the aim of this study is to investigate the day-to-day variations of morphine, M6G and M3G serum concentrations during stable chronic oral and sc morphine administration to cancer patients.
Methods
Patients
We included twenty-nine patients admitted during a nine-month period to the Palliative Care Unit at the University Hospital in Trondheim. The inclusion criteria were; verified malignant disease, expected survival time less than 6 months, scheduled morphine treatment started at least three days prior to inclusion, stable scheduled doses of morphine for a minimum of three days and age more than eighteen years. The exclusion criteria were; planned hospitalisation less than three days and lack of ability to communicate (e.g. dementia, deafness).
All patients gave their written informed consent before inclusion. The study was conducted according to the guidelines of the Helsinki declaration. The Regional Committee for Medical Research Ethics, Health Region IV, Norway, approved the study.
Study design
Inclusion
The patients were included in the study within three days after admission to the Palliative Care Unit. Each patient was followed for four days. Patients readmitted to the Palliative Care Unit were allowed to a new trial period identical to the first trial period. No patients were included in more than three trial periods.
The patients' age, gender, primary malignant diagnosis, presence of metastasis, and other medications were registered. Morphine treatment during the last 24 hours was registered with respect to route of administration, morphine formulation, scheduled dose and consumption of rescue morphine for breakthrough pain. The patients' functional status was assessed using the Karnofsky performance status score [15].
Blood samples
Blood samples were obtained each day during the trial period. The samples were obtained at the same time each day during the routine morning round for collecting blood samples.
Observations
In order to observe if the patients were studied during stable treatment conditions the scheduled morphine dose, rescue morphine consumption and route of administration were registered each study day. The use of other medications was also registered daily. Pain, nausea and sedation were assessed at day study two, three and four during the trial period using a 5 category verbal rating scale (VRS) score ranging from no to very severe. All symptoms were assessed for the last 24 h.
Analyses
The blood samples were placed in EDTA tubes until separated by centrifugation (3000 rpm, ten minutes) and stored at -85°C until analysed. All samples were analysed for serum concentrations of morphine, M6G and M3G applying liquid chromatography mass spectrometry [16]. The limits of detection were for morphine 0,35 nmol/l and for M6G and M3G 2,2 nmol/l. The analytical coefficients of variation obtained in quality control samples (CVAnalytical) were for morphine 3,0%, for M6G 5,5% and for M3G 7,0%. The analytical coefficients of variation were determined at 100 nmol/l for morphine and 1000 nmol/l for M6G and M3G. Serum values of creatinine concentrations, alanin aminotransferase activities (ALAT), aspartat aminotransferase activities (ASAT) and albumin concentrations were determined using standard analytical methods.
Statistical evaluation
Total use of morphine for each trial day was calculated by adding scheduled morphine doses and rescue morphine consumption. Samples obtained less than two hour after the administration of a morphine rescue dose were excluded from the analyses.
Day-to-day variations of morphine and its metabolites are presented as biological coeffecients of variation. This biological variation (CVBiological), expressed in terms of percent coefficient of variation, was calculated for each patient in each trial period using the equation [15,16]:
CVBiological = CVObserved - CVAnalytical
The observed coefficients of variation (CVObserved) for morphine, M6G and M3G, which represent the variation in serum concentrations for each patient during each trial period, were calculated using the equation [17,18]:
At least three observations were needed in order to calculate an observed coefficient of variation.
Statistical comparisons between the trial days and trial periods were performed using one-way analysis of variance tests. Due to multiple comparisons statistical significance was defined as p < 0,01.
The statistical software SPSS version 9.0 for Windows was used throughout the analyses.
Results
Patient characteristics
The patients (16 males and 13 women) median age at inclusion was 68 years (range; 39–89). The patients' Karnofsky performance status, primary tumor diagnoses and presence of metastases are shown in Table 1. The median serum creatinine concentrations at inclusion were 72 μmol/l (range; 45–121). The median values at inclusion of ASAT and ALAT were 31 IU/l (range; 7–154) and 17 IU/l (range; 5–65), respectively. No patient had clinical significant liver failure. The median serum albumin concentrations at inclusion were 32 g/l (range; 23–42).
Table 1 Patient characteristics
Karnofsky performance status (median (range)) 60 (40–80)
Cancer Diagnoses
Prostate 11
Colorectal 5
Kidney 3
Breast 3
Pancreatic 2
Lung 2
Gastric 1
Malignant melanoma 1
Leiomyosarcoma 1
Metastases
Liver 7
Bone 16
Other 16
Antidepressants 4
Neuroleptics 1
Benzodiazepines 6
Corticosteroids 13
Antiemetics 7
Ten patients used non-opioid analgesics (nine paracetamol, one acetylsalicylic acid). The patients used a median number of 5 (range; 1–10) non-pain medications. The numbers of patients using psychotropic drugs, antiemetics or corticosteroids are given in Table 1. All except one patients received laxatives, lactulose and bisacodyl, during the study period. All medications were stable during the study period. Similar pain, nausea and sedation scores were observed throughout the trial periods (Table 2). Twenty-seven patients had died at the time of manuscript preparation. The median survival time from inclusion was three months.
Table 2 Symptom scores All scores were obtained using a 5 category verbal rating scale score (scores; 1–5). All results are given as mean (SD). No significant differences in scores were observed between trial days.
Trial day 2 Trial day 3 Trial day 4
Pain 2.8 (0.7) 2.2 (1.1) 2.2 (1,1)
Nausea 1.7 (1.0) 1.7 (1.1) 1.8 (0.9)
Sedation 3.4 (1.2) 3.4 (1.0) 3.2 (1.1)
Of the nineteen patients receiving oral morphine sixteen patients completed one trial period, two patients completed two trial periods and one patient completed three trial periods. The corresponding numbers for the ten patients receiving sc morphine were four, five and one, respectively. Six patients were excluded during a study period. The reasons were; discharge from hospital (n = 2), opioid treatment changed to fentanyl patch (n = 1) and fatigue (n = 3). Opioid induced adverse effects caused no exclusions. Sixteen blood samples were not obtained due to circumstances related to the patients' or relatives' needs (e.g. visits from relatives at the time of a planned blood sample).
Morphine treatment
The median duration of morphine treatment before entering the study was 7 months (range; 0–29). The median morphine dose at inclusion for the patients receiving oral treatment (controlled-release morphine) was 90 mg/24 h (range; 20–1460). The median morphine dose for the patients receiving sc morphine infusions was 135 mg/24 h (range; 30–340). The morphine doses varied between the study days because the patients were allowed to use rescue morphine. This variation, however, was minor (Table 3 and 4).
Table 3 Serum concentrations for morphine and metabolites for the oral route The morphine doses (mg/24 h) vary because of variable doses of rescue morphine. A total of 23 trial periods in 19 patients were studied. All data are given as median and range.
Day 1 Day 2 Day 3 Day 4
Morphine dose (mg/24 h) 90 (20–1460) 80 (20 – 1700) 95 (30 – 1520) 90 (20 – 1580)
Serum morphine (nmol/l) 255 (46–2520) 59 (17–1437) 94 (12–1429) 77 (9–2296)
Serum M6G (nmol/l) 1156 (149–7874) 568 (66–7874) 516 (66–9678) 620 (80–8026)
Serum M3G (nmol/l) 6341 (1734–31997) 3696 (404–36887) 3226 (595–41452) 3778 (526–43043)
Table 4 Serum concentrations for morphine and metabolites for the subcutaneous route The morphine doses (mg/24 h) vary because of variable doses of rescue morphine. A total of 17 trial periods in 10 patients were studied. All data are given as median and range.
Day 1 Day 2 Day 3 Day 4
Morphine dose (mg/24 h) 135 (30–340) 163 (30 – 335) 164 (50 – 440) 150 (84 – 440)
Serum morphine (nmol/l) 240 (42–741) 254 (62–1297) 305 (106–1045) 373 (103–1222)
Serum M6G (nmol/l) 723 (78–1811) 674 (88–2867) 1009 (374–2023) 1225 (400–2339)
Serum M3G (nmol/l) 5350 (578–11784) 4490 (779–16312) 5631 (3028–8342) 6119 (2777–13715)
The diurnal distributions of rescue morphine administrations were recorded in order to assess the possible influence from rescue morphine on the serum concentration observations. Three blood samples were obtained during the two-hour interval following an administration of rescue morphine. The results from these samples were excluded from the analyses.
Morphine, M6G and M3G serum concentrations
The median serum concentrations of morphine during oral morphine treatment ranged from 59 to 255 nmol/l during the four study days. The median serum concentrations for M6G and M3G on each study day for patients receiving oral morphine are given in Table 3.
The median serum concentrations were more stable during sc morphine treatment compared with oral treatment. The median serum concentrations of morphine during sc morphine treatment ranged from 240 to 373 nmol/l during the study days. The median serum concentrations for M6G and M3G on each study day for patients receiving sc morphine are given in Table 4.
Day-to-day variation
The median biological coefficient of variation (CV) for morphine serum concentrations was 46% (range; 13–103) during oral morphine therapy and 10% (range; 0–36) during sc morphine infusions (Table 5). The median biological CV values for M6G were 25% (range; 1–72) during oral therapy and 13% (range; 2–40) during s.c. therapy. The corresponding results for M3G serum concentrations were 18% (range; 0–57) and 9% (range; 0–34), respectively (Table 5).
Table 5 Biological coefficients of variation (CV) of morphine, morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) serum concentrations during four consecutive days of oral or subcutaneous morphine treatment. All values are given as median and range.
Biological coefficient of variation %
Oral morphine Subcutaneous morphine
Morphine 46 (13–103) 10 (0–36)
M6G 25 (1–72) 13 (2–40)
M3G 18 (0–57) 9 (0–34)
Discussion
Intraindividual fluctuation of drug serum concentrations not explained by changes in doses, administration of other drugs or by time for collection of blood samples, is the day-to-day variation. Routine measurements of serum concentrations of morphine and metabolites are of questionable value because of the large variability of minimum effective serum concentration and the lack of a direct relationship between serum concentrations and adverse effects [19]. However, measurements of serum concentrations of morphine and metabolites are of importance in patients displaying unexpected opioid toxity [4,7] Physicians assessing results from serum drug concentrations determinations should be aware to what extent serum concentrations of drugs fluctuate during stable treatment conditions. Without this knowledge differences and changes in serum concentrations observations may be unduly interpreted.
The available data on day-to-day variability during chronic morphine treatment is sparse. Vermeire et al. reported day-to-day variations during morphine treatment in eight cancer patients receiving continuous sc morphine infusion for 1 to 23 weeks. The individual CV values observed in their study varied between 26% to 56% for morphine, 20% to 51% for M6G and 20% to 49% for M3G [14].
We observed less day-to-day variations of morphine and metabolites concentrations during sc morphine treatment (morphine 10%, M6G 13%, M3G 9%) compared to the fluctuations reported by Vermeire et al.. One explanation for this discrepancy is that Vermeire et al. obtained blood samples during treatment periods up to 23 weeks. This study design may overestimate day-to-day variability since patient related factors will vary more during long time intervals than between consecutive days.
To our knowledge this is the first study to assess the day-to-day variation of morphine and morphine metabolites serum concentrations during oral morphine therapy. The median observed CV values for serum concentrations of morphine, M6G and M3G during oral morphine therapy (morphine 46%, M6G 25%, M3G 18%) were higher than the CV values observed in patients receiving sc morphine treatment (morphine 10%, M6G 13%, M3G 9%). This observation was expected due to a more stable delivery rate and since absorption during sc administration is not influenced by food intake, gastric retention, malabsorption, vomiting or variable first-pass metabolism.
The results in our study, as in the study by Vermeire et al., represent day-to-day variability in cancer patients admitted to a palliative care unit. In this patient population pharmacological observations will be influenced by variations in food intake, gastric retention, malabsorption, effects from other drugs on gastric emptying, vomiting and drug interactions. In order to perform a study on day-to-day variation not suspect to these confounding factors patients or volunteers must be recruited into a controlled experimental environment. We believe that studies in controlled experimental environments and studies in patients with advanced cancer disease are complementary to each other. The first targets the pharmacokinetic phenomenon of day-to-day fluctuations, the second targets the fluctuations met during clinical real-life conditions.
We recognise some limitations in our study. First, blood samples were collected during four trial days. An extended trial period in order to obtain a larger number of samples from each patient gives a more precise estimate of day-to-day variation. However, due to ethical considerations, taking into account the strain on each patient from serial blood sampling, we chose to not extend the trial periods beyond four days. A second potential confounding factor is absorption peaks in serum concentrations caused by rescue doses of morphine. We chose to allow for rescue morphine because we wanted to observe the variability of serum concentrations as observed in a normal clinical setting in patients considered to be clinical stable in respect to pain treatment. We belive that the variability caused by serum concentration peaks is limited since samples obtained within a time interval of two hours after administration of a morphine rescue dose were excluded. However, it is important to recognize that in order to observe the exact pharmacological day-to-day variability of serum concentrations of morphine and morphine metabolites a design with a stable baseline morphine dose and a non-morphine alternative for breakthrough pain should be applied. Third, the use of rescue morphine implies that the daily morphine doses were not constant. However, the small changes in daily morphine doses can not explain the observed day-to-day variability.
In this study we assessed clinical symptoms related to opioid treatment in order to verify the stable intensities of symptoms during the study period. We did not attempt to explore the relationships between serum concentrations and clinical outcome measures. As a rule of thumb 25 patients are required per independent variable in order to give valid results in studies exploring the effects from factors predicting clinical observations [20]. Consequently, the size of this study was not sufficient to investigate the relationships between opioid serum concentrations and clinical symptoms.
Conclusions
Morphine, M6G and M3G serum concentrations vary considerably in samples obtained on consecutive days. Such variability is present during stable morphine doses and stable clinical symptoms. The day-to-day variability was lower in patients receiving continuous sc morphine infusions compared with patients receiving oral morphine. These findings indicate that results from blood samples taken in order to assess a patient's pharmacological morphine status should be interpreted with the understanding of that variability is partly caused by day-to-day variation.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
PK, PH and JM participated in the design of the study, running of the study and preparation of the manuscript. SK, PCB and OD participated in the design of the study and preparation of the manuscript. KZ was responsible for measurements of morphine and morphine metabolite serum concentrations.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank Bjørn Fougner for interviewing patients and Per Balstad for support in data management. The study was financial supported by grants from The Norwegian Cancer Society and the Norwegian Research Council.
==== Refs
Glare PA Walsh TD Clinical pharmacokinetics of morphine Ther Drug Monit 1991 13 1 23 2057987
Portenoy RK Thaler HT Inturrisi CE Friedlander-Klar H Foley KM The metabolite morphine-6-glucuronide contributes to the analgesia produced by morphine infusion in patients with pain and normal renal function Clin Pharmacol Ther 1992 51 422 31 1563212
Klepstad P Kaasa S Borchgrevink PC Start of oral morphine to cancer patients: effective serum morphine concentrations and contribution from morphine-6-glucuronide to the analgesia produced by morphine Eur J Clin Pharmacol 2000 55 713 9 10663448 10.1007/s002280050003
Bodd E Jacobsen D Lund E Ripel Å Mørland J Wiik-Larsen E Morphine-6-glucuronide might mediate the prolonged opioid effect of morphine in acute renal failure Hum Exp Toxicol 1990 9 317 21 2261246
Hagen NA Foley KM Cerbone DJ Portenoy RK Inturrisi CE Chronic nausea and morphine-6-glucuronide J Pain Symptom Manage 1991 6 125 8 2016556 10.1016/0885-3924(91)90961-3
Skalke C Scmidt H Geisslinger G Lötsch J Analgesic effects of morphine and morphine-6-glucuronide in a transcutaneous electrical pain model in healthy volunteers Clin Pharm Ther 2003 73 107 21 10.1067/mcp.2003.5
Sjøgren P Thunedborg LP Christrup L Hansen LH Franks J Is development of hyperalgesia, allodynia and myoclonus related to morphine metabolism during long-term administration ? Six case stories Acta Anaesthesiol Scand 1998 42 1070 5 9809090
Gong Q-L Hedner J Bjorkman R Hedner T Morphine-3-glucuronide may functionally aantagonize morphine-6-glucuronide induced antinociception and ventilatory depression in the rat Pain 1992 48 249 55 1589243 10.1016/0304-3959(92)90065-J
Smith MT Watt JA Cramond T Morphine-3-glucuronide – a potent antagonist of morphine analgesia Life Sci 1990 47 579 85 2402182 10.1016/0024-3205(90)90619-3
Penson RT Joel SP Bakhshi K Clark SJ Langford RM Slevin ML Randomized placebo-controlled trial of the activity of the morphine glucuronides Clin Phar Ther 2000 68 667 676 10.1067/mcp.2000.111934
McQuay HJ Carroll D Faura CC Gavaghan DJ Hand CW Moore RA Oral morphine in cancer pain: influences on morphine and metabolite concentration Clin Pharmacol Ther 1990 48 236 44 2401122
Ashby MA Fleming BG Wood MJ Somogyi A Plasma morphine and glucoronide (M3G and M6G) concentrations in hospice inpatients J Pain Symptom Manage 1997 14 157 67 9291702 10.1016/S0885-3924(97)00020-1
Klepstad P Dale O Kaasa S Zahlsen K Aamo T Fayers P Borchgrevink PC Influences on serum concentrations of morphine, M6G and M3G during routine clinical drug monitoring:: A prospective survey in 300 cancer patients Acta Anaesthesiol Scand 2003 47 725 731 12803591
Vermeire A Remon JP Rosseel MT Belpaire F Devoulder J Bogaert MG Variability of morphine disposition during long-term subcutaneous infusion in terminally ill cancer patients Eur J Clin Pharmacol 1998 53 325 30 9516031 10.1007/s002280050387
Karnofsky DA Abelmann WH Craver LF Burchenal JH The use of nitrogen mustards in the palliative treatment of carcinoma Cancer 1948 1 634 656
Bogusz MJ Maier R-D Driessen S Morphine, morphine-3-glucuronide, morphine-6-glucuronide, and 6-monoacetylmorphien determined by means of atmospheric pressure chemical ionization-mass-spectrometry-liquid cromatography in body fluids of heroin victims J Anal Toxicol 1997 21 346 55 9288586
Fraser CG Harris EK Generation and application of data on biological variation in clinical chemistry Crit Rev Clin Lab Sci 1989 27 409 37 2679660
Ahokoski O Virtanen A Huupponen R Scheinn H Salminen E Kairisto V Irjala K Biological day-to-day variation and daytime changes of testosterone, follitropin, lutropin and oestradiol-17- in healthy men Clin Chem Lab Med 1998 36 485 91 9746274 10.1515/CCLM.1998.081
Klepstad P Borchgrevink PC Dale O Zahlsen K Aamo T Fayers P Fougner B Kaasa S Routine drug monitoring of serum concentrations of morphine, morphine-3-glucuronide and morphine-6-glucuronide do not predict clinical observations in cancer patients Palliat Med 2003 17 679 687 14694919
Fayers P Machin D Sample size: how many patients are necessary? Br J Cancer 1995 72 1 9 7599035
| 15482600 | PMC526196 | CC BY | 2021-01-04 16:30:54 | no | BMC Med Educ. 2004 Oct 13; 4:20 | latin-1 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-20 | oa_comm |
==== Front
BMC Clin PharmacolBMC Clinical Pharmacology1472-6904BioMed Central London 1472-6904-4-71546181810.1186/1472-6904-4-7Research ArticleDay-to-day variations during clinical drug monitoring of morphine, morphine-3-glucuronide and morphine-6-glucuronide serum concentrations in cancer patients. A prospective observational study Klepstad Pål [email protected] Priscilla [email protected] Jorunn [email protected] Stein [email protected] Petter C [email protected] Kolbjørn [email protected] Ola [email protected] Department of Circulation and Imaging, Faculty of Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway2 Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway3 Department of Clinical Pharmacology, St. Olavs University Hospital, Trondheim, Norway2004 4 10 2004 4 7 7 28 5 2004 4 10 2004 Copyright © 2004 Klepstad et al; licensee BioMed Central Ltd.2004Klepstad 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 feasibility of drug monitoring of serum concentrations of morphine, morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) during chronic morphine therapy is not established. One important factor relevant to drug monitoring is to what extent morphine, M6G and M3G serum concentrations fluctuate during stable morphine treatment.
Methods
We included twenty-nine patients admitted to a palliative care unit receiving oral morphine (n = 19) or continuous subcutaneous (sc) morphine infusions (n = 10). Serum concentrations of morphine, M6G and M3G were obtained at the same time on four consecutive days. If readmitted, the patients were followed for another trial period. Day-to-day variations in serum concentrations and ratios were determined by estimating the percent coefficient of variation (CV = (mean/SD) ×100).
Results
The patients' median morphine doses were 90 (range; 20–1460) mg/24 h and 135 (range; 30–440) mg/24 h during oral and sc administration, respectively. Intraindividual fluctuations of serum concentrations estimated by median coefficients of day-to-day variation were in the oral group for morphine 46%, for M6G 25% and for M3G 18%. The median coefficients of variation were lower in patients receiving continuous sc morphine infusions (morphine 10%, M6G 13%, M3G 9%).
Conclusion
These findings indicate that serum concentrations of morphine and morphine metabolites fluctuate. The fluctuations found in our study are not explained by changes in morphine doses, administration of other drugs or by time for collection of blood samples. As expected the day-to-day variation was lower in patients receiving continuous sc morphine infusions compared with patients receiving oral morphine.
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Background
Morphine is degraded in the liver to several metabolites of which morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) are biological active [1]. M6G is shown to contribute to the analgesia produced by morphine and may cause opioid related adverse effects such as sedation or nausea [2-5]. Due to first pass metabolism and slow accumulation of M6G in the brain the analgesic activity of M6G is most prominent during oral long-term treatment with morphine while single dose studies show less contribution from M6G to the analgesic effects from morphine [2,3,6]. M3G may in exceptional cases cause excitatory adverse effects such as delirium, myoclonus or allodynia [7]. Animal studies observed that M3G have an anti-nociceptive effect [8,9], but this effect was not reproduced in a study administering M3G to volunteers exposed for human experimental pain [10].
The most obvious determinants for serum concentrations of morphine, M6G and M3G are morphine doses, route of administration and renal function. However, a considerable variation of serum concentrations between patients remains after correcting for dose and route of administration [3,11-13].
Measurements of morphine, M3G and M6G serum concentrations can explain individual responses in patients where morphine treatment turns out to have unexpected effects and help physicians to determine changes in pain treatment. Physicians tend to believe that samples obtained for therapeutic drug monitoring during steady state conditions will be representative irrespective of which day the sample is collected. However, morphine, M6G and M3G serum concentrations may also have fluctuations not caused by changes in morphine doses, administration of other drugs or by time for collection of blood samples. This variation represents the day-to-day variability. In order to evaluate the clinical implications from morphine and metabolites serum concentrations measurements it is necessary to know if these serum concentrations have fluctuations not related to changes in drug administrations.
The day-to-day variability in serum concentrations of morphine, M6G and M3G are previously reported in a study of 8 cancer patients treated with subcutaneous (sc) morphine infusions. This study observed coefficients of variation (CV) ranging from 26%–56% for morphine, 20% to 51% for M6G and 20%–49% for M3G [12]. To our knowledge, the day-to-day variations of morphine, M6G and M3G serum concentrations obtained from consecutive days or during chronic oral administration of morphine are not previously reported.
Thus, the aim of this study is to investigate the day-to-day variations of morphine, M6G and M3G serum concentrations during stable chronic oral and sc morphine administration to cancer patients.
Methods
Patients
We included twenty-nine patients admitted during a nine-month period to the Palliative Care Unit at the University Hospital in Trondheim. The inclusion criteria were; verified malignant disease, expected survival time less than 6 months, scheduled morphine treatment started at least three days prior to inclusion, stable scheduled doses of morphine for a minimum of three days and age more than eighteen years. The exclusion criteria were; planned hospitalisation less than three days and lack of ability to communicate (e.g. dementia, deafness).
All patients gave their written informed consent before inclusion. The study was conducted according to the guidelines of the Helsinki declaration. The Regional Committee for Medical Research Ethics, Health Region IV, Norway, approved the study.
Study design
Inclusion
The patients were included in the study within three days after admission to the Palliative Care Unit. Each patient was followed for four days. Patients readmitted to the Palliative Care Unit were allowed to a new trial period identical to the first trial period. No patients were included in more than three trial periods.
The patients' age, gender, primary malignant diagnosis, presence of metastasis, and other medications were registered. Morphine treatment during the last 24 hours was registered with respect to route of administration, morphine formulation, scheduled dose and consumption of rescue morphine for breakthrough pain. The patients' functional status was assessed using the Karnofsky performance status score [15].
Blood samples
Blood samples were obtained each day during the trial period. The samples were obtained at the same time each day during the routine morning round for collecting blood samples.
Observations
In order to observe if the patients were studied during stable treatment conditions the scheduled morphine dose, rescue morphine consumption and route of administration were registered each study day. The use of other medications was also registered daily. Pain, nausea and sedation were assessed at day study two, three and four during the trial period using a 5 category verbal rating scale (VRS) score ranging from no to very severe. All symptoms were assessed for the last 24 h.
Analyses
The blood samples were placed in EDTA tubes until separated by centrifugation (3000 rpm, ten minutes) and stored at -85°C until analysed. All samples were analysed for serum concentrations of morphine, M6G and M3G applying liquid chromatography mass spectrometry [16]. The limits of detection were for morphine 0,35 nmol/l and for M6G and M3G 2,2 nmol/l. The analytical coefficients of variation obtained in quality control samples (CVAnalytical) were for morphine 3,0%, for M6G 5,5% and for M3G 7,0%. The analytical coefficients of variation were determined at 100 nmol/l for morphine and 1000 nmol/l for M6G and M3G. Serum values of creatinine concentrations, alanin aminotransferase activities (ALAT), aspartat aminotransferase activities (ASAT) and albumin concentrations were determined using standard analytical methods.
Statistical evaluation
Total use of morphine for each trial day was calculated by adding scheduled morphine doses and rescue morphine consumption. Samples obtained less than two hour after the administration of a morphine rescue dose were excluded from the analyses.
Day-to-day variations of morphine and its metabolites are presented as biological coeffecients of variation. This biological variation (CVBiological), expressed in terms of percent coefficient of variation, was calculated for each patient in each trial period using the equation [15,16]:
CVBiological = CVObserved - CVAnalytical
The observed coefficients of variation (CVObserved) for morphine, M6G and M3G, which represent the variation in serum concentrations for each patient during each trial period, were calculated using the equation [17,18]:
At least three observations were needed in order to calculate an observed coefficient of variation.
Statistical comparisons between the trial days and trial periods were performed using one-way analysis of variance tests. Due to multiple comparisons statistical significance was defined as p < 0,01.
The statistical software SPSS version 9.0 for Windows was used throughout the analyses.
Results
Patient characteristics
The patients (16 males and 13 women) median age at inclusion was 68 years (range; 39–89). The patients' Karnofsky performance status, primary tumor diagnoses and presence of metastases are shown in Table 1. The median serum creatinine concentrations at inclusion were 72 μmol/l (range; 45–121). The median values at inclusion of ASAT and ALAT were 31 IU/l (range; 7–154) and 17 IU/l (range; 5–65), respectively. No patient had clinical significant liver failure. The median serum albumin concentrations at inclusion were 32 g/l (range; 23–42).
Table 1 Patient characteristics
Karnofsky performance status (median (range)) 60 (40–80)
Cancer Diagnoses
Prostate 11
Colorectal 5
Kidney 3
Breast 3
Pancreatic 2
Lung 2
Gastric 1
Malignant melanoma 1
Leiomyosarcoma 1
Metastases
Liver 7
Bone 16
Other 16
Antidepressants 4
Neuroleptics 1
Benzodiazepines 6
Corticosteroids 13
Antiemetics 7
Ten patients used non-opioid analgesics (nine paracetamol, one acetylsalicylic acid). The patients used a median number of 5 (range; 1–10) non-pain medications. The numbers of patients using psychotropic drugs, antiemetics or corticosteroids are given in Table 1. All except one patients received laxatives, lactulose and bisacodyl, during the study period. All medications were stable during the study period. Similar pain, nausea and sedation scores were observed throughout the trial periods (Table 2). Twenty-seven patients had died at the time of manuscript preparation. The median survival time from inclusion was three months.
Table 2 Symptom scores All scores were obtained using a 5 category verbal rating scale score (scores; 1–5). All results are given as mean (SD). No significant differences in scores were observed between trial days.
Trial day 2 Trial day 3 Trial day 4
Pain 2.8 (0.7) 2.2 (1.1) 2.2 (1,1)
Nausea 1.7 (1.0) 1.7 (1.1) 1.8 (0.9)
Sedation 3.4 (1.2) 3.4 (1.0) 3.2 (1.1)
Of the nineteen patients receiving oral morphine sixteen patients completed one trial period, two patients completed two trial periods and one patient completed three trial periods. The corresponding numbers for the ten patients receiving sc morphine were four, five and one, respectively. Six patients were excluded during a study period. The reasons were; discharge from hospital (n = 2), opioid treatment changed to fentanyl patch (n = 1) and fatigue (n = 3). Opioid induced adverse effects caused no exclusions. Sixteen blood samples were not obtained due to circumstances related to the patients' or relatives' needs (e.g. visits from relatives at the time of a planned blood sample).
Morphine treatment
The median duration of morphine treatment before entering the study was 7 months (range; 0–29). The median morphine dose at inclusion for the patients receiving oral treatment (controlled-release morphine) was 90 mg/24 h (range; 20–1460). The median morphine dose for the patients receiving sc morphine infusions was 135 mg/24 h (range; 30–340). The morphine doses varied between the study days because the patients were allowed to use rescue morphine. This variation, however, was minor (Table 3 and 4).
Table 3 Serum concentrations for morphine and metabolites for the oral route The morphine doses (mg/24 h) vary because of variable doses of rescue morphine. A total of 23 trial periods in 19 patients were studied. All data are given as median and range.
Day 1 Day 2 Day 3 Day 4
Morphine dose (mg/24 h) 90 (20–1460) 80 (20 – 1700) 95 (30 – 1520) 90 (20 – 1580)
Serum morphine (nmol/l) 255 (46–2520) 59 (17–1437) 94 (12–1429) 77 (9–2296)
Serum M6G (nmol/l) 1156 (149–7874) 568 (66–7874) 516 (66–9678) 620 (80–8026)
Serum M3G (nmol/l) 6341 (1734–31997) 3696 (404–36887) 3226 (595–41452) 3778 (526–43043)
Table 4 Serum concentrations for morphine and metabolites for the subcutaneous route The morphine doses (mg/24 h) vary because of variable doses of rescue morphine. A total of 17 trial periods in 10 patients were studied. All data are given as median and range.
Day 1 Day 2 Day 3 Day 4
Morphine dose (mg/24 h) 135 (30–340) 163 (30 – 335) 164 (50 – 440) 150 (84 – 440)
Serum morphine (nmol/l) 240 (42–741) 254 (62–1297) 305 (106–1045) 373 (103–1222)
Serum M6G (nmol/l) 723 (78–1811) 674 (88–2867) 1009 (374–2023) 1225 (400–2339)
Serum M3G (nmol/l) 5350 (578–11784) 4490 (779–16312) 5631 (3028–8342) 6119 (2777–13715)
The diurnal distributions of rescue morphine administrations were recorded in order to assess the possible influence from rescue morphine on the serum concentration observations. Three blood samples were obtained during the two-hour interval following an administration of rescue morphine. The results from these samples were excluded from the analyses.
Morphine, M6G and M3G serum concentrations
The median serum concentrations of morphine during oral morphine treatment ranged from 59 to 255 nmol/l during the four study days. The median serum concentrations for M6G and M3G on each study day for patients receiving oral morphine are given in Table 3.
The median serum concentrations were more stable during sc morphine treatment compared with oral treatment. The median serum concentrations of morphine during sc morphine treatment ranged from 240 to 373 nmol/l during the study days. The median serum concentrations for M6G and M3G on each study day for patients receiving sc morphine are given in Table 4.
Day-to-day variation
The median biological coefficient of variation (CV) for morphine serum concentrations was 46% (range; 13–103) during oral morphine therapy and 10% (range; 0–36) during sc morphine infusions (Table 5). The median biological CV values for M6G were 25% (range; 1–72) during oral therapy and 13% (range; 2–40) during s.c. therapy. The corresponding results for M3G serum concentrations were 18% (range; 0–57) and 9% (range; 0–34), respectively (Table 5).
Table 5 Biological coefficients of variation (CV) of morphine, morphine-6-glucuronide (M6G) and morphine-3-glucuronide (M3G) serum concentrations during four consecutive days of oral or subcutaneous morphine treatment. All values are given as median and range.
Biological coefficient of variation %
Oral morphine Subcutaneous morphine
Morphine 46 (13–103) 10 (0–36)
M6G 25 (1–72) 13 (2–40)
M3G 18 (0–57) 9 (0–34)
Discussion
Intraindividual fluctuation of drug serum concentrations not explained by changes in doses, administration of other drugs or by time for collection of blood samples, is the day-to-day variation. Routine measurements of serum concentrations of morphine and metabolites are of questionable value because of the large variability of minimum effective serum concentration and the lack of a direct relationship between serum concentrations and adverse effects [19]. However, measurements of serum concentrations of morphine and metabolites are of importance in patients displaying unexpected opioid toxity [4,7] Physicians assessing results from serum drug concentrations determinations should be aware to what extent serum concentrations of drugs fluctuate during stable treatment conditions. Without this knowledge differences and changes in serum concentrations observations may be unduly interpreted.
The available data on day-to-day variability during chronic morphine treatment is sparse. Vermeire et al. reported day-to-day variations during morphine treatment in eight cancer patients receiving continuous sc morphine infusion for 1 to 23 weeks. The individual CV values observed in their study varied between 26% to 56% for morphine, 20% to 51% for M6G and 20% to 49% for M3G [14].
We observed less day-to-day variations of morphine and metabolites concentrations during sc morphine treatment (morphine 10%, M6G 13%, M3G 9%) compared to the fluctuations reported by Vermeire et al.. One explanation for this discrepancy is that Vermeire et al. obtained blood samples during treatment periods up to 23 weeks. This study design may overestimate day-to-day variability since patient related factors will vary more during long time intervals than between consecutive days.
To our knowledge this is the first study to assess the day-to-day variation of morphine and morphine metabolites serum concentrations during oral morphine therapy. The median observed CV values for serum concentrations of morphine, M6G and M3G during oral morphine therapy (morphine 46%, M6G 25%, M3G 18%) were higher than the CV values observed in patients receiving sc morphine treatment (morphine 10%, M6G 13%, M3G 9%). This observation was expected due to a more stable delivery rate and since absorption during sc administration is not influenced by food intake, gastric retention, malabsorption, vomiting or variable first-pass metabolism.
The results in our study, as in the study by Vermeire et al., represent day-to-day variability in cancer patients admitted to a palliative care unit. In this patient population pharmacological observations will be influenced by variations in food intake, gastric retention, malabsorption, effects from other drugs on gastric emptying, vomiting and drug interactions. In order to perform a study on day-to-day variation not suspect to these confounding factors patients or volunteers must be recruited into a controlled experimental environment. We believe that studies in controlled experimental environments and studies in patients with advanced cancer disease are complementary to each other. The first targets the pharmacokinetic phenomenon of day-to-day fluctuations, the second targets the fluctuations met during clinical real-life conditions.
We recognise some limitations in our study. First, blood samples were collected during four trial days. An extended trial period in order to obtain a larger number of samples from each patient gives a more precise estimate of day-to-day variation. However, due to ethical considerations, taking into account the strain on each patient from serial blood sampling, we chose to not extend the trial periods beyond four days. A second potential confounding factor is absorption peaks in serum concentrations caused by rescue doses of morphine. We chose to allow for rescue morphine because we wanted to observe the variability of serum concentrations as observed in a normal clinical setting in patients considered to be clinical stable in respect to pain treatment. We belive that the variability caused by serum concentration peaks is limited since samples obtained within a time interval of two hours after administration of a morphine rescue dose were excluded. However, it is important to recognize that in order to observe the exact pharmacological day-to-day variability of serum concentrations of morphine and morphine metabolites a design with a stable baseline morphine dose and a non-morphine alternative for breakthrough pain should be applied. Third, the use of rescue morphine implies that the daily morphine doses were not constant. However, the small changes in daily morphine doses can not explain the observed day-to-day variability.
In this study we assessed clinical symptoms related to opioid treatment in order to verify the stable intensities of symptoms during the study period. We did not attempt to explore the relationships between serum concentrations and clinical outcome measures. As a rule of thumb 25 patients are required per independent variable in order to give valid results in studies exploring the effects from factors predicting clinical observations [20]. Consequently, the size of this study was not sufficient to investigate the relationships between opioid serum concentrations and clinical symptoms.
Conclusions
Morphine, M6G and M3G serum concentrations vary considerably in samples obtained on consecutive days. Such variability is present during stable morphine doses and stable clinical symptoms. The day-to-day variability was lower in patients receiving continuous sc morphine infusions compared with patients receiving oral morphine. These findings indicate that results from blood samples taken in order to assess a patient's pharmacological morphine status should be interpreted with the understanding of that variability is partly caused by day-to-day variation.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
PK, PH and JM participated in the design of the study, running of the study and preparation of the manuscript. SK, PCB and OD participated in the design of the study and preparation of the manuscript. KZ was responsible for measurements of morphine and morphine metabolite serum concentrations.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank Bjørn Fougner for interviewing patients and Per Balstad for support in data management. The study was financial supported by grants from The Norwegian Cancer Society and the Norwegian Research Council.
==== Refs
Glare PA Walsh TD Clinical pharmacokinetics of morphine Ther Drug Monit 1991 13 1 23 2057987
Portenoy RK Thaler HT Inturrisi CE Friedlander-Klar H Foley KM The metabolite morphine-6-glucuronide contributes to the analgesia produced by morphine infusion in patients with pain and normal renal function Clin Pharmacol Ther 1992 51 422 31 1563212
Klepstad P Kaasa S Borchgrevink PC Start of oral morphine to cancer patients: effective serum morphine concentrations and contribution from morphine-6-glucuronide to the analgesia produced by morphine Eur J Clin Pharmacol 2000 55 713 9 10663448 10.1007/s002280050003
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| 15488147 | PMC526202 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 15; 5:150 | latin-1 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-150 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-361538002710.1186/1471-2202-5-36Research ArticleActivation of Erk and JNK MAPK pathways by acute swim stress in rat brain regions Shen Chang-peng [email protected] Yelena [email protected] Christopher [email protected] Emanuel [email protected] Department of Psychiatry, New York University School of Medicine, 550 First Ave, MHL HN511, New York, NY 10016, USA2 Cell Signaling Technology, 166B Cummings Center, Beverly, MA 09115, USA2004 20 9 2004 5 36 36 25 6 2004 20 9 2004 Copyright © 2004 Shen 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 mitogen-activated protein kinases (MAPKs) have been shown to participate in a wide array of cellular functions. A role for some MAPKs (e.g., extracellular signal-regulated kinase, Erk1/2) has been documented in response to certain physiological stimuli, such as ischemia, visceral pain and electroconvulsive shock. We recently demonstrated that restraint stress activates the Erk MAPK pathway, but not c-Jun-N-terminal kinase/stress-activated protein kinase (JNK/SAPK) or p38MAPK, in several rat brain regions. In the present study, we investigated the effects of a different stressor, acute forced swim stress, on the phosphorylation (P) state of these MAPKs in the hippocampus, neocortex, prefrontal cortex, amygdala and striatum. In addition, effects on the phosphorylation state of the upstream activators of the MAPKs, their respective MAPK kinases (MAPKKs; P-MEK1/2, P-MKK4 and P-MKK3/6), were determined. Finally, because the Erk pathway can activate c-AMP response element (CRE) binding (CREB) protein, and swim stress has recently been reported to enhance CREB phosphorylation, changes in P-CREB were also examined.
Results
A single 15 min session of forced swimming increased P-Erk2 levels 2–3-fold in the neocortex, prefrontal cortex and striatum, but not in the hippocampus or amygdala. P-JNK levels (P-JNK1 and/or P-JNK2/3) were increased in all brain regions about 2–5-fold, whereas P-p38MAPK levels remained essentially unchanged. Surprisingly, levels of the phosphorylated MAPKKs, P-MEK1/2 and P-MKK4 (activators of the Erk and JNK pathways, respectively) were increased in all five brain regions, and much more dramatically (P-MEK1/2, 4.5 to > 100-fold; P-MKK4, 12 to ~300-fold). Consistent with the lack of forced swim on phosphorylation of p38MAPK, there appeared to be no change in levels of its activator, P-MKK3/6. P-CREB was increased in all but cortical (prefrontal, neocortex) areas.
Conclusions
Swim stress specifically and markedly enhanced the phosphorylation of the MAPKKs P-MEK1/2 and P-MKK4 in all brain regions tested without apparent alteration in the phosphorylation of P-MKK3/6. Curiously, phosphorylation of their cognate substrates (Erk and JNK) was increased to a much more modest extent, and in some brain regions was not altered. Similarly, there was a region-specific discrepancy between Erk and CREB phosphorylation. Possible explanations for these findings and comparison with the effects of restraint stress will be discussed.
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Background
Increasing evidence implicates stress as an important factor in the vulnerability to depressive and other mental illnesses [1-3]. Consequently, understanding the cellular and biochemical mechanisms that transduce stress signals may inform our insight into the factors that lead to depression, as well as provide potential new avenues for therapeutic intervention. Although current therapies for anxiety and depression usually focus on modulating serotonergic and/or noradrenergic activity in the brain [4], recent data suggest that neurotrophins such as BDNF may ameliorate depression [5]. Neurotrophins activate MAPK signaling, and increased signaling via at least one of the MAPKs, Erk, has been shown to regulate plasticity in the brain by modulating gene expression. Thus, activation of the Erk pathway via both a cAMP/PKA-dependent mechanism leading to activation of CREB, and a non-cyclase-linked pathway, leading to activation of CREB and/or the serum response element (SRE) binding protein Elk-1, have been shown to occur in the hippocampus during acquisition of learning, memory consolidation and long-term potentiation (LTP) [6-8]. It is therefore not unreasonable to speculate that these signal transduction pathways may also be involved in the neuronal plasticity that is presumed to underlie aberrant brain function [9].
The term MAPK is used to denote a family of signal transduction mediators, extensively distributed throughout the central nervous system [10,11], that regulate a diverse array of cellular functions [12-15]. The most common MAPKs are the extracellular signal-regulated kinases Erk1 and 2 (also known as p44 and p42MAPK, respectively), which primarily regulate cellular growth and differentiation, and the p38MAPK and c-Jun-N-terminal kinase/stress-activated protein kinases (JNK/SAPK), which mainly function as mediators of cellular stresses such as inflammation and apoptosis. Physiologically stressful stimuli, including seizure induction [16], ischemic insult [17], visceral pain [18] and electroconvulsive shock (ECS) [19,20] have also been shown to rapidly activate MAPKs in various brain regions.
Subjecting rats or mice to acute stressors such as restraint or forced swim results in activation of c-fos gene and/or Fos protein expression in many brain regions) [21-24]. CRE and SRE are DNA sequences found in the promoter regions of many immediate early genes (IEGs) such as c-fos and zif286. P-CREB and P-Elk-1, by binding to these elements, enhance the transcription of the IEGs and regulate gene expression [25,26]. Recently it was shown that swim stress increased P-CREB expression in several brain regions [27]. Thus, given that, on one hand, stress activated CREB and increased c-fos expression, and on the other, that certain stressful stimuli (see above) activated Erk, it was reasonable to predict that acute physiological stressors would activate the Erk (and perhaps other) MAPK pathway(s). The brain regions most likely to show such an effect would be those implicated in the response to stress, including the hippocampus [28,29], prefrontal [30-32] and other cortical areas [21,33], and the amygdala [21,34-36]. In a previous study we tested this hypothesis and found indeed that acute restraint stress increased P-Erk concentrations in several stress-relevant brain areas; however, neither JNK nor p38MAPK phosphorylation were altered by restraint stress [37]. The present study followed up that investigation by examining the effects of a different common stressor, forced swim stress, extended the number of brain areas examined, and also evaluated changes in the phosphorylation state of the respective upstream MAPKK activators MEK1/2, MKK4 (also known as SEK1 and JNKK) and MKK3/6. While these studies were in progress, others reported that swim stress activated the MKK4/JNK pathway in mice [38].
Results
Swim stress elicits large increases in phosphorylation of MEK1/2 in all brain regions
An acute 15 min session of forced swimming produced obvious large increases in the phosphorylation of the MAPKK MEK1/2 in the hippocampus, neocortex, prefrontal cortex, amygdala and striatum (Fig. 1, top). Surprisingly, phosphorylation of the substrate(s) of activated MEK1/2, Erk1/2, was increased to a much smaller extent, and in some brain regions (hippocampus, amygdala) not at all (Fig. 1, bottom). Swim stress significantly increased P-MEK1/2 levels from 4.5-fold (hippocampus) to more than 100-fold (striatum) in the various brain areas (Fig. 2). P-Erk2 levels, on the other hand, were modestly elevated only in neocortex (2.3-fold), prefrontal cortex (3.3-fold) and striatum (2.3-fold) (Fig. 2). Because P-Erk1 signals were generally much weaker, and in short exposure blots nearly undetectable, only P-Erk2 bands were quantitated. However, qualitative changes in P-Erk1 concentrations paralleled those in P-Erk2 (Fig. 1, bottom: neocortex, amygdala, striatum); this conclusion was borne out in over-exposed blots, which, however, were difficult to quantitate for P-Erk1 because of overlap with the large adjacent P-Erk2 signal.
Swim stress increases phosphorylation of MKK4 in all brain regions
Analogous to its effects on P-MEK1/2, but much more dramatically, forced swimming enhanced the levels of P-MKK4, the immediate upstream activator of JNK, in all brain areas tested (Fig. 3, top). Increases ranged from 12.6-fold (hippocampus) to ~300-fold (striatum) (Fig. 4). It should be noted that all increases greater than about 10–15-fold are approximate, since the relationship between optical density and the film exposure-response is no longer linear beyond this range, as determined previously [37]. Thus, increases greater than 10–15-fold are likely underestimates of the actual changes. Nevertheless, it seemed reasonable to utilize the calculated numerical estimates for statistical purposes. In contrast, and again analogous to the findings in the MEK/Erk pathway, much smaller changes were observed in P-JNK1 (p46) and P-JNK2/3 (p54) levels (Fig. 3, bottom). Both P-JNK1 and P-JNK2/3 were significantly elevated in all brain areas, with the single exception of a non-significant increase in P-JNK1 in the amygdala (Fig. 4). Significant swim stress-induced increases for these kinases ranged from 1.4–5.4-fold across brain regions (Fig. 4).
Effects of swim stress on activation of the p38MAPK pathway and phosphorylation of CREB
In contrast to its effects on the Erk and JNK pathways, swim stress exhibited no effect on the phosphorylation of p38MAPK in any brain region, with the exception of a small but significant increase (55%) in the hippocampus (Figs. 5 and 6). As shown above, the major impact of swim stress was to activate the corresponding upstream MAPKKs of Erk and JNK, i.e., to increase P-MEK1/2 and P-MKK4, respectively. Since the major activators of p38MAPK are the closely related, dual specificity kinases phosphorylated MKK3 and MKK6 [39], we attempted to determine if swim stress increased the phosphorylation of this upstream kinase. Fig. 7 shows that the P-MKK3/6 antibody (CST # 9231) detected at least two swim stress-induced bands in the hippocampus, at ~37 and 48 kD. However, the expected molecular weight is ~40 kD; indeed, cell extracts of UV-treated NIH/3T3 and anisomycin-treated C6 glioma cells both exhibited induced bands at ~40 kD (Fig. 7). It seems likely, therefore, that basal levels of brain P-MKK3/6 are very low (cf. basal levels of P-MKK4, Fig. 3), and, unlike P-MEK1/2 and P-MKK4, are not induced by swim stress. Interestingly, use of a different P-MKK3/6 antibody (sc-7994-R, Santa Cruz) yielded very similar results, specifically, swim stress induced a large increase in an ~48 kD band (not shown). Other brain regions examined with the P-MKK3/6 antibody yielded the same pattern; no band at ~40 kD was evident (not shown). We considered the possibility that the swim-induced protein at ~48 kD might be P-MKK7, since that is the approximate molecular weight of this kinase. Moreover, MKK7, like MKK4, phosphorylates JNK [40]. Since the phosphopeptides used to raise the P-MKK4, P-MKK7 and P-MKK3/6 antibodies share a substantial degree of homology in their amino acid sequences, it was possible that the P-MKK3/6 antibody was detecting a swim-induced increase in P-MKK7. However, at a concentration of 1 ng/ml, neither the P-MKK4 nor the P-MKK7 phosphopeptides affected the protein band pattern observed using the P-MKK3/6 antibody, whereas the P-MKK3/6 phosphopeptide completely abolished all signals (data not shown). This appears to rule out the possibility that the 48 kD band is P-MKK7. The identities of the swim stress-induced bands observed with the P-MKK3/6 antibodies remain unknown.
In partial agreement with a previous report [27], swim stress induced significant increases in CREB phosphorylation in the hippocampus and amygdala (as well as in the striatum in the present study); however, we were unable to replicate reported P-CREB increases in neocortex and prefrontal cortex (Figs. 5 and 6). The reasons for this discrepancy are not apparent; however, while the same rat strain (Wistar) and swim protocol (15 min, followed by immediate sacrifice) were used, other differences (e.g., P-CREB antibody source, halothane vs. CO2 anesthesia, etc.) may account for the difference in results. Note, moreover, that the regional pattern of changes in P-CREB (Fig. 6) and P-Erk (Fig. 2) differed markedly. Whether this is due to a simple difference in time course or, more substantively, in activation mechanism, awaits further investigation.
Time course of swim stress-induced MAPK kinase phosphorylation: onset and offset
The effect of varying the duration of swim on the phosphorylation of MEK1/2 and MKK4 was examined in neocortex and hippocampus. The effect on both kinases gradually reached a maximum at 15 min (longer times were not assessed), except for the increase in P-MEK1/2 in neocortex, which peaked at 5 min (Fig. 8). The levels of P-MEK1/2 and P-MKK4 in both neocortex and hippocampus were maximal immediately after termination of swim stress (Fig. 9, top), declined gradually, and all except P-MKK4 in neocortex returned to basal levels by 60 min. The levels of the corresponding phosphorylated substrates of the MAPK kinases, P-Erk2 and P-JNK (combined P-JNK1 and P-JNK2/3) followed a very similar pattern of decline post-swim stress (Fig. 9, bottom), and all except neocortical P-JNK were at control levels by 60 min. Note that hippocampal P-Erk2 was unchanged at all times after swim stress, consistent with the results shown above in Figs. 1 and 2.
Discussion
The results presented establish unambiguously that forced swim stress elicits a rapid and profound but relatively transient increase in the phosphorylation (and consequently the presumed activation) of the MEK-Erk and MKK4-JNK signaling pathways in, at the least, the five brain regions examined. These effects displayed specificity in that the MKK3/6-p38MAPK pathway was essentially unchanged. (Although in the experiment shown in Figs. 5 and 6, there was a very modest increase in P-p38MAPK that was restricted to the hippocampus, there was no significant change in this phosphoprotein in an independent replication of this experiment; data not shown). Our results may be compared to a very recent study on the effects of forced swim in mice, reported by Liu et al. [38] while the present study was nearing completion. Both studies are in agreement that swim stress elicits a very large increase in P-MKK4 levels throughout the brain, and that P-JNK levels are correspondingly elevated in the same brain regions. In addition, both studies agree that P-p38MAPK levels are unaffected. However, Liu et al. [38] did not determine the effects of swim stress on P-MEK1/2 levels (which we found were also markedly elevated: Figs. 1 and 2). Moreover, whereas Liu et al. [38] did not find any significant change in P-Erk levels, our studies indicated region-specific alterations in this kinase: increases in cortical areas (prefrontal and neocortical) and striatum, but not in the hippocampus or amygdala (Figs. 1 and 2). The reasons for the dissimilar results on P-Erk are not apparent, but it should be noted that the studies utilized different species (mice vs. rats), which may have contributed to the observed differences. Also, it is possible but unlikely that stress duration (30 min in the study of Liu et al. and 15 min here) accounts for the difference in results.
One of the most interesting aspects of the present results was the apparent discrepancy between the magnitude of the changes in the MAPKKs (P-MEK1/2 and P-MKK4) and the corresponding MAPKs (P-Erk and P-JNK). It is obvious from Figs. 2 and 4 that whereas swim stress elevated P-MEK1/2 and P-MKK4 levels dramatically, the effects on their substrates, P-Erk2 and P-JNK, respectively, were much more modest. (This difference appears to be much smaller in mice; [38]). One possible explanation is a temporal difference in the phosphorylation of the upstream MAPKKs and their downstream MAPK substrates. A gradual increase in P-Erk2 and P-JNK phosphorylation, subsequent to activation of MEK1/2 and MKK4, may have been obscured by the fact that animals were sacrificed immediately after termination of the swim. However, this is clearly not the case, as the time-course of the swim-induced increases in phosphorylation of the MAPKKs and MAPKs were very similar in both the hippocampus and neocortex (Fig. 9). Another explanation centers on potential differences in phosphorylation stoichiometry, i.e., levels of basal phosphorylation among the various phosphoproteins may vary greatly. Thus, one may posit that kinases (e.g., MEK1/2, MKK4) whose basal state of phosphorylation is very low may be subject to large increases, whereas those whose basal state is comparatively larger (e.g., Erk, JNK) demonstrate much smaller increases. For example, activation of tyrosine hydroxylase, the rate-limiting enzyme for catecholamine biosynthesis, is related to phosphorylation of the protein on at least three different serine sites [41]. The basal phosphorylation stoichiometry of these sites varies not only among the sites but also between brain regions [42]. Interestingly, haloperidol-induced phosphorylation of the sites, as visualized on immunoblots using phosphosite-specific antibodies, appears to be negatively related to the basal phosphorylation state [42]. To our knowledge, the basal phosphorylation stoichiometry of the various rat brain kinases examined in the present study are not known. However, a recent technique has been described [43] that may lend itself to estimating basal and swim stress-induced phosphorylation stoichiometry in various brain regions.
Nevertheless, differences in phosphorylation stoichiometry cannot explain why, in some brain regions (i.e., hippocampus and amygdala, Figs. 1 and 2), there were no discernable increases in P-Erk after swim stress, despite large increases in P-MEK1/2. Activated MAPKKs, unlike their upstream activators or downstream targets, are known to exhibit narrow substrate specificity [13]. Indeed, Erk1/2 activity is believed to be exclusively regulated by MEK1/2 [15]. Thus, a large increase in MEK1/2 phosphorylation/activation would be expected to translate into substantial phosphorylation/activation of Erk. However, the activity of P-MEK1/2 is regulated by a negative feedback mechanism involving activation of protein phosphatase(s) that rapidly dephosphorylate P-Erk1/2 [44]. A large number of potential phosphatases have been implicated in the regulation of Erk phosphorylation [44]; consequently, identifying a specific phosphatase that may be responsible for such an effect was beyond the scope of the present study. Recently, a detailed kinetic analysis of eleven different protein phosphatases implicated three specific phosphatases as the most likely mediators of Erk2 dephosphorylation, including mitogen-activated protein kinase phosphatase 3 (MKP-3) and protein phosphatase 2A (PP2A) [45]. It might be instructive therefore in a future study to assess the activity of these protein phosphatases in tissue lysates. A final alternative hypothesis is that swim stress activates a mechanism that serves to uncouple MEK1/2 phosphorylation (fully or in part) from activation of Erk. That such uncoupling is possible has been reported in mammalian cells undergoing mitosis [46], but the mechanism deduced in that instance is highly unlikely to apply in the present case; nevertheless, the possibility is worthy of consideration.
In a previous communication we reported that 30 min of restraint stress elicited modest (1.6–2.5-fold) increases in P-Erk levels in hippocampus and prefrontal and cingulate cortex [37]. As for swim stress, P-p38MAPK was not altered. In contrast to the effects of swim stress, P-JNK levels were not altered by restraint. That study did not assess changes in any of the MAPKKs, limiting further comparisons. However, in a preliminary experiment we found that restraint stress increased P-MKK4 levels in both the hippocampus and striatum, but the effects were more modest than after swim stress (data not shown). Interestingly, Liu et al. [38] reported that restraint, like swim, elevated both P-MKK4 and P-JNK levels in mice, but the effects in the former stress model were smaller than in the latter. However, P-Erk and P-p38MAPK levels were unchanged in both restrained and swim stressed mice. It thus appears that both the rodent species and type of stress may be important determinants of the specific regional changes occurring in the various kinases. Additional studies will be required to sort out the influence of a variety of factors on the phosphorylation pattern of the MAPK signaling pathways in the brain regions of different animal species. In particular, restraint and swim differences in ambient temperature and physical activity may impact the results. Finally, because these two forms of stress yield different patterns of effects on MAPKs in brain, we can ask which MAPKs (and which brain regions) are the more relevant to stress effects, i.e., which stress model is more appropriate. Answering this question will require correlating the behavioral and MAPK sequelae of different stress treatments (see below).
The nature of the extracellular signaling molecule(s) that are recruited upon subjecting animals to stress are unknown. The pathways leading from stimulation of a particular receptor on the cell surface to activation of MAPK signaling cascades are diverse and complex [13,14,47-50]. For example, Erk1 and Erk2 can be activated by a variety of extracellular signaling molecules, including growth factors, hormones and neurotransmitters [48-52]. Calcium influx in particular has gained currency as potentially of prime importance [53]. As mentioned earlier, stress is well known to activate c-fos gene expression) [21-24], and such activation in neurons can occur, at least in part, because of an increase in Erk activity via NMDA glutamate receptor-stimulated calcium influx [54-56]. Stress also activates CREB phosphorylation [27], which may be mediated at least in part by NMDA receptor activation [57]. Thus, it is attractive to speculate that NMDA receptor activation (at least in some brain regions) may be the initial trigger leading to activation of the Erk pathway (calcium influx may also lead to activation of the JNK pathway via other signaling mechanisms; see [53]). NMDA receptors themselves are subject to regulation by phosphorylation on their NR1, NR2A and NR2B subunits [58-60]. Interestingly, dopamine D1 receptor-mediated CREB phosphorylation appears to involve NMDA NR1 subunit phosphorylation [61]. In preliminary experiments we have found that swim stress increased P-NR1 levels on immunoblots of the hippocampus, neocortex and striatum (data not shown), suggesting that stress-induced activation of the MEK-Erk pathway may involve NMDA receptor-mediated calcium influx. Further studies will be directed toward explicating the differential effects of swim stress on the degree of phosphorylation of the MAPKKs vs. the MAPKs, assessing NMDA receptor involvement, and determining whether (and which) MAPKK pathways participate in some behavioral sequelae of repeated swim stress treatment (e.g., immobility and analgesia).
Conclusions
Acute swim stress initiated a rapid increase in phosphorylation of the MAPKKs MEK1/2 and MKK4 in hippocampus, neocortex, prefrontal cortex, amygdala and striatum. Concomitantly, their corresponding substrates Erk and JNK were also phosphorylated, but not always in register with the changes in the MAPKKs. Moreover, the magnitude of the increase in phosphorylation of MEK1/2 and MKK4 was much greater than for their cognate substrates. While it is clear that stressors such as forced swim and restraint activate these signaling pathways, much work will be required to define the mechanism(s) responsible for these effects, and to relate potential alterations in the activity of these molecules to stress-related influences on the development of anxiety and depressive disorders.
Methods
Animals
Male Wistar rats (200–300 g; Taconic Farms, Germantown, NY) were used throughout. Rats were housed 2/cage and maintained on a 12 h light/dark cycle with food and water ad libitum; they were acclimated to handling and transportation for 2–3 weeks before being used in experiments. All experimental procedures were carried out in accordance with the NIH Guide for the Care and Use of Laboratory Animals, and were approved by the NYU School of Medicine Institutional Animal Care and Use Committee.
Materials
Precast 10% polyacrylamide gels were obtained from Cambrex BioScience, Rockland, ME. Complete® protease inhibitor cocktail was from Roche Molecular Biochemicals, Indianapolis, IN and colored molecular weight markers were purchased from Bio-Rad, Hercules, CA. Biotinylated protein ladder marker and anti-biotin were from Cell Signaling Technology (CST), Beverly, MA. West Pico Super Signal enhanced chemiluminescence (ECL) reagent was obtained from Pierce Biotechnology, Rockford, IL, and Re-Blot Plus stripping buffer was from Chemicon International, Temecula, CA. All primary antibodies were rabbit polyclonal except as noted. Because suppliers often offer more than a single antibody for the same antigen, catalog numbers are specified in parentheses. P-Erk (monoclonal, sc-7383), pan Erk (sc-93) and pan MKK4/SEK1 (sc-964) were all from Santa Cruz Biotechnologies, Santa Cruz, CA. The following antibodies were obtained from CST: P-MEK1/2 (9121), pan MEK1/2 (9122), P-MKK4/SEK1 (9151), P-JNK (monoclonal, 9255), pan JNK (9252), P-MKK3/6 (9231), pan CREB (9192) and pan p38MAPK (9212). P-CREB (06-519) was from Upstate Biotechnology, Inc. (Waltham, MA), while P-p38 was either from CST (9212) or Chemicon (AB3828). Control cell extracts for P-MKK3/6 were obtained from CST: +/- UV-treated NIH/3T3 cell lysates (9233) and +/- anisomycin-treated C6 glioma cell lysates (9213).
Stress procedure
Groups of rats (n = 4–6) were subjected to forced swim stress in a round glass tank (24 cm W × 44 cm H) filled to a depth of 30 cm with water (25 ± 1°C). In most experiments rats were forced to swim for 15 min and sacrificed by decapitation immediately after narcotization with carbon dioxide for 20 sec. In some experiments the duration of swim stress (2–15 min) or the post-swim interval (0–60 min) was varied as described in the legends to Figures. Control rats were not treated and were sacrificed in parallel as above.
Tissue dissection
Brains were rapidly removed, briefly chilled in saline and placed in a stainless steel brain matrix (Activational Systems, Warren, MI). Sections of 1–2 mm in thickness were cut, frozen quickly on dry ice, and brain areas of interest (prefrontal cortex, neocortex, amygdala and striatum) were micropunched using the atlas of Paxinos and Watson [62] as a guide. Hippocampus was dissected freehand from the remaining portion of the brain (approximately posterior to bregma -3.80). All samples were stored at -80°C until processed.
Tissue processing
Tissues were mechanically disrupted, in glass homogenizers using a Teflon pestle, in 10–20 volumes of buffer (50 mM Tris-HCl, pH 7.4 containing 300 mM NaCl, 1% Nonidet P-40, 10% glycerol, 1 mM EDTA, 1 mM Na3VO4, 1 mM NaF, 0.5 μM okadaic acid and Complete® protease inhibitor cocktail). After centrifugation at 30,000 g for 20 min, lysates were mixed with 5X sodium dodecyl sulfate (SDS) sample buffer, boiled for 5 min and stored at -80°C. Protein content of lysates was determined using the bicinchoninic acid assay kit (Pierce).
Protein separation and immunoblotting
Proteins (10–40 μg/lane) were separated by SDS-PAGE on precast polyacrylamide gels. Gels were also loaded with colored molecular weight markers to assess electrophoretic transfer, and biotinylated protein ladder marker to estimate molecular weights of bands of interest. Following electrophoretic transfer to nitrocellulose, blots were incubated in blocking buffer (5% nonfat dry milk in Tris-buffered saline containing 0.05% Tween-20 (TBST)) for 1 hr at room temperature (RT), washed 3 × 10 min in TBST and incubated with primary phospho-specific antibodies overnight at 4°C (monoclonal antibodies in 5% milk/TBST, polyclonal antibodies in 5% BSA/TBST). Blots were washed 3× in TBST, incubated with the appropriate secondary antibody plus anti-biotin for 1 hr at RT, washed again, treated with ECL reagent and exposed to film. Blots were then stripped by incubation for 15 min at RT with Re-Blot Plus, re-blocked, washed and incubated for 1 hr at RT with the corresponding pan antibody which recognizes total antigen protein (phosphorylated and nonphosphorylated). This was followed by incubation for 1 hr at RT with the appropriate secondary antibody plus anti-biotin. Antigens were again visualized by treatment with ECL reagent and exposure to film.
Immunoblot analysis
A computerized image analysis system (MCID-M4, Imaging Research, St. Catherines, Ontario, Canada) was utilized to analyze immunoblots. Image analysis was carried out as described previously [63]. For each blot, relative phosphoprotein levels were calculated from the ratio of absorbance of the phosphoprotein/pan protein to correct for small differences in protein loading. Differences between experimental and control conditions were obtained by comparison to the normalized control ratio (arbitrarily set at 100).
Statistical analysis
Control and swim-stressed groups were compared using Student's t test (SigmaStat, ver. 2.03, SPPS, Inc., Chicago, IL).
Authors' contributions
C-PS conducted the majority of the immunoblotting studies. YT performed some of the experiments and quantitated many of the films using image analysis. CS assisted in the design of some of the experiments, carried out the MKK3/6 experiments, and assisted in the writing of the manuscript. EM designed the study, supervised and facilitated the experiments, analyzed the data and wrote the manuscript.
Acknowledgements
This work was supported by a grant (MH-60592) to EM from the National Institute of Mental Health, USA.
Figures and Tables
Figure 1 Swim stress increased phosphorylation of MEK1/2 and Erk2 in brain. Representative immunoblots of changes in P-MEK1/2 and P-Erk2 after subjecting rats to forced swim for 15 min. Total (pan) MEK1/2 and Erk2 were unchanged by swim stress. In this and all other immunoblot figures, molecular weight markers (not shown) corroborated the expected size of target proteins. C, control; S, swim stress.
Figure 2 Quantitative changes in P-MEK1/2 and P-Erk2 following swim stress. The results are the mean ± standard error of 4–6 rats/group. * p < 0.05; **p < 0.01; ***p < 0.001. Hipp, hippocampus; NCx, neocortex; PFC, prefrontal cortex; Amyg, amygdala; Str, striatum.
Figure 3 Swim stress-induced phosphorylation of MKK4, JNK1 and JNK2/3 in brain regions. Representative immunoblots are shown. In the JNK blots, the lower band corresponds to the 46 kD JNK1 isoform, while the upper band represents the (unseparated) 54 kD JNK2 and 3 isoforms. Abbreviations as in the legend to Fig. 1.
Figure 4 Quantitative changes in P-MKK4 and P-JNK1 and P-JNK2/3 after swim stress. N = 4–6 rats per group. HP, hippocampus; NC, neocortex; Amy, amygdala; other abbreviations and statistics as in the legend to Fig. 2.
Figure 5 Swim stress generally did not alter p38MAPK phosphorylation but elevated P-CREB in some brain regions. Representative blots are shown. Abbreviations as in the legend to Fig. 1.
Figure 6 Quantitative regional alterations in P-p38MAPK and P-CREB after swim stress. Abbreviations as in the legends to Figs. 2 and 4; statistics as in the legend to Fig. 2. Mean ± standard error of 4–6 rats/group.
Figure 7 Swim stress did not induce phosphorylation of MKK3/6. Note the absence of a band for P-MKK3/6 at ~40 kD, either in control or swim stressed hippocampal lysates, whereas an induced band is visible in the positive control cell extracts. Also note the unknown ~37 and 48 kD proteins induced by swim stress. Other brain regions examined exhibited a similar pattern. 3T3, lysates of NIH/3T3 cells treated with (+) or without (-) UV radiation (40 mJ, 45 minutes recovery time); C6, lysates of C6 glioma cells treated with (+) or without (-) anisomycin (25 μg/ml, 30 min); MW, molecular weight markers.
Figure 8 Time-course of the onset of swim stress-induced increases in phosphorylation of MEK1/2 and MKK4. Rats were sacrificed immediately upon termination of the indicated swim periods. Shown are the means of duplicate samples from a representative experiment. The experiment yielded similar results upon repetition.
Figure 9 Decline of swim-induced increases in phosphorylation of MAPK signaling proteins after stress termination. Rats were subjected to a 15 min swim stress session and sacrificed at various times after swim termination. Top, time-course of the falloff in P-MEK1/2 and P-MKK4 phosphorylation in hippocampus (HIPP) and neocortex (NCx); bottom, time-course for P-Erk2 and P-JNK (all 3 isomers quantitated together). Shown are means of duplicate samples from a representative experiment that was repeated with similar results.
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| 15380027 | PMC526203 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Sep 20; 5:36 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-36 | oa_comm |
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BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-4-171548557610.1186/1471-2261-4-17Research ArticleEffect of the G-308A polymorphism of the tumor necrosis factor (TNF)-α gene promoter site on plasma levels of TNF-α and C-reactive protein in smokers: a cross-sectional study Gander Marie-Louise [email protected] Joachim E [email protected] Friedrich E [email protected] Känel Roland [email protected] Department of General Internal Medicine, University Hospital, Bern, Switzerland2 Institute for Behavioral Sciences, Federal Institute of Technology, Zürich, Switzerland3 Institute of Clinical Chemistry, University Hospital Zürich, Switzerland2004 14 10 2004 4 17 17 10 5 2004 14 10 2004 Copyright © 2004 Gander et al; licensee BioMed Central Ltd.2004Gander 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
Plasma levels of tumor necrosis factor (TNF)-α and of C-reactive protein (CRP) are elevated in smokers. Previous studies failed to show an association between the G-308A polymorphism in the promoter region of the TNF-α gene and coronary artery disease (CAD). We investigated whether smoking would interact with the TNF-α G-308A polymorphism in determining plasma levels of TNF-α and CRP.
Methods
Study participants with a complete data set in terms of smoking and the TNF-α G-308A polymorphism were 300 middle-aged male and female industrial employees. After excluding 24 irregular smokers, analyses were performed on 198 "non-smokers" (life-long non-smokers or subjects who quit smoking >6 months ago) as compared to 78 "regular smokers" (subjects currently smoking >3 cigarettes/day). All subjects had a fasting morning blood draw to measure plasma levels of TNF-α and CRP by high-sensitive enzyme-linked immunosorbent assays.
Results
The cardiovascular risk factor adjusted analysis regressing log-transformed CRP levels against smoking status, genotype, and smoking-status-genotype interaction revealed a significant main effect for smoking status (F1,250 = 5.67, p = .018) but not for genotype (F1,250 = 0.33, p = .57). The interaction-term between genotype and smoking status was not significant (F1,250 = 0.09, p = .76). The fully adjusted model with plasma TNF-α failed to show significant main effects for smoking and genotype, as well as for the smoking-status-genotype interaction.
Conclusions
The findings suggest that the TNF-α G-308A polymorphism does not mediate the effect of smoking on plasma CRP levels. It remains to be seen whether other genetic polymorphisms along the inflammatory pathway may modulate vascular risk in smokers.
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Background
Coronary artery disease (CAD) is associated with chronic inflammatory processes in which blood-derived macrophages play a key role [1]. Tumor necrosis factor (TNF)-α is essentially produced by monocytes and macrophages, and, in turn, it is the strongest known paracrine activator of monocytes and macrophages [2]. Upon stimulation, these cells secrete a variety of products including interleukin (IL)-6 stimulating the liver to produce the acute phase reactant C-reactive protein (CRP) [3]. CRP is an acknowledged indicator of increased systemic inflammation across a wide range of diseases [4]. TNF-α and CRP both are found in considerable quantities in atherosclerotic lesions [5,6], and they have also been associated with increased cardiovascular risk in numerous large population-based studies [[7,8]; for review].
Smoking is one of the strongest cardiovascular risk factors for atherosclerotic diseases [9]. Several studies have revealed increased plasma levels of TNF-α and of CRP in smokers as compared to non-smokers [10,11], suggesting that part of the coronary risk associated with smoking may relate to increased inflammatory activity. However, the prevalence of cardiovascular disease varies substantially among smoking individuals [12]. This could indicate that genetic factors are important determinants of the biological pathways linking smoking with cardiovascular disease risk [13]. In fact, it has been recently shown that the CC polymorphism in the promoter region of the CD14 gene (CD14 -159C/T) was associated with common carotid artery intima-media thickness in smokers, but not in non-smokers [14]. Similarly, in young and healthy individuals carrying the C allele of the interleukin-6 promoter polymorphism -174 genotype, those who smoked had higher leukocytes, lymphocytes, and monocytes than those who did not smoke [15].
Another candidate polymorphism that might mediate the cardiovascular risk with smoking, and that is at the initiation of the inflammatory cascade, is the G-308A polymorphism of the TNF-α gene promoter site. The TNF-α G-308A polymorphism is a single base pair polymorphism located at position 308 in the TNF-α gene that maps to human chromosome 6 (p21.1-p21.3) resulting in substitution of the nucleotide adenine (A) for guanine (G) [16]. In the present study, we investigated the possible impact of this polymorphism on the association between smoking severity and plasma levels of TNF-α and CRP in vivo.
While the TNF-α G-308A polymorphism has been associated with increased production of TNF-α in vitro [17,18], previous studies failed to show an association of this polymorphism with CAD [19-25]. However, there is a dearth of data regarding possible interactions between environmental risk factors for cardiovascular disease (e.g., smoking) and the G-308A polymorphism on plasma levels of proinflammatory markers. It is conceivable that the TNF-α G-308A polymorphism alone is of negligible importance in CAD, but that the presence of certain environmental conditions (i.e., exposure to tobacco smoke) and specific alleles may influence CAD risk [26-28].
If alleles are randomly distributed between smokers and non-smokers, case-control studies not explicitly investigating the possible environmental-genetic interaction might fail to unravel the role of genetic polymorphisms in CAD. Therefore, we speculated that smoking would affect plasma TNF-α and CRP levels depending on the TNF-α genotype. If so, the genotype might affect the rate of disease progression rather than the existence of atherosclerotic lesions.
Methods
Study participants
The study was conducted as part of a larger project in an airplane manufacturing plant in southern Germany. From a total of 1,760 employees, participation was offered to a representative sample of 647 men and women. Of those, 537 (accrual rate 83%) volunteered to participate. The Institutional Review Board approved the study protocol. All 537 subjects completed questionnaires on medical and psychosocial health status. A subsample of 332 subjects agreed to have a variety of biological measures assessed. Three hundred of these individuals had complete data on smoking status, smoking history and the TNF-α G-308A gene polymorphism.
Based on self-reported smoking history, we categorized life-long non-smokers or those, who had quit smoking for at least 6 months as „non-smokers" (n = 198), smokers who were currently smoking >3 cigarettes per day as "regular smokers" (n = 78), and smokers reporting to consume up to 3 cigarettes/day or who had stopped smoking for less than 6 months as „irregular smokers" (n = 24). Because of the resulting small numbers of irregular smokers, the latter 24 individuals were excluded from further analyses. Of the remaining 276 individuals, we excluded those with a history suggestive of symptomatic atherosclerotic disease, individuals reporting intake of drugs or conditions that might affect CRP levels (including chronic inflammatory diseases such as active rheumatoid arthritis), and subjects for whom CRP data were missing because of occasional assay problems. Subjects reporting a positive history of elevated blood glucose were not excluded. This selection procedure resulted in a final dataset of 261 subjects.
Experimental protocol
Data were collected on two occasions: First, subjects completed a medical questionnaire and examination. The medical assessment consisted of a 96-item questionnaire assessing the medical history and smoking behavior. The questionnaire was based on the Nurses Health Study [29] with questions asking for smoking behavior adapted from the MONICA study [30].
After completion of questionnaires, subjects had a 15-min rest period while sitting. Thereafter, systolic and diastolic blood pressure (BP) were measured twice within 5 min by sphygmomanometry, and the average of the two readings was computed. The waist-to-hip ratio was calculated based on waist circumference (as measured at its narrowest point between the ribs and iliac crest) and hip circumference (as measured at the maximal buttocks). Blood samples after overnight fasting were collected within one day to two weeks after having obtained medical data. Blood sampling was scheduled two hours after awakening to minimize circadian variation in variables of interest.
Biochemical analyses
Venous blood was obtained using cooled (4°C) citrate tubes for the TNF-α assay. Plasma was snap-frozen after centrifugation until further processing. We chose high-sensitive enzyme-linked immunosorbent assays (ELISA) to measure plasma concentrations of TNF-α (Quantikine HS, R&D Systems Europe, Abington, United Kingdom) and of CRP (detection limit 0.1 mg/l; Immunolite, DPC Biermann GmbH, Germany). In contrast to standard CRP assays, the high-sensitivity assay for CRP allows stratification of subjects with CRP levels below the range used for infectious disease workup [31]. Moreover, it has been argued that the high-sensitive CRP assay is required for risk assessment of cardiovascular disease [32]. Low-density lipoprotein cholesterol (LDL-C) cholesterol and high-density lipoprotein cholesterol (HDL-C) as well as hemoglobin A1c (HbA1c) were determined by a commercial laboratory (Synlab, Augsburg, Germany) applying standard procedures.
Gene analysis
To determine the TNF-α -308 G/A gene polymorphism, we extracted genomic DNA from the leukocyte-containing pellets remaining after centrifugation of coagulated blood using the QIAmp DNA Blood Mini Kit (Qiagen, Hilden, Germany). The TNF-α G-308A polymorphism was assessed by fluorescent real-time polymerase chain reaction with melting curve analysis on a LightCycler (Roche Diagnostics, Rotkreuz, Switzerland) using the TNF-α G-308A ToolSet for LightCycler (Genes-4U, Neftenbach, Switzerland) containing specific primers and fluorescent mutation detection oligonucleotide probes, in conjunction with the Roche Light Cycler Hyb Probe Master Mix (Roche Diagnostics, Rotkreuz, Switzerland) according to the manufacturer's protocols. For statistical analyses, we used the following groups: a) the GG variant, and b) the rarer AA and GA genotypes combined.
Statistical analyses
Descriptive data are presented as means ± SD or as median and interquartile range for severely skewed data. To approximate a normal distribution, we log transformed TNF-α and CRP values. General linear models were employed to elucidate the proportion of variance explained of log-transformed plasma TNF-α and CRP values (dependent variables). Independent variables were smoking status (regular smokers vs. non-smokers), gene variant, and an interaction term between smoking status and gene variant. Following this crude analysis, we entered possible covariates (age, gender, self-reported physical exercise, self-reported alcohol intake, gender, BP, and lipoproteins) into the equation. Results were considered statistically significant at the p ≤ .05 level; all tests were 2-tailed. To minimize possible type II errors when assessing interaction terms, we considered interaction terms when the F-statistic on the interaction term had a p-value < 0.2 [33]. All regression and variance analyses were performed using generalized linear models (PROC MIXED) to account for the unbalanced nature of the data (SAS version 8.2, SAS Inc, Cary, NC). Analyses were repeated including the small group (n = 24) of irregular smokers either with the non-smokers or with the regular smokers. None of these additional analyses changed our main results to a relevant degree. Therefore, only the results comparing non-smokers with regular smokers are reported.
Results
Study population and smoking status
Table 1 compares health variables between non-smokers (n = 198), irregular smokers (n = 24) and regular smokers (n = 78). Regular smokers tended to be younger (p = 0.09), and they had significantly lower HDL-C levels (p = 0.01) than non-smokers. Regular smokers also had higher plasma levels of CRP than non-smokers (median 1.75 mg/l vs. 1.0 mg/l, p = 0.006). In our study sample, irregular smokers had plasma levels of CRP comparable to non-smokers (median 0.81 vs. 1.0 mg/l, p = 0.94). TNF-α was not significantly different between regular smokers and non-smokers.
Table 1 Subject characteristics in relation to smoking status
Smoking status
Variable Non-smokers for >6 months n = 198 Irregular smokers n = 24 Regular smokers n = 78 P-valuea
Gender [% male] 86 80 88 0.52
Age [years] 42.1 ± 9.0 40.6 ± 9.2 40.0 ± 9.6 0.09
LDL [mg/dl] 122.3 ± 28.5 117.4 ± 30.4 115.9 ± 28.3 0.10
HDL [mg/dl] 45.3 ± 10.8 47.1 ± 8.3 41.4 ± 9.2 0.01
Systolic blood pressure [mmHg] 132.9 ± 15.2 128.7 ± 9.9 131.2 ± 13.6 0.43
Diastolic blood pressure [mmHg] 82.9 ± 9.7 79.2 ± 8.0 81.6 ± 8.3 0.33
Glycosylated hemoglobin [%] 5.15 ± 0.5 5.1 ± 0.4 5.25 ± 0.4 0.12
Tumor necrosis factor-α [ng/l] 1.8 ± 0.5 1.8 ± 1.0 2.2 ± 2.5 0.61
C-reactive protein [mg/l] 1.0 (0.47–1.9) 0.80 (0.5–2.0) 1.75 (0.7–3.0) 0.006
a P-values for comparisons between non-smokers and regular smokers. Tumor necrosis factor-α and C-reactive protein (CRP) levels were compared using the Wilcoxon test; for all other continuous variables the Student t-test was employed. Values are means ± SD except for CRP where the median and interquartile range is provided. LDL = Low-density lipoprotein cholesterol, HDL = High-density lipoprotein cholesterol
Gene distribution
After excluding irregular smokers, we found the GG wild type polymorphism in 203 subjects (74%); GA heterozygote were 70 participants (25%) and AA homozygote were 3 participants (1%). Calculated allele frequencies amounted to 0.86 for the G allele and to 0.14 for the A allele. Regular smokers were slightly though not significantly more frequent amongst subjects with the GG genotype than amongst individuals carrying the GA or AA genotype (odds ratio 1.41; 95% CI 0.77–2.6, p = 0.26). Table 2 compares individuals with the GG wild type with participants having the GA or the AA genotype. The table reveals that genotype was not associated with any of the examined health variables in crude bivariate comparisons, including plasma levels of TNF-α and CRP.
Table 2 Subject characteristics in relation to the TNF-α G-308 polymorphism
GG n = 203 GA/AA n = 73 P-value
Gender [% male] 87 86 0.73
Age [years] 41.0 ± 9.1 42.9 ± 9.4 0.37
Regular smokers [%] 28.3 21.8 0.26
Low-density lipoprotein cholesterol [mg/dl] 119.5 ± 32.6 124.8 ± 29.1 0.22
High-density lipoprotein cholesterol [mg/dl] 44.1 ± 11.9 44.62 ± 10.9 0.76
Systolic blood pressure [mmHg] 131.7 ± 11.7 134.6 ± 17.4 0.18
Diastolic blood pressure [mmHg] 82.5 ± 11.1 82.7 ± 8.1 0.87
Glycosylated hemoglobin A1c [%] 5.1 ± 0.5 5.2 ± 0.6 0.18
Tumor necrosis factor-α [ng/l] 1.7 (1.3–2.2) 1.9 (1.4–2.2) 0.39
C-reactive protein [mg/l] 1.04 (0.53–2.36) 1.05 (0.49–3.14) 0.35
Tumor necrosis factor (TNF)-α and C-reactive protein (CRP) levels were compared by the Wilcoxon test; for all other continuous variables the Student t-test was employed. Values are means ± SD except for TNF-α and CRP where the median and interquartile range is provided.
Genotype, smoking status, TNF-α and CRP levels
The adjusted analysis regressing log-transformed CRP levels against smoking status, genotype, and smoking-status-genotype interaction revealed a main effect for smoking status (F1,250 = 5.67, p = .018), but not for genotype (F1,250 = 0.33, p = .57). The interaction-term between genotype and smoking status failed to gain significance (F1,250 = 0.09, p = .76), indicating that the effect of smoking on plasma levels of CRP is not affected by the TNF-α -308G/A polymorphism (Figure). Of the considered covariates, gender (F1,250 = 12.5, p = .0005), age (F1,250 = 4.55, p = .03), and HDL-C (F1,250 = 3.4, p = .065) were associated with CRP levels, but not BP or LDL-C. Men, older individuals, and those with lower HDL-C had higher plasma levels of CRP.
Figure 1 The figure depicts plasma levels of C-reactive protein (CRP) in relation to smoking status and the TNF-α G-308 polymorphism in the final sample of 261 subjects. CRP levels are anti-log transformed least square means estimates from the fully adjusted model controlling for age, gender, blood pressure and blood lipids. Error bars denote the standard error of the mean estimate. Simple effects comparing genotypes across smokers were not significant (p = .14).
The fully adjusted model with plasma levels of TNF-α also failed to show significant main effects for genotype (F1,246 = 1.08, p = .30) and for smoking (F1,246 = 0.33, p = .56), as well as for the smoking-status-genotype interaction. However, the adjusted model revealed a significant main effect for HDL-C (F1,246 = 20.7, p < .0001), suggesting that individuals with higher plasma levels of TNF-α had lower HDL-C. Post-hoc analyses revealed no interaction between smoking status and HDL-C or genotype and HDL-C.
Discussion
Genes, health-behavior and the psychosocial environment interact to determine whether or not, and, if at all, how rapidly silent atherosclerosis will progress to the clinical manifestation of CAD [34,35]. The development of coronary artery sclerosis is a life-long process that probably has its onset in childhood [36]. Particularly, as suggested by post mortem studies, inflammation-related endothelial damage plays an important role in atherosclerosis onset and progression early in life [37]. The understanding of atherosclerosis as an inflammatory disease [1] has kindled much interest in a number of genetic polymorphisms coding for inflammatory molecules potentially related to CAD [38].
While plasma levels of the proinflammatory cytokine TNF-α are regulated by several polymorphisms of the TNF-α gene [39], it is the TNF-α G-308A gene polymorphism which has been most intensely scrutinized as one candidate polymorphism underlying CAD [8]. Plasma TNF-α levels predicted second myocardial infarction [40], and have been associated with common carotid intima-media thickness [41]. TNF-α also stimulates the liver to produce CRP [8], which, itself, has been shown to predict coronary risk in numerous population based studies [7].
Interestingly, blood cells from individuals who carry the A allele of the TNF-α G-308A gene polymorphism express more TNF-α in vitro upon stimulation with lipopolysaccharide than cells from individuals being homozygous for the G allele [18]. Despite this association, several studies did not find a significant association between the TNF-α G-308A gene polymorphism and incident CAD [19-25]. There are, however, no studies examining whether established cardiovascular risk factors might interact with the TNF-α G-308A gene polymorphism in determining plasma levels of TNF-α and eventually CRP downstream in the inflammatory cascade. We thus investigated the effect of an interaction between smoking severity and the G-308A polymorphism of the TNF-α gene on plasma levels of these two proinflammatory markers. Our specific hypothesis was that there was a cumulative increase of TNF-α and CRP related to the TNF-α G-308A polymorphism in subjects who regularly smoke as compared to non-smokers.
In spite of two recent studies, which found an interaction between smoking and polymorphisms of molecules participating in the inflammatory response [14,15], the results from the present study fail to support our hypothesis. More precisely, we found that the interaction between smoking status and the TNF-α G-308A polymorphism did not significantly affect plasma levels of TNF-α and CRP in both unadjusted and adjusted analyses. Also, there was no main effect for the polymorphism investigated in terms of plasma levels of TNF-α and CRP. On the other hand, although not an aim of our study, we confirmed previous findings of increased plasma CRP in regular smokers as compared to non-smokers [11], while, rather unexpectedly, plasma levels of TNF-α were not different between smokers and non-smokers.
It must be noted that our findings are preliminary, and, they do not allow us to reject the overall hypothesis of a smoking-gene interaction modifying inflammatory processes contributing to atherosclerosis initiation and progression [14,15]. For instance, because the number of homozygous carriers of the A allele in our study population was low reflecting low frequency of the AA genotype in the general population, we were unable to analyze whether there might be a "dose-response" relationship between the A allele dosage and CRP levels. Larger sample sizes are clearly needed to detect a potential difference in regulation of proinflammatory markers in plasma across the GG, AG, and AA polymorphism and with respect to their interaction with different cardiovascular risk factors. This reasoning becomes even more obvious with respect to the higher absolute difference in the mean estimates of plasma CRP levels between the GG and the GA/AA genotypes in smokers as compared to non-smokers (Figure). A highly powered study might raise the odds of this absolute difference to become statistically significant. Moreover, the biological plausibility of our hypothesis was straightforward given the important role of smoking, inflammation and their link in CAD [9-11]. However, we do not know in how far interactions between smoking, the TNF-α G-308A polymorphism, and other polymorphisms of molecules involved in the inflammatory pathways not investigated in our study [14,15] might affect plasma TNF-α and CRP levels in an unexpected way.
The lack of a difference in plasma TNF-α levels between subjects with the A allele as compared to those homozygous for the G allele stands in contrast to previous in vitro studies [17,18]. However, aside from a power issue, our measured values of TNF-α levels only slightly exceeded the assay's sensitivity limit, incurring a larger chance of measurement error. We may speculate that a relation between gene variant, smoking, and circulating TNF-α levels might have been uncovered among patients with atherothrombotic disorders or other inflammatory conditions. Moreover, we measured systemic TNF-α; circulating TNF-α may not necessarily reflect TNF-α secretion at sites of confined subendothelial atherosclerotic lesions, where regulatory polymorphisms are most likely to affect reactions of immune cells. Finally, interactions also involving IL-6 polymorphisms [15] may play a role in the association between smoking, elevated CRP, and increased CAD risk. Future studies thus may want to investigate whether IL-6 polymorphisms might be associated with plasma CRP levels and whether they interact with TNF-α and CRP in smokers.
Conclusions
Our study suggests that both plasma TNF-α and CRP levels are not regulated by an interaction between smoking and the G-308A polymorphisms of the TNF-α gene promoter site. We thus remain far from adopting a clinical practice that would counsel smokers to quit smoking based on a particular gene polymorphism. Nonetheless, our results do not refute the overall hypothesis that genetic polymorphisms along the inflammatory pathway may account for the differential effect of tobacco consumption on the cardiovascular risk in individuals who smoke.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MG participated in the design of the study and drafted the first version of the manuscript.
JF participated in the design of the study, in data acquiring, performed the statistical analyses, and critically revised the manuscript.
FM carried out and supervised the molecular genetic studies.
RvK participated in the design of the study and wrote the final version of the manuscript.
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 supported by a grant from the Federal Institute of Technology, Zürich, Switzerland
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| 15485576 | PMC526204 | CC BY | 2021-01-04 16:30:02 | no | BMC Cardiovasc Disord. 2004 Oct 14; 4:17 | utf-8 | BMC Cardiovasc Disord | 2,004 | 10.1186/1471-2261-4-17 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-421545856810.1186/1471-2458-4-42Research ArticleFear of nuclear war increases the risk of common mental disorders among young adults: a five-year follow-up study Poikolainen Kari [email protected]älä Terhi [email protected] Annamari [email protected] Mauri [email protected]önnqvist Jouko [email protected] Finnish Foundation for Alcohol Studies, PO Box 220, FIN-00531 Helsinki, Finland2 National Public Health Institute, Department of Mental Health and Alcohol Research, Mannerheimintie 166, FIN-00300 Helsinki, Finland3 Helsinki University Hospital, Hospital for Children and Adolescents, Department of Child Psychiatry, Helsinki, Finland4 Helsinki University Hospital, Department of Adolescent Psychiatry, Peijas Hospital, FIN-01400 Vantaa, Finland5 Tampere School of Public Health, University of Tampere, FIN-33014 Finland6 Department of Psychiatry, University of Helsinki, Helsinki, Finland2004 30 9 2004 4 42 42 27 11 2003 30 9 2004 Copyright © 2004 Poikolainen et al; licensee BioMed Central Ltd.2004Poikolainen 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
Evidence on the relation between fear of war and mental health is insufficient. We carried out a prospective cohort study to find out whether fear of nuclear war is related to increased risk of common mental disorders.
Methods
Within two months preceding the outbreak of Persian Gulf War in January 1991, 1518 adolescents [mean age 16.8 years, SD 0.9] filled in a self-administered questionnaire. Of the 1493 respondents, 47% gave their written informed consent to participate in the follow-up study. There were no material differences between those who chose to respond anonymously and those who volunteered to give their name and address for the follow-up study. In 1995, the response to the follow-up questionnaire was 92%. Common mental disorders were assessed by 36-item version of the General Health Questionnaire [GHQ]. A score 5 or higher was considered to indicate caseness. We excluded 23 cases which had used mental health services in the year 1991 or earlier and two cases with deficient responses to GHQ. This left 626 subjects for analysis [400 women].
Results
After adjusting for significant mental health risk factors in logistic regression analysis, the risk for common mental disorders was found to be significantly related to the increasing frequency of fear for nuclear war, high scores of trait anxiety and high scores of immature defense style. Elevated risk was confined to the group reporting fear of nuclear war once a week or more often [odds ratio 2.05; 95% confidence interval 1.29–3.27].
Conclusion
Frequent fear of nuclear war in adolescents seems to be an indicator for an increased risk for common mental disorders and deserves serious attention.
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Background
Risks of war and terrorism are threatening our health, both directly in actual life and also indirectly by the increasingly violent content of video games and other forms of entertainment. How does this affect mental health? Earlier during the cold war period, fear of war was found to be common among adolescents, and more prevalent among girls than boys [1-5]. Little is known about the influence of fear of war on mental health of adolescents. On one hand, it has been argued that worrying about nuclear war is related to positive aspects of mental health [6]. On the other, fear of nuclear war has been found to associate with several measures of psychological distress in cross-sectional studies [4,7-9]. To our knowledge, no follow-up studies have been published. However, high perceived risk of nuclear war might be related not only to transient psychological distress but also to more long-term mental disorder among vulnerable adolescents. We have followed up a cohort of adolescents first studied during the period of increasing international tension before the outbreak of the Persian Gulf War in January 17, 1991, and report here on the relation between fear of nuclear war at that time and incident common mental disorders five years later.
Methods
Design
Between December 4, 1990, and January 16, 1991, 1518 adolescents from five high schools in Helsinki and five in Jyväskylä, Finland, representing a cross-section of school entrance requirement levels filled in a self-administered questionnaire during an ordinary classroom hour. Of the 1493 respondents, 709 (47%) gave their written informed consent to participate in the follow-up study. There were no significant differences between those who chose to respond anonymously and those who volunteered to give their name and address for the follow-up study with respect to baseline predominance of mature, immature or neurotic defence styles, trait anxiety, trait depression, the number of positive and that of negative life-events, self-esteem, coherence of future, or availability of social support. Anonymous respondents reported less somatic symptoms than those who gave informed consent to follow up. The absolute difference in the symptom score was not very large, however. The mean scores (SE) were 21.8 (0.22) and 22.8 (0.29) for men, 24.2 (0.23) and 25.0 (0.23) for females, respectively [10]. Of the 709 subjects who gave signed consent, two were excluded from the follow-up due to deficient completion of the baseline questionnaire, and one died. The sample eligible for follow-up comprised 706 subjects. In 1995, the response to the follow-up questionnaire was 92%. Design and sample has been described earlier in more detail [10].
Participants
We excluded 23 cases who reported having used mental health services in the year 1991 or earlier and two cases with deficient responses to GHQ. This left 626 subjects for analysis, of whom 400 were women. At the baseline, the age range was 15 to 19 years (mean 16.8, SD 0.9).
Measures
Baseline examination in 1990
Frequency of fearing nuclear war during past four weeks (scores in parentheses) was assessed by a question with six options: not at all (0), less than once a week (0.5), 1–2 times a week (6), 3–5 times a week (16), almost daily (22) and daily (28).
The Defence Style Questionnaire (DSQ) consisted of 72 statements assessing possible conscious derivatives of 20 defences. It is based on the 88-item version of the Bond's Defense Style Questionnaire [11]. Andrews et al. [12] reviewed the items to make the labelling consistent with the Diagnostic and Statistical Manual of Mental Disorders (3rd ed. revised, DSM-III-R) by the American Psychiatric Association [13]. The defence styles were grouped into three levels: mature, neurotic, and immature defence styles. Individual defences are (mature:) sublimation, humour, anticipation, suppression, (neurotic:) undoing, altruism, idealisation, reaction formation, (immature:) projection, passive aggression, acting out, isolation, devaluation, autistic fantasy, denial, displacement, dissociation, splitting, rationalisation and somatization [14].
The Trait Anxiety Inventory was used to measure trait anxiety as a general tendency of feeling [15]. Trait anxiety is used to screen neurotic anxiety problems and vulnerability for anxiety disorders.
Depressive trait [16] was assessed by questions following the style, scoring and response options of the Trait Anxiety Inventory. The questions dealt with a general tendency to have obvious depressive mood.
An abbreviated version of the Life Event Checklist [17] consisted of 20 defined life events considered to be the most common ones among Finnish adolescents and of four open items. Number of negative and that of positive life events was analysed.
The Somatic Symptom Score is an abbreviated 14-item version of an original 18-item score used earlier in Finnish studies on adults and adolescents [18]. The 14 items comprised physical symptoms common in adolescence but only rarely associated with a physical disease, such as headache, abdominal pains, fatigue or weakness, lack of energy, diarrhoea or irregular bowel function. Respondents were asked "Have any of the following symptoms bothered you, and how often during the last six months?" The response options were never, sometimes, quite often, and often or continuously.
The self-esteem scale by Rosenberg [19] consists of ten items measuring the self acceptance aspect of self-esteem. Rosenberg relates positive self-esteem to many social and interpersonal consequences such as less shyness and depression, more assertiveness, and more extra-curricular activities. The response scorings were inverted so that a high total score indicated high self-esteem.
Coherence of future was measured by three items (no. 11, 22 and 27) from the Sense of Coherence Scale [20] relating to the meaningfulness and manageability of one's own personal future.
Social support was ascertained by asking "Do you have a significant other person with whom you may discuss your personal activities and problems?".
Social class assessment was based on father's occupation or on mother's occupation when the father was not living in the family of the adolescent. Use of the City of Helsinki Social Group Classification divided the sample into four categories: (i) professionals, managers and higher administrative or clerical employees, (ii) lower clerical employees, (iii) skilled workers, and (iv) unskilled workers.
Follow-up examination in 1995
The General Health Questionnaire (GHQ) [21,22] is a measure for common mental disorders [23]. It is a widely used and well-validated self-administered test. The GHQ focuses on discontinuities in normal functioning and the experience of new phenomena of a distressing nature. It covers feelings of strain, depression, inability to cope, anxiety-based insomnia, lack of confidence and other psychological problems [24]. GHQ has been found to be very accurate at detecting anxiety and depression with anxiety [25]. We employed the 36-item version, which is derived from the original 60-item questionnaire by excluding items measuring somatic symptoms [26]. We applied the standard scoring method, counting the two highest response options as pathological. As commonly done earlier [27,28], a score 5 or higher was considered to indicate common mental disorders. Treatment contacts with mental health professionals before the follow-up examination were ascertained in 1995.
Statistical analysis
Data were analysed with SPSS 11. Logistic regression was used to model the relationship between assumed risk factors and high GHQ score [5 or more]. Initial models included sex, social class, availability of social support [dichotomous variables], age, self-esteem, coherence of future, number of positive and that of negative life-events, neurotic, immature and mature defence styles, trait anxiety, trait depression, somatic symptom score [continuous variables]. Second-level interactions were studied by adding product terms to the models. Because of missing values, the number of cases was lower than 626 in some analyses.
Only significant [p < 0.05] confounders remained in the final models. To evaluate relative risk, fear of nuclear war and the significant confounders were categorised and odds ratios with their 95% confidence interval were estimated.
Results
Of the 400 women, 27.5% reported having feared nuclear war once a week or more often in 1990. The respective figures for men were 226 and 13.7%. Thirty-six per cent of the women and 22.1 % of the men scored 5 or higher on GHQ. The initial full model included all putative confounders under study (Table 1). There were no interactions. Significant and almost significant explaining variables were retained in the final model with continuous variables. The risk for common mental disorders was found to be significantly related to high frequency of fear for nuclear war, high scores of trait anxiety and high scores of immature defense style (Table 2). While the odds ratios suggested a dose-response relation between fear of nuclear war and common mental disorders, significantly elevated risk was confined to the group reporting fear of nuclear war once a week or more often. This group showed a 2-fold risk compared to subjects that did not report fear of nuclear war (Table 3). High immature defense style and high trait anxiety were also related to higher risk for common mental disorders. Applying a GHQ cut-off score 6 did not materially change the results.
Table 1 Logistic regression analysis, General Health Questionnaire score on unit change in all potential explanatory variables (n = 607)
Explanatory variable Odds ratio Regression coefficient SD p-value
Frequency of fearing nuclear war 1.04 0.039 0.015 0.007
Sex 0.75 -0.287 0.231 0.2
Age 1.10 0.093 0.114 0.4
Social class II 1.05 0.052 0.240 0.8
Social class III 0.64 -0.439 0.263 0.095
Social class IV 1.40 0.336 0.559 0.5
Number of positive life events 1.01 0.005 0.041 0.9
Number of negative life events 1.05 0.052 0.056 0.4
Social support 1.07 0.063 0.426 0.9
Self esteem 0.97 -0.029 0.030 0.3
Coherence of future 1.00 -0.005 0.159 0.98
Trait anxiety 1.04 0.042 0.019 0.03
Trait depression 1.10 0.091 0.108 0.4
Mature defense style 0.94 -0.065 0.116 0.6
Neurotic defense style 1.10 0.096 0.120 0.4
Immature defense style 1.32 0.274 0.163 0.09
Somatic symptom score 1.02 0.021 0.025 0.4
Table 2 Logistic regression analysis, General Health Questionnaire score on unit change in significant explanatory variables (n = 621)
Explanatory variable Odds ratio Regression coefficient SD p-value
Frequency of fearing nuclear war 1.04 0.04 0.014 0.004
Trait anxiety 1.07 0.069 0.014 <0.001
Immature defense style 1.43 0.359 0.143 0.012
Table 3 Logistic regression analysis, General Health Questionnaire (GHQ) score on categorized significant explanatory variables (n = 626)
Explanatory variable Number of cases Odds ratio (95% confidence interval)
GHQ ≥ 5 GHQ <5 Unadjusted Adjusted
Fear of nuclear war never 63 199 1 (reference) 1 (reference)
less than once a week 72 151 1.51 (1.01–2.24) 1.35 (0.88–2.05)
once a week or more 59 82 2.27 (1.47–3.52) 2.01 (1.26–3.21)
Trait anxiety <32 26 167 1 (reference) 1 (reference)
≥ 32 < 38 95 195 3.13 (1.94–5.06) 2.58 (1.58–4.23)
≥ 38 73 70 6.70 (3.95–11.4) 4.48 (2.53–7.91)
Immature defense style <3.3 40 159 1 (reference) 1 (reference)
≥ 3.3 < 4.0 56 148 1.50 (0.95–2.39) 1.24 (0.76–2.01)
≥ 4.0 98 125 3.12 (2.02–4.82) 2.01 (1.24–3.25)
Discussion
A positive association was found between frequent fear of nuclear war at baseline examination and common mental disorders among adolescents in a five-year follow up. The temporal order of exposure and response suggest that this relation could be causal. Our measure for common mental disorders, the GHQ, rates recent change (within the past month) in mental health at follow-up examination, i.e. incident problems. False positives might have included individuals with mild or transient psychological disturbance, which should have biased the association towards the null. Still, the relation was significant. However, some caveats should be discussed.
Could the association be due to some confounding factors? We controlled for several potential confounders. Those, known to increase or decrease the risk of mental disorders, included neurotic, immature and mature defence styles [29-31], trait anxiety [32,33], trait depression [34,35], life-events [36,37], somatic symptom score [38], self-esteem [39], coherence of future [40,41] and social support [42,43]. Nevertheless, on one hand there always remains the possibility of bias due to some unknown or otherwise not controlled variable, and, on the other, one cannot be sure that such a variable would also be an actual confounder in the data set at hand. In our data, the close correspondences of unadjusted and adjusted risk ratios suggest that no material residual confounding remained [44].
Adolescents not willing to answer a mental health questionnaire may have more mental health risk factors and problems than participants. However, we found no significant differences between the anonymous and identifiable respondents in possible mental health risk factors analysed except that anonymous respondents reported slightly more somatic symptoms than those who identified themselves. The difference was, however, small in absolute terms (data presented above in section on design). This suggests that subjects with high risk were not underrepresented in the present sample.
The degree of perceived threat of nuclear war may depend on several factors, such as (i) actual presence and size of the nuclear weapon arsenal, (ii) actual political tensions and threats, (iii) media coverage of the former, (iv) mental, conscious and unconscious processing of information, and (v) psychological developmental influences specific to adolescence.
Part of the fear may be based on realistic evaluation of the threat. Our baseline examination was carried out within two months before the outbreak of the Persian Gulf War in January 1991 and before the reductions in nuclear weapon arsenals in the United States and in Russia started. A quote from a novel describing the life experience of one teen-age girl during the pre-detente period may be illustrative:
"One was obliged to think about something important. One was obliged to think about the crisis between China and Soviet Union. A war could break out, the World War III and nuclear fallout would burn everything. The familiar fear for war pressed me inside so that it was difficult to breathe."
Laura Honkasalo. Sinun lapsesi eivät ole sinun. Jyväskylä: Gummerus, 2001, p. 132.
But similar experiences were not unknown among boys either, as witnessed by a seasoned cook from New York:
"I grew up thinking the Big One could come at any moment, and this country – or fear of it, the way my country reacted to the threat – radicalized, marginalized, and alienated me in ways that still affect me."
Alan Bourdain. A cook's tour in search of the perfect meal. London: Bloomsbury, 2001. p. 80.
Widespread media coverage on any potential danger may bring about considerable increase in perceived fear [45]. Mass media have been found to be the most important source of information about the issue of nuclear war among adolescents in Finland [5].
Perceptions of the threat of nuclear war as well as other dangers are processed mentally. Conscious or unconscious intentions are often projected to or mixed with dangerous external events, and they may distort the association between the actual threat of war and perceived fear. There is growing evidence that violent films and video games may trigger fear, aggression and violence among adolescents vulnerable to such content [46], and perceived fear of nuclear war might cause mental distress in vulnerable adolescents in similar vein.
Studies on the prevalence of fearing or worrying about nuclear war during periods of low political tension suggest that this phenomenon is common in adolescence and disappears or at least diminishes later in life [47]. Cognitive maturation and lessening of egocentrism seem to explain why fears with a major irrational component decrease from early adolescence to adulthood [48].
Global threats may vary in time as well as in their appraisal. In addition to old risks of nuclear war and aircraft hijacking, international terrorism and biological warfare loom at present. How should we handle these risks? We might inquire into the fears of our patients, appraise the risks realistically, point out that widespread media coverage tends to exaggerate the risks, and, as Durodié and Wessely [49] point out, suggest that we should not become victims of our fears.
Conclusions
A clear positive association was found between fear of nuclear war and common mental disorders among adolescents. Fear of nuclear war may either be a risk indicator produced by an underlying vulnerability to psychopathological process or have a more direct causal role in the onset of mental disorder among adolescents. In either case, frequent fear of nuclear war in adolescence seems to be an indicator for an increased risk for common mental disorders that deserves serious attention.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
KP and JL planned and designed the study. KP wrote the study proposal and received funding. KP, TA-S, AT-H, MM and JL designed the follow-up, supervised the data collection, and interpreted data. KP analysed data and drafted the paper. KP revised it with contributions from all authors. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank the men and women who completed the study questionnaires. We also thank Riitta Kanerva who contributed to the planning of the baseline examination and Tuuli Pitkänen for participating in the planning, data collection and data management of the follow-up examination. This study was partly funded by the Medical Research Council of the Academy of Finland. We are grateful for the constructive comments from the reviewers.
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| 15458568 | PMC526205 | CC BY | 2021-01-04 16:28:46 | no | BMC Public Health. 2004 Sep 30; 4:42 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-42 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-5-361548558210.1186/1471-2474-5-36Research ArticlePatterns of use, dosing, and economic impact of biologic agent use in patients with rheumatoid arthritis: a retrospective cohort study Gilbert Thomas D [email protected] Daniel [email protected] Daniel A [email protected] Market Research, The Ipsen Group, Inc., 27 Maple Street, Milford, MA 01757, USA2 Analytic Operations, PharMetrics, Inc., 311 Arsenal Street, Watertown, MA 02472, USA2004 14 10 2004 5 36 36 26 5 2004 14 10 2004 Copyright © 2004 Gilbert 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
Variability in dosing and costs of biologics among patients with rheumatoid arthritis (RA) is of interest to healthcare descision-makers. We examined dosing and costs among RA patients newly treated with infliximab or etanercept under conditions of typical clinical practice.
Methods
Integrated pharmacy and medical claims data were obtained from 61 U.S. health plans. RA patients newly treated with infliximab or etanercept between July 1999–June 2002 were selected. A maintenance number of infliximab vials was determined after the "loading period" (2–3 infusions); those with ≥ 2 occurrences of an increase in vials or an interval between infusions of <49 days were considered to have had escalated. For etanercept patients, escalation was based on ≥ 2 instances of increased average daily dose. Multiple logistic regression analyses were conducted to assess variables associated with dose escalation. RA-related costs at one year post-initiation also were examined; comparisons were made using generalized linear models.
Results
A total of 1,548 patients were identified (n = 598 and 950 for infliximab and etanercept respectively). Infliximab recipients were somewhat older (50.5 vs. 46.6 years for etanercept). Nearly 60% of infliximab patients increased their dose at one year, compared to 18% for etanercept. Infliximab patients who escalated dose incurred a 25% increase in mean one-year costs ($20,915 vs. $16,713 for no increase; p < 0.0001). Costs among etanercept patients did not substantially differ based on dose escalation ($14,482 vs. $13,866 respectively).
Conclusions
Infliximab is associated with higher rates of dose escalation relative to etanercept, which contributes to substantially higher one-year medical costs.
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Background
Rheumatoid arthritis is a costly and debilitating autoimmune disorder that is characterized by joint pain, stiffness, and impaired functionality. Symptoms arise from the inflammation and degradation of the synovial membrane, causing progressive disability in joint function [1]. As the disease progresses, patients require more frequent invasive procedures (e.g., joint injections, synovectomy) as well as the eventual replacement of affected joints. Consequently, the economic costs of RA are considerable, as the estimated direct and indirect costs of related care in the US totals $19 billion annually [2].
Because there is no known cure for RA, the goal of therapy is to treat the disease's symptomatology while attempting to slow or halt its overall progression. Pharmacotherapy is the cornerstone of treatment where symptoms may be treated with various combinations of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, and narcotic analgesics. In addition, disease-modifying antirheumatic drugs (DMARDs) are sometimes administered in an effort to alter the disease's progression. The effectiveness of DMARDs, however, is offset by the high levels of toxicity experienced by some patients taking these medications, and is problematic for long-term therapy [3].
In addition to the use of various DMARDs either alone or in combination with other therapies, several new DMARDs have recently been introduced for the treatment of RA. These include two biologic agents, etanercept (Enbrel®, Amgen, Inc./Wyeth, Inc., Thousand Oaks, CA and St. David's, PA) and infliximab (Remicade®, Centocor Inc., Malvern, PA) which provide anti-rheumatic activity by inhibiting tumor necrosis factor (TNF), another important mediator of an inflammatory response. The use of such agents in combination with the DMARD methotrexate has been shown to be clinically superior to methotrexate alone in controlled clinical trials [4-12].
The results of recent observational studies examining the effectiveness of infliximab indicate that increased or more frequent dosing (i.e., beyond what is mentioned in the product labeling) may provide additional benefits for patients with rheumatoid arthritis [13,14]. However, the costs associated with use of biologic agents is already far greater than other DMARDs. Therefore, economic considerations may impact physicians' willingness to prescribe as well as commercial insurers' placement of biologic agents in the sequence of care.
Given the wide disparity in costs between therapy alternatives and the potential impact of dose escalation on these costs, it is essential to examine the total costs of RA-related care associated with each form of therapy from a perspective of typical U.S. clinical practice. In this study, the direct costs of RA-related care were estimated on an annual basis after initial treatment with anti-TNF biologics. Resource utilization and cost estimates were also stratified by dosing status (increase in dose during follow-up vs. no increase), using retrospective claims data from commercial insurers in the US.
Methods
Data source
Medical and pharmaceutical service claims were obtained from the PharMetrics Patient-Centric Database, and spanned the period from January 1999 to June 2002. At the time of this study, the database contained fully adjudicated service claims from 61 health plans across the US. Inpatient and outpatient diagnoses (ICD-9-CM format) and procedures (CPT-4 and HCPCS formats), as well as standard and mail order prescription records, are included in the data set. Reimbursed payments and charged amounts are available for all services rendered, as well as dates of service for all claims. Additional data elements include demographic variables (e.g., age, gender, geographic region), product type (e.g., HMO, PPO), payor type (e.g., commercial, self-pay), provider specialty, and start and stop dates for plan enrollment. All patients who met the sample selection criteria specified below were included in the analyses.
Sample selection
Patients with a diagnosis of rheumatoid arthritis (ICD-9-CM 714.XX) who newly started on infliximab or etanercept between July 1999 and June 2001 were initially selected for inclusion in the study sample. A hierarchical procedure was then implemented to stratify patients into treatment cohorts according to their first utilization of a particular medication at a specific point in time. For example, a patient initially receiving infliximab and later receiving etanercept would be classified as an "infliximab" patient for the duration of the study period. An index date for each therapy was established based on the first occurrence of a claim. Patients with no claims activity for the index therapy for six months prior to the index date were deemed "newly started".
Those patients not continuously enrolled during the six-month pretreatment and 12-month follow-up periods were excluded from all analyses. Additionally, patients must have had at least five infusions or prescriptions for their index medication. Patients 65 and older who were not enrolled in a Medicare "risk" plan (i.e., a commercial plan that agrees to undertake full financial risk for a Medicare beneficiary) were excluded from each of the analyses, as such patients may not have had fully visible utilization and cost values due to coordination of medical benefits. All medical and pharmaceutical claims spanning the period January 1, 1999 to June 30, 2002 were then extracted for eligible patients in the data set.
Measures
The primary measures of interest in this evaluation were the frequency and economic impact of an escalation in biologic dose. Dose escalation was assessed for patients new to infliximab and etanercept during the study period described above. Infliximab and etanercept doses reported at the third infusion/prescription respectively were considered to be the maintenance dose levels. Subsequent utilization was then examined to determine dose escalation. Dose escalation for patients initiating infliximab was based on at the presence of least two occurrences of an increase in the number of vials reported on the infusion claim. The standard period (as indicated on the prescribing information for infliximab) between infusions following the third infusion is eight weeks. Therefore, patients with two infusions within seven weeks on two or more occasions were also considered to have an increase in dose. Dose escalation for patients initiating etanercept therapy was determined according to a change in the average daily dose (expressed in terms of mg per day); average daily dose was calculated based on data from pharmacy claims, using the following formula:
metric strength(25 mg)*quantity dispensed (in vials)/days supplied
Patients having two or more prescriptions with a higher average daily dose than that reported on their maintenance dose were considered to have an increase in dose. Dose escalation results were reported on an overall basis and stratified by age (<18, 18–34, 35–44, 45–54, 55–64, and 65 years and over respectively), geographic region (East, South, Midwest, West), calendar year of biologic therapy initiation (1999, 2000, or 2001), and quartile of pre-index RA-related costs.
In addition to dose escalation, the demographic and clinical characteristics of the sample also were assessed and stratified among patients who did and did not escalate their dose. Characteristics of interest included age, gender, health plan type, geographic region, physician specialty as of the index date, presence of selected pre-index medications, procedures, and comorbid diagnoses, co-diagnosis of Crohn's disease, and pre-index total (i.e., RA-related and unrelated) healthcare costs.
During follow-up, the numbers of prescriptions or infusions of biologic therapy were tracked, as were the costs of all appropriate medical interventions, inpatient, outpatient, and pharmacy services. Costs were tallied for both RA-related and unrelated services and medications. Costs were deemed to be RA-related based on the presence of a relevant diagnosis, medication claim, or procedure (See Appendix 'Additional file 1' for ICD-9-CM, CPT-4, and GPI drug codes).
Analyses
Based on the sample described above, a series of analyses were conducted as follows:
1. Dose Escalation – compared the rate of dose escalation at one year among patients initiating etanercept or infliximab therapies;
2. Predictive Model for Dose Escalation – identified RA patients most likely to experience an increase in dose at one year, isolating specific medical, pharmaceutical and demographic characteristics that served as predictors for patients increasing drug utilization; and
3. Comparison of Annual Costs – compared costs between etanercept and infliximab stratified by whether or not the patient escalated their dose.
The proportion of patients escalating dose was compared between patients receiving etanercept and infliximab using a chi-square test or Fisher's Exact Test (for cell sizes less than five). In addition, a multiple logistic regression model was applied to identify characteristics that were most predictive of patients experiencing an escalation in dose (including the index biologic therapy). The selection of predictors for inclusion in the models began with a univariate analysis of each variable to determine the frequency of the observations associated with each treatment cohort. An initial model run was performed using the following variables: age group, gender, region, biologic therapy group, plan type, prescribing specialty, RA-related costs during the six month pre-index period, non-RA related costs during the pre-index period, maintenance dose, dummy variables (1 = present, 0 = absent) for the receipt of other RA related medications during the pre-index period, and selected comorbidities. A stepwise method was employed to produce the final model specification using an "entry" level of α = 0.15 and a "stay" level of α = 0.05.
The amount of costs (reimbursed amounts paid by health plans) for all services previously described were calculated on an annual per patient basis for each cohort. Total RA-related, unrelated, and overall costs during the one-year follow-up period were compared controlling for differences in age, gender, pre-index RA related costs and other appropriate variables between the cohorts. A generalized linear model using a gamma distribution was used to control for these differences.
Results
Patient demographics and clinical characteristics
Clinical and demographic characteristics of the study sample (N = 1,548) are presented in Table 1. Overall, approximately one-third of patients (31%) were aged 55 or older. However, infliximab use was more concentrated among older patients, as 37% of patients on this therapy were 55 or older; compared to 27% of etanercept patients. Not surprisingly, females had a greater representation than males in the study sample, accounting for nearly three quarters of the study population (74%). Rates were similar for the infliximab and etanercept groups respectively (76.4% and 72.1%). Most patients in the sample were members of an HMO or PPO product; however, the use of infliximab was much lower in the HMO group compared to etanercept (30% vs. 45% respectively) and substantially higher among PPO patients (49% vs. 35% respectively).
The use of other RA-related medications prior to biologic use differed numerically by treatment group. NSAIDs were used more frequently by etanercept users relative to infliximab (42% vs. 26% respectively), as were Cox-II inhibitors (35% vs. 27% respectively) and leflunomide (26% vs. 19% respectively). The rate of pretreatment methotrexate use was similar among the etanercept and infliximab groups (55% and 56%).
Utilization of joint aspiration procedures was numerically higher during the pre-index period for patients in the infliximab group relative to the etanercept sample (35% and 28% respectively). Additionally, pre-index RA related costs also were somewhat higher among infliximab patients ($3,916 vs. $3,585).
Dose escalation
Patients who initiated infliximab therapy experienced significantly higher rates of dose escalation during the first year of follow-up relative to patients who were initiated on etanercept (58% vs. 18%; p < 0.001) (Table 2). When stratified by pre-index costs, patients initiating infliximab therapy had consistently higher rates of escalation relative to patients on etanercept therapy, although the rate of dose escalation generally increased with pre-index costs; the rate of escalation at one year for patients with the lowest pre-index costs was 50% for the infliximab cohort compared to 17% for patients initiating etanercept (p < 0.001); corresponding rates were 62% and 21% in the highest cost group (p < 0.001). When stratified by year of therapy initiation, age, and geographic region, rates of escalation were significantly higher among patients initiating infliximab therapy relative to the etanercept cohort across all groups. The rate of escalation increased by calendar year for patients receiving infliximab, but declined among etanercept users. Interestingly, while the rate of dose escalation increased with increasing age in the etanercept group, this rate declined in the infliximab group as age increased (beyond age 35); for example, 66.4% of those aged 35–44 in the infliximab group increased their dose, versus 39.3% in patients aged 65 and older. Finally, rates of dose escalation varied considerably by region, with the highest rates observed in the South and Midwest.
Predictive model for dose escalation
Modeled dose escalation results for patients who initiated infliximab or etanercept therapy are presented in Table 3. The type of biologic therapy was by far the most significant predictor of dose escalation, as patients starting infliximab were over 6 times more likely to increase dose than patients starting on etanercept (Transformed Odds Ratio [OR] = 6.38; p < 0.0001). Patients who were members of an HMO were less likely to have an increase in dose than those who did not. RA patients with a listed comorbid diagnosis of Crohn's disease were less likely to escalate dose (OR = 0.48; p = 0.0477) than those without, while patients utilizing Cox II therapy were significantly more likely to experience an increase in dose over the course of the year (OR = 1.36; p = 0.0175). Patients in the West were much less likely to experience an increase in dose. Patients in the Northeast were 1.92 times more likely to increase dose (p = 0.0215), while patients in the South and Midwest were 1.87 and 1.89 times more likely to dose escalate (p = 0.0102 and 0.0063 respectively). Age and gender were not significant predictors of dose escalation, although the likelihood of dose escalation increased by 4% with every additional $1,000 of RA-related pretreatment costs.
In an effort to better understand the factors associated with infliximab dose escalation, an additional model was conducted among patients initiating infliximab only. Results are presented in Table 4. In this model, patients who belonged to an HMO were significantly less likely to have an increase in dose at one year relative to patients with other coverage (OR = 0.68; p = 0.0372). Patients utilizing methotrexate during pretreatment were more likely to escalate dose than those without (OR = 1.48; p = 0.0216). There was a trend towards significance in terms of an age effect, as infliximab users between the ages of 35–44 were more likely to escalate dose relative to younger patients (OR = 1.94; p = 0.0682).
Comparison of annual costs
Overall, costs for patients initiating infliximab therapy were numerically higher than for patients in the etanercept group ($19,144 vs. $13,977). Much of the cost difference was due to the difference in drug costs ($13,470 vs. $10,159 respectively). In addition, infliximab patients had higher costs for physician management visits ($691 vs. $381), ancillary services ($1,511 vs. $866), and hospitalizations ($2,277 vs. $1,322). Use of alternative biologic therapy (i.e., use of etanercept in a patient starting on infliximab and vice versa) was minimal, as illustrated by extremely low average annual costs for these alternative strategies.
Average annual RA-related, unrelated, and total costs are also stratified by whether patients underwent dose escalation in Table 5. Patients in the infliximab group who experienced an increase in dose had significantly higher RA-related costs relative to those who remained at maintenance levels ($20,915 vs. $16,713; p < 0.0001). This difference was primarily manifested in lower pharmacy costs for patients not escalating dose; for example, annual infliximab costs were 60% higher for patients escalating dose ($15,998 vs. $10,000; p < 0.001). Ancillary costs were also higher for patients with an increase in dose ($1,601 vs. $1,387). However, RA-related hospitalization costs were lower for patients who had an increase in dose ($1,516 vs. $3,323).
Discussion
In an effort to better understand the differences in dosing patterns and costs among RA patients on biologic therapy, a retrospective analysis of pharmacy and medical claims for patients new to biologic therapy was undertaken. Dosing frequency and quantity was examined, as were RA-related costs at one year after therapy initiation.
Dosing guidelines suggest that etanercept patients receive two 25 mg vials a week; the use of higher doses has not been studied. The recommended dosing for infliximab is 3 mg/kg of body weight for the first dose, and then at two and six weeks and every eight weeks thereafter. Patients experiencing an inadequate response may increase dose to 10 mg/kg; or they may receive treatment as frequently as every four weeks [15]. The flexibility in these guidelines appears to be necessary, as infliximab patients in our study were much more likely to experience a dose escalation than patients on etanercept. There also appeared to be a strong relationship between the utilization of Cox-II inhibitors as well as pretreatment RA-related costs and dose escalation, indicating that disease severity may play a role in the decision to increase dose. Recent evidence suggests, however, that the relationship between dose escalation and disease activity is nonlinear. In a recent examination of infliximab and etanercept use in Sweden, improvement in disease activity levels following infliximab dose escalation was similar to that observed among infliximab patients not escalating dose as well as etanercept recipients [16].
Lastly and most importantly, differences in RA-related cost among patients new to infliximab and etanercept therapy ($19,144 vs. $13,977) were manifested mainly in the treatment costs ($13,470 vs. $10,159). Management and ancillary services accounted for most of the remaining difference. The difference in treatment costs may be attributed to the higher rate of dose escalation among the infliximab group. These patients had treatment expenses that were ~60% higher than patients who did not dose escalate ($15,998 vs. $10,000), while infliximab patients who did not dose escalate had costs similar to patients in the etanercept group. There was little difference in drug therapy costs among etanercept patients who experienced an increase in dose and those with no change ($10,427 vs. $10,100). These findings highlight the differences in treatment patterns and associated costs among patients new to etanercept and infliximab.
Our study was subject to some important limitations. First, as this was a retrospective analysis of claims data, results were based on amounts billed to health plans. As a result, the unit of measurement for infliximab is billed whole vials. For example, if 1.2 vials were administered to a patient, 2 vials would be billed to the health plan. Therefore, these results may not reflect the true amount of infliximab utilized and may in fact under- or overstate the rate of dose escalation – for example, a patient who increases from 1.2 to 1.7 vials will be shown to have utilized 2 vials in both instances; in contrast, a patient moving from 1.9 to 2.1 vials will appear as having moved from 2 to 3 vials.
In addition, information regarding body mass and/or patient weight was not available. As stated above, infliximab dosing levels may range from 3 mg/kg to 10 mg/kg. Dosing changes resulting from weight changes alone were therefore undetectable. Also, one method of estimation of dose change for infliximab was based on two infusions within seven weeks on two or more occasions. It is possible that some patients may have had these non-standard queuing times simply as a result of scheduling availability, and not as a result of dose escalation. Clear estimates of dose increase due to increased frequency may only be obtained through a more controlled observational study.
Furthermore, no information is available in this administrative database regarding the reason for dose escalation – lack of efficacy, increase in symptoms, other reasons. Our major focus for this study was therefore to simply document that standard dosing assumptions regarding infliximab may lead to erroneous conclusions regarding its cost, given the high level of escalation seen in this and other studies. In addition, the database lacks clinical detail on levels of disease severity as well as other potentially important variables (e.g., working status) for consideration of the full clinical and economic impact of dose escalation.
As with all retrospective study, we cannot rule out the possibility that differences in disease progression and/or severity between patients who do and do not escalate dose may have influenced our findings. Nevertheless, our results remained statistically significant even after controlling for observable differences between groups, indicating that any selection bias would likely only affect the magnitude, not the direction, of our findings.
Finally, while the data used represent final, adjudicated claims in a health plan setting, it is possible that the data elements used are subject to coding or misclassification error. Nevertheless, if such an error rate exists, it is likely not a systematic phenomenon – that is, there is no reason to expect that coding errors would disproportionately affect the infliximab or etanercept samples in our study.
Conclusions
Despite the limitations noted above, we believe our study has important implications. While, the results of recent observational studies suggest that both infliximab and etanercept are highly effective in clinical practice [17], our findings suggest that patients with rheumatoid arthritis who initiate infliximab therapy are much more likely to experience an increase in dose over the course of one year relative to patients who initiate etanercept. This increase in dose leads to significantly higher pharmacy costs, as well as increases in many other RA-related medical costs. While there may be clinical factors in the decision to pick one route of therapy administration over another, public and private payers alike should carefully consider the economic implications of coverage decisions when targeting appropriate candidates for anti-TNF therapy.
Competing interests
All authors were employed by PharMetrics, Inc. at the time of this analysis, which was conducted based on an unrestricted research grant from Abbott Laboratories, Inc. No other competing interests are declared by any author, including stocks or other holdings, other financial interests, or non-financial interests.
Authors' contributions
TG was involved in the conception and design of the study, oversaw and provided quality assurance on all study output, and drafted the methods and results sections of the manuscript. DS was responsible for the design and conduct of all descriptive and statistical analyses. DO was responsible for the conception and design of the study, drafting of the background, discussion, and conclusions sections of the manuscript, and all formal correspondence regarding the study. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
File consists of ICD-9-CM codes, CPT-4 codes, GPI drug codes to describe the various diagnoses, type of drugs and procedures.
Click here for file
Figures and Tables
Table 1 Demographic/Clinical Characteristics (6 month pre-period)
Characteristic Infliximab (N = 598) Etanercept (N = 950)
Age (%)
< 18 0.2 4.1
18 – 34 7.0 8.7
35 – 44 19.9 23.3
45 – 54 35.8 37.2
55 – 64 32.4 25.1
65 + 4.7 1.7
Age
Mean 50.5 46.6
SE 0.4 0.4
Min 16 2
Max 80 88
Gender (% female) 76.4 72.1
Plan Type (%)
HMO 29.8 44.8
PPO 48.7 34.5
POS 12.5 16.3
Indemnity 7.5 2.6
Other 1.5 1.7
Geographic Region (%)
East 8.7 15.7
South 45.0 15.9
Midwest 38.8 57.4
West 7.5 11.1
Physician Specialty (%)
FP/GP 6.4 3.4
Internal Medicine 3.5 3.8
Rheumatology 52.7 64.1
Other 27.6 18.8
Unknown 9.9 9.9
Infusions/Rxs (Infliximab/Etanercept)
Mean 7.0 10.4
Median 7.5 11.0
SD 2.1 3.8
Pre-index Therapies (%)
NSAID 26.3 41.6
Cox-II 27.1 34.5
Other Misc. Anti-inflammatory 1.3 3.2
Gold Compound 2.7 3.3
Methotrexate 56.5 55.2
Leflunomide 19.4 25.9
Other DMARD 20.7 26.6
Other RA related therapy 74.9 78.8
Pre-index Medical Diagnoses (%)
Osteoporosis 9.2 7.5
Depression 3.8 3.2
Selected Pre-index Procedures (%)
Joint Aspiration/Injection Procedures 34.6 27.7
Synovectomy 0.7 0.7
Arthroplasty 1.5 0.8
Arthrodesis 0.7 0.9
Arthroscopy 0.8 1.4
Liver Function Tests 42.5 54.5
Urinalysis 31.3 33.8
Hematologic/Serologic Tests 81.3 75.8
Bone/Joint Imaging 33.8 31.5
Centesis Procedures 24.1 20.9
Upper Respiratory Infections 5.7 5.8
Chest X-ray 21.4 17.3
Pre-index Total Healthcare Costs ($)
Mean 3,916.40 3,585.11
SE 225.46 252.39
Min 60.23 0.00
Max 47,306.12 178,388.26
Table 2 Percent of Patients with an Increase in Dose After One Year
Infliximab Etanercept
Patients with 3+ infusions Pct with an increase in dose Patients with 3+ infusions Pct with an increase in dose p-value
Overall 598 57.9% 950 18.1% <0.001
by Pre-period RA Costs
Quartile 1 – $0 to $502.89 157 49.7% 230 16.5% <0.001
Quartile 2 – $502.90 to $975 157 62.4% 230 18.7% <0.001
Quartile 3 – $975.15 to $176 135 57.8% 252 15.9% <0.001
Quartile 4 – $1766.22 + 149 61.7% 238 21.4% <0.001
by Year
1999* 0 0.0% 196 22.4% <0.001
2000 309 53.1% 602 17.9% <0.001
2001 289 63.0% 152 13.2% <0.001
by Region
East 52 53.8% 149 20.1% <0.001
South 269 60.2% 151 17.2% <0.001
Midwest 232 57.3% 545 19.4% <0.001
West 45 51.1% 105 9.5% <0.001
by Age Group
< 18 Years 1 100.0% 39 33.3% <0.001
18 – 34 Years 42 50.0% 83 14.5% <0.001
35 – 44 Years 119 66.4% 221 16.7% <0.001
45 – 54 Years 214 60.3% 353 17.3% <0.001
55 – 64 Years 194 54.1% 238 18.9% <0.001
65 + Years 28 39.3% 16 25.0% <0.001
*Infliximab dosing for RA was not detectable via HCPCS code until June of 2000
Table 3 Dose Escalation Regression Estimates – Inflixim ab and Etanercept Dependent Variables (Increase in dose for Inflixim ab or Etanercept 1 = yes, 0 = no)
Parameter Level Estimate Odds-Ratio Transformed Odds Ratio Conditional Probability p-value
Intercept -0.3129 0.73 n.a. n.a. 0.1702
HMO Yes -0.2246 0.80 1.00 0.37 0.0826
No 0.0000 1.00 1.25 0.42
Treatment Inflixim ab 0.0000 1.00 6.38 0.42
Etanercept -1.8529 0.16 1.00 0.10 <.0001
Region West 0.0000 1.00 1.00 0.42
Northeast 0.6505 1.92 1.92 0.58 0.0215
Midwest 0.6382 1.89 1.89 0.58 0.0063
South 0.6252 1.87 1.87 0.58 0.0102
Pre-Period Non RA Related Costs ('000s) Yes 0.0350 1.04 1.04 0.43 0.0185
No 1.00 1.00 0.42
Crohn's Disease Yes -0.7328 0.48 1.00 0.26 0.0477
No 0.0000 1.00 2.08 0.42
Cox-IIs Yes 0.3064 1.36 1.36 0.50 0.0175
No 0.0000 1.00 1.00 0.42
Table 4 Dose Escalation Regression Estimates – Inflixim ab Dependent Variables (Increase in dose for Inflixim ab 1 = yes, 0 = no)
Parameter Level Estimate Odds-Ratio Conditional Probability p-value
Intercept -0.0732 0.93 n.a. 0.8209
Age Group
0–34 0.0000 1.00 0.48
34–54 0.6639 1.94 0.64 0.0682
45–54 0.3808 1.46 0.58 0.2594
55+ 0.0648 1.07 0.50 0.8469
HMO HMO -0.3810 0.68 0.39 0.0372
Pre-Index Therapy Methotrexate 0.3900 1.48 0.58 0.0216
Table 5 Total Costs of Care for RA Patients at One Year
Infliximab N = 598 Etanercept N = 950 Infliximab: Dose Escalation vs. No Escalation (p-value)
Dose Escalation No Escalation Dose Escalation No Escalation
N = 346 N = 252 N = 172 N = 778
Measure Cost per patient (mean, SE), $
RA Related Costs $20,914.98 (631.15) $16,713.20 (1,315.86) $14,482.45 (519.97) $13,865.48 (306.88) <0.0001
Pharmacy:
Infliximab $15,997.65 (413.51) $9,999.87 (293.82) $313.88 (175.79) $197.56 (53.23) <0.0001
Etanercept $101.26 (45.32) $178.18 (63.03) $10,426.90 (254.66) $10,099.71 (124.94)
Other $1,049.00 (69.20) $1,045.57 (145.45) $1,089.33 (88.71) $1,009.24 (48.61)
Total Pharmacy $17,147.90 (418.81) $11,223.62 (350.97) $11,830.12 (292.80) $11,306.51 (136.87)
Outpatient:
Management $635.68 (53.29) $766.75 (112.25) $377.98 (29.58) $382.19 (41.21)
Emergency Room $13.85 (4.11) $12.54 (6.32) $4.72 (1.81) $7.09 (1.76)
Ancillary $1,601.47 (116.33) $1,387.33 (118.42) $842.11 (122.13) $871.60 (90.63)
Total Outpatient $2,251.00 (136.95) $2,166.62 (176.82) $1,224.81 (135.67) $1,260.88 (120.64)
Hospitalization Costs $1,516.08 (393.06) $3,322.95 (1,191.16) $1,427.52 (411.72) $1,298.08 (249.13)
Non-RA Related Costs $5,370.17 (536.68) $6,104.37 (1,210.40) $3,954.81 (415.94) $4,177.57 (281.46) 0.0302
Grand Total $26,285.15 (1,022.19) $22,817.56 (2,431.57) $18,437.26 (809.69) $18,043.04 (496.95) <0.0001
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| 15485582 | PMC526206 | CC BY | 2021-01-04 16:03:42 | no | BMC Musculoskelet Disord. 2004 Oct 14; 5:36 | utf-8 | BMC Musculoskelet Disord | 2,004 | 10.1186/1471-2474-5-36 | oa_comm |
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BMC BiotechnolBMC Biotechnology1472-6750BioMed Central London 1472-6750-4-231548558310.1186/1472-6750-4-23Methodology ArticleUse of polyethyleneimine polymer in cell culture as attachment factor and lipofection enhancer Vancha Ajith R [email protected] Suman [email protected] Kishore VL [email protected] Madhuri [email protected]ález-García Maribel [email protected] Rafael P [email protected] Departments of Chemistry and Biology, Texas A&M University-Kingsville, Kingsville, TX 78363, USA2004 15 10 2004 4 23 23 15 5 2004 15 10 2004 Copyright © 2004 Vancha 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
Several cell lines and primary cultures benefit from the use of positively charged extracellular matrix proteins or polymers that enhance their ability to attach to culture plates. Polyethyleneimine is a positively charged polymer that has gained recent attention as a transfection reagent. A less known use of this cationic polymer as an attachment factor was explored with several cell lines.
Results
Polyethyleneimine compared favorably to traditional attachment factors such as collagen and polylysine. PC-12 and HEK-293 cells plated on dishes coated with polyethyleneimine showed a homogeneous distribution of cells in the plate, demonstrating strong cell adhesion that survived washing procedures. The polymer could also be used to enhance the adherence and allow axonal outgrowth from zebrafish retinal explants. The effects of this coating agent on the transfection of loosely attaching cell lines were studied. Pre-coating with polyethyleneimine had the effect of enhancing the transfection yield in procedures using lipofection reagents.
Conclusion
Polyethyleneimine is an effective attachment factor for weakly anchoring cell lines and primary cells. Its use in lipofection protocols makes the procedures more reliable and increases the yield of expressed products with commonly used cell lines such as PC-12 and HEK-293 cells.
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Background
Molecular cell biology experimentation often requires the culture of primary cells or immortalized cell lines. The most common substratum used in cell culture consists of a plastic dish that offers a negatively charged surface. A drawback of this technology is that some anchorage-dependent cell types do not produce sufficient amounts of positively charged extracellular matrix proteins, adhering only weakly to the plastic substratum. Pre-coating of the plastic surface with extracellular matrix proteins such as collagen, fibronectin, laminin, etc., usually enhances the attachment of these cell types [1,2]. Synthetic polymeric cations such as polylysine or polyornithine have also been used as attachment promoting factors in numerous studies [3,4].
Polyethyleneimine (PEI) is an organic polymer that has a high density of amino groups that can be protonated. At physiological pH, the polycation is very effective in binding to DNA and can mediate the transfection of eukaryotic cells [5]. The original PEI-based protocol has been used in our laboratory successfully for transfections with HEK-293 (human embryonic kidney) and PC-12 (rat pheochromocytoma) cells. However, lipofection using some of the last generation cationinc lipids, LipofectAMINE-2000 or LipofectAMINE-Plus (Invitrogen), yielded higher efficiencies of gene delivery, particularly with PC-12 cells (M. González-García and R.P. Ballestero, unpublished observations). Several modifications of the original PEI-based protocol have been described that improve the efficiency of transfection significantly, rivaling the lipofection protocols [6-10], but they have not been attempted yet in our laboratory. A notable difference, related to the attachment of the cells to the substratum, was observed during the transfection experiments. Whenever the cells were transfected by the original PEI-based protocol, the cells remained firmly anchored to the plates throughout histochemical procedures. HEK-293 and PC-12 cells are considered weakly anchoring cells [11-14], and many were detached from the plates during histochemical stainings of cells transfected by lipofection. This initial observation prompted us to study the attachment-enhancing properties of PEI and its effects on lipofection of eukaryotic cells. PEI has been used previously to coat surfaces to promote the attachment of neurons in primary cell cultures [15,16]. A modified version of PEI that incorporates hydrophobic groups was demonstrated to be highly effective in the attachment of several cell lines, allowing differentiation of neurons and preventing cell loses during multiple washing steps [17].
This report presents a comparison of PEI with other traditional coating agents as attachment factors for several cell lines and primary cultures of retina tissue. Additionally, an enhancement of the transfection yield of weakly anchoring cell lines is shown by the combination of PEI coating with lipofection.
Results
PEI promotes the attachment of weakly anchoring cells and primary tissues
PC-12 cells are used as a model of neuronal differentiation in the laboratory, as treatment of these cells with nerve growth factor (NGF) induces neurite extension and the expression of biochemical markers of the sympathetic neuronal phenotype [18]. They grow as weakly anchoring cells and collagen or polylysine polymers are frequently used for pre-coating plates for PC-12 cell culture [13,19,20]. PC-12 cells were cultured in the laboratory in naive wells or in wells pre-coated with diverse attachment factors (multiwell-12 tissue culture dishes), to observe the effect on the anchorage of the cells to the substratum. In the absence of any coating agent, the cells showed a characteristic tendency to form clusters of cells that accumulate towards the center of the well; these cells are firmly attached among themselves, but very weakly attached to the tissue culture dish (Figure 1D). This results in a very heterogeneous distribution of cells (very few cells remain towards the edge of the wells) and significant loss of cells during washing procedures or changes of medium. Pretreatment of the plates with PEI resulted in a significantly more homogeneous distribution of cells in the well, with cells attaching to the plate firmly, showing a much lower tendency to clustering (Figure 1A). For comparison, plates were also pretreated with other commonly used coating agents, collagen (Figure 1B) and poly-D-Lysine (PDL; Figure 1C), resulting also in firmer attachment of the cells to the plates and a more homogeneous distribution of cells.
To test further the anchoring enhancement property observed with the PEI pretreatment of the culture dishes, a second system was utilized. Retinal explants from teleost fish have been used in the study of nerve regeneration [21,22]. When a lesion is applied to the optic nerve of the fish, a regeneration response is initiated and the retinal ganglion cells (RGCs) of the retina re-extend their axon towards the tectal target tissue. If the retina from such a "primed" fish is explanted and cultured, the regeneration response is observed in vitro by the accelerated extension of long neurites. However, this phenomenon requires the use of extracellular matrix or attachment factors (for example collagen or PDL coating), as the explants show very low affinity for the uncoated plastic surface. Experiments were conducted to observe if PEI could act as an attachment factor conducive to axonal outgrowth from zebrafish retinal explants. Retinal explants from control zebrafish eyes were able to attach to PEI-pretreated culture dishes (Figure 1E). Furthermore, retinal explants from fish that received a conditioning lesion were able to extend axons vigorously when cultured using PEI as attachment factor (Figure 1F).
The results with the retina explants suggested that neuronal cells attached to PEI-coated dishes can differentiate, generating neurites that attach well to the substrate. To test this hypothesis with the pro-neuronal PC-12 cells, differentiation experiments by NGF treatment were performed with cells attached to dishes coated with PEI. The PC-12 cells treated with NGF remained firmly attached to the plate over several days, generating networks of neurites (Figure 2). The results indicate that PEI is permissive for the differentiation process and for the attachment of the neurites to the dish. The differentiated cells remained firmly anchored throughout immunocytochemistry experiments (M. Challa, G. R. Chapa, M. González-García and R. P. Ballestero, unpublished results).
Strength of anchoring of eukaryotic cells to culture dishes pretreated with PEI and other attachment factors
Three different cell lines were selected to test the ability of PEI to promote strong anchoring to plastic culture dishes. PC-12 and HEK-293 cells have been described above as weakly anchoring. On the other hand, MYS cells are primary fibroblasts that adhere strongly to plastic culture dishes, growing to form a monolayer of cells on the surface. To test the strength of anchoring of the diverse cells to plates, a protocol was performed in which 4 consecutive washes with isotonic buffer were performed, followed by the use of a colorimetric protocol for counting the cells that remained in the plate (based on the vital dye neutral red). Plates pretreated with the diverse attachment factors were compared with plates that received no pretreatment (untreated). Experiments were performed in triplicate, and the averages of dye retained in the plates that received each treatment were calculated. Those averages were normalized to the average obtained with the PEI pretreatment, which was assigned the arbitrary value of 100.0% in all the experiments. The results from representative experiments with the 3 cell lines are shown in Figure 3 (at least three independent experiments were conducted with each cell line). PC-12 cells attached almost equally well to plates coated with PEI, collagen or PDL (relative cell counts of 100.0% ± 5.3%, 89.3% ± 5.0% and 96.3% ± 5.8%, for PEI, collagen and PDL respectively). However, a significant loss of cells was observed when comparing untreated plates with PEI-pretreated plates, with a relative cell count of 43.9% ± 5.8% (Figure 3A). In the case of HEK-293, the cells attached strongly to both PEI- and PDL-pretreated wells. In the representative experiment shown in Figure 3B, the relative counts with those two treatments were 100.0% ± 0.4% and 96.8% ± 1.7% respectively. However, a large number of cells were lost when plated in the untreated wells (relative count of 8.3% ± 0.6%), or in the wells that were pretreated with collagen (11.5% ± 1.2%), suggesting that these cells attach fairly loosely to plastic or to collagen-coated wells. Finally, when the fibroblast cells (MYS cells) were utilized, the cells seemed to attach rather well to all four surfaces, including to the untreated wells (Figure 3C). Figure 3C shows a representative plot, displaying relative MYS cell counts of 100.0% ± 2.2%, 85.9% ± 7.8%, 73.7% ± 6.6%, and 77.1% ± 1.4%, with wells pretreated with PEI, collagen, or PDL, or untreated wells respectively. This cell line therefore attaches fairly well to the untreated plastic surface comparatively to the PC-12 and HEK-293 cell lines.
To provide an indication of the variation among experiments, the averages ± standard deviations of the relative cell counts obtained in the independent experiments were calculated for each cell line and treatment (note that since in all the experiments the count for PEI-pretreated wells was set at 100.0%, the value for the global average with this coating agent is exactly 100.0% for all the cell lines). The comparative results for the other treatments are indicated below. For the PC-12 cells, the relative counts were 81.5% ± 8.8% for collagen-pretreated wells (n = 4), 93.9% ± 21.2% for PDL-pretreated wells (n = 4), and 52.1% ± 13.7% for untreated wells (n = 4). For the HEK-293 cells, the relative counts were 16.3% ± 12.7% for collagen-pretreated wells (n = 3), 99.6% ± 2.5% for PDL-pretreated wells (n = 3), and 9.0% ± 4.1% for untreated wells (n = 3). In the experiments with MYS cells, the average of the relative counts with collagen-pretreated wells was 92.7% ± 16.2% (n = 3), 71.1% ± 6.7% for PDL-pretreated wells (n = 3), and 78.4% ± 11.5% for untreated wells (n = 3). Statistical analysis indicates that the enhancement of adhesion of both PC-12 and HEK-293 cells to PEI-pretreated dishes versus untreated plastic is significant (p < 0.05 and p < 0.01 respectively, t-test analysis), while it is not statistically significant for MYS cells (p > 0.05).
To further characterize the properties of PEI as an attachment factor for weakly anchoring cells, experiments were carried out to analyze the dosage of PEI that can provide optimal cell anchoring, the range of cell numbers that can benefit from the presence of PEI, and the stability of PEI as attachment factor. Figure 4A shows the results from a representative experiment testing the strength of attachment of HEK-293 cells to dishes coated with various doses of PEI. The cell counts were normalized to the value obtained for the treatment with 25 μg/ml of PEI, which was set at 100.0%. The results indicate that concentrations of PEI of 2.5 μg/ml or higher resulted in maximal attachment enhancement, suggesting that the surface of the plastic dish is fully coated with the polymer at these concentrations. The higher concentrations of the polymer did not seem to have toxic effects on the cells if the excess of PEI remaining in solution was removed thoroughly (slight toxic effects were observed at the 250 μg/ml concentration if the solution was not fully removed after the treatment). The experiment shown is representative of 4 independent experiments. Figure 4B represents the results obtained with various numbers of PC-12 and HEK-293 cells. The figure shows relative cell counts, where an arbitrary value of 100.0% was assigned to the average count obtained from the PEI-treated wells with the highest number of each cell line (3.2 × 105 HEK-293 cells and 1.5 × 106 PC-12 cells). The results indicate that PEI worked well as an attachment factor over a wide range of cell numbers for both PC-12 and HEK-293 cells. Lower cell numbers could not be tested reliably because they were close to the limit of sensitivity of the neutral red assay. The results shown are representative of at least 4 independent experiments performed with each cell line. Figure 4C shows the results of an experiment conducted to test the stability of PEI as an attachment factor. In this experiment, a set of plates were treated with PEI and then kept in PBS at 4°C for 10 days. In a second test, plates were treated with PEI and kept (with medium) in the 37°C CO2 incubator for 3 days, with a medium change performed every 24 h. The performance of PEI in these plates was then compared with plates treated with PEI using the standard protocol described for previous experiments. The results show relative cell counts normalized to the average of the absorbances obtained with the standard PEI-treated plates, which was given the arbitrary value of 100.0%. The results indicate that the PEI coating remains stable on the surface of the plastic dish for at least 10 days of refrigeration, and that it is not removed by incubation at 37°C in medium or even by repeated medium changes. Results are representative of 4 independent experiments performed in triplicate.
PEI pretreatment enhances lipofection of weakly anchoring cells
Transfection of eukaryotic cells by lipofection involves several steps, media additions and replacements, which can take a toll in weakly anchoring cells. Even if care is practiced to avoid cell loses, the weakly anchored cells may be less efficient in the uptake of the transfection complexes. Since PEI pretreatment of cell culture dishes increased the strength of attachment of cells to such surfaces, it was hypothesized that the attachment factor may have a positive effect in transfection yields obtained by lipofection. The three cell lines described above were used to test this hypothesis utilizing the coating agents previously examined. Transfections were performed with a plasmid encoding the reporter enzyme β-galactosidase. The transfection yield was monitored by determination of reporter enzyme activity in lysates from the transfected cells. At least two independent assays in triplicate were performed with each cell line. Representative plots are shown in Figure 5. The yields (β-galactosidase activities) were normalized to the activity obtained with PEI pre-coating, which was set as a reference at 100.0%. Generally, it was observed that the transfection yields were improved in the weakly anchoring cells by the pre-coating with agents that promote cell attachment to the plate. In the case of PC-12 cells, pre-coating of wells with PEI, collagen or PDL resulted in a 2- to 3-fold enhancement of transfection yield related to the untreated wells: relative yields of 100.0% ± 1.4% for PEI, 87.0% ± 8.7% for collagen, and 100.1% ± 2.4% for PDL, versus 42.9% ± 2.2% for the untreated wells (Figure 5A). The improvement of transfection yield by PEI pretreatment was more pronounced for HEK-293 cells. The experiment in Figure 5B shows a relative activity of 21.1% ± 3.4% for the cells in the untreated wells, when compared with 100.0% ± 8.4% for the PEI-pretreated wells (approximately 5-fold increase). Pretreatment with collagen or PDL resulted in more modest inductions (approximately 2- to 3-fold in Figure 5B). The lowest improvement was observed with collagen, similarly to the lower enhancement of attachment of HEK-293 cells that was previously observed in Figure 3B. Finally, for the strongly attaching MYS fibroblasts, there was not a significant positive effect observed by using attachment factors in the transfection procedure. Variations were usually less than 20% for all the experimental conditions. The representative experiment shown in Figure 5C in fact shows a slightly higher transfection yield for the untreated wells than for the PEI-pretreated plates (relative activity of 117.7% ± 3.2% for the untreated wells versus 100.0% ± 4.8% for the PEI-pretreated wells, or approximately 1.2-fold higher transfection yield in the control).
Compiling the results from all the experiments performed, the average fold-induction (± standard deviation) observed in the transfection yield of PC-12 cells anchored to PEI as compared with cells on untreated wells was 2.4-fold (± 0.1-fold; n = 3), while the enhancement with HEK-293 cells was 6.3-fold (± 0.5 fold; n = 2). t-test statistical analysis indicates that both increases are significant (p < 0.05). No significant transfection enhancement was observed in the experiments with MYS cells, with an average fold-change of 1.0-fold (± 0.3-fold; n = 2) by the pretreatment with PEI (basically identical to the yield without treatment).
Discussion
While some cell lines are able to produce extracellular matrix components in sufficient quantities to allow them to attach well to plastic culture dishes, some others have a more limited capacity, resulting in cells that are loosely attached to the dish. Natural extracellular matrix factors such as collagen, laminin, fibronectin, etc., can be utilized in the culture of these cells [2] and in the performance of multiple laboratory procedures that require the cells to remain attached to the culture vessel, for example immunocytochemical procedures. Other more economical alternatives have been developed, such as the use of polylysine or polyornithine [3,4]. A much less utilized polymer, PEI, offers a successful alternative. The most economical of all the mentioned factors, PEI seems to perform the task of attachment factor efficiently with primary neurons (see Figure 1 and references [15,16]), and with weakly anchoring cell lines (see Figures 1, 2 and 3, and reference [17]). The PEI solution is very easy to prepare, it can be frozen for several months without any loss of activity, and remains active on the plastic surface for numerous days after the treatment (Figure 4C). The anchoring effect of PEI seemed to work well for a wide range of cell numbers for both PC-12 and HEK-293 cells (see Figure 4B). PEI was efficient with high numbers of PC-12 cells probably by preventing cell-cell interactions (that result in the "clumping" of the cells), favoring instead the attachment to the dish surface. PEI worked well with HEK-293 cells in the range of 4 × 104 to 3.2 × 105 cells per well. When larger numbers of HEK-293 cells were utilized the results were more variable, suggesting that when these cells are near confluency they may be able to form monolayers that attach more firmly to the plate, however further experiments are necessary to study this hypothesis. The effects of PEI and the other cationic attachment factors on PC-12 and HEK-293 cells suggest that these cells have a limited capacity to produce an effective extracellular matrix to allow them to attach to the plastic surface, resulting in weak anchoring to the dishes on their own. Cationic polymers such as PEI and PDL seem to work very effectively to coat the surface of the dish and substitute for the absence of the extracellular matrix components. On the other hand, fibroblast cells (such as the MYS cells) are very efficient producing extracellular matrix proteins, which allows them to attach firmly to the culture dishes, therefore no benefit was observed by the use of attachment factors with these cells (see Figure 3).
Lipofection is an efficient transfection procedure that requires culture dish manipulations and media changes. This fact causes difficulties when using cells that are not well anchored to the culture dish. The experiments presented support the idea that attachment factors can enhance the yield of lipofection of weakly anchoring cells. PEI provided very good results with PC-12 and HEK-293 cells, while the other factors tested showed more specific and limited effects (see Figure 5). Based on the results regarding the anchoring promotion effect of PEI and the other factors, it is likely that the increases in the yields of β-galactosidase observed are due to a larger number of cells remaining in the dishes that were treated with attachment factors. However, the transfection procedures were performed trying to minimize the cell loses, therefore it is also possible that part of the effect observed could be derived from a higher transfection efficiency of the cells that are more firmly attached versus the cells that are loosely attached or forming clusters. Cells in suspension are often harder to transfect than cells anchored to the substratum [9,10]. Preliminary experiments in which the transfection yield was normalized to the amount of protein in the lysate suggested that the increased yield is due primarily to the larger number of cells rather than an increase in efficiency (S. Govindaraju, K.V.L. Parsa, M. González-García and R.P. Ballestero, unpublished results). Future experiments will analyze these possibilities in more detail. In any case, the PEI pretreatment offers additional advantages, for example the firmly attached cells can be used in immunocytochemical and immunofluorescence analysis with great ease and virtually no cell loss (data not shown). Most protocols of transfection of PC-12 cells suggest the use of an attachment factor, however this is usually not the case for protocols involving the widely used HEK-293 cells [19,20,23,24]. Our experiments suggest that pretreatment of culture surfaces with PEI is advantageous in lipofection protocols with both cell lines.
Conclusions
PEI is used frequently as a transfection reagent. A second application of this reagent as an attachment factor is much less recognized. A comparison of PEI with two very commonly used attachment factors for cell and tissue culture showed that PEI is highly efficient and convenient. Two commonly used cell lines, PC-12 and HEK-293, attached firmly to plastic dishes coated with PEI. Although the anchoring properties of PEI had been previously recognized, its use with these two cell lines had not been characterized in detail in comparison with other frequently used coating agents. PEI will likely work with a variety of weakly anchoring cells, for example PEI was shown to be effective with primary retinal explants in this report. Additionally, the results presented indicate that the use of coating agents can enhance lipofection protocols. Cell culture and transfection protocols using PC-12 cells frequently involve the use of coating agents (commonly collagen or polylysine), but this is not so for protocols with HEK-293 cells. PEI was very effective in improving lipofection protocols with both cell lines. The firm attachment afforded by PEI allowed transfections with cationic lipids (lipofection) to provide higher yields and more consistent results.
Methods
Tissue culture and microscopy
PC-12 (rat pheochromocytoma, ATCC CRL-1721) cells were cultured with RPMI complete medium: RPMI medium (Invitrogen) supplemented with 10% horse serum and 5% fetal bovine serum (both from Hyclone), 100 u/ml of penicillin, 100 μg/ml of streptomycin, 10 mM HEPES, 2 mM glutamax and 1 mM sodium pyruvate (all from Invitrogen). HEK-293 (293, human embryonic kidney, ATCC CRL-1573) and MYS (MYS-Cl-2-BCF1, mouse yolk sac, ATCC CRL-9292) cells were cultured in DMEM complete medium: DMEM medium (Invitrogen) supplemented with 10% fetal bovine serum (Hyclone), 100 u/ml of penicillin, 100 μg/ml of streptomycin and 2 mM glutamax (all from Invitrogen). Cell lines were maintained at 37°C in a 5% CO2 atmosphere in a tissue culture incubator (NuAire). Zebrafish retinal explants were maintained in L-15 complete medium: L15 medium (Sigma) supplemented with 1% fetal bovine serum (Hyclone), 100 u/ml penicillin, 100 μg/ml of streptomycin and 2 mM glutamax (all from Invitrogen). The explants were incubated at 28°C at normal atmospheric CO2 concentrations.
For the culture and microscopic observation of PC-12 cells, the wells of a multiwell-12 tissue culture dish (MW12; Nunclon) were pretreated for 20 min with 100–200 μl of the different solutions of attachment factors: PEI-800 kDa, from Fluka, at 25 μg/ml in 150 mM NaCl; PDL, from ICN, at 100 μg/ml in deionized water; bovine dermal collagen, from ICN, 3 mg/ml aqueous solution. Usually, each column of wells in the MW12 was pretreated with one of the attachment factors, while the last column was left untreated. After removal of the different attachment factor solutions, 1 ml of a PC-12 cell suspension (approximately 0.5 × 106 cells) was added to each well of the MW12 and the plate was incubated at 37°C in a 5% CO2 atmosphere for 48 h. The cells were then examined under a phase contrast Olympus-CK40 microscope and photomicrographs were obtained with a Pixera Penguin 150 CL camera. The experiment was performed in triplicate and pictures were taken at the center, near the edge and midway between center and edge of the plate, to evaluate the distribution of cells on the wells.
For the microscopic observation of retinal explants, optic nerve crush (conditioning lesion) was performed retro-orbitally with anesthetized zebrafish 10 days prior to explantation, using protocols previously described [21,25]. Control explants were obtained from unoperated fish. The retinal pieces were laid on the wells of a MW12 that were pretreated with PEI as described above, and cultured in 200 μl of L15 complete medium at 28°C for 3 days. Photomicrographs were obtained as previously described.
PC-12 differentiation with NGF
The wells of a MW12 were pretreated with PEI as described above. The wells were then seeded with 2.5 × 105 PC-12 cells and cultured with RPMI complete medium as previously described. After 24 h, the medium was replaced by RPMI medium with low serum (same as RPMI complete medium, except for lower concentrations of sera: 1% horse serum and 0.5% fetal bovine serum), which was supplemented with NGF (Sigma) at 100 ng/ml to induce differentiation. The culture medium was replaced by new RPMI low serum medium with 100 ng/ml of NGF every two days. At different times during the differentiation process (0, 24, 48, and 96 h after the initial NGF addition), photomicrographs were obtained as previously described.
Cell number measurement with neutral red dye
Pretreatment of MW12 dishes with the attachment factors was performed as described above. PC-12, HEK-293 and MYS cells were counted using a Neubauer hemocytometer, and the MW12 plates were seeded with 1 × 106 cells per well for the experiments with PC-12 cells, 1.5 × 105 cells for the experiments with HEK-293 cells, or 3 × 104 cells for the experiments with MYS cells. The MW12 dishes were incubated at 37°C in a 5% CO2 atmosphere for 48 h in the case of PC-12 cells, or 24 h for HEK-293 and MYS cells. In all the experiments, the wells were washed 4 times with phosphate buffered saline (PBS) to test the strength of anchorage to the substratum. To estimate the number of cells remaining after the multiple washings, a procedure that utilizes the vital dye neutral red was employed [26]. Briefly, the cells were incubated for 90 min at 37°C with 750 μl of a solution of 0.01% neutral red in PBS, and then the excess dye was removed with two washes in PBS. The dye retained by the cells was extracted with 900 μl of ethanol-citrate solution (1:1 mixture of 0.1 M citrate pH 4.2 solution and ethanol) for 20 min with gentle agitation. Relative cell counts were determined by measuring the amount of dye spectrophotometrically at 540 nm (Beckman DU500 spectrophotometer). All experiments were performed in triplicate. To compare the experiments, absorbances were normalized to the values obtained from the PEI-pretreated wells.
Statistical analyses
Two-sample (unpaired) t-test analyses were performed with a Microsoft Excel worksheet designed for this purpose. The worksheet calculates the t-factor by comparing 2 independent sets of data and estimates the probability (p) of obtaining that result assuming that the two samples came from the same population (null hypothesis: mean-1 = mean-2; alternate hypothesis: mean-1 < > mean-2) based on the Student's t-distribution (two-tailed). The averages of the two samples from the same experiment were considered statistically different whenever p was lower than 0.01. For comparisons of the independent experiments, one-sample t-test analyses were utilized to estimate the p values and assess the statistical significance of the difference observed between the PEI-treated wells and the untreated controls. The effect of PEI was considered significant whenever p was lower than 0.05.
Determination of effective PEI dosages
For the determination of optimal concentrations of PEI for the pretreatment procedure, a series of solutions of PEI were prepared so that their final concentrations of PEI polymer (w/v) were 0.025, 0.25, 2.5, 25 and 250 μg/ml. They were used to coat MW12 tissue culture dishes as previously described for the 25 μg/ml solution. To test the efficacy of coating of the solutions, 2 × 105 HEK-293 cells were seeded onto the dishes and incubated at 37°C for 24 h. Untreated wells were seeded in the same manner as controls.
The dishes were then subjected to the multiple washing procedure described above and the number of cells remaining in the dishes was measured by the neutral red dye procedure as described previously. All experiments were performed in triplicate and the relative cell counts were calculated by normalization to the values obtained with 25 μg/ml of PEI, which was assigned a value of 100.0%.
Determination of effective cell number ranges for PC-12 and HEK-293 cells
The wells of MW12 culture dishes were pretreated with 25 μg/ml of PEI as described above. The wells were then seeded with various numbers of either HEK-293 cells (4 × 104, 8 × 104, 1.6 × 105 and 3.2 × 105) or PC-12 cells (2.5 × 105, 5 × 105, 1 × 106 and 1.5 × 106). Control untreated MW12 culture dishes were seeded with the same numbers of cells in parallel with the treated plates. After 24 h for the HEK-293 cells or 48 h for the PC-12 cells, the strength of anchoring of the cells to the dishes was measured as described before and relative cell numbers were determined (normalized to the raw absorbance values obtained from the PEI-pretreated wells with the highest numbers of each cell line tested, 3.2 × 105 for HEK-293 cells and 1.5 × 106 for PC-12 cells, which were assigned arbitrarily a value of 100.0%). All experiments were performed in triplicate.
Stability of PEI coating experiments
To analyze the stability of the PEI attachment factor onto the plastic surface of the tissue culture dish, two tests were performed. One MW12 dish was coated with 25 μg/ml of PEI as previously described, then the PEI solution was replaced by PBS and the dish was kept at 4°C for 10 days. A second MW12 dish was coated with 25 μg/ml of PEI, then the PEI solution was replaced by DMEM complete medium and the dish was kept at 37°C in the CO2 incubator for 24 h. Afterwards, the medium was replaced by fresh DMEM complete medium and incubated for a further 24 h. This process was repeated for a third time, so that the dish was incubated for a total of 96 h with 3 medium changes. These two dishes were then seeded with 2 × 105 HEK-293 cells, in parallel with a third MW12 in which a column of wells was pretreated with PEI using the standard protocol described earlier (an untreated column of wells in this dish was used for the controls). The strength of attachment of cells to these dishes was determined as mentioned before, and relative cell counts were calculated by normalization to the value obtained with the standard PEI-treated wells, which was assigned arbitrarily a value of 100.0%. All experiments were performed in triplicate.
Transfection yield determinations
MW12 culture dishes were pretreated and seeded with cells as indicated above for the cell number measurements. The cells were subjected to lipofection with 1 μg per well of the plasmid pcDNA3-βgal [27] and LipofectAMINE-2000 (PC-12 and HEK-293 cells) or LipofectAMINE-Plus reagent (MYS cells; both reagents from Invitrogen), following the protocols recommended by the manufacturer. After incubation at 37°C in a 5% CO2 atmosphere for 48 h for the PC-12 cells, or 24 h for the HEK-293 and MYS cells, the transfection yield was determined by measuring β-galactosidase activity in cell extracts [28]. The cells were lysed using reporter lysis buffer (Promega). The reaction mixture was made of 50 μl of lysate supernatant, 100 μl of reporter lysis buffer, and 150 μl of 2X-ONPG substrate solution (1.33 mg/ml O-nitro phenyl β-D-Galactopyranoside in 164 mM Na2HPO4, 36 mM NaH2PO4, 2 mM MgCl2 and 100 mM β-mercaptoethanol). The reaction mixture was incubated at 37°C until development of yellow coloration was apparent (usually within a few hours). Reactions were stopped by the addition of 500 μl of 1 M Na2CO3. The relative levels of β-galactosidase activity were obtained by determination of the absorbance at 420 nm in a spectrophotometer (Beckman DU500). Parallel assays with cells transfected with pcDNA3 plasmid (Invitrogen) were conducted as negative controls in every experiment, and subtracted from the experimental absorbances to correct for endogenous activities. The experiments were conducted in triplicate. Absorbances were normalized to the values obtained with PEI-pretreated wells.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
A.R.V. performed the experiments comparing the various attachment factors. S.G. and K.L.V.P. performed the PEI dosage, cell number and PEI stability analysis experiments, as well as the NGF differentiation experiments with PC-12 cells. M.J. participated in the experiments with MYS cells. R.P.B. and M.G.G. collaborated as Principal Investigators in this project, participating in the design of the experiments and the writing of the manuscript. R.P.B. performed the experiments with zebrafish retinal explants.
Acknowledgements
This work was supported by NIH-MBRS-SCORE grant # S06 GM08127–28 to M.G.G. and R.P.B. and by a Welch Foundation Grant to the Department of Chemistry at Texas A&M University-Kingsville.
Figures and Tables
Figure 1 Attachment of PC-12 cells and neuronal explants to culture dishes. PC-12 cells attached better to plastic surfaces that were pretreated with anchoring-promoting factors, such as PEI (panel A), collagen (panel B), or PDL (panel C), as compared to the untreated plastic surface control (panel D), which contained cells in clusters and very loosely attached to the substratum (pictures were taken midway between center and edge of the plate). PEI coating resulted in a homogeneous distribution of cells firmly attached to the dish substratum (panel A). PEI promotes attachment to culture dishes of retinal explants from zebrafish (panel E). This attachment factor was supportive of neurite extension in retinal explants primed for axonal growth by a pre-conditioning lesion to the optic nerve (panel F). The bar in the photomicrographs corresponds to 25 μm in panels A-D, 50 μm in panel E, and 100 μm in panel F.
Figure 2 Differentiation of PC-12 cells attached to culture dishes coated with PEI. PC-12 cells attached to PEI coated dishes remained firmly anchored to the plate and extended neurites upon treatment with NGF. The pictures show the progress of the differentiation of the treated cells over time: 0 h (panel A), 24 h (panel B), 48 h (panel C) and 96 h (panel D) after the initiation of treatment with 100 ng/ml of NGF. The bar in the photomicrographs corresponds to 25 μm.
Figure 3 PEI coating promotes firm attachment of weakly anchoring cells to the substratum. The loss of cells induced by a protocol that involved multiple washings was measured to assess the strength in the attachment of cells to culture dishes coated with factors that promote cell anchoring. PEI was compared to other attachment factors (collagen, PDL) and to untreated control dishes. The relative cell counts were normalized by assigning an arbitrary value of 100.0% to the PEI-pretreated dishes. PEI coating resulted in significant enhancement of anchoring strength in PC-12 cells (graph A) and in HEK-293 cells (graph B), comparing favorably to other commonly used attachment factors. No benefits were observed in the attachment of a strongly anchoring cell line (MYS cells, graph C). The bars show the average ± standard deviation of triplicate assays, which are representative of at least three different experiments performed (* significantly higher than untreated control, p < 0.01; ** significantly higher than untreated control in the experiment presented, p < 0.01, but significance not maintained in independent experiments).
Figure 4 Characterization of the properties of PEI as an attachment factor. Graph A: Increasing doses of PEI were tested to determine the concentration range for optimal coating. PEI showed full attachment enhancement at concentrations of 2.5 μg/ml and higher. Graph B: PEI promoted attachment of both PC-12 and HEK-293 cells over a wide range of cell numbers (numbers tested are indicated in X axis). Graph C: PEI coating remained stable onto the culture dish surface over long incubation periods and medium changes (10d-PBS: PEI-treated dish incubated for 10 days in PBS at 4°C; 96 h-3MC: PEI-treated dish incubated for 96 h with 3 medium changes at 37°C). For all the graphs, the bars show the average ± standard deviation of triplicate assays, which are representative of at least 3 independent experiments (* significantly higher than untreated control, p < 0.01).
Figure 5 PEI enhances transfection of weakly anchoring cells. Transfection yields were determined by β-galactosidase assays and normalized to the yields obtained with PEI coating of the culture dish (to which it was assigned an arbitrary value of 100.0%). PEI and other attachment factors enhanced the transfection of loosely attaching cell lines such as PC-12 cells (graph A) and HEK-293 cells (graph B). No significant effect was observed with tightly attaching cells such as MYS fibroblasts (graph C). The bars show the average ± standard deviation of triplicate assays, which are representative of at least two independent experiments (* significantly higher than untreated control, p < 0.01).
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| 15485583 | PMC526208 | CC BY | 2021-01-04 16:02:57 | no | BMC Biotechnol. 2004 Oct 15; 4:23 | utf-8 | BMC Biotechnol | 2,004 | 10.1186/1472-6750-4-23 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-221548815210.1186/1472-6920-4-22Research ArticleStudent evaluation of an OSCE in paediatrics at the University of the West Indies, Jamaica Pierre Russell B [email protected] Andrea [email protected] Michelle [email protected] J Michael [email protected] Celia DC [email protected] Department of Obstetrics, Gynaecology and Child Health, University of the West Indies, Jamaica2 Undergraduate Affairs, Dean's Office, Faculty of Medical Sciences, Jamaica2004 16 10 2004 4 22 22 8 4 2004 16 10 2004 Copyright © 2004 Pierre et al; licensee BioMed Central Ltd.2004Pierre 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 Faculty of Medical Sciences, University of the West Indies first implemented the Objective Structured Clinical Examination (OSCE) in the final MB Examination in Medicine and Therapeutics during the 2000–2001 academic year. Simultaneously, the Child Health Department initiated faculty and student training, and instituted the OSCE as an assessment instrument during the Child Health (Paediatric) clerkship in year 5. The study set out to explore student acceptance of the OSCE as part of an evaluation of the Child Health clerkship.
Methods
A self-administered questionnaire was completed by successive groups of students immediately after the OSCE at the end of each clerkship rotation. Main outcome measures were student perception of examination attributes, which included the quality of instructions and organisation, the quality of performance, authenticity and transparency of the process, and usefulness of the OSCE as an assessment instrument compared to other formats.
Results
There was overwhelming acceptance of the OSCE in Child Health with respect to the comprehensiveness (90%), transparency (87%), fairness (70%) and authenticity of the required tasks (58–78%). However, students felt that it was a strong anxiety-producing experience. And concerns were expressed regarding the ambiguity of some questions and inadequacy of time for expected tasks.
Conclusion
Student feedback was invaluable in influencing faculty teaching, curriculum direction and appreciation of student opinion. Further psychometric evaluation will strengthen the development of the OSCE.
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Background
The assessment of student's clinical competence is of paramount importance, and there are several means of evaluating student performance in medical examinations [1,2]. The Objective Structured Clinical Examination (OSCE) is an approach to student assessment in which aspects of clinical competence are evaluated in a comprehensive, consistent and structured manner, with close attention to the objectivity of the process [3]. The OSCE was introduced by Harden in 1975 [4], and first described as an assessment format in Paediatrics (Child Health) by Waterson and colleagues [5]. Since its inception, the OSCE has been increasingly used to provide formative and summative assessment in various medical disciplines worldwide [6], including non-clinical disciplines [7].
The University of the West Indies was established in 1948 as a medical college of the University of London, which granted external degrees to those who successfully completed the course [8]. The Faculty of Medical Sciences located on four campuses, on the islands of Jamaica, Bahamas, Barbados and Trinidad and Tobago, conducts bi-annual final examinations at the end of year 5. The 'traditional' format of examination that included long case, short cases and oral examination, was preserved until recent changes in the curriculum. In response to recommendations to improve the validity and fairness of the examination through adoption of proven methods and approaches in assessment and evaluation in medical education, the Faculty of Medical Sciences (FMS), University of the West Indies (UWI) initiated the OSCE as a formal method of assessment for the final examination in Medicine and Therapeutics, Child Health, Community Health and Psychiatry, in November 2000. Students and faculty were exposed for the first time to a relatively new assessment instrument in which aspects of competence (communication, history-taking and technical skills) were assessed in a structured, formal manner.
The Section of Child Health, Mona, Jamaica, implemented the OSCE examination as an end-of clerkship assessment for students in their 5th year, during the 1999–2000 academic year. It was felt timely in order to (a) direct and motivate student learning in areas not previously assessed in the 'traditional' curriculum, (b) verify students' competence in fundamental paediatric clinical skills, and (c) provide a forum for feedback to students on their strengths and weaknesses in clinical skills. It was thought that it would enhance faculty and student acceptance of this new assessment tool and promote faculty training for the newly introduced final OSCE examination.
In the absence of any previous information from this institution, the study was designed to evaluate student overall perception of the end-of-clerkship OSCE, determine student acceptability of the process and provide feedback to enhance further development of the assessment.
Methods
The OSCE comprised a circuit of thirteen stations, which involved completion of a number of tasks such as examination of a system, eliciting a focussed history, counselling or communicating a problem, performing a procedure and problem-solving oriented around patient and laboratory data, and photographic material (Figure 1). The areas assessed included cardiovascular, respiratory, abdomen, neurological, developmental, dysmorphism and nutrition. This assessment format allowed the controlled exposure of students to a wide variety of paediatric clinical skills within a relatively short time period. Each station was 7 minutes duration with the exception of the 14-minute history-taking station. One minute was given between stations to facilitate change and the reading of instructions. With the inclusion of strategically placed rest stations, to reduce student and patient fatigue, all students completed the circuit over a 2-hour period.
Figure 1 Plan of OSCE circuit
A standardised technique of marking was used and student performance was assessed by criterion reference for each station. Criterion-based scoring was used, with each checklist item scored as 0 (omitted, incorrect or inadequate), or 1–2 (correct or adequate).
Face and content validity of each checklist was established by review and consensus by a core group of senior paediatricians. Stations were first selected to represent the curricular goals and objectives and to reflect authentic clinical situations. Checklists were designed to include the features thought to be most important by the development committee. Through discussions, consensus was achieved on the checklist items and structure.
The study was conducted during the period July 2001 to December 2002. Five groups of students participated in the process, during their respective clerkship rotations. Student groups had at least two briefing sessions before the OSCE, and included an orientation about the examination process (both end-of-clerkship and final MB) and a review of commonly assessed competences. They were also apprised of the valuable contribution they could make towards improving the assessment and encouraged to participate in the evaluation.
A cross-sectional survey using a 32-item self-administered questionnaire was completed at the end of each OSCE [9]. Students were asked to evaluate the content, structure, and organization of the OSCE, rate the quality of performance and objectivity of the OSCE process, and to give their opinion about the usefulness of the OSCE as an assessment instrument compared to other forms which they had experienced (essays, multiple choice questions, long and short cases, general clerkship rating).
Participation was on a voluntary basis and students were assured that those who declined involvement in the survey would not be penalised. The Curricular Affairs Section handled the administration and analysis of the questionnaires. Ethical approval was received from the University Hospital of the West Indies/University of the West Indies Faculty of Medical Sciences Ethics Committee. Following completion of the questionnaire, an OSCE review session was conducted with the students for feedback and teaching purposes, at the end of the clerkship. Students were given the opportunity to review their individual performances at the respective stations. Examiner evaluations were also used in the feedback process.
Data were collated and descriptive and non-parametric tests applied using Stata version7 [10]. Basic statistical analysis of the Likert items was conducted by calculating frequencies, means and standard deviations. Qualitative analysis was done through a form of content analysis by identifying themes in student responses and grouping responses according to thematic content. Two of the authors individually conducted this content analysis and identified themes and final grouping of responses were developed by consensus.
Results
OSCE evaluation
Eighty-one students responded to the questionnaire, representing 92 % (81/88) of those who completed the Clerkship.
The majority of students agreed that the OSCE was comprehensive and covered a wide range of knowledge (95%) and clinical competencies (86%) in Child Health. Three quarters (78%) also agreed that the assessment process helped to identify weaknesses and gaps in their competencies (Table 1).
Table 1 OSCE evaluation
Question Agree % Neutral % Disagree % No comment %
Exam was fair 68 19 12 1
Wide knowledge area covered 95 5
Needed more time at stations 70 22.5 7.5
Exams well administered 73 16 11
Exams very stressful 67 20 13
Exams well structured & sequenced 81.5 17 2.5
Exam minimized chance of failing 28 40.5 30 1.5
OSCE less stressful than other exams 15 40 35 10
Allowed student to compensate in some areas 67 21 12
Highlighted areas of weakness 78 13 9
Exam intimidating 48 32 20
Student aware of level of information needed 53 26 21
Wide range of clinical skills covered 86 6 8
Most (73–82%) felt that the exam was well administered, and that the stations were arranged in an organised and well-sequenced order.
Students believed that the assessment was fair (68%). Fifty-three percent were aware of the level of information required at each station, yet 28% felt that the examination process minimized their chances of failing.
Students found the OSCE to be intimidating (48%) and more stressful (35%) than other assessment formats to which they were previously exposed. And most (70%) felt that they needed more time to complete the stations.
Performance testing
The majority of students felt they were well oriented about the exam and that the required tasks were consistent with the actual curriculum that they were taught. They also felt that the process was fair but were not as satisfied with the time allocation for each station (Table 2).
Table 2 Quality of performance testing
Question Not at all % Neutral % To great extent %
Fully aware of nature of exam 4 9 87
Tasks reflected those taught 4 23 73
Time at each station was adequate 44 35 21
Setting and context at each station felt authentic 18 24 58
Instructions were clear and unambiguous 15 27 58
Tasks asked to perform were fair 3 27 70
Sequence of stations logical and appropriate 13 30 57
Exam provided opportunities to learn 11 21 69
Most saw the OSCE as a useful learning experience and that the content reflected real life situations in Child Health. More than half of the students were satisfied with the conduct, organisation and administration of the OSCE.
Perception of validity and reliability
Although half of the students believed that the scores were standardised, they were unsure whether their scores were an actual reflection of their paediatric clinical skills (Table 3). Student responses to the question about bias due to gender, personality or ethnicity, were not interpretable.
Table 3 Student perception of validity and reliability
Question Not at all % Neutral % To great extent %
OSCE exam scores provide true measure of essential clinical skills in paediatrics 14 43 43
OSCE scores are standardized 8 37 55
OSCE practical and useful experience 4 23 73
Personality, ethnicity and gender will not affect OSCE scores 18 19 63
Comparing assessment formats
Students were asked to rate the following assessment instruments to which they had been exposed (multiple choice questions, essays / short answer questions, general clerkship ratings, OSCE). A likert scale was used to assess each according to the evaluative labels (Table 4).
Table 4 Student rating of assessment formats
Question: Difficult % Undecided % Easy %
Which of the following formats is easiest?
MCQ 48 26 26
Essay/SAQ 38 44 18
OSCE 43 45 12
Clerkship ratings 21 47 32
Question: Unfair % Undecided % Fair %
Which of the following formats is fairest?
MCQ 29 28 43
Essay/SAQ 7 25 68
OSCE 4 16 80
Clerkship ratings 16 26 58
Question: Learn very little % Undecided % Learn a lot %
From which of the following formats do you learn most?
MCQ 28 37 35
Essay/SAQ 12 37 51
OSCE 15 25 60
Clerkship ratings 20 18 62
Question: Used much less % Undecided % Used much more %
Which of the following formats should be used more often in the clinical years of the programme?
MCQ 31 59 10
Essay/SAQ 9 52 39
OSCE 5 43 52
Clerkship ratings 12 56 32
MCQ – multiple choice question; SAQ – short answer question; OSCE -objective structured clinical examination
Thirty-two percent of students felt that the clerkship rating was the easiest, while 48% rated MCQ as a more difficult form of assessment. The OSCE was overwhelmingly considered the fairest assessment format (80%), and essays (68%) to a lesser extent. OSCE (60%) and clerkship ratings (62%) were considered the most useful learning experiences. Compared to the other assessment formats, 52% considered that the OSCE should be used most in the clinical years.
Qualitative data
Students were asked follow-up questions related to positive and negative aspects of the OSCE and suggestions for improvement. The open-ended responses were grouped by thematic content.
Among the positive attributes of the OSCE, students re-affirmed that the assessment was comprehensive (44 comments) and that it was an objective and fair process (43 comments). Some indicated that the opportunity for feedback helped to motivate them and drive the learning process (21 comments).
Students felt that the time allocated to perform expected tasks was insufficient (36 comments), and that the procedure was stressful (18 comments) and tiring (13 comments). Technical problems (28 comments) included unclear instructions, inadequate time provision and instructions between stations and detention of some candidates at stations by examiners.
Suggestions for improvement included increasing the duration of stations (29 comments), ensuring clear instructions (8 comments) and having more realistic expectations of students for the expected tasks. A few students wished to have more training with the OSCE and suggested that the examination should be videotaped to increase objectivity and permit review.
Discussion
Students overwhelmingly perceived that the OSCE in Child Health had good construct validity. This was demonstrated by the favourable responses concerning transparency and fairness of the examination process, and the authenticity of the required tasks per station. Excellent levels of acceptance of the OSCE by students have been previously described in the literature [11-14]. They however expressed concerns and uncertainty about whether the process would minimize their chances of failing or that the results were a true reflection of their clinical skills. This was understandable, since it was their first encounter with this type of assessment.
Several felt that the examination was stressful and intimidating, yet paradoxically some students perceived it as an enjoyable, practical experience. Studies surveying student attitudes during the OSCE have documented that the OSCE can be a strong anxiety-producing experience, and that the level of anxiety changes little as students progress through the examination [15].
It is well recognised that assessment is a catalyst for both curriculum change and student learning. The students recognised the value of the instrument for formative evaluation. In addition, as many medical schools have adopted a student-centred approach to medical education, greater student participation in quality assurance exercises must be encouraged. Students perceived the OSCE to be fairer than any other assessment format to which they were exposed. The findings were somewhat similar to the views of students at Newcastle medical school [16]. Although student views on fairness may not be consistent with published literature, the impact and influence on acceptability of the instrument should be noted.
They offered constructive criticism of the structure and organisation of the process. At some stations they felt that the instructions were ambiguous and that the time allocation was inadequate for the expected tasks. The feedback was invaluable and facilitated a critical review and modification of the station content and conduct of the examination over time. Faculty perceived that the concerns about time allocation per station and the degree of stress expressed by the students were due to inadequate preparation for the examination, particularly in competences not previously assessed in the 'traditional' examination.
The high student response rate has helped to ensure that the findings presented are a valid representation of student opinion. Students have traditionally viewed the end-of-clerkship assessment as a 'high-stake' examination and also perceive it as predictive of their performance at their final MB examination. Student perception of the OSCE however, may have been influenced by anxiety and lack of confidence associated with a new assessment. The responses may also have been affected by the timing of the inquiry (immediately after the examination); hence student stress and fatigue should be taken into consideration. Whereas the high response rate ensured that the views were reasonable representative of the students, differences in assessors could have influenced the interpretation of the results of open-ended responses.
Implementing the OSCE in Child Health at the University of the West Indies, Jamaica has been challenging, however student participation in the evaluation and their overall acceptance of the instrument have been encouraging. Feedback from students and faculty has been useful in effecting improvements to the process and greater emphasis has been placed on the teaching and evaluation of history taking, communication and technical competencies. It is also sending a clear message to students that the achievement of overall competence is imperative to clinical practice in the current environment. Ultimately, these provide the loop necessary to drive the continuum of curriculum development. This has been timely considering that the Faculty of Medical Sciences, Jamaica is undergoing significant reform [17]. Further developments involving psychometric evaluation will strengthen the process.
Conclusions
In summary, the findings highlight the need for student participation in the development of new assessment tools in medical curricula. Student acceptance will be more favourable for assessment formats that they perceive to be transparent, authentic and valid. 'Traditional' medical curricula must be responsive to global paradigm shifts in undergraduate medical education.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
RP conceptualised the study; developed the proposal, coordinated the conduct of the project, completed initial data entry and analysis, and wrote the report. AW participated in the design of the study, coordinated the conduct of the project, performed the statistical analysis, and assisted in writing the report. MB was the main organizer of the clerkship OSCE, and assisted in editing the final report. MBr and CC participated in overall supervision of project and revision of report. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We wish to thank Dr. Jerome De Lisle of the Centre for Medical Science Education (CMSE), EWMSC, Trinidad, for professional advice and permission to use the questionnaire in this study. We also express our gratitude to the participating students and lecturers in Child Health who contributed to the implementation of the OSCE in the department.
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| 15488152 | PMC526209 | CC BY | 2021-01-04 16:30:54 | no | BMC Med Educ. 2004 Oct 16; 4:22 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-22 | oa_comm |
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BMC NursBMC Nursing1472-6955BioMed Central London 1472-6955-3-41546961610.1186/1472-6955-3-4Research ArticleCommunity nursing needs more silver surfers: a questionnaire survey of primary care nurses' use of information technology Chan Tom [email protected] Sarah [email protected] Lusignan Simon [email protected] Kent Surrey and Sussex Primary Care Research Network (KSSnet.) Surrey and Hampshire Borders Community Trust Camberley, Surrey, UK2 Kent Surrey and Sussex Primary Care Research Network (KSSnet.) Three Bridges Practice, Crawley, West Sussex, UK3 Senior Lecturer – Primary Care Informatics Department of Community Health Sciences St. George's Hospital Medical School LONDON SW17 ORE UK2004 7 10 2004 3 4 4 13 5 2004 7 10 2004 Copyright © 2004 Chan et al; licensee BioMed Central Ltd.2004Chan 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 the UK the health service is investing more than ever before in information technology (IT) and primary care nurses will have to work with computers. Information about patients will be almost exclusively held in electronic patient records; and much of the information about best practice is most readily accessible via computer terminals.
Objective
To examine the influence of age and nursing profession on the level of computer use.
Methods
A questionnaire was developed to examine: access, training received, confidence and use of IT. The survey was carried out in a Sussex Primary Care Trust, in the UK.
Results
The questionnaire was sent to 109 nurses with a 64% response rate. Most primary care nurses (89%) use their computer regularly at work: 100% of practice nurses daily, compared with 60% of district nurses and 59% of health visitors (p < 0.01). Access to IT was not significantly different between different age groups; but 91% of practice nurses had their own computer while many district nurses and health visitors had to share (p < 0.01). Nurses over 50 had received more training that their younger colleagues (p < 0.01); yet despite this, they lacked confidence and used computers less (p < 0.001). 96% of practice nurses were confident at in using computerised medical records, compared with 53% of district nurses and 44% of health visitors (p < 0.01.) One-to-one training and workshops were the preferred formats for training, with Internet based learning and printed manuals the least popular (p < 0.001).
Conclusions
Using computers in the surgery has become the norm for primary care nurses. However, nurses over 50, working out in the community, lack the confidence and skill of their younger and practice based colleagues.
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Background
In the UK its National Health Service (NHS) is investing in computerisation. Large contracts have been set that will run until 2013 to provide electronic patient records [1]. Over this timeframe, with the exception of patient-held records, written medical records will largely be phased out, and replaced by the computerised. Instead of each hospital, or other NHS service that a patient attends creating its own record in isolation separate records will be linked via an "electronic spine." To help integration of records the whole NHS is due to move to a single coding system. Currently most of general practice uses Read codes to record structured data in computers [2,3]. There are many other systems currently in use in the other parts of the health service, but this is all due to come to an end with the whole NHS moving to use a classification system called SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms [4,5]) In addition, computer use will be become more and more necessary, as ever increasing amounts of information to support learning and the delivery of high quality care, are placed on-line [6,7]. Every general practice premises has a connection to NHSnet and via this the Internet, to facilitate access [8].
In this environment primary care professionals will need the skills to read and enter data in computerised medical records, access guidelines and other information on-line, receive alerts about changes in practice via email and use computer as a learning resource. Nurses: practice nurse, district nurses, health visitors, and others, make up a substantial proportion of the primary care workforce. Previous studies have suggested that they have limited access and low levels of confidence and competence in using IT (Information Technology) [9-11]. However, the data reported in these publications is at least two years old. We therefore conducted this study to discover the extent to which primary care nurses had access to, and were using IT; and to examine whether any particular groups were being left behind.
Methods
A literature review was carried out of standard bibliographic data bases: CINAHL, British Nursing Index and Medline to identify any recent publications about use of IT or computerised medical record by practice or district nurses and health visitors. In addition the primary care electronic library (PCEL) [12], and information departments in the Community Practitioners and Health Visitors Association (CPHVA) [13] and Royal College of Nursing (RCN) [14] were contacted by email. We did not find any contemporary reports of UK primary care nurse use of IT.
The study took place in a single Primary Care Trust in Sussex, a mixed urban and rural county in southeast England. A Primary Care Trust is a geographically defined locality of approximately 1 – 200,000 people that commissions hospitals, general practitioners and other health providers to deliver comprehensive local NHS healthcare. A PCT can also provide care itself. Most community nurses are employed directly by their PCT, and this PCT is no exception.
All the practices in this PCT are computerised with computers linked to the patient database located in all consulting and treatment rooms; and most offices. In common with nearly all UK general practice the practice is networked and the network is connected to the internet via a fixed ISDN-2 (Integrated Services Digital Network) link. The ISDN (64 K link) actually connects the practices to the NHS own intranet (NHSnet) and via secure gateways to the internet [8]. The NHSnet connection also provides primary care professionals email, as part of this service. In theory there should not be problems with access to IT in NHS primary care.
This PCT has a predominantly female (97%) workforce with one-and-a-half times as many nurses in the over 40 age group compared with the national average for nursing [15]. The Nursing and Midwifery Council does not keep separate age-sex details for community nurses, however some information was available from the RCN. The RCN provided information about the age range of community nurses in southeast England. These, although collected using different age bands, show a similar population distribution as that found in this sample. The department of health produces details of community nurses by age band [16]. Although, these data do not include practice nurses, and are a national sample, they also show a similar distribution. The proportions by age group are shown in Figure 1.
Figure 1 Comparison of the age distribution of the study population with the Royal College of Nursing community nurse members in the southeast of England.
A questionnaire was considered the most appropriate way to gather the information required. Two previous studies indicated that primary care nurses appeared willing to complete the questionnaires and provide rich data [10,11]. The design consisted of questions which required "tick box" answers, or rating choices on a simple four point scale. It had 12 questions spread over three sides of A4 with a fourth side available for comments; it could be completed in less than five minutes. A copy of the questionnaire is attached as an appendix to this paper [see Additional file 1], or can be downloaded from: .
The questions were based on those used in two earlier studies; with the clarity of questions was improved. Especially distinguishing between a clinical computer system (i.e. one that is used to enter data into an electronic patient record) from other "work" computer systems; and, between home and work use. The questionnaire was tested by a pilot group of nurses located in another PCT in a neighbouring county. The pilot group of six nurses were asked to complete the questionnaire on their own. They were then invited to join a discussion group – where the purpose of the study was explained. Then the questions were gone through one by one to identify any ambiguity. The nurses were also invited to suggested alternative or additional questions. They were given SB's contact details should anything come to mind later.
Ethical approval was obtained from the local research ethics committee to conduct the study.
Primary care nurses were identified using a list provided by the PCT. The questionnaire was sent out with a covering letter explaining the nature of the study. A courier addressed envelope was provided so the questionnaire could be returned without cost. A reminder and second questionnaire was sent out to non-responders.
The questionnaire responses were coded and entered into SPSS (Statistical Package for Social Sciences) for analysis. The results were analysed by age bands and professional grouping as we wished to test whether either of these factors predetermined the level of computer use. Additional comments were entered into Microsoft Word, and the SPSS data from the questionnaire was placed alongside the relevant comments – they were flagged with the professional group and age group of their originator. Microsoft Excel was used to allow them to be sorted by age group and profession.
To simplify the analysis only three age-groups were considered: age under 30, 30 to 39, 40 to 49, and 50 and over. There were no nurses under 30, and only one over 60. The nursing professions were divided into: District nurses, health visitors and midwives, practice nurses, and others. In this study qualified nurses who work in the community primarily supporting district nurses or health visitors were analysed as part of that nursing specialist group. The health visitors and community midwives were combined into the "health visitors" group. The term "primary care nurse" is used to embrace all qualified nurses included in this survey; "community nurse" refers to nurses who still do a proportion of their work in patients' homes, Figure 2.
Figure 2 Typology of UK primary care nurses
Results
There was a 67% (67/105) response rate to the questionnaires. There was some variation between nursing groups: the response from practice nurses was 73% (n = 22/30), for district nurses 51% (n = 17/33), for health visitors and midwives 67% (n = 19/28), and other nurses, community psychiatric nurses and managers 64% (9/14).
Computers were readily available to primary care nurses. Over 90% of nurses had access to a personal computer (PC) at work, though for nearly half (45%) this was shared access; home access was even higher – 96%, with very few (12%) describing their access as shared. The remainder had access elsewhere: library, college etc. Not all the computers were connected to the Internet: 11% of those provided a PC at work and 6% at home could not go on line. After taking into account "other" access to the Internet only 3% of primary care nurses lacked access.
The pattern of access to computers, the Internet, and receiving training changed with age. The younger nurses had more access to PCs and the Internet at work, while older nurses had higher levels of access at home, and had received more training. These trends are shown in Table 1; only the receipt of more training in nurses 50 and over was statistically significant (Chi-squared test.) There were more striking differences between the different nursing professions. 90.1% of practice nurses had their own computer at work, with the balance of 9.1% having shared access – three-quarters (76.2%) had Internet access via this computer. By way of contrast their community based colleagues had significantly less access – only 56% of district nurses and 17% of health visitors had their own computer. Most had shared access and a few, 6.3% and 8.7% respectively, no access at all (p < 0.001).
Table 1 Trends in access to computers, the Internet and receipt of training with age
Age 30–49 Age 50 & over All Pearson X2 p=
Has access to own PC when working 59.1% 37.0% 50.7% 0.074
Has own access to Internet when working 47.7% 29.6% 40.8% 0.225
Has Access to PC at home 81.8% 85.2% 83.1% 0.656
Has Access to Internet at home 77.3% 77.8% 77.5% 0.999
Have received computer training 50% 81.5% 62.0 0.008
(n=) 44 27 71
62% of nurses had received some training, although it was often superficial and run in house to provide basic knowledge of how to use the clinical computer system. Some learned from family members, colleagues or were self-taught, in the use of Microsoft Word and Excel. A few respondents received more formal training on the use of Internet for literature searching from a library, evening classes or as part of high education. Open question data shows that the training received are very diverse both in subjects and level of skills:
"...two fifteen minute sessions in 1991 when we first got computers, a second when system was updated"
"...only one day. All other courses since have been at difficult times or locations"
"...very basic"
"...not sufficient though"
"...no formal courses, only shown by colleague when I was new in post"
"Protected time for PC training would be most valuable – so often this is hurriedly given during the odd 5-minute break rather than in a training session."
"...adequate training is really necessary and protected time; 10–15 minutes between patients is not adequate."
Respondents were asked to rate their preferred format for ICT training on a 4-point scale: from 0 for least desirable to 3 for highly desirable. The result showed that 'one-to-one' training and workshops were the preferred formats for training, printed manual the least (Table 2.)
Table 2 Preferred format for training
Training format Mean rank
Printed manual 1.73
Tutorial on the Internet 2.68
Lectures 2.46
Workshops 3.75
One to one 4.38
(Friedman Test: n = 42; X2 = 87.01; df = 4; p < 0.001).
The preferred location for the training reflected the format of training requested (Table 3.) Most wanted training to take place one-to-one in their workplace with those wanting workshops looking to see them held in an education or teaching centre. No age or professional group differences were found for the format or venue of ICT training required.
Table 3 Preferred location of IT training.
% of responses % of cases
At work 63.7 84.1
In education/teaching centre 29.7 39.1
Library 2.2 2.9
Home (self directed learning) 4.4 5.8
100% 131.9%
(Multi-response analysis: 3 missing cases; 69 valid cases)
Confidence with computers appeared to be age related, with younger nurses having higher levels of confidence across all the areas of competence than their more senior colleagues. However, only two of these trends, use of spread sheets and electronic patient records (EPR) were found to be statistically significant, see Table 4; though the latter is critically important for patient care. Practice nurses had significantly higher levels of confidence in working with the EPR. 95.5% of practice nurses felt confident compared with 53% of district nurses and 44% of health visitors (p < 0.01).
Table 4 Confidence in the use of IT among primary care nurses
Age 30–39 Age 39–49 Age 50 & over X2
Mouse 97.7% 96.3% 97.2% 0.724
Key board 93.3% 81.5% 88.7% 0.291
Word processor 70.5% 51.9% 63.4% 0.128
Spread sheet 43.2% 25.9% 36.6% 0.095
e-mail 79.5% 66.7% 74.6% 0.243
Internet 81.8% 70.4% 77.5% 0.457
Electronic library 52.3% 33.3% 45.1% 0.292
Electronic medical records 79.5% 37.0% 63.4% P = 0.001
n= 44 27 71
The raw data also suggested that there was a discernable trend in computer usage with age. 90% of nurses age 30 to 39 years used their workplace computer daily, 70% of those 40 to 49, and only 59.3% of those over 50. This trend was not significant, see Table 5.
Table 5 Use of computers at work for different age groups of community nurses
Age 30–49 Age 50 & over Total
At Least daily 75.0 59.3 69 (n = 49)
At least weekly 13.6 25.9 18.3 (n = 13)
At least monthly or never 11.4 14.8 12.7 (n = 9)
n= 44 27 71
Colleagues were the most used source of information across all ages and nursing professions. Older nurses tended to use books and journals from their personal collections, and to use libraries. However, the use of libraries, books and journals is quite low.
Younger nurses seem to use journals at work or electronic resources more. Neither of these trends, shown in Table 6, were statistically significant. A larger proportion of practice nurses were more confident about using electronic libraries (63%), compared with health visitors (35%), and district nurses (24%); this trend was also not statistically significant.
Table 6 Sources of information and knowledge for primary care nurses
Age 30–49 Age 50 & over All Fisher 1 tail p =
Books personal collection 75.0% 85.2% 78.9% 0.238
Journals personal collection 43.2% 51.9% 46.5% 0.320
Libraries 36.4% 29.6% 33.8% 0.376
Journals at work 75.0% 77.8% 76.1% 0.513
Electronic resources 29.5% 33.3% 31.0% 0.469
Books at work 68.2% 74.1% 70.4% 0.401
n= 44 27 71
Nurses summarised their experiences in access information in a number of free text comments:
"don't know where to look", "don't know website address",
"vast amounts of irrelevant articles under same headings",
"I am not as good as I should be with CIHNAL, Medline",
"information not found or not relevant some difficulty sometimes refining the search for specific information"
"lack of training/unable to access information I am looking for quickly enough – quicker to look in a book"
"like trying to get a glass of water from Niagara Falls"
Discussion
This study demonstrated that primary care nurses have high levels of access to IT and only a small minority have no access at all. Practice nurses, working within the surgery, all use the computerised medical record and nearly all feel confident to use it; over three-quarters have access to the Internet. However, community bases nurses in the second half of their careers despite receiving more training; have less access and are less confident about computers, especially use of the EPR, and prefer to use paper-based information resources. Across all ages and professions using libraries, reading journals and accessing on-line resources are minority activities. One to one learning in the workplace, and workshops were believed to be the best way to learn, with printed manuals and on-line tutorials of least value.
The implication of this study is that community nursing needs more silver surfers. Silver surfers are those over 50 years who enjoy using the Internet. The primary care nurses over 50, despite more training, are still looking for information on paper; using their computers less at work; and less confident in computer use. Market strategies are making the over 50 s the fastest expanding group of Internet users this decade [17,18]; but the benefits of this growth in the market have not as yet had sufficient impact on primary care nurses. The successful aspects of these strategies need to be replicated for community over 50 years if they are to become effective users of the EPR and access the ever increasing number of online information resources. Nurses have clear ideas about the sort of training they want; however their chosen options is the most labour intensive and potentially expensive for the health service to provide.
The small sample size and single locality of this study are its principal weaknesses. The former prevented any more detailed sub-group analysis, as the groups became too small. A further reminder may have improved the response rate.
This study shows that nurses have made enormous strides in acquiring IT skills and access to computers since earlier surveys [10-12]; although these surveys included a smaller proportion of practice nurses – the group that appears to have embraced information technology the most. However, the use of Internet sources of information has changed relatively little. The change strategies proposed by the nurses fit with much of the literature, even though most of this was written in the context of implementing evidence based guidelines. It is likely that lessons about changing one type of behaviour in medicine are likely to apply to others [19-21].
Further research is needed with a larger sample to see if some of the non-statistically significant trends seen in this data become significant in a larger sample drawn from a national sample. It is important for the NHS to understand what input is needed to raise the level of use of the EPR and online information; and how quickly this change can be achieved. What nurses believe to be most effective ways of changing behaviour, and promoting computer use needs to be tested.
Conclusions
There is a consensus among primary care nurses of the sort of training they require to improve their computer use. A significant proportion of community nurses in the second half of their careers need this additional support if they are to achieve silver surfer status; and, have the skills needed to work in the new computerised NHS. Studies need to test whether providing the training wanted overcomes the barriers reported. Although primary care nurses have acquired more computer skills than previously reported there is no room for complacency – community nurses need the opportunity to develop into silver surfers.
Abbreviations
CPHVA Community Practitioners and Health Visitors Association, a representative body of community nurses.
EPR Electronic patient record, synonymous with Computerised Medical Record.
IT Information Technology
NHS National Health Service. The UK's state funded health service.
PCEL Primary Care electronic Library, an on-line information resource for primary care.
PC personal computer
PCT Primary Care Trust – organises and commissions care for a locality of about 100,000 population.
RCN Royal College of Nursing
SNOMED CT Systematized Nomenclature of Medicine – Clinical Terms
SPSS Statistical Package for Social Sciences – a computerised statistical package.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors conceived the study, and contributed to all aspects of the paper. The major contributions of each author is as follows: TC Analysed the data for the study, SB Recruited the nurses, sent out and collated the questionnaire, SdeL helped develop the questionnaire used in a earlier study and wrote the initial draft of the paper.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Community nurses access to and use of computers: questionnaire
Click here for file
Acknowledgements
Indi Munasinghe at CPHVA, and the information officer at RCN, for information about the age distribution of community nurses. This study was supported by KSSnet (Kent Surrey and Sussex Primary Care Research Network); in turn funded by the NHS Research Directorate.
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| 15469616 | PMC526210 | CC BY | 2021-01-04 16:30:13 | no | BMC Nurs. 2004 Oct 7; 3:4 | utf-8 | BMC Nurs | 2,004 | 10.1186/1472-6955-3-4 | oa_comm |
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J CarcinogJournal of Carcinogenesis1477-3163BioMed Central London 1477-3163-3-141548814110.1186/1477-3163-3-14ResearchFeeding of soy protein isolate to rats during pregnancy and lactation suppresses formation of aberrant crypt foci in their progeny's colons: interaction of diet with fetal alcohol exposure Linz Amanda L [email protected] Rijin [email protected] James G [email protected] Pippa M [email protected] Thomas M [email protected] Frank A [email protected] Arkansas Children's Nutrition Center, 1120 Marshall Street, Little Rock, Arkansas, 72202, USA2 Department of Physiology and Biophysics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, 72202, USA3 Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, 72202, USA2004 15 10 2004 3 14 14 13 7 2004 15 10 2004 Copyright © 2004 Linz et al; licensee BioMed Central Ltd.2004Linz 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.
Soy protein isolate (SPI) in the diet may inhibit colon tumorigenesis. We examined azoxymethane (AOM)-induced aberrant crypt foci (ACF) in male rats in relation to lifetime, pre-weaning, or post-weaning dietary exposure to SPI and also within the context of fetal alcohol exposure. Pregnant Sprague Dawley rats were fed AIN-93G diets containing casein (20%, the control diet) or SPI (20%) as the sole protein source starting on gestation day 4 (GD 4). Progeny were weaned on postnatal day (PND) 21 to the same diet as their dams and were fed this diet until termination of the experiment at PND 138. Rats received AOM on PND 89 and 96. Lifetime (GD 4 to PND 138) feeding of SPI led to reduced frequency of ACF with 4 or more crypts in the distal colon. Progeny of dams fed SPI only during pregnancy and lactation or progeny fed SPI only after weaning exhibited similarly reduced frequency of large ACF in distal colon. Number of epithelial cells, in the distal colon, undergoing apoptosis was unaffected by diet. SPI reduced weight gain and adiposity, but these were not correlated with fewer numbers of large ACF. Lifetime SPI exposure similarly inhibited development of large ACF in Sprague Dawley rats whose dams were exposed to ethanol during pregnancy. In summary, feeding of SPI to rat dams during pregnancy and lactation suppresses numbers of large ACF in their progeny, implying a long-term or permanent change elicited by the maternal diet. Moreover, results support the use of ACF as an intermediate endpoint for elucidating effects of SPI and its biochemical constituents in colon cancer prevention in rats.
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Background
Colorectal cancer is the third leading cause of cancer-related deaths in the U.S.; an estimated 147,500 new cases of this disease will have occurred in the U.S. during 2003 [1]. Globally, this cancer is a significant health problem as its incidence is increasing with the burgeoning of the aged population and with 'Westernization' of diets. Most recently, the cancer incidence rates for the ascending colon have increased whereas those for all other colon subsites (transverse, descending, sigmoid and rectum) have stabilized or declined slightly [2,3]. The incidence of colorectal cancer, like that for breast and prostate cancers, is lower in Asian than American and European populations [4]. This has implied a possible protective effect of the Asian diet and of soy foods in particular, on colon cancer incidence. Epidemiological studies are generally supportive of the postulated protection against colorectal cancer incidence by consumption of soy components and soy foods [4-6]. However, these studies were not designed to decipher possible effects of lifetime vs. developmental stage-specific dietary exposure to soy. In this regard, soy consumption during adolescence may preferentially reduce subsequent risks for breast cancer in later adults [7,8].
Soy foods are the main dietary sources of isoflavones, which are implicated in cancer prevention [4,5,9]. Soy foods are a source of other potentially cancer-preventive substances as well, including saponins, protease inhibitors, and other bio-active peptides and proteins [9-13]. However, the literature concerning effects of pure isoflavones or processed soy products (soy flour, soy protein) in animal models of colon cancer is mixed. Many investigators utilized the rat and administered a chemical carcinogen, either dimethylhydrazine (DMH) or azoxymethane (AOM). Both agents rapidly induce formation of aberrant crypt foci (ACF) and subsequent development of colon adenomas and adenocarcinomas. Investigators have utilized ACF number and type, tumor number and type, or the combination, as endpoints to examine effects of soy and soy constituents on colon cancer. Dietary genistein (the predominant soy isoflavone) was inhibitory to ACF formation in AOM-treated rats [14,15]. Order of protection (measured as ACF number) was genistein > defatted soy flour > full-fat soy flakes > soy concentrate (isoflavone-depleted) [16]. Similarly, soy bean saponins inhibited ACF incidence in AOM-treated mice at 14 weeks post-initiation [11]. However, in the study of Gee et al., [17], feeding of isoflavone-containing soy protein isolate or of genistein (with casein) for 7 days prior to DMH treatment (and switching to casein diet thereafter) actually promoted ACF numbers in the distal colon; whereas, feeding of these same diets for 42 days immediately after carcinogen administration had no effect. Davies et al. [18] formulated diets to mimic the Western type (i.e., high fat, low calcium) diet supplemented with low or high isoflavone-containing soy protein isolates, and ACF and tumors were subsequently measured in AOM-rats. Increased numbers of small ACFs were found at 12 weeks, post-carcinogen administration in the isoflavone-enhanced group. Therefore, there is a lack of consensus for effects of soy or soy components on chemically-induced ACFs in rodents and the underlying basis of these discrepancies is unknown.
The picture is also unclear when tumors rather than ACFs are used as the end point. In an early study, soybean protein did not differ from beef protein in terms of relative numbers of colon tumors in DMH-treated rats [19]. Defatted soybean meal was not protective in the same model [20]. However, we previously reported the protection afforded by lifetime-feeding of a soy protein isolate, against colon carcinoma in AOM-treated male Sprague Dawley rats [21]. Dietary genistein, on the other hand, had no effect on colon adenocarcinoma incidence or multiplicity of invasive colon carcinoma, yet actually increased noninvasive and total adenocarcinoma multiplicity [22]. In contrast, soy protein isolates with two levels of total isoflavones did not elicit differences in colon tumorigenesis in the Min mouse model of intestinal cancer [23]. However, feeding of a high molecular weight insoluble fraction from proteinase-treated soybean protein isolate suppressed colon tumor numbers in rats [24,25].
The above studies used diets made with soy protein isolates or soy components prepared in different ways and supplemented to varying levels. Furthermore, diets usually were fed just prior to or concurrent with the chemical carcinogen in order to focus on initiation or progression of tumorigenesis. Some studies did not account for the fact that commercial rodent diets can include soy protein; moreover there is significant maternal-fetal transfer of isoflavones in rats fed soy-containing diets [26]. Thus, pre-exposure to soy constituents during various stages of an animal's life cycle potentially complicates interpretation of the reported results. With the single exception of the study from our group [21], no studies examined the effects of lifetime (including gestational) exposure, by feeding of soy or purified soy components, on colon tumor or ACF incidence. In view of the significant use of soy-based infant formulae, which accounts for greater than 25% of the infant formula currently sold in the United States [9,27], it is important to examine potential SPI effects on colon tissue pre-disposition to cancer. Here, we utilize male Sprague Dawley rats fed AIN-96G diets formulated with casein or SPI to examine dietary effects on AOM-induced ACF incidence and multiplicity. We also examine effects of dietary SPI at pre- and post-weaning vs. lifetime consumption. Lastly, we confirm the SPI effect on colon ACF biogenesis in a second model, namely, AOM-treated rats whose dams were exposed to ethanol during their pregnancy.
Methods
Solid Diets
Diets contained either casein or SPI as the sole protein source (200 g/Kg diet) and their formulation has been previously described [21,28]. Casein (ALACID 741) was from New Zealand Milk Products (North America) Inc. (Santa Rosa, CA). SPI was a gift from DuPont Protein Technologies (St. Louis, MO). Total isoflavone content was 3.70–3.98 mg/g protein and total aglycone equivalents were 2.13–2.32 mg/g protein for the SPI. Corn oil replaced soybean oil and essential amino acid content was maintained at levels for that of the AIN-93G diet [29]. Diets were prepared by Harlan Teklad (Madison, WI).
Animals
Animals were housed in an AAALAC-approved animal facility at the Arkansas Children's Hospital Research Institute; animal use protocols were approved by the University of Arkansas for Medical Sciences Institutional Animal Care and Use Committee. Animals were housed in polycarbonate cages and allowed ad libitum access to diet and water. Animal rooms had constant humidity and a 12-h light-dark cycle.
Expt. I. Lifetime, Pre-weaning and Post-weaning Diets
Pregnant Sprague Dawley dams from Harlan, Inc. (Indianapolis, IN) were received at gestation day (GD) 4 and immediately assigned in random fashion to casein or SPI diet. At postnatal day (PND) 2, each litter was culled to 5 males and 5 females (females were used in an unrelated experiment). At weaning, animals were divided into four diet groups: lifetime (GD 4 to PND 138) casein, n = 25; lifetime SPI, n = 25; casein to SPI, n = 25; and SPI to casein, n = 25. Diet switchovers were performed at PND 21 and all rats were given AOM on PND 89 and 96. Rats were injected subcutaneously with AOM (Midwest Research Institute) in saline, 15 mg/kg body weight. Animals were weighed weekly from birth and were euthanized at PND 138 for ACF determination or TUNEL assay.
Expt. II. Intra-gastric Infusion of Ethanol-containing CAS and SPI Liquid Diets during Pregnancy
Pregnant rats at GD 5 were surgically implanted with an intra-gastric cannula as previously described [30]. Dams were fed casein plus ethanol (n = 5) or SPI plus ethanol (n = 7) by total enteral nutrition (TEN) from GD 6 – GD 19 [30]. TEN diets were isocaloric and met NRC requirements for normal pregnancy [30]. Amounts of casein hydrolysate (MPH 955; New Zealand Milk Products) and SPI (Soy Clinical Blend IB1.2; total isoflavone content was 3.98 mg/g protein, total aglycone equivalents were 2.32 mg/g protein; DuPont Protein Technologies) added to liquid TEN diets were 31.5 and 31 g/l, respectively. Ethanol was infused at increasing amounts to reach a maximum of 10 g/Kg body weight. At GD 19, ethanol was no longer fed, however, the TEN diets were infused until parturition and simultaneously, corresponding solid diet was added to cages. After parturition, only water was infused (25 ml/23 h) and solid diets were continued ad libitum. At PND 2, each litter was culled to 5 males and 5 females (females were used in an unrelated experiment). Progeny were weaned to the same diet as for their dam. Number of male progeny allocated to casein and SPI diets was 24 and 29, respectively.
Visualization of ACF
Fifteen animals of each diet group in Experiments I and II were used for ACF determination. Colon contents were removed by flushing from the cecal end with ~20 ml of PBS via syringe. Each colon was slid onto a 2 ml pipette and was fixed in this position for 10 min in 10% neutral buffered-formalin. The colon was opened, laid flat and placed between sheets of labeled filter paper in fixative in the cold. Tissue was removed from formalin, divided into proximal and distal halves, and stained in 0.2% methylene blue in PBS for 5–7 min or until the tissue had a uniform blue appearance. Tissues were rinsed with PBS for ~1 min and stored in 0.4% formalin-PBS at 4°C. Aberrant crypt foci were viewed immediately after staining using a Nikon AMZ800 stereoscope at 40× magnification with side illumination. Proximal and distal colon halves were reviewed along their entire lengths and all ACF were counted. Aberrant crypt foci were categorized according to crypt complexity (1, 2, 3, 4, 5, etc., crypts per ACF). All colons were scored in blinded fashion by a single observer. Colons that failed to yield useable data due to poor fixation and/or staining were excluded from statistical analysis.
TUNEL
Colons not used for ACF determination (i.e., whole-mount fixation) were divided into proximal and distal halves and the midpoints of each half taken for fixation. Tissue was fixed in 10% neutral buffered-formalin, processed through a graded series of ethanol and xylene washes, embedded in paraffin, 4 μm sections obtained, and these were subjected to TUNEL assay. The TdT-FragEL DNA Fragmentation Detection kit (Oncogene Research Products, San Diego, CA) was used for this purpose. Approximately 200 crypt columns were examined from each of 4–5 animals in each diet group.
Adiposity
Body composition data were obtained on anesthetized rats by dual energy x-ray absorptiometry (DXA) using a Hologic QDR 4500A instrument (Bedford, MA). Five rats (at 33 days post-AOM treatment) were randomly chosen from casein and SPI diet groups (Expt. I) for DXA analysis. Percentage of global fat (% fat) was determined.
Statistical Analyses
The ACF endpoints were discrete variables (numbers of crypts, numbers of ACF, etc.) and hence were not normally distributed. We took the natural logarithm of all data points since log-transformed values were less skewed than the original data and more amenable to analysis. However, for simplicity, figures and tables present the original (not log-transformed) data. To examine diet effects, we used the SAS System's (SAS, SAS Institute, Inc., Cary, NC) PROC GLM procedure. Unpaired t tests were also used to examine differences between proximal and distal colon for several endpoints. Incidence of large ACF (with 5 or more crypts per focus) was analyzed using Fisher's Exact Test (SigmaStat for Windows Version 2.03, SPSS Inc.). For all analyses, a P value less than 0.05 was considered significant, while 0.05 < P < 0.1 was deemed marginal. Body weights and body fat content were compared by t-test (SigmaStat).
Results
Effects of Lifetime Consumption of Casein and SPI on Colonic ACF Frequency
In casein-fed animals, the distal half of the colon had 2–3 fold more ACF of each size class than did the proximal half (Fig. 1). In rats lifetime-fed casein or SPI, differences in ACF content were observed for distal colon (Table 1). SPI feeding led to fewer numbers of ACF with 4, 5 and >5 crypts and as a consequence, a reduced overall ACF crypt multiplicity for distal colon. Crypts/focus was slightly reduced (by ~8%) by SPI in the proximal colon (P = 0.01). SPI did not differ from the casein group in the frequencies of ACF containing 1, 2 or 3 aberrant crypts in either colon region.
Figure 1 ACF occurrence in proximal and distal colon of Sprague Dawley rats (n = 12) lifetime-fed casein. Shown are means ± SEM of ACF (per rat) containing: 1, 2, 3, 4, 5 or greater than 5 crypts (5+) per ACF; crypt multiplicity (number of aberrant crypts/focus); total number of ACF; and total number of aberrant crypts (no. ACF × crypts/focus). P values indicate all statistically significant differences between proximal and distal colon.
Table 1 Effect of diet on ACF distribution by colon region*
Proximal Colon Distal Colon
CAS SPI CAS SPI
ACF – 1 Crypt 5.50 ± 1.03 6.55 ± 1.35 10.58 ± 1.77 12.55 ± 2.45
ACF – 2 Crypts 9.92 ± 2.28 8.65 ± 1.61 18.33 ± 2.36 16.27 ± 2.81
ACF – 3 Crypts 4.67 ± 0.96 5.18 ± 1.17 11.42 ± 1.57 8.18 ± 1.91
ACF – 4 Crypts 1.50 ± 0.40 a 0.82 ± 0.38 5.83 ± 0.99 b 2.45 ± 0.73
ACF – 5 Crypts 0.42 ± 0.26 0.27 ± 0.20 1.67 ± 0.33 c 0.64 ± 0.47
ACF – 5+ Crypts 0.08 ± 0.08 0.09 ± 0.09 0.42 ± 0.19 d 0.09 ± 0.09
Crypts/Focus 2.17 ± 0.05 a 2.00 ± 0.09 2.38 ± 0.07 c 2.02 ± 0.04
ACF Total 22.08 ± 4.29 21.54 ± 4.08 48.25 ± 5.98 40.18 ± 6.93
Crypt Total 47.92 ± 9.47 44.55 ± 8.98 115.67 ± 14.83 e 83.18 ± 15.55
* CAS, n = 12; SPI, n = 11; shown are means ± SEM per animal for ACF of differing sizes; 5+ = no. ACFs containing 6 or more aberrant crypts; crypt total = sum of ACF × crypts/focus.
aP = 0.01, bP = 0.005, cP = 0.001, dP = 0.091, eP = 0.044; P values for differences between diets within each region; SAS.
Dietary Switchovers at Weaning and Subsequent ACF Formation
To examine the possibility that dietary exposure to SPI over the period encompassing fetal and neonatal development could mimic effects of lifetime SPI, we performed diet switchovers of Sprague Dawley rats at weaning and surveyed their colons six weeks after AOM administration, relative to animal's lifetime-fed SPI diet concurrently. The opposite switchover, from casein to SPI at weaning, was carried out in parallel to examine effects of SPI, from post-weaning through to adulthood.
ACF frequency for each diet switchover generally mimicked that for lifetime SPI (Fig. 2). Analysis for the relative incidence rather than the mean number (per animal) of the largest ACF (those containing 5 or >5 crypts) for all diet groups is shown in Table 2. These data further support a protective role for dietary SPI (lifetime, CAS/SPI or SPI/CAS regimens) on appearance of large ACF.
Figure 2 Frequency distribution of ACF in Sprague Dawley rats lifetime-fed SPI or switched, at weaning, from CAS to SPI or from SPI to CAS. Analysis of proximal and distal colon halves is shown. Shown are means ± SEM, per rat, of ACF containing: 1, 2, 3, 4, 5 or greater than 5 crypts (5+) per ACF; crypt multiplicity (number of aberrant crypts/focus); total number of ACF; and total number of aberrant crypts. P value indicates the only statistically significant difference between switchover diets and SPI.
Table 2 Relative incidence of largest ACFs (5 or >5 Crypts/ACF)*
CAS SPI CAS/SPI SPI/CAS
% rats with ACF(s) containing 5 crypts
Proximal 25 18 0 38
Distal 83 18a 57 54
Entire 83 36b 57 69
% rats with ACF(s) containing >5 crypts
Proximal 8 9 0 0
Distal 33 9 0 15
Entire 42 18 0 15
* CAS, n = 12; SPI, n = 11; CAS/SPI, n = 7; SPI/CAS, n = 13.
a, bCompared to CAS: aP = 0.003; bP = 0.036; Fisher's Exact Test.
Apoptosis
At six weeks, post-AOM, there were no observable differences in the relative apoptotic state of total colonic epithelium in distal colons of CAS, SPI, CAS/SPI and SPI/CAS groups (Table 3). However, the upper third crypt region of the CAS/SPI group had ~2-fold more apoptotic cells than did the lifetime SPI or SPI/CAS groups.
Table 3 Effect of diet regimen on TUNEL positive cells in the distal colona
CASb SPI CAS/SPI SPI/CAS
Totalc 18.0 ± 2.55 13.8 ± 3.20 24.6 ± 5.80 16.0 ± 3.89
Upperd, e 6.8 ± 3.02 3.2 ± 1.07 11.4 ± 2.02 2.25 ± 0.48
Middle 4.6 ± 0.93 3.2 ± 1.02 9.2 ± 4.68 9.5 ± 3.12
Lower 6.8 ± 2.63 7.2 ± 1.93 4.4 ± 1.29 4.75 ± 0.75
aCAS, n = 5; SPI, n = 5; CAS/SPI, n = 5; SPI/CAS, n = 4 animals; PND 138.
bMean values are expressed as the number of positive cells per 200 crypt columns ± SEM.
cData are for entire crypts.
dData represent upper one-third, middle one-third or lower one-third regions of crypts, respectively.
eOverall diet effect (P = 0.024); CAS/SPI > SPI and >SPI/CAS, P < 0.05; One-way ANOVA and Student-Newman-Keuls method.
Growth and Body Composition
SPI-fed animals weighed less than corresponding casein-fed counterparts (Fig. 3). The body weight differences between the casein and SPI groups were evident as early as PND 10 postnatal and prior to weaning (Fig. 3). Administration of AOM resulted in temporary cessation of growth and a small amount of weight loss in all groups (Fig. 3); however after a short lag period, all animals resumed growth. Growth curves of casein/SPI and SPI/casein switch-over groups were intermediate between those for lifetime casein and SPI groups (data not shown). Analysis of body composition (at 33 days post-AOM treatment) by DXA revealed significant differences in global % fat between groups (CAS > SPI, difference of ~3.3 percentage points; Fig. 4) and this somewhat mimicked the observed final differences in body weight. Regression analysis did not identify any significant associations between final body weight and proximal or distal ACF numbers or crypts/focus (Fig. 5 and data not shown).
Figure 3 Feeding of casein or SPI to pregnant dams and their progeny elicits differential growth rates. Top panel shows body weight (mean ± SEM) gain in relation to postnatal day (PND) and time of administration of azoxymethane (AOM) for progeny of Sprague Dawley dams. Bottom panel shows divergence in body weights during early postnatal development. P values indicating differences between CAS and SPI groups (t-test) are shown in lower panel.
Figure 4 Dietary protein type influences body fat content. DXA was performed on five animals per group (CAS: casein; SPI: soy protein isolate) at 33 days after the second AOM administration. Means (±SEM) are statistically different for the two diets.
Figure 5 Lack of association of final body weight with ACF size.
ACF in AOM-treated Adult Rats Previously Exposed to Ethanol as Fetuses
We confirmed the inhibitory effect of SPI on occurrence of large ACF in a second model, namely, progeny of Sprague Dawley dams that received ethanol + casein or ethanol + SPI by total enteral nutrition (TEN) during gestation and which were weaned to the same diet as their dams (paradigm shown in Fig. 6). We chose this model since we were interested in examining how diet, in combination with ethanol, might affect ACF distribution in progeny. As is evident from Fig. 7, SPI inhibited the occurrence of large ACF relative to the casein diet in animals exposed to ethanol as fetuses. Unlike the results from Expt. I. however, SPI-fed animals exhibited reduced ACF numbers in both proximal and distal colons and these ACF now included those with 3 aberrant crypts (Fig. 7). Moreover, there was an increased occurrence of ACF with three or more crypts in the proximal colons of casein-fed rats in Expt. II vs. those in Expt. I. In the absence of a true no-ethanol control for Expt. II, we cannot make any final conclusions about the specific effect of fetal alcohol exposure on ACF frequency in the later adult stage. However, these data do suggest that fetal alcohol exposure favors the development of large ACF which was inhibited by SPI in the diet.
Figure 6 Experimental design used for evaluation of diet effects on ACF in progeny of dams exposed to ethanol during pregnancy.
Figure 7 Frequency distribution of ACF in rat progeny lifetime fed casein (n = 10) or SPI (n = 14) and whose dams were exposed to ethanol during pregnancy (Fig. 6). Analysis of proximal and distal colon halves is presented. Shown are mean ± SEM, per rat, of ACF containing: 1, 2, 3, 4, 5 or greater than 5 crypts (5+) per ACF; crypt multiplicity (number of aberrant crypts/focus); total number of ACF; and total number of aberrant crypts. P values indicate statistically significant differences between diets.
Discussion
The objectives of this study were to: a) elucidate effects of SPI on colon ACF frequency in rat models, b) study effects of dietary SPI on somatic growth and adiposity in view of their potential interactions with colon ACF incidence, c) examine developmental and lifetime 'exposures' to dietary SPI and consequent effects on ACF indices, and d) correlate ACF data with our previously published colon tumor data obtained with animals lifetime fed SPI [21]. ACF may provide a more time- and cost-effective means to study SPI effects on colon cancer prevention in animal models and we sought to further explore this possibility in the current study. We report that SPI reduced the incidence of the largest size classes of ACF in AOM-treated, adult Sprague Dawley male rats and, regardless of whether SPI was fed during GD 4 – PND 21, only after PND 21, or from GD 4 to PND 138. This effect of SPI also was manifested in animals whose dams were exposed to ethanol during pregnancy; thereby suggesting the generality of the SPI effect.
ACF have been identified on the colon luminal surface in rats and mice treated with chemical carcinogens (AOM, DMH, and NMU) and in humans with and without overt colon carcinoma [31,32]. AOM stimulates both proliferation and apoptosis in the colonic mucosa [33] and ACF are thought to be the earliest observable pre-neoplastic lesions to arise in this tissue [34,35]. The validity of ACF as an intermediate biomarker for colon cancer is however, controversial. Larger multi-cryptal ACF are generally correlated with tumor incidence in rodents and humans [32,36-42]. Feeding a Western-type diet can induce ACF as well as adenomas and carcinomas in normal mouse colon [43]. Thiagarajan et al. [16] reported a trend for diets that promoted ACF numbers to also increase colon tumor numbers. In other studies, however, DMH or AOM induced the occurrence of adenomas and adenocarcinomas in the distal colon which were correlated with ACF number; whereas the proximal colon developed signet-ring type carcinomas which were not correlated with ACF [44-46]. Moreover, there may be discrete subset(s) of ACF, not easily recognized by the standard assay, that progress to microadenomas and which are characterized by increased dysplasia, altered oncoprotein and tumor suppressor expression and/or genomic instability [37,47-51]. Previously, we reported that Sprague Dawley rats, lifetime fed casein-AIN-93G diet had a 50% incidence, whereas those fed SPI-AIN-93 G diet had a 12% incidence of colon tumors, at 40 weeks post-AOM [21]. In our previous study, tumor incidence was similarly reduced for proximal and distal regions by SPI. However, in the present study, we found that SPI reduced crypt multiplicity and frequency of large ACF (those containing 4 or more crypts) mainly in the distal colon, which at six weeks post-AOM had more ACF of each size than did the proximal region. Therefore, the present ACF data are in general concordance with our previous tumor data but only for the distal colon. The basis for the discordance of ACF and tumor data for the proximal colon is unknown but is in general agreement with other studies showing correlations of larger ACFs with distal but not proximal colon tumors [44-46].
A diet switching paradigm examined whether the suppressive effect of SPI on larger ACF required lifetime exposure or could be mimicked with a shorter developmental period of SPI feeding. We also questioned if 'protective' effects could be transferred maternally to the offspring. The results of this experiment are of interest for a number of reasons. The observation that SPI elicited reduced growth by day 10, postnatal, may point to a 'maternal effect' of SPI, during gestation or lactation or both that is transmitted to progeny to affect their growth. The exact nature of this effect awaits further clarification. Both diet switchovers generally mimicked the effects of lifetime SPI on ACF incidence, although the casein/SPI regimen appeared to be slightly more effective than the SPI/casein dietary treatment in this regard. We therefore surmise that SPI can manifest long lasting effects on ACF incidence and that at least some of these effects can be transmitted to the offspring through feeding of SPI to pregnant and/or lactating dams.
Obesity is considered by some to be a promoter of oncogenesis in rat models of chemically induced colon cancer [52,53] and of colon cancer in humans [54]. Positive interactions of soyfoods with increasing body mass index to reduce breast cancer risk in humans have been suggested [55]. In the current study, DXA analysis identified differences in global % fat that could account for the differences between final body weights of casein and SPI animals; although we examined only a limited number of animals in this regard. However, we were unable to find any statistically significant associations of final body weight and ACF for the diet groups. Therefore, our results do not indicate any obvious positive relationship between relative fat content and ACF incidence.
Kállay et al. [56] observed that the feeding of soy protein isolate selectively suppressed or enhanced colonic gene expression (relative to casein). Their work as well as the present results underscores the need for further elucidating direct and indirect actions of dietary SPI and its protein and non-protein constituents on colonic mucosal growth and differentiation during development through to adulthood. The somewhat conflicting literature regarding effects of soy on colon carcinoma in the rat may derive in part from differences in the nature of the dietary casein and SPI used, and the relative developmental timing of SPI feeding. Studies that used commercial diets containing soy constituents for propagation of rat colonies for subsequent experiments may also have been confounded by pre-exposure to soy. It may be important to standardize this developmental soy exposure in future studies so as to reach consensus on the colon cancer-preventive actions of soy and its constituent(s).
Conclusions
SPI inhibited the development of large ACF, some of which may be tumor precursors. Feeding of SPI to rat dams during pregnancy and lactation led to a suppression in numbers of large ACF in their progeny, implying a long-term or permanent anti-carcinogenic effect elicited by the SPI-based diet. Body weight and body composition were differentially affected by soy protein isolate or casein in the diet. ACF may be a valid intermediate endpoint for elucidating effects of SPI and its biochemical constituents in tumor prevention in the colons of Sprague Dawley rats.
Author's contributions
ALL performed the whole-mount and TUNEL assays, performed data analysis, and assisted with tissue collection. RX performed data analysis, assisted with tissue collection, and prepared the final figures. JGP and PMS performed statistical analyses. TMB participated in the design of the study and oversaw the animal and diet manipulations. FAS conceived of the study, participated in its design, interpreted the data and prepared the manuscript. All authors read, modified and approved the final manuscript.
Acknowledgements
We thank Matthew Ferguson, Renea Eason, Leon Chatman, Renee Till, Michael Velarde, Xin Shi, Jamie Badeaux, Misty Reeves, Chris Curtis, Tammy Dallari, Alexis Bussell, Trae Pittman and Kim Hale for assistance with animals, diets and tissue collection. We acknowledge Mark Robinette and Pam Treadaway for helping with data management, analysis and presentation. Thanks to Drs. Rosalia C.M. Simmen, Martin J.J. Ronis, Shanmugam Nagarajan, Rick Helm and Ling He for helpful discussions and for critically reading the manuscript. We also thank Dr. Daniel D. Gallaher and Cindy Gallaher (University of Minnesota) for sharing their protocol for whole-mount fixation of colons. This work was supported by USDA CRIS 6251-5100-002-06S to the Arkansas Children's Nutrition Center and by NIH 2RO1AA008645. This research was performed, in part, using compound(s) provided by the National Cancer Institute's Chemical Carcinogen Reference Standards Repository operated under contract by Midwest Research Institute, NO. N02-CB-07008.
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| 15488141 | PMC526211 | CC BY | 2021-01-04 16:39:16 | no | J Carcinog. 2004 Oct 15; 3:14 | utf-8 | J Carcinog | 2,004 | 10.1186/1477-3163-3-14 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-711547154310.1186/1477-7827-2-71ResearchCloning of a novel inhibin alpha cDNA from rhesus monkey testis Bernard Daniel J [email protected] Teresa K [email protected] Tony M [email protected] Department of Neurobiology and Physiology, Northwestern University, 2205 Tech Drive, Evanston, IL 60208, USA2 Center for Biomedical Research, Population Council and The Rockefeller University, 1230 York Ave., New York, NY 10021, USA3 University of Pittsburgh School of Medicine, Departments of Cell Biology and Physiology, and Obstetrics, Gynecology and Reproductive Sciences, 3550 Terrace Street, Pittsburgh, PA 15261, USA2004 7 10 2004 2 71 71 16 9 2004 7 10 2004 Copyright © 2004 Bernard et al; licensee BioMed Central Ltd.2004Bernard 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
Inhibins are dimeric gonadal protein hormones that negatively regulate pituitary FSH synthesis and secretion. Inhibin B is produced by testicular Sertoli cells and is the primary circulating form of inhibin in most adult male mammals. Inhibin B is comprised of the inhibin alpha subunit disulfide-linked to the inhibin/activin betaB subunit. Here we describe the cloning of the cDNAs encoding these subunits from adult rhesus monkey testis RNA.
Methods
The subunit cDNAs were cloned by a combination of reverse transcriptase polymerase chain reaction (RT-PCR) and 5' rapid amplification of cDNA ends (RACE) RT-PCR from adult rhesus monkey testis RNA.
Results
Both the inhibin alpha and betaB subunit nucleotide and predicted protein sequences are highly conserved with other mammalian species, particularly with humans. During the course of these investigations, a novel inhibin alpha mRNA isoform was also identified. This form, referred to as rhesus monkey inhibin alpha-variant 2, appears to derive from both alternative transcription initiation as well as alternative splicing. rmInhibin alpha-variant 2 is comprised of a novel 5' exon (exon 0), which is spliced in-frame with exon 2 of the conventional inhibin alpha isoforms (variant 1). Exon 1 is skipped in its entirety such that the pro-alpha and part of the alpha N regions are not included in the predicted protein. rmInhibin alpha -variant 2 is of relatively low abundance and its biological function has not yet been ascertained.
Conclusion
The data show that the predicted inhibin B protein is very similar between monkeys and humans. Therefore, studies in monkeys using recombinant human inhibins are likely to reflect actions of the homologous ligands. In addition, we have observed the first inhibin alpha subunit mRNA variant. It is possible that variants will be observed in other species as well and this may lead to novel insights into inhibin action.
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Background
The inhibins are dimeric gonadal protein hormones that negatively regulate pituitary FSH synthesis and secretion [1,2]. Inhibins are comprised of an α subunit (inhibin α) and one of two inhibin β subunits (inhibin βA or inhibin βB). In adult male mammals, inhibin B (α-βB dimer) appears to be the primary circulating form of the hormone, whereas females produce both inhibin A and B and do so in discordant fashion across the reproductive cycle [3-15]. One exception to this general pattern is in rams, where inhibin A appears to be the primary circulating form [16]. Historically, investigations of inhibin action have relied principally upon recombinant preparations of inhibin A because inhibin B has not been available in sufficient quantities to permit in vivo studies of its role in the negative feedback regulation of gonadotropin secretion [17,18]. Because inhibin B is the biologically relevant ligand in male primates, this has placed some constraints on our understanding of inhibin action in these animals. For this reason, we cloned the inhibin B subunit cDNAs from adult monkey testis as a requisite first step to producing recombinant monkey inhibin B. In the course of cloning the monkey inhibin α subunit, we identified a novel transcript, which has not been observed in other species. In this paper, we describe the new transcript called rhesus monkey inhibin α-variant 2.
Methods
RNA extraction
Total RNA was extracted from frozen testis samples of two adult male rhesus monkeys (Macaca mulatta) (#1861 and #2333) using Trizol following the manufacturer's instructions (Invitrogen, Carlsbad, CA). RNA was dissolved in diethyl pyrocarbonate-treated H2O and quantified by spectrophotometry. Animals were treated in accordance with institutional and federal guidelines.
Reverse transcriptase polymerase chain reaction (RT-PCR)
Contaminating genomic DNA was removed from RNA samples using RQ1 DNase (Promega) following standard protocols. Four μg of DNased RNA (from #1861) was reverse transcribed into cDNA using 100 ng random hexamer primers and 100 U MMLV-RT (Promega). Four hundred ng of cDNA was subjected to PCR to amplify part of the αN domain and the entirety of the mature domain (αC) of the inhibin α subunit using the following primer set: 5'-CCYTTCCTGGTGGCCCACACT (forward) and 5'-TTAGATACAAGCACAGTGYTG (reverse) (see primers A and B in Fig. 3). Reactions were subjected to 35 cycles of 94C for 30 sec, 55C for 30 sec, and 72C for 30 sec. No amplified products were observed in H2O or RT- controls (data not shown). The amplified 465 bp product was ligated into pCR3.1 (Invitrogen) following the manufacturer's instructions. Recombinant clones were screened by colony hybridization using the gel purified PCR product as probe. Plasmids were purified from hybridizing clones and sequenced using DyeTerminator Cycle sequencing (ABI). All hybridizing clones corresponded to inhibin α.
Figure 3 Rhesus monkey inhibin α gene structure. Schematic representation of the genomic organization of the inhibin α subunit in rhesus monkey. Boxed regions reflect exons and the intervening straight line is the intron (the 2 kb is an estimate based on the 2051 bp intron in humans). Black boxes reflect 5' and 3' UTRs. White boxes reflect sequences encoding the signal peptide (in inhibin α-variant 1) or a domain of unknown function (inhibin α-variant 2). The other shaded regions correspond to the pro-α, αN, and αC domains of the inhibin α prepro-hormone and are labeled in the figure. Inhibin-α variant 1 is the canonical inhibin α mRNA described previously in other species and is produced through splicing of exons 1 and 2. Inhibin α-variant 2 is produced through splicing of the novel exon 0 and exon 2. In this latter form, the entirety of exon 1 is removed, in addition to intron 1. An A to G transition in the monkey genomic sequence (relative to the human sequence) at the end of exon 0 appears to introduce a GT 5' splice donor site, which is absent in other species. This allows the splicing event observed in inhibin α-variant 2 when an upstream transcription start site is utilized. This variant is not predicted to exist in other species and also appears to be rare in monkey. The approximate positions of the primers used in different RT-PCR analyses are shown. A and B refer to the forward and reverse primers, respectively, designed to amplify the final 465 bp of the open reading frame. C and D are the outer and inner gene specific reverse primers used in the 5' RACE procedure.
The full-length monkey inhibin α cDNA was amplified by RT-PCR from monkey testis RNA as described using a primer (5'-ATGGTGCTGCCCCTACTGCT) directed against the putative start of translation as determined by 5' rapid amplification of cDNA ends (RACE) (see below) and the reverse primer described above. Three prominent bands of approximately 1100, 700, and 400 bp were amplified. The top band was of the predicted size and was purified, cloned and sequenced. The identities of the other two bands have not yet been determined.
The mature region of the inhibin βB subunit cDNA was amplified from monkey (#1861) testis RNA by RT-PCR as described for the α subunit using the following primer set: 5'-AGCTGGCCGTGGTGCCBGTGTT (forward) and 5'-TCAGGCGCAGCCGCACTCCTC (reverse). The resulting 455 bp product was gel purified and sequenced directly.
5' RACE RT-PCR
5' RACE was performed on monkey (#1861) testis RNA using RNA ligase mediated (RLM)-RACE reagents following the manufacturer's instructions (Ambion, Austin, TX). The primary PCR was performed using the Outer Adapter Primer (Ambion) and the following gene specific primer: 5-GGCAGGTTTGGTGGGATGTGCA (Fig. 3, primer C). The secondary PCR was performed on 4 μl of a 1:100 dilution of the primary PCR reaction using the Inner Adapter Primer and the following nested gene specific primer: 5'-GGAAGGAGATGTTCAGTGCTAC (Fig. 3, primer D). For both PCR reactions, the following reaction conditions were used with AmpliTaq (Perkin-Elmer): 35 cycles of 94C for 30 sec, 55C for 30 sec, and 72C for 1.5 min. Three prominent amplicons were observed in the secondary PCR reaction. No bands were observed in the primary PCR or in any of the negative controls (data not shown). A pool of the different RACE products was ligated into pCR2.1 (Invitrogen). Plasmids were isolated from recombinant clones screened by α complementation and were sequenced as described.
Northern blot
Twenty μg of total RNA prepared from testes of two adult rhesus monkeys were run on a 1% MOPS-formaldehyde agarose gel. RNA was transferred to Hybond N+ charged nylon membrane by capillary action using 20X SSC. The blot was first probed with a 32 P-labeled (Ready-to-go; Amersham Pharmacia) cDNA corresponding to the last 465 bp of the coding sequence of monkey inhibin α. The blot was hybridized overnight at 42C in 50% formamide, 5X SSC, 1X Denhardt's, 20 mM NaPO4 (pH 6.8), 1% SDS, and 100 μg/ml denatured salmon sperm DNA using a modified sandwich method [19]. Following washes in 2X SSC/0.1% SDS at RT and 70C, the blot was exposed to X-ray film (Kodak) overnight with an intensifying screen at -85C. The blot was subsequently stripped and re-probed with a 32 P-labeled cDNA corresponding to 256 bp of monkey inhibin α exon 1. Hybridization and washing conditions were as described for the first probe.
Results
Cloning of the rhesus monkey inhibin α cDNA
The inhibin α cDNA was cloned from rhesus monkey testis using a combination of RT-PCR based approaches. First, the cDNA encoding the last 20 amino acids of αN and the entirety of the mature (αC) domain was amplified by RT-PCR using adult monkey testis RNA as starting material. The resulting PCR product was cloned and sequenced. BLASTN of the non-redundant database showed highest sequence identity (97%) to the human inhibin α cDNA. Within the 402 bp encoding the αC domain, sequence identity was also 97% and the predicted amino acid sequence was 98% conserved (Fig. 1). Within the 134 amino acid αC domain there are three non-conservative differences between human and rhesus monkey (L4P, S72P, and Y86P; the first letter refers to the amino acid in human and the number denotes the residue in the αC domain); however, all occur at residues that vary between the mammalian inhibin α subunits sequenced thus far (Fig. 1). Proline at position 4 of rhesus monkey is also observed in pig, horse, cow and sheep. The proline at position 72 is observed in all mammalian species examined, except human. Finally, the proline at residue 86 is leucine in all non-human mammalian species examined thus far and is tyrosine in human.
Figure 1 Inhibin α amino acid sequence alignment. Alignment of the inhibin α mature domain (αC) amino acid sequence in several mammalian species. Differences from the human sequence are bolded and underlined. Differences between the human and monkey sequences are indicated by arrows.
To clone the full-length cDNA, we used 5' RACE to amplify the remainder of the αN and the pro-α regions. Three prominent RACE products were amplified. The longest (919–927 bp) and shortest (642 bp) were cloned and sequenced. The intermediate sized amplicon has not yet been definitively characterized. The long product was within the expected size range and was somewhat heterogeneous in that the clones had 5' ends that extended to differing extents. This likely reflects differences in transcription start sites, but all were within 8 bp of each other. The 5' untranslated region (UTR) ranged from 105 to 113 bp, which is slightly shorter than the 144 bp described in humans (GenBank acc.# NM_002191). When combined with the original PCR fragment, the contiguous sequence contained an open reading frame of 1098 bp predicted to encode a 366 amino acid prepro-hormone, consistent with the size of the human prepro-inhibin α.
To confirm expression of a transcript containing this uninterrupted open-reading frame, PCR primers were designed against the start and end of the translation and the predicted 1101 bp fragment (including the stop codon) was amplified. DNA sequencing confirmed its identity. A monkey placental EST (CB548960) overlapped with and confirmed the sequence of the final 183 bp of the monkey inhibin α open reading frame and included an additional 188 bp of 3' UTR (not including the poly A+ tail). Thus, the mRNA encoding the inhibin α subunit would be predicted to be approximately 1.4 kb. Northern blot analysis of monkey testis RNA using a probe directed against sequence within αC domain (exon 2 probe) hybridized to an mRNA of approximately 1.7 kb (Fig. 2A). The slight size discrepancy may result from a long polyA+ tail, the use of alternative polyadenylation sequences (although a consensus AAUAAA sequence appears 21 nt upstream of the polyA+ tail in the EST), and/or alternative transcriptional start sites. An additional transcript of about 4 kb was also detected with this probe (top arrow in Fig. 2A). The identity of this less abundant transcript has not yet been ascertained. The monkey inhibin α sequence from the end of the longest 5'RACE product through the end of the open reading frame has been deposited in GenBank (GenBank Acc. #AY574369).
Figure 2 Inhibin α mRNA expression in monkey testis. Northern blots showing inhibin α mRNA expression in adult monkey testes. Equal amounts of total RNA from two adult males were run on a MOPS-formaldehyde gel. RNA was transferred to a nylon membrane and hybridized consecutively with 32 P-labeled cDNA probes corresponding to 465 bp of exon 2 (A) and 256 bp of exon 1 (B) of monkey inhibin α. Both probes detected transcripts of 1.7 and 4 kb (top two arrows). The exon 1 probe also detected a 0.4 kb transcript (bottom arrow in B). Molecular weight standards (in kb) are shown at the left of each panel. Hybridization patterns were the same in both animals.
Novel inhibin α mRNA in monkey testis
The short RACE product when cloned and sequenced was determined to correspond to a novel variant of the inhibin α subunit. In humans (and other species), inhibin α has been described as a two exon gene (Fig. 3). Exon 1 encodes the 5' UTR, pro-α, and 85 bp of the αN region. The remainder of αN, αC and the 3'UTR are contained within exon 2. The 546 bp at the 3' end of the short RACE product corresponded exactly to sequence within monkey exon 2 (based on the human nomenclature). The 96 bp at the 5' end of the amplicon did not, however, correspond to the exon 1 sequence determined in the long RACE product. Upon BLASTN search, this 96 bp sequence was determined to show highest identity (91–97%) with human (GenBank acc.# AF272341), mouse (GenBank acc.# M95526), pig (GenBank acc.# AF510728), cow (GenBank acc.# S72864), and rat (GenBank acc.# M32754) inhibin α proximal promoter sequences (Fig. 4A). These data suggested that transcription of the short RACE product was initiated in what is conventionally thought of as inhibin α promoter (5' flanking sequence) in all species described to date.
Figure 4 DNA sequence alignment of novel exon 0 in monkey inhibin α. A) Alignment of the novel exon 0 in inhibin α-variant 2 with inhibin α promoter sequences from Human (GenBank acc.# AF272341), mouse (GenBank acc.# M95526), pig (GenBank acc.# AF510728), cow (GenBank acc.# S72864), and rat (GenBank acc.# M32754). Note that the numbering is relative to the 96 bp of the monkey exon 0 and does not reflect the numbering in the GenBank entries. Bolded and underlined bases reflect differences from the monkey sequence and spaces (-) are added where needed to facilitate the alignment. The non-consensus cAMP responsive element (CRE), which is important for basal and FSH stimulated expression of inhibin α, is boxed and is conserved in all species. The arrows denote the start sites of the human (h, GenBank acc.# CB997542), mouse (m1, GenBank acc.# BY303064; m2, GenBank acc.# BI082792), and pig (p, GenBank acc.# BP457576) ESTs referred to in the text. B) Alignment of the end of monkey exon 0 and the start of exon 1 with human inhibin α genomic sequence. The AT dinucleotide in human is GT in monkey, thereby introducing a novel 5' splice donor site. The bold and underlined base reflects a difference from the monkey sequence.
We used this 96 bp sequence to screen the expressed sequence tag (EST) database to determine whether or not this portion of the inhibin α gene was included in transcripts in other species. BLASTN showed significant homology to four entries from three species: human placenta (GenBank acc.# CB997542), mouse dpc 14.5 Rathke's pouch (GenBank acc.# BY303064), mouse mammary tumor (GenBank acc.# BI082792), and pig ovary (GenBank acc.# BP457576). None of the ESTs extended as 5' as the monkey short RACE sequence, but one mouse EST (BY303064) started 15 bp 3' of where monkey RACE product began (Fig. 4A). In all cases, the EST sequences where contiguous with previously described 5' UTRs in exon 1 of the various species. Therefore, these ESTs appear to define alternative transcription initiation sites in the inhibin α gene and ostensibly increase the length of the 5' UTRs, but do not alter the exon-intron structure of the gene nor do they alter the open reading frame of the mRNA. This contrasts with what we observed in monkey.
We aligned both the short and long (see above) RACE product sequences to human genomic sequence derived from a BAC clone in GenBank (acc.# AC009955). The 96 bp unique to the short RACE product terminated 12 bp 5' of where the longest RACE product began (Fig. 4B). We noted that the first two bp of this intervening sequence was AT in human. We hypothesized that if the adenine in the first position in human was guanine in monkey, this would provide a 5' splice donor site (GT; [20]) and might explain how exon 1 sequence was skipped in its entirety in this transcript. We designed PCR primers corresponding to sequences flanking the intervening region and amplified genomic DNA extracted from monkey testis. Resulting amplicons of the predicted size were gel purified and sequenced directly. The results confirmed that the first two bp of the intervening sequence were GT in monkey, and therefore potentially provided a novel 5' splice site (Fig. 4B). The same 3' splice acceptor used in the long transcript also appears to be used in the short transcript, such that exon 2 is spliced identically in both cases (Fig. 3). We propose to call the unique sequence in the short RACE product exon 0. The resulting transcript reflects the splicing together of exon 0 and exon 2 (Fig. 3). Exon 1 and the intron are removed in the process. The long RACE product reflects transcription initiation from a downstream (more common?) site and is produced through splicing together of the canonical exon 1 and exon 2 (Fig. 3). We propose to call the transcript identified in the short RACE product rhesus monkey inhibin α-variant 2 (GenBank acc.# AY574370) and the canonical form inhibin α-variant 1.
The short RACE product was initiated from a reverse primer directed against sequence within exon 2 (Fig. 3, primer D). By virtue of its positioning, this primer excluded the last 287 bp of the open reading frame in exon 2. To confirm that inhibin α-variant 2 extended at least as far as the stop codon in exon 2, we used RT-PCR to amplify a contiguous sequence from the 5' end of exon 0 to the end of the ORF in exon 2. A faint band was amplified, but was of insufficient abundance to clone or directly sequence. We therefore performed nested PCR on this amplicon using primers directed against exon 0 (15 bp 3' of the first primer) and exon 2 (170 bp 5' of the first primer; primer C in Fig. 3). A band of the predicted size was amplified and directly sequenced following gel purification. The product corresponded to inhibin α-variant 2, indirectly confirming that the entirety of the open reading frame in exon 2 is contained within this transcript.
The ORF of inhibin-α variant 2 is 888 bp, potentially encoding a protein of 296 amino acids. A putative AUG start codon is observed 38 bp from the 5' end. However, the surrounding sequence does not conform to the consensus Kozak sequence [21]. The next AUG is observed 129 bp 3' of the first, within the αN encoding portion of exon 2. This potential start site also fails to conform to the consensus Kozak sequence. Therefore, it is not clear which, if either, of these codons may be used to initiate translation of inhibin α-short. The putative protein contains 19 novel amino acids (encoded by exon 0) at its N-terminus followed in-frame by amino acids 90–366 of inhibin α variant 1. The N-terminal 19 amino acid peptide does not encode a signal sequence nor does it possess significant homology to sequences in the public databases. The amino acids from exon 2 encode the majority of αN and the entirety of αC (see Fig. 3).
The northern blot in Fig. 2A was probed with a cDNA corresponding to exon 2, which is contained in both inhibin α-variants 1 and 2. Two transcripts were detected. To determine whether the transcripts might encode these two alternative forms, we stripped the blot and re-probed it with an exon 1 specific probe (Fig. 2B). Both transcripts were again detected, indicating that both contained exon 1 sequence and, by extension, that neither transcript encoded inhibin α-variant 2 (which lacks exon 1). This is perhaps not surprising in light of the difficulty we experienced in amplifying inhibin α-variant 2 by RT-PCR and is consistent with the notion that it is a relatively low abundance mRNA. Surprisingly, a smaller transcript of ~0.4 kb was also detected with the exon 1 probe. These data suggest that yet another inhibin α transcript may be expressed in monkey testis. The identity of this mRNA species is currently unknown; however, it is predicted to be truncated and contain some or all of exon 1 sequence.
Cloning of the rhesus monkey inhibin βB cDNA
The mature domain of inhibin βB is highly conserved across all species investigated to date. We used RT-PCR to amplify this region of the cDNA in rhesus monkey (GenBank acc.# AY574371). Not surprisingly, the 115 amino acid domain shared 99% sequence identity with several other mammalian species, including human (Fig. 5). The one amino acid difference was a non-conservative threonine to alanine substitution at position 75 of the mature domain. This amino acid is also divergent (proline) in rat and mouse.
Figure 5 Inhibin βB amino acid sequence alignment. Alignment of the inhibin βB mature domain amino acid sequence in several mammalian species. Differences from the human sequence are bolded and underlined. An arrow indicates the one amino acid difference between the human and monkey sequences.
Discussion
In this report, we describe the cloning of the inhibin B subunit cDNAs from testis of the adult rhesus monkey. The results indicate that both the inhibin α and inhibin βB subunits are highly conserved with other mammalian species, particularly within the mature domains of the prepro-hormones. For inhibin α, monkey and humans share 131 out of 134 amino acids. The three differences are all non-conservative; however, all occur at residues that vary across mammalian species. The human and monkey βB mature regions share 114 of 115 amino acids. Again, the one difference is non-conservative but occurs in a residue that varies across mammalian species. Collectively, these data suggest that the mature inhibin B is nearly identical in the human and rhesus macaque. As a result, current assays developed to measure human inhibin B are expected to accurately measure native monkey inhibin B. In addition, because rh-inhibin A and rh-inhibin B are equipotent at suppressing FSH secretion by monkey gonadotrophs in primary culture (Winters and Plant, unpublished observations), it seems reasonable to assume that the FSH suppressing potency of rh-inhibin A may be comparable to that of native monkey testicular inhibin B. If this assumption is substantiated when recombinant monkey inhibin B becomes available, then it will allow the physiological significance of previous and future studies using rh-inhibin A administration in male monkeys to be placed into perspective.
In the course of cloning the inhibin α subunit, we identified at least one alternative inhibin α mRNA, which we call rhesus monkey inhibin α-variant 2. The canonical inhibin α subunit mRNA, rhesus monkey inhibin α-variant 1, is comprised of two exons. Monkey inhibin α-variant 2 is produced through a combination of alternative transcription initiation and alternative splicing. As a result, a novel exon (exon 0) is used in place of exon 1 observed in inhibin α-variant 1. Both transcripts incorporate exon 2. 5' RACE indicated that transcription of inhibin α-variant 2 initiates about 108 bp 5' to the start of inhibin α-variant 1 (exon 1). As a result, the short variant contains an additional sequence at its 5' end that is conventionally referred to as inhibin α promoter or 5' flanking sequence. In monkey, an A to G transition (relative to the human sequence) in the genomic sequence upstream of the conventional transcription start site (in exon 1) leads to the introduction of a novel 5' splice donor site. As a result, exon 1 and the intron are removed from the pre-mRNA and a new exon (exon 0) is spliced to exon 2. The putative protein encoded by this variant is predicted to contain the majority of the αN and the entirety of the αC domains. At its N-terminus, however, it lacks a signal sequence as well as the pro-α region. Thus, if produced, it is unlikely that the protein would be secreted. We have not yet tried to express the protein to see if it is indeed synthesized in mammalian cells and where it may be trafficked. The putative translation initiation codon does not conform to the consensus sequence so there is some question about the efficiency with which this variant may be translated. In addition, its low abundance (at least at the mRNA level) also calls into question its functional significance.
At this point, we do not know to what extent inhibin α-variant 2 may be expressed in other species, nor have we examined its expression in monkey ovaries. However, this isoform may be unique to monkey because of the A to G transition (relative to human) in the genomic sequence. An alignment of the relevant "promoter" sequences in human, mouse, rat, and cow indicates that the splice donor (GT) in monkey is AT in human and GG in the other three species (not shown). Moreover, screening of the EST database with the monkey inhibin α-variant 2 unique sequence (exon 0) identified very few clones and in each case merely extended the 5' UTR in these species. That is, where monkey inhibin α-variant 2 skipped exon 1 entirely, the mouse, human, and pig EST sequences were contiguous with exon 1.
Perhaps the most important aspect of the identification of monkey inhibin α-variant 2 is that in this primate and perhaps in other species, transcription can be initiated further 5' than previously considered. As a result, sequences previously characterized as promoter may actually be 5' UTR, at least in some mRNA species. For example, a mouse EST contains sequence in its 5' UTR previously identified as a conserved non-consensus CRE in the inhibin α promoter [22] (Fig. 4A). Because this EST contains the CRE sequence, its transcription may not be cAMP dependent.
Conclusions
Cloning of the inhibin B cDNAs from rhesus monkey testis indicates that the mature inhibin B is highly conserved in monkeys and humans. Therefore, the results in monkeys obtained with recombinant human inhibins may accurately reflect results that would be obtained with recombinant homologous ligands. The characterization of the inhibin subunit cDNAs in monkeys will greatly facilitate the production of macaque inhibins and will permit a direct test of this hypothesis. In addition, a novel inhibin α mRNA isoform was isolated in this investigation. This is the first example of an inhibin α mRNA variant described in any mammalian species. The results of both northern blot (Fig. 2) and RT-PCR analyses indicate that additional inhibin α mRNA variants also exist. Future studies will not only more thoroughly characterize these variants, but will examine their expression in other species. Moreover, functional analyses may highlight heretofore-unknown aspects of inhibin biology and function.
Authors' contributions
DJB participated in the design of the study, performed all of the molecular biological experiments and analyses, and drafted significant portions of the manuscript. TKW participated in the design of the study and critically revised the manuscript. TMP provided the animal tissues, participated in the design of the study, and drafted sections of the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors thank Jaro Jelen for technical assistance. This work was supported by NICHD/NIH through cooperative agreement (U54-HD-08610) as part of the Specialized Cooperative Centers Program in Reproduction Research. DJB was supported, in part, by a Lalor Foundation post-doctoral fellowship.
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| 15471543 | PMC526212 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Oct 7; 2:71 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-71 | oa_comm |
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Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-3-221547947310.1186/1476-072X-3-22ReviewCurrent practices in the spatial analysis of cancer: flies in the ointment Jacquez Geoffrey M [email protected] BioMedware, 516 North State Street, Ann Arbor, MI, 48104-1236, USA2004 12 10 2004 3 22 22 28 9 2004 12 10 2004 Copyright © 2004 Jacquez; licensee BioMed Central Ltd.2004Jacquez; 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.
While many lessons have been learned from the spatial analysis of cancer, there are several caveats that apply to many, if not all such analyses. As "flies in the ointment", these can substantially detract from a spatial analysis, and if not accounted for, can lead to weakened and erroneous conclusions. This paper discusses several assumptions and limitations of spatial analysis, identifies problems of scientific inference, and concludes with potential solutions and future directions.
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Introduction
'Dead flies cause the ointment of the apothecary to send forth a stinking savor; so doth a little folly him that is in reputation for wisdom and honour.'
Ecclesiastes 10:1
The term "flies in the ointment" is occasionally used to describe minor defects in some endeavor. But this quote from Ecclesiastes has a much wider scope than a few dead flies – it is the ointment itself that stinks, and the entire endeavor is thereby ruined. By analogy, there are several caveats that apply to many, if not all spatial analyses of cancer data. As "flies in the ointment", these caveats can substantially detract from a spatial analysis, and if not accounted or otherwise controlled for, can lead to weakened or erroneous conclusions. Several of these caveats have been identified in the papers in this collection; others have yet to be described. This paper brings them together in one location, where they are discussed under three broad headings.
• Problems of inference;
• Assumptions and limitations; and
• Potential solutions and future directions.
Problems of inference: what can we learn from spatial analysis?
This section provides an overview of the scientific method as applied to spatial data, limitations inherent in the study of spatial systems, including those on inference, spatial methods and data, and finally, limitations imposed on spatial analyses of human health data by society and the context from which health data arise.
Overview of the scientific method
The Classic Paradigm of Karl Popper. Popper [1] posed an approach to gaining knowledge from bodies of data that has come to be known as the "Scientific Method". Although his approach has been criticized as not necessarily being applicable to how scientific knowledge advances in practice, with fortuitous circumstance and flashes of insight (as occurred in the discovery of penicillin) receiving no mention, Popper's philosophy is useful because it incorporates inductive and deductive reasoning, and uses falsifiable predictions based on clearly stated hypotheses.
The useful lessons from Popper are first, that hypotheses and theories emerge from patterns and relationships in a set of observations; second, that the validity of the hypotheses is evaluated by distilling falsifiable predictions from them; third, that experiments designed to test these falsifiable predictions result in new data collected specifically to evaluate the predictions (these are designed experiments); and fourth, in order to avoid a tautology predictions cannot be tested on the data that gave rise to them. It is important to realize that useful predictions are falsifiable ones. Popper's approach can never prove a theory or hypothesis to be true. Rather, a body of evidence is collected from a series of experiments designed to test specific predictions, thereby increasing confidence that the hypothesis on which the predictions are based is true. Popper thus saw science as advancing much in the way used by Sir Arthur Conan Doyle's fictitious character Sherlock Holmes: If the alternative explanations are disproven, then the remaining explanation, no matter how unlikely, must be true.
Recognizing this, Platt [2], proposed what he called "Strong Inference". Strong Inference begins with a set of hypotheses regarding observed phenomenon. The researcher then designs a series of critical experiments to systematically test each hypothesis. Platt recognized that the set of alternative hypotheses may change during the course of the experimental process, and Strong Inference is thus more closely aligned with the experimental process as it is used in practice.
Limitations inherent in the study of spatial systems
Spatial systems typically are large, and the spatial phenomena of interest in public health (e.g. cancer mortality rates, risk behaviors, demographic characteristics, and environmental exposures) are often difficult to observe directly and/or change slowly through time. This makes it difficult, if not impossible, to conduct designed experiments, and in any event there are substantial ethical considerations with experimentation on human populations. The spatial health researcher must often work with encountered data that have been collected for some purpose other than her specific study. In some instances the data are sampled in a systematic way from a spatially distributed population. But in each of these instances spatial analysis plays a critical role in identifying spatial and temporal relationships in population-level data, giving rise to hypotheses that can then be evaluated on additional data to be collected from the same system or on data from analogous spatial systems (spatial controls).
Under both Popper's and Platt's inference frameworks, study designs that attempt to confirm rather than reject hypotheses are not particularly useful. Repeating spatial studies to search for confirmation is less useful than undertaking analyses that are designed specifically to reject scientifically meaningful alternative hypotheses. But because it is so difficult to manipulate spatial systems, it can be difficult to design and undertake the critical analytical experiments that test falsifiable predictions.
Limitations on inference
The spatial analyst's tool box includes techniques for quantifying spatial patterns, modeling risk surfaces, and assessing relationships between cancer outcomes and potential exposures. These techniques allow researchers to determine whether observed spatial patterns are statistically significant, to identify the locations of clusters, hotspots and cool spots, to construct maps showing excesses and deficits relative to a risk model, and to quantify association between two spatial variables (such as cancer incidence and putative environmental exposures). Although these techniques can be quantitatively powerful, the inferences that can be drawn from them have attendant limitations. We now consider three limitations on the inferences that can be reached from analyses (1) of spatial patterns, (2) of spatial associations, and (3) by using randomization (Monte Carlo)-based techniques.
Pattern does not demonstrate causation. As noted by Waller and Jacquez [3] tests for spatial pattern employ alternative hypotheses of two types; the omnibus "not the null hypothesis" or more specific alternatives. Tests with specific alternatives include focused tests [4] that are sensitive to monotonically decreasing risk as distance from a putative exposure source (the focus) increases. Acceptance of either of these types (the omnibus or a more specific alternative) only demonstrates that some spatial pattern exists, and does not implicate a cause. When the alternative hypothesis is highly specific, as for a focused test, it may correspond to a potential causal mechanism. For example, Waller et al [5] employed focused tests to explore a possible association between leukemia and lymphoma in New York State and exposure to TCE injected into ground water at industrial sites. While the score test employed was highly significant, demonstrating increased risk near several ground water injection wells, this finding did not demonstrate a causal relationship, or even that persons close to the injection wells had increased exposure to TCE. The existence of a spatial pattern alone cannot demonstrate nor prove a causal mechanism.
Association is not causation. The spatial analyst has an increasingly diverse suite of tools for documenting and quantifying associations between the spatial patterns of two or more variables. These techniques include cross-correlograms and related measures [6,7]., the bivariate LISA [8,9]., boundary overlap [10], polygon area overlap [11], as well as other approaches. Not intended to replace traditional statistical methods for association (such as the Pearson product-moment correlation), these methods assess the extent to which the spatial patterns in two variables (such as lung cancer incidence and ambient air toxic concentrations, see for example Jacquez and Greiling 2003) [12] coincide or "match up". But, as for traditional correlation techniques, a demonstration of spatial association does not demonstrate causality.
Randomization limits inference to the data set. Many disease cluster techniques and approaches to spatial modeling employ randomization, either based on sampling algorithms from spatial models (e.g. the Bernoulli model for the locations of cases and controls; the heterogeneous Poisson model for area-based cluster tests, and so on) or on distributional assumptions of randomization (e.g. the randomization hypothesis for Moran's I). Traditional statistics based on distribution theory (e.g. student's test, ANOVA etc) are able to make inferences regarding the "Universe" from which the population sample was drawn. Inferences for methods based on randomization, however, are limited only to the data set to which they were applied. This is one of the critical distinctions between methods based on distribution theory and "distribution free" techniques based on re-sampling a data set to construct empirical distributions [13].
Limitations imposed by methods
All methods have attendant limitations, and this is true as well for techniques in the spatial analyst's toolbox. We now consider limitations imposed by spatial methods including the amount of knowledge required to use them, the selection and specification of spatial weights, and the subjectivity of the methods themselves.
Amount of knowledge Different analysis approaches require different amounts of knowledge. A distinction often is made between exploratory analysis, models of data, and models of process. When working with spatial data, a corresponding distinction can be made between Exploratory Spatial Data Analysis (ESDA), spatial data models, and spatial process models. Each of these (ESDA, models of data, and models of process) has different inferential/predictive abilities, and requires different amounts of data and knowledge of the spatial system itself. ESDA quantifies spatial pattern, models of data are used for interpolation and prediction, and models of process are used for prediction and the assessment of proposed perturbations to the spatial system. ESDA (including techniques such as autocorrelation analysis and disease clustering) aims to identify spatial patterns and to generate hypotheses that might explain those patterns. It requires relatively little knowledge of the system being studied. In fact, the objective of exploratory techniques is to explore and quantify relationships in order to increase the analyst's knowledge of the spatial system. Models of data (such as spatial regression, geostatistical models, risk surface models, and Bayesian techniques) require data of sufficient quality to estimate model parameters, and that the researcher possesses sufficient knowledge to be able to identify dependent and independent variables, and their relevant parameters. However the forms of these models do not convey any information regarding causal relationships. Models of process require a detailed understanding of the mechanics of the system being studied, and incorporate this understanding directly into the model itself. Spatial compartmental models that incorporate population and disease processes such as birth, death, migration and risk have been applied to model infectious diseases [14,15]. This kind of model has also been used to model the transport and fate of mutagenic compounds that are known carcinogens (e.g. [16]). But to date there are few if any process models that link population-level cancer outcomes to environmental exposures.
Spatial weights Each of the 3 types of approaches outlined above require the use and specification of spatial relationships among the objects (e.g. individuals, places of residence, areas of spatial support) being studied. In ESDA these are referred to as spatial weights. In models of data these may be called kriging weights (in geostatistics), autoregressive parameters (in spatial regression), or spatial filters (in Bayesian smoothing). In models of process spatial relationships are quantified to correspond to the underlying mechanics of the system, for example in an infection model, by how likely pairs of nearby susceptible and infectious individuals are to contact one another. As one moves from ESDA to models of process, the methods used for quantifying spatial relationships become increasingly meaningful in terms of the spatial system being studied.
For ESDA spatial weights model the spatial disease pattern (the alternative hypothesis). The selection and specification of spatial weights in ESDA is undertaken in the most "knowledge poor" circumstance, yet is critical since these weights quantify the alternative hypothesis of the pattern recognition statistic. For area-based data, commonly used spatial weights include first and higher order adjacencies, and functions of common border length. Some techniques evaluate nearest-neighbor and adjacency relationships on the centroids of areas, an approach that disposes of highly relevant geographic information (such as common borders) readily obtainable from polygon geometry. More advanced and realistic techniques are now being developed that account, not only for geographic relationships, but also for co-information such as population size [17]. But in general many of the spatial weights in common use are geographically crude (e.g. employ area centroids) and based entirely on Euclidean spatial relationships that ignore relevant co-information such as population size. Later in this paper we discuss the use of spatial weights to represent exposure mechanisms.
Subjectivity Most researchers recognize that all analytical methods impose a model, of one type or another, on the data and are therefore subjective. For example, the product-moment correlation coefficient imposes a linear model and is thus sensitive to linear relationships in bivariate data. Similarly, all techniques for spatial pattern analysis and modeling are founded on assumptions and are sensitive to or descriptive of different aspects of spatial pattern. For example, reliance on a single cluster statistic can only reveal those disease patterns that are consistent with that test's alternative hypothesis (e.g. circular or elliptical clusters for spatial scan statistics). This has prompted some researchers to employ a battery of spatial pattern methods to better describe different aspects of the morphology of geographic patterns in cancer incidence [12]. While employing a variety of techniques doesn't remove subjectivity, it does illuminate different aspects of spatial patterns, thereby providing a richer and more accurate description of geographic variation.
Limitations imposed by data
The spatial data used in many geographic studies of cancer have inherent limitations attributable to granularity, spatial and temporal mismatch, under-reporting, misdiagnosis, the use of location as an exposure surrogate, human mobility, location and attribute uncertainty, static representation, as well as topological errors that result in erroneous spatial weights.
Granularity has to do with the spatial resolution of the data. For human health applications, death certificates are often georeferenced to location of place of residence at time of diagnosis or death. Point-based methods then use these coordinates directly. Area-based methods require the point locations to be aggregated to provide raw or adjusted rates within areas, and these areas might be census units, metropolitan statistical areas, counties, states and so forth. Because of the need to protect patient privacy, publicly available data are often aggregated to a sufficient extent to prevent the disclosure or reconstruction of patient identity. So, for example, point maps displaying patient place-of-residence typically cannot be disclosed by researchers and public health agencies. But due to the Modifiable Areal Unit Problem (MAUP) how these data are aggregated can dramatically impact analysis results, and incompatible geographies (e.g. census vs. ZIP Code) make tests for association problematic [18]. The ability to detect and model spatial pattern depends on granularity. One cannot, for example, detect clusters of counties using health data that is aggregated at the state-level. It is worth noting, however, that methods of spatial unmixing for raster-based data have been developed that support the construction of higher resolution maps from lower resolution information [19]. Unmixing approaches for disaggregating census and spatially aggregated health data that will allow spatial analyses using a common spatial support across variables are now available [20].
Spatial and temporal mismatch Cancer data, information on covariates and on environmental exposures typically do not "match up" in space or in time. For example, Jacquez and Greiling [12] analyzed lung cancer data on Long Island, and contrasted spatial patterns (geographic boundaries) with data on airborne toxics from EPA's (Environmental Protection Agency) National Air Toxics Assessment (NATA) program. Mismatch occurred between the cancer and air toxics data both in space (lung cancer incidence was reported at ZIP+4 level; air toxics data for census block groups) and in time (lung cancer incidence was reported for 1994–97; the air toxics data was based on emissions reported during 1996). The problem of spatial mismatch was solved by using spatial tests for association (boundary overlap) that account for the differing geographies within the randomization procedure. Temporal mismatch was problematic because latency for lung cancer is on the order of 15–20 years, and air toxics information could not be reconstructed over that time span. Thus while they found a positive geographic association between the air toxics and lung cancer incidence, the substantial temporal mismatch means a more detailed exposure reconstruction is required before any conclusions can be reached.
Location and attribute uncertainty Uncertainty in spatial health data occurs in two data components: the locations (e.g. coordinates of place of residence) and attributes (the values recorded at the locations). Also referred to as positional uncertainty, the impacts of location uncertainty on spatial pattern analysis and modeling have been well documented in the geographical and natural resource sciences [21,22]. In the health sciences, Jacquez and Waller [23] evaluated the impacts of location uncertainty on three tests for space-time interaction, and found the Mantel, Knox and k-nn tests to differ in their sensitivity to location uncertainty, with the k-nn test less likely to report false negatives as uncertainty increased. Location uncertainty can be modeled using several approaches, including lists of alternative locations for point-based data, and polygon, population, and risk-based models for area-based data [24]. Nonetheless, many spatial analyses of cancer assume locations are known with 100% certainty and that the spatial weights calculated from those locations are precise and without error in either representation (e.g. is it reasonable to use place of residence to represent human activity patterns?) or measurement.
Location as an exposure surrogate Location uncertainty has different sources, one of which is human mobility. Attempts at describing such mobility that transcend the use of place-of-residence to represent location include daily activity spaces[25,26], and constructs such as time geography and pathogenic paths [27-29]. But while almost all researchers acknowledge that many causative exposures occur outside of the home, most spatial analyses still rely on place-of-residence to georeference locations of health events. When might place of residence reasonably be used to georeference health data? For infectious diseases exposure events require contact between infected and susceptible individuals of sufficient duration to allow the pathogen to pass from one to the other. The exposure route varies from one type of pathogen to another, and a given pathogen may have several exposure mechanisms. These includes fecal-oral (e.g. the Norberg virus that recently has been the bane of cruise ships), intimate sexual contact (e.g. STD's and HIV), air-borne droplets (e.g. tuberculosis), and contaminated foods (e.g. hepatitis), among other mechanisms. Zoonotic and vector-borne diseases involve an animal host or reservoir, and exposure mechanisms may include animal-human as well as human to human routes. Spatial weights for such exposure routes may incorporate measures of geographic proximity, but also should be constructed to reflect the probability of exposure between pairs of individuals (for individual-based models) and for groups (for population-based models). Although exposure routes for infectious diseases are numerous and often quite complex, exposure reconstruction for cancers with long latency and for which mechanisms of carcinogenesis are only partially known is even more problematic.
Use of place-of-residence in spatial analyses of cancer, and calculating purely spatial weights from those locations, seems appropriate only when individuals have resided at that location for as long or longer than the latency period, and when potential causative exposures occur either in the household or in the surrounding neighborhood. For what cancers might causative exposures occur in the home? Lung cancers attributable to household radon are a good example, as well as cancers caused by combustion by-products from cooking and second-hand smoke. Cancers of childhood reasonably may use place-of-residence as an exposure surrogate since the latency period is short and children tend to stay near the home. For other cancers and at larger scales of aggregation, such as census and ZIP Code geography, human mobility, especially in commuter communities, poses a substantial challenge to spatial analysis of cancer, and the finding of a geographic cluster can thus be difficult to interpret when place of residence is used to represent locations of individuals. Recently, Meliker et al [30] used the constructs of time geography within a space-time information system to undertake the space-time modeling of individual-level exposure to arsenic. They were able to reconstruct individual arsenic exposure based on specific assumptions regarding occupational exposures and the ingestion of arsenic in drinking water. The time-geographic approach appears to provide a robust quantitative foundation for exposure reconstruction that is not possible when a single location is used to represent an individual's location in space-time.
Under reporting and misdiagnosis: Uncertainty in the attributes (e.g. case identifiers and the numerators in incidence and mortality rates) arises from under reporting and misdiagnosis. Under reporting is especially an issue when working with data that encompasses health districts with different recording and reporting practices. Because states maintain their own cancer registries, differences in reporting practices can pose a special problem for data sets that cross state boundaries. For most cancers, diagnostic accuracy decreases as one works with retrospective data when the physician's diagnostic arsenal was not as robust. In addition, classifications of disease change through time, as when the International Classification of Disease (ICD) code is updated. When either differences in reporting and diagnosis are present, once cannot preclude the possibility that observed spatial variation in cancer rates is attributable to these causes.
Static view: GIS typically represent the world as "snapshots" in time and do not effectively represent temporal change [31]. The importance of time in health geography is well recognized, since almost all geographic disease patterns are the result of space-time processes [32]. There thus are substantial limitations that arise from using conventional GIS technology, especially for the mapping, representation, and analysis of health, socioeconomic, and environmental information for populations that are dispersed or mobile and in which space-time relationships are dynamic. Advances in space-time information system technology address this deficiency using space-time coordinates and object representations that include motion and morphing, as well as attribute change models [30].
Polygons, Topology and computational geometry: The spatial analysis of area-based data requires the calculation of statistics such as polygon contiguity, length of common boundaries, areas and centroids. Calculation of these statistics employs methods of computational geometry that assume the polygons are correctly represented in the Euclidean plane. These assumptions usually are that polygons are closed (e.g. Jordan curves), and are not folded or joined together at single points to form "bow ties". When these assumptions are not met, techniques such as polygon triangulation will either fail or yield incorrect results, and resulting statistics, including placement of area centroids and spatial weights, will be wrong. Despite the importance of this problem, most spatial analysis software does not check shapefiles (which lack topological information) to determine whether the polygons are topologically well-conditioned. While this may seem an arcane problem, we have discovered in practice that a substantial proportion of the shapefiles shared among researchers for use in spatial analysis are flawed to a sufficient extent so that the resulting spatial weights are incorrect. One example is the primary care service area file that has 60 of 6000 polygons self intersecting, a 1% error rate.
Limitations imposed by society and context
Limitations on inference in cluster investigations. Many disease cluster investigations are initiated by reports from concerned citizens, and the attendant increase in the probability of false positives due to such preselection bias is well known [33,44]. Others have pointed out that the investigation of preselected clusters is not a scientifically valid endeavor [34], because of the tautology of testing hypotheses on the data from which they emerged, as well as other reasons. Several authors [35] have noted limitations of the hypothesis-testing framework relative to a more flexible spatial modeling approach. Nonetheless, it is the mission of public health departments to respond to public health concerns [36], and cluster investigations are likely to continue to be undertaken within a hypothesis testing framework such as that advocated by the Centers for Disease Control [37].
Limitations arising from lack of communication with community stakeholders Within public health departments spatial analyses of cancer data are best undertaken by teams comprised of a community stakeholder (e.g. community end-user of the study results), a political decision maker whose constituency is the subject of analysis, a public health practitioner capable of putting in place an intervention should the results be positive, a spatial analyst with a detailed understanding of the spatial analytic methods, and a GIS specialist to manage data and undertake mapping tasks. Such a team effort is most likely to translate analytical results into community action [43].
Information democracy vs. protection of privacy Efforts such as the National Spatial Data Infrastructure project are leading to the advent of data portals designed specifically to facilitate sharing and dissemination of spatial information. The DataWeb is a network of online data libraries created in a collaboration between the CDC and the US Census Bureau. The libraries consist of both microdata and aggregate data, and include census, economic, health, income and unemployment, population, labor, cancer, crime, transportation, family dynamics, vital statistics, and other georeferenced data. Information in DataWeb is accessed through DataFerret, an application that prepares data sets for the user to download. It allows users to select a databasket of variables and then recode those variables as needed. Users develop and customize data tables and download them to their desktop (download formats include ASCII, SAS, SPSS, and Excel/Access).
Launched on June 30th, 2003, the Geospatial One Stop program is a web-based portal for one-stop access to maps, data and other geospatial services that simplifies access to geospatial data collected by government agencies and other organizations. Geodata.gov is accelerating development and implementation of the National Spatial Data Infrastructure (NSDI) and includes state, local and tribal governments along with the private sector and academia as data providers. Geodata.gov offers access to thousands of data bases in 17 categories.
The promise of these and like efforts is an information democracy in which all citizens have ready access to information describing health, the environment, services, resources, the economy and other data, both for their immediate neighborhood as well as larger areas. While freedom of information is arguably one of the pillars of a democratic society, the need to protect individual privacy is a substantial countervailing consideration. There are other important ethical considerations with the sharing of spatial data of very high resolution. For example, satellite imagery is now publicly available at 1 m and sub 1 meter spatial resolution, and hyperspectral imagery at comparable resolution will soon be available. From such imagery it will be possible to identify and classify microhabitat for disease vectors [38], and even to map the transport and fate of heavy metals in rivers and streams [39]. But such information also will allow even small pockets of microhabitat of economically valuable species to be targeted for exploitation (for example, in the U.S. there is a black market in native endangered frogs, turtles, snakes and lizards). How will the need for spatial data sharing consistent with an information democracy be balanced with individual rights to privacy and related ethical considerations?
Assumptions and limitations of spatial analysis of cancer data
Every study is based on assumptions, and ideally these are made explicit when the results are reported. This section describes several assumptions and considerations typical of spatial analyses of cancer, including ability to infer causality, the ecologic fallacy, and the role of higher-order interactions. This section concludes with a discussion of the lack of utility of the word "cluster" as a spatial pattern descriptor.
Power to disprove but not confirm causality
Earlier we pointed out than an important limitation of spatial analyses of cancer data is that demonstration of significant geographic patterns and associations is never sufficient to demonstrate causality. This is particularly true of cancers because of long latencies, the substantial difficulties posed by exposure reconstruction, and because of our lack of a full our understanding of the environmental bases of carcinogenesis. However, the exploration and modeling of spatial cancer patterns can disprove predictions based on causal hypotheses that are expressed in spatial terms. For example, hypothesized exposure mechanisms that involve proximity to point sources, or for which attributed risks vary geographically, can be evaluated systematically using spatial analytic approaches.
Ecologic fallacy, and arbitrary spatial partitions
Studies of geographic clusters and cancer data must include consideration of the potentially misleading aspects of ecologic studies. Even ZIP Codes and census tracts can be considered coarse spatial units for aggregating cancer cases and for estimating exposures, and exposure and health data often are not available at the same resolution. Exposure data often are reported at spatial levels, such as census tracts, that partition the geography in a manner inappropriate for the exposure process. Other spatial divisions may be better descriptors of environmental exposure, including, for example, watersheds, aquifers, local public water systems for water-borne substances, or "windsheds" for airborne substances. But because of privacy concerns for the patients and the limitations on existing environmental data, the data used often are simply the data that are available. Every data collection protocol has a design. Is it appropriate to use data for purposes other than for which they were collected? ZIP Code, census tract or place of residence at diagnosis is an inadequate descriptor of an individual's location during the development of cancer. For example, using the ZIP Code of residence assumes the patient lived within that ZIP Code area during the period of time required to develop cancer following exposure to an environmental compound that influenced cancer risk. Hence the degree of exposure to the potential risk factors over a multi-year period has been estimated for each study subject based on their place of residence, aggregated at the census tract level. This assumption is clearly tenuous given the mobility of human populations and the arbitrariness of the spatial partition for the environmental data [45].
In the 1980's many epidemiologists considered ecologic studies likely to lead to erroneous conclusions, and that the most accurate findings arise from individual-level data. Since the late 1990's, however, the potential of adding "contextual variables" to multi-level analyses has provided a sound methodological mechanism for combining individual-level data with higher geographical contextual data. Nonetheless, issues regarding the definition of spatial partitions, patient privacy, and the appropriate use of data still pertain.
Higher order interactions
Especially for complex relationships (such as those between environment, genetics and cancer), apparent bivariate associations may be driven by multivariate interactions that are not directly quantified by the two variables under scrutiny. For example, elevated air pollution may be associated with lower housing prices (because of proximity to industrial sites), which in turn attracts poorer households with higher smoking rates. In this instance, an observed bivariate correlation between air pollution and cancer would actually overestimate the degree of association between these two variables. But because of their complexity, higher order multivariate interactions are difficult to quantify in spatial cancer studies.
The term "cluster" has little meaning
The term "cluster" by and of itself is so generic as to be almost meaningless for describing spatial variation in cancers. What is a cluster? Is it an excess of cancer, and, if so, how much extra is considered an excess? Do we use likelihood statistics to find an excess, or should we use some other statistical framework? Are we looking globally to identify clusters anywhere in the study area, or do we define patterns locally, or relative to a putative source? These kinds of questions suggest that the declaration of a "cluster" is meaningless without a precise description of the statistical test being employed and the patterns to which the test it is sensitive. Because different clustering techniques are sensitive to different aspects of cluster morphology, analytic approaches that employ several pattern recognition methods can be more informative, especially in the ESDA phase of an analysis, with the caveat that the multiple tests will need to be accounted for should accurate estimates of P-values be required. Analyses that rely on just one kind of cluster test are incomplete in the sense that they will have power to detect only one type of cluster. Cancer morbidity and mortality evinces rich geographic variation, and it thus can make sense to employ a variety of techniques to more fully describe relevant aspects of spatial pattern.
The future
This last section discusses salient trends and methodological challenges in the changing landscape of the spatial analysis of cancer. It summarizes expected improvements in cancer data, exposure measures, and genetic information, and concludes with some anticipated methodological and technological challenges for the next decade.
Improved availability of cancer data
Recent trends in cancer registries are resulting in improved reporting and linking of spatially referenced data, although there is substantial variation in quality from state to state. There is a trend towards increased availability of aggregated cancer statistics over the World Wide Web. Not withstanding the inherent limitations of ZIP Code data, a good exemplar of improved availability is New York State, which is publishing online atlases of cancer incidence at ZIP Code level geography. A second example is the National Cancer Mortality Atlas published by the National Cancer Institute. New York State also makes available findings from spatial analyses of the cancer incidence data using the spatial scan statistic [40], and the National Cancer Atlas provides a narrative interpretation of the cancer mortality patterns and their potential causes. In coming months and years the quality of and speed with which cancer incidence and mortality data are made available is expected to improve, with some of these benefits attributable to improved Public Health Surveillance infrastructure currently being funded by bioterrorism and first responder initiatives.
Improved exposure and population data
Efforts cited earlier in this paper such as the CDC's dataweb and geospatial onestop are making georeferenced information on socioeconomic, census, environmental, remote sensing and other data readily available for downloading over the web. In remote sensing, the trend is towards higher spatial, spectral and temporal resolutions which together hold great promise for improving environmental risk assessment, habitat classification, and change detection [41]. Modeling efforts by organizations such as the Environmental Protection Agency are integrating exposure models with spatial models of air-borne and other toxins, incorporating both point and non-point source information. Coupled with improved data on cancer health outcomes, enhanced exposure estimates, along with detailed descriptions of the affected populations, hold the promise of more detailed, accurate and predictive spatial modeling of cancer outcomes. However, this promise can only be realized when the data obtained from disparate sources is temporally matched, aggregated in an appropriate fashion, and collected at compatible geographic granularities.
Improved genetic data
The recent revolution in gene sequencing, bioinformatics and proteomics is making possible a detailed understanding of genetic predispositions as well as the cascade of genetic changes that cause normal cells to turn into cancer cells. Research currently underway at the NCI is seeking to elucidate gene-environment interactions and how these interactions can lead to cancer, but in general spatial analysis has contributed little to the study of gene-environment interactions. In fact, such studies would require population-level information on genetic profiles and biomarkers sufficient to calculate human genetic distances, and this kind of data are not yet available. Some research has conducted on European populations to explore relationships between genetic distances calculated from blood polymorphisms and differences in cancer mortality [42]. But to fully exploit the potential of spatial analysis for the study of gene-environment interactions, more detailed data on the genetic profiles of human populations in the United States is needed.
Improved technology
As noted earlier, the static view of GIS makes it difficult to represent human mobility and temporal change in cancer, environmental and socioeconomic data. GIS typically are based on spatial data models that apply to static spatial systems such as those found in geology, forestry, and physical geography. However, this purely spatial data model inadequately characterizes the "what, where, when" needed to effectively analyze cancer data and health-environment relationships. GIS built on spatial, rather than space-time data structures, cannot deal readily with space-time georeferencing nor space-time queries [31], and instead are best suited for analyzing static systems. Loytonen [32] and others have called for a "higher-dimensional GIS" (a Space-Time Information System or STIS) to better represent space-time dynamics. STIS provide a rich framework for the generation and evaluation of epidemiologic hypotheses founded on the exploration of space-time disease patterns in relation to their putative causes and covariates [9]. The advent of mobile computing and location-based services provide substantial opportunities for increasing our understanding of human activity patterns, and an important challenge for the spatial analysis of cancer will be to more fully exploit the temporal dimension as this information becomes more readily available.
The methodological challenge
In the near future we will need techniques and methods that take full advantage of the burgeoning data stream while maintaining the values and ethos of an open, democratic society. Information detailing place of death, genetic makeup, socioeconomic status, product use, and lifestyle indicators will be available at unprecedented spatial and temporal resolution. Using these data, substantial benefits to society are expected to accrue from the rapid identification of cancers and other health risks. Syndromic and health surveillance systems are now being deployed that could make it possible to rapidly identify local increases in cancer risk, and even relate them to spatial patterns and changes in environmental data thought linked to causative exposures. But the benefits of analyzing such high spatial and temporal resolution data must be balanced against the need to maintain individual privacy, while at the same time providing equitable information access to all strata of society. Certain aspects of this problem can be met by the development of appropriate analysis techniques. Coming up with these techniques and applying them in a responsible fashion is a substantial challenge that will require the cooperation of researchers, funding agency program managers, and legislators.
Note
The author is President of a commercial company (BioMedware) that develops software for the exploratory spatial and temporal analysis of health and environmental data.
Acknowledgements
This contribution is one of the products of a working group on the spatial analysis of cancer data organized by Linda Pickle of the National Cancer Institute. Lance Waller chaired the meeting of this working group and shepherded the resulting papers through a preliminary review process. Frank Boscoe and two anonymous reviewers provided suggestions and criticisms that substantially improved the manuscript. The author's efforts were funded in part by grant R01CA92669 from the National Cancer Institute. The perspectives expressed in this publication are those of the author and do not necessarily represent the official views of the National Cancer Institute.
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| 15479473 | PMC526213 | CC BY | 2021-01-04 16:39:01 | no | Int J Health Geogr. 2004 Oct 12; 3:22 | utf-8 | Int J Health Geogr | 2,004 | 10.1186/1476-072X-3-22 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-301548259410.1186/1476-4598-3-30Short CommunicationIs cumulative frequency of mitochondrial DNA variants a biomarker for colorectal tumor progression? Aikhionbare Felix O [email protected] Masood [email protected] Delicia [email protected] Joel [email protected] Rodney [email protected] Department of Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA2 School of Public Health, University of Alabama, Birmingham, AL 35294, USA3 Comprehensive Cancer Center, University of Alabama, Birmingham AL 35294, USA4 Department of Surgery, Morehouse School of Medicine, Atlanta GA 30310, USA2004 13 10 2004 3 30 30 16 8 2004 13 10 2004 Copyright © 2004 Aikhionbare et al; licensee BioMed Central Ltd.2004Aikhionbare 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.
To examine the relationship between mitochondrial DNA (mtDNA) alterations and colorectal tumorigenesis, we used high-resolution restriction endonucleases and sequencing to assess the mitochondrial genome from three histologic subtypes of colorectal adenomas (tubular = 8; tubulovillous = 9; and villous = 8), colorectal cancer (CRC) tissues = 27, and their matched surrounding normal tissue (MSNT) = 52. The mitochondrial genomes were amplified using 9 pairs of overlapping primers and systematically analyzed by means of high-resolution analysis. DNA fragments showing a shift in banding patterns between the three adenomas, CRC, in comparison to the MSNT were sequenced to identify the mtDNA alterations. A total of thirty-eight germ-line mtDNA variants were observed in this study. Twenty-two of the thirty-eight were identified as mutations and 59% (13 of 22) were silent mutations and one was a 1-bp insertion. Sixteen of thirty-eight were distinct SNPs in flanking regions of the restriction sites and, 6 of the 16 (37%) SNPs were not previously reported. Most of these mutations/SNPs were homoplasmic and distributed in various regions of mitochondrial genes including the 16S and 12S rRNA. Based on our results, mtDNA germline variants increased in prevalence with adenoma CRC progression. To the best of our knowledge, this is the first report to show an increased prevalence of mitochondrial gene variants in CRC tumorigenesis.
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Findings
Colorectal polyps are a frequent occurrence in the general population and adenomatous changes in these polyps are associated with the overwhelming majority of CRC. These adenomas are the precursor lesions of colon cancer [1]. Currently, clinical management of individuals with colorectal polyps is guided by the histology of the lesion [2]. However, the accuracy of an appropriate staging of colorectal polyps progression to cancer continues to confound clinical pathologists as well as surgeons. Alternative strategies, which augment pathological findings such as the identification of molecular markers that are associated with the colorectal tumor progression, may prove to be a useful as a prognostic tool and a preoperative stage-specific evaluation. Recently, there have been reports of the potential use of mtDNA mutations as a biomarker in the solid tumor of other cancers [3-6] due its repaired less efficiently compared with that of nuclear DNA. Also, Mitochondria have been implicated in cancer given their role in apoptosis [7,8] and its vulnerability to mutation due to the close proximity to a major source of reactive oxygen species (ROS). Nevertheless, definitive mtDNA mutations associated with the progression of tumors such as CRC have yet to be established. Given the connection between mitochondria, ROS, and neoplasia, mtDNA from CRC adenoma, cancer tissues, and pathologically determined matched surrounding non-cancerous tissue were screened for variants, which may be used as biomarkers for colorectal cancer progression. We speculated that there is an association between one or more mtDNA mutations in the adenomatous polyps and CRC progression and the cumulative frequencies of such mtDNA mutations may eventually be demonstrated to be an important marker in adenoma colorectal progression.
This study was approved by the Institutional Review Board of Morehouse School of Medicine and the Research Oversite Committee of the Grady Memorial Hospital, Atlanta, Georgia and the University of Alabama at Birmingham School of Medicine, Birmingham, Alabama. Primary fresh frozen and paraffin embedded tissues from 3 histologic subtypes of adenomatous polyps (8 tubular; 9 tubulovillous; and 8 villous), 27 CRC tissues, and histologically MSNT, n = 52 were obtained from the Tissue Procurement Network at the University of Alabama at Birmingham and the Department of Surgery of Morehouse School of Medicine/Grady Hospital. Adenomas were defined by histologic type, degree of dysplasia, and presence of infiltrating adenocarcinoma in adenoma and classified as tubular, tubulovillus, and villous. CRC diagnosis was confirmed by histological examinations of biospied specimens for all patients and pathological tumor staging for these was based on American Joint Committee on Cancer [9]. The mean age of the study subjects was 66.3 ± 5.7 years.
Genomic DNA was extracted from both frozen and paraffin embedded remnant tissues using Tri-Reagent (Molecular, Research Center Inc., Cincinnati, Ohio) and Purgene DNA purification kits (Gentra Systems, Minneapolis, Minnesota) according to the manufacture's protocol. The mtDNA from each tissue sample was amplified by PCR using nine overlapping mtDNA primer pairs as previously described [10,11], which resulted in large PCR products that exclude the possibility that nuclear pseudogenes were amplified. Each PCR product was digested with 14 restriction endonucleases (AluI, AvaII, BamHI, Dde I, HaeII, HaeIII, HhaI, HincII, HinfI, HpaI, MspI, MboI, RsaI, and TaqI) and then subjected to direct sequencing of both sense and anti-sense strands with ABI 3100 Genetic Analyzer to determine the exact nature of new length polymorphisms/mutations detected by restriction analysis. Sequences were compared (BLAST) to the human mitochondrial DNA sequence (Genbank Accession #J01415). The MITODAT database was used to identify mitochondrial genome sequence variants.
The analysis of mtDNA samples by restriction digestion and sequencing of the DNA fragments showed non-predicted banding patterns either as homoplasmic single bandshifts or heteroplasmic multiple bands on the gel. The PCR product data point in overlapping regions were counted only once. However overlapping regions were used as internal controls for the identification of the variants. Results of the high-resolution restriction analyses and sequencing of the entire mitochondrial genome yielded 38 sequence variants (including 16 SNPs from the flanking restriction sites) for the precursor adenomatous polyps and cancer tissues. In no instance was a variant detected in the adenomatous tissue found in the histologically matched adjacent surrounding tissue, indicating that these were germ-line origin.
Based upon our assessment of the mitochondrial DNA from non-cancer, precancerous (adenomatous polyps), and colorectal cancer tissues using a high-resolution restriction analysis, we have identified sequence variants. To our knowledge this is the first assessment of the mitochondrial genome using solely primary tissue rather than cell lines. This is important since tissues in culture can undergo clonal evolution which can distort frequency data. Although, the restriction analyses identified a number of band shifts indicating site gains or losses, these data only indicated that the predicted sequence had changed. They did not identify specific base pair changes. We therefore used the original primers and a series of nested primers to sequence the band shifts in order to identify these specific changes. A total of thirty-eight germ-line mtDNA variants were observed and all mutations/SNPs were considered as germ-line origin since the variants found in the colorectal adenoma polyps or cancerous tissues were also found in the MSNT tissues. Twenty-two of the thirty-eight were identified as mutations and 59% (13 of 22) were mostly silent mutations of T-to-C. or a G-to-A transition, which is consistent with the mutagenic spectra of oxidative, damage [12,13]. Among them, C3316T/A (ND1; Met-to Met/Ile), G2758A (16SrRNA), T2352C (16SrRNA), A4769G (ND2; Met-to-Met), A3759G (ND1;CUN), G5178T (ND2; CUN-to-Met) G7028A (COI; Ala-to-Ala) T7055C/G (COI; Gly-to-Gly) C7498T (S(UCN)), G6260A (COI; Glu-to-Gly), G8251A (COII; Gly-to-Gly) T8784C (ATPase 6; Gly-to-Gly) A8618G (ATPase6; Ile-to-Thr), G9055A (ATPase 6, Ala-to-Thr), A8860G (ATPase 6 Thr-to-Ala), T11641C (ND4; Met-to-Met), A10398G (ND3; Thr-to-Ala), C10400T (ND3; Thr-to-Ala), C12633A(ND5;S(UCN)), C16390T(D-loop), T16519C(tRNAVal). However, one was a 9-bp Ins5892C in a non-coding region between MTTY and MTCOI. Some of these mutations are located in ATP synthase genes that are involved in mtDNA genome maintenance and integrity in yeast [14]. Seventy-five percent (39 of 52) of a G8860A (Thr-to-Ala) mutation in ATPase 6 gene was detected among CRC adenomas and cancer tissues, compared to 14% (7 of 52) in MSNT tissues. This mutational spectrum in ATPase gene could lead to a less efficient mtDNA replication and abnormalities as previously suggested by Maximo et al. [15]. Sixteen of thirty-eight were distinct SNPs in flanking regions of the restriction sites and 10 of the 16 (63%) have been reported, and 6 were not recorded in the MITODAT database [#J01415]. The identified SNPs are 3107delG (ND1; Frameshift), T2914G (16SrRNA), A2706G (16SrRNA), A2768G (16SrRNA), T2885C (16SrRNA), C7521T (tRNAAsp), 7335insC (COI; Frameshift), G7256A (COI; Asn-to-Asn), T7146A (COI; Thr-to-S(UCN)), G8206A (COI; Met-to-Met), A16325G (D-loop), T16309G (D-loop), G16294A (D-loop), G16266A (D-loop), G16233A (D-loop), 8269-9bp del (noncoding). Worth mentioning is also the high frequency of 16S rRNA gene variants (> 65%) in the mtDNA among CRC tissue versus the < 25% in the precancerous tissues. Mutations in the 16S rRNA gene were commonly found in different cancers, except those of the thyroid [16].
We then sought to determine whether or not any of mtDNA variants observed in flanking regions of these restriction site variants may prove to be informative biomarkers. Although, a total of 38 of the mtDNA variants were found, none of the variants appeared to be a marker for a particular adenoma CRC tumor tissue type. Nevertheless, cumulative frequencies of mtDNA variants in the different tissue types resulted in a high prevalence of mtDNA sequence variants in CRC tissue and the trend was for the number of variants to be lowest in the precancerous (Fig. 1), suggesting that this may be a useful approach to distinguishing the progressive stages of CRC adenomas as previously observed in tumour progression in the thyroid [17]. Our sample of CRC adenomas is relatively small and these data must not be overinterpreted at this point. The data however are suggestive and the cumulative frequency approach is currently being followed-up. It is possible that the high frequency of variants in mtDNA in CRC cancer tissues may result from the high rate of mtDNA replication. Furthermore, most of the mtDNA mutations in this study could be a result of mtDNA aggression affected by reactive oxygen species [18-21] and could occur via a slipped replication mechanism [22].
Figure 1 Frequencies of mitochondrial genome variants based on high-restriction analysis in all of the colorectal tumors (tubular, tubulovillous, villous), cancer tissues in each of the 9 primer sets. Numbers on the abscissa represent PCR products obtained using overlapping primers sets 1–9, termed haploblocks (Rank methods; for all comparsion).
These data should be interpreted cautiously as they are based on small number of sample size. Similar studies have looked at precancerous tissue types [20], however they have not assessed cumulative frequencies of mutations in these same tissues as we have done. Our study is also different in that we did not use cell lines for this work. Moreover, consistent with our findings were reports of cumulative mitochondrial DNA damage in the aging process as well as in cancer [23]. A similar mechanism may be involved in colorectal cancer progression, since age is a risk factor for CRC.
Abbreviations
MtDNA, mitochondrial DNA; ATP, adenosine triphosphate; CRC, colorectal cancer; MSNT, matched surrounding Normal tissue; SNPs single nucleotide polymorphisms; PCR polymerase chain reaction.
Authors' contributions
FOA performed few assays while MK performed almost all assays. DC provided the statistical support. JO provided some tissue samples and suggestions for finalization of the manuscript. RG provided some technical supervision to MK and suggestions for finalization of the manuscript. All authors read and approved the manuscript.
Acknowledgements
The authors would like to thank Drs. Birdsong of Grady Hospital, Atlanta and Grizzle of University of Alabama-Birmingham for providing some initial frozen and paraffin embedded tissues used in this study. We also thank Dr. Bracie Watson Jr. for his initial involvement with project. This work was supported by MSM/UAB partnership NIH-NCI grant #U56-CA92080.
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| 15482594 | PMC526214 | CC BY | 2021-01-04 16:36:33 | no | Mol Cancer. 2004 Oct 13; 3:30 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-30 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-191547155210.1186/1476-7120-2-19ResearchAssociation of mitral annulus calcification, aortic valve calcification with carotid intima media thickness Sgorbini Luca [email protected] Angelo [email protected] Massimo [email protected] Francesco [email protected] Cardiologic Unit I.N.R.C.A.-I.R.C.C.S. Via Cassia 1167, 00100 ROMA, ITALY2 Geriatric Unit I.N.R.C.A.-I.R.C.C.S. Via Cassia 1167, 00100 ROMA, ITALY2004 8 10 2004 2 19 19 2 7 2004 8 10 2004 Copyright © 2004 Sgorbini et al; licensee BioMed Central Ltd.2004Sgorbini 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
Mitral annular calcification (MAC) and aortic annular calcification (AVC) may represent a manifestation of generalized atherosclerosis in the elederly. Alterations in vascular structure, as indexed by the intima media thickness (IMT), are also recognized as independent predictors of adverse cardiovascular outcomes.
Aim
To examine the relationship between the degree of calcification at mitral and/or aortic valve annulus and large artery structure (thickness).
Methods
We evaluated 102 consecutive patients who underwent transthoracic echocardiography and carotid artery echoDoppler for various indications; variables measured were: systemic blood pressure (BP), pulse pressure (PP=SBP-DBP), body mass index (BMI), fasting glucose, total, HDL, LDL chlolesterol, triglycerides, cIMT. The patients were divided according to a grading of valvular/annular lesions independent scores based on acoustic densitometry: 1 = annular/valvular sclerosis/calcification absence; 2 = annular/valvular sclerosis; 3 = annular calcification; 4 = annular-valvular calcification; 5 = valvular calcification with no recognition of the leaflets.
Results
Patient score was the highest observed for either valvular/annulus. Mean cIMT increased linearly with increasing valvular calcification score, ranging from 3.9 ± 0.48 mm in controls to 12.9 ± 1.8 mm in those subjects scored 5 (p < 0.0001). In the first to fourth quartile of cIMT values the respective maximal percentual of score were: score 1: 76.1%, score 2: 70.1%, score 4: 54.3% and score 5: 69.5% (p > 0.0001).
Conclusion
MAC and AVC score can identify subgroups of patients with different cIMT values which indicate different incidence and prevalence of systemic artery diseases. This data may confirm MAC-AVC as a useful important diagnostic parameter of systemic atherosclerotic disease.
carotid artery diseaseheart diseaseatherosclerosisimaging
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Background
Mitral annular calcification (MAC) and aortic annular calcification (AVC) are observed in populations that develop significant atherosclerosis and more frequently in the elderly [1]. Previous pathological studies have suggested they represent a degenerative process that progresses with advancing age [2-4]. Consistently with this hypothesis, several ultrasound cardiovascular studies demonstrated a significant association between MAC and coronary artery disease, aortic atheroma and peripheral arterial atherosclerotic disease [5-8].
Currently, there are no accurate and standardized methods to quantify the degree of MAC an AVC; many studies have been performed with the aim to detect categorical scoring systems derived from echocardiographic annular-valvular morphology [9-12].
Alterations in vascular structure such as increased arterial wall thickness, as indexed by the intima media thickness (IMT), are also increasingly recognized as significant independent predictors of adverse cardiovascular outcomes [13-17].
We therefore undertook a cross-sectional study to examine the relationship between the degree of calcification at mitral and/or aortic valve annulus and large artery structure (thickness).
Subjects and Methods
We evaluated 128 consecutive patients who underwent transthoracic echocardiography and carotid artery echoDoppler for various indications. Patients with significant common carotid artery stenosis, rheumatic valvular disease, cardiomyopathy, prosthetic valves, ischemic heart disease and carotid artery surgery were excluded. Thus, 102 subjects were enrolled for the present study. All participating patients gave informed consent; the study protocol was approved by the institutional ethics committee.
Variables Measured
Blood pressure
Blood pressure determinations were performed with subjects in the supine position, and following a ten minute quiet resting period. Blood pressure was measured in the nondominant arm with a mercury sphygmomanometer using an appropriately sized cuff. The blood pressure values used in this study are the average of the second and third measurements. Values for systolic blood pressure (SBP) and diastolic blood pressure (DBP) were defined by Korotkoff phase I and V, respectively. Hypertension was defined as either systolic or diastolic elevation of blood pressure (>140/90 mmHg) or ongoing antihypertensive pharmacological therapy. Pulse pressure was computed as PP = (SBP-DBP); mean BP was computed as MBP = DBP +(PP/3).
Anthropometry and smoking status
Height and weight were determined for all participants. Body mass index (BMI) was determined as body weight (kg) / height (m)2. Smoking status was ascertained by a questionnaire that classified each subject as a non smoker, former, or current smoker. For the purpose of the present study, current smoker status was used.
Plasma lipids and fasting blood glucose
Blood samples were drawn from the antecubital vein between 7 and 8 AM after an overnight fast. Subjects were not allowed to smoke, engage in significant physical activity or take medications prior to the collection of the sample. The concentrations of plasma triglycerides and total cholesterol were determined by an enzymatic method [18,19]. HDL-cholesterol levels were obtained by selective precipitation with dextran-MgCl2 [20]. Serum LDL-cholesterol concentrations were estimated by the Friedewald's formula [21]. Fasting plasma glucose concentration was measured. Diabetes was defined by a fasting glucose >120 mg/dl, ore use of insulin ore ipoglicemic medication.
Echocardiographic measurements
All the echocardiographic examinations were performed using the PHILIPS® SONOS 5500 with a S3 probe. All patients had an adequate 2D echocardiogram. Evaluation of mitroaortic sclerosis/calcification was made, off line, with the acoustic quantification-densitometry package (PHILIPS® Medical System) wich restitute values based on echogray scale (0 db = black, 64 db = white, fig. 1). From parasternal short/long axis and apical 4 – 5 chamber scans, 3 or more subsequent ECG triggered cardiac cycles were acquired (gain setting: 50, compression: 55, mechanical index: 1.4); focus and region of analysis (ROI) were positioned at the level of mitral/aortic annular/valvular sclerosis/calcification; the dimensions of ROI were 11 × 11 mm. The mitral/aortic lesions were graded by five qualitative independent scores based on 2D morphology and on acoustic densitometry values: 1= annular/valvular sclerosis/calcification absence; 10–25 dB (fig. 2); 2 = annular/valvular sclerosis; 26–35 dB (fig 3); 3 = annular calcification; 36–40 dB (fig. 4); 4 = annular-valvular calcification; 41–45 dB (fig. 5); 5 = valvular calcification with no recognition of the leaflets; > 46 dB (fig. 6). The resulting patient score was the highest observed for either valvular annulus. All the images were stored in digital format for off-line analysis and independently evaluated by three blinded operators which resulted always concordant in assigning the patient's scores.
Figure 1 Panel A Acoustic quantification-densitometry (AD): echogray scale, black = 0 dB; Panel B echogray scale, white = 64 dB.
Figure 2 Score 1. Panel A: AD of mitral annulus (apical scan); Panel B: AD of aortic valve (parasternal scan).
Figure 3 Score 2. Panel A: AD of mitral valve (apical scan); Panel B: AD of aortic valve (parasternal scan: short axis).
Figure 4 Score 3. Panel A: AD of aortic valve (apical scan); Panel B: AD of mitral valve (apical scan).
Figure 5 Score 4. AD of aortic valve (apical scan).
Figure 6 Score 5. Panel A: AD of aortic valve (parasternal scan: short axis); Panel B: AD of mitral valve (apical scan).
Carotid Ultrasonography
High-resolution B-mode carotid ultrasonography was performed with a linear-array 5- to 10-MHz transducer. The subject lay in the supine position in a dark, quiet room. The right common carotid artery (CCA) was examined with the head tilted slightly upward in the midline position. The transducer was manipulated so that the near and far walls of the CCA were parallel to the transducer footprint and the lumen diameter was maximized in the longitudinal plane. A region 1.5 cm proximal to the carotid bifurcation was identified, and the carotid intima media thickness (cIMT) of the far wall was evaluated as the distance between the luminal-intimal interface and the medial-adventitial interface. cIMT was measured on the frozen frame of a suitable longitudinal image with the image magnified to achieve a higher resolution of detail. The cIMT measurement was obtained from 5 contiguous sites at 1-mm intervals, and the average of the 5 measurements was used for analyses. All the measurements were performed by a single sonographer.
Statistical analysis
All analyses were performed using the SPSS 8.0 package. Data are presented as mean ± SD unless otherwise specified.
Comparison of groups based on different calcification score was made by ANOVA, followed by Bonferroni's test for all two-way comparisons, or by chi-square analysis – as appropriate. Geometric mean values of vascular end points, adjusting for traditional cardiovascular risk factors, were calculated across categorized features by means of General Linear Model.
Results
Of the 102 patients evaluated, 24 were scored 1, 19 were score 2, 20 were score 3, 18 were score 4 and 21 were score 5. There were no statistically significant intergroup differences in age, sex distribution, total cholesterol, HDL and LDL cholesterol, smoking habits, diabetes mellitus, and positive family history of coronary artery disease (tab.1). Similarly, clinical indications for ultrasound examinations were not significantly different in the 5 score groups (tab. 2).
Table 2 Clinical Indications
Score 1 Score 2 Score 3 Score 4 Score 5 All pts
Carotid murmur % 38.2 37.1 39.1 39.9 39.3 38.7
Cardiac murmur % 34.8 34.3 33.8 34.1 35 34.4
Stroke % 15.3 15.6 14.8 15.1 15.4 15.2
Cardiac surgery % 11.7 13 13.3 10.9 10.3 11.8
Systolic blood pressure showed a non statistical increase from group 1 to 5 while pulse pressure values raised significantly (tab. 1; p < 0.04).
Table 1 Baseline characteristics
Score 1 Score 2 Score 3 Score 4 Score 5 All pts ANOVA p
N° of pts 24 19 20 18 21 102
Age yrs 68.4 ± 11.6 65.9 ± 9.8 68.4 ± 4.7 69.3 ± 4.8 65.3 ± 6.8 67.4 ± 7.5 .25
BMI Kg/m2 27.8 ± 5.1 29.7 ± 5.9 29.5 ± 7.8 28.4 ± 4.8 29.6 ± 4.9 29 ± 5.6 .76
Male Sex % 33 30 38 37 48 37 .31
Current Smoker % 33 21 17 32 35 27 .21
CAD Family history % 36 36 23 48 43 37 .42
Diabetes % 10 11 15 19 17 14 .18
SBP mmHg 142.5 ± 12.9 144.6 ± 11.4 140.5 ± 17.5 142.5 ± 16.3 149.1 ± 18.0 143.8 ± 15.2 .07
DBP mmHg 83.8 ± 8.2 84.6 ± 9.7 83.1 ± 10 82.8 ± 9.8 83.1 ± 9.3 83.4 ± 9.4 .29
PP mmHg 58.7 ± 10.4 60 ± 13.8 57.4 ± 11.5 59.7 ± 13.9 66 ± 17.8 60.3 ± 13.5 .03
Total Chol mg/dl 214.6 ± 42.4 204.9 ± 32.7 216.3 ± 40.6 218.3 ± 36.1 209.9 ± 36.4 212.4 ± 37.6 .62
HDL Chol mg/dl 48.4 ± 9.4 48.2 ± 10.7 52.2 ± 13.5 48.7 ± 11.3 46.7 ± 12.0 48.8 ± 11.3 .47
LDL Chol mg/dl 136.4 ± 33.7 126.9 ± 32.6 139.0 ± 37.8 140.1 ± 38.2 130.3 ± 35.9 134.5 ± 35.6 .52
TGC mg/dl 148.6 ± 56.8 149.3 ± 85.5 125.3 ± 49.1 147.3 ± 35.8 164.8 ± 49.7 147.6 ± 55.3 .19
Fasting blood Glucose mg/dl 98.9 ± 29.7 117.1 ± 45.4 113.1 ± 40.6 103.1 ± 21.9 98.0 ± 20.5 106 ± 31.6 .11
Fibrinogen mg/dl 302.1 ± 59.4 302.1 ± 56.4 325.5 ± 103.8 305.7 ± 57.4 323.0 ± 88.9 311.8 ± 73.1 .61
Pts: patients, BMI: body mass index, CAD: coronary artery disease, SBB: systolic blood pressure, DBP: diastolic blood pressure, PP: pulse pressure, Total Chol: total cholesterol, TGC: triglycerides.
Vascular characteristic of the five score groups are shown in Fig 7; mean cIMT increased linearly with increasing valvular calcification score, ranging from 3.9 ± 0.48 mm in controls to 12.9 ± 1.8 mm in those subjects with score 5 (p < 0.0001).
Figure 7 Positive association between cIMT and score groups; *p < 0.0001
ANCOVA analysis confirmed that the association of valvular calcification score with cIMT was independent of age, sex, BMI, HDL and LDL cholesterol, smoker and diabetes (table 3). In the first to fourth quartile of cIMT values the respective maximal percentual of score were: score 1: 76.1%, score 2: 70.1%, score 4: 54.3% and score 5: 69.5% (p > 0.0001) (fig. 8); multivariate analysis showed a significant influence of systolic blood pressure from first to fourth quartile and of HDL cholesterol.
Table 3 ANCOVA analysis
Age Sex BMI C.S HP D HDLC LDLC
p (cIMT) .609 .23 .479 .948 .699 .471 .625 .387
Age: years, BMI: body mass index Kg/m2, C.S: current smoker, HP: hypertension, D: diabetes, HDLC: HDL cholesterol mg/dl, LDLC: LDL cholesterol mg/dl.
Figure 8 Distribution of quartiles of cIMT across scores of valvular calcification; *chi-square = p < .001
Discussion
The present study is the first to show a strong and significant association between the presence of MAC-AVC and cIMT values. Patients with severe MAC-AVC (scored 5) had higher values of cIMT.
Previous pathologic studies demonstrated that foam cells which represent early atherosclerotic lesions can be found in subjects already during adolescence on the endothelium of the epicardial coronary arteries, the ventricular surface of the posterior mitral leaflet and the aortic aspects of each aortic leaflet [1,22]. Experimentally-induced systemic vascular atherosclerosis is also associated with the deposition of fatty plaques on the aortic surface of aortic valve cups and the ventricular surface of the posterior mitral leaflet [22]. These findings suggest that coronary atherosclerosis, MAC and AVC have a similar aetiology and pathophisiology, particularly in the elderly: as the fatty plaques grow, their nutritional needs fail to be fulfilled and they degenerate into calcific deposits[1].
Many recent studies showed a clear association between mitral annulus calcification and the presence of aortic atheromas, atheroma thickness and carotid artery disease [4,5] these studies also found that MAC patients have higher prevalence of carotid artery stenosis [6], coronary artery stenosis [13] and peripheral artery stenosis [8], supporting the theory that MAC is a form of polisegmental atherosclerosis. Adler [23] in a recent prospective trans oesophageal echocardiographic study showed a significant association between the presence and severity of MAC and aortic atheroma, suggesting MAC as an important marker of aortic atherosclerosis; the author concluded that this association may explain in part the high prevalence of systemic emboli and stroke in patients with MAC. Even the presence and extent of AVC has been demonstrated, in many recent studies, to be directly correlated with atherosclerotic risk factors and atherosclerotic disease suggesting how AVC could represent a marker of polisegmental atherosclerosis [2,3,7,24-26].
In our study we investigated the possible association between AVC, MAC and cIMT in elderly patients; we found a clear and strong significant linear correlation between cIMT and AVC/MAC values; there were no significant influence of the considered variables on this correlation. In addition, we found a strong association of incremental values of score with first to fourth quartile of cIMT Another exclusive characteristic of our study is the creation of a semi quantitative way of AVC-MAC evaluation; in fact, scoring the evolution of AVC and MAC, we were able to correlate this parameter with the continuous variable cIMT.
Furthermore, a recent study [26] showed a strong association of aortic valve sclerosis and systemic endothelial dysfunction evaluated by ultrasonography of the brachial artery; since it is well established and demonstrated that IMT is an early marker of endothelial-organ damage and an initial precursor of systemic atherosclerotic disease [1-8] our results are consistent with those obtained by Poggianti and her collegues.
Our data indicate that AVC-MAC could be considered a form of polisegmental atherosclerosis; the semi quantitative evaluation of AVC-MAC is strongly associated cIMT; this semi quantitative way of grading mitral-aortic valvular-annular sclerosis and calcification was also able to identify the quartile of cIMT. These results indicate the score evaluations as an important echocardiographic tool for atherosclerotic disease evaluation.
Limitations of the study
This study evaluated only elderly patients, therefore the eventual correlation of MAC-AVC and cIMT can be only supposed in younger patients; future studies are needed to demonstrate this hypothesis. We viewed cIMT as a marker for sub clinical atherosclerosis. Although the significance of carotid thickening, particularly in the distal common carotid artery, continues to be debated, its association with prevalent and future cardiovascular events has been supported by a number of studies [15-17,24,25]. Nonetheless, it should be recognized that atherosclerosis may progress in other vascular districts at different rates. Further work is need to test if these results may apply to atherosclerosis in other major vascular territories (peripheric big arteries, thoracic and abdominal aorta).
Additionally, whether our findings are sufficient to indicate a widespread use of carotid ultrasound in those with presence of MAC-AVC requires further studies.
Conclusions
MAC-AVC presence and their scoring can be detected by transthoracic echocardiography, a simple noninvasive imaging method. Using MAC and AVC values we can identify subgroups of patients with different cIMT values, a well-established precursor of systemic atherosclerosis, which indicate different incidence and prevalence of carotid, coronary and aortic artery diseases. Therefore, mitral or aortic valve calcification should not be regarded as a natural correlate of aging, rather as markers of generalized atherosclerosis.
List of abbreviations
MAC: Mitral annular calcification
AVC aortic annular calcification
IMT intima media thickness
cIM carotid intima media thickness
CCA right common carotid artery
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| 15471552 | PMC526215 | CC BY | 2021-01-04 16:38:29 | no | Cardiovasc Ultrasound. 2004 Oct 8; 2:19 | utf-8 | Cardiovasc Ultrasound | 2,004 | 10.1186/1476-7120-2-19 | oa_comm |
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Hum Resour HealthHuman Resources for Health1478-4491BioMed Central London 1478-4491-2-131537738210.1186/1478-4491-2-13ReviewImbalance in the health workforce Zurn Pascal [email protected] Poz Mario R [email protected] Barbara [email protected] Orvill [email protected] Department of Human Resources for Health, World Health Organization, Geneva, Switzerland2004 17 9 2004 2 13 13 18 8 2004 17 9 2004 Copyright © 2004 Zurn et al; licensee BioMed Central Ltd.2004Zurn 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.
Imbalance in the health workforce is a major concern in both developed and developing countries. It is a complex issue that encompasses a wide range of possible situations. This paper aims to contribute not only to a better understanding of the issues related to imbalance through a critical review of its definition and nature, but also to the development of an analytical framework. The framework emphasizes the number and types of factors affecting health workforce imbalances, and facilitates the development of policy tools and their assessment. Moreover, to facilitate comparisons between health workforce imbalances, a typology of imbalances is proposed that differentiates between profession/specialty imbalances, geographical imbalances, institutional and services imbalances and gender imbalances.
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Introduction
Imbalance in the health workforce is a major challenge for health policy-makers, since human resources – the different kinds of clinical and non-clinical staff who make each individual and public health intervention happen – are the most important of the health system's inputs [1]. Imbalance is not a new issue, as nursing shortages were reported in hospitals in the United States of America as early as 1915 [2]. It remains a major concern to this day, reported in both developed and developing countries and for most of the health care professions.
Although imbalance in the health workforce is an important issue for policy-makers, various elements contribute to obscuring policy development. First, many reports of shortages are not borne out by the evidence. Rosenfeld and Moses [3] show that an overwhelming majority of newspapers, journals and newsletter articles describing the nursing situation in the United States presume the existence of a shortage. They found that even in those areas where concrete evidence of a shortage was not available, the term "nursing shortage" still appeared. Second, the notion of shortage is a relative one: what is considered a nursing shortage in Europe would probably be viewed differently from an African perspective. Finally, imbalances are of different types and their impact on the health care system varies. In consequence, there is a general need to critically review the imbalance issue.
The objective of this paper is to contribute to a better understanding of the issues related to imbalance through a critical review of its definition and nature and the development of an analytical framework.
Definition
There are various approaches to defining imbalances [4]. From an economic perspective, a skill imbalance (shortage/surplus) occurs when the quantity of a given skill supplied by the workforce and the quantity demanded by employers diverge at the existing market conditions [5]. Labour market supplies and demands for occupational skills fluctuate continuously, so at times there will be imbalances in the labour market. In other words, a shortage/surplus is the result of a disequilibrium between the demand and supply for labour. In contrast, non-economic definitions are usually normative, i.e. there is a shortage of labour relative to defined norms [6]. In the case of health personnel, these definitions are based either on a value judgement – for instance, how much care people should receive – or on a professional determination – such as deciding what is the appropriate number of physicians for the general population.
Nature
One of the key questions regarding imbalances is how long these last: Is the imbalance temporary or permanent? In a competitive labour market, we should expect most imbalances to be resolved over time. Imbalances will tend to disappear faster the greater the reaction speed and also the greater the elasticity of supply (or demand) [7]. This type of imbalance (shortage or surplus) is defined as dynamic.
In contrast, a static imbalance occurs because supply does not increase or decrease; market equilibrium is therefore not achieved. For instance, wage adjustments may respond slowly to shifts in demand or supply as a result of institutional and regulatory arrangements, imperfect market competition (monopoly, monopsony) and wage-control policies. Another example is physicians' education: because of the length of time required to educate physicians, changes in available supply take a long time to react significantly. Lack of information on the state of the various labour markets can also be a factor in the speed of market adjustment. To make proper labour market decisions, households and firms must be informed of the existing market conditions across markets. They must therefore know what wages are paid and the nature and location of job openings and available workers.
Moreover, we should also differentiate between qualitative and quantitative imbalance. In a tight labour market, employers might not find the ideal candidate, but will still recruit someone. Under these conditions, the issue is the quality of job candidates rather than the quantity of people willing and able to do the job [8]. From the employers' perspective, a shortage of workers exists; from the job-market perspective, the existence of a shortage could be questioned because the jobs are filled. One negative hidden impact of a qualitative shortage is the number of positions that are filled with ineffective individuals [9].
A conceptual framework
To better understand the role of factors affecting health workforce imbalances and to facilitate the development of policy tools, a conceptual framework is presented in this section.
Introduction
Factors affecting health workforce imbalances are numerous and complex, but focusing on crucial elements should permit insight into the issue of health workforce imbalances. The framework is depicted in Fig. 1 and contains six main components: the demand for health labour, the supply of health labour, the health care system, policies, resources and "global" factors.
Figure 1 Framework for imbalance of human resources for health
Central to this framework are the demand for and supply of health labour. Also included in the framework is the health care system, and in particular, some of its features that are likely to have an impact on health workforce imbalances. Policies constitute another crucial element of the framework. In effect, health policies but also non-health-oriented policies can have an impact on health workforce imbalances. The framework also incorporates financial, physical and knowledge resources that contribute to model the health workforce.Finally, "global" factors such as economic, sociodemographic, political, geographical and cultural factors are included. These elements contribute directly or indirectly to shaping and transforming the entire society and hence the health workforce.
The demand for health personnel
The first element of the framework to be examined is the demand for health personnel. The demand for health personnel can be considered as a derived demand for health services. Accordingly, we should consider factors determining the demand for health services. Personal characteristics – such as health needs, cultural and sociodemographic characteristics – and economic factors play an important role.
It has often been proposed that the planning of human resources for health be based solely on estimates of health needs in the population [10]. However, relying only on the concept of need is difficult, because it can be defined either broadly or restrictedly and accordingly lead to a perception of either systematic shortage or surplus. Health needs is only one of the factors affecting the demand for health personnel.
Several studies have attempted to estimate the impact of economic factors on the demand for health care. In particular in the United States, studies have attempted to estimate price and income elasticities of demand for medical services [11-13]. Measurements of price or income elasticities make it possible to evaluate the impact of a change in price or income on the demand for health care. Most studies reported elasticities in the range between 0.0 and -1.0, indicating that consumers tend to be responsive to price changes but that the degree of price sensitivity is not very large compared to that for many other goods and services [14].
Another element influencing the demand for health care is the value of a patient's time, such as travel time and waiting time. Acton [15] found that in the United States, elasticity of demand with respect to travel time ranged between -0.6 and -1, meaning that a 10% increase in travel time would induce a reduction of 6%-10% in the demand for health care.
Other factors affect the demand for health labour. In particular, some specific features of the health care system and its features, policies, resources and environmental factors do have an impact on the demand for health labour. Their respective role will further discussed later.
The supply of human resources for health
After reviewing factors affecting the demand for health labour, we shall now turn to those affecting the supply of the health workforce. In particular, we shall consider the following elements: factors affecting the choice for a health professional training/education, participation in the health labour market and migration.
Education/professional training choice
The availability of a renewed health workforce, as well as the type of profession and specialty chosen by individuals, is a major concern for health decision-makers. These issues are of particular relevance, especially since the number of younger people, predominantly women, choosing a nursing career is declining in some countries and since in professional training/education, individuals' choices do not always match the absorptive capacity of the market.
From an economic perspective, the decision to undertake professional training/education is considered an investment decision. To emphasize the essential similarities of these investments to other kinds of investments, economists refer to them as investment in human capital [16]. Since investment decisions usually deliver payoffs over time, we must consider the entire stream of costs and benefits. The expected returns on human capital investments are a higher level of earnings, greater job satisfaction over one's lifetime and a greater appreciation of non-market activities and interests.
Based on the human capital approach, rate of return on education can be estimated. An average rate of return that is high and rising for a given profession will attract more individuals to that profession. On the other hand, a lower and decreasing average rate of return will discourage individuals from choosing that profession. Nowak and Preston [17], using the human capital approach, found that Australian nurses are poorly paid in comparison to other female professionals. The declining interest in nursing can be partly explained by the expansion of career opportunities in traditionally male-dominated occupations over the last three decades that entail a higher rate of return [18]. The number of young women entering the registered-nurse workforce has declined because many women who would have entered nursing in the past – particularly those with high academic ability – are now entering managerial and professional occupations that used to be traditionally male.
Besides the human capital approach, the choice of a profession can also be explained by sociopsychological factors. For instance, individuals may choose a profession because it is highly valued by the society or for family tradition. In the health sector, the satisfaction afforded by caring for people and assisting them to improve their health is an important element used by nursing schools to attract new enrollees. In the light of this approach, the decline in the number of individuals choosing nursing as a career might also be explained by the fact that this profession is now less socially valued than before [19,20].
Participation in the labour market
The economic theory of the decision to work views the decision as a choice concerning how people spend their time. Individuals face a trade-off between labour and leisure. They decide how much of their time to spend working for pay or participating in leisure activities, the latter being activities that are not work-related.
An issue that has drawn a lot of attention recently is the impact of wage increases on labour participation, in particular for nurses. In the short term, higher wages can have at least two effects on the labour supply of current qualified nurses: first, qualified nurses who are working in other occupations may return to nursing activities; second, nurses now in practice may respond by working more hours. In the long run, higher wages in nursing relative to other occupations make nursing an attractive profession and will draw more people into nurse training programmes.
In their literature review of wage elasticity of nursing labour supply, Antonazzo et al. [21] and Chiha and Link [22] found that most of the studies indicate a positive relationship, although not a strong one, between wages and labour supply. Accordingly, increases in nursing wages are unlikely to cause significant increases in labour participation. A literature review on the women's workforce undertaken by Killingsworth and Heckman [23] indicated that in addition to wage rate, women's participation is responsive to changes in unearned income, spouse's wage and having children (particularly of pre-school age). Another aspect of labour supply decisions that has been investigated by Philips [24] is the costs associated with entering the nursing labour market (such as costs of child care and housework). The elasticity of participation with respect to changes in working costs was evaluated at -0.67 for all nurses. This suggests that a subsidy leading to a decrease of 10% in these costs would increase the participation of nurses by 6.7%.
Moreover, hospitals are also using a variety of strategies to recruit new staff. A survey of hospitals in the United States shows that richer benefits, such as health insurance and vacation time, are the most common incentives used. In addition, hospitals may offer other recruitment and retention benefits, such as tuition reimbursement, flexible hours and signing bonuses based on experience or length of commitment [25]. Many countries, but particularly developed ones, use such incentives to recruit new staff.
Economic factors also play a role in physician's participation in the labour market, as demonstrated by the impact of cost-containment policies in Canada, where most provincial governments have implemented an assortment of controls of health care expenses. Threshold reductions were introduced, so that fees payable to individual physicians were reduced as billing exceeded an agreed threshold. As a consequence, physicians who had billed at the threshold level chose to take leaves of absence rather than receive a level of reimbursement they considered inadequate [26].
When health personnel choose an alternative or additional occupation, this is likely to have consequences on health labour supply. In developing countries, and particularly in Africa, attempts to reform the health care sector have frequently failed to respond to the aspirations of staff concerning remuneration and working conditions. Salaries are often inadequate and may be paid late, and health workers try to solve their financial problems in a variety of ways [27]. Private practice is only one of the many survival strategies that health personnel use to supplement their income and increase their job satisfaction. Teaching, attending training courses, supervision activities, research, trade and agriculture are some of these alternative strategies [28].
Labour market exit
Parker and Rickam [29] examined the economic determinants of the labour force withdrawal of registered nurses in the United States, i.e. nurses leaving the profession to pursue a non-nursing occupation and employed nurses withdrawing from the labour force. Their results suggest that a significant number of registered nurses withdraw, at least temporarily, from the labour force. Among the significant elements influencing the withdrawal decision are the wage rate, other family income, presence of children and full-time/part-time work status. Increasing registered nurses' wages and working full-time is expected to reduce the probability of labour force withdrawal, whereas higher education levels, age and other family income increase the probability of labour force withdrawal.
The relative importance of wage is also emphasized by studies investigating job satisfaction. There is support in the empirical literature for the existence of job dissatisfaction among nurses, and the link between job dissatisfaction and job exit [30,31]. In the United States the most important factors in nurses' resignation were, in order of importance: workload, staffing, time with patients, flexible scheduling, respect from nursing administration, increasing nursing knowledge, promotion opportunities, work stimulation, salary and decision-making. These studies suggest that salary is just one of the reasons why nurses are quitting. The relative importance of wage is confirmed by Shields and Ward [32]. Their results suggest that dissatisfaction with promotion and training opportunities has a stronger impact than workload or pay.
Migration
Migration of health personnel can have a serious impact on the supply of human resources in health, because it may exacerbate health personnel imbalances in "sending" countries. It is suggested that migration is an "individual, spontaneous and voluntary act" that is motivated by the perceived net gain of migrating – that is, the gain will offset the tangible and intangible costs of moving [33]. Decisions to migrate are often a family strategy to produce a better income and improve survival chances [34].
The reality for many health workers in developing countries is to be underpaid, poorly motivated and increasingly dissatisfied and sceptical [35]. The relevance of motivation to migration is self-evident. There can be little doubt that for many health workers an improvement in pay and conditions will act as an incentive to stay in the country. Improved pensions, child care, educational opportunities and recognition are also known to be important [36-38]. In Ghana it is estimated that only 191 of the 489 doctors who graduated between 1985 and 1994 were still working in the country in 1997 [39].
Health system characteristics
As the health workforce is part of the health care system, we shall also consider features of the health care system that are likely to have an impact on the demand and supply of health labour. In particular, we shall examine market failures, the diversity of stakeholders, the supply-demand adjustment time lag and hospitals' potential monopsony power.
Market failures
From an economic perspective, the health care market is characterized by market failures – that is, the assumptions for perfect competition are violated. From a societal perspective, in the presence of market failures such as externalities – imperfect knowledge, asymmetry of information and uncertainty – market mechanisms lead to a non-optimal demand and/or supply in health services. In other words, shortages and surpluses are likely to result from the health care market.
Most markets are characterized by market failures, but what is unique to the health services market is the extent of these market failures [40]. Governments try to correct health care market failures through policy interventions. A classic example of public intervention in the presence of a positive externality is the introduction of a policy of mandatory vaccination. However, implementing such policies is sometimes difficult and may result in only partial correction of the market failures.
Stakeholders
The health care system is characterized by a wide range of institutional stakeholders involved in shaping human resources for health, all of whom may have different objectives [41,42]. The objectives of a union or professional association do not necessarily coincide, for example, with those of a government ministry, a hospital manager or the central government. Unions/professional associations seek to increase their members' market power, employment and income [43], whereas the ministry of finance will want more budget equilibrium and will favour measures to limit health care expenditures.
In the case of Mozambique, whereas the policy of employing national professionals by cooperation agencies has met with warm support from national cadres, its effect on the health sector is problematic [44]. The prospect of immediate financial gains puts pressure on qualified professionals to leave their posts within the Mozambique National Health Service to take up management or consultant positions. The substantial investment in their training is therefore producing dubious direct returns to the National Health Service. More seriously perhaps, the presence of donor-paid jobs outside the health sector (as programme coordinators, researchers, etc.) is creating pressure on the Ministry of Health itself, exacerbating the imbalances in the National Health Service and creating incentives for trained Mozambicans to leave the public sector.
Time lag
Moreover, adjustments between the demand and supply for health personnel may take a long time. In the health care field; the time lag between education and practising may be quite substantial. To obtain licensure to practise medicine requires lengthy education and training, and the long lag time between a changed student intake and a change in supply has been noted [45]: supply adjustment for physicians is not immediate, but takes a long time.
Hospitals' potential monopsony power
A single entity that is the sole purchaser of labour is a monopsony. One example is the potential monopsony power of hospitals in hiring nurses or the ministry of health in hiring the health workforce. The amount of labour demanded will influence the price the monopsonist must pay for it. In contrast to the situation in a competitive market, the monopsony is a price maker, not a price taker. Monopsony results in lower wages and lower employment of nurses compared to a competitive market.
A number of studies have tested whether or not hospitals possess monopsony power with respect to nurses, and the results are contradictory. Sullivan [46] and Staiger et al. [47] concluded that hospitals have a substantial degree of monopsony power. In contrast, Hirsch and Schumacher [48] did not find empirical support for the monopsony model. Nurses' wages were found not to be related to hospital density and to decrease rather than increase with respect to labour market size.
Provider power/monopoly
In contrast, providers' power may enable the latter to restrict the supply of human resources for health. Seldon, Jung and Cavazos [49] suggest that physicians in the United States have market power through such avenues as restricting supply and price-fixing. In France, trade unions are granted an institutional role at establishment level [50]. In India and Sri Lanka, a clear constraint to support-services contracting was the inability to counter the power of the public service unions in dictating employment terms and conditions [51]. The varying degree of homogeneity of the different professional groups may also explain their relative success in maintaining a monopoly of practice. In Iceland, one of the factors that contributed to breaking the professional monopoly of pharmacists was division within the profession [52].
Regulations
The type off regulation associated with a profession plays an important role regarding the supply of members of a profession. Regulation has, by tradition, been achieved through a combination of direct government regulation and, to a large extent, through rules adopted by professional associations, whose self-regulatory powers enable them to establish both entry requirements and rules regarding professional conduct [53].
Such barriers to entry exist in particular for doctors, but also in other health professions, such as dentistry. Some argue that these barriers constitute a means to limit entry into the profession, and hence maintain high incomes. Muzondo and Pazderka [54] established, for Canadian professional licensing restrictions, a relationship between different variables of self-regulation and higher income. Seldon et al. [55] suggest that physicians in the United States have market power through such sources as restricting supply and price-fixing. However, the proponents of self-regulation claim that these barriers are a means to provide health care of quality and to protect patients from incompetent providers. In contrast, although most countries have a professional nursing association, nurses tend to have limited power to regulate entry to the profession. This could be associated with a large diversity of specialist groups in nursing failing to unite on issues related to professional regulation [56].
Health and non-health policies
Health and non-health policies contribute to shaping the health care system and have an influence on the demand and supply of health labour.
Health policy can be defined as a formal statement or procedure within institutions (notably government) that defines priorities and the parameters for action in response to health needs, available resources and other political pressures. Health policy is often enacted through legislation or other forms of rule-making that create regulations and incentives for providing health services and programmes and access to them. For instance, the decision to introduce or expand health insurance coverage is likely to have an impact on the demand for health services. This is illustrated by the RAND Health Insurance Experiment, a controlled experiment that increased knowledge about the effect of different insurance copayments on use of medical services. Insurance copayments ranged from zero to 95%. The RAND study concluded that as the co-insurance rose, overall use and expenditure fell for adults and children combined [57].
Non-health policies reflect state interventions in areas such as employment, education and regional development that contribute to shaping the health workforce. These policies do not directly address health issues, but have an indirect impact on such issues. In France, a controversial new regulation was introduced that reduced the workweek to a maximum of 35 hours in an attempt both to create hundreds of thousands of new jobs and to achieve greater flexibility in the labour force. Unions responded by demanding the creation of more posts in public hospitals.
Financial, physical and knowledge resources
Financial, physical and knowledge resources are crucial to any type of health care workforce. The level of resources attributed to the health care system, and how these resources are used, will have a significant impact on health workforce issues.
In terms of financial resources, human resources account for a high proportion of national budgets assigned to the health sector [58]. Health expenditure claims an increasingly important share of the gross domestic product and, in most countries, wage costs (salaries, bonuses and other payments) are estimated to account for between 65% and 80% of the renewable health system expenditure [59,60].
Physical resources include human resources within the health sector and other sectors; buildings and engineering services such as sanitation, water and heating systems for community use and for the use of medical care institutions; and equipment and supplies. Finally, the health workforce is also constrained by its human capital. This human capital can be associated with the qualification and education of the health workforce. Education of the health workforce is the systematic instruction, schooling or training given in preparation for work.
Global factors
Economic, sociodemographic, cultural, and geographical factors contribute to shaping and transforming society and hence have a direct or indirect impact on health workforce issues.
From an economic perspective, for instance, there is evidence of a correlation between the level of economic development of a country and its level of human resources for health. Countries with higher GDP per capita are said to spend more on health care than countries with lower income, as demonstrated by cross-sectional studies, [61] and hence would also tend to have larger health workforces.
Moreover, both the demand and supply are likely to be affected by sociodemographic elements such as the age distribution of the population. On the demand side, the ageing of the population is giving rise to an increase in the demand for health services and health personnel, especially nurses for home care.
On the supply side, the ageing of the health workforce, and in particular of nurses, has serious implications for the future of the nursing labour market. For example, the Institute of Medicine noted that older registered nurses have a reduced capacity to perform certain tasks [62]. It was found that between 1983 and 1998 the average age of practising registered nurses increased by more than four years, from 37.4 to 41.9 years [63]. In contrast, the average age of the United States workforce as a whole increased by less than two years during the same period. Furthermore, the proportion of the registered-nurse workforce younger than 30 years decreased from 30.3% to 12.1% during this period.
Geographical and cultural factors also play a role in determining the demand and supply of human resources. Geographical characteristics affect the organization of health services delivery. For instance, a country with many islands or with isolated population groups will face particular challenges in terms of health workforce issues. Similarly, significant climatic changes are likely to give rise to changes in health needs, which in turn will call for changes in health services and in the health workforce. Finally, both cultural and political values also affect the demand for and supply of human resources for health.
Health workforce imbalances: a typology
This section considers a typology of imbalances, and differentiates between the following:
• Profession/specialty imbalances: Under this category, we consider imbalance in the various health professions, such as doctors or nurses, as well as shortages within a profession, e.g. shortage of one type of specialists.
• Geographical imbalances: These are disparities between urban and rural regions and poor and rich regions.
• Institutional and services imbalances: These are differences in health workforce supply between health care facilities, as well as between services.
• Gender imbalances: These are disparities in female/male representation in the health workforce.
Profession/specialty imbalances
Imbalances have been reported for almost all health professions, and in particular for nurses. The United States General Accounting Office [64] reports a nursing shortage. However, the nursing shortage has not been institution-wide but is concentrated in specialty care areas, particularly intensive care units and operating rooms [65]. The shortage of registered nurses in intensive care units is explained in part by the sharp decline in the number of younger registered nurses, whom intensive care units have historically attracted. Shortages in operating rooms probably reflect that many registered nurses who work in this setting are reaching the age when they are beginning to reduce their hours worked or are retiring altogether.
Major variations occur in the number of health care workers per capita population and in the skill mix employed across countries, as depicted in Fig. 2. The nurse/doctor ratio varies widely from one country to another, as shown in Fig. 2. The nurse/doctor skill mix is important and may have consequences for the respective tasks of nurses and doctors [66]. It is also interesting to note that these variations are taking place among countries with a relatively similar economic development level.
Figure 2 Distribution of physicians, nurses, midwives, dentists and pharmacists in selected countries. WHO data, 2000.
Geographical imbalances
Virtually all countries suffer from a geographical maldistribution of human resources for health, and the primary area of concern is usually the physician workforce [67]. In both industrialized and developing countries, urban areas almost invariably have a substantially higher concentration of physicians than rural areas.
Understandably, most health care professionals prefer to settle in urban areas, which offer opportunities for professional development as well as education and other amenities for themselves and their families. But it is in the rural and remote areas, especially in the developing countries, that most severe public health problems are found.
The geographical maldistribution of doctors has been the object of particular attention. In general there is a higher concentration of general practitioners in the inner suburbs of the metropolitan areas. According to the Australian Medical Workforce Advisory Committee [68], the reasons for high concentration of general practitioners in inner city areas are:
• historical
• lifestyle-related: access to amenities
• spouse/husband-related: greater employment opportunities
• child-related: better access to secondary and tertiary education services
• professional, family and social ties and professional ambitions.
The geographical distribution of health care personnel is an important issue in many countries. Managua, the capital of Nicaragua, contains one-fifth of the country's population but around half of the available health personnel [69]. In Bangladesh, most of the doctors (35%) and nurses (30%) in health services are located in four metropolitan districts where only 14.5% of the population lives [70]. This concentration pattern is characteristic of developing countries.
In Indonesia the geographical distribution of physicians is a particular concern, since Indonesia's vast size and difficult geography present a tremendous challenge to health service delivery [71]. It is difficult to place doctors in remote islands or mountain or forest locations with few amenities, no opportunities for private practice, and poor communications with the rest of the country.
To improve the geographical distribution of physicians, governments often have used combinations of compulsory service and incentives. So far, there is virtually no country in the world that has solved the problem of a rural/urban imbalance of the physician workforce [67]. This does not necessarily mean that policies and programmes designed to reduce the imbalance have had no effect. For example, Thailand has successfully begun to stem the migration of health professionals from rural to urban areas and from public to private facilities with a range of strong financial incentives [72].
Institutional and services imbalances
Institutional imbalances occur when some health care facilities have too many staff because of prestige, working conditions, ability to generate additional income, or other situation-specific factors, while others are understaffed [73]. Institutions such as magnet hospitals, for example, are hospitals characterized by adequate to excellent staffing, low turnover, rich nursing skill mix and greater job satisfaction, among other factors, even in the face of a general health personnel shortage [74].
Imbalance between the types of health services provided may also arise. In particular, we can consider the issue of curative versus preventive care. In effect, it has been estimated that most diseases (80%) and accidents are preventable through known methodologies, yet at present there is an imbalance in the funding of medical research, with only 1%-2% going to prevention and 98%-99% spent on curative approaches [75]. This imbalance in funding raises the question of a health workforce imbalance between preventive and curative care.
Gender imbalances
In many countries, women still tend to concentrate in the lower-status health occupations and to be a minority among more highly trained professionals and managers. In Bangladesh, the distribution by gender of the health workforce shows that the total proportion of women accounts for little more than one-fifth in health services [76]. The distribution of women by occupational category is biased in favour of nurses. Women are very poorly represented in other categories, such as dentists, medical assistants, pharmacists, managers/trainers and doctors. The underrepresentation of women in managerial and decision-making positions may lead to less attention to and poorer understanding of the problems specific to women and the particularities of their utilization patterns [77].
Female general practitioners have been shown to practise differently from males, managing different types of medical conditions, with some differences due to patient mix and patient selectivity, and others inherent in the sex of physician. In some more traditional areas, some women will not seek care for themselves or even for their children because they do not have access to a female provider [76].
Discussion
This framework can be used to assess policy reforms and their impact on health workforce imbalances; it also provides a common framework for cross-country comparisons. This framework emphasizes the number and type of factors affecting health workforce imbalances, illustrating the complexity of this issue. From a policy perspective, it is particularly interesting to identify factors that policy-makers can influence in order to remedy imbalance problems.
Various monetary and non-monetary incentives are used to influence the supply and/or demand for the health workforce. For example, subsidies, grants and scholarships are examples of incentives that can be used to attract more nursing students, whereas wage increases, additional benefits and working hours flexibility are examples of commonly used incentives to attract or retain the health workforce.
The numerous factors and actors involved in the health workforce imbalance issue call for a coherent health workforce vision and policy. In that context, health planning plays an important role since it contributes to shaping the health care system. Moreover, since from a societal perspective market mechanisms alone do not allow an adequate demand/supply of health personnel to be reached, public interventions such as human resources planning are a means to correct for market failures. Health planning involves a time horizon. Forecasting the future number of health personnel needed and developing policies to meet such figures are common to any health care system. Physicians represent the profession for which more planning effort has been expended to achieve a workforce of appropriate size than for any other health profession. Countries' desire to meet population health needs and to avoid social welfare losses resulting from a shortage or an oversupply are factors explaining, to a large extent, the importance attributed to planning in the context of public health policies.
The policy implications of forecasting either a shortage or a surplus of health care personnel are different, and hence attempts at projections must be rigorous. For instance, referring to previous studies predicting significant surpluses, Cooper [78] notes that such large surpluses have not occurred so far, because of a decrease in physician work effort. Factors such as age, sex and lifestyle contributed to this evolution.
As a result of forecasted physician surpluses, various policy recommendations have been formulated. The United States Institute of Medicine [79] published a report recommending, among other things, that there be no new medical schools, that existing schools should not increase their class size and that the number of first-year residency positions should be reduced. The Pew Health Professions Commission Report [80] issued a report recommending more severe steps, such as the closing of some medical schools and tightening the visa process for international medical graduates.
This framework also apprehends the different types of imbalances. This is important since the choice of a policy will also depend on the type of imbalance. Significant disparities in human resources for health between health occupation, regions, gender or health services are recognized as classic problems of imbalance. However, the question of a public/private imbalance is more debatable. One the one hand, we can argue that for equity and access, a health care system should have a strong public component. On the other hand, we can imagine a private-sector oriented health care system with mechanisms to ensure access to the poor.
Conclusion
In an attempt to contribute to a better understanding of imbalances in the health workforce, this paper has discussed a framework for human resources for health and proposed a typology of imbalances. Although the term "imbalance" is commonly used with respect to the health workforce, it is clear that imbalance in the health workforce encompasses a wide range of possible situations and is a complex issue. The use of a framework should facilitate the development of policy tools and their assessment.
Competing interests
None declared.
Authors' contributions
All authors participated in writing the original text and read and approved the final manuscript.
Acknowledgements
Many thanks are due to Eivind Hoffmann, International Labour Office, Geneva; Gilles Dussault, World Bank Institute, Washington DC; and Bill Savedoff and Guy Carrin, World Health Organization, Geneva, for their useful comments and suggestions.
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| 15377382 | PMC526216 | CC BY | 2021-01-04 16:37:43 | no | Hum Resour Health. 2004 Sep 17; 2:13 | utf-8 | Hum Resour Health | 2,004 | 10.1186/1478-4491-2-13 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-311546267810.1186/1742-4690-1-31ResearchReduced proviral loads during primo-infection of sheep by Bovine Leukemia virus attenuated mutants Debacq Christophe [email protected] Alcaraz Maria Teresa [email protected] Franck [email protected] Pierre [email protected] Richard [email protected] Luc [email protected] Molecular and cellular biology, FUSAGx, Gembloux, Belgium2 Unité d'Oncogenèse Virale, CNRS UMR5537, Centre Léon Bérard, Lyon, France3 Department of Virology, Veterinary and Agrochemical Research Centre, Uccle, Belgium2004 5 10 2004 1 31 31 23 6 2004 5 10 2004 Copyright © 2004 Debacq et al; licensee BioMed Central Ltd.2004Debacq 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 early stages consecutive to infection of sheep (e.g. primo-infection) by Bovine leukemia virus mutants are largely unknown. In order to better understand the mechanisms associated with this period, we aimed at analyzing simultaneously three parameters: B-lymphocytosis, cell proliferation and viral replication.
Results
Sheep were experimentally infected either with a wild type BLV provirus or with selected mutants among which: a virus harboring an optimalized LTR promoter with consensus cyclic AMP-responsive elements, two deletants of the R3 or the G4 accessory genes and a fusion-deficient transmembrane recombinant. Seroconversion, as revealed by the onset of an anti-viral antibody response, was detected at 3 to 11 weeks after inoculation. At seroconversion, all sheep exhibited a marked increase in the numbers of circulating B lymphocytes expressing the CD5 and CD11b cluster of differentiation markers and, interestingly, this phenomenon occurred independently of the type of virus. The net increase of the absolute number of B cells was at least partially due to accelerated proliferation as revealed, after intravenous injection of bromodeoxyuridine, by the higher proportion of circulating BrdU+ B lymphocytes. BLV proviral DNA was detected by polymerase chain reaction in the leucocytes of all sheep, as expected. However, at seroconversion, the proviral loads were lower in sheep infected by the attenuated proviruses despite similar levels of B cell lymphocytosis.
Conclusions
We conclude that the proviral loads are not directly linked to the extent of B cell proliferation observed during primo-infection of BLV-infected sheep. We propose a model of opportunistic replication of the virus supported by a general activation process of B lymphocytes.
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Background
Bovine leukemia virus (BLV) is an oncogenic retrovirus closely related to the primate T-cell leukemia viruses [1]. These viruses are exogenous to their host species [2,3], have similar genomic organizations [4], integrate into dispersed sites within the host genome [5,6] and appear transcriptionally silent in vivo (reviewed by [7]). However, BLV is unique in the HTLV family of retroviruses because it infects and dysregulates B lymphocytes instead of T cells. The natural host for BLV is cattle but the virus can also be experimentally transmitted to sheep [8]. The pathogeneses in these species are globally similar despite higher frequencies of leukemogenesis in sheep (up to 100%) and shorter latency periods (1–4 years versus 4–10 in cattle) [1]. Following BLV infection, the hosts, either cattle or sheep, develop a persistent antibody response to viral proteins and virions can be isolated from ex vivo cultured leucocytes [9]. Detection of antibodies in the infected animals correlates with a transient B cell lymphocytosis [10-13]. In most cases, BLV infection remains clinically silent, a stage referred to as the asymptomatic or aleukemic stage of the disease [7]. Only 30% of BLV-infected cattle develop persistent lymphocytosis (PL), a polyclonal expansion of B cells coexpressing high levels of surface IgM, myeloid (CD11b) or T-specific (CD5) markers [14-16] and less than 5% will die from a fatal leukemia, lymphoma or lymphosarcoma [17].
A major advantage of the BLV system is the possibility to study viral genetic determinants in relation with infectivity and pathogenicity in vivo. A strategy, which we previously described, is based on the use of a cloned BLV provirus whose sequence can be mutagenized in vitro. Well characterized mutants can subsequently be injected into sheep and compared to the wild type virus (WT) [18]. This experimental protocol permitted the correlation of viral determinants with defined phenotypes in vivo. In particular, we showed that:
(i) the R3 and G4 accessory genes are required for efficient viral spread in vivo, although their deletion or mutation does not hamper infectivity (mutants CRX3 and IG4 described by [1,19,20]).
(ii) restoring a CRE consensus (cyclic-AMP response element; TGACGTCA) in the triplicate motif of the imperfectly conserved Tax-responsive sequence (TxRE: AGACGTCA, TGACGGCA, TGACCTCA) increases LTR promoter activity, as expected, but restricts the proviral loads in vivo, suggesting that repression of expression is required for immune escape (CRE mutant; [21]).
(iii) formation of multinucleated syncytia by envelope dependent cell fusion in vitro is paradoxically not required for infectivity or efficient viral spread in vivo (A60V recombinant; [22]).
Importantly, only the A60V envelope mutant behaves as wild type in terms of infectivity and pathogenesis, in contrast to the others (CRX3, IG4 and CRE) therefore referred to as attenuated. The two categories of viruses, either wild type (WT and A60V) or attenuated (CRX3, IG4 and CRE) thus permit to characterize and compare the processes occurring during primo-infection.
Results
The extent of transient lymphocytosis is similar in sheep infected by wild type or mutant proviruses
BLV recombinants with optimized consensus CRE sequences (CRE clone) and mutants deleted in the R3 and G4 genes (CRX3 and IG4 proviruses) are impaired in their ability to propagate efficiently within their host [1,19,21]. The early stages occurring soon after infection by these mutants and, more particularly, the extent of the transient B lymphocytosis are however unknown. Therefore, sheep were infected with well-characterized molecular clones of BLV proviruses. As control, the hematological parameters were first determined in uninfected animals, as illustrated on figure 1A (sheep 4533 BLV-). To this end, PBMCs (Peripheral Blood Mononuclear Cells) were isolated and analyzed by flow cytometry for the presence of IgM, CD4 and CD8 markers. Despite some variations in the absolute numbers of leucocytes, the B (squares), CD4+ (triangles) and CD8+ (crosses) T cell populations remained remarkably constant over extended periods of time, as expected (Figure 1A and data not shown for sheep 4534). In contrast, a marked increase of the absolute numbers of leucocytes occurred between 2 and 3 weeks post-inoculation of the wild type BLV provirus (from 6,331 103/ml to 10,262 103/ml, respectively, at days 21 and 28 in sheep 4536 BLV+ WT, Figure 1A; and data not illustrated for animal n° 4535).
Figure 1 Transient B cell lymphocytosis at seroconversion of sheep infected by BLV-mutants. A. Sheep n° 4536 was injected with 100 μg of proviral DNA (wild type strain 344 cloned into plasmid pBLV344) whereas animal n° 4533 was used as a negative control. At different times post-injection, leucocytes were counted using a Coulter Counter ZN and the total numbers of lymphocytes were estimated after examination under a microscope. In parallel, Peripheral Blood Mononuclear Cells (PBMCs) were isolated by Percoll gradient centrifugation and the proportions of B, CD4+ T and CD8+ T-cells were determined by flow cytometry. The vertical line represents the detection by immunodiffusion of antibodies directed against the virus (day 28: sheep n° 4536). B. Graphic representation of absolute B cell numbers at viral inoculation (Day 0, yellow bars) and at seroconversion (grey bars) of sheep infected by the BLV wild type virus (WT) or mutants (IG4, CRX3, CRE and GP30). The sheep coordinates are indicated. C. Fold increase of the B cell population in the infected sheep. The bars correspond to the mean difference (± standard deviation) between the absolute numbers of B cells at the day of virus inoculation and those determined at seroconversion (means of three values).
Phenotypic analysis of the sheep n° 4536 PBMCs revealed that leucocytosis is essentially due to a marked increase in the B cell numbers (from 2,294 103/ml of blood to 4,858 103/ml, respectively, at days 21 and day 28; see squares), while the absolute counts of CD4+ (triangles) and CD8+ (crosses) T cells remained relatively constant (as did the γδ T cell population, data not shown). Furthermore, maximal B cell accumulation corresponded to the day of seroconversion characterized by the onset of an anti-BLV humoral response (the vertical line at day 28 on Figure 1A).
A similar experiment was performed in parallel with 4 selected BLV mutants (IG4, CRX3, CRE and GP30), the latter behaving as wild type in terms of pathogenesis and infectivity in vivo. Interestingly, B cell lymphocytosis occurred in all sheep independently of the type of provirus (Figure 1B, compare absolute numbers of B cells at day of seroconversion). A rate of induction was calculated by dividing the absolute numbers of the B cell population around seroconversion with those measured at the day of proviral injection (day 0). This induction rate (represented in fold increase on Figure 1C) was independent of the type of provirus, no significant difference being observed between the wild type and the mutants.
Expression of the CD5 and CD11b cluster of differentiation markers has been associated with BLV-infection, although B lymphocytes negative for these receptors are also less efficient targets for the virus [23,24]. Therefore, we aimed to determine if lymphocytosis detected at the seroconversion period is preferentially due to an accumulation of B+CD5+ and/or B+CD11b+ cells. For this purpose, PBMCs were dually labeled with an anti-IgM antiserum together with anti-CD5 or anti-CD11b monoclonal antibodies and analyzed by two-color flow cytometry (illustrated on Figure 2A for BLV wild type infected animal n° 4535: x axis = CD5 or CD11b labeling; y axis = B labeling). At day 0, two third of the B lymphocytes expressed CD5 (3,119 in a total of 1,585 + 3,119 B cells) whereas, at seroconversion, most of them became positive for this marker (6,906 cells versus 1,273). In parallel, the number of B+CD11b+ cells also drastically increased (3,547 cells at day 0 versus 7,526 at the seroconversion day). Of note, although most B lymphocytes should be CD5 and CD11b double positive cells, this assumption could not be formally demonstrated because of cross reactions between two IgG1 isotypes. However, the dot plots at right on the figure 2A clearly show that the majority of B cells should express both markers.
Figure 2 The lymphocytosis is due to an accumulation of B lymphocytes expressing CD5 and /or CD11b. A. PBMCs were isolated from BLV wild type infected sheep (n° 4535) and labeled with anti-sIgM 1H4 monoclonal in combination with CD5 or CD11b antibodies. Ten thousand cells analyzed by flow cytometry are represented as dot plots (Y axis = B cells; X axis = CD5 or CD11b expressing cells). Illustrated data correspond to the dot plots performed at day 0 (provirus injection) and at the seroconversion day. The total numbers of B cells are indicated in the upper quadrants. B. Histogram representation of the absolute numbers of B cells expressing (right panels) or not (left panels) CD5 or CD11b in sheep infected with wild type viruses (n° 4535 and 4536), or mutants (pBLVIG4 in n° 4537 / 4538, pBLVCRX3 in n° 4539 / 4541, pBLVCRE3X in n° 4542 / 4543, pBLVA60V in n° 4544 / 4545). Sheep n° 4533 and 4534 were used as uninfected controls. * represents the absolute number of cells at day 24 of the experiment.
In terms of absolute cell counts (i.e. normalized to the cell numbers per ml of blood), it appeared that B+CD5+ or B+CD11b+ lymphocytes accumulated at the seroconversion day in wild type infected sheep 4535 and 4536 (compare yellow and shaded bars on the right panels of Figure 2B). This accumulation did not happen in the CD5- or CD11b-negative B cell populations (left panels) or in negative controls (uninfected sheep n° 4533 and 4534), although a relative increase was observed in some infected animals (particularly 4542 and 4543, Figure 2B, B+CD5-). Importantly, the net increase of sIgM+CD5+ or sIgM+CD11b+ populations also occurred in animals infected by the different mutants independently of the type of provirus (Figure 2B).
Together, these data demonstrate that primo-infection in sheep experimentally infected either with a wild type or mutant BLV proviruses is associated with a transient lymphocytosis, extending previous reports [11,12]. At seroconversion, all sheep exhibited a marked increase in the numbers of circulating B lymphocytes expressing for most of them the CD5 and CD11b cluster of differentiation markers and, interestingly, this phenomenon occurred independently of the type of mutant.
Increase of in vivo B cell proliferation during seroconversion
In terms of cell dynamics, lymphocytosis is the consequence of a homeostatic deregulation provoked by an imbalance in the rates of proliferation and/or death. Alternatively or concomitantly, the net increase in B lymphocyte numbers might be the result of a cell mobilization between the lymphatic system and the peripheral blood. We have previously established a protocol allowing to quantify these parameters in BLV-infected sheep [25]. This experimental approach is based on intravenous injection of 5-bromo-2'-deoxyuridine (BrdU) which permits, after its incorporation into DNA, the identification by flow cytometry of cells that have undergone proliferation.
In order to label proliferating B lymphocytes during primo-infection, BrdU was injected weekly over a two months period and blood was collected at three days after each injection, a delay required for maximal detection of labeled cells (see methods) [25]. Figure 3A illustrates an example of IgM+BrdU+ dual flow cytometry analysis performed at days 0, 24 and 31 after proviral injection into sheep n° 4536. It appeared that the numbers of B+BrdU+ cells in this sheep increased at day 24 (cell counts of 99 at day 0 versus 385 at day 24 amongst 10,000 events) and remained high at day 31 (324 counted events). In contrast, in sheep n° 4533 used as a negative control, no variation was observed in terms of absolute numbers (72, 66 and 75 at days 0, 24 and 31) as well as in proportion of the sIgM-positive cell population (Figure 3B, yellow bars). In BLV-infected sheep n° 4536, however, this relative proportion of BrdU-labeled B lymphocytes peaked at day 24, indicating that these cells underwent proliferation (black bars on Figure 3B). Interestingly, the burst of B+BrdU+ cells just preceded the onset of seroconversion (vertical line at day 28 on Figure 3C) and correlated with the mid phase of total B lymphocytes counts in the blood. The most straightforward interpretation is that the net increase of the B cell population is at least partially due to proliferation.
Figure 3 The lymphocytosis correlates with increased B cell proliferation at seroconversion. Once per week over a 2 months period, 500 mg of bromodeoxyuridine (BrdU) were injected intravenously into BLV-infected (n° 4536) and control (n° 4533) sheep. An aliquot of blood (1 ml) was collected at three days post-BrdU injection. After lysis of the red blood cells, B lymphocytes were labeled with an anti-sIgM antibody. Next, cells were stained with anti-BrdU FITC in the presence of DNase and analyzed by two-color flow cytometry. A. Dot Plot graphs of B lymphocytes (Y axis) labeled in combination with BrdU (X axis) in uninfected control animal (n° 4533) and in a BLV wild type infected sheep (n° 4536) performed at days 0, 24 and 31. Day 31 corresponds to the first data obtained just after seroconversion of sheep n° 4536. Ten thousand events were acquired by flow cytometry and PBMCs were selected by the FSC/SSC gating method. The total numbers of B cells are indicated in the upper quadrants. B. Histogram of the proportions of BrdU+ labeled B cells (in % of the total B lymphocyte population). C. Graphic representation of the proportions of B+BrdU+ cells within the total B lymphocyte population (at days 3 and 7 post-injection) and the corresponding absolute numbers of B lymphocytes observed during the experiment. The BrdU was injected at days 14, 21, 28, 35, 42 and 52. The vertical line represents the detection of the seroconversion day.
Since BLV mutants also induce a transient lymphocytosis, a kinetics of BrdU incorporation was performed in 4 additional sheep infected with proviruses IG4, CRX3, CRE and GP30 (respectively animals n° 4538, 4541, 4543 and 4544; see figure 4). In all sheep, the high absolute numbers of B lymphocytes (squares) observed at seroconversion (vertical line) was preceded by a net increase in the BrdU positive population (lozenges).
Figure 4 In vivo B lymphocyte proliferation in sheep infected with BLV mutant proviruses. Graphic representation of the proportions of B+ BrdU+ cells within the total B lymphocyte population (at days 3 and 7 post-injection) and the corresponding absolute numbers of B lymphocytes in sheep (n° 4538, 4541, 4543 and 4544) infected, respectively, with BLV mutants (IG4, CRX3, CRE and GP30). The BrdU was injected at days 14, 21, 28, 35, 42 and 52. The vertical line corresponds to the seroconversion day as determined by an immunodiffusion test.
Together these data demonstrate that transient lymphocytosis occurring in BLV infected-sheep is at least partially due to an increase in cell proliferation as assessed by BrdU incorporation and, surprisingly, this phenomenon occurred independently of the type of virus.
Lymphocytosis and B cell proliferation are independent of the proviral loads
With the aim to quantify the proviral loads, viral DNA levels within the circulating blood of all sheep were determined by semiquantitative PCR. The genomic DNAs were extracted from blood samples collected at seroconversion and the sequences corresponding to the viral tax gene were amplified by PCR. The number of PCR cycles was adapted in order to compare the relative amounts of proviral sequences in the blood samples. The amplicons were then analyzed by Southern Blotting using a BLV tax probe. Amplification of the gapdh gene was used as internal control for chromosomal DNA integrity (Figure 5A). As controls for PCR contaminations, no signal was generated in DNA samples from uninfected sheep at day 0 (n° 4533 and 4534). In contrast, PCR amplification of the tax gene using the genomic DNA from WT infected sheep (sheep n° 4535 an 4536) yielded a 1 kb fragment as expected. Under similar conditions, a weaker signal was generated by amplification of genomic DNA isolated from animals infected by mutant proviruses (Figure 5A). Phosphorimager quantification of these hybridization signals revealed that mutants IG4, CRX3 and CRE replicated at lower proviral loads compared to the wild type or the non-attenuated GP30 viruses (Figure 5B). Using real-time PCR performed as described by [26], the proviral loads were estimated to be 270 and 245 copies per 1,000 cells in sheep infected, respectively, with the wild type virus and the GP30 recombinant whereas the levels yielded by the other mutants were significantly reduced (27, 40 and 24 copies / 1,000 cells for IG4, CRX3 and CRE, respectively) (Figure 5B).
Figure 5 Reduced proviral loads at seroconversion in sheep infected with attenuated mutants. A. At seroconversion day, DNA was extracted from blood isolated from wild type or mutant-infected sheep, as indicated. Proviral sequences were amplified by 25 cycles of PCR using tax specific primers. The amplification products were resolved on a 1% agarose gel and analyzed by Southern blotting with a BLV tax probe. Blood samples from uninfected sheep n° 4533 and 4534 were used as controls for PCR contamination. A PCR amplification of the gapdh gene was used as an internal control for DNA integrity. B. Mean proviral loads at seroconversion. For each category of mutant-infected sheep, the mean values of the proviral loads (in arbitrary units ± standard deviations as determined after Phosphorimaging scanning) were statistically compared using the Student t test to the mean proviral load of the wild type group (WT). The data result from three independent experiments using DNAs extracted around the seroconversion period (NS: p > 0.05 non-significance; * 0.01 < p < 0.05; *** p < 0.001, Student t test). Using real-time PCR, the proviral loads were estimated to be 270 and 245 copies per 1,000 cells in sheep infected, respectively, with the wild type virus and the GP30 recombinant whereas the levels yielded by the other mutants were significantly reduced (27, 40 and 24 copies / 1,000 cells for IG4, CRX3 and CRE, respectively).
Together these data show that, around the seroconversion period, the proviral loads are reduced in sheep infected by attenuated mutants.
Conclusions
We have studied here the interplay between the efficiency of viral spread, the cellular proliferation within the host and the extent of cell accumulation during the period consecutive to the infection of sheep by bovine leukemia virus. We compared two categories of viruses based on their ability to infect and expand within their host: those behaving as wild type (WT and GP30) or so-called attenuated mutants (IG4, CRX3 and CRE). Experimental infection of sheep with these two types of viruses led to a surprising observation, namely, a similar extent of transient lymphocytosis independently of the proviral loads (see Figure 6). In other words, the total B lymphocyte accumulation within the peripheral blood is not modulated by the amount of viral copies. Another contribution of this report is the demonstration that transient lymphocytosis arising just prior to seroconversion is at least partially due to an increase in B cell proliferation (Figure 6). In fact, since lymphocytes rest within the peripheral blood in the G0/G1 phase of the cell cycle (unpublished data), proliferation occurs in other sites i.e. the bone marrow, spleen, lymph nodes and Peyer's Patches. Therefore, the accumulation of BrdU+ B lymphocytes might also be the consequence of an increased outflow from these sources estimated, in non-infected sheep, at 30 × 106 cells per gram of lymph node in one hour [27]. Conversely, impaired recirculation to the lymphatic system would also create an imbalance in the B lymphocyte counts (1 g of lymph node receives 1.2 × 108 cells per hour [27]). Answering to this question would require canulation of lymph nodes allowing the precise quantification of the cellular flows.
Figure 6 Schematized summary of proliferation rates, proviral loads and lymphocytosis associated with primo-infection. Sheep, which were experimentally infected with BLV wild type or mutant proviruses (virus inoculation), exhibited a similar extent of transient accumulation of B-lymphocytes (—). This transient lymphocytosis arising just before seroconversion is at least partially due to an increase in B cell proliferation (peak of B cell proliferation •••••). In contrast, the proviral loads greatly differ among the two categories of sheep, e.g. infected with wild type (- - -) or attenuated viruses (— ••).
With the aim to correlate lymphocytosis with a defined B cell sub-population, we have demonstrated that two surface molecules, CD5 and CD11b, whose expression has been previously associated with late stages of BLV infection [13-15,23,24] are also important markers at the seroconversion period. In human and mice, the B lymphocytes expressing the CD5 and CD11b proteins are referred to as B-1a cells. B lymphocytes that are CD5- and CD11b+ are called B-1b and have a similar function than the B-1a subset [28]. Compared to the conventional B-2 cells (e.g. CD5- CD11b-), the B-1 population exhibits different developmental schemes, phenotypes, antibody repertoires, localization and behaviors. In humans, elevated numbers of B-1 cells have been reported in patients with Sjorgen's syndrome, rheumatoid arthritis, chronic lymphocytic leukemia and AIDS [29-32]. In mice, increased numbers of B-1 cells have been observed in a number of naturally occurring and genetically manipulated strains that develop autoimmune manifestations [33]. B-1 cells are believed to be the major source of natural IgM, a polyreactive and weakly autoreactive antibody, which is produced in the absence of exogenous antigenic stimulation [34]. Consistent with this model, IgM specificities within the B-1 repertoire include phosphorylcholine, phosphatidylcholine, thymocytes, lipopolysaccharide and influenza virus [33]. Interestingly, in sheep, the B-1 population also expands in response to infection with other pathogens like Trypanosoma evansi and Pasteurella haemolytica [35,36]. In this context, we speculate about an opportunistic mechanism of BLV replication supported by a general activation of B-1a lymphocyte proliferation, which could thus be a primary B-cell humoral response. Successive divisions of B-1a cells would thereby expand the number of potential targets for the virus. During this period, the ability of the virus to colonize new cells would be crucial, a fact that is reflected by the differential proviral loads between the wild type and attenuated viruses.
Differences in the infectious potential of the attenuated viruses are the consequence of mutations in accessory genes (R3 and G4) or in the LTR promoter (CRE) [19,21]. Although the role of R3 is still unknown, its homologue expressed by HTLV-1 (p12) interacts with the IL-2 receptor as well as with calcineurin and is crucial during the initial steps of infection [37-39]. BLV G4 and HTLV-1 p13 bind to the same cellular protein, farnesyl pyrophosphate synthetase [40] but modulate differentially cell transformation in vitro [20,41]. The defect in BLV propagation associated with the CRE mutant relates to its inability to repress basal expression [21]. Optimization of the imperfect CRE enhancer sequences present in the LTR creates a more efficient promoter, as expected, but restricts the proviral loads indicating that transcriptional silencing is required for viral persistence and spread. The reduced capacity of the CRE, G4 and R3 mutants to propagate are thus caused by different genetic defects and data presented in this report further extent our previous observations [1,19,21]. More surprisingly is the replication efficiency exhibited by the GP30 envelope mutant. Unable to induce membrane fusion, at least as measured by classical syncytia formation tests, the GP30 virus propagates at wild type levels [22]. Besides a possible experimental caveat based on the lack of sensitivity of the syncytium assay in vitro, our previous (and most straightforward) hypothesis postulated that viral spread mainly occurred via clonal expansion of the infected lymphocytes with few or non-limiting cell-to-cell transmissions. Figure 5 demonstrates that the proviral loads of the GP30 mutant are at wild type levels even during the early steps of infection, a period thought to be associated with active infection of novel cells. Preliminary inverse PCR amplification data indicate that the number of target cells carrying integrated GP30 or wild type proviruses are not significantly different (F. Mortreux, ongoing work). We thus have to reconsider our interpretation and propose that the syncytium assay does not reflect the infectious potential in vivo. In terms of its biological properties in sheep, the GP30 recombinant should thus be considered as, or close to, wild type at least during primo-infection.
In conclusion, we have characterized here the initial steps consecutive to BLV infection of sheep. We show that this period is characterized by a transient accumulation of CD5+ / CD11b+ B lymphocytes resulting, at least in part, from increased proliferation. Furthermore, the extent of B cell lymphocytosis is not directly linked to the proviral loads reached by the wild type and mutant viruses. On basis of a comparative leukemia approach, these results could be informative for the related human T-lymphotropic viruses.
Methods
Experimental animals
Twelve sheep of one year old were kept under controlled conditions at the Veterinary and Agrochemical Research Centre (Machelen, Belgium). Two animals (n° 4533 and 4534) were used as uninfected controls whereas sheep n° 4535 and 4536 were experimentally infected with a BLV wild type cloned provirus (strain 344) [18]. Briefly, 100 μg of plasmid DNA were mixed with 200 μl of N-[1-(2,3 dioleoloxyl)propyl]-N,N,N-trimethylammonium methylsulfate (DOTAP; Roche Diagnostics) in 1 ml of HBS (20 mM HEPES-150 mM NaCl, [pH 7.4]) and injected intradermally into the back of each sheep. Plasmids containing the mutant proviruses pBLVIG4 (harboring a stop codon in the G4 open reading frame; [19], pBLVCRX3 (deleted in R3; [1]), pBLVCRE3X (in which the CRE imperfect sequences were mutated to TGACGTCA; [21]) and pBLVA60V (alanine codon 60 of the GP30 transmembrane gene being mutated into valine; [22]) were injected, respectively, in sheep n° 4537 / 4538, n° 4539 / 4541, n° 4542 / 4543 and n° 4544 / 4545. Twice a week, the total leukocyte counts were determined by using a Coulter counter ZN, and the number of lymphocytes was estimated after examination under the microscope after staining with May-Grunwald Giemsa. In parallel, the sera from each sheep were analyzed for BLV seropositivity using immunodiffusion and enzyme-linked immunosorbent assay (ELISA) techniques [42].
Immunophenotyping of sheep
Peripheral blood mononuclear cells (PBMCs) were isolated by Percoll gradient centrifugation and their viability was estimated by trypan blue dye exclusion [43]). PBMCs were labeled with monoclonal antibodies (Mabs) directed against surface immunoglobulin M (anti-sIgMs, clone 1H4, mouse IgG1; Pig45A2, mouse IgG2b), CD4 (ST4, mouse IgG1), CD5 (CC17, mouse IgG1), CD8 (CC58, mouse IgG1) and CD11b (CC125, mouse IgG1) provided by C. Howard (Institute for Animal Health, Compton, United Kingdom) and by I. Schwartz-Cornil (INRA, Jouy-en-Josas, France) or obtained from VMRD Inc. Cells were then labeled with a rat anti-mouse IgG1 phycoerythrin (PE)-antibody (Becton Dickinson Immunocytometry Systems) or with a goat anti-mouse IgG2b fluorescein isothiocyanate (FITC)-conjugate (Caltag Laboratories). Finally, PBMCs were analyzed by flow cytometry on a Becton Dickinson FACScan flow cytometer. Ten thousand events were collected for each sample and data were analyzed with the Cellquest software (Becton Dickinson Immunocytometry Systems).
Analysis of 5-bromo-2'-deoxyuridine in vivo
Each week during two months, sheep were injected intravenously with 500 mg of 5-bromo-2'-deoxyuridine (Sigma Aldrich) resuspended in physiologic serum (NaCl 0.9%). To evaluate BrdU-incorporation, blood was collected at three and seven days after each BrdU injection. The red blood cells were lysed with 1× FACS Lysing Solution (Becton Dickinson Immunocytometry Systems), the leucocytes were washed twice with PBS containing 0.5% Bovine Serum Albumin (BSA) (Sigma Aldrich) and incubated in the presence of biotinylated 1H4 monoclonal antibody for 30 min at 4°C. Next, the cells were labeled with streptavidin-phycoerythrin (Becton Dickinson Immunocytometry Systems) and incubated with 1× FACS Permeabilizing Solution (Becton Dickinson Immunocytometry Systems). Finally, leucocytes were stained with anti-BrdU FITC antibody in the presence of DNase (Becton Dickinson Immunocytometry Systems) and analyzed by flow cytometry.
Semiquantitative PCR analysis
DNA isolations were performed directly on blood using the Wizard®Genomic DNA Purification Kit (Promega). An aliquot of 300 μl of blood were mixed with 900 μl of Cell Lysis Solution and incubated for 10 minutes at room temperature. After two washes with the same buffer, the cells were resuspended in 300 μl of Nuclei Lysis Solution and incubated for one hour at 37°C. Then, the samples were digested during 15 minutes at 37°C in the presence of 1.5 μl of RNase Solution. Proteins were precipitated by adding 100 μl of Protein Precipitation Solution to the nuclear lysates. After centrifugation at 13,000 g, the supernatant was mixed with an equal volume of isopropanol, centrifuged and ethanol precipitated. Five hundred nanograms of the purified DNAs were amplified in the presence of 200 μM of deoxynucleotides, 2.5 U of Taq DNA polymerase, and 200 ng of primers. The primers used (PCRTA 5'-CTCTTCGGGATCCATTACCTGA-3' and PCRTC 5'-CCTGCATGATCTTTCATACAAAT-3') encompass the region from position 7999 to 6990 of the BLV tax gene [44]. In parallel, the primers G3PDHA (5'-CATGTGGGCCATGAGGTCCACCAC-3') and G3PDHS (5'-GACCCCTTCATTGACCTCAACTACA-3') were used to amplify the gapdh gene. The samples were denatured for 5 min at 94°C, and amplified by 25 cycles of PCR (30 s at 94°C, 30 s at 57°C, and 1 min at 72°C). After a final elongation step of 10 min at 72°C, 20 μl of the amplification products were resolved on a 1% agarose gel, transferred to a Hybond N+ membrane (Amersham Pharmacia Biosciences), and hybridized either with a BLV tax (a 1-kb ClaI insert from plasmid pGEM7zfLOR1) or with a gapdh probe labeled with α-32P dCTP. Quantification of 32P signal was performed using a PhosphorImager (Personal Molecular Imager FX System,Biorad).
Real-time PCR was performed using 6FAM-labeled MGB probes specific for the BLV pol gene and the 18S ribosomal DNA sequences essentially as described in reference [26].
List of Abbreviations
BLV: Bovine Leukemia Virus; BrdU: 5-bromo-2'-deoxyuridine; CRE: Cyclic-AMP Response Element; PBMCs: Peripheral Blood Mononuclear Cells; WT: Wild Type.
Competing Interests
The author(s) declare that they have no competing interests.
Authors' Contributions
CD carried out the most experimental work and drafted the manuscript. MS and FM performed the sample collections and the determination of the proviral loads. PK was responsible for the sheep studies. RK participated to experimental design and interpretation of data. LW conceived the study, its design and coordination. All authors read and approved the final manuscript
Acknowledgments
We thank the "Belgian Federation against Cancer", the "Fortis Bank Assurance", the "Fonds national de la recherche scientifique" (FNRS) and the "Interuniversity Attraction Poles Programme – Belgian Science Policy P4/30" for financial support. RK and LW are research directors of the (FNRS) whereas CD is a "Télévie" fellow. The antibodies were kindly provided by K. Walravens (CODA/CERVA, Uccle, Belgium), JJ. Letesson (FUNDP, Namur, Belgium), D. Portetelle (FSAGx, Gembloux, Belgium), C. Howard (Institute for Animal Health, Compton, United Kingdom) and I. Schwartz-Cornil (INRA, Jouy-en-Josas, France). We are grateful to M. Boxus, C. Burteau, J. Defoiche, G. Manfouo Foutsop, J.M Londes, Y. Muhovski, P. Urbain and G. Vandendaele for experimental help.
==== Refs
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| 15462678 | PMC526217 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Oct 5; 1:31 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-31 | oa_comm |
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BMC SurgBMC Surgery1471-2482BioMed Central London 1471-2482-4-121545651710.1186/1471-2482-4-12Study ProtocolProbiotic prophylaxis in patients with predicted severe acute pancreatitis (PROPATRIA): design and rationale of a double-blind, placebo-controlled randomised multicenter trial [ISRCTN38327949] Besselink Marc GH [email protected] Harro M [email protected] Erik [email protected] Vincent B [email protected] Louis MA [email protected] Hein G [email protected] members of the Dutch Acute Pancreatitis Study Group [email protected] Department of Surgery, University Medical Center Utrecht PO Box 85500, HP G04.228, 3508 GA Utrecht, The Netherlands2 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht PO Box 85060, 3500 AB Utrecht, The Netherlands2004 29 9 2004 4 12 12 21 7 2004 29 9 2004 Copyright © 2004 Besselink et al; licensee BioMed Central Ltd.2004Besselink 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
Infectious complications are the major cause of death in acute pancreatitis. Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the vast majority of these infections. Goal of this study is to determine whether selected probiotics are capable of preventing infectious complications without the disadvantages of antibiotic prophylaxis; antibiotic resistance and fungal overgrowth.
Methods/design
PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial in which 200 patients will be randomly allocated to a multispecies probiotic preparation (Ecologic 641) or placebo. The study is performed in all 8 Dutch University Hospitals and 7 non-University hospitals. The study-product is administered twice daily through a nasojejunal tube for 28 days or until discharge. Patients eligible for randomisation are adult patients with a first onset of predicted severe acute pancreatitis: Imrie criteria 3 or more, CRP 150 mg/L or more, APACHE II score 8 or more. Exclusion criteria are post-ERCP pancreatitis, malignancy, infection/sepsis caused by a second disease, intra-operative diagnosis of pancreatitis and use of probiotics during the study. Administration of the study product is started within 72 hours after onset of abdominal pain. The primary endpoint is the total number of infectious complications. Secondary endpoints are mortality, necrosectomy, antibiotic resistance, hospital stay and adverse events. To demonstrate that probiotic prophylaxis reduces the proportion of patients with infectious complications from 50% to 30%, with alpha 0,05 and power 80%, a total sample size of 200 patients was calculated.
Conclusion
The PROPATRIA study is aimed to show a reduction in infectious complications due to early enteral use of multispecies probiotics in severe acute pancreatitis.
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Background
Infection of pancreatic necrosis is the major cause of death in acute pancreatitis [1-4] Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the majority of these infections [5-9] Antibiotic prophylaxis has been studied in several trials [10-13]. Recently, a well-designed placebo-controlled trial failed to show a reduction of infectious complications [14]. A multicenter trial from the Netherlands, using topical and enteral antibiotics to reduce bacterial overgrowth (selective bowel decontamination, SBD) showed a reduction in infected necrosis [15]. Despite favourable results, SBD has not been widely implemented due to the workload associated with it and the reluctance to use antibiotics for a long period of time with risks of bacterial resistance and fungal infection [16,17]. Microbial antibiotic resistance has become a worldwide problem due to excessive use. The World Health Organisation has advocated the use of microbial interference therapy: non-pathogens (probiotics) to restrain pathogens [18]. It is the goal of the present study to investigate the use of prophylactic probiotics as an alternative strategy.
Several trials with enteral probiotics have shown a significant reduction of infectious complications both in acute pancreatitis and in patients undergoing major abdominal surgery [19-21] A well-designed placebo-controlled trial with Lactobacillus plantarum in patients with acute pancreatitis showed very interesting results: a significant reduction of infected pancreatic necrosis (1/22 versus 7/23 infected necrosis)[19]. However, some criticised this study because of the exclusion of biliary pancreatitis patients and some statistical flaws [22,23]. In these and other trials a single probiotic strain was used.
It has been suggested that multispecies probiotics are more effective, because effects are strain specific. Combinations of probiotics can be designed so that strain-specific properties are additive or synergistic. Based on in vitro data, a selection of 6 out of 75 probiotic strains was made by Winclove Bio Industries (Amsterdam, the Netherlands) in co-operation with the Departments of Paediatric Immunology and Surgery, of the UMC Utrecht (Utrecht, The Netherlands). This paper describes the rationale of this product and the design of the study.
Rationale for the efficacy of multispecies probiotics in acute pancreatitis
The bacteria responsible for infection of (peri-)pancreatic necrosis most often originate from the gut [2,5,27] The pathophysiology of infection of peri-pancreatic necrosis and the steps amenable to therapeutic intervention are essentially unknown. Upper gastrointestinal dysmotility (UGID) has been observed in acute pancreatitis (AP) as well as in cholestasis and sepsis [6-8] UGID may lead to small bowel bacterial overgrowth (SBBO)[6]. In immunocompromised patients this bacterial overgrowth may lead to bacterial translocation (BT) [5,6] BT is not only held responsible for infectious complications, but it also contributes to the overproduction of pro-inflammatory cytokines during acute necrotising pancreatitis (ANP) [28]. These cytokines are key factors in the pathogenesis of multi-organ failure and sepsis.
Prevention of UGID, SBBO and BT may lead to prevention of infected pancreatic necrosis and the resulting systemic complications. Intravenous antibiotic prophylaxis is considered an option to prevent pancreatic infection, but results from randomised clinical trials are conflicting [10-14].
Probiotics are living micro-organisms that upon oral delivery exert a range of health promoting properties. For a growing number of inflammatory diseases (gastrointestinal, airway or skin) probiotics are being used, with variable clinical outcome. It is hypothesised that probiotics have an effect on different levels. We developed a multispecies probiotic preparation that aims to prevent SBBO and BT in ANP through (1) stimulation of the production of anti-inflammatory cytokines, especially interleukin-10, at the level of the intestinal mucosa[29], (2) stimulation of gastrointestinal motility[30] and (3) competitive inhibition of opportunistic pathogens[31]. The individual strains each have their own capacity to inhibit growth of specific potential pathogenic micro-organisms (PMO's), as for instance Escherichia Coli or Enterococcus species. The combination of these probiotics, in vitro, inhibits the growth of all relevant PMO's known to infect pancreatic necrosis [31].
Methods / design
Study objectives
The study objective is to show that probiotics are effective in reducing the number of infectious complications during the course of acute pancreatitis.
Primary endpoint
The primary endpoint is the total numbers of infectious complications during the hospital stay for acute pancreatitis, see table 1.
Secondary endpoints
Secondary endpoints are mortality, necrosectomy, use of antibiotics, total hospital stay, intensive care stay, side effects, abdominal complaints by a patient visual analogue scale questionnaire, sequential organ failure assessment (SOFA) scores, bacterial resistance and total costs.
Design
PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial. The randomisation is stratified according to the aetiology of the acute pancreatitis (ie. biliary versus non-biliary), also block-randomisation per hospital is used. Its design and timing of the investigations are presented in Figure 1 and Table 2, respectively.
Figure 1 PROPATRIA according to CONSORT.
Table 1 Infectious complications
Complication Definition
Bacterial infection body temperature > 38 degrees and increased number of neutrophils and CRP in peripheral blood and one of the below:
Infected pancreatic necrosis Positive fine needle aspiration culture or air bubbles in the pancreatic necrosis on CT-scan.
Pneumonia Coughing, dyspnoea, radiography with infiltrative abnormalities, lowered arterial bloodgass. On the intensive care unit a positive endotracheal culture is mandatory.
Urinary tract infection Dysuria with bacteraemia (>10.000 CFU/mL)
Setting
Patients will be enrolled from all 8 Dutch University Hospitals and 7 non-University hospitals.
Patients
A total of 200 adult patients with a first episode of predicted severe acute pancreatitis will be randomised.
Eligibility criteria
Inclusion Criteria
• age equal to or above 18 years
• first episode of acute pancreatitis
• written and oral informed consent
Exclusion criteria
• post-ERCP pancreatitis
• malignancy
• infection/sepsis caused by a second disease
• intra-operative diagnosis
• immunocompromised patients
• use of probiotics during admission
Randomisation criteria
After inclusion in the study, patients with predicted severe acute pancreatitis, represented by at least one of the following scores: 3 Imrie criteria, CRP 150 mg/L, APACHE II score 8, are randomised within the first 72 hours after the onset of abdominal pain. Patients with a predicted mild attack of acute pancreatitis do not receive the study product. They do give informed consent and are monitored.
Ethics, informed consent
This study is conducted in accordance with the principles of the Declaration of Helsinki and 'good clinical practice' guidelines. The independent ethics committee of all 15 participating hospitals approved the final protocol. Oral and written informed consent in form is obtained from the patient before inclusion in the trial.
Safety
All the probiotics used in this study have a long history of use in the food industry. Probiotics have been studied in many critical ill and immunocompromised patients without any serious adverse events being noted. There is one trial that studied probiotics in acute pancreatitis patients and no serious advents were noted. If an infection with one of the administered probiotics might occur, this could be treated with antibiotics. During administration of the study-product both the patient and the nursing staff are asked to register any potential side effect or adverse event. An independent monitoring committees will discuss all reported (serious) adverse events.
Statistical analysis
Intention- to-treat
The analysis will be performed on the basis of an intention-to-treat (ITT) population and with respect to ITT principles. Also a per-protocol analysis and an analysis for necrotising versus non-necrotising pancreatitis will be performed.
Interim-analysis
For ethical reasons it is desirable to end a therapeutic experiment once a statistical significant difference in treatment results has been reached. This study uses the stopping-rules according to Snapinn [32]. An interim-analysis will be performed after the data of the first 100 patients (50% fraction) is obtained. According to Snappin, the trial will be ended at this interim-analysis at p < 0,0081. The study will also be ended in case of adverse events without possibility of positive outcome, p > 0,382. The monitoring committee will discuss the results of the interim-analysis and advice the steering committee. The steering committee decides on the continuation of the trial.
Sample size
It is anticipated that probiotics will lead to a reduction of infectious complications from 50% (% of patients) to 30%. The sample size calculation is based on α = 0.05, and a power of 80% This leads to a required sample size of 188 patients. Taking into account a 5% loss-to-follow up, a total of 2 × 100 patients will be randomised. Based on hospital data of 2002 about 500 patients have to be included in order to randomise 200 patients with predicted severe acute pancreatitis. There is one post-discharge follow-up after three months. The expected study end is in 2006 (2 years inclusion period).
Randomisation
The randomisation list was generated by using the website Randomization.com . According to this list a stratified random allocation of probiotics and placebo was performed. Each participating hospital received a series of subsequently numbered identical containers with probiotics or placebo. Patients with biliary cause of the pancreatitis are allocated to the lowest possible number available whereas patients with non-biliary cause are allocated to the highest possible number available (stratification).
Blinding
Both the probiotics and placebo are packed in identical, numbered sachets. These sachets are packed in identical, numbered containers. The probiotics and placebo are identical in weight, colour, smell and taste. All doctors, nurses, research staff and patients involved are unaware of the treatment administered to the patient.
Treatment program
Patients eligible for inclusion are followed during 72 hours after onset of the abdominal pain. When a patient meets a randomisation criteria (preferably within 24 hours), a nasojejunal feeding tube is passed and administration of the study product (Ecologic® 641, Winclove Bio Industries, Amsterdam, The Netherlands) and fibre enriched tube feeding (Multifibre®, Nutricia, Zoetermeer, The Netherlands) is started. Ecologic® 641 consists of 6 strains of viable and freeze-dried bacteria, namely 4 lactobacilli: Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus salivarius, Lactococcus lactis, and 2 bifidobacteria: Bifidobacterium bifidum and Bifidobacterium lactis in a total daily dose of 1010 bacteria. The study-product is administered twice daily through a nasojejunal tube for a maximum of 28 days. The treatment is stopped when a patient is diagnosed with infected pancreatic necrosis, is discharged or dies. A standard protocol for the treatment of acute pancreatitis is followed. During ERC with sphincterotomy in case of biliary pancreatitis antibiotic prophylaxis is allowed. Prophylactic use of proton pump inhibitors is only allowed in case of a clinical history of peptic diseases like peptic ulcer disease and reflux esophagitis. Prophylactic use of antibiotics is not allowed. At 7–10 days after admission, a routine CT scan is performed to detect pancreatic necrosis. Fine needle aspiration in (peri)pancreatic collections is performed only in case of clinical suspicion of infected necrosis. Further culturing, imaging and treatment are all based on clinical findings.
Monitoring
A research nurse monitors the participating centres and patients. Every 6 months all centres are visited by a second independent research nurse who checks, at least, 10% of every patient's data.
Follow-up
Patients are followed during their hospital stay. There is one follow-up visit, 3 months after discharge, including an abdominal ultrasound and a short questionnaire regarding abdominal pain and daily activities.
Discussion
Only patients with predicted severe acute pancreatitis are randomised. Patients with mild acute pancreatitis are considered not eligible for randomisation because of their low risk to develop infectious complications. If such patients would be included, a very large number of patients would be needed to demonstrate a significant effect between the treated and the non-treated patients. To properly introduce the concept and potential of probiotic prophylaxis, the use of prophylactic antibiotics was discussed during the preparations of the study. About 30% of the hospitals participating in the study commonly used prophylactic antibiotics, once a patient was diagnosed with pancreatic necrosis. Because of the lack of evidence it was decided, even before the results of the most recent German trial[14] were presented, not to administer prophylactic antibiotics in case of pancreatic necrosis without clinical suspicion of infected necrosis. When patients do receive antibiotics, for instance because of an urinary tract infection, effort is made to administer the study product with a 4-hour interval in order to minimise interference of the antibiotics with the probiotics.
The maximum of 72 hours between onset of symptoms and start of treatment was decided upon, based on the consideration that probiotics are expected to prevent infection of pancreatic necrosis. Therefore the probiotics should be administered prior to the stage that bacterial overgrowth starts and intraluminal bacteria migrate across the mucosal barrier. Experimental studies have shown that bacterial overgrowth occurs very early, within 24 hours after onset, in the course of acute pancreatitis and reduction of the bacterial load in the proximal small bowel by intraluminal antibiotics reduces the risk of infection of pancreatic necrosis [8,33,34].
The presence of pancreatic necrosis can only be detected reliably 5–7 days after onset [35]. This is too late to effectively prevent bacterial overgrowth and translocation and therefore "predictive laboratory scores" are used as randomisation criteria and not diagnosis of pancreatic necrosis on CT scan. The scoring systems used are also simple and generally available. A major disadvantage though, is the limited positive predictive value and the high number of false positives [36,37]. During interim-analysis the number of false positives will be calculated and the sample size may be adjusted.
All randomised patients will receive early enteral feeding by a jejunal feeding tube. Since patients with predicted severe pancreatitis would develop severe pancreatitis in only 50% of the cases, the fraction with a mild course would normally not receive a nasojejunal feeding tube. It is unavoidable to prevent this over treatment for patients with a mild course because the current scoring systems fail to select all patients with a severe course and early intervention is warranted.
The primary outcome parameter 'total of infectious complications', was chosen because it was shown in previous trials that also the number of pulmonary and urinary tract infections can be reduced by probiotics [19-21] This fits in with the concept that such complications are secondary to bacterial translocation.
All of the infectious complications are documented in the study period until the patient reaches one of the study-endpoints: infected necrosis, discharge or in-hospital death.
Conclusion
PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial that aims to show a reduction in infectious complications by the enteral use of a multispecies probiotics preparation in patients with predicted severe acute pancreatitis.
Authors' contributions
MB drafted the manuscript
HT, EB, VN, LA and HG edited the manuscript
All authors participated in the design of the study
MB and EB performed the statistical analysis.
All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
PROPATRIA committee members
Steering Committee-HG Gooszen (chairman), MGH Besselink (principal investigator), HM Timmerman, LMA Akkermans, VB Nieuwenhuijs, E Buskens, UMC Utrecht; H van Goor, GT Bongaerts, UMC St. Radboud Nijmegen.
Monitoring Committee-IHM Borel Rinkes (chairman), B Oldenburg, Y van der Graaf, W Renooij, UMC Utrecht; E Stobberingh, University Hospital Maastricht.
Key staff at coördinating centre
MGH Besselink (principal investigator), VJM Zeguers (trial research nurse), J Oors (auditor), HG Rijnhart (data manager), HM Timmerman, LMA Akkermans, HG Gooszen, UMC Utrecht.
Clinical centres and investigators
The last investigator per hospital is the local principal investigator. University Hospital Groningen: RJ Ploeg, MR Kruijt Spanjer, HS Hofker; University Medical Center St. Radboud Nijmegen: H Buscher, A Nooteboom, H van Goor; University Hospital Maastricht: JP Rutten; CHC De Jong; St. Elisabeth Hospital Tilburg: T Drixler; C van Laarhoven; Erasmus Medical Center Rotterdam: G van ’t Hof, EJ Kuipers CHJ van Eijck; Canisius Wilhelmina Hospital Nijmegen: B Houben, L Ootes, A Tan, C Rosman; Medical Center Rijnmond Zuid Rotterdam: N Wijffels, L van Walraven, J Lange; Leiden University Medical Center: A Haasnoot, S Schaapherder; Gelderse Vallei Hospital Ede: B Witteman; St. Antonius Hospital Nieuwegein: TL Bollen, B van Ramshorst; University Medical Center Utrecht: KJ van Erpecum; Meander Medical Center Amerfoort: R Frankhuisen, MA Brink; Vrije Universiteit Medical Center Amsterdam: CJ Mulder, MA Cuesta; Rijnstate Hospital Arnhem: E Spillenaar Bilgen, P Wahab; Academic Medical Center Amsterdam: DJ Gouma, O van Ruler, MA Boermeester.
List of abbreviations
CRP c-reactive protein
APACHE II Acute Physiology and Chronic Health Evaluation II
ERC endoscopic retrograde cholangiography
CT scan computer tomographic scan
Table 2 Study Flowchart
Visit Admission Day 2 Day 5 Day 10 Day 14 Day 21 Day 28 Day 35 Discharge 3 months
Informed consent X
Clinical scores X X X X X X X X X
US# X X
CT* X
Laboratory X X X X X X X X
Study product X§ X§ X X X X X
# US = abdominal ultrasound to detect gallstones (at admission) or pseudocyste (at follow-up)
* CT = abdominal computer tomographic scan to detect (peri-)pancreatic necrosis
§ = Within 72 hours after onset of pain the administration of the study product is started.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank Winclove Bio Industries, Amsterdam, for the help in the design of the multispecies probiotics and for the supply of both probiotics and placebo. Senter, an agency of the Dutch Ministry of Economic Affairs, funded this study (grant number: TSGE3109).
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| 15456517 | PMC526218 | CC BY | 2021-01-04 16:28:03 | no | BMC Surg. 2004 Sep 29; 4:12 | utf-8 | BMC Surg | 2,004 | 10.1186/1471-2482-4-12 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1401545858010.1186/1471-2105-5-140Research ArticleA comprehensive comparison of comparative RNA structure prediction approaches Gardner Paul P [email protected] Robert [email protected] Department of Evolutionary Biology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen Ø, Denmark2 Faculty of Technology, University of Bielefeld, PO Box 10 01 31, 33501 Bielefeld, Germany2004 30 9 2004 5 140 140 12 8 2004 30 9 2004 Copyright © 2004 Gardner and Giegerich; 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
An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms.
Results
Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance.
Conclusions
We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research.
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Background
Motivation
RNA, once considered a passive carrier of genetic information, is now known to play a more active role in nature. Many recently discovered RNAs are catalytic, for example RNase P which is involved in tRNA maturation and the self-splicing introns involved in mRNA maturation [1]. In addition, there is evidence that RNA based organisms were an essential step in the evolution of modern DNA-protein based organisms [2,3]. The number of non-coding RNAs (ncRNA) in humans remains a mystery, but progress in this direction suggests the number of ncRNAs produced is comparable to the number of proteins [4-6]. Surprisingly, the number of protein coding genes does not correlate with our concept of "organism complexity", hence it has been hypothesised that control of gene expression via a combination of alternative splicing and non-coding RNAs are responsible for this, implying that the "Central Dogma" (RNA is transcribed from DNA and translated into protein) at least in higher eukaryotes is woefully inadequate [7,8].
A fundamental tenet of biology is that a stable tertiary structure is essential for biological function. In the case of RNA the secondary structure (the base-pair set for an RNA molecule) provides a scaffold for the tertiary structure [9,10]. Yet, the experimental determination of RNA structure remains difficult [11]; Researchers increasingly turn to computational methods. To date the most popular structure prediction algorithm is the Minimum Free Energy (MFE) method for folding a single sequence, this has been implemented by two packages: Mfold [12] and RNAfold [13]. However, there are several independent reasons why the accuracy of MFE structure prediction is limited in practise (see discussion below). Generally the best accuracy can be achieved by employing comparative methods [14]. This paper explores the extent to which this statement is true, given the current state of the art, for automated methods. There are currently three approaches to automated comparative RNA sequence analysis where the comparative study is supported by available algorithms (see plans A, B, and C, figure 1). A researcher following plan A may align sequences using standard multiple sequence alignment tools (i.e. ClustalW [15], t-coffee [16], prrn [17],...), then use signals provided by structure neutral mutations for the inference of a consensus structure. Frequently the mutual-information measure is used for this [18-20]. Recently tools have been developed that use a combination of MFE and a covariation-score [21,22] or probabilistic models compiled from large reference data-sets [23,24]. However, a multiple-sequence-alignment step assumes a well conserved sequence. This is often not so with swiftly evolving ncRNA sequences, in this case incorrect sequence alignments can destroy any covariation signal.
This has motivated plan B, the use of the "Sankoff-Algorithm", an algorithm designed for the simultaneous alignment, folding and inference of a protosequence for a set of homologous structural RNA sequences [25]. The recurrences combine sequence alignment and Nussinov (maximal pairing) folding [26]. The algorithm requires extreme computational resources (O(n3m) in time, and O(n2m) in space, where n is the sequence length and m is the number of sequences). Current implementations, Foldalign [27,28], Dynalign [29] and PMcomp [26], are restricted implementations of the Sankoff-algorithm which impose pragmatic limits on the size or shape of substructures.
The final approach (plan C) applies when no helpful level of sequence conservation is observed. We may exclude the sequence alignment step, predict secondary structures for each sequence (or sub-group of sequences) separately, and directly align the structures. Because of the nested branching nature of RNA structures, these are adequately represented as trees. The concept of a similarity measurement via edit operations, a standard procedure for string comparisons, has been generalised to trees [30-33]. Tree comparison and tree alignment models have been proposed [34,35] and implemented [13,36-39]. The crucial point in plan C is the question whether the initial independent folding produces at least some structures that align well and hence give clues as to the underlying consensus structure – when one exists. An increasing number of researchers have recently released novel RNA structure analysis and prediction algorithms [22,23,37,40-43]. Yet few algorithms are tested upon standardised example data-sets, and often they are not compared with algorithms of the same pedigree. Algorithm evaluation is a regular event for protein structure prediction groups [44-47], gene-prediction [48-50] and multiple sequence alignments [51-54]. Based on reliable data-sets, we evaluate:
• the viability of plan A, B, or C given tools available today, and
• the relative performance of the tools used within each plan.
We shall explicitly not evaluate computational efficiency, which (by necessity) differs widely between the tools. We also do not evaluate user friendliness (such as ease of installation and convenience of input or output formats, etc.) except for some remarks in the discussion section. Data-sets, documentation and relevant scripts are freely available from .
Structural alignments and consensus structures
RNA secondary structure inference is the prediction of base-pairs which form the in vivo structure, given only the sequence of bases. Three general considerations apply: (1) The in vivo structure is not only predetermined by the primary structure, but also by cellular components such as chaperones, base modifications, and even by the transcriptional process itself. There are currently no computational tools available that assess these effects. (2) There are 'ribo-switches', whereby two or more functional structures exist for a given sequence [55-57]. Such cases will fool all the tools studied here, because asking for a single consensus structure is simply the wrong question. On the other hand, the potential of conformational switching can be reliably detected [58-60]. (3) Structures may contain pseudo-knots, which are ignored by most current tools due to reasons of computational complexity and scarcity of these motifs. We do not consider pseudoknots here. However, several comparative approaches that include pseudoknots are currently under development, and certainly merit a comparative study of their own. Note that in an application scenario, we often do not know whether the considerations (1–3) apply.
The comparative approach to structure inference is initiated from a set of homologous RNA sequences. Attempts are made to infer the in-vivo structure for each of them, as well as a consensus structure that captures the common, relevant structural aspects. The consensus structure per se does not exist in vivo, and so some mathematical rigour should be applied when working with this notion.
An RNA sequence is a string over the RNA alphabet {A, C, G, U}. An RNA sequence B = b1,...,bn contains n bases, but no structural information. For comparative analysis, we are given the RNA sequences B1,...,Bk. A secondary structure can be associated with each sequence B as a string S over the alphabet {"(",".",")"}, where parentheses in S must be properly nested, and B and S must be compatible: If (si, sj) are matching parentheses, then (bi, bj) must be a legal base-pair. A base-pair is also denoted as bi·bj, si·sj, or simply i·j when the sequence is clear from the context. Both sequences and structures may be padded with a gap symbol "-", in order to align sequences and structures of different lengths. For compatibility of padded sequences and structures, we require that bi = "-" iff si = "-".
A multiple structural alignment is a multiple sequence alignment of the 2 * k sequences, B1, S1,..., Bk, Sk, such that Bi is compatible with Si, and the following consistency criterion is satisfied: For any Si and Sj and any base-pair , we have ≠ ")" and ≠ "(", and if = "(" or = ")", then . This means that if one partner of a base-pair in Sj is aligned to one partner in Si, their partners must also be aligned to each other (see figure 2 for an illustration).
A consensus structure C for a multiple structural alignment can be determined by a majority rule approach using a threshold p with 0.5 <p ≤ 1. We define ck = x if = x for at least sequences Si, and ck = ".", otherwise. The latter definition is somewhat arbitrary; when relating the consensus structure to a particular sequence B in the alignment, we quietly turn those dots into gaps that align with gaps in B. For p = 1, we speak of a strict consensus, and the base-pair set in C is the intersection of the base-pairs in all Si.
A consensus structure exhibits base-pairs shared by the majority of structures under consideration, but has no sequence information associated with it. Each individual structure for a concrete sequence typically has additional base-pairs which are properly nested between those that constitute the consensus. Given a consensus structure C and a sequence B compatible with it, we can obtain a structure refold(B, C) which is the best thermodynamic folding for B that exhibits the base-pairs specified by C, plus additional ones that do not conflict with the former. Refolding can be achieved by RNAfold with option -C (this option is used to constrain the minimum free energy prediction with prior knowledge – such as known base-pairs, unpaired regions, etc). If B and S contain gaps, we remove them before refolding and reintroduce them in the same positions afterwards.
Given a consistent structural alignment, it is easy to derive a consensus structure, as we can count majorities at individual positions. If the 5' partner of a base-pair passes the majority threshold, consistency implies that its 3' partner also makes it into the consensus.
Given a consensus structure and a sequence alignment without structural information, we can approximate a structural alignment by computing Si = refold(Bi, C). We call this structural alignment reconstruction. While all Si will be consistent with C, and with each other as far as the base-pairs of C are concerned, they may be inconsistent for the base-pairs introduced in refolding. This is tolerable, since if we trust the consensus to capture the relevant common structural features, there is no need to require that all members of a family agree upon extra-consensus features.
We note in passing that it seems worthwhile to study the conditions under which consensus derivation and structural alignment reconstruction are mutually inverse operations, but such theoretical issues are outside our present scope.
Interpreting database information
While the plans A, B and C we are about to evaluate strive to find a good consensus structure from sequence data, the "truth" available to us comes in a different form. Structural databases only convey a consensus by example: They provide a reference sequence, say B1, with an experimentally proved structure S1, and provide a multiple sequence alignment of B1, S1 and additional sequences B2,..., Bn in the family under consideration. The sequence alignment is chosen to exhibit structural similarities between the reference structure and the other family members, but in general, we do not know the precise model of achieving similarity, nor do we know whether this model has been solved to optimality.
One consequence of this situation would be to conclude that the reference structure is the only reliable anchor point available to us for evaluation. Comparative analysis tools would then be evaluated by the capacity to predict this particular structure by using family information. This would be a meaningful way to proceed, however, the effect of structural homogeneity within a sequence family would go unmeasured, and so would the difficulty or success of exploiting it. We therefore proceed in a different way which we call consensus reconstruction.
The reference structure S1 need not be compatible with any Bi except for i = 1. However, we can still compute Si := refold(Bi, S1) by treating bases as unpaired where they violate compatibility. (This is also achieved with RNAfold, option -C.) What we obtain in this way is a reconstructed structural alignment, which will be consistent to the extent that the reference structure indeed describes the common structural features, and to the extent that the database sequence alignment reflects these. In all our test cases, this alignment was overall consistent, an indicator that the families and their structural features are in fact well defined. From this alignment, we derive a consensus structure as explained above using a threshold p = 0.5, which will serve as the standard of truth in our evaluation.
One may argue that our approach to reconstruct the truth is somewhat ad-hoc and should be replaced by a more systematic method. However, this is what the tools we evaluate try to achieve, and we should not add one of our own as the standard of truth. Hence, our consensus reconstruction is designed to stay as close as possible to the database information.
Caveats
Results of observations based on the above measures must be interpreted with care. We list a number of caveats that must be kept in mind when proceeding to the subsequent sections.
Use of defaults
In all tests, one could possibly obtain better predictions by tuning the program's parameters. We felt that it would be inappropriate to do so, since in the evaluation, we know the correct result and could use this knowledge in the tuning, whereas in a true application context, one does not have such guidance. Hence we used the recommended defaults in all cases.
Tool abuse
In some cases we apply a tool to data where we know that the model structure has features not recognised by the tool. An example is a structure with multiloops or pseudoknots, searched for with a tool that explicitly excludes such structures. We permit such cases, because again, in a true application context one does not know whether the tool is appropriate or not, and it is still of interest to see how close to the correct structure one can get.
Standard of truth
We take for granted the correctness of structural alignments taken from the literature, and the consensus reconstructed thereof. Should one of the tested algorithms produce a result that is actually better (closer to the functionally important structure), it may be penalised. Also, we do not consider a large number of data-sets here, it is possible that performance of some algorithms improves on a different selection of data-sets.
Tools improve
Our data reflect the state of the art in 2004. Most of the tools tested are very recent, and their authors are still improving them. Hence, not all observations will remain reproducible. In fact, we hope this study helps to obtain better results in the future.
Methods
We have compiled RNA sequence alignments consisting of up to 11 sequences derived from reliable sources (see table 1). These have been used to test several RNA analysis packages. Each alignment contains at least one reference sequence B1 with (preferably) an experimentally verified secondary structure S1. Experimental verification of a structure may be from a variety of sources: x-ray crystallography, NMR, enzymatic structure probing or phylogenetic inference. A comparison of phylogenetic with x-ray crystallographic structures has shown the phylogenetic predictions of rRNA to be very reliable (sensitivity > 97%) [61]. This data specifies a "consensus by example", as explained above, to which our consensus reconstruction was applied to obtain the "true" consensus.
To avoid results bias, we constructed test alignments, with corresponding phylogenies that, wherever possible, were free of highly similar clades. In addition, we endeavoured to ensure that the reference sequence was central to the phylogeny, or more specifically, not an out group. To meet these requirements, sequences from large data-sets were sorted into high-similarity and medium-similarity groups (with respect to the model sequence), from which maximum-likelihood phylogenies [62] were constructed. These were pruned until the desired size and topology was achieved. For each data-set two sequence alignments were constructed, one of high sequence identity (approximately 90–99%) and the other more diverse data-set of medium sequence identity (approximately 70–90%).
Our data-sets are quite diverse and must for the purposes of this study be considered difficult to analyse in structural terms. The shape of ribosomal RNA is believed to be influenced by interaction with ribosomal proteins. The shape of RNase P shows relatively little sequence and structure conservation, and furthermore, it contains pseudoknots which are generally excluded by prediction algorithms. Transfer RNAs are known to be a hard case for thermodynamic folding, primarily due to the propensity of modified bases which influence structure formation. All tools tested may perform better upon less complex data-sets, but the purpose of this study is not to show how good the algorithms are but to compare relative performance when prediction is difficult.
Performance Measures
Sensitivity (X) and selectivity (Y) are common measures for determining the accuracy of prediction methods [63]. Selectivity is also known as the "specificity" [28] and "positive predictive value" [64,65]. We use slightly modify versions of the standard definitions of X and Y for examining RNA secondary structure prediction:
where TP is the number of "true positives" (correctly predicted base-pairs), FN is the number of "false negatives" (base-pairs in the reference structure that were not predicted) and FP is the number of "false positives" (in-correctly predicted base-pairs). However, not all FP base-pairs are equally false! We classify FP base-pairs as either inconsistent, contradicting or compatible. Predicted base-pairs which conflict with a base-pair in the reference structure are labelled inconsistent (i.e. i·j is predicted where either i·k and/or h·j are paired in the reference structure (h ≠ i and j ≠ k)). Predicted base-pairs (i·j) which are non-nested with respect to the reference structure are labelled contradicting (i.e. there exists base-pairs k·l in the reference satisfying k <i <l <j). Note that some base-pairs may both contradict and be inconsistent with the reference structure. Predicted base-pairs which are neither true positive, contradicting or inconsistent are labelled compatible and can be considered neutral with respect to algorithm accuracy. Hence these are subtracted in the selectivity evaluation, their number is ξ in the above equation. It is of interest to note that the base-pair metric [66,67] between the reference and predicted structures dBP(Sref, Spred) is the sum of FN and FP, and hence is different from the measure used here.
A measure combining both selectivity and sensitivity is useful for ranking algorithms. For this we employ the Matthews correlation coefficient [63] defined below:
MCC ranges from -1 for extremely inaccurate (TP = TN = 0) to 1 for very accurate predictions (FP - ξ = FN = 0). When comparing RNA structures TN = 0 occurs only in extreme examples, hence MCC generally ranges from 0 to 1. Furthermore, for the specific case of RNA structure comparisons, MCC can be approximated by the arithmetic-mean or geometric-mean of X and Y [28].
Results
Single sequence methods
The accuracy of the MFE single sequence method has been evaluated elsewhere and was found to have an accuracy of 73% when averaged over many different RNAs and "base-pair slippage" was tolerated in the evaluation [68]. A recent and more stringent work found MFE predictions had a sensitivity of 56% and selectivity of 46% for RNase P, SRP and tmRNA structures [64]. Similar values are also reported by the "Gutell Lab" for tRNA and rRNA structures [69-71]. We need to clarify the accuracy of this method on the particular data-sets we employ here for comparison with the multi-sequence methods. After all, if MFE folding worked perfectly for our given data-sets, there would be no need to resort to comparative methods.
Mfold & RNAfold
Mfold [12,72] and RNAfold [13,73] both implement the Zuker-Stiegler algorithm for computing minimal free energy (MFE) structures assuming a "nearest neighbour model" and using empirical estimates of thermodynamic parameters for neighbouring interactions and loop entropies to score structures. The algorithm is O(n3) in time and O(n2) in memory where n is the sequence length. Both employ the same thermodynamic parameters [68]. Hence, differences in the predictions are generally minor and are the result of slightly different implementations. There appears to be no significant differences in terms of algorithm accuracy.
The sensitivity, selectivity and correlation of MFE methods (for the four data-sets considered here) ranged from 22–63%, 20–60% and 0.18–0.61 respectively (See figures 3 &4). The low accuracies (22%, 20% & 0.18) are due to an alternative long-stem conformation of S. cerevisiae tRNA-PHE which the free energy methods favour. Mfold infers 'suboptimal' structures by calculating minimum free energy structures with the restriction that every possible base-pair is forced in a one-by-one fashion. Unique structures are then ranked by energy. Investigating the top two suboptimal structures from Mfold resulted in an overall increase in the range of sensitivity, selectivity and correlation, 22–69%, 20–67% and 0.18–0.68 respectively. The predictions shown here are used to illustrate the potential advantages of using comparative analyses over single sequence methods.
Sfold
Sfold [41,74] represents another energy-based single-sequence folding algorithm. For a given RNA, Sfold stochastically samples all possible structures in the Boltzmann ensemble of secondary structures using conditional probabilities which are computed with the partition function [75]. Clustering techniques could then be used to obtain representative ' likely ' structures. Instead, the current implementation samples 1000 structures, sorts these by energy, the minimum and maximum energy structures are computed and the energy range divided into 10 equally sized energy blocks. The minimum energy structure from each block is returned with ranking 1 to 10. We consider the top 3 structures labelled 'Sfold (1–3)'. In terms of accuracy, the results are very similar to those of the Zuker-Stiegler single sequence methods, although with a slightly higher variance (See figures 3 &4).
Intrinsic limits of single sequence methods
There are systematic limits to the accuracy of single sequence prediction methods. The thermodynamics may not be accurate, as some parameters are extrapolated and parameter measuring conditions in vitro are different from in vivo conditions. Indeed the thermodynamic model itself is an estimate of the real physics of RNA folding. Also, many bases of structural RNAs are chemically modified by sugar methylation, pseudo-uridine, dihydrouracil, etc, these are generally ignored by these methods. Kinetics of folding are also ignored. Given only a single sequence, we have no way to distinguish base-pairs and structure elements important for the consensus from those that are peculiar for the given sequence. Finally, some functional RNAs have bistable structures, while in others, the structure is irrelevant, hence not conserved, and the optimal MFE structure is biologically meaningless. This is some justification of why researchers proceed to comparative methods.
Comparative method: alignment folding (plan A)
To simulate realistic RNA folding studies we use ClustalW [15] to re-align each of our test data-sets, then folded these using each of the methods mentioned below. The resultant predicted structures were then compared to our reconstructed consensus structures.
RNAalifold
RNAalifold [21,76] implements an extension of the Zuker-Stiegler algorithm for computing a consensus structure from RNA alignments. The algorithm computes an averaged energy matrix (where N is the number of sequences in the alignment) and a covariation score matrix, augmented with penalties for inconsistent sequences, Bij. A standard trace-back procedure is performed to recover a consensus structure with the optimal sum-of-average-energy-and-covariation-score . The algorithm is remarkably efficient O(N·n2 + n3) in time and O(n2) in memory.
The sensitivity, selectivity and correlation of the RNAalifold predictions ranged from 57–91%, 57–100% and 0.57–0.95 respectively, showing a significant increase in the accuracy measures when compared to the MFE-methods.
Pfold
Pfold implements a "stochastic context free grammar" (SCFG) designed to produce a "prior probability distribution of RNA structures" for an RNA alignment input [23,24,77]. A maximum-likelihood phylogeny is used to weight posterior probabilities computed from large reference data-sets.
The algorithm is generally accurate and efficient. Hence, the over-all sensitivity, selectivity and correlation of the Pfold predictions ranged from 0–100%, 0–100% and 0.0–1.0, respectively. But removing those points where Pfold predictions were empty structures (LSU rRNA (H & M) and SSU rRNA (M), see figure 3), the prediction accuracies ranged from 66–100%, 89–100% and 0.77–1.0, respectively. The zeros are due to 'under-flow errors', a solution is presently under construction by the authors (pers. commun. Bjarne Knudsen).
ILM
ILM (iterated loop matching) is one of the few comparative RNA folding algorithms which can return pseudo-knotted structures [22,78]. It uses a combination of thermodynamic and mutual information content scores [18] to produce a secondary structure. All possible stems ("small" internal loops and bulges inclusive) are generated and ranked according to a combination of thermodynamic and mutual-information scores. The stem with maximal score is selected, scores are updated and stems conflicting the selection removed, then the next highest scoring stem is selected. This algorithm is iterated until no stems remain. ILM generally ranked low in terms of selectivity and was not as sensitive as either RNAalifold or Pfold on the high similarity data, but did improve on the medium similarity data-sets (see figure 3). The over-all sensitivity, selectivity and correlation of ILM predictions ranged from 44–100%, 37–75% and 0.40–0.86, respectively. To ensure the low selectivity values weren't due to the reference-structure being pseudo-knot free we re-evaluated ILM with reference-structures replete with pseudo-knots. The new sensitivity, selectivity and correlation values ranged from 31–100%, 26–75% and 0.29–0.86, in fact evaluating with pseudo-knotted structures did little to increase ILM selectivity. But, keep in mind that the sensitivity of the other (non-knot-inclusive) methods must decrease when a significant proportion of the true base-pairs are engaged in pseudo-knots.
The inclusion of pseudo-knots prediction vastly increases the number of possible secondary structures, this is why they are generally excluded from exhaustive folding algorithms. In addition, there is a general lack of experimentally derived thermodynamic parameters which include pseudo-knots. ILM is a method still under development, hence the performance may improve once pseudo-knots can be more accurately modelled.
Comparative method: simultaneous sequence alignment and folding (plan B)
Sankoff
The Sankoff algorithm is a dynamic programming approach to obtain a common base-pair list with maximal sum of base-pair weights. Basically, this is a merger of sequence alignment and Nussinov [79] (maximal-pairing) folding dynamic programming methods [26]. Sankoff's algorithm can be used to obtain both an alignment and consensus structure. Full implementations of the "Sankoff algorithm" for the solution of simultaneous RNA folding, alignment and protosequence problems have proven too computationally taxing (O(n3m) in time, and O(n2m) in space for sequence length n and m sequences) to be practical [25]. Hence, three restricted versions of this algorithm have been implemented. These are Foldalign [27], Dynalign [29] and recently PMcomp has also been published [26]. Carnac [80,81] is another recent innovation designed to detect conserved stems in unaligned sequences, we include it here as a relative of the Sankoff approach.
Foldalign
Foldalign [27] can be interpreted as "a mixture of local alignment and maximum number of base-pairs algorithm" [28,82]. A combination of "clustal" [15] and "consensus" [83] heuristics are used to build multiple sequence alignments from pair-wise comparisons. Restricting maximum motif size (for this study 50 was used) and forbidding bifurcating structures (multi-loops) reduces the time complexity to O(n4N) in time (where N is the number of sequences and n is the length of the longest sequence). A simple match-based scoring scheme is used to rank putative conserved structure elements.
The Tool Abuse Caveat generally applies to the tool Foldalign as all of our data-sets contain multi-loops. The use of Foldalign for the prediction of global, multi-looped secondary structures is not recommended-as Foldalign is specifically designed for the location of short regulatory motifs such as IREs [84] where the motifs are only related at the level of (non-bifurcating) structure and not at the level of sequence. Hence the relatively poor sensitivity, selectivity and correlation, which ranged from 5–24%, 23–36% and 0.11–0.27 respectively, for our test data-sets.
Dynalign
Dynalign [29,85] is a pairwise implementation of the Sankoff algorithm, which uses a "full energy model" to locate a common low energy structure (including multi-loops) and align two structural RNAs. The computational complexity of the full Sankoff is reduced by restricting the difference in the indices i and j of aligned nucleotides (where i indexes positions in sequence 1 and j indexes sequence 2) to be less than M. In addition, Dynalign uses the same method employed by MFold to reduce the conformation space, by limiting the size of internal loops [29,86]. The complexity is thus reduced to O(n3M3).
The current Dynalign implementation is restricted to pair-wise sequence comparisons. Rather than compute all pairwise foldings we compared all sequences with the reference structure. Due to the computational expense of this algorithm it could only be used to predict tRNA and RNase P structures. Dynalign performed well on the tRNA, medium sequence homology data-set (sensitivity, selectivity and correlation of 94%, 95% and 0.94 respectively, when averaged over all pairwise alignments with the reference sequence). With this one high-scoring point removed, averaged sensitivity, selectivity and correlation values ranged from 32–54%, 33–54% and 0.32–0.54 respectively. Comparing the performances of MFold and Dynalign showed that MFold performance was always superior on the RNase P data-set, Dynalign however did much better on the shorter and more diverse tRNA sequences. Performance gains could be made by investing more computer time and refolding RNase P with larger ' maximum insert size', which was set to 10 during this study. The use of Dynalign on the RNase P data-sets in this study is therefore a case of tool-abuse, as the parameters recommended by the authors of Dynalign were not used (to ensure calculations completed in reasonable time).
Carnac
The Carnac algorithm, as mentioned previously, is not strictly an implementation of the Sankoff algorithm. A set of filters are employed through which sets of sequences are passed in a pair-wise fashion [80,81,87]. Sequences are scanned for stems and "high similarity" regions of sequences (dubbed "anchor points") are identified, a dynamic program is used to select conserved stems using anchor point and covariation information.
The Carnac algorithm was remarkably selective at base-pair predictions. However, the sensitivity of the algorithm was generally low, although when evaluated with the correlation coefficient it is comparable to RNAalifold and Pfold. Sensitivity, selectivity and correlation values for Carnac predictions ranged from 45–71%, 92–100% and 0.65–0.82 respectively. The sensitivity of Carnac can be increased by constraining a minimum free energy fold (i.e. with "RNAfold-C") with the Carnac predicted structure, but this cost in terms of selectivity. On average this increased the sensitivity by 22.5, decreased the selectivity by 17.2 and slightly increased the correlation by 0.05.
Alignment of predicted structures (plan C)
RNA forester
RNAforester [37,88] implements the tree alignment model. In contrast to approaches that produce only a similarity value, but no underlying alignment, it computes pairwise alignments of two input structures. RNAforester can produce either global or local alignments; we used the global mode. A structure alignment is itself a branching (tree-like) structure; the set of matched base-pairs can be derived from it and evaluated as with the other approaches.
We used the tRNA and RNase P data-sets and generated structure single sequence predictions with RNAfold. All predicted structures were aligned pairwise and a neighbour-joining approach used to cluster and align high similarity sequences and structure profiles. The highest scoring alignment was used to derive a predicted consensus that was evaluated against the consensus tRNA model structures. Sensitivity, selectivity and correlation ranges of consensus structures computed from the highest scoring RNAforester alignments were 29–67%, 27–67% and 0.26–0.66 respectively. It seems likely that much of the inaccuracy of this approach is due to MFE structure prediction, however the structure-clustering approach frequently separates mis-folded MFE predictions from the accurate folds.
MARNA
The MARNA algorithm [39,89] proceeds by constructing edge weights between nucleotides in a pairwise fashion. Weights are structure-enhanced-sequence-similarities transformed from edit distances proposed by Zhang [90]. Phase two pipes the set of alignment edges into t-coffee [16] for multiple alignment production. The resultant alignments are not strictly structural alignments in the sense defined above. Rather, these are sequence alignments influenced by structure.
Sensitivity, selectivity and correlation values of consensus structures computed from MARNA alignments of MFE structures ranged from 29–52%, 32–84% and 0.30–0.65 respectively. We also tried trimming high entropy base-pairs from the MFE predictions using the bound Qij > 1, where , , and pij are pair-probabilities computed using McCaskilPs partition function [75]. The new accuracy ranges were 29–71%, 92–100% and 0.53–0.84. A related approach for trimming of low probability was recently shown to improve the selectivity of MFE predictions [65]. MARNA is generally less dependant upon the accuracy of the input structures hence performs slightly better with the poorly predicted tRNA structures than RNAforester.
Discussion
We have evaluated three different strategies for comparative structure prediction, and altogether eight tools (not counting the single sequence methods). The results of which are summarised in figures 3 &4. A surprising discovery given that the test data-sets are so diverse is that algorithm specific clusters formed in sensitivity versus selectivity scatter plots, indicating algorithm-specific eccentricities. A number of algorithms which might have been evaluated here have been excluded, primarily due to the heavy computational costs of the various implementations on our longer data-sets. We favoured recent algorithms which could be compiled on modern computers and those with input and output which could be simply dealt with (for example returning dot-bracket [13,37,91] or tabular-connect type formats [12,29,41], rather than coordinates and lengths of stacks or graphic (gif/pdf) representations favoured by a minority of researchers).
Practical recommendations
For well aligned short sequences, both Pfold and RNAalifold generally perform well, PFold performed marginally better than RNAalifold. It is likely that some moderate refinements to RNAalifold would improve accuracy without altering the efficiency, for example, if gaps were not penalised in the free-energy evaluation and a more sophisticated model for scoring mutations was employed, perhaps ribosum matrices [92] could be used to weight base-pair bonuses and penalties. For well aligned, long sequences the performance and speed of RNAalifold was excellent. For data-sets consisting of short (< 200 bases) and diverse sequences Dynalign might do well, as it does not require sequence similarity – in fact the scoring function does not include sequence comparison. Otherwise, one might choose to use a mixture of RNAalifold and/or Pfold to fold similar clades and RNAforester and/or MARNA to align folded clades. Advocates of plan A should note that many multiple sequence alignment algorithms generally do not favour transitions over transversions or employ ad hoc 2-parameter methods to model these (ClustalW [15] for example). Structural RNA sequences however evolve rapidly via structure neutral mutations which are frequently transitions and rarely transversions [92,93]. Multiple sequence algorithms which employ more complex yet more accurate models of sequence evolution will undoubtedly produce "better" alignments for folding.
Carnac produced highly selective structures for all the test data-sets, which if used to constrain a free energy fold produced sensitive predictions with a cost to selectivity. The consistency of Carnac performance is remarkable, for all the data-sets considered here this heuristic approach performed well. It is however unclear how Carnac will perform on highly diverse data-sets.
For advocates of plan C, we have an encouraging message: Both MARNA and RNAforester perform better on the medium similarity data than on high similarity data. This seems paradoxical at first glance, but one must understand that for an approach purely based on predicted structures, high sequence similarity can be a curse rather than a blessing: If sequences are very similar, they may jointly fold into the wrong MFE structure. With more sequence variation, it becomes more likely that at least some family members have good predictions, which by their mutual similarity can be picked out from the rest. This means that especially in the case of low sequence similarity, where nothing else works, plan C, currently the least explored strategy of all, has a certain promise.
Conclusions
Finally, let us outline some directions for future research.
An implementation of the single sequence pseudoknot algorithms [42,43,94] employing similar strategies to RNAalifold [21] for alignment folding would be most useful. Based upon the RNAalifold results this approach would dramatically increase the accuracy of these algorithms upon certain data-sets. Also, an extension of these allowing constrained foldings to incorporate prior knowledge would be of assistance, this has proved extremely useful for MFE predictions. Sampling structures from reference alignments is also likely to prove beneficial. The implementation of fast and accurate variants of the Sankoff algorithm remains an open problem.
Again allowing constrained foldings and alignments would be useful. The further development of "BLAST-like" folding heuristics for this should be a priority, obviously Carnac is a good start. The MARNA approach for producing structurally enhanced multiple alignments produced rather selective results after trimming high-entropy base-pairs from MFE predictions. This suggests that weighting edit-distances with partition-function derived probabilities or entropies will produce reasonable RNA alignments. A consensus structure could then be derived from MFE-structures or from PFold or RNAalifold predictions on the resultant alignment. This approach would effectively decouple the Sankoff algorithm into manageable structure-enhanced-alignment and folding stages.
Note added in proof
Two further developments are likely to increase the power of plan C. Pure multiple structure alignment (as opposed to pairwise alignment used here) presented in [95] may leave out some misfolded structures from a progressively constructed profile aligment. A small but representative set of near-optimal structures can now be derived by abstract shape analysis [96]. Combining both approaches, one could consider a progressive multiple alignment approach where these representative, near-optimal structures are included for each sequence.
More training data is essential for this field to progress, for this homology search tools are essential. Infernal [91,97] used to construct the Rfam database [98,99] is an excellent approach but sensitivity might be increased with a phylogenetic approach and RNA-specific sequence search tools. The implementation of methods combining energetics, covariation [21] and co-transcriptional folding [100] in a statistically reasonable manner is also a potentially fruitful direction for development.
Authors' contributions
PPG carried out the experiments, the analysis and drafted the manuscript. RG suggested comparing comparative structure prediction methods and assisted in the manuscript preparation. All authors read and approved the final manuscript.
Acknowledgements
The authors thank the numerous researchers who provided access, documentation and installation assistance for their algorithms; Notably Ivo Hofacker, Dave Mathews, Bjarne Knudsen, Matthias Hochsmann and Sven Siebert, authors of RNAalifold, Dynalign, Pfold, RNAforester and MARNA respectively. PPG thanks Niels Hansen and Andreas Wilm for useful discussions and advice. PPG was supported by a DFG (German Research Foundation) post-doctoral scholarship and a Carlsberg Foundation Grant (21-00-0680). The basis of much of this work was conceived at the ESF and NIH funded 2003 computational RNA workshop in Benasque, Spain. The authors thank the (mostly) anonymous reviewers for their constructive comments.
Figures and Tables
Figure 1 RNA analysis. Current automated approaches to analysing homologous RNA sequences and structures usually follow one of three "plans". Plan A uses aligned sequences (usually produced by a standard multiple sequence alignment algorithm) to infer a consensus secondary structure from the evolutionary and energetic information contained in an alignment. This is a highly successful approach, but is limited to data-sets with sequence homology high enough for the alignment step to work yet divergent enough for detection of structurally consistent mutations. Plan B employs the "Sankoff algorithm" to simultaneously align and infer a consensus structure. This algorithm requires extreme amounts of memory and time. Plan C aligns RNA structures rather than sequences. This approach can be used in the rare situation where reliable structures are known. Representative algorithms which could be used for each plan are indicated within the figure.
Figure 2 Alignment consistency. A violation of RNA structural alignment consistency is shown (left), together with a possible correction (right) – see text for details. Note that the inconsistent alignment may maximise sequence similarity, showing 3 mismatches versus 1 mismatch and 2 indels, with the concrete outcome depending on the gap scoring used. Inconsistency is the reason why it is dangerous to align two structures in string representation by a standard sequence alignment algorithm. Inconsistency is hard to detect by human eye inspection, and structural alignments in databases are not always free from consistency violations.
Figure 3 Prediction correlation with reality. Matthews correlation coefficient versus the logarithm of the sequence length for a range of different ncRNAs and structure prediction algorithms. Inset A shows accuracies of thermodynamic single sequence prediction algorithms. Insets B and C shows accuracies of comparative methods on the high and medium similarity data-sets respectively.
Figure 4 ROC plots. We use ROC (receiver operating characteristic) plots to simultaneously display both sensitivity and selectivity for plans A, B and C respectively. Accuracies of the MFE methods (MFold, RNAFold and SFold) are shown in each plot to provide a base-line. Points on the line X = Y are as sensitive as they are selective, points below this line indicates a greater selectivity, points above indicate greater sensitivity. Points below the line X = 100 - Y are worse than "random" assignments; Assuming base-pairs are independent of each other (this is false for base-pairing). Points in the top right corner are "perfect" predictions. Interestingly many algorithms form characteristic clusters in these plots. Where the variance is sufficiently small these have been indicated with a closed curve.
Table 1 Characteristics and sources of the four test data-sets, columns from left to right show: data-set, lengths, mean pair-wise sequence similarity (mean pair-wise Kimura "2-parameter" distance is shown in parentheses [109]), the number of sequences in each alignment and the alignment and structure sources are given.
Test data-set characteristics and sources
Data-set length mean pairwise seq. identity Number of Sequences Alignment source Structure source
High Med. High Med.
E. coli LSU rRNA 2904 88.1 (0.12) 72.0 (0.35) 11 11 Wuyts et al., (2001) Cannone et al., (2002)
E. coli SSU rRNA 1542 90.7 (0.08) 80.0 (0.21) 11 11 Wuyts et al., (2002) Cannone et al., (2002)
E. coli RNase P 377 81.5 (0.09) 67.1 (0.41) 9 11 Brown, (1999) Brown, (1999)
S. cerevisiae tRNA-PHE 73 84.4 (0.19) 60.0 (0.71) 11 11 Griffiths-Jones et al., (2003) Sundaralingham & Rao, (1975)
Table 2 The following tables display results of several structure predictions using a variety of algorithms upon data-sets containing either S. cerevisiae tRNA-PHE, E. coli RNase P, E. coli SSU rRNA or E. coli LSU rRNA sequences. Reading columns from left to right we show: prediction method, number of base-pairs in the reference structure, number of base-pairs in the predicted structure, the number of true positive base-pairs in the prediction (% sensitivity as described earlier in parentheses), the number of false positive base-pairs in the prediction (% selectivity as described earlier in parentheses), correlation values are the "Matthews correlation coefficient" (with approximate correlation in parentheses). Each of these MFE-based attempts to predict the famous S. cerevisiae tRNA-PHE structure converges on an alternative lengthy-helix type structure. Adding prior knowledge, such as forcing modified bases in the RNA sequence to be unpaired can produce dramatic improvements.
S. cerevisiae tRNA-PHE: Single Sequence Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
RNAfold 18 23 4 (22.2) 16 (20.0) 0.178 (21.1)
Mfold (1) 18 21 4 (22.2) 14 (22.2) 0.191 (22.2)
Mfold (2) 18 22 8 (44.4) 11 (42.1) 0.409 (43.3)
Mfold (3) 18 23 4 (22.2) 16 (20.0) 0.178 (21.1)
Sfold (1) 18 23 4 (22.2) 16 (20.0) 0.178 (21.1)
Sfold (2) 18 23 4 (22.2) 16 (20.0) 0.178 (21.1)
Sfold (3) 18 21 4 (22.2) 14 (22.2) 0.191 (22.2)
Table 3 Generally the comparative approaches perform much better than MFE methods at determining S. cerevisiae tRNA-PHE structure. For the consensus predictions of RNAalifold and Carnac we also computed "filled" structures using constrained MFE predictions. This usually improved the sensitivity of the methods. PFold a built-in stem-extension procedure to fill structures. As the tRNA structure contains a multi-loop Foldalign is not expected to perform well here. Dynalign performed well on the most diverse data-set (M) but didn't do well on the high similarity data-set. The structure alignment methods generally did poorly here. Most probably due to the miss-folded MFE structure which were used as input. Trimming high entropy base-pairs from the input structures produced modest improvements.
S. cerevisiae tRNA-PHE: Comparative Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
Plan A: ClustalW Alignment
RNAalifold (H) 21 20 19 (90.5) 0 (100.0) 0.950 (95.2)
RNAalifold (H) + RNAfold-C 21 21 21 (100.0) 0 (100.0) 1.000 (100.0)
RNAalifold (M) 18 14 14 (77.8) 0 (100.0) 0.880 (88.9)
RNAalifold (M) + RNAfold-C 18 21 18 (100.0) 0 (100.0) 1.000 (100.0)
ILM (H) 21 24 16 (76.2) 7 (69.6) 0.722 (72.9)
ILM (M) 18 30 18 (100.0) 6 (75.0) 0.863 (87.5)
Pfold (H) 21 21 20 (95.2) 0 (100.0) 0.975 (97.6)
Pfold (M) 18 21 18 (100.0) 0 (100.0) 1.000 (100.0)
Plan B: Unaligned sequences
Carnac (H) 21 17 15 (71.4) 1 (93.8) 0.815 (82.6)
Carnac (H) + RNAfold-C 21 21 19 (90.5) 1 (95.0) 0.925 (92.7)
Carnac (M) 21 13 12 (57.1) 1 (92.3) 0.722 (74.7)
Carnac (M) + RNAfold-C 21 22 16 (76.2) 5 (76.2) 0.757 (76.2)
Dynalign (H) 21 22.40 11.50 (54.78) 10.20 (54.45) 0.5353 (54.59)
Dynalign (M) 21 21.10 19.80 (94.27) 1.20 (95.00) 0.9448 (94.64)
Foldalign (H) 21 16 5 (23.8) 11 (31.2) 0.259 (27.5)
Foldalign (M) 21 16 5 (23.8) 10 (33.3) 0.268 (28.6)
Plan C: Structure alignment
MARNA (H) 21 19 6 (28.6) 12 (33.3) 0.295 (31.0)
MARNA (M) 21 22 7 (33.3) 15 (31.8) 0.311 (32.6)
MARNA-trim (H) 21 6 6 (28.6) 0 (100.0) 0.530 (64.3)
MARNA-trim (M) 21 15 15 (71.4) 0 (100.0) 0.843 (85.7)
RNAforester (H) 21 23 6 (28.6) 16 (27.3) 0.263 (27.9)
RNAforester (M) 21 21 14 (66.7) 7 (66.7) 0.659 (66.7)
Table 4 Note the improvement in prediction accuracy on the supposedly more difficult and longer E. coli RNase P data-set. This shows that MFE methods are less sensitive to folding errors on longer data-sets but are also less likely to resolve the entire structure. There is little difference in algorithm accuracy for each of the methods explored here. Each employs the same energy parameters so differences are due to slightly different implementations.
E. coli RNase P: Single Sequence Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
RNAfold 110 116 69 (62.7) 46 (60.0) 0.612 (61.4)
Mfold (1) 110 118 67 (60.9) 49 (57.8) 0.591 (59.3)
Mfold (2) 110 114 67 (60.9) 46 (59.3) 0.599 (60.1)
Mfold (3) 110 118 76 (69.1) 37 (67.3) 0.680 (68.2)
Sfold (1) 110 116 73 (66.4) 42 (63.5) 0.647 (64.9)
Sfold (2) 110 119 86 (78.2) 28 (75.4) 0.767 (76.8)
Sfold (3) 110 117 61 (55.5) 55 (52.6) 0.538 (54.0)
Table 5 RNase P is a difficult data-set to study. Five sequences in the high similarity data-set are truncated at both the 5 and 3 prime ends (due to the primers used for sequencing these). Sequences in the medium similarity data-set are full-length but do not align well using traditional tools such as ClustalW. Values corresponding to the re-evaluation of ILM with pseudo-knot inclusive reference structures are indicated by "ILM-pknot".
E. coli RNase P: Comparative Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
RNAalifold (H) 71 113 56 (78.9) 16 (77.8) 0.782 (78.3)
RNAalifold (H) + RNAfold-C 71 119 55 (77.5) 16 (77.5) 0.773 (77.5)
RNAalifold (M) 54 66 31 (57.4) 23 (57.4) 0.571 (57.4)
RNAalifold (M) + RNAfold-C 54 77 33 (61.1) 16 (67.3) 0.639 (64.2)
Pfold (H) 71 67 47 (66.2) 6 (88.7) 0.765 (77.4)
Pfold (M) 54 87 47 (87.0) 4 (92.2) 0.895 (89.6)
ILM (H) 71 124 31 (43.7) 54 (36.5) 0.395 (40.1)
ILM (M) 54 133 38 (70.4) 31 (55.1) 0.620 (62.7)
ILM-pknot (H) 110 124 53 (48.2) 65 (44.9) 0.463 (46.5)
ILM-pknot (M) 110 133 44 (40.0) 75 (37.0) 0.382 (38.5)
Plan B: Unaligned sequences
Carnac (H) 71 40 36 (50.7) 0 (100.0) 0.712 (75.4)
Carnac (H) + RNAfold-C 71 116 50 (70.4) 25 (66.7) 0.684 (68.5)
Carnac (M) 97 80 63 (64.9) 3 (95.5) 0.787 (80.2)
Carnac (M) + RNAfold-C 97 118 78 (80.4) 25 (75.7) 0.779 (78.1)
Foldalign (H) 71 41 14 (19.7) 25 (35.9) 0.265 (27.8)
Foldalign (M) 97 24 5 (5.2) 17 (22.7) 0.107 (13.9)
Dynalign (H) 71 95.13 28.63 (40.31) 41.50 (39.59) 0.3974 (39.96)
Dynalign (M) 97 103.20 31.00 (31.95) 61.50 (32.80) 0.3208 (32.39)
Plan C: Structure alignment
MARNA (H) 71 89 37 (52.1) 23 (61.7) 0.566 (56.9)
MARNA (M) 97 60 48 (49.5) 9 (84.2) 0.645 (66.8)
MARNA-trim (H) 71 52 37 (52.1) 3 (92.5) 0.694 (72.3)
MARNA-trim (M) 97 43 39 (40.2) 1 (97.5) 0.625 (68.9)
RNAforester (H) 71 114 40 (56.3) 31 (56.3) 0.562 (56.3)
RNAforester (M) 97 117 64 (66.0) 44 (59.3) 0.624 (62.6)
Table 6 E. coli SSU rRNA with a length of approximately 1600 nucleotides is beyond the reach of many structure prediction algorithms such as RNAforester and Dynalign. The minimum free energy methods, however, can produce results.
E. coli SSU rRNA: Single Sequence Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
RNAfold 468 493 207 (44.2) 271 (43.3) 0.437 (43.8)
Mfold (1) 468 480 240 (51.3) 224 (51.7) 0.515 (51.5)
Mfold (2) 468 487 242 (51.7) 229 (51.4) 0.515 (51.5)
Mfold (3) 468 487 202 (43.2) 273 (42.5) 0.428 (42.8)
Sfold (1) 468 481 232 (49.6) 229 (50.3) 0.499 (49.9)
Sfold (2) 468 499 231 (49.4) 249 (48.1) 0.487 (48.7)
Sfold (3) 468 475 232 (49.6) 230 (50.2) 0.498 (49.9)
Table 7 The probabilistic approach of PFold can, on occasion, suffer from "under-flow" errors caused by multiplying many probabilities together producing numbers too low to be dealt with on modern computers. This is what has happened on the medium similarity data-set.
E. coli SSU rRNA: Comparative Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
Plan A: ClustalW Alignment
RNAalifold (H) 460 472 275 (59.8) 179 (60.6) 0.601 (60.2)
RNAalifold (H) + RNAfold-C 460 483 273 (59.3) 195 (58.3) 0.588 (58.8)
RNAalifold (M) 441 433 372 (84.4) 32 (92.1) 0.881 (88.2)
RNAalifold (M) + RNAfold-C 441 469 388 (88.0) 44 (89.8) 0.889 (88.9)
Pfold (H) 460 377 326 (70.9) 26 (92.6) 0.810 (81.7)
Pfold (M) 441 0 0 (0.0) 0 (0.0) 0.000 (0.0)
ILM (H) 460 565 236 (51.3) 313 (43.0) 0.469 (47.1)
ILM (M) 441 564 264 (59.9) 249 (51.5) 0.554 (55.7)
ILM-pknot (H) 468 565 236 (50.4) 311 (43.1) 0.466 (46.8)
ILM-pknot (M) 468 564 266 (56.8) 258 (50.8) 0.537 (53.8)
Plan B: Unaligned sequences
Carnac (H) 460 233 206 (44.8) 12 (94.5) 0.650 (69.6)
Carnac (H) + RNAfold-C 460 470 332 (72.2) 112 (74.8) 0.734 (73.5)
Carnac (M) 448 294 259 (57.8) 18 (93.5) 0.735 (75.7)
Carnac (M) + RNAfold-C 448 471 337 (75.2) 110 (75.4) 0.753 (75.3)
Table 8 E. coli LSU rRNA is approximately 3350 nucleotides in length. The longest member of our test-set. The highest ranked Sfold prediction is remarkably poor, resolving just 5.8% of the reference structure.
E. coli LSU rRNA: Single Sequence Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
RNAfold 839 906 435 (51.8) 430 (50.3) 0.510 (51.1)
Mfold (1) 839 883 458 (54.6) 383 (54.5) 0.545 (54.5)
Mfold (2) 839 892 480 (57.2) 364 (56.9) 0.570 (57.0)
Mfold (3) 839 889 454 (54.1) 392 (53.7) 0.539 (53.9)
Sfold (1) 839 903 49 (5.8) 811 (5.7) 0.057 (5.8)
Sfold (2) 839 878 432 (51.5) 411 (51.2) 0.513 (51.4)
Sfold (3) 839 882 384 (45.8) 463 (45.3) 0.455 (45.6)
Table 9 Pfold predictions on both the high and medium similarity data-sets underflow on E. coli LSU rRNA. RNAalifold and Carnac, however, produce reasonable results.
E. coli LSU rRNA: Comparative Methods
Algorithm number of bps in reference number of bps in prediction True Positives (% sensitivity) False Positives (% selectivity) Correlation (%)
Plan A: ClustalW Alignment
RNAalifold (H) 794 879 627 (79.0) 195 (76.3) 0.776 (77.6)
RNAalifold (H) + RNAfold-C 794 871 629 (79.2) 185 (77.3) 0.782 (78.2)
RNAalifold (M) 819 721 614 (75.0) 53 (92.1) 0.831 (83.5)
RNAalifold (M) + RNAfold-C 819 790 691 (84.4) 78 (89.9) 0.871 (87.1)
Pfold (H) 794 0 0 (0.0) 0 (0.0) 0.000 (0.0)
Pfold (M) 819 0 0 (0.0) 0 (0.0) 0.000 (0.0)
ILM (H) 794 1048 389 (49.0) 602 (39.3) 0.438 (44.1)
ILM (M) 819 1161 560 (68.4) 405 (58.0) 0.630 (63.2)
ILM-pknot (H) 869 1048 272 (31.3) 759 (26.4) 0.287 (28.8)
ILM-pknot (M) 869 1161 377 (43.4) 629 (37.5) 0.403 (40.4)
Plan B: Unaligned sequences
Carnac (H) 816 422 390 (47.8) 7 (98.2) 0.685 (73.0)
Carnac (H) + RNAfold-C 816 873 674 (82.6) 156 (81.2) 0.819 (81.9)
Carnac (M) 821 508 463 (56.4) 14 (97.1) 0.740 (76.7)
Carnac (M) + RNAfold-C 821 865 682 (83.1) 147 (82.3) 0.827 (82.7)
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| 15458580 | PMC526219 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Sep 30; 5:140 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-140 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1481547655810.1186/1471-2105-5-148Methodology ArticleRank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data Martin Dietmar E [email protected] Philippe [email protected] Michael N [email protected] Michel [email protected] Biozentrum, University of Basel, CH-4056 Basel, Switzerland2 CRBM, FRE2593, CNRS, Montpellier, France2004 11 10 2004 5 148 148 22 7 2004 11 10 2004 Copyright © 2004 Martin 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
A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions.
Results
We describe a new method, Rank Difference Analysis of Microarrays (RDAM), which estimates the total number of truly varying genes and assigns a p-value to each signal variation. Information on a group of differentially expressed genes includes the sensitivity and the false discovery rate. We demonstrate the feasibility and efficiency of our approach by applying it to a large synthetic expression data set and to a biological data set obtained by comparing vegetatively-growing wild type and tor2-mutant yeast strains. In both cases we observed a significant improvement of the power of analysis when our method is compared to another popular nonparametric method.
Conclusions
This study provided a valuable new statistical method to analyze microarray data. We conclude that the good quality of the results obtained by RDAM is mainly due to the quasi-perfect equalization of variation distribution, which is related to the standardization procedure used and to the measurement of variation by rank difference.
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Background
In a typical microarray experiment, thousands of genes have their relative expression levels measured in parallel under different biological states [1,2]. To identify differentially-abundant genes, most published methods [3-5] progress through a similar sequence of elementary steps. First, a normalizing procedure is applied to make data sets comparable. If certain experimental conditions comprise several replicates, methods based either on parametric or nonparametric tests usually reduce the number of values generated by using their means. Then, gene variation is quantified by a statistic derived from intensity measurements. Knowledge of the null distribution of the gene variation, which is the distribution of its statistic when only random fluctuations occur, allows p-values to be assigned to observed variations and genes to be ranked according to the significance of their variation. As the test is repeated as many times as there are genes, p-values are corrected accordingly, and the false discovery rate (FDR, "the expected proportion of false positives among all genes declared significantly differentially expressed"[6]) is estimated.
We describe here, in detail, a new analysis method that has been used to analyze the transcriptome in yeast [7]. This method is original in several respects. First, Rank Difference Analysis of Microarrays (RDAM) replaces raw signal by its rank (R), expressed on a 0–100 scale, and we show that this simple transformation is a powerful normalizing procedure. Also, RDAM does not reduce replicated signals to their means, but instead only considers variations, expressed as rank differences (RD), between individual experimental points. An essential step is the standardization of RD observed between two replicates, permitting easy access to the empirical null distribution and allowing accurate and precise p-values to be assigned to observed standardized RD (zRD). When dealing with replicated points, RDAM uses a random variable, the product of p-values (ppv), for which the null distribution is straightforward to compute in a manner that is independent of the experimental conditions. Finally, RDAM estimates the total number of truly varying genes (TV), assigns a p-value to each gene variation, characterizes the selection of a gene using the FDR and the percentage of truly varying genes included in the selection (sensitivity, S).
Analysis of synthetic data sets allowed us to specify the error distribution of all the estimators used (FDR, TV and S), and to demonstrate the strong predominance that the number of varying genes and the distribution of their variation have on the quality of the results.
We also analysed the transcriptional effects of the TOR2-controlled signaling function using a genome-wide microarray approach in yeast. In S. cerevisiae, TOR2 has two essential signaling functions. One, shared with TOR1, is required for translation initiation, transcription, and cell growth in response to the presence of nutrients [8-10]. The second is unique to TOR2, and functions in cell-cycle-dependent actin polarization and possibly in transcription [8,11]. A previous genetic screen for mutants defective in the TOR-shared and the TOR2-unique functions identified several TOR2 temperature-sensitive alleles [12]. In this study, we compared total transcription profiles for strain SH121, which is specifically defective in the TOR2-unique function, and its isogenic wild type counterpart SH100 [12].
Results
Standardization of positive variations
The simplest system to which our method can be applied comprises three experimental points, of which two are replicates, as described by the expression {Exp1A, Exp1B, Exp2A}, where the number refers to the biological condition and the final letter refers to the replicates. To identify significant variations in the comparison Exp2A vs. Exp1A, we have to first calculate the variation of gene i, VARi. From among several possibilities, we tested three different variation units: the fold change (FC), corresponding to the ratio of signals, the signal difference (SD), and the rank difference (RD). The RD uses a standardized signal measure that is independent of the scanner settings, because the signal is replaced by its rank, expressed on a 0–100 scale. This normalizing procedure consists of first calculating the absolute rank (AR) of each gene by ordering their signals from 0 to N (with the signals of all genes having a negative signal being set to zero, and N representing the number of non-null and non-negative signals) and then transforming the absolute rank value into a relative one (R = AR*100/N). In this way, all the signals are expressed on the same scale and are directly comparable.
We studied the variation distribution between the two replicates, i.e. Exp1A and Exp1B, reasoning that the observed empirical variation distribution would be an excellent approximation of the null distribution corresponding to the null hypothesis. The null hypothesis we have in mind states that all observed mRNA changes occurring under replicated conditions are due to a combination of biological and technological noise, and are not the result of any biologically significant process.
We first restricted our study to positive variations. As the distribution of positive variations should be the same in both comparisons – Exp1B vs Exp1A and Exp1A vs Exp1B – we plotted the positive variations against rank for both comparisons on a single graph. This revealed that the most salient property of variation distribution, common to all tested measures of variation, is its dependence on the signal rank. This is exemplified for the RD in figure 1A, which shows the absolute value of RD against the minimum of the ranks (i.e., |Ri(Exp1A) - Ri(Exp1B)| vs min{Ri(Exp1A), Ri(Exp1B)} for gene i). This mode of presentation can be interpreted in either of two ways: as a plot of the positive variations of both comparisons, or as the plot of the positive and the negative variations of a single comparison, with both of variation being represented by a positive number. Whichever interpretation is prefered, it should be underlined that this presentation allows all the gene variations to be taken in account, and ensures the uniqueness of the resulting variation distribution.
We tried to eliminate dependency of positive variation distribution on the signal rank by standardizing variations according to the general formula:
where VAR is to be replaced by any one of the variation units tested (SD, FC or RD). Using this expression, the sample mean and standard deviation (μVAR and stdVAR) were calculated for all genes having a rank within a given neighbourhood of Ri, the rank of gene i. This notation reflects the fact that the VAR distribution is not gene specific, but rank dependent.
Figure 1B shows the results obtained by applying this procedure to the distribution of zRD. In concrete terms, we traced, as shown in figure 1A, two standardization curves, μRD and stdRD, which provide for a given rank the local mean and standard deviation of all the genes with a similar rank. Then, each RDi was standardized according to (1). Figure 1B illustrates the beneficial effect of this standardization procedure: first, the mean and the std are no longer dependant upon the rank. Second, the distribution is equalized all along the rank scale, as shown by QQ-plots in figure 2A.
Comparable results are obtained if the standardization is applied to the FC or the SD, but the zRD gives the best results in terms of distribution equalization: figure 2B shows, for example, that QQplots derived from zFC are more erratic than those derived from zRD, which are almost identical to the first diagonal up to the 99th percentile.
The fact that the zRD distribution is independent of rank can be explained by the fact that each gene's zRDi follows the same zRD distribution. Therefore, we reasoned that the empirical cumulative frequency distribution, ecfd(zRD), approximates the distribution of zRD for any gene i under the null hypothesis, and we used Fo = 1 - ecfd(zRD), based on a comparison between two replicated experiments, to assign a p-value to any zRD calculated in a comparison between two different biological conditions (the p-value is defined as the probability of zRD of an unchanged gene i to be equal to or greater than the observed zRDi under the null-distribution Fo). Because of the very large number of genes present on a chip, the null distribution is sampled a great number of times, generating a quasi continuous set of points that spans a wide range of values. This improves the precision of the Fo curve and allows accurate p-values to be assigned for even large variations.
The entire procedure can then be applied to the comparison Exp2A vs. Exp1A. Because standardization curves constructed on the basis of the two replicates are used in the standardization process, the calculated zRD can be justifiably compared to the null distribution and interpolated on the Fo curve in order to assign a p-value to each gene variation. At this step positive and negative variations can be processed together, although it is necessary to keep track of the actual type of variation, i.e. positive or negative, in order to conduct subsequent analysis.
Standardization of negative variations
We also tested to see if it is possible to apply the same standardization techniques to negative variations. In figure 3A, we have plotted the opposite of absolute RD value against the maximum of the ranks (i.e., -|Ri(Exp1A) - Ri(Exp1B)| vs max{Ri(Exp1A), Ri(Exp1B)} for gene i). It is clear that the weak signals are characterized by a truncation of their variation distribution, as evidenced by the clear alignment of points between ranks 0 and 20. This explains why the standardization procedure applied in figure 3B fails to equalize the variation distribution, and also why the power of the test is lower for down-regulated genes than it is for up-regulated genes (see Discussion).
False Discovery Rate (FDR), Total Variation (TV) and Sensitivity (S)
Because the test is repeated N times, issues related to multitesting must be considered: the more tests that are performed, the more an outlier outcome becomes probable. In view of this, we first compute the observed distribution of zRD in the comparison Exp2A vs. Exp1A for increased and decreased genes, giving two curves: FINC for the positive variations and FDEC for the negative variations (in both cases F = 1 - ecfd (zRD)). Then we plot FINC (or FDEC) and F0 on the same graph, corresponding to the observed positive (or negative) variation distributions and to the expected variation distribution according to the null hypothesis, respectively (figure 4). In the following discussion, the rationale is the same for increased and decreased variations, and F stands for either FINC or FDEC (same remark for TV, FDR, S, K and N). In most encountered situations, F is on top of F0. F(x) gives the probability, for any variant or invariant gene i, of observing a zRDi that is at least as high as x, and NF(x) gives the corresponding number of genes. If 0 <= K <= 1 is the fraction of invariant genes, then KNF0(x) is the number of invariant genes that have a zRDi at least as high as x. As a consequence, NF(x) - KNF0(x) is an estimate of the number of variant genes with zRD equal to or greater than x. We can call k the value of x that gives the maximum number of variant genes, such as NF(k) - KNF0(k) = max(NF(x) - KNF0(x)), and use this value as an estimate of the total variation (TV), that is, the number of truly varying genes. As these quantities must verify the equation N = KN + TV, i.e., N = KN + NF(k) - KNF0(k), we can deduce that .
For each value of x, we estimate the False Discovery Rate,
and the sensitivity,
In the context of a transcriptome analysis, p-values reflect how probable it is that a variation reaches or exceeds an observed value. P-values can always be used to rank genes, but the selection of significant variations in the context of multiple testing requires defining significance levels that are far more stringent than 0.01 or 0.05, as used in single testing. FDR and S parameters allow this difficulty to be overcome: for each c used as a potential critical value, S(c) reflects the fraction of truly-varying genes that are selected by zRD>c, and FDR(c) estimates the fraction of selected genes that are likely to be invariant genes. It is therefore possible to plot FDR and S against c, and to construct, for positive and negative variations, what we call a selection abacus (figure 4).
Analysis of replicates
The simplest example of a replicated experimental scheme is the system {Exp1A, Exp1B, Exp2A, Exp2B}. While it would be tempting to average signals or ranks for each experimental condition and apply the method described above, this is not possible because averaging changes statistics and we have no practical way of obtaining the corresponding empirical null distribution.
In a first round of comparison, we conducted two analyses in parallel by applying RDAM to the first comparison Exp2A vs. Exp1A and to the second comparison Exp2B vs. Exp1B. Based on this first round of comparison, we obtained two p-values for gene i: p1i and p2i. It could occur that gene i is detected as an increasing variation in the first comparison and as a decreasing variation in the second comparison. In this case, we apply a direction rule to decide on the final direction of variation. We consider simply that the lowest p-value is in favor of its corresponding variation direction, and we set the p-value of the discordant comparison to one. Once we have calculated and possibly corrected the p-values, we construct a new random variable, the product of p-values, ppvi = p1i × p2i. To obtain an unbiased value for ppv, we apply the same procedure to a second round of comparison by exchanging Exp2A and Exp2B between the two comparisons, giving a second ppv value. The direction rule is applied to the two ppv before obtaining the final, averaged ppv.
The advantage of the random variable ppv is the ease of constructing its null distribution. In fact, the cfd(pi|H0) is a uniform distribution over the interval [0,1]. Therefore, for cases in which two independent comparisons between two sets of duplicates were to be considered, we constructed two sets, U1 and U2, of 100000 points uniformly distributed over the interval [-1,1], to take into account the possibility of increased and decreased variation for each point. These sets were randomized to make them independent in order to model the independence of measurement according to the null hypothesis. This hypothesis states that all variations are due to noise, and that for a particular gene all corresponding p values must be independent. Then, we apply the direction rule to the pair U1, U2 and calculate ppv for genes that are detected as increased. Thus, the F0 = 1 - cfd(log10(1/ppv)) curve allows the significance of any value for ppv to be tested. The significance of ppv combines the significance of variation within each individual comparison and the significance of the correlation between these variations. F curve is, as usual, the observed 1 - ecfd(log10(1/ppv)), and we get exactly the same kind of selector abacus, as shown in figure 4. Simulation of the ppv null distribution used exactly the same steps that the analysis process follows, i.e. application of the direction rule and construction of the product of p-values, and resulted in a null distribution model we found appropriate seeing, both with experimental and synthetic data sets, that the observed distribution of ppv matches the null distribution when no variation occurs (data not shown).
The system {Epx1A, Exp1B, Exp2A, Exp2B} allows the construction of two sets of sandardizing curves, one from Exp1 replicates and the other from Exp2 replicates. As these curves are not equivalent, it is necessary to carry out both analyses and then use the more conservative one, whichever has the lower F curve.
The generalization of the entire procedure to more than two replicates is straightforward. For example, with three replicates {Exp1A, Exp1B, Exp1C, Exp2A, Exp2B, Exp2C}, there are 3 × 2 ways of arranging the experiments in order to obtain different sets of comparisons. Each round of comparison gives three p-values for the gene i - p1i, p2i and p3i – and the direction rule is applied the following way: in case the gene i is detected as an increasing variation in the first comparison and as a decreasing variation in the two other comparisons, we compare p1i to p2i × p3i and determine the variation direction. Once we have calculated and possibly corrected the p-values, we obtain the product of p-values for gene i, ppvi = p1i × p2i × p3i. As the number of comparison rounds increases very rapidly with the number n of replicates (n!), we simply apply a circular permutation – the circular permutation of ABC consists in the subset of permutations ABC, BCA and CAB – to the replicates inside one of the biological condition which allows the number of rounds of comparison to be restricted to the number of replicates (n).
Generation of synthetic data
In order to test our method, we devised a way of generating synthetic data having similar statistical properties as real biological data. We selected two replicated experiments, Exp1A and Exp1B, as seeds for generating synthetic data, traced standardization curves, and calculated ecfd(zRD) for (Exp1A, Exp1B). We randomly selected half of the genes and exchanged the signals for Exp1A and Exp1B, giving two new data sets Exp1A' and Exp1B'. Random numbers uniformly distributed over interval [0,1] are generated for each gene. Each random number is interpolated on the inverse of the cfd of zRD to assign a random standardized variation zRDi to each gene i. New values Ri are obtained by adding or subtracting to the rank R'i of Exp1A' the rank difference RDi calculated by applying to zRDi the inverse of the normalization function (1). Rank values are finally back-converted into signal values by interpolation of the rank on the graph of signal vs. rank constructed with one of the original data sets. This procedure allows two data sets to be obtained, Exp1C and Exp1D, which are statistically indistinguishable from the original data. To obtain a synthetic data set in which a pre-determined subset of genes receives a significant variation value we can possibly add a second step. We selected in Exp1C and Exp1D a random subset of genes, for example 500 increasing genes and 500 decreasing genes. To these genes, a second random variation value is applied, but instead of drawing random numbers on the interval [0,1], we limit the selection to the interval [0,p]. If we set the limiting p to 0.10, then the variation applied to the subset will have a p-value <= 0.1. For the genes receiving an additional variation contribution, the mean magnitude of zRD that is calculated between the synthetic and the original data is proportional to the magnitude of the applied p-value, as shown in figure 5.
This entire procedure, composed of two successive steps, results in synthetic data sets of high quality, because the generation of data mimics the observed variation of genes. We compared the synthetic data sets Exp1C and Exp1D to the same natural data set Exp1A, and plotted the corresponding zRD of one comparison against the other, as shown in figure 5. We observed that these zRD were independent for genes that had not received an additional variation contribution, but were correlated for genes that had been changed. In general, however, this correlation was not absolute, except for some decreased genes. This phenomenon is explained by the high noise that characterizes weak signals: for such genes, there is a high probability that negative variation makes them reach the minimal rank value (zero). For high signals, there also exists a limit for the rank variation, but the noise is very small, and the truncation effect is not visible. We also observed that a small proportion of genes receiving an increased (decreased) variation contribution could be detected as increased (decreased) in one comparison, but decreased (increased) in another. All of these properties support the realistic nature of the synthetic data generated by our algorithm.
RDAM performances
We generated several synthetic data sets from the two experiments (Wt_t0a and Wt_t0b) by letting the number of increased and decreased genes equal 0, 100 or 500, and letting the maximum p-value of extra variation equal 0.3, 0.1, 0.05 or 0.01.
The first question we addressed relates to the effectiveness of our scoring method in discriminating among real variations: does the overall process rank the genes correctly? To address this point, genes were ranked according to their ppv, and the number of hits was computed in a series of sublists of increasing length, selected from the top. From this number of hits, and the number of genes in the sublist, we calculated the real S and FDR. Plotting FDR against S allowed us to visualize the respective effects of the number of replicates, the magnitude of the variations, the number of varying genes, and the direction of variation on the performance of our rank difference method. Figure 6 shows the effect of the first two parameters on the ranking of increasing genes, in this case 100 varying genes. The FDR50, defined as the FDR observed when S = 50%, can be used to demonstrate these effects. Increasing the number of replicates improves the scoring performance, but this improvement is strongly modulated by the magnitude of the applied variation. For example, when the number of replicates equals, successively, 2, 3 and 5, the FDR50 equals, respectively, 85%, 74% and 41% for small variations (p <= 0.3) and 3%, 0% and 0% for large variations (p <= 0.01). For variations that we consider from our experience to be realistic, i.e., p <= 0.10, the FDR50 is equal to 58%, 30% and 5%, respectively. The number of varying genes also has an important impact, since under the same variation conditions, but with 500 increased genes instead of 100, we measured FDR50 values of 24%, 8% and 1%, respectively, which represents a mean decrease in the FDR50 of 20 percentage points.
The second question we addressed is the quality of the FDR and S estimators. The genes were ranked according to their estimated FDR (or S), and the number of hits computed in a series of sublists of increasing length, selected from the top. From this number of hits, and from the number of genes in the sublist, we calculated the mean real FDR and S (figure 7). Both FDR and S are overestimated in this case, except for the point at 5% FDR in the groups with two replicates. If we consider individual comparisons, the distribution of errors has a higher variance for small estimator values. Despite this dispersion of errors in the low FDR range, the absolute number of genes attributed to a faulty category is always negligible and mainly conservative (overestimation of false positives), as shown in Table 1. Figure 7 shows that the ratio between real and estimated S is rather constant, and we found that this ratio was close to the ratio between the estimated and real TV.
Finally, we tested to see whether the independent analysis of positive and negative variations subsequent to standardization was dispensable, or if a one-step procedure could be used instead. In order to illustrate this point, we have constructed, from the experimental replicates Exp1A and Exp1B (sh100 at t = 0 h), a first group of two synthetic replicates having 500 increased and no decreased genes and a second group of two synthetic replicates without changed genes in order to reveal any clear differences that may exist between the one-step and the two-step procedures. Table 2 shows the number of genes selected at several FDR levels when the two competing methods were applied to the comparison between the two groups of synthetic data We can see that with the one-step analysis the number of true positives is lower and the estimate of FDR is largely biased toward higher values relative to the two-step analysis.
Comparison with SAM on synthetic data
The generation of synthetic data is also a powerful tool for comparing different methods of analysis. As an example, we conducted a systematic comparison between RDAM and SAM. We selected this method because it is popular, easy to use (there exists an Excel add-in), and can be considered as representative of numerous other nonparametric methods, which apply Monte Carlo procedures to estimate the distribution of the statistics used to quantify the relative difference of gene expression. Figure 6 shows that for two and three replicates our scoring procedure generates less FDR than SAM does across the entire sensitivity scale. For five replicates, the scoring procedure of SAM is better only in the low sensitivity (<20%) range. In terms of practical gain, and particularly when experimental costs are considered, the improvement obtained with RDAM is important because we have the same overall ranking quality as SAM but with one replicate less. We also compared the errors made on the estimation of FDR by the two methods, when the nominal FDR equals 20% (Table 3). We concluded that in this particular case FDR estimation was as good in RDAM (mean error of -4 percentage points and extreme error values of -7 and -1 percentage points) than in SAM (mean error of 0 percentage point and extreme error values of -6 and +5 percentage points). Other comparisons show that this conclusion holds true for all other conditions used to generate synthetic data sets (data not shown). However, we observed that SAM estimator was unable to reach the nominal level of FDR detection in case of few replicates and/or small extra variations (for example in case of two replicates and extra variation with p >= 0.10, the smallest FDR estimation delivered by SAM is greater than 20%). In conclusion the large differences in the number of true and false positives found between the RDAM and SAM methods (Table 3) are mainly explained by difference of scoring procedure efficiency between the two methods.
Analysis of the TOR experiment
When strains SH121 and SH100 were shifted to 37°C, RDAM detected roughly 2300–2500 genes as being either increased or decreased in each strain (column TV, Table 4). Most of these gene variations were caused by the temperature shift and were common to both strains, as shown by the differential analysis which detected only up-regulation of genes as a consequence of TOR2 temperature inactivation: 106 genes at 2 h and 92 genes at 6 h (column TV, Table 4). After 2 hours at 37°C, 19 annotated genes showed significant induction with a 10% FDR, whereas after 6 hours 39 genes were induced (supporting Tables 5,6 and 7 [see additional file 1, 2 and 3]). However, these two groups of genes do not overlap, i.e. the shift to the nonpermissive temperature leads to a subsequent and transient increase in transcription of a small set of defined genes. We note that among these 39 genes, 2 are known to be regulated by the amino-acid-responsive transcriptional activator Gcn4 ([13], see Table 7 in additional file 3). With a selection criterion of 20% FDR, five other Gcn4 regulated genes are detected (CPA2, THI11, SNO1, SNZ1 and PRB1). Therefore, it seems that inhibition of the TOR2-unique function leads to an significant increase in the transcription of known Gcn4 target genes. It is still unclear, however, how the TOR2-unique pathway is connected to nutrient sensing or, vice versa, how nutrient sensing interferes with actin polarization.
Comparison with SAM on the TOR experiment
We ran our method in parallel with SAM in two situations displaying contrasting transcriptional responses. We tested a first comparison, Wt-t1 vs Wt-t0, which is characterized by a high number of varying genes and a good reproducibility between replicates, facilitating the detection of changes as reflected by the results of RDAM analysis which selected 620 increased genes at S = 50% and FDR = 6%. As SAM does not detect any increased genes at this selection level, we compared results obtained by the two methods at FDR = 10% and observed that among the 817 and 804 genes selected repectively by RDAM and SAM, only 426 were found in common. These results match what we found with synthetic data sets in case of high strength of variation (e.g. p <= 0.01 in figure 6D). We then considered comparisons of biological interest, i.e Mu-t1 vs. Wt-t1 and Mu-t2 vs. Wt-t2, and in this situation RDAM did not select any decreased genes and found only a few increased genes (see Discussion and Table 4). On the contrary SAM failed to detect any genes, either increased or increased.
Discussion
Analysis of the TOR experiment
RDAM is a method for identifying genes with changing expression levels using the user-determined FDR and/or S selection parameters. This method was used to study the effects of a thermosensitive mutation of TOR2 in yeast. RDAM succeeded in identifying the few genes that are differentially regulated by the TOR mutation from among the entire mass of genes perturbed by the temperature shift. Recently it has been shown that TOR controls the translation of Gcn4 via the eIF4alpha kinase Gcn2 [14]. Under conditions of TOR inactivation by rapamycin, Gcn4 translation is enhanced, leading to the activation of Gcn4-mediated transcription. Our data also demonstrate that TOR2 inactivation leads to enhanced transcription of Gcn4-controlled target genes (biological results based on RDAM analysis are discussed in a forthcoming paper). Further experiments may show how the TOR2-unique function is integrated into nutrient- (or amino acid-) responsive signaling pathways.
Normalizing of signal
Apart from randomly-distributed noise, microarrays are also prone to systematic effects that can bias the measurement of signal. All analysis methods are sensitive to systematic bias and include a preliminary normalizing step to make chips comparable. This is a limitation of this kind of approach, because the final result depends on the normalizing procedure used. Considering that all normalizing procedures rely on monotonous transformations that do not change the rank of raw data, we reasoned that if we used a statistics based on rank there would be no need to optimize the normalizing procedure. The rank unit we describe is similar to quantile normalization [15], but does not depend on the signal values of a particular chip as a reference: it can therefore be considered as an invariant. For example, if we focus specifically on the Affymetrix platform, we observe that the signal distribution changes with the different versions of the software: in MAS5, the 50th percentile is around 100, as compared to 1000 in MAS4. In our system, the rank of the genes at the same position in the signal distribution would not change, and would always be roughly equal to 50. This rank unit allows the drawing of plots in which all data are evenly distributed alongdimensions representing a signal. In addition, the linear density of points on the corresponding axes is constant, and the skewness of signal distribution has no effect on the graphical representation.
In our system, all values different from 0 or 100 are assigned to one and only one gene, because ordering of signals always delivers a series of contiguous rank values, even in cases of equivalent signal values. 0 is assigned to all unexpressed genes, as long as a robust method is available to detect them, and 100 to all genes for which the signal is saturated. In Affymetrix technology, especially with the scanner setup presently used, saturation is not a matter of concern and in our analysis, the value 100 is simply assigned to the highest signal. It is a complex problem to identify genes that are not expressed in a given experiment, and we decided to consider as absent only genes having a signal less than zero, as they occur in results delivered by MAS4.
Rank normalization results in transformation of the original signal distribution which is heavily skewed towards low values into a uniform distribution. As a consequence high rank variation could be assigned to small signal variations of weakly expressed genes, and it could be argued that our rank normalization method may bias variation detection towards genes with low signal. By using comparison between synthetic data sets we found no evidence of such a bias (data not shown).
Systematic usage of duplicates and standardization of variation
Our method has been developed within the framework of hypothesis testing and requires knowledge of the variation distribution for each gene when the null hypothesis is verified. The rationale of our approach considers replicated experiments as precisely representing a system in which all genes follow this hypothesis. However, it has long been recognized that the variation distribution expressed as a ratio or fold change is dependent upon the level of gene expression [16], and we show here that this property subsists when difference of signals or difference of ranks is used to measure variation. In theory it could be possible to use numerous replicates to obtain the empirical variation distribution of each gene. This is not possible for practical reasons, however, and we found that the classical centered-reduced standardization procedure can render variation distribution totally independent of gene expression level, as demonstrated by the QQplot analysis of figure 2A, and allow us to use duplicates to obtain the null variation distribution.
Independent analysis of positive and negative variations
In the algorithmic implementation of our method, we chose to proceed in two steps and deal with increased and decreased variations independently. First, this ensures that symmetrical comparisons (e.g. Exp1 vs. Exp2 and Exp2 vs. Exp1) give perfectly symmetrical results. Second, even if it were possible to devise another method that would allow one to proceed in one step, it seems more logical to consider increased and decreased variation separately. To clarify this point, a clear distinction must be made between up- or down-regulated mRNAs and increased and decreased variation. Regardless of the experimental points that are being compared, one always observes increased and decreased variations, but these variations have no absolute meaning because one only has to reverse the comparison to change the direction of variation. On the contrary, we can speak of up- or down- regulated mRNAs only when a causal effect exists, such as in a differentiation process or a kinetics or drug assay. In other words, a positive variation observed, for example, between two successive time points in a kinetic can be considered as an up-regulation whatever its mechanism – gene or post-transcriptional regulation – but it is meaningless to invoke any particular form of regulation when comparing, for example, two unrelated cancer tissues. In the case of down-regulated genes, the variation distribution of all weakly-expressed genes is truncated, due to the impossibility of a decreasing signal crossing the zero line. In the case of up-regulated genes, we do not observe the same effect for increasing variation of highly-expressed genes, first because the signal distribution is heavily skewed towards low values, and second because the variance of highly expressed genes is very small (figure 1A). The observation that the reproducibility of variation was lower for down-regulated genes than it was for up-regulated genes [17] is partly explained by this reason, and we were able to demonstrate the statistical difference between up- and down-regulated genes by using synthetic data and observing that all FDR vs. S curves (figure 6) constructed with down-regulated genes were lower than the corresponding curves for up-regulated genes (data not shown). Moreover, we conducted a test showing that the joint analysis of increased and decreased variations degrades the quality of FDR estimation and reduces the number of true positives detected.
Replicates
We did not try to reduce the amount of raw data when using replicates, and devised a two-step method. A first statistics, the standardized rank difference zRD is constructed on each independent comparison, and p-values are assigned by considering an empirical distribution that matches the null hypothesis. Then a second statistics, the product of p-values ppv, is calculated and p-values are assigned from the null distribution obtained by simulation. Simulation of the null distribution used exactly the same steps that the analysis process follows, i.e. application of the direction rule and construction of the product of p-values, and resulted in a null distribution model we found appropriate seeing, both with experimental and synthetic data sets, that the observed distribution of ppv matches the null distribution when no variation occurs (data not shown). It turned out that this scheme is very flexible and of general applicability: because the second step is rooted in a rigorous statistical method that uses only p-values as input data, it is possible to adapt or to improve the entire process simply by focusing on the first step of p-value estimation. For example, to apply our method to cDNA glass arrays, the only step to be modified would be the variation standardization. Alternatively we could use the segmental approach proposed by Yang and colleagues [18], which is claimed to equalize log ratio distribution, or the variance stabilization method of Huber et al [19], which is efficient in equalizing variation distribution of transformed intensity measurements in both cDNA and oligonucleotide platforms.
FDR, Total Variation and Sensitivity Estimation
The way in which we estimated FDR is exactly the same as that suggested by B. Efron et al. in their demonstration of the equivalence of empirical Bayes and frequentist approaches (Efron B, Storey J. D. and Tibshirani R. , see equation 3.8 and [20]). We did, however, use another heuristic approach to estimate TV because we observed that the estimator proposed by Storey et al [20] could be very difficult or impossible to calculate when the expression of a small fraction of genes changes. We demonstrated using synthetic data that our estimator was not prone to this type of instability (not shown), and that under realistic conditions (additional variation of p <= 0.10) our estimate was 60%, 65% and 80% of the true TV in the case of two, three and five replicates, respectively. The accuracy of this estimator is obviously dependent on the power of the test, which is itself under the control of the number of replicates. We have also shown that estimated sensitivity was biased by a constant factor that was mostly determined by the error made in TV estimation. Finally, it must be emphasized that the error made in TV estimation has little effect on FDR estimation, as demonstrated by forcing RDAM to use the true TV and K values during the process of synthetic data analysis (data not shown).
Synthetic data sets
Using the empirical noise distribution observed between two replicates, we devised a method for constructing synthetic data sets. Most published methods add noise to a signal that is supposed to represent the true signal of the gene. We showed here that raw signals without denoising could be used and gave excellent result as judged both by the final distribution of signals and by indirect controls such as the preservation of variation distribution and the possibility of successfully analyzing synthetic data substituted for the original data.
Synthetic data sets are well adapted for judging the respective performances of different analysis methods. To characterize the scoring procedure of a particular method, we used a new type of diagram that plots FDR vs. S, two quantities that relate to the subset of selected genes and that seem better adapted than Receiver Operator Characteristic (ROC, [21]), which relates to both selected and rejected genes (FDR vs. specificity). We proposed using FDR50, the FDR at S = 50%, as a comparative index between different methods and showed that RDAM has an FDR50 that is 30 percentage points smaller than SAM in the case of three replicates and applied changes with p <= 0.10 (figure 6).
Conclusions
RDAM is a new statistical method whose performances have been precisely evaluated through extensive analysis of synthetic data sets. When applied to TOR experiment, our method succeeded in finding the few genes of biological interest which were concealed in the mass of varying genes induced by the temperature shift. Comparison with SAM showed that our method obtained the same (if not better) results but with a smaller consumption of chips We conclude that the good quality of the results obtained by RDAM is mostly due to the use of replicates to calibrate the noise and to the quasi-perfect equalization of variation distribution, which is related to the standardization procedure used and to the measurement of variation by rank difference.
Methods
Preparation of RNA
Saccharomyces cerevisiae strains SH100 and SH121 [12] were grown overnight in yeast extract peptone glucose (YPD), diluted to an optical density measured at 600 nm of 0.05 (OD600 = 0.05), and grown for an additional 4 hours the next day. The main cultures were then inoculated in YPD medium and grown at 25°C or shifted to 37°C for 2 or 6 hours. All cultures were grown as independent duplicates and were harvested at a final OD600 of 0.8 to 0.9 to minimize the influence of differences in growth phase.
Upon harvesting by centrifugation (2 min, 3000 × g) at 4°C, cells were washed once in ice-cold water, centrifuged again, and the cell pellet was flash frozen in liquid nitrogen. Total RNA was extracted using a hot phenol method essentially as described by Schmitt, M.E. et al. [22].
Microarray hybridization
Affymetrix™ S98 Yeast Genome GeneChips, containing 6,400 S. cerevisiae (S288C strain) genes and 600 additional probe sets representing putative open reading frames [23], were used throughout this study. Synthesis of cDNA and in vitro transcription of biotin-labeled cRNA, as well as microarray hybridisation, washing and staining procedures, were carried out according to standard protocols as recommended by the manufacturer. Two independent preparations were used for each experimental point.
Data processing
The scanned microarray images were analysed using the algorithm implemented in MAS 5.0 (Affymetrix, Santa Clara, CA) and the generated raw data were further processed by scripts written in Matlab language (MathWorks, Natick, MA.). SAM analysis [3] of synthetic data was made using version 1.21 of the program [24] with the following default parameters: unlogged data, number of permutations set to 100 and "K-Nearest Neighbors Imputer" used.
Raw data files were uploaded to NCBI's GEO repository under the series number GSE1814 .
Abbreviations
RD, rank difference; zRD, standardized rank difference; FDR, false discovery rate, S, sensitivity; TV; total variation
Authors' contributions
DM and MH initiated and conducted the TOR experiment. DM and PD processed probe preparation and chip hybridization. MB developped and tested RDAM method. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Table 5 - Genes found decreased in the comparison sh121 vs sh 100 at 0 h and selected at FDR = 10%
Click here for file
Additional File 2
Table 6 - Genes found increased in the comparison sh121 vs sh 100 at 2 h and selected at FDR = 10%
Click here for file
Additional File 3
Table 7 - Genes found increased in the comparison sh121 vs sh 100 at 6 h and selected at FDR = 10%
Click here for file
Acknowledgements
We thank Estelle Chanudet for her participation in the development of the synthetic data generator, Reinhold Koch and Gunnar Wrobel for helpful discussions, and Lydia Michaut for her comments on the manuscript. We thank anonymous reviewers for their useful comments. This work was supported by grants from the Genopole of Montpellier to MB, and from the Canton of Basel and the Swiss National Science Foundation to MNH.
Figures and Tables
Figure 1 Effect of positive variation normalization. A – Increasing rank differences in the comparison Wt-t0b vs Wt-t0a are plotted (in red) against the rank of Wt-t0a. Similarly, increasing rank differences in the comparison Wt-t0a vs Wt-t0b are plotted (in blue) against the rank of Wt-t0b. In the nomenclature of the TOR experiment, Wt and Mu refer, respectively, to strains SH100 and SH121, t0, t1 and t2 refer to 0 h, 2 h and 6 h time points, and a and b indicate the replicates. A sliding window of 100 points was moved by steps of 30 points. At each position, the mean (upper magenta curve) and std (lower green curve) were plotted and the resulting jagged lines were smoothed by a fitting procedure [25]. The vertical arrows indicate the position of 10, 100 and 1000 signal on the 0–100 rank scale. B – Distribution of zRD (mean (magenta curve) and std (green curve) are not smoothed).
Figure 2 QQ plots of rRD. A – Percentiles of zRD distribution in one bin were plotted against corresponding percentiles of another bin. A bin is the set of all genes having a rank in Wt-t0a that falls between two successive decimal values (e.g. between 30 and 40). Blue dashed, green dotted and red continuous lines indicate, respectively, QQ-plots between contiguous bins, between bins three bins apart, and between the first and the last bin. 50th, 90th and 99th percentiles are marked, respectively, by a yellow circle, a magenta square, and a red diamond, B – QQ plot of zFC.
Figure 3 Effect of negative variation normalization. A – Decreasing rank differences in the comparison Wt-t0b vs Wt-t0a are plotted (in blue) against the rank of Wt-t0a and decreasing rank differences in the comparison Wt-t0a vs Wt-t0b are plotted (in red) against the rank of Wt-t0b. A sliding window of 100 points was moved by steps of 30 points. At each position, the mean (lower magenta curve) and std (upper green curve) were plotted and the resulting jagged lines were smoothed by a fitting procedure [25]. The vertical arrows indicate the position of 10, 100 and 1000 signal on the 0–100 rank scale. B – Distribution of zRD (mean (magenta curve) and std (green curve) are not smoothed).
Figure 4 Selection abacus. The following functions of x are displayed: F (red dashed curve), KFo (magenta continuous curve), FDR (green continuous curve) and S (blue dotted curve). The crossed circle marks the position at which the maximum of F-Fo (cyan continuous curve) is detected. Interpolation on the FDR curve shows that FDR~30% at S = 100% (green continuous straight lines). Similarly, interpolation on the S curve shows that S~65% at FDR = 10% (blue dotted straight lines). Abscissa x could be either log10(1/p-value(zRD)) in the case of a simple comparison or log10(1/ppv) if replicates are used. All the curves are constrained to be strictly decreasing functions except F-Fo.
Figure 5 plot of zRD of the comparison Exp1D vs Exp1A against zRD of the comparison Exp1C vs Exp1A. Exp1C and Exp1D are synthetic data sets made from the experimental replicates Exp1A and Exp1B (sh100 at t = 0 h). As zRD can be negative, we have plotted positive variations as zRD+2 and negative variations as -zRD-2 to obtain a clear separation of increased and decreased genes. All of the unchanged genes have their zRD set to zero. Red and blue circles mark the genes that received, respectively, an increasing (500 genes) or decreasing (500 genes) additional variation contribution. Black points mark all the genes that did not receive an additional variation contribution. The additional variation p-value was set to 0.30 (A), 0.10 (B), 0.05 (C) and 0.01 (D).
Figure 6 Performance of the scoring procedure. Comparisons were made between two groups of 2, 3 or 5 replicates. The first group is composed of synthetic data sets made from the experimental replicates Wt-t0a and Wt-t0b, in which 200 genes (100 increased and 100 decreased) are changed with different strengths (p <= 0.30(A), 0.10(B), 0.05(C) or 0.01(D)), and the second group is composed of synthetic data sets without extra variation. Ten comparisons with different combinations of data sets were made, and the resulting mean FDR of increased genes is plotted against given values of S (0.05 to 1, by steps of 0.05). Replicates 2, 3 and 5 are, respectively, plotted with blue interrupted, green dotted, and red continuous lines. Lines marked with circles and without circles refer, respectively, to the results of RDAM and SAM. Only one comparison was used for SAM analysis.
Figure 7 Error of FDR and S estimators. Comparisons were made between two groups of 2, 3 or 5 replicates. The first group is composed of synthetic data sets made from the experimental replicates Wt-t0a and Wt-t0b, in which 1000 genes (500 increased and 500 decreased) are changed (p <= 0.10), and the second group is composed of synthetic data sets without extra variation. Ten comparisons with different combinations of data sets were made, and the resulting mean errors of FDR (upper panel) and S (lower panel) for several values of the estimator (5 to 90%, by steps of 5%) are plotted (error is computed by substracting the estimated value from the real value and expressed in relative percentage point difference). Replicates 2, 3 and 5 are marked, respectively, by blue circles, green triangles and red squares.
Table 1 Error on FDR estimation in the case of two replicates and 500 increased genes (p <= 0.1)
FDR #1 #2 #3 #4 #5 #6 #7 #8 #9 #10
5% 1 (12) 2 (8) 0 (3) 0 (3) 0 0 (0) 0 (0) 0 (0) 0 (0) 0 (2)
10% 2 (73) 3 (25) 0 (14) -2 (37) -1 (2) 2 (20) 0 (0) 1 (12) -1 (20) -2 (20)
15% 1 (160) 0 (84) -3 (68) -4 (59) -3 (5) 2 (58) -4 (41) -3 (40) -4 (63) -4 (57)
20% 2 (217) -6 (157) -11 (130) -9 (129) -5 (65) 0 (134) -8 (152) -6 (81) -11 (146) -10 (136)
Comparisons were made between two groups of two replicates. The first group is composed of synthetic data sets made from the experimental replicates Wt-t0a and Wt-t0b, in which 1000 genes (500 increased and 500 decreased) are changed (p <= 0.10), and the second group is composed of synthetic data sets without extra variation. Ten comparisons with different combinations of data sets were made. For each set of selected genes we computed the difference between the real number of false positives and the estimated number of false positives. In the first column the FDR estimation is indicated; other columns give the number of increased genes either faultly assigned as true positive (positive difference) or as false positive (negative difference) in each of the ten comparisons (numbered from #1 to #10). In parentheses is indicated the number of genes selected for the given value of the FDR estimator.
Table 2 Comparison of one step and two step procedures in the case of two replicates and 500 increased genes
One-step analysis Two-step analysis
# Selected # Target Est FDR True FDR # Target Est FDR True FDR
100 75 49 25 88 17 12
200 133 58 34 167 23 17
300 183 67 39 238 29 21
400 229 75 43 298 35 26
500 272 80 47 336 44 33
Two groups of data were compared under two conditions. The first group is composed of two synthetic data sets made from the experimental replicates Wt-t0a and Wt-t0b, in which the same subset of 500 genes is increased (p <= 0.10), and the second group is composed of two synthetic data sets made from the same experimental replicates, in which no gene was changed. The one-step analysis consists of processing all of the variations together after the standardization step, leading to the generation of only one F curve, whereas with the two-step analysis the observed variation distributions of increased and decreased genes are considered independently, with each having its own F curve (Finc and Fdec). FDR was chosen so as to obtain a predetermined number of selected genes indicated in the first column. The second and fifth columns (# Target) give the number of true positivesselected. The third and sixth column (Est FDR) indicate the level of selection applied and give an estimate of the FDR. The fourth and seventh columns give the true FDR value, corresponding to the ratio of the number of true positives to the number of genes selected.
Table 3 Comparison of RDAM and SAM in the case of two, three or five replicates and 500 increased genes
FDR50 FDR True Positive False Positive
R p var RDAM SAM RDAM SAM RDAM SAM RDAM SAM
2R 0.3 55% 63% 33% - 4 - 2 -
0.1 25% 49% 21% - 172 - 45 -
0.05 9% 39% 11% 14% 281 23 35 4
0.01 1% 26% 15% 23% 452 175 77 51
3R 0.3 42% 47% 17% - 55 - 11 -
0.1 7% 28% 16% 20% 344 156 64 40
0.05 3% 19% 13% 18% 409 246 60 54
0.01 0% 11% 14% 22% 473 381 80 109
5R 0.3 17% 21% 18% 25% 261 264 58 88
0.1 2% 4% 15% 20% 455 395 81 101
0.05 0% 2% 16% 20% 472 419 87 106
0.01 0% 0% 19% 19% 474 443 111 105
Comparisons were made between two groups of two replicates. The first group is composed of synthetic data sets made from the experimental replicates Wt-t0a and Wt-t0b, in which 1000 genes (500 increased and 500 decreased) are changed, and the second group is composed of synthetic data sets without extra variation. Results are shown for increased genes. The first column (R), indicates the number of replicates used. The second column (p var), gives the p-value of the extra variation introduced to the changed genes. The third column displays the FDR50, and the fourth column (FDR) the real FDR corresponding to the set of genes selected at FDR = 20%. For the same level of selection, columns 5 and 6 give, respectively, the number of true and false positives.
Table 4 Results of selection at S = 50% and FDR = 10%
Decreased Increased
FDR = 10% S = 50% FDR = 10% S = 50%
TV Nb S Nb FDR TV Nb S Nb FDR
MU vs WT t0 12 4 30% 7 13% 217 0 0% 196 45%
MU vs WT t1 0 0 0% 0 0% 106 19 16% 73 27%
MU vs WT t2 0 0 0% 0 0% 92 39 38% 51 11%
t1 vs t0 WT 791 311 35% 459 14% 1164 817 63% 620 6%
t2 vs t1 WT 231 0 0% 244 53% 233 45 17% 189 38%
t2 vs t0 WT 1195 759 58% 641 8% 1275 1074 76% 660 3%
t1 vs t0 MU 1158 644 50% 642 10% 1093 856 71% 564 3%
t2 vs t1 MU 382 19 5% 351 44% 387 180 42% 226 14%
t2 vs t0 MU 1289 725 51% 714 10% 1064 954 84% 543 2%
WT and MU refer, respectively, to strains SH100 and SH121, and t0, t1 and t2 refer to 0 h, 2 h and 6 h time points. TV is the total variation, Nb the number of genes selected, S the sensitivity and FDR the false discovery rate as estimated by RDAM. The second header line gives the selection parameter used.
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| 15476558 | PMC526220 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 11; 5:148 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-148 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1521549149210.1186/1471-2105-5-152Methodology ArticleIncidence of "quasi-ditags" in catalogs generated by Serial Analysis of Gene Expression (SAGE) Anisimov Sergey V [email protected] Alexei A [email protected] Section for Neuronal Survival, Wallenberg Neuroscience Center, Lund University, 221 84 Lund, Sweden2 Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA2004 18 10 2004 5 152 152 27 5 2004 18 10 2004 Copyright © 2004 Anisimov and Sharov; 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
Serial Analysis of Gene Expression (SAGE) is a functional genomic technique that quantitatively analyzes the cellular transcriptome. The analysis of SAGE libraries relies on the identification of ditags from sequencing files; however, the software used to examine SAGE libraries cannot distinguish between authentic versus false ditags ("quasi-ditags").
Results
We provide examples of quasi-ditags that originate from cloning and sequencing artifacts (i.e. genomic contamination or random combinations of nucleotides) that are included in SAGE libraries. We have employed a mathematical model to predict the frequency of quasi-ditags in random nucleotide sequences, and our data show that clones containing less than or equal to 2 ditags (which include chromosomal cloning artifacts) should be excluded from the analysis of SAGE catalogs.
Conclusions
Cloning and sequencing artifacts contaminating SAGE libraries could be eliminated using simple pre-screening procedure to increase the reliability of the data.
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Background
Serial Analysis of Gene Expression (SAGE) is a rapid method to study mRNA transcripts in cell populations [1]. Two major principles underline SAGE: (1) short expressed sequenced tags (ESTs) are sufficient to identify individual gene products, and (2) multiple tags can be concatenated and identified by sequence analysis [1,2]. With the ever-expanding sequence information available in public databases, identification of gene transcripts with SAGE tags has greatly facilitated transcriptome comparisons and gene identification [3].
SAGE data are usually analyzed with software packages like "SAGE300" or "SAGE2000". The majority of SAGE libraries use NlaIII or Sau3A (SalAI) as anchoring enzymes (AE) to create SAGE tags. Both of these enzymes have 4-bp palindromic recognition sequences (CATG for NlaIII and GATC for Sau3A) that flank individual ditags within concatemers. A major component of the software analysis is the identification of anchoring enzyme recognition sequences (AERS) that flank target sequences (SAGE ditags). After finding the first AE recognition sequence, the software continues reading the sequence until it finds the next one. The software then compares the distance between these recognition sequences with predicted ditag lengths (20–24 bp in the case of NlaIII or Sau3A), and ditags that are too short (<20 bp) or too long (>24 bp) are excluded. However, if the length of the AERS-flanked sequence satisfies the size criteria, it is identified as a ditag. This algorithm relies on the assumption that all sequences have correctly organized ditag concatemers; however, the cloning efficiency of SAGE rarely reaches 100%. In this report, we show that up to 5% of ditags from some SAGE libraries should be omitted from the final analysis. These false ditags (termed "quasi-ditags") result from genomic contaminants and apparently random combinations of nucleotides generated by cloning or sequencing errors. Using a mathematical model to simulate the frequency of quasi-ditags in DNA, we propose a method to exclude quasi-ditags from SAGE catalogs.
Results
From twelve independent SAGE libraries, we analyzed numerous clones lacking organized ditag concatemers that would be excluded by SAGE software packages, including clones lacking inserts, clones with inserts containing bacterial or rodent genomic sequences, and clones with unidentifiable sequences (Figure 1). Depending on the quality of the SAGE library, examples of clones in Figure 1A,1B,1C can represent up to 50% of the total volume of clones sequenced [4], but generally range from 2–20%. A more typical example is taken from our R1 ES cell and AMH-II SAGE libraries [5,6], which contained 5,988 and 4,478 clones, respectively. The cloning efficiency was ~79% and ~76% (4,714 and 3,413 clones with inserts, respectively). Among these, 411 and 167 clones in the R1 ES SAGE library and 305 and 194 clones in the AMH-II library contained sequences with only 1 or 2 ditags, respectively.
During our sequence analysis of the clones that had produced a least number of ditags (1–2 per clone), we identified a subset of sequences (up to 40%) that contain ditags that may be false. Importantly, some of these "ditags" matched bacterial genomic sequences (Figure 2A), while others seemed to represent random combinations of nucleotides. Figure 2B show an example of a clone that contains a single ditag sequence embedded within a sequence of unidentifiable origin. Because most of this sequence is not composed of concatenated ditags, this embedded ditag may therefore represent a quasi-ditag, which should be excluded from further analysis. These two examples, among others, suggest that some inserts in pZErO-1 contain sequences that just by chance mimic SAGE ditags.
To predict the potential frequency of randomly occurring quasi-ditags, we employed a stochastic model system to generate random sequences. We then used both computer-generated random sequences and true genomic DNA sequences to test this possibility. Random sequences were generated and analyzed with a Visual Basic program designed to mimic SAGE software analysis of ditags. The simulated sequences varied in length from 600 to 1200 nucleotides, which corresponds to the average sequence lengths generated by automated sequence analyzers. One million random sequence strings with L = 600, 700, 800, 900, 1000, 1100, and 1200 nucleotides were generated. Table 1 shows expected frequencies of quasi-ditags according to the model (equation (5)) and the observed frequencies based on computer simulations. The line plots of the expected (model) and observed (computer simulation) quasi-ditag frequencies are almost identical (Figures 3 and 4). Fragmented Saccharomyces cerevisiae genomic DNA that lack SAGE ditag concatemers was also employed for in vivo / in silico model validation, and a number of quasi-ditags was detected in these (Figures 4 and 5). When compared to Saccharomyces cerevisiae genomic DNA [7], quasi-ditag frequencies were somewhat less abundant than those generated by the computer, potentially due to the presence of nucleotide repeats and unequal frequencies of individual nucleotides in the Yeast genomic sequences. These data, however, support our hypothesis that quasi-ditags can be generated randomly from potential sequencing errors or from genomic contaminants. This analysis furthermore underscores the limited extent that quasi-ditags occur: the distribution of expected number of quasi-ditags per clone is clearly bimodal, with peaks at 1 and 2 ditags (Q1 and Q2, respectively). At the same time, the frequency of occurrence for three quasi-ditags (Q3) is extremely low (0.01% for L = 600 to 0.02% for L = 1200), such that the value of P3 (20–24) effectively converges to zero for the majority of the SAGE catalogs (i.e. <3000–5000 clones) (Figure 4). Accordingly, the clones that include ditag concatemers of higher length should lack quasi-ditags.
Clones containing only one or two ditags/quasi-ditags, however, could be excluded from SAGE analyses, without adversely affecting the data set (Figure 6). As an example, we extracted sequences from clones that produce 1–2 total ditags from AMH-II and R1 ES cell libraries. This reduced the total number of tags by 1.06% for ES R1 and 1.94% for AMH-II, but it effectively removed all contaminating bacterial sequences and improved the data reliability. However, the total AMH-I library (2,365 clones, ~78% cloning efficiency; [6]) had a larger proportion of ditags extracted as being too long (>24 bp), as indicated by lower tag per clone ratio (average insert size of 12.2 tags/clone vs. 22.6 in AMH-II library) amid the same average sequence length, suggesting higher proportion of quasi-ditags. Analysis of the AMH-I SAGE library has revealed 353 and 52 clones that contained just 1 or 2 ditags, respectively. Exclusion of these sequences decreased the total number of tags by 5.21% (calculated after duplicate dimer exclusion), and proved critical to our subsequent quantitative SAGE comparisons [6]. Failure to remove these quasi-ditag sequences decreased the quantitative reproducibility (R values) between AMH-I and AMH-II SAGE libraries, showing that quasi-ditags can adversely affect the reliability of SAGE libraries.
Discussion
SAGE is an important tool of modern molecular biology widely used in a number of applications. We hypothesized that actual SAGE catalogs could be contaminated by false ditags ("quasi-ditags") of various origins. Although SAGE software packages are designed to ignore sequences that lack 20–24 bp sequences flanked by two anchoring enzyme recognition sites, it does not exclude quasi-ditags originating from genomic contaminants or unknown sequences that may arise as cloning or sequencing artifacts (Figure 2). Negative controls (self-ligated vector) do not produce any colonies after Zeocin selection and cannot account for the appearance of background clones and quasi-ditags in Zeocin-resistant bacteria. Since some quasi-ditags, however, originate directly from E. Coli, we suggest that one probable source for these contaminanting tags is from recombination events that occur in E. Coli. Indeed, such a mechanism has already been documented [8] and has led to the development of Stbl2 bacteria that are mcrA-/mcrBC-hsdRMS-mrr-. Since pZErO-1 was not translated into recombination deficient bacteria (DH10B), large-scale amplifications of this plasmid within bacteria would be expected to lead to some random recombinations, and the generation of quasi-ditags (e.g. Figure 2A).
Some of the ditags derived from the clones that had produced a least number of ditags (1–2 per clone) do not match genomic sequences and thus might be originated from sequencing errors. We therefore suggested a model that provides a mathematical basis for the hypothesis that such a possibility exists. The mathematical model presented in the manuscript is an attempt to predict the frequency distribution of quasi-ditags in random sequences. The phenomenon itself is rather complex and there is no simple model that would capture it in full complexity. We, however, believe that we have selected a reasonable level of model complexity that captures the major pattern of frequency distribution.
Using the computer simulation we show that random combinations of nucleotides generated could be indeed recognized by SAGE software as valid SAGE ditags. We also demonstrate that quasi-ditags may constitute a non-negligible proportion of SAGE catalogs. Our model, which simulates the frequency of quasi-ditags in DNA (equations (1–6)), suggests that single or double ditags may represent quasi-ditags; however, the results of the in silico experiments show that the probability of finding more than two quasi-ditags in the same sequence converges effectively to zero (Table 1 and Figure 4). Based on these findings, we suggest that additional steps be performed with SAGE libraries. We recommend removing clones with sequences containing ≤ 2 ditags at a pre-processing step ("clean-up"). The removal of clones containing 1 or 2 ditags can effectively remove bacterial genomic sequences and potential sequencing artifacts from SAGE libraries. The overall number of SAGE tags excluded by this additional step (authentic and quasi-ditags) is usually low, and generally does not exceed 1.0–1.8% of the total number of sequenced SAGE tags [5,6,9,10]; however, the frequency of potential quasi-ditags could be high (>5%) in some SAGE libraries. In AMH-I library, for example, the fraction of clones lacking appropriate ditag concatemers was >20%. In these instances, quasi-ditags significantly contribute to the final SAGE tag count, and should be removed.
Chart in Figure 6 plots values for ditag distribution from both the model-based simulations (L = 800 bp) and actual clones from the SAGE libraries that had sequences of the same mean length (L ≈ 800 bp). The expected maximum frequency of 1–2 quasi-ditags in the plotted model data approximated the observed frequency of clones with 1–2 total ditags detected in the pool of the actual SAGE clones. Contrary to that, the frequency of occurrence of three or more quasi-ditags predicted by the model is extremely low, demonstrating a divergence in the distribution of expected quasi-ditags and valid SAGE ditags for higher number of ditags per clone. Note that owing to the gel-purification of concatemers the majority of clones in the representative samples belong to the clusters of higher ditag numbers (AMH-II and ES R1 libraries, 13–26 total ditags; AMH-I library, 4–11 total ditags).
Comparing values of observed frequencies of the actual SAGE clones that produce 1–2 total ditags with those of expected quasi-ditag frequencies for the sequences of given length might be indicative on the possible contribution of cloning and sequencing artifact-derived quasi-ditags (Figure 6). The possible contribution of quasi-ditags to the final tag yield in SAGE libraries cannot be accurately predicted in advance but a failure to report the cloning efficiency and the number of clones with 1 or 2 ditags precludes an evaluation of potential false tags present in SAGE catalogs. Current SAGE protocols do not ensure 100% accurate size fractioning of concatemers: some of the smallest concatemers could therefore be cloned and sequenced. We recognize that some authentic tags (representing valid, but extremely short inserts that were not extracted during gel-purification of concatemers) will be excluded by removing all clones containing only 1 or 2 ditags. Nevertheless, we suggest that any potential loss of authentic ditags in the clean-up procedure is negligible compared to the advantage of having more reliable SAGE results.
SAGE protocols are extremely complex technologically and every possible mean should be employed to ensure qualitative and quantitative accuracy of catalogs on both the experimental and analytical steps. Evaluation of the cloning efficiency and precision (e.g. with RAST-PCR [11]) and sequencing accuracy are therefore essential on the stage preceding large-scale sequencing of the clones. Nonetheless, introduction of the simple pre-processing step eliminating false ditags would further improve the accuracy of the method resulting in its wider application.
Conclusions
We have hypothesized that actual SAGE catalogs could be contaminated by false ditags (termed "quasi-ditags") of various origins and employed a mathematical model to predict the frequency of quasi-ditags in random nucleotide sequences. Cloning and sequencing artifacts contaminating SAGE libraries could be eliminated using simple pre-screening procedure to increase the reliability of the data.
Methods
SAGE
Serial analysis of gene expression (SAGE) was performed according to the original protocol [1] with minor modifications [5,12]. Human (PC3) and mouse (P19, R1, D3, EG-1, MEF) cells and tissues (adult and old heart) have been employed for construction of SAGE libraries and sequence analysis to illustrate the "clean-up" process. SAGE tags were generated with NlaIII and BsmFI restriction enzymes (New England Biolabs, Beverly, MA, USA). Sequencing was performed by Perkin-Elmer Applied Biosystems / Celera Genomics (Foster City, CA, USA) and Agencourt Bioscience Corporation (Beverly, MA, USA).
Stochastic model
Anchoring enzyme recognition sites (AERS) are 4 bp long. Assuming for simplicity that all 4 nucleotide bases (A, T, C, and G) have equal frequencies, a probability that a random combination of 4 nucleotides would match the AERS is 4-4 = 1/256. In a sequence of length L, the expected number of AERS (e.g. CATG for NlaIII anchoring enzyme) is L/256. Thus, the probability of finding k tags CATG in a random sequence of length L is determined by the Poisson distribution:
If two CATG sequences (AERS(CATG)) are located within the sequence of length L, then the probability that they are separated by a 20–24 bp distance (P(20–24)) is approximately:
where 10 is the number of possible relative positions of two AERS(CATG) that yield a quasi-ditag and 24 is the mean distance from the center of one SAGE tag to the end of the sequence that does not leave enough space for another tag to form a quasi-ditag.
If >2 AERS are present in the sequence, then there is a chance that additional AERS would appear within the quasi-ditag formed by first two AERS. A probability that additional AERS will not appear within the quasi-ditag is approximately:
where 30 is the average length of a nucleotide string outside of the ditag.
If the total number of AERS(CATG) equals k, then the number of possible AERS pairs is:
Taken together, a probability of at least one quasi-ditag in the sequence that has exactly k AERS(CATG) is:
Then, a probability (Q1) to find at least one quasi-ditag in a sequence of given length L is:
where p(k) is given by equation (1).
There is also a probability that more than one quasi-ditag exists within the sequence. In some cases the same AERS(CATG) could serve as a portion of the two neighboring quasi-ditags (...CATG-(N)20–24-CATG-(N)20–24-CATG...). In other cases, two or more quasi-ditags can be located independently in the sequence. If a sequence with k tags already has one quasi-ditag bounded by two tags, then other (k-2) tags may form additional quasi-ditags. The probability of existence of additional quasi-ditags on condition that one ditag is already present is approximately q(k-2). Then the total probability that any random sequence has at least two quasi-ditags is:
In the same way,
and so on.
The probability that a random sequence has exactly n quasi-ditags is:
Rn = Q(n) - Q(n + 1) (9).
Software and analysis
A random nucleotide generator (for L = 600–1200) and analysis program that mimics "SAGE300" or "SAGE2000" software algorithms was written in Visual Basic and is available upon request. Genomic DNA sequences of Saccharomyces cerevisiae that lack SAGE ditag concatemers were also employed for in vivo / in silico model validation. Randomly selected S. cerevisiae chromosomes were downloaded from GenBank, fragmented to create a minimum of 300 sequences (L = 600–1200) and searched for quasi-ditags using "SAGE2000" software (available at SAGE website [13]). Frequency distribution of the number of ditags was analyzed in raw sequences from 3 randomly chosen 96-well plates from AMH-I, AMH-II and ES R1 SAGE libraries (285 sequences for each library) using the same software.
Authors' contributions
SVA developed the hypothesis, overall plan and performed SAGE, computer simulations, and analysis of Yeast genome fragments. AAS developed and implemented the mathematical model predicting the appearance of "quasi-ditags" in random sequences of given length. Both authors have contributed to the writing and approved the final manuscript.
Acknowledgements
We would like to thank Dr. Paul Pullen (NIA/NIH, USA) for writing a code for software effecting computer simulations and Dr. Kenneth Boheler (NIA/NIH, USA) for the valuable help in preparing this manuscript.
Figures and Tables
Figure 1 Raw SAGE sequence data showing cloning and potential sequencing artifacts excluded by SAGE software. (A) Clone with fragment of E. Coli genomic DNA. Italics denote E. Coli sequence (AE000256). (B) Clone containing a fragment of rodent genomic DNA. Italics denote M. musculus sequence (AI894042). (C) Clone with unidentifiable insert, which lack normal SAGE concatemer. pZErO-1 sequences are underlined; Anchoring enzyme recognition sites (AERS(CATG)) are shown in bold.
Figure 2 Raw SAGE sequence data showing cloning and potential sequencing artifacts not excluded by SAGE software. (A) Clone with fragment of E. Coli genomic DNA. Italics denote E. Coli sequence (AE000307). (B) Clone with unidentifiable insert, which lack normal SAGE concatemer. Sequences like (A-B) represent quasi-ditags that should have been removed. pZErO-1 sequence is underlined; Anchoring enzyme recognition sites (AERS(CATG)) are shown in bold, and potential SAGE tags are shown by dotted underlines.
Figure 3 Probability (p(k)) to find k AERS(CATG) in a random sequence for L = 600 and L = 1200 bp. Dotted lines represent p(k) mean values. L, sequence length; Model, mathematical modeling; CompSim, computer simulation (1,000,000 simulations).
Figure 4 Probability to find various numbers of quasi-ditags (QN) in the same nucleotide sequence of the given length (L = 1200 bp). L, sequence length; Model, mathematical modeling; CompSim, computer simulation (1,000,000 simulations); In vivo Sim, fragments of S. Cerevisiae chromosome (300 simulations).
Figure 5 Probability of finding one or more quasi-ditag in the nucleotide sequence of a given length (L = 600 to 1200 bp). Model, mathematical modeling; CompSim, computer simulation (1,000,000 simulations for each L value); In vivo Sim, fragments of S. Cerevisiae chromosomes (300 simulations for each L value). Dotted line represents trendline for In vivo Sim.
Figure 6 Frequency distribution of the number of ditags in SAGE output. Probability to find various numbers of ditags in the clone sequence has been plotted as a function of a number of total ditags per clone. Model, mathematical modeling; CompSim, computer simulation (1,000,000 simulations); In vivo Sim, fragments of S. Cerevisiae chromosome (300 simulations); AMH-I, -II, ES R1, actual SAGE data (sequences from 3 randomly chosen 96-well plates). Sequence length (L) = 800 bp for Model, CompSim and In vivo Sim; average sequence length ≈ 800 bp for all three SAGE libraries.
Table 1 Probability to find one or more "quasi-ditag" in the nucleotide sequence of the given length (P(20–24))
Sequence Length (L) Frequency of ≥ 1 quasi-ditags in sequence S. Cerevisiae chromosome 3
Mathematical model Computer simulation 1 In vivo 2 simulation (S. Cerevisiae)
600 bp 0.039392 0.039858 0.016666 IV [NC_001136]
700 bp 0.046231 0.046655 0.026666 X [NC_001142]
800 bp 0.053070 0.053383 0.036666 XIV [NC_001146]
900 bp 0.059909 0.060051 0.040000 VIII [NC_001140]
1,000 bp 0.066748 0.066743 0.046666 V [NC_001137]
1,100 bp 0.073587 0.073225 0.066666 IX [NC_001141]
1,200 bp 0.080426 0.079793 0.070000 XI [NC_001143]
1 For computer simulation, 1,000,000 files consisting of the sequence-imitating random combination of A, C, G and T nucleotides of selected length were analyzed in search of SAGE "quasi-ditags".
2 For in vivo / in silico simulations 300 sequences were created by fragmentation of randomly selected chromosomes of Saccharomyces Cerevisiae for each L value. Larger samplings (900–1,400 sequences) were created and tested for selected sequence lengths and did not change results significantly.
3 GenBank database accession numbers are given in brackets.
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SAGE
| 15491492 | PMC526221 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Oct 18; 5:152 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-152 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1531549149710.1186/1471-2105-5-153Research ArticleInter-residue distances derived from fold contact propensities correlate with evolutionary substitution costs Williams Gareth [email protected] Patrick [email protected] Wolfson Centre for Age-Related Diseases, The Wolfson Wing, Hodgkin Building, Kings College London, London SE1 1UL, UK2004 18 10 2004 5 153 153 2 6 2004 18 10 2004 Copyright © 2004 Williams and Doherty; 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 wealth of information on protein structure has led to a variety of statistical analyses of the role played by individual amino acid types in the protein fold. In particular, the contact propensities between the various amino acids can be converted into folding energies that have proved useful in structure prediction. The present study addresses the relationship of protein folding propensities to the evolutionary relationship between residues.
Results
The contact preferences of residue types observed in a representative sample of protein structures are converted into a residue similarity matrix or inter-residue distance matrix. Remarkably, these distances correlate excellently with evolutionary substitution costs. Residue vectors are derived from the distance matrix. The residue vectors give a concrete picture of the grouping of residues into families sharing properties crucial for protein folding.
Conclusions
Inter-residue distances have proved useful in showing the explicit relationship between contact preferences and evolutionary substitution rates. It is proposed that the distance matrix derived from structural analysis may be useful in aligning proteins where remote homologs share structural features. Residue vectors derived from the distance matrix illustrate the spatial arrangement of residues and point to ways in which they can be grouped.
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Background
The large number of protein crystal structures available has naturally led to statistical analyses of protein folding and protein interaction in the hope that these will point to intrinsic residue characteristics and therefore serve as aids in protein fold and interaction prediction. The first such analysis was performed by Miyazawa and Jernigan [1-3], where a statistical protein folding potential, the MJ matrix, was deduced from residue contact propensities in a set of monomeric protein crystal structures. The MJ matrix has been used in various in silico folding experiments, reviewed by Jernigan et al [4], and shown to point to the essentially hydrophobic nature of folding interactions [5]. An analysis of the MJ matrix has enabled the reduction in sequence complexity by grouping residues into families [6]. A more detailed study of crystal interactions focusing on hydrogen bond distributions has resulted in mean force potentials that have been successfully used in ligand prediction [7]. It is reasonable therefore to conclude that the statistical approach has pointed to an intrinsic residue:residue potential. In this study we will show that crystal contact statistics can be used to define an inter-residue similarity score that is strongly correlated with an evolutionary substitution cost. As this score is not based on aligning homologous proteins it can serve as a complement to similarity scores derived from substitution matrices when faced with the problem of aligning remotely homologous but structurally similar proteins.
The observation that evolutionarily close residues appear to have similar contact propensities leads us to postulate that the extent of similarity between the contact propensities corresponding to two particular amino acids is related to the ease with which these amino acids can be mutated into each other. We define the contact propensity as , where Nij is the number of possible pairings between residue type i and residue type j and Cij is the number of these parings corresponding to residues in contact. Only non-neighbouring residues on the protein chain are considered and a pair of residues is defined to be in contact if any of their side chain atoms are within a given distance of each other. The difference in contact propensities for two amino acid types can be measured by their rms difference and we define as our amino acid difference measure or distance matrix.
If we have really got a measure of the distances between residue types then it should follow that residues sharing physical properties are close together. More crucially, we expect that residues that are distant according to D(P) will be difficult to mutate into one another and vice versa. This is because the factors involved in determining mutation rates are dominated by those affecting the structural integrity of the protein. Such factors are residue hydropathy, size, charge and etc. Substitution matrices such as PAM and Blosum are determined from mutation rates in aligned protein sequences [8,9]. We can define an amino acid distance matrix in a similar way to Dij above.
That is, , where S is the substitution rate matrix. We show below that D(P) is indeed strongly correlated with D(S). It must be stressed that D(P) and D(S) are independently derived, with one based on structure and the other on sequence alignment. Their strong correlation is indicative of the validity of our definition of inter-residue distance.
Relating amino acids through a structurally defined distance measure should provide a useful tool for aligning remotely homologous protein sequences. Also, a distance measure naturally leads us to look for a vector representation of the amino acids. In much the same way as average hydropathy plots are useful in structural analysis we expect that average vector profiles will also pick out various structural features. Given a vector for each residue type we can visualise the residues in some abstract space and look for natural groupings of residues and thereby find ways of reducing the effective number of residues.
Results
A representative set of crystal structures was compiled from the PDBselect25 database [10], which contains structures sharing at most 25% sequence homology. We made sure that side chain coordinates were defined and restricted chain lengths to be greater than 50 and less than 500 residues long. In short we arrived at 1073 structures and performed the statistical analysis on these. Residues are held to be in contact if any of their respective side chain atoms are within a given distance of each other. Only residue pairs that are not neighbours along the chain are considered in the analysis of intra-molecular contacts.
As explained above the contact propensities can be converted to a distance matrix D(P). If this matrix is really a measure of residue similarity then we should be able to correlate it with an equivalent matrix constructed from an evolutionary substitution rate matrix. In what follows we will take PAM250 as the substitution matrix. In Figure 1a we show the contour plots of D(S) in the top triangle and D(P) in the bottom triangle for a contact cut-off of 4.5Å, where the pearson correlation (r) is maximal, with r = 0.82. See additional table 1 for explicit values of P and D(S). The correlation can be seen explicitly in Figure 2b. The extent of correlation is roughly constant over a large range of cut-offs (4~8Å) and only drops when the cut-off is small and contacts are rare or when the cut-off is so big that non-interacting residues are scored, see Figure 1c. We expect that, due to the wide range of side chain sizes, a full atom representation is more accurate than a centroid representation and we find that the centroid D(P) is consistently less well correlated with D(S), peaking at r = 0.64 for a cutoff of 8Å, see Figure 1c.
We have defined inter-residue distances and this implies that there must be a vector representation for the residues. In this case the distance matrix will be , where are the residue vectors. Explicitly, the vectors are defined such that is minimal. When these vectors are derived it becomes clear that Cysteine is quite separate from the other residues in this property space and this maybe due to the unique role played by Cysteine in stabilising folds. Though it must be made clear that the distance matrix is independent of the frequency of an amino acid contacting its own kind and therefore does not count Cysteine bridges in the structures. Without Cysteine the distance matrix can be projected onto a plane i.e. the vectors can be taken to be two-dimensional and this vector space is illustrated in Figure 2a. It is a reasonable postulate that neighbouring residues share physical characteristics and we see similar residue groupings in a standard amino acid Venn diagram [11]. Indeed the vector grouping may serve as a way of reducing the effective amino acid number [6]. It is illuminating to compare vector spaces derived from other statistical analyses. The substitution rate vector space Figure 2b is, as expected, similar to that of the contact propensity vector space, though in D(P) residues with opposite hydropathies tend to be further apart. This is consistent with hydropathy playing a pivotal role in protein folding. In contrast, the MJ energy matrix vector space is shown in Figure 2c and here the residues effectively lie on a line, which is in accordance with Li et al [5], where the MJ matrix was shown to be dominated by its principal eigenvector reduction. However, for the contact propensity and evolutionary substitution rate spaces, a lot of information is lost in such a linear projection and our analysis clearly points to a higher dimensional representation of the residues. Nonetheless, to make a concrete comparison of our vectors with existing scalar representations of amino acid properties we are forced to project our vectors onto a line. See additional table 2 for the explicit vector components of the contact propensity, substitution rate and MJ energy matrices.
The dominant driving force of folding, at least in defining the crude fold, is hydrophobicity and it is apparent that residues with similar hydrophobicities are grouped together. It also seems that residues of similar size tend to be close in this space. To make a direct comparison between existing residue scales and our vectors we can project the residue vectors onto a line. Here the amino acid scalars, one-dimensional vectors, di are defined such that is minimal. We find that these distance matrix derived scalars have a correlation of 0.65 with the Kyte-Doolittle hydrophobicity scale [12] and a correlation of 0.53 with an amino acid volume scale [13]. It is clear therefore that the residue vectors capture a combination of factors determining protein folding.
It is worth noting that a scalar reduction of the distance matrix can be got by a principal eigenvector analysis. In a principal eigenvector reduction of the contact propensity matrix we have Pij = λeiej, where λ is the principal eigenvalue and ei is the principal eigenvector, and consequently our distance matrix has a scalar representation, . It is not surprising that the eigenvector is closely related to our scalar, in fact r(e,d) = 0.98. There are many hydrophobicity scales in the literature [14] and some are remarkably similar to our scalar amino acid representation, for example r = 0.95 for Wertz & Scheraga scale [15]. However, the highly correlated scales are derived from residue burial statistics in protein structures and are therefore not independent of our statistic.
Discussion
We have generated full atom residue:residue contact propensity profiles for intra-molecular interactions from a non-redundant crystal structure database. Recasting the contact propensity matrix as a distance matrix we see that close residues are those with a low evolutionary substitution cost. The structure derived distance measures can serve as additional scores when aligning proteins where remote homologs share structural features. The distance matrix led us naturally to derive effective residue vectors. We found that residues sharing similar physical characteristics, such as hydrophobicity and volume, are grouped together. In contrast to the MJ matrix analysis, we find that a scalar representation for the residues is inadequate to capture the complexity of the propensity distance matrix. The most successful scalar representation for the amino acid residues has been the hydropathy scale. Representing a sequence as a smoothed hydropathy profile through wavelet analysis or simple averaging has resulted in many effective analytical tools, such as periodic structure prediction [16], remote homology analysis, helix prediction [14], transmembrane prediction [17] etc. It is then probable that a higher dimensional vector representation of the amino acids may lead to a more subtle sequence analysis. The distance matrix may also serve as an additional tool in sequence alignment as it gives one a measure of the structural cost of residue mutation and this is an idea we hope to pursue in a future study.
Conclusions
In this study we have shown that inter-residue distance matrices and residue vectors allow us to make an explicit connection between amino acid interaction preferences observed in protein structures and amino acid evolutionary substitution costs. When problems are encountered with aligning structurally related proteins that are remote homologs then the structurally defined distance matrix may prove to be an effective supplement to existing substitution rate derived matrices. The distance matrix leads naturally to an amino acid vector representation. Projecting the vectors onto a two-dimensional plane illustrates ways in which the amino acids can be grouped and their effective number thereby reduced.
Methods
The database used in the present study was compiled from the PDBselect25 [10] list of representative proteins with known crystal structure that share less than 25% sequence homology. The structural coordinates were downloaded by automated ftp from the NCBI protein data bank. All programmes were written in C, compiled with Metrowerks CodeWarrior and run on a PC. In brief, the contact propensity statistics were compiled by reading the amino acid sequence and atomic coordinates for the specified chain of each pdb structure file in turn. The number of possible pairings of amino acid type i with amino acid type j, Nij were counted together with the number of these pairings corresponding to a pair with side chain atoms within a given distance of each other, Cij. The contact propensity matrix is given by . The residue vectors were defined such that is minimal. The minimisation was carried out by a standard Newton-Raphson steepest descent iteration.
Supplementary Material
Additional table 1
Contact propensities and distance matrix derived from the structural database with contact cut-off set at 4.5Å.
Click here for file
Additional table 2
Two dimensional residue vector components derived from the contact propensity distance matrix, the PAM250 distance matrix and the MJ distance matrix.
Click here for file
Figures and Tables
Figure 1 Comparison of the distance matrices derived from intra-molecular crystal contacts and from the PAM250 evolutionary substitution rates. In (a) D(S) is plotted in the upper triangle and D(P) in the lower, larger distances correspond to lighter shades. We have scaled D to average unity i.e. <D> = 1. It is apparent that the two matrices have a similar pattern. The correlation is shown explicitly in (b). The extent of correlation varies with the contact cut-off distance and peaks at a cut-off of 4.5Å, see (c). When contacts are scored by residue centroid proximity then the correlation is less strong, see (c).
Figure 2 A two-dimensional representation of the amino acid vectors. The contact propensity distance matrix derived vectors are shown in (a) and it is clear that residues with similar hydropathies and sizes are grouped together. A similar vector space can be obtained from an evolutionary substitution rate matrix and this is shown in (b). The residue positions are similar in (a) and (b), but in (a) residues with opposite hydropathies appear to lie further apart. In contrast, the vector space derived from the MJ energy matrix appears to be roughly linear. Here, the residues group in essentially the same way as reported by Wang & Wang [6].
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| 15491497 | PMC526251 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Oct 18; 5:153 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-153 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-221547947510.1186/1471-2210-4-22Research ArticleIn vitro pharmacokinetics of anti-psoriatic fumaric acid esters Litjens Nicolle HR [email protected] Strijen Elisabeth [email protected] Gulpen Co [email protected] Herman [email protected] Dissel Jaap T [email protected] H Bing [email protected] Peter H [email protected] Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands2 Department of Dermatology and Venereology, Erasmus Medical Center, Rotterdam, The Netherlands2004 12 10 2004 4 22 22 6 2 2004 12 10 2004 Copyright © 2004 Litjens et al; licensee BioMed Central Ltd.2004Litjens 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
Psoriasis is a chronic inflammatory skin disease that can be successfully treated with a mixture of fumaric acid esters (FAE) formulated as enteric-coated tablets for oral use. These tablets consist of dimethylfumarate (DMF) and salts of monoethylfumarate (MEF) and its main bioactive metabolite is monomethylfumarate (MMF). Little is known about the pharmacokinetics of these FAE. The aim of the present study was to investigate the hydrolysis of DMF to MMF and the stability of MMF, DMF and MEF at in vitro conditions representing different body compartments.
Results
DMF is hydrolyzed to MMF in an alkaline environment (pH 8), but not in an acidic environment (pH 1). In these conditions MMF and MEF remained intact during the period of analysis (6 h). Interestingly, DMF was hardly hydrolyzed to MMF in a buffer of pH 7.4, but was rapidly hydrolyzed in human serum having the same pH. Moreover, in whole blood the half-life of DMF was dramatically reduced as compared to serum. The concentrations of MMF and MEF in serum and whole blood decreased with increasing time. These data indicate that the majority of the FAE in the circulation are metabolized by one or more types of blood cells. Additional experiments with purified blood cell fractions resuspended in phosphate buffered saline (pH 7.4) revealed that at concentrations present in whole blood monocytes/lymphocytes, but not granulocytes and erythrocytes, effectively hydrolyzed DMF to MMF. Furthermore, in agreement with the data obtained with the pure components of the tablet, the enteric-coated tablet remained intact at pH 1, but rapidly dissolved at pH 8.
Conclusion
Together, these in vitro data indicate that hydrolysis of DMF to MMF rapidly occurs at pH 8, resembling that within the small intestines, but not at pH 1 resembling the pH in the stomach. At both pHs MMF and MEF remained intact. These data explain the observation that after oral FAE intake MMF and MEF, but not DMF, can be readily detected in the circulation of human healthy volunteers and psoriasis patients.
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Background
Psoriasis is a chronic inflammatory skin disease characterized by epidermal hyperplasia and infiltration of inflammatory cells into skin lesions. Anti-psoriatic therapies are mainly anti-inflammatory. Long-term use of many of these anti-psoriatic therapies is often hampered by serious adverse effects [1-5]. In this connection it is of interest that already in 1959, Schweckendiek introduced fumaric acid, an intermediate of the citric acid cycle, for the treatment of his psoriasis [6]. The main adverse effect of fumaric acid therapy, i.e. induction of gastric ulcers, was overcome by application of a mixture of fumaric acid esters (FAE) with great bioavailability [7]. This mixture, consisting of dimethylfumarate (DMF) and salts of monoethylfumarate (MEF), was formulated as enteric-coated tablets. This systemic therapy, successfully applied by several German [8,9] and Dutch [10,11] dermatologists, can be taken by patients for a long period due to the excellent safety profile [12]. Adverse effects that do occur are mostly mild and transient and include facial flushing and gastro-intestinal complaints. Pharmacokinetic data of FAE therapy are very limited and mainly based on personal communications [8,13]. For such a pharmacokinetic study, we first developed a highly sensitive method to determine concentrations of FAE in human blood (Litjens et al., manuscript submitted). In the present study, we investigated the hydrolysis of DMF to its most bioactive metabolite monomethylfumarate (MMF) and the stability of MMF, DMF and MEF in different environments representing various body compartments using this methodology.
Results
Stability of FAE and hydrolysis of DMF to MMF in buffers of various pH
DMF, MMF and MEF remained completely intact in a buffer of pH 1 mimicking the pH in the stomach (Figure 1A and 1F). However, at pH 8 resembling the pH in the small intestines DMF, the most abundant component of the FAE tablet, was hydrolyzed to MMF (the half-life of DMF amounted to 1.5 hr) (Figure 1B). Addition of MEF, the other component of the FAE tablet, did not affect the half-life of DMF (1.7 hr) (Figure 1G). MMF remained intact (Figure 1B) in this buffer during the period of analysis (6 hr) as did MEF (Figure 1G).
Figure 1 Changes in the concentrations of the various FAE in different environments. DMF at a concentration of 2 mg/L or the combination of 2 mg/L DMF and 1.4 mg/L MEF were placed at 37°C in 0.1 N HCl; pH 1 (A, F), 0.1 M sodium phosphate buffer; pH 8 (B, G), 0.1 M sodium phosphate buffer; pH 7.4 (C, H), normal human serum (D, I) or whole blood (E, J). At various intervals thereafter samples were collected and the MMF (squares), DMF (circles) and MEF (triangles) concentrations were measured using HPLC. Results are a representative experiment of at least 3 independent experiments.
To further examine the pH-dependency of the hydrolysis of DMF to MMF, we measured concentrations of DMF and MMF in phosphate buffers (with pH ranging from 6.5–8) supplemented with DMF or the combination of DMF and MEF. The results revealed that the half-life of DMF dramatically decreased with increasing pH values and the maximal hydrolysis of DMF to MMF was seen at pH 8 (half life of DMF was 1.5 hr). For example, at pH 7.4 the half-life of DMF amounted to 12.7 ± 1.0 hr (n = 3) (Figure 1C). In agreement with these results we observed that the Fumaraat 120 tablet disintegrated completely between 1.5 and 2.5 hr in the alkaline, but not in the acidic, environment. The half-life of DMF in the tablet amounted to approximately 2.3 hr (data not shown).
Changes in the concentrations of DMF, MMF and MEF in serum and whole blood
Since FAE must enter the circulation to exert their anti-psoriatic effects at the affected skin site [14], we determined the hydrolysis of DMF to MMF and examined the stability of MMF, DMF and MEF in both normal human serum and whole blood (both with a pH of 7.4). The half-life of DMF in serum (Table 1 and Figure 1D) is dramatically shorter (p < 0.05) than that in a buffer of the same pH. MMF (Figure 1D and 1I) and MEF (Figure 1I) concentrations slowly decreased in serum during the period of analysis (6 hr). Furthermore, the half-life of DMF was even shorter (p < 0.05) in whole blood than in serum (Table 1 and Figure 1E), indicating that circulating cells are also involved in the hydrolysis of DMF to MMF. Furthermore, concentrations of MMF (Figure 1E and 1J) and MEF (Figure 1J) in whole blood decreased steadily during the period of analysis (6 hr), indicating that they may be metabolized by blood cells as well.
Table 1 Hydrolysis rate of DMF to MMF and half-lives of DMF in different environments. To analyze under which circumstances DMF can be hydrolyzed to MMF and whether MEF affects the hydrolysis of DMF into MMF, we determined the hydrolysis rates for DMF in different environments. In short, a 0.1 M sodium phosphate buffer, human serum and whole blood (all pH 7.4) were spiked with either 2 mg/L of DMF or with the combination of 2 mg/L of DMF and 1.4 mg/L of MEF and at several intervals thereafter, samples were taken and prepared in order to measure the concentration of DMF, MMF and MEF by HPLC. Subsequently, after calculating the area under the curves for DMF (AUC_DMF) and MMF (AUC_MMF), the following model [16] was used to fit the concentrations of MMF and to estimate the kDMF (rate of hydrolysis of DMF into MMF) in these solutions: [MMF]t = i = (kDMF*AUC_DMF)-(kMMF*AUC_MMF) + [MMF]t = 0. In addition, the half-life was calculated using the following formula: t1/2 = ln(2)/k. Data are means and SD (n = 3). # and * significant (p < 0.05) different value between buffer and and serum and serum and whole blood, respectively.
kdmf (h-1) t1/2 (h)
Buffer
Spiked with:
DMF 0.06 (0.004) 12.72 (1.04)
DMF+MEF 0.05 (0.01) 15.17 (1.88)
Serum
Spiked with:
DMF 1.96 (0.47) 0.37 (0.08) #
DMF+MEF 2.20 (0.25) 0.32 (0.05) #
Whole blood
Spiked with:
DMF 8.01 (3.78) 0.10 (0.04)*
DMF+MEF 10.08 (2.74) 0.07 (0.02)*
To find out which blood cell type(s) is (are) responsible for the hydrolysis of FAE in whole blood, hydrolysis of DMF in a buffer of pH 7.4 by purified blood cell fractions was analyzed. The results revealed that monocytes/lymphocytes (Figure 2A), but not granulocytes (Figure 2B) and erythrocytes (Figure 2C), at concentrations present in whole blood effectively hydrolyzed DMF to MMF.
Figure 2 Hydrolysis of DMF to MMF by various types of blood cells. Monocytes/lymphocytes, granulocytes, and erythrocytes were purified from blood of healthy volunteers using centrifugational techniques. Next, the various cell types were resuspended in PBS pH 7.4 to concentrations present in whole blood, e.g. 1 × 106/mL monocytes/lymphocytes (A), 5 × 106/mL granulocytes (B) and 5 × 109/mL erythrocytes (C), and then DMF was added to a final concentration of 2 mg/L. At various intervals thereafter samples were collected and the MMF (squares) and DMF (circles) concentrations were measured using HPLC. Results are a representative experiment of 3–4 independent experiments.
Discussion
A major finding of the present study is that hardly any DMF was hydrolyzed in a buffer of pH ≤ 7.4, whereas at pH 8, resembling the pH of the small intestines, this FAE was effectively hydrolyzed to its active metabolite MMF. It should be noted that MMF (and MEF) remained stable in these buffers. We realize that using acidic or alkaline buffers to mimick the conditions in body compartments, like the stomach and the small intestines, is only a first attempt to investigate the in vitro pharmacokinetics of FAE. For example, no enzymes, e.g. esterases, are present in these buffers whereas they are in these body compartments. In this connection, Werdenberg and collegues [15] recently showed that in the small intestines, the concentrations of MEF and MMF remained unaffected, whereas concentrations of DMF decreased by the action of esterases, such as carboxyl- and choline-esterases in this compartment. Esterase activity is also present in the liver which can cause a rapid disappearance of the various FAE from the circulation. Absorption of FAE from the small intestines into the circulation is not only dependent on the permeability of the intestinal membrane for the various FAE (permeability increases with increased acyl-chain length and increased lipophilicity), but also on the stabilities of the various FAE in the small intestines and liver. Clearly, hydrolysis of DMF to MMF is not only dependent on the pH of the environment but also on the activities of esterases.
Another important finding of this study is that the half-life of DMF in whole blood is considerably shorter than that in serum, although the pH of both blood and serum is 7.4. To explain this difference in hydrolysis of DMF in whole blood and serum we considered the possibility that blood cells also hydrolyze DMF to MMF. Using purified blood cell fractions resuspended in PBS (pH 7.4) we found that monocytes/lymphocytes, but not granulocytes and erythrocytes, at concentrations present in whole blood effectively hydrolyzed DMF to MMF. The rapid removal of DMF from PBS after addition of granulocytes and erythrocytes suggests that these blood cells bind DMF.
It should be realized that MMF (and MEF) most likely enter the circulation of psoriasis patients in order to exert their antipsoriatic effects in the skin lesions. In agreement we detected MMF and MEF, but not DMF, in the circulation of healthy volunteers and psoriasis patients after oral intake of Fumaraat 120® tablets [[16], Litjens et al., manuscript submitted; Litjens et al., unpublished data]. Our observation that the MMF is more rapidly removed from whole blood than from serum could indicate that MMF (and MEF) is taken up by blood cells and perhaps further metabolized into FA, which subsequently fuels the citric acid cycle, as suggested earlier by Joshi (personal communication). The different interactions between FAE and blood cells may affect their functional activities, as has been reported earlier [14,17-19], thus contributing to the beneficial effects of FAE therapy.
Conclusions
Together, these in vitro data indicate that DMF is almost completely hydrolyzed to MMF at an alkaline pH, but not at an acidic pH, suggesting that this hydrolysis occurs mainly within the small intestines and not in the stomach. Most likely, MMF and MEF are then absorbed in the circulation where they interact with blood cells and perhaps cells in the psoriatic lesions. The different interactions between these FAE and the various cell types may explain the beneficial effects of FAE in psoriasis. Finally, these in vitro experimental data will be key to the pharmacokinetic analysis of oral FAE in human healthy volunteers and psoriasis patients.
Methods
Fumaric acid esters (FAE)
The following FAE were used: dimethylfumarate (DMF; purity > 97%, TioFarma, Oud-Beijerland, The Netherlands), calcium-monoethylfumarate (MEF; purity > 97%, Tiofarma), monomethylfumarate (MMF; purity > 97%, AstraZeneca R&D, Charnwood, Loughborough, UK). In addition, the enteric-coated, magisterial manufactured tablet (named Fumaraat 120®; TioFarma), containing 120 mg of DMF and 95 mg of calcium-MEF was investigated in this study.
DMF, MMF and MEF in acidic and alkaline environments
To investigate the stability of DMF, MMF and MEF and the hydrolysis of DMF to MMF in several environments representing various aspects of different body compartments, 0.1 N HCl with pH 1 and 0.1 M sodium phosphate buffer with pH 8 were spiked with 2 mg/L of DMF, MMF, MEF or the combination of 2 mg/L of DMF and 1.4 mg/L MEF, to resemble the ratio of these two components in the Fumaraat 120® tablet. In addition, to determine the release of the contents of a Fumaraat 120® tablet and the hydrolysis of DMF to MMF at pH 1 (0.1 N of HCl) and pH 8 (0.1 M of sodium phosphate buffer), the tablet was placed in these buffers and at various intervals samples were taken and prepared for measurement of the concentrations of DMF, MMF and MEF by high-performance liquid chromatography (HPLC) as described below.
To further investigate the effect of the pH on the hydrolysis of DMF to MMF, 0.1 M sodium phosphate buffers with pH values ranging from 6.5–8 were spiked with DMF and the combination of DMF and MEF. At several intervals thereafter, samples were taken, and then prepared for measurement of the various FAE by HPLC.
As the current buffers lack proteins, no extraction procedure was necessary and the concentrations of the various FAE in the samples could be directly quantified by HPLC (see below).
Concentrations of DMF, MMF and MEF in serum and whole blood
As described above, serum and whole blood from 3 volunteers was spiked with 2 mg/L of DMF, MMF, MEF or the combination of 2 mg/L of DMF and 1.4 mg/L MEF. All volunteers were healthy as assessed by a full medical screening.
At several intervals, samples were taken, and then prepared for measurement of the various FAE by HPLC. In short, serum and whole blood contained proteins known to interfere with the measurement of FAE. To overcome this problem, the various FAE were extracted from serum and whole blood samples and subsequently the concentrations were measured by HPLC (see below).
Effects of purified blood cell fractions on the hydrolysis of DMF in PBS (pH 7.4)
The various blood cell fractions were obtained from blood of healthy volunteers using centrifugational techniques as described earlier [17,19]. In short, blood was subjected to Ficoll Amidotrizoate (ρ = 1.077 gm/L; Dept. of Pharmacy, Leiden University Medical Center, Leiden, The Netherlands) density gradient centrifugation (440 g 20 min at 18°C). After resuspension of the cells in the pellet in phosphate buffered saline (PBS; pH 7,4) the granulocytes were purified by plasmasteril (Fresenius AG, Bad Homburg, Germany) sedimentation (1 g) for 10 min at 37°C, washed with PBS and the contaminating erythrocytes were lysed with distilled water. Erythrocytes were obtained after washing the cells in the Ficoll-Amidotrizoate pellet three times with PBS supplemented with 0.1 IU heparin. Cells in the Ficoll-Amidotrizoate interphase (monocytes/lymphocytes) were washed three times with PBS containing 0.5 IU heparin and then resuspended in PBS pH 7.4. Next, suspensions of 1 × 106 monocytes and lymphocytes/mL PBS, 4 × 106 granulocytes/mL PBS, and 5 × 109 erythrocytes/mL PBS were spiked with 2 mg/L DMF. Again, at several intervals samples were taken and concentrations of the various FAE were measured as described below.
Sample preparation and HPLC analysis
The concentrations of the various fumarates in serum samples were determined as described (Litjens et al., submitted for publication). Briefly, after precipitation of serum proteins with acetonitrile, DMF in the samples was quantitated by HPLC. The sample preparation for MMF and MEF required a protein precipitation step with metaphosphoric acid followed by extraction with diethylether and additional pH-lowering to pH 0.5. Next, sodium chloride was added before centrifugation at 12,000 g. Thereafter, the ether layer was transferred to a glass vial and after evaporation the residue reconstituted in methanol: 0.1 M potassium phosphate buffer (KH2PO4/K2HPO4; pH 7.5) supplemented with 5 mM tetrabutylammonium dihydrogen phosphate 1:1 (v/v).
Concentrations of DMF, MMF, and MEF were determined on a HPLC apparatus (Spectra SERIES P100, Thermo Separation Products, Breda, The Netherlands) equipped with an Alltima C18 (5 μ 250*4.6; Alltech, Lokeren, Belgium) column and an Alltima Guard C18 precolumn (5 μ 7.5*4.6; Alltech, Lokeren, Belgium) using methanol:water 30:70 (v/v) as an eluent for DMF and methanol: potassium phosphate buffer supplemented with 5 mM tetrabutylammonium dihydrogen phosphate 20:80 (v/v) as eluent for MMF and MEF. The limit of detection for all three compounds amounted to 0.01 mg/L, the coefficient of variation for MMF, DMF and MEF was 7%, 8% and 9% at 0.5 mg/L, respectively (n = 4), and the recovery of MMF, DMF and MEF amounted to 75 ± 7%, 98 ± 3%, 67 ± 7% (n = 6). Standard curves constructed with purified FAE in buffers or human serum were used to quantify the concentrations of FAE in the various samples of these buffers and human serum or whole blood, respectively.
Authors' contributions
NL participated in the design of the study, analysis of the data and drafted the manuscript. ES carried out the optimisation of the HPLC method and performed all HPLC analyses. CvG was responsible for the optimisation of the HPLC method and participated in the design of the study. HM participated in the design of the study and analysis of the data. JvD, HT and PN conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank Dr. J. Tio (TioFarma) for providing the enteric-coated tablets and purified components of the tablet. This study was financially supported by a grant from AstraZeneca R&D Charnwood, Loughborough, UK).
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| 15479475 | PMC526253 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 12; 4:22 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-22 | oa_comm |
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-121546268310.1186/1471-2369-5-12Research ArticleRenal outcome in adults with renal insufficiency and irregular asymmetric kidneys Neild Guy H [email protected] Gill [email protected] Dorothea [email protected] Robin G [email protected] John O [email protected] Christopher RJ [email protected] Institute of Urology and Nephrology, University College London, W1T 3AA, UK2 Renal Unit, Middlesex Hospital (UCL Hospitals), Mortimer St., London, W1T 3AA, UK3 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel St., London, WC1E 7HT, UK2004 5 10 2004 5 12 12 5 5 2004 5 10 2004 Copyright © 2004 Neild et al; licensee BioMed Central Ltd.2004Neild 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 commonest cause of end-stage renal failure (ESRF) in children and young adults is congenital malformation of the kidney and urinary tract. In this retrospective review, we examine whether progression to ESRF can be predicted and whether treatment with angiotensin converting enzyme inhibitors (ACEI) can delay or prevent this.
Methods
We reviewed 78 patients with asymmetric irregular kidneys as a consequence of either primary vesico-ureteric reflux or renal dysplasia (Group 1, n = 44), or abnormal bladder function (Group 2, n = 34). Patients (median age 24 years) had an estimated GFR (eGFR) < 60 ml/min/1.73 m2 with at least 5 years of follow up (median 143 months). 48 patients received ACEI. We explored potential prognostic factors that affect the time to ESRF using Cox-regression analyses.
Results
At start, mean (SE) creatinine was 189 (8) μmol/l, mean eGFR 41 (1) ml/min 1.73 m2, mean proteinuria 144 (14) mg/mmol creatinine (1.7 g/24 hrs). Of 78 patients, 36 (46%) developed ESRF, but none of 19 with proteinuria less than 50 mg/mmol and only two of 18 patients with eGFR above 50 ml/min did so. Renal outcome between Groups 1 and 2 appeared similar with no evidence for a difference. A benefit in favour of treatment with ACEI was observed above an eGFR of 40 ml/min (p = 0.024).
Conclusion
The similar outcome of the two groups supports the nephrological nature of progressive renal failure in young men born with abnormal bladders. There is a watershed GFR of 40–50 ml/min at which ACEI treatment can be successful at improving renal outcome.
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Background
Nearly half the children and young adults who develop end-stage renal failure (ESRF) have asymmetric irregularly shaped kidneys [1]. This appearance, often referred to as bilateral renal scarring, is frequently associated with vesico-ureteric reflux (VUR) and sometimes with a history of urinary tract infection (UTI). It is generally a consequence of congenital malformations of the kidneys and urinary tract and is variously described as `reflux nephropathy' or `chronic pyelonephritis.'
Such patients fall into two broad groups. Firstly, there is a group who appear to have normal bladders without outflow obstruction and normal calibre ureters when not micturating, described as having either primary VUR or primary renal dysplasia. Secondly, there is a group with some form of bladder outflow dysfunction which causes a secondary VUR and dilated upper urinary tracts, of which a posterior urethral valve (PUV) in males is the most common cause.
The primary group have a bimodal presentation. Commonly they present in childhood with UTI; the rest present in early adult life with renal insufficiency and often with no preceding history of UTI [2-6]. Traditionally the diagnosis was made by recognising the characteristic appearance of calyceal clubbing and irregular `scarring' of the kidney on intravenous urography (IVU) [7,8]. With significant renal insufficiency, however, these changes can be impossible to see clearly by IVU [2], and the irregular, asymmetrical kidney is more sensitively visualised by 99 mTc-dimercaptosuccinic acid (DMSA) renography [9,10]. In this adult population a micturating cysto-urethrogram (MCU) frequently will not show evidence of VUR as reflux usually ceases spontaneously in childhood [2,4,5]. In fact, the finding of VUR is a weak predictor of renal damage in children admitted with an UTI [11].
The appearance of proteinuria and progressive renal failure indicates glomerular capillary hypertension (glomerular hyperfiltration) and progressive focal and segmental glomerulosclerosis (FSGS) [12,13]. Risk factors for patients with reflux nephropathy developing progressive renal failure after childhood are proteinuria, renal insufficiency, bilateral scarring of the kidneys and hypertension [2,4,5]. Patients with congenital bladder outflow obstruction and secondary reflux, however, have usually been excluded from such outcome studies, and very little has been published from a nephrological perspective about their long-term outcome.
In this retrospective observational review, from a large, single centre nephro-urological practice, we have examined the natural history and progression to ESRF of patients with primary and secondary reflux with asymmetric irregular kidneys and moderate to severe renal insufficiency. We have tested the null hypothesis of no difference in renal outcome between patients with primary and secondary reflux.
Methods
Patients
Patients with bilaterally scarred kidneys and glomerular filtration rate (GFR) 15–60 mls/min/1.73 m2 were identified from a review of the records of outpatients and of patients receiving renal replacement therapy at the Renal Unit of the Middlesex Hospital (UCL Hospitals Trust). Most patients had been referred, as adolescents, from the nephrology and urology clinics at the Great Ormond Street Hospital for Children. All patients had renal scarring confirmed by DMSA or 99 mTc-mercaptoacetyltriglycine (MAG-3) renography, although most patients had undergone extensive investigations.
For inclusion in this study, patients had:
• an isotopic 51Cr-edetic acid (EDTA) GFR < 60 ml/min/1.73 m2; or estimated GFR < 60 ml/min/1.73 m2
• apparently stopped growing and with a steady body weight (so that plasma creatinine could be used to estimate serial GFRs), and
• data for at least 5 years of follow up.
Patients specifically excluded from this study were those with bladder exstrophy, neuropathic bladders, or any form of urinary diversion (conduit or reservoir). In our analysis, the patients were divided into two broad groups:
Group 1: those with normal calibre ureters and normal bladders (Primary group)
Group 2: those with megaureters, hydronephrosis and abnormal bladders (Secondary group).
Data
Glomerular filtration rate (GFR) was estimated by single exponential analysis of the plasma clearance of 51Cr-edetic acid (EDTA) following a single intravenous injection with blood samples taken after 2 and 4 hours [14]. Plasma (PCr) and urine creatinine concentration were measured by the Jaffe technique using an autoanalyser (Chemlab Instruments, Hornchurch, UK) and urinary protein (Uprot) by turbidometric assay following precipitation with trichloroacetic acid.
GFR was estimated by different formulae and compared with the isotopic GFR. The Jelliffe formulae (I and II) [15,16] have been shown to approximate most closely low values of GFR when compared with the inulin clearance [17].
Jelliffe I (ml/min/1.73 m2) [15]:- "(100 × 88/ PCr μmol/l) - 12" for males; and "(80 × 88/creatinine μmol/l) - 7" for females.
Jelliffe II (ml/min/1.73 m2) [16]:- "(98–0.8(age-20)) × 88/ PCr μmol/l" for males. (Jelliffe II × 0.9 for females). [The original formulae used creatinine mg/dl. Conversion to μmol/l introduces the factor 88].
We found that a mean of these 2 formulae gave closer approximations to measured isotopic GFR than the other methods. We have termed this mean value `estimated GFR' (eGFR). All measured values of 51Cr EDTA GFRs were compared with the eGFR calculated using the contemporary value of plasma creatinine. 151 values of corrected isotopic GFR (ml/min/1.73 m2) from a range of 12–60 ml/min/1.73 m2 were found to have no significant bias (-0.34 ml/min/1.73 m2 with a 95% CI of -1.19 to 0.51 ml/min/ 1.73 m2) and an agreement within limits of -11.0 to 10.3 ml/min/1.73 m2.
Normal bladder: patients presenting after adolescence were considered to have a normal bladder if they had no bladder outflow symptoms, a normal urine flow rate (> 15 ml/sec) and no residual urine volume seen by ultrasound after voiding.
Declining renal function: the rate of progression of CRF, -delta GFR (`-ΔGFR'), was calculated as the rate of change of eGFR and is shown as ml/min/year.
Proteinuria: was initially measured as the amount of protein (g) in a 24-hour urine collection. Since 1995 proteinuria was more commonly measured on a random (spot) sample of urine at clinic visits with the proteinuria expressed as mg protein/mmol creatinine (normal laboratory range 0–13 mg/mmol). As all 24 hour urine data (Uprot) included creatinine excretion (mmol/24 hours) we have been able to calculate protein/creatinine ratios (Up/Cr). Using the data from 161 separate 24-hour collections, we assessed how proteinuria in g/day predicts protein/creatinine ratios. Paired values ranged from 0.1–8.6 g/day and 8–700 mg protein/mmol creatinine, with a high correlation (r = 0.90). The regression equation was [Up/Cr = 90.3 × Uprot0.94].
Hypertension was defined as either blood pressure consistently > 140/85 mmHg, or patients receiving blood pressure lowering therapy.
End-stage renal failure (ESRF): was taken to be the date when the patient began dialysis. To calculate changes in renal function with time the eGFR was assumed to be 8 ml/min at this time.
Outcome: Renal outcome was defined as having reached ESRF or not at last review.
ACEI therapy
Since June 1986 some patients were started on ACEI therapy when anti-hypertensive therapy was required. In addition, some anti-hypertensive regimens were changed to ACEI therapy. A small group with blood pressure <140/85 were started on ACEI therapy because of increasing proteinuria. Some patients never received ACEI because ESRF was reached before the use of ACEIs in renal insufficiency had become routine.
Patients, who were started on ACEI for hypertension, were advised to restrict their salt intake and the initial aim was for a blood pressure of ≤ 130/70. If ACEI alone did not lower the blood pressure to the target a diuretic was added. The latter was not always possible in Group 2 patients who might already have features of hypovolaemia secondary to their renal tubular pathology. No patient ever received any immunosuppressive drug.
Data collection
Only data from the start and end of the study are presented from all 78 patients. Of the 48 patients who were treated with ACEI, we also report data on 28 of the patients (for whom it was available for at least 18 months before and 48 months after the introduction of ACEI.) at the start of ACEI therapy and again at 2 years after start of therapy.
`Statistical Analysis'
Means were compared within groups by paired samples t-test, and between groups by Wilcoxon rank-sum test. The time scale was time since birth, since the setting was of a congenital disease eventually leading to renal failure. It was checked graphically that there was no obvious pattern to loss to follow up over time. Renal outcome (reaching ESRF or not) was compared between groups using Kaplan-Meier survival plots. Outcomes were quantified as the median survival outcome in months (with 95% Confidence Intervals [CI]). This is equivalent to the median time for 50% of the group to reach ESRF. Differences in renal outcome over time were tested using log-rank tests. For graphical examination of the proportionality, continuous variables were grouped into approximate tertiles. The Cox proportional hazards model was chosen for further analysis. Continuous variables were centred on the mean. Protein/urine creatinine ratios were log-transformed for further analyses. Treatment with ACEI was entered as a time-changing variable to take account of the variable start of treatment after referral. Univariable regression models estimated crude (unadjusted) effects of the prognostic variables. Since it is possible that the effect ACEI depends on the GFR at start of treatment (entered as continuous variable), especially in patients with moderate to severe renal insufficiency, an interaction between those variables was fitted. Variables were entered in the multivariable model in a stepwise forward fashion. Analyses were performed using SPSS for Windows v10.1. and Stata 8 (Stata Corporation, Texas, USA).
Results
Demography
Data from 78 patients, who were first seen between December 1969 and February 1988, were analysed. Demographic details are presented in Table 1. The age of patients at the start of the study period ranged from 15–49 years (median 23 years) with one lady aged 65 years.
Table 1 Demographic Details At Entry (n = 78). Data shown are means (SE); Dates (month/year) are medians. Because isotopic GFRs were not always performed this data is not shown in Table, but 26 Group 1 patients had a mean (SE) isotopic-GFR of 41.2 (2.1) ml/min/1.73 m2 with a contemporaneous mean eGFR of 44.2 (2.3) ml/min/1.73 m2, and the 26 Group 2 patients with an isotopic GFR of 40.2 (2.3) had an eGFR 41.5 (2.0) ml/min 1.73 m2.
Group 1 (Primary reflux) Group 2 (Secondary reflux) TOTAL
Total 44 34 78
Male:Female 22:22 32:2 54:24
Date at start (median) 8/1988 6/1986 2/1987
Age (year) 28.9 (1.6) 22.2 (1.4) 26 (1.1)
Creatinine (μmol/l) 178 (9.0) 203 (13) 189 (7.7)
eGFR (ml/min) 42.3 (1.8) 40.3 (2.1) 41.4 (1.3)
Proteinuria (g/d) 1.63 (0.19) 1.83 (0.33) 1.72 (0.2)
Protein/creatinine (mg /mmol) 136 (14) 154 (27) 144 (14)
Hypertension 18 (41%) 5 (15%) 23 (29%)
Treatment with ACEI 32 (73%) 16 (47%) 48 (62%)
Treatment date (median) 4/1992 8/1993 11/1992
Total months of follow up 145 (11) 143 (9) 144 (7)
Group 1 (n = 44; 50% female) were patients who had primary VUR or primary renal dysplasia. Twenty two patients (71% female) presented in childhood with a UTI. In each case, MCU performed at a median age of 7 years showed reflux which was either bilateral (82%) or unilateral (18%). In contrast, the remainder (n = 22) presented at a median age of 24 years. Only 32% were female and they almost invariably presented either with hypertension after starting a contraceptive pill or with complications during pregnancy. Only 2 had had a MCU performed and neither showed VUR.
Group 2 patients (n = 34; 6% female) had the following diagnoses: PUV (n = 15), prune belly syndrome (n = 2), single dysplastic kidney with megaureter (n = 2), renal dysplasia with abnormal bladder function (n = 2), bilateral megaureters (n = 1), megacystis and megaureters (n = 4), and finally a group (n = 8) in whom the initial diagnosis (pre-1979) had included "bladder neck obstruction". Six of these had megacystis and megaureters and might now be termed `pseudo-prune belly syndrome'.
Renal outcome
By Group: The median survival time of Group 1 versus Group 2 was compared by log rank test: 231 months (95% CI: 153–309) vs. 162 months (95% CI: 135–189) respectively, p = 0.35. There was no evidence for a major difference in renal outcome between these patients with primary and secondary reflux. Thus for subsequent analyses the data from all 78 patients was combined.
By eGFR: We compared renal outcome for all patients after they were stratified by initial eGFR into four groups (15–30, 31–40, 41–50, and 51–60 ml/min/1.73 m2) (see Fig 1). Of 18 patients with eGFRs >50 ml/min, only 2 (11%) reached ESRF: however, eGFR still declined in the other 16 by 1.30 ml/min/yr and mean proteinuria rose from 35 to 55 mg/mmol creatinine, despite 12 patients (75%) receiving ACEI.
Figure 1 Renal outcome stratified for eGFR (ml/min/1.73 m2) at start. eGFR 51–60 vs 41–50: p = 0.17; eGFR 41–50 vs 31–40: p = 0.004; eGFR 31–40 vs 15–30: p = 0.041.
By Proteinuria: We compared renal outcome for all patients after they were stratified by initial proteinuria into three groups (0–99, 100–199, and ≥ 200 mg/mmol) (Fig 2). The great significance of proteinuria is emphasised further by the observation that seven patients with proteinuria ≥ 200 mg/mmol reached ESRF despite initial eGFRs ≥ 40 ml/min. In contrast, none of 19 patients with proteinuria <50 mg/mmol creatinine at start developed ESRF after a median follow up of 160 months (range 87–227).
Figure 2 Renal outcome stratified for proteinuria (mg/mmol) at start. 10 – 99 vs 100–199 mg/mmol: p = 0.009; 100–199 vs >200 mg/mmol: p = 0.002.
Proteinuria increased with time and declining function in both Groups (Table 2) but levels were consistently higher in Group 2 compared with Group 1 patients (Table 1). There was a strong correlation between rate of loss of function and proteinuria at start (R = 0.63, p < 0.0001), and end of study: (R = 0.69).
Table 2 Creatinine, eGFR, proteinuria and ACE-I stratified by renal function at outset. Data are medians (range). Proteinuria*: b) vs c) p = 0.06, c) vs d) p = 0.031; -Δ eGFR†: b) vs c) p= 0.14, c) vs d) p = 0.012; Total -ΔGFR is the rate of change of function in ml/min/yr from start to last follow up; Rx ACEI is the percentage of patients receiving ACEI treatment
Renal Function groups N= Creatinine eGFR Proteinuria -Δ eGFR Total -Δ eGFR post-ACEI Rx ACEI
μmol/l ml/min/1.73 m2 mg/mmol ml/min/yr ml/min/yr
a) 15–30 ml/min 16 295 (220–450) 24 209 (57–680) 2.94 (0.55–5.7) 2.63 (1.51–4.3) 38%
b) 31–40 ml/min 14 198 (160–233) 36 200 (71–275)* 3.05 (0.95–7.4) 1.7 (0.9–3.45) 50%
c) 41–50 ml/min 30 160 (130–185) 46 100 (10–276)* 1.71 (0.44–8.21)† 1.68 (0.66–7.85) 77%
d) 51–60 ml/min 18 130 (115–153) 55 38* (10–250) 1.34 (0.24–3.41)† 1.76 (0.24–3.73) 72%
ACEI treatment
48 patients commenced ACEI therapy at a median of 48 months (range 0–311) after the start of the study, by which time their median eGFR had fallen from 46 (range 15–60) to 36 (10–60) ml/min/1.73 m2.
Effect on renal outcome
Table 3 shows the results of both the univariable and multivariable analyses. For every variable entered into the model the assumption of proportionality of hazards was met. It was notable, given the small number of patients of our sample, that we were able to detect an interaction between treatment with ACEI and renal function at treatment start. The effect of ACEI was estimated to have its main effects just above a GFR of 40 ml/min. In the crude, as well as in the full model, neither sex nor type of reflux seem to have a significant effect upon time to ESRF since referral. At entry to study, both the amount of proteinuria and eGFR were important prognostic variable towards ESRF in crude as well as adjusted analyses. There was one 65 year old lady. Refitting the final model omitting this record did not affect the estimates.
Table 3 Estimated crude and adjusted hazard ratios for incidence of ESRF in all patients. *full model includes all variables, since analyses were conducted on the age-scale, effects are taking account of current age; **interaction parameters (95%CI): crude model: -0.088 (-0.162,-0.014); p = 0.019 full model: -0.093 (-0.174,-0.012); p = 0.024; #effect of 100 mg/mmol creatinine = (displayed hazard ratio)0.7 effect of 200 mg/mmol creatinine = (displayed hazard ratio)1.4; ##effect of 10 ml/min/1.73 m2 decrease = (displayed hazard ratio)2 effect of 15 ml/min/1.73 m2 decrease = (displayed hazard ratio)3
Hazard ratios
Estimated effects of categories/unit crude 95%CI p-value Adjusted* 95%CI p-value
Gender female 1.00 1.00
male 1.17 (0.55, 2.50) 0.677 0.55 (0.21,1.39) 0.205
Type of reflux primary 1.00 1.00
secondary 1.28 (0.65, 2.55) 0.478 1.73 (0.73, 4.06) 0.211
Proteinuria per 50 mg/mmol proteinuria increase# 1.71 (1.33, 2.20) <0.001 1.50 (1.17, 1.91) 0.001
eGFR** per 5 ml/min/1.73 m2 decrease## 1.38 (1.16, 1.64) <0.001 1.29 (1.05, 1.58) 0.016
ACE inhibitor** at 30 ml/min/1.73 m2 0.53 (0.22, 1.29) 0.162 0.67 (0.27, 1.67) 0.393
at 35 ml/min/1.73 m2 0.34 (0.12, 1.00) 0.051 0.42 (0.14, 1.26) 0.121
at 40 ml/min/1.73 m2 0.22 (0.06, 0.84) 0.027 0.27 (0.07, 1.04) 0.058
at 45 ml/min/1.73 m2 0.14 (0.03, 0.73) 0.02 0.17 (0.03, 0.91) 0.039
Effect on rate of progression and proteinuria
We examined the effect of ACEI on 28 patients with deteriorating function for whom data was available for at least 18 months before and 48 months after the introduction of ACEI. ACEI reduced the rate of loss of renal function during the first 24 month period of follow-up, but the benefit was greater in the subsequent follow-up period with the rate slowing from a median of -1.86 before ACEI to -1.48 ml/min/yr (p = 0.007). ACEI treatment was associated with a reduction in proteinuria after 24 months of therapy. However, proteinuria had increased at last follow up owing to loss of the anti-proteinuric effect in half the group (Fig 3).
Figure 3 Effect of ACEI on Proteinuria. Time points are 1) start of study, 2) at begin of ACEI therapy, 3) 2 years after begin ACEI; 4) at end of study. Proteinuria* at ACEI vs +2 years post-ACEI; p < 0.0001.
Analysis of 30 patients who did not receive an ACEI provides indirect support of benefit from this treatment. The median rate of loss of renal function for 25 of these patients who started with a eGFR ≤ 50 ml/min was 2.80 ml/min/yr and if initial proteinuria was ≥ 50 mg/mmol, (n = 23) the rate of loss of renal function was 3.0 ml/min/yr.
Blood pressure
At the start of the study 18 (41%) Group 1 patients and 5 (15%) Group 2 patients were hypertensive. Group 2 patients tended to be normotensive and some were started on an ACEI for proteinuria. At ESRF or last follow up, all patients were on conventional anti-hypertensive therapy or ACEI, except for two Group 2 patients.
Discussion
There is a consensus that patients with renal insufficiency and proteinuria have progressive renal failure, that the rate of decline of function is proportional to the magnitude of the proteinuria, and that angiotensin antagonists both slow the rate of progression and reduce proteinuria [18-20]. Our data show that this nephro-urological group of patients is no exception.
While VUR patients with abnormal bladders almost invariably present in early childhood, patients with normal bladder function have a bimodal presentation. In one series from New Zealand, 42 patients (36 adults) had ESRF with reflux nephropathy. Many had presented with advanced renal insufficiency, hypertension, and proteinuria, and only 22% of males and 58% of females had a history of UTI. Similarly, in our study, VUR had been proven in 50% of patients with primary reflux nephropathy and these patients (71% female) had almost invariably presented with an UTI in childhood (median age 7.0 years). In contrast, the other half (32% female) presented at a median age of 24 years with advanced disease and persistent reflux was not demonstrated in the 2 patients who underwent a MCU. Similar to the New Zealand experience [5,21,22], nearly all our women presented with hypertension after starting a contraceptive pill or with complications during pregnancy, whereas the men were found to have proteinuria, hypertension or renal insufficiency – usually on routine investigation.
Although reflux nephropathy is frequently viewed as a disease of little girls with recurrent UTIs [7], our data confirms findings of others regarding the late presentation of adults with asymmetric irregular kidneys. In a UK series from Newcastle, only 9% presented under 20 years of age [2], in Australia 22% under 15 years [4], and from Italy 10% under 12 years [6]. Furthermore it is clear from published reviews [2,4-6] and dialysis programmes [5,23], that there is no female preponderance at ESRF.
In 1978 Kincaid-Smith and her colleagues [12] reported that progressive renal failure with primary VUR was very unlikely unless proteinuria was in excess of 1.0 g/day (equivalent to 100 mg protein/mmol creatinine). In a subsequent report, in which 147 such patients were followed for a mean of 6.9 years, renal function deteriorated in 37% and 14% progressed to ESRF. Proteinuria, elevated creatinine and hypertension at presentation were associated with relative risks (RR) of 25, 24 and 4.5 respectively for the development of progressive renal failure [4].
In an Italian study [6], 80 patients were followed for a mean of 5.6 years and retrospectively stratified into those with stable renal function and those with slowly or rapidly progressive renal failure. For those with progressive nephropathy, there was no difference in initial renal function but proteinuria was much greater in the rapidly progressive group. Loss of function was unusual with creatinine ≤ 1.7 mg/dl (150 μmol/l) and inevitable above that concentration [6].
In a UK study from Newcastle, proteinuria and renal insufficiency (plasma creatinine >130 μmol/l) were present from presentation in 21% and 13% of 125 patients respectively, and with time (mean 5.9 years) a further 21% developed proteinuria and 22% renal insufficiency. In all the 16 patients with progressive renal failure, the decline was linear. In a subsequent report, progressive renal failure did not develop in 138 adult patients with normal function at the start (plasma creatinine < 90 μmol/l) [3].
Our data support the established relationship between the risk of progressive nephropathy and renal insufficiency with proteinuria, but suggests a watershed range for renal function as a predictor of outcome. When the eGFR exceeds 40–50 ml/min/1.73 m2 nephropathy rarely progresses, but disease progression is invariable when function is worse. Nakashima et al. similarly reported that an isotopic GFR less than 49 ml/min predicted decline to ESRF [24].
The other determining factor of poor renal outcome is proteinuria, and we find that deterioration can be expected when proteinuria exceeds 50 mg/mmol (0.5 g/d). In a Japanese study, serial biopsy samples from patients with reflux nephropathy confirmed the close association between the degree of renal scarring, the extent of the glomerular pathology, and proteinuria [25,26]. Once extensive glomerular sclerosis was present there was conspicuous glomerular hypertrophy which correlated with increasing proteinuria. This is consistent with our findings that proteinuria increased as renal function declined. The prognostic importance of proteinuria is emphasised by our observation that 6 of the 25 (24%) patients with proteinuria ≥ 200 mg/mmol developed ESRF despite initial eGFRs exceeding 40 ml/min. On the other hand, the survival outcome benefit of ACEI treatment was most conspicuous when patients with proteinuria = 100 mg/mmol were compared (p < 0.00001).
In a 10-year follow up study of 52 children, randomised to medical or surgical management of severe bilateral VUR (grades III-IV) between 1985–1989, progressive renal failure developed in only 4 children (2 from each group) all of whom had GFRs at or below 40 ml/min/1.73 m2 at outset [27].
Despite the few long-term studies of adult patients with primary VUR and reflux nephropathy [2-5], there is almost no renal outcome data on adult patients born with abnormal bladder function (Group 2) [28]. It had been our clinical impression that those with secondary reflux did less well, but although there was a trend for Group 2 patients to do less well this was not statistically significant, although a difference might emerge if large numbers were studied.
Progressive renal damage due to congenital outflow tract obstruction may be averted by urological intervention. This is not, however, always successful and even treatment in utero may not prevent progressive renal damage [29] and the development of ESRF. Despite correction of urethral obstruction, 30% of boys with PUV develop ESRF by the age of 15 years and this may be due to continuing bladder dysfunction [28]. Most boys born with abnormal bladders who develop ESRF have posterior urethral valves, but in our series 35% had presented with megacystis/megaureters. Before 1975, this was attributed to bladder neck obstruction and treated by bladder neck surgery but subsequently it has been determined that most of this group are born with gross bilateral VUR. The dilated bladders and ureters are attributed to the constant recycling of refluxed urine, sometimes exacerbated by the nephrogenic polyuria [30] although urodynamic studies often show high voiding pressure suggestive of detrusor/sphincter dyssynergia. The concept that progressive renal failure is often `nephrogenic' in origin, rather than urological, is supported by our data which show that adults with abnormal bladders do not behave significantly differently to those with primary reflux – although the numbers are relatively small. Nevertheless, if renal function deteriorates in a urological patient in the absence of proteinuria, then some other cause (such as obstruction) must be sought.
Apart from small numbers, the limitations of this study are the usual ones in observational research with effect estimates that are potentially confounded. We believe, however, that it would not now be possible to design a prospective study in which ACEI therapy was with held from one group. In this study, ACEI therapy improved renal outcome although non-ACEI patients were generally from an earlier period and not necessarily seen in a specialist clinic. The analysis demonstrated a long-term benefit for ACEI both in slowing the rate of loss of function and in reducing proteinuria. However, ACEI therapy appeared to be ineffective if started when the eGFR was already = 30 ml/min.
We have studied a group of patients whose renal pathology is not immunological and whose residual functioning tissue is not homogenously distributed. The pathophysiology is similar to experimental sub-total nephrectomy which results in proteinuria, progressive glomerulosclerosis and renal failure [13,31]. Like the experimental models, we have found that there is a watershed level of GFR above which ESRF is unlikely and below which it is invariable, and that the most important harbinger of poor outcome is proteinuria. Why, however, one patient with a GFR of 45 ml/min should have no proteinuria and do well, while another with a GFR of 50 and 2 g/day of proteinuria does badly, remains to be determined. Long term studies [27] and follow up observations on outcome [23] in patients with reflux nephropathy confirm no benefit from anti-reflux surgery. This is consistent with our current view that progressive nephropathy is nephrogenic rather than urological in origin.
Conclusions
Patients with a GFR of <60 ml/min need careful follow up and we would recommend that anyone with increasing proteinuria, or proteinuria >50 mg/mmol (0.5 g/d) is started on an angiotensin antagonist to reduce proteinuria and slow the rate of progression of the renal failure.
Competing interests
GHN has been a consultant and lectured for MSD regarding both ACE inhibitors and angiotensin II receptor blockers. G Thomson, D Nitsch, RG Woolfson, JO Connolly, and CRJ Woodhouse have no conflict of interest.
Authors' contributions
GN was responsible for the study design, management of patients, and writing of the report. GT collected clinical data from medical records; DN contributed to analyses and writing of the report; RW and JC contributed to patient management and writing of the report. CW was responsible for the continuing urological supervision of the patients and writing of the report. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Prof TM Barratt for many discussions and his advice writing this paper. We wish to acknowledge the paediatric surgeons at Great Ormond Street Hospital for Children, in particular Sir David Innes Williams and Mr PG Ransley, whose great surgical skills allowed us to study so many patients. We would like to thank our colleagues Dr FD Thompson, Dr MA Mansell, Dr SL Cohen, and Dr BR Leaker for their help with the management of patients.
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| 15462683 | PMC526254 | CC BY | 2021-01-04 16:32:50 | no | BMC Nephrol. 2004 Oct 5; 5:12 | utf-8 | BMC Nephrol | 2,004 | 10.1186/1471-2369-5-12 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-481549149410.1186/1471-2458-4-48Study ProtocolThe ProActive trial protocol – a randomised controlled trial of the efficacy of a family-based, domiciliary intervention programme to increase physical activity among individuals at high risk of diabetes [ISRCTN61323766] Williams Kate [email protected] A Toby [email protected] Simon [email protected] Wendy [email protected] William [email protected] David [email protected] Stephen [email protected] Ulf [email protected] Nicholas [email protected] Ann Louise [email protected] General Practice and Primary Care Research Unit, Department of Public Health and Primary Care, Institute of Public Health, Forvie Site, Robinson Way, Cambridge CB2 2SR, UK2 Department of Radiology, 146 N Canal St, Suite 300, University of Washington, Seattle, WA, 98103, Box 358853, USA3 MRC Biostatistics Unit, Institute of Public Health, Forvie Site, Robinson Way, Cambridge CB2 2SR, UK4 MRC Epidemiology Unit, Strangeway Research Laboratory, Wort's Causeway, Cambridge CB1 8RN, UK2004 18 10 2004 4 48 48 2 10 2004 18 10 2004 Copyright © 2004 Williams et al; licensee BioMed Central Ltd.2004Williams 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
Increasing prevalence of obesity and disorders associated with sedentary living constitute a major global public health problem. While previous evaluations of interventions to increase physical activity have involved communities or individuals with established disease, less attention has been given to interventions for individuals at risk of disease.
Methods/design
ProActive aims to evaluate the efficacy of a theoretical, evidence- and family-based intervention programme to increase physical activity in a sedentary population, defined as being at-risk through having a parental family history of diabetes. Primary care diabetes or family history registers were used to recruit 365 individuals aged 30–50 years, screened for activity level. Participants were assigned by central randomisation to three intervention programmes: brief written advice (comparison group), or a psychologically based behavioural change programme, delivered either by telephone (distance group) or face-to-face in the family home over one year. The protocol-driven intervention programme is delivered by trained facilitators, and aims to support increases in physical activity through the introduction and facilitation of a range of self-regulatory skills (e.g. goal setting). The primary outcome is daytime energy expenditure and its ratio to resting energy expenditure, measured at baseline and one year using individually calibrated heart rate monitoring. Secondary measures include self-report of individual and family activity, psychological mediators of behaviour change, physiological and biochemical correlates, acceptability, and costs, measured at baseline, six months and one year. The primary intention to treat analysis will compare groups at one-year post randomisation. Estimation of the impact on diabetes incidence will be modelled using data from a parallel ten-year cohort study using similar measures.
Discussion
ProActive is the first efficacy trial of an intervention programme to promote physical activity in a defined high-risk group accessible through primary care. The intervention programme is based on psychological theory and evidence; it introduces and facilitates the use of self-regulatory skills to support behaviour change and maintenance. The trial addresses a range of methodological weaknesses in the field by careful specification and quality assurance of the intervention programme, precise characterisation of participants, year-long follow-up and objective measurement of physical activity. Due to report in 2005, ProActive will provide estimates of the extent to which this approach could assist at-risk groups who could benefit from changes in behaviours affecting health, and inform future pragmatic trials.
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Background
This trial addresses the rise in the burden of disease associated with sedentary living: a major public health problem. Physical inactivity accounts for up to 11.7% of all deaths in developed countries [1] and it has been causally associated with coronary heart disease, diabetes, osteoporosis and some cancers. The rise in the prevalence of obesity in many countries may be associated with a decline in physical activity.
Reversal of this trend will require not only public health programmes to increase activity at societal level, but also interventions to help high-risk individuals increase physical activity and maintain beneficial activity patterns [2]. This is a trial of such an intervention. It aims to overcome the limitations of previous studies through careful choice and characterisation of the target population, study design, measures, and the interventions under evaluation themselves. The intervention programme, based on theories and evidence from psychology about how best to support behavioural change and maintenance, is potentially generalisable to other settings, target groups and behaviours.
Target population
The study targets people with a parental family history of Type 2 diabetes and a sedentary lifestyle, who constitute a clearly identifiable high-risk population [3]. A consistent direct relationship exists between sedentary living and Type 2 diabetes [3,4]. People with a family history of diabetes have a three-fold increased risk of developing diabetes compared to those without; a risk that is magnified by physical inactivity and weight gain [3,5]. At least 40% of the excess risk associated with weight gain might be avoided in such people if their BMI did not exceed 30 kg/m2 [2,3], and prospective studies support the idea that physical activity reduces weight gain [5,6].
Limitations of previous trials
Most trials have evaluated increasing physical activity in the context of established disease. The few published trials of primary prevention in high-risk groups have methodological limitations. They have mainly evaluated brief interventions to increase exercise in the general population delivered through primary care practitioners [7-9], often with very short follow-up (a few weeks). Those such as the Activity Counselling Trial ("ACT"), offering follow-up for two years, are based on an unknown proportion of willing attendees at ambulatory care facilities [10,11]. Evaluation of exercise prescriptions delivered through leisure centres has not been encouraging [12]. Moreover, participants have been poorly characterised in terms of risk, and most studies have relied on self-reports of exercise. This may inflate differences between groups due to recall bias, and cannot capture changes in either total energy expenditure, or physical activity related energy expenditure [13]. Measuring total energy expenditure is relevant if an increase in one component of activity results in a decline in another, as activities are substituted, and measuring physical activity related energy expenditure (i.e. total energy expenditure adjusted for resting energy expenditure) is important if this is the aetiologically relevant factor.
Three trials among individuals with impaired glucose tolerance in China [14], Finland [15] and USA [16], have established that intensive approaches to lifestyle changes including physical activity can delay progression to diabetes by 58% over three years [15,16] and possibly longer [14]. Few studies have modelled the long-term effects of physical activity, although available work suggests that interventions for primary prevention of Type 2 diabetes might be cost-effective [17]. A full review of primary prevention trials [5] identified no interventions aimed at increasing physical activity alone, without accompanying dietary change or intended weight loss. The current study addresses this gap, and previous methodological shortcomings, by careful characterisation of the participants, year-long follow-up, objective measurement of physical activity, and modelling of the relationship between current behaviour change and future disease risk.
Limitations of previous interventions
(i) Poorly specified interventions
Many of the available trials evaluated interventions that were not explicitly based on psychological theory and evidence, and did not specify clearly which behaviour change techniques were applied by the providers [18-20]. In addition, interventions often used relatively ineffective behaviour change techniques, for instance giving people advice about behaviour change [21,22]. More effective interventions to promote physical activity have applied psychological theory and evidence about how best to support behaviour change [16,23]. The development of the ProActive intervention programme included a review of psychological theories and evidence, through systematic reviews [18,20] and expert meetings, and a one-year feasibility study among 15 willing participants and their families. Based on this work, the Theory of Planned Behaviour (TPB) [24] was selected as the theoretical framework to inform behavioural determinants targeted in the intervention. Determinants include beliefs and attitudes towards the behaviour (here physical activity), which are elicited at an individual level. Tailoring interventions to personal beliefs is an innovative, but theoretically appropriate application of the TPB. Systematic reviews and expert meetings then informed the selection of potentially effective techniques aimed at changing beliefs. They include reinforcement of positive beliefs, and problem solving in relation to negative beliefs, in order to strengthen motivation.
(ii) Attention to adoption and maintenance of physical activity
A range of behaviour change techniques with evidence for their effectiveness was used to bridge the gap between intention and action: goal setting and review, action planning, use of prompts, self-monitoring, and reinforcement [2,18,23,25]. Use of a causal model, linking measured beliefs and attitudes to behaviour will allow subsequent process analysis to better specify both determinants and intervention (Hardeman et al., 2004. A causal modelling approach to the development of theory-based behaviour change programmes for trial evaluation. Submitted).
Major challenges in promoting physical activity are maintenance of behaviour change, and the avoidance of drop out rates that can approach 50% [5,26]. Reviews suggest that theoretical advances in facilitating behaviour maintenance have not been applied in intervention programmes [27,28]. The highest levels of participation have been achieved by home-based interventions, involving frequent professional contact, and promoting enjoyable, informal exercise of moderate intensity, such as walking [2,8]. Behavioural maintenance may be best supported by building habits, using self-regulatory strategies such as repetition of behaviours over time in a constant environment, ongoing goal review, self-monitoring, reinforcement, and relapse prevention [28,29]. The use of mail and telephone contacts is a promising cost-effective approach [23], especially the use of frequent, brief, support calls [2]. All these approaches are incorporated into ProActive.
ProActive objectives
The primary objective of ProActive is to determine the effects of a theoretical- and evidence-based intervention programme on objectively measured physical activity after one year, in sedentary individuals at risk of diabetes and related metabolic abnormalities due to their family history. Three questions are posed:
1. Behaviour change: Can an innovative approach to increasing physical activity achieve clinically important change in this behaviour when offered to a group at increased risk of diabetes?
2. Disease impact: If so, what is the potential for the changes in behaviour achieved in mid-life to reduce the incidence of diabetes in later life?
3. Dose finding: How does delivery of the approach, at two levels of intensity, affect acceptability, efficacy and costs?
The trial will estimate the extent to which physical activity and its key psychological mediators are altered by the intervention programme, and assess its acceptability to this high-risk group. It will document the extent to which behaviour change is associated with reduction in weight gain and improvement in physiological and biochemical correlates, and will model the potential impact of the intervention on future risk of diabetes.
Intensive, face-to-face interventions may not be a feasible health service model, and there is some evidence that less intensive, continuous support may be as effective [2,23]. The intervention programme is therefore being evaluated at two levels of intensity: 'face-to-face' (delivered at the participants' homes and by telephone) and 'distance' (delivered by one home visit and telephone and correspondence) over one year, in order to inform the most cost-effective intervention programme for wider evaluation. If potential efficacy is demonstrated, we intend to proceed to a multi-centre pragmatic trial of the cost-effectiveness of the approach in practice.
Methods/design
ProActive is a four-year study with a complex randomised trial design [30], with central randomisation of willing participants to intervention programmes or comparison. The trial is managed from the Institute of Public Health, University of Cambridge, following MRC guidelines. Ethical approval has been obtained from the Eastern MREC, and West Suffolk, Cambridge, Huntingdon and West Essex LRECs.
The study design and patient flows (achieved at recruitment closure, October 2003, and projected to end of study) are shown in Figure 1. The focus of measurement is the adult offspring of a Type 2 diabetic parent, but the focus of the intervention programme is this individual within a family context. Participants were recruited via parents with diabetes on primary care registers (20 practices), or directly through records of their family history of diabetes (seven of the 20 practices). Sedentary individuals and their families were randomised to facilitation, either 'face-to-face' or 'distance', or to a comparison arm offering a leaflet providing brief advice on the benefits of activity. Psychological, physiological, anthropometric and biochemical data were collected at baseline and one year after randomisation, with psychological data also collected at six months after randomisation.
Figure 1 Trial design and patient flows; Oct 2003 (recruitment closure)
The study is explanatory in design, and the quality-assured intervention programmes are delivered by carefully trained and supervised family health facilitators with experience of working in primary care or the community, and backgrounds in health promotion, dietetics and nursing.
Setting, recruitment and screening
The study is set in urban, suburban and rural Cambridgeshire, Essex and West Suffolk, England, in the homes of participants and their families. The study population consists of offspring of people with Type 2 diabetes, aged 30–50 years, without a diagnosis of diabetes, and not considered very active based on self-report at the start of the study (see below). This age range defines a group at risk of weight gain [31,32]. Any individuals found at study entry to have fasting hyperglycaemia [33] were referred to their family doctor, but retained in the trial.
Practice recruitment
Once the relevant ethical and PCT approval had been obtained, 53 practice teams in the locality were approached by letter, inviting them to take part in the study, and highlighting the reimbursement of all costs involved. Personalised letters were sent to the practice manager (who we asked to collate responses and reply using a reply slip and Freepost envelope), all partners and nursing staff. Included with each letter was a brief summary of the study and a Research Information Sheet for Practices (RISP) form [34]. If no response was received, a follow-up phone call was made to the practice manager. A principal investigator and member of the trial team visited interested practice teams, to discuss the study in further detail. All relevant practice staff were encouraged to attend, particularly those who would be involved in the administration of proposed patient surveys. The 20 practices that agreed to take part then received a 'set-up' visit by the trial team. A 'Practice Survey Manual' was created for the practice staff, and the trial team supported the practice teams as needed throughout the survey period.
Participant recruitment
Initially participants were recruited through their parents; patients with Type 2 diabetes on the diabetes registers of 20 practices ('recruitment method 1'). Patients were written to by their general practitioner, with a description of the study, and asked to provide contact information for any offspring aged 30–50 years, living locally. Consent was also sought for the practice to pass the contact details of the offspring to the research team so that they could invite the offspring directly into the study. Piloting demonstrated feasibility and acceptability of the method, and one reminder was sent after three weeks if no reply was received.
From 20 practices, 2631 patients were approached and 2025 (77%) replied, yielding 1238 potentially eligible offspring who were invited to take part in the study. The ratio of approximately one potentially eligible offspring to two patients with diabetes was half our pilot projections, so to increase recruitment we developed a second recruitment approach ('recruitment method 2'). This approach recruited potential participants with a recorded family history of diabetes directly from practices with family history registers, and was feasible in seven of the 20 practices. General practitioners wrote to all patients aged 30–50 years with a recorded family history of diabetes, enclosing a study information sheet, and asking those willing to complete and return to the practice a questionnaire to determine which family member(s) had diabetes, and of which type. Consent was sought for this information and contact details to be passed on to the research team. Using this method, with again one reminder letter, 1340 patients were written to, and 896 (67%) responses were received, with 283 patients interested and eligible. Both recruitment approaches provided 1521 potential trial participants. Practitioners used their discretion in applying both approaches to the exclusion of patients who were physically or mentally unwell.
Study population: inclusion and exclusion criteria
Activity levels
Potential participants recruited by both methods were next written to by the research team with full information about the study and a screening activity questionnaire, describing occupational and leisure activity, based on published questionnaires [35,36], to exclude very active individuals. Two reminder letters with questionnaires were sent at two-week intervals if necessary, giving a response rate of 74%. Respondents were excluded if they reported their occupational activity as 'heavy manual work' [35]; or 'physical work' if their total score on the leisure questionnaire [36] was ≥ 20; or 'sedentary' or 'standing' work if their total leisure activity score was ≥ 30. This resulted in exclusion of approximately 30% of those screened, a figure that matches well with the proportion of the UK population designated as active in prevalence surveys [37].
Study requirements
To fulfil measurement requirements participants had to be able to walk briskly, without help, on the flat for 15 minutes. Participants also had to live within reach of the measurement centre and the Family Health Facilitators; defined as a 30-minute average travel time from the study co-ordination centre. Other exclusion criteria included individuals with serious physical or psychiatric illness limiting programme involvement; people with life issues interfering with the study; those known to be pregnant or have diabetes before baseline measurement; and those planning to move away. As shown in Figure 1, application of these criteria reduced the 837 'interested' responses to 465 potentially eligible individuals, who were telephoned by a trained interviewer to confirm eligibility. Eligible and interested individuals were then scheduled for baseline measurement at either the Ely Research Centre or the Addenbrooke's Hospital Wellcome Trust Clinical Research Facility, where written consent was obtained.
Eligible offspring were registered with general practitioners in the Eastern Region of the UK. Prior to both baseline measures and randomisation, these doctors were individually informed about their registered patients' intention to participate in ProActive. Brief details of the trial were sent, together with a request for feedback if the practitioner had any concerns about the offspring's participation, or about the safety of the facilitators making home visits.
Randomisation
Randomisation was carried out centrally by the trial statistician, using a partial minimisation procedure that dynamically adjusted the randomisation probabilities in order to balance important covariates; body mass index, sex, age, physical activity (individually calibrated heart rate monitoring, see below), family size, and behavioural intentions. Randomisation thus used baseline measures. Thirty-two pairs of siblings and two sibling-triples were cluster randomised to the same study group to avoid contamination, and the remaining 295 participants (81%) were individually randomised. Overall, 365/465 (78%) of those eligible went forward to randomisation.
Baseline measures and follow-up
At baseline and the end of the study, all participants attend the study centre at either Ely or Cambridge for questionnaires, physiological and anthropometrical measures, and venesection. At six months, psychological and self-reported physical activity data are collected by postal questionnaires. Measures relating to the intervention programme evaluation are collected by the facilitators during the intervention, and we assess reported use of self-regulatory strategies by participants to increase their activity levels at six and twelve months.
Compliance with follow-up
In similar primary care based trials we have achieved attrition rates of 30% or less [38,39], and at current rates we will exceed the required 300 to complete the study (100 in each group, see Figure 1).
Maximising retention is an important issue, particularly as the comparison group do not benefit from regular contact with a facilitator. At recruitment, the introductory leaflets for all three arms emphasised the importance of follow-up, irrespective of treatment group. Participants who drop out of the intervention programme are contacted by a principal investigator, and offered an opportunity to give feedback and to confirm drop out from the intervention programme only, or from trial measurement as well.
Measurement
The distribution of measures across baseline, six-month and one-year follow-up are shown in Table 1. The principle outcome is an objective measurement of physical activity energy expenditure, the daytime physical activity ratio (dayPAR), which is the ratio of daytime energy expenditure to resting energy expenditure measured using heart rate monitoring with individual calibration for the heart rate-energy expenditure relationship [40,41]. This allows more precise quantification of the relationship between energy expenditure and relevant disease end points than self-report [13]. The method has been validated against the gold standard techniques of doubly-labelled water and whole-body calorimetry [42]. Physical activity is also measured by a validated questionnaire covering work, recreation and domestic activity over the previous month and year [43], and offspring report of usual physical activity patterns among family members and how they changed over the previous year.
Table 1 Study measures
Measures Baseline 6 months 12 months
Questionnaire measures:
1. Godin / EPIC self-reported physical activity [36]
2. Short form State anxiety [45]
3. Risk / worry diabetes+
4. Theory of Planned Behaviour [24]+
5. General Questionnaire, comprising:
A) Rose Angina questionnaire
B) Smoking, Alcohol & Physical Activity+
(smoking &
physical activity
only)
C) Occupation & Social Class
D) SF-36 & EQ-5D [44,46]
6. EPAQ (2) [43]
7. Clinical measurement questionnaire (physiological measures and family history)
8. Physical activity of family members+
9. Injury questionnaire+
10. Intervention programme satisfaction+*
11. Skills acquisition+*
Physiological measures: [13,40-42,51]
Cardiorespiratory fitness & dayPAR parameters
Weight, height, % body fat, blood pressure, ECG
Biochemical parameters (fasting plasma glucose, glycosylated haemoglobin, insulin, lipids)
Blood stored for future genetic testing
Costs:
Cost to the NHS of facilitator training & salary+
Costs of intervention programme delivery+
+ questionnaires developed for study
* intervention programme participants only
Oxygen uptake (ml O2/kg/body weight) is measured by indirect calorimetry during a submaximal graded treadmill exercise test, and maximal cardiorespiratory fitness (VO2max) is estimated using predicted maximal heart rate (i.e. 220 minus age) [40,42].
Self-report measures of well-being and quality of life include subjective health and energy (SF-36) [44], anxiety [45], worry about diabetes and perceived vulnerability, and EuroQol (EQ-5D) [46].
The frequency and severity of physical activity related injury is assessed by study questionnaire at one year.
Psychological mediators of physical activity include intention to increase activity over the next year, and its predictors (attitude, subjective norm, perceived behavioural control). These key measures have been developed for the study following the recommendations of Ajzen [24]. Physiological correlates of behaviour include weight measured on standard scales calibrated at three monthly intervals, body fat percentage measured by bio-electrical impedance (Bodystat, Isle of Man, UK), and systolic/diastolic blood pressure, measured using an automatic sphygmomanometer (Accutorr, UK). Biochemical correlates include fasting plasma glucose, glycosylated haemoglobin, insulin and lipids. We are storing EDTA whole blood samples for future genetic testing. Sociodemographic factors and ECG are also documented at baseline.
Cost of the intervention
The economic analysis will explore the impact of a physical activity intervention programme on NHS costs. As the study is explanatory in design, we will not conduct a full cost-effectiveness analysis, but aim to provide a cost-description of the delivery of the intervention programmes. We are measuring the costs of delivering the 'face-to-face' and 'distance' intervention programmes via family health facilitators. These costs primarily comprise the training of facilitators, educational materials, travel, and the time that facilitators spend contacting and visiting families (including cancelled visits). Travel costs and contact time are recorded by the facilitators for every trial participant. The cost of facilitator time will be based on national average salaries, employment costs, qualifications, overheads and indirect costs [47]. Although we do not expect the ProActive intervention programme to have an impact on health service costs in the short term, we are monitoring health service utilisation (hospital, primary and community care) in the last 20% of participants recruited to the study.
Participant safety
The primary safety concerns for participants in ProActive are cardiovascular and musculoskeletal events associated with the laboratory procedures of treadmill exercise testing and injuries sustained as a consequence of increasing physical activity in everyday life. The cardiorespiratory fitness test used in this study is submaximal, and only undertaken following extensive screening procedures. If a participant exhibits a positive Rose angina questionnaire [48], a positive physical activity readiness questionnaire [49] or an abnormal ECG, they are referred to a clinical member of the measurement team for a more detailed medical review. If there are clinical concerns, participants are excluded from the study, and referred to their general practitioner. In over 3000 such tests undertaken by our group using this protocol, no significant adverse events have occurred. Supervising staff are trained and hold current cardio pulmonary resuscitation certificates.
Ranges for acceptable results are set for all clinical measures. If these are exceeded, the information is sent to the general practitioner, and the participant informed and advised to consult.
As the intervention programme is based on participants' own preferred activities, and emphasises small achievable goals set by the participants, the risk of excess injury is small. Group information about injury will be reported.
Participants previously unaware of their familial risk of diabetes may experience anxiety related to awareness of their increased risk status. This is considered in facilitator training, and measures of anxiety, worry about diabetes and perceived vulnerability are included (see above).
Data management, quality assurance and exclusion of bias
Physiological and anthropometric measures are made in two centres by observers unaware of individuals' group allocation. Biochemical measures are made in one laboratory with established quality assurance systems. Randomisation was undertaken by the trial statistician, independently of the trial co-ordination team, and the data entry team are unaware of study group.
The administrative database (participant information), dayPAR values and blood test results are managed in-house, with the latter being double entered. Numeric fields have limiters set so that values outside a defined range cannot be entered. Additionally, any blood results outside the 'normal range' are flagged for confirmation of value. Random checks on administrative data are performed regularly, checking the data on the database against paper records and correcting any errors found.
Double data entry of all anthropometric and questionnaire measures is undertaken by an experienced, independent agency, blind to study group (Wyman Dillon Research and Data Management, Bristol, UK). In addition, random checks are applied as described above.
Intervention (see Figure 1)
Intervention programme contacts
The family health facilitator contacts participants randomised to the 'face-to-face' and 'distance' interventions, and arranges a home interview including family members. At this introductory interview, personal reasons for increasing physical activity are elicited and reinforced, family participation is encouraged, and the relationships between physical activity, weight gain and prevention of Type 2 diabetes are explained and discussed.
In the 'face-to-face' arm this is followed by four visits and two brief support telephone calls over five months. During these interactions the participant and willing family members learn strategies to increase physical activity, for instance selecting activities that they enjoy doing, setting achievable goals, defining action plans, self-monitoring, self-reinforcement and relapse prevention. Pedometers are available for self-monitoring among participants who have chosen walking as their goal. A key difference between this intervention and others currently under evaluation (e.g. ACT) is that there is no absolute target for physical activity defined at the outset. Family members are encouraged to make gradual and continuous increases in their activity, as much as they feel able to, on the understanding that all increases, if maintained, are beneficial. Follow-up continues by monthly telephone calls up to one year, to discuss any difficulties in applying the strategies, and to encourage family members to increase activity further.
In the 'distance' arm, following the introductory meeting the intervention programme is delivered by six telephone calls over five months, and then monthly by post up to one year, with content similar to the 'face-to-face' arm. During the phone calls the facilitators encourage the participants to involve family members. Visits and telephone calls take approximately one hour and 45 minutes, respectively.
Materials
An arm-specific introductory leaflet is used, but otherwise materials are the same for the face-to-face and distance arms. All introductory leaflets include text to encourage retention in the trial. In the comparison arm the leaflets offer brief advice on the benefits of physical activity. Participants in the intervention programme arms are given an educational manual describing the strategies that participants are encouraged to use to increase their habitual activity in a step by step fashion.
Promotion of fidelity of intervention delivery
Various mechanisms are used to promote the fidelity of delivery of the intervention programme to the underlying psychological theories and intervention programme protocols. A detailed training manual and protocols for each contact were developed, and a Training Officer appointed. Facilitators attended a five day phased course in psychological theories, behaviour change techniques and experiential training in techniques, with six half-days initially, followed by refresher sessions at six months and continuing supervised practice by a clinical psychologist and through peer-appraisal. Facilitators complete a checklist for the introduction of and mastery of self-regulatory strategies by the participant after each contact, and monitor intervention programme attendance and drop-out for each participant.
Assessment of fidelity and evaluation of the intervention programme
An assessment of adherence by facilitators to the behaviour change techniques specified in the protocols was conducted among a random sample of 27 participants, using reliable coding frames and transcripts of the sessions. The intervention programme evaluation includes: an assessment of the frequency of meetings and telephone calls, proportion of progress reports and postcards sent and progress reports returned, satisfaction with the intervention programme, reported use of self-regulatory strategies by participants at six months and one year, and drop-outs at one year.
Statistical procedures
Sample size
The sample size calculation was initially based on physical activity level (PAL), the ratio of total energy expenditure to estimated basal metabolic rate [40,41], and required 100 individuals completing one-year follow-up in each group. Prior to the measurement of any follow-up data, and endorsed by the Trial Steering Committee, a proposal was made to change the primary outcome measure to dayPAR, the ratio of daytime energy expenditure to resting energy expenditure, on grounds that this outcome consisted entirely of measured rather than estimated quantities. The calculations were based on the Ely cohort study data [41,50], in which the residual standard deviation of one-year change in dayPAR adjusting for baseline was 0.53. With 100 individuals in each group, there is 80% power to detect a difference in mean dayPAR of 0.18 between the combined intervention programme groups and the control group with a two-sided test at the 5% level of significance. This is equivalent to 2 MET hours/day, 30 minutes of brisk walking on the level, or 20 minutes of leisurely bicycling or swimming; a plausible and important increase. The observed difference in mean dayPAR between any pair of groups will be estimated with a 95% confidence interval having the width ± 0.15, equivalent to ± 1.75 MET hours/day, ± 25 minutes brisk walking or ± 15 minutes bicycling or swimming. Calculations were based on equal numbers in each group, and require 300 participants with outcome data at one-year follow-up. Recruitment of 400 participants allowed for 25% attrition after randomisation, and lower interim attrition rates will enable a lower recruitment target of 365 participants (Figure 1).
Main analyses will be at one year, comparing combined 'face-to-face' and 'distance' versions of the intervention programme with 'brief advice', comparing 'face-to-face' with 'distance' modes of the intervention programme, and estimating the difference between each intervention programme group and 'brief advice' to inform a larger pragmatic trial. Analysis by intention-to-treat will retain individuals within their randomised group regardless of participation. Comparisons will involve an adjustment for baseline physical activity and other variables used in the randomisation. We will undertake sensitivity analyses, assuming a range of potential outcomes for non-completers, informed by available baseline and interim data on non-completers. Non-completers will have multiple data imputed with a 'missing at random' assumption and with sensitivity analyses to represent optimistic and pessimistic scenarios for drop out. Clustering effects by family will be estimated for the primary outcome.
A secondary 'dose-response' analysis will use all three randomised groups, over baseline, six months and one year. A 'per protocol' analysis will also be undertaken among those completing the intervention programme.
The incremental cost of delivering the 'face-to-face' intervention programme will be compared to the 'distance' and 'brief advice' groups.
Modelling will comprise a series of stages
Stage 1) The trial will provide evidence on the relationship between observed behaviour change, weight change, and biochemical and physiological correlates. Modelling is facilitated by reference to the Ely Cohort; a prospective population cohort study that began in 1990 and involved 1122 people without known diabetes [40]. Measurements identical to those used in the Ely Cohort Study are included in ProActive.
Stage 2) Using models based on past cohort data, the influence of behaviour change on future diabetes incidence [40,41,51,52] will be projected, appropriately allowing for uncertainty in the parameter estimates. Simulation methods will be adopted. At this stage other risk factors (e.g. smoking, diet) will be assumed fixed.
Stage 3) We will undertake sensitivity analyses on the projections at Stage 2, using a range of plausible assumptions about how behaviour change might affect other risk factors and hence indirectly influence future diabetes risk.
Discussion
ProActive is the first efficacy trial of physical activity promotion in a defined high-risk group accessible through primary care, evaluating an intervention programme based on theory and evidence. It supports increases in informal activity, through the introduction and facilitation of self-regulatory strategies with regular reinforcement by the facilitator. Due to report in 2005, ProActive has the potential to make substantial contributions to understanding the extent to which such approaches could assist the wide range of at risk groups who could benefit most from increasing their physical activity.
The trial team brings together expertise in the epidemiology of diabetes [53] with intervention development and evaluation [18-21,30], measurement from beliefs to self-reported behaviour [26] and objectively measured energy expenditure [13,40,54] and trials [55], especially in primary care [38,39]. Their complementary contributions will allow both the answering of the main study questions in a robust manner, and the development of theory and method for future studies. Further exploratory work on interactions between genotype, social class and physical activity are planned, which may in the future lead to refinement in selection of the at-risk group. As an adjunct to the measurement of physical activity related energy expenditure by individually calibrated heart rate monitoring, we are also employing measurement of body movement using the MTI-Actigraph [54] on a proportion of the participants. The combination of the two measurement techniques has the potential to overcome the limitations with either method used alone, and improves the estimates of physical activity related energy expenditure [56], since the measurement errors associated with the methods are not positively correlated.
In terms of the intervention itself, careful measurement along the hypothesised causal path from cognition, through self-reported behaviours to energy expenditure, will enable testing of the application of the Theory of Planned Behaviour in this setting, and of the relationship between the everyday activities that the programme has as its focus and the objectively measured physical activity (Hardeman et al., 2004. A causal modelling approach to the development of theory-based behaviour change programmes for trial evaluation. Submitted). This will enable replication and further strengthening of effective intervention steps, as well as development of theory.
Together, it is expected that the findings will inform the design of future larger scale and more pragmatic preventive programmes promoting physical activity in at-risk groups.
List of abbreviations
dayPAR = daytime physical activity ratio; the ratio of daytime energy expenditure to resting metabolic rate measured using heart rate monitoring with individual calibration
ECG = electrocardiogram
MET = metabolic equivalent
PAL = physical activity level; the ratio of total energy expenditure to estimated basal metabolic rate measured using heart rate monitoring with individual calibration
TPB = Theory of Planned Behaviour
VO2max = maximal oxygen uptake (ml O2/kg/min)
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
ALK, NW, SG, SS, WH, DS, – Principal Investigators
TP – Trial Statistician
KW – Trial Co-ordinator
Will H – Trial Economist
UE – Physical activity measurement
All authors read and approved the final manuscript. ALK is the paper guarantor.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The Medical Research Council provided funding for the research and the measures, NHS R&D the intervention, together with the RCGP Scientific Foundation (early phases of intervention development and piloting), and Diabetes UK fund the assessment of the fidelity of intervention delivery (ref. no. RG35259).
We would like to thank the practice teams for all their hard work helping to recruit the participants, and the trial co-ordination, measurement and intervention teams for looking after them at the various stages of the study. The final thank you goes to our participants: without their enthusiasm, commitment and willingness to help, we would not have a study.
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| 15491494 | PMC526256 | CC BY | 2021-01-04 16:28:46 | no | BMC Public Health. 2004 Oct 18; 4:48 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-48 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-421548815110.1186/1471-2334-4-42DebateAutoimmune inflammatory disorders, systemic corticosteroids and pneumocystis pneumonia: A strategy for prevention Sowden Evin [email protected] Andrew J [email protected] Department of Dermatology, The James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW UK2004 16 10 2004 4 42 42 2 6 2004 16 10 2004 Copyright © 2004 Sowden and Carmichael; 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
Pneumocystis pneumonia (PCP) is an increasing problem amongst patients on immunosuppression with autoimmune inflammatory disorders (AID). The disease presents acutely and its diagnosis requires bronchoalveolar lavage in most cases. Despite treatment with intravenous antibiotics, PCP carries a worse prognosis in AID patients than HIV positive patients. The overall incidence of PCP in patients with AID remains low, although patients with Wegener's granulomatosis are at particular risk.
Discussion
In adults with AID, the risk of PCP is related to treatment with systemic steroid, ill-defined individual variation in steroid sensitivity and CD4+ lymphocyte count. Rather than opting for PCP prophylaxis on the basis of disease or treatment with cyclophosphamide, we argue the case for carrying out CD4+ lymphocyte counts on selected patients as a means of identifying individuals who are most likely to benefit from PCP prophylaxis.
Summary
Corticosteroids, lymphopenia and a low CD4+ count in particular, have been identified as risk factors for the development of PCP in adults with AID. Trimethoprim-sulfamethoxazole (co-trimoxazole) is an effective prophylactic agent, but indications for its use remain ill-defined. Further prospective trials are required to validate our proposed prevention strategy.
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Background
Pneumocystis carinii was identified a hundred years ago by Chagas [1] and recognised as a pathogen in marasmic children at the end of World War II [2]. The organism came to the fore again in the early 1980s when apparently healthy homosexual men developed PCP and heralded the acquired immunodeficiency syndrome (AIDS) epidemic [3]. With highly active antiretroviral therapy (HAART) and prophylactic antibiotics, attention has turned to PCP in human immunodeficiency virus (HIV) negative individuals. We recently treated a young woman with steroid-based immunosuppression for dermatomyositis. Four months after diagnosis, she was admitted acutely breathless to the intensive care unit with a presumptive diagnosis of PCP. Although the final diagnosis was rapidly progressive alveolitis related to dematomyositis, it prompted us to consider whether we should use PCP prophylaxis for selected patients receiving systemic steroids for AID. In this article we explore the background of PCP in HIV negative patients, consider the incidence of PCP in AID, discuss predisposing factors and propose a strategy for prevention.
Epidemiology
Pneumocystis pneumonia is caused in humans by the recently renamed Pneumocystis jiroveci (Frenkel 1999), previously known as Pneumocystis carinii and now thought to be related to fungi on the basis of DNA analysis, despite morphological similarities to protozoa [4]. The organism shows significant genetic divergence and is host specific with no cross-species infectivity. There has been considerable debate about the nature of the relationship between humans and P. jiroveci. At one time it was hypothesised that infection occurred through reactivation of colonisation acquired in childhood, as specific antibody was found in seven out of eight normal adults [5]. This suggestion has been refuted by the absence of detectable P. jiroveci in bronchoalveolar lavage specimens from healthy volunteers, despite amplification by the polymerase chain reaction [6]. In addition, genotypic analysis of P. jiroveci from infected adults identified strains found in patients' place of residence, rather than their place of birth [7]. Investigation of apparent clusters has shown different genetic strains affecting most cases, indicating that transmission from affected cases to susceptible persons does not account for the majority of infections [8,9], despite the recognised transmission of Pneumocystis DNA from affected patients to their immunocompetent contact health care workers to produce colonisation [10]. As P. jiroveci has been isolated in samples of air [11] and pond water [12], it is likely that the environment represents the main source of infection for most patients. Nevertheless, isolation of known cases of PCP is advisable.
Pneumocystis pneumonia is almost always exclusive to immunocompromised hosts. Two thirds of cases occur in HIV positive patients and constitutes the initial manifestation of AIDS in 46% of these patients [13]. A third of cases arise in HIV negative patients [14], a group consisting of organ transplant recipients (0–75%), haematological malignancies (9–58%), solid organ tumours (4–17.5%) and AID, usually on immunosuppressive treatment. The latter group accounts for 13–36% of cases in HIV negative patients [15-19]. It is this group we wish to consider in more detail.
Pathogenesis
P. jiroveci trophozoites proliferate and attach to type I alveolar pneumocytes causing desquamation, leading to a foamy eosinophilic exudate visible on hematoxylin-eosin staining and a "honey comb" appearance of lung tissue. Both antibody and cell mediated immunity have been postulated as being involved in host protection [20].
Clinical presentation
HIV-negative patients with PCP are older (48–61 years) than HIV positive patients. Males are affected more often than females (male to female ratio 1:1.4). The clinical course is typically more acute, with an average duration of 6–13 days. The most frequent clinical symptoms are dyspnoea (63–100%), cough (55–74%), weakness (47%), loss of appetite (38%) expectoration (22–25%), sweating (19%), weight loss (13%), haemoptysis (9%) and thoracic pain (9%). Physical findings include fever >38°C (63–85%), râles (55–66%), tachycardia (25%) and tachypnoea (22%). Chest radiography may show bilateral abnormalities (68–88%), interstitial opacities (64–80%) and alveolar opacities (31–47%), but may be normal (5–7%) [14-16,18,21]. The clinical picture can be altered by the use of aerosolised pentamidine prophylaxis, resulting in extra-pulmonary disease [20]. P. jiroveci in HIV negative patients is associated with concurrent pulmonary infections in over 50% of cases. The implicated pathogens include Cytomegalovirus (35%), Candida (18%) and Mycobacterium tuberculosis, which contribute significantly to the mortality of PCP [17,18,22].
Diagnosis
P. jiroveci infection is diagnosed in most cases by bronchoalveolar lavage, which, depending on the staining method used, has been reported to have a sensitivity of 81 to 90% and a specificity of 90 to 100% [23]. Other methods used include hypertonic saline induced sputum production, which is less sensitive, and definitive open lung biopsy. Cysts or trophozoites are morphologically identified by methenamine-silver nitrate or giemsa stains respectively. Immunospecific stains are now available and have increased the sensitivity of detection in sputum and bronchoalveolar lavage fluid [24]. The number of organisms in diagnostic specimens is high in HIV positive patients, but often low in HIV negative cases. An induced sputum sample may therefore be insufficient in the latter situation and bronchoalveolar lavage and/or transbronchial biopsy is the preferred method of investigation [20]. In patients who are at risk of PCP it is essential to have a high index of suspicion and low threshold for investigation, to allow early diagnosis and treatment. This is particularly important in those with AID, as PCP may mimic pulmonary involvement by the underlying condition, such as dermatomyositis, as exemplified by our patient who prompted this review. In SLE, 33% of patients die from infections, with 62.5% of all fatal infections being opportunistic, but only 10% of opportunistic pathogens are detected ante-mortem [25].
Management
Two antibiotic regimes have been shown to work, co-trimoxazole and parenteral pentamidine isethionate. Efficacies are comparable, although side-effects are particularly common with co-trimoxazole in HIV associated PCP and affect up to 60% of such patients [26]. These include rash, neutropenia, gastrointestinal upset and liver enzyme disturbance. Although side-effects are less common with parenteral pentamidine, toxicity tends to be more severe and includes pancreatitis, hypoglycaemia, neutropenia, thrombocytopenia and orthostatic hypotension. Treatment with co-trimoxazole is usually given for 21 days in HIV cases and 14 days in HIV negative PCP. Co-trimoxazole is used in 90% of cases of PCP [15] and is associated with fewer reactions (15%) in HIV negative patients [22].
Prognosis
The prognosis of HIV negative PCP is worse than for those HIV positive, with intensive care admission in 31–60%; mechanical ventilation in 14–64%; overall mortality 19–47%, rising to 50–71% on intensive care, although some series suggest an improved mortality in recent years [14-18,27]. Poor prognosis has been associated with tachypnoea, tachycardia, elevated C-reactive protein, raised lactate dehydrogenase, mechanical ventilation, and some studies suggest a correlation with previous mean steroid dose and treatment with cyclophosphamide [17,18]. The mortality rate of patients with underlying AID appears to be worse than for other HIV negative patients [15] and varies according to underlying pathology: 63% in Wegener's granulomatosis; 58% in inflammatory myopathy; 48% in polyarteritis nodosa; 31% in rheumatoid arthritis and 17% in systemic sclerosis [27]. Several case reports suggest that PCP in inflammatory myopathy may follow a fulminant course [28].
While PCP in HIV positive patients has been associated with a high incidence of relapse after successful therapy, this has not been seen in patients with AID, even without secondary prophylaxis and despite ongoing immunosuppressive treatment. This is thought to be related to better clearance of organism, confirmed by repeat bronchoalveolar lavage [22].
Incidence
There is no specific surveillance system in the United Kingdom (UK) for PCP, other than in HIV infection. Although laboratories are invited to report isolates of P. jiroveci to the Communicable Disease Surveillance Centre, it is estimated that only a fifth of clinically diagnosed cases of PCP are reported [29]. As the laboratory-reported number of cases was 0.36 per million in 1999, we would estimate the true incidence of PCP to be approximately 1.8 per million in that year. While Pneumocystis is the second commonest reported invasive mycosis, this estimate suggests that PCP is still a rare pathogen in the UK.
The overall incidence of PCP from 1990 to 1999 has declined in the UK [29], as a result of the advent of HAART for AIDS. However, probably as a result of the use of ever more immunosuppressive therapy, the number of cases of PCP diagnosed in HIV negative patients increased throughout the 1980s and 1990s [14-16,18,27]. These two opposing trends have resulted in HIV negative patients constituting an ever increasing proportion of the total number of cases of PCP.
Attempts have been made to characterise the incidence of PCP amongst patients with AID, usually by retrospective analysis of case records. Ward & Donald's review of 223 cases of PCP in AID is the largest series and covered 2.6 million hospitalisations over the period 1983 to 1994 [27]. They found the underlying AID in this group to be SLE (42%), rheumatoid arthritis (18%), Wegener's granulomatosis (14%), inflammatory myopathy (12%), polyarteritis nodosa (9%) and systemic sclerosis (5%). An estimate of the incidence of PCP in a particular AID was derived by determining the number of cases of PCP in a particular AID per 1,000 hospitalisations with the said AID per year. The results were: 8.9/1,000 hospitalisations /year for Wegener's granulomatosis; 6.5 for polyarteritis nodosa; 2.7 for inflammatory myopathy; 1.2 for SLE; 0.8 for systemic sclerosis and 0.2 for rheumatoid arthritis. Clearly the denominator in these frequencies reflects hospital admissions per year with a particular AID. For an AID where the average annual rate of admission is less than once per year, the true incidence for that condition will be less than the rate quoted and vice versa. These frequencies, with the exception of polyarteritis nodosa, are broadly comparable with findings in other studies which report a long-term risk of PCP of 6–12% for Wegener's granulomatosis and less than 2% for other AID [14,22,30]. There are only a few isolated reports of PCP in dermatoses treated with medium-term systemic steroids such as pemphigus, pemphigoid, cutaneous necrotizing vasculitis and Behçet's syndrome [22,31].
Discussion
Pneumocystis pneumonia in AID is unusual in the absence of steroid treatment. Corticosteroids have been recently administered in over 90% of cases in most series [14,17,18,22] and were the sole immunosuppressant in 17–28% of AID patients [18,22]. The median duration of treatment prior to the diagnosis of PCP is three to four months. Occurrence within a month of starting treatment is uncommon, with the exception of inflammatory myopathies [28,32]. Most cases have taken prednisolone in excess of 15 mg per day, or equivalent doses of corticosteroid. Notable is the profound inter-subject variation in response to standard steroid doses as measured by in vitro inhibition of lymphocyte proliferation [33], indicating that host factors are likely to have a significant, but as yet ill-defined role. Several mechanisms have been postulated to explain the role of steroids in promoting the development of P. jiroveci including CD4+ lymphocyte depletion and immune dysfunction [17,30]. Porges et al. found an association between the risk of PCP and the dose of prednisone used in SLE [32]. Similarly, Hellman et al. found an association between prednisolone dosage and risk of fatal opportunistic infection in SLE, the commonest cause of which was P. jiroveci [26]. However, other studies have failed to show an association between cumulative steroid dose and risk of PCP [34].
A number of cytotoxic and other immunosuppressive agents commonly used in the treatment of AID are frequently associated with PCP, including cyclophosphamide, azathioprine, methotrexate and ciclosporin [14]. Cyclophosphamide is routinely used in the treatment of Wegener's granulomatosis and has transformed the previous one year survival figure of 20% [35] into the present eight year survival of 80% [36]. Godeau et al. [34] showed a significant association between cyclophosphamide cumulative dose and the risk of PCP. However, this was not an independent factor in multivariate analysis when lymphopenia was taken into account [19]. In one series involving 180 patients with Wegener's granulomatosis, no cases of PCP were identified amongst patients on cytotoxic therapy alone (although the authors did not specify the numbers involved), suggesting a permissive role for corticosteroids [30].
Data on PCP associated with AID indicates lymphocytopenia (<1,000 cells/mm3) is almost a prerequisite, with 91% of patients exhibiting a low lymphocyte count. Fifty percent of such PCP patients have total lymphocyte counts of <400 cells/mm3 [22]. The pre-treatment lymphocyte count and lymphocyte counts during the first three months of immunosuppressive treatment in Wegener's granulomatosis have been shown to be predictive for PCP in multivariate analysis. A total lymphocyte count <600 cells/mm3 was recorded in ten (83%) of 12 patients with PCP, but such a low lymphocyte count was recorded in 11 (34%) of 32 with Wegener's unaffected by PCP [34]. A similar association was found in a prospective study involving patients with SLE [37]. Porges et al [32] proposed a cut off of total lymphocyte count of <350 cells/mm3 which captured 4 out of 6 cases with PCP and SLE, but only 1 of 20 patients with SLE unaffected by PCP.
Information on CD4+ counts, which have been shown to be highly predictive of the risk of PCP in HIV infected individuals [38], is less well documented in AID patients. The issue was addressed by Mansharamani et al [19] who prospectively observed 171 patients in various risk categories for PCP, including 22 patients with active PCP. They found that patients who were at high risk of PCP had significantly lower CD4+ counts than patients at low risk. They noted that 91% of cases of PCP had CD4+ counts <300 cells/mm3 at the time of diagnosis. Their findings are echoed by an increased risk of respiratory colonisation by P.jiroveci in HIV negative patients with CD4+ counts of <400 cells/ mm3 [39].
Kadoya et al [37] in their study on the occurrence of PCP in 75 patients with inflammatory myopathy and SLE, noted a significant association between radiological interstitial pulmonary fibrosis (IPF) and the risk of PCP (8.8% IPF in non-PCP vs 100% IPF in PCP, p < 0.001). In contrast, PCP has only rarely been reported in idiopathic pulmonary fibrosis [15], indicating that more than systemic steroids and pulmonary fibrosis are required to put patients at excessive risk of PCP.
Prevention
Co-trimoxazole is commonly used for PCP prophylaxis in Wegener's granulomatosis when CD4+ counts are <300 cells/mm3 [15] or even with normal counts in some centres [21]. This combination of antibiotics has been shown to be effective prophylaxis when used daily or thrice weekly at a dose of 960 mg in HIV positive patients [40]. Adverse effects occur in less than 20% of patients, usually manifesting as a rash, which resolves on temporary discontinuation and often does not recur on re-challenge [41,42].
Apart from Wegener's granulomatosis, identifying patients with AID who are at risk of PCP has proved a challenge, as the overall incidence is low. Nevertheless it remains an important issue, as AID patients contribute a considerable proportion of cases of PCP in HIV negative patients (up to 36%) and have a particularly poor prognosis, as discussed earlier.
Any method used to select patients for prophylactic treatment needs to be assessed against set standards and have a high sensitivity and specificity. The standards should address the percentage of PCP cases captured by the selection criteria (ideally 100% but in practice >80%) and the risk of the condition in the selected group (which should be significant). In HIV patients who meet the criteria for PCP prophylaxis as set out by the US Public Health Service [43], the annual risk of PCP is 18% [38]. As the mortality from PCP in HIV negative patients is approximately double that of HIV-positive patients [14-18,27], we would suggest that an annual risk >9% of PCP would be sufficient to justify prophylaxis.
In the study by Mansharamani et al, their proposed cut off of <300 CD4+ cells/mm3 would capture 91% of cases of PCP in all HIV negative patients, but would also include 39–46% of patients on systemic steroids, most of whom would be unaffected by PCP. Administering prophylaxis to such large numbers of patients would unnecessarily expose patients to drug side-effects and potentially encourage drug resistance. However, analysis of their data reveals that the subgroup of patients with AID who developed PCP had CD4+ counts of <250 cells/mm3 and six out of eight had counts <200 cells/mm3 [19].
Given the laboratory costs, we would argue in favour of performing CD4+ counts after one month's immunosuppression only on patients who satisfy the following three screening criteria:
• Steroid dosage >15 mg prednisolone or equivalent/day
• >three months corticosteroid treatment proposed
• total lymphocyte count <600 cells/mm3
A CD4+ count <200 cells/mm3 might then warrant the use of prophylactic co-trimoxazole, if the annual risk of PCP in these patients is greater than 9%. Most cases of PCP in patients with AID would be captured by these criteria, according to published series.
Clearly, further prospective investigation is required to gather sufficient data to validate any selection method. To justify our proposed threshold for prophylaxis we would need to know the risk of PCP for patients on steroid-based immunosuppression for AID with CD4+ counts of <200 cells/mm3, information which is currently unavailable.
Summary
P. jiroveci infection in HIV negative patients presents more acutely and has a worse prognosis. The incidence of PCP in patients with AID is low, although these patients still represent a considerable proportion of all HIV negative cases. It is important to have a high index of suspicion of PCP when treating AID with steroid based immunosuppressive regimes, as early treatment could improve prognosis. The risk of infection is related to treatment with systemic steroid, ill-defined individual variation in steroid sensitivity and CD4+ lymphocyte count. Effective and relatively safe prophylaxis is available. Rather than opting for PCP prophylaxis on the basis of disease or treatment with cyclophosphamide, we argue the case for carrying out CD4+ lymphocyte counts on selected patients as a means of identifying individuals who are most likely to benefit from PCP prophylaxis. Further prospective trials are required to validate our proposed prevention strategy.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
AJ Carmichael initiated the discussion, appraised results, lead departmental debates and helped revise the manuscript. ES carried out the literature search, presented at meetings and wrote the original manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank Drs WD Taylor, B McCarron, M Plant and P McCormack from The James Cook University Hospital for their helpful comments and suggestions.
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| 15488151 | PMC526257 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Oct 16; 4:42 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-42 | oa_comm |
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BMC Ear Nose Throat DisordBMC Ear, Nose, and Throat Disorders1472-6815BioMed Central London 1472-6815-4-21550069210.1186/1472-6815-4-2Research ArticleReadmission and overstay after day case nasal surgery Singh Gurminder [email protected] David [email protected] David R [email protected] Guy's, King's, & St. Thomas' School of Medicine, London, UK2 3 Bakewell Close, Mickleover, Derby DE3 9JS UK3 The ENT & Audiology Department, 3rd Floor Thomas Guy House, Guy's Hospital, SE1 9RT UK2004 22 10 2004 4 2 2 30 4 2004 22 10 2004 Copyright © 2004 Singh et al; licensee BioMed Central Ltd.2004Singh 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
A readmission is classified as a patient necessitating readmission to hospital due to a post-operative complication following discharge. An overstay however, is classified as a patient having to stay longer than the planned duration in hospital (not having been discharged in the interim) due to a post-operative complication. This study aims to investigate patient-related factors that predispose to readmission or overstay and thus make recommendations to decrease the likelihood of readmission or overstay.
Method
In this retrospective study 312 'day-case nasal procedures', were selected from a total cohort of 4274 ENT patients over a 17-month period. This sub-group was investigated for a range of demographic factors including, age, gender and ethnicity with regards to their relationship to readmission rates and overstay frequency and duration.
Results
The rates were 2.88% and 9.62% for readmission and overstay respectively. The total number of days spent in hospital as a result of readmission was 27. Epistaxis was the leading cause for readmission/overstay (28.9%) followed by high levels of post-operative pain preventing them from being discharged (23.7%). All procedures in this study had readmission rates that were below those recommended in the guidelines set by the Royal College of Surgeons of England. Women overstayed significantly longer (t = 1.65, p < 0.05) than men.
Conclusions
Suitable candidates for day-case ENT surgery highlighted by this study include healthy individuals between the ages of 20 and 60. Operating in the morning would increase the immediate post-operative recovery time, which may reduce the numbers of patients who complain of high levels of pain at the time of discharge. Procedures such as septorhinoplasty being performed routinely in the ambulatory setting require additional research into more effective methods of pain control. Standards need to be improved so that the causes of overstay and readmission are clearly identifiable in patient records.
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Background
Day-case surgery has rapidly expanded as a cost-effective and resource-conserving surgical intervention to the point that well in excess of two million operations are performed in a day-case/ambulatory setting in the United Kingdom alone each year [1]. Day-case surgery is based on operating on a patient and aiming to discharge them on the same day. Ear, nose and throat procedures account for a significant proportion of these ambulatory procedures and include operations such as functional endoscopic sinus surgery and rhinoplasty. It has been suggested that day-case surgery should be confined to those procedures where less than 3% of patients require admission post-operatively [1]. Septorhinoplasty is generally considered to be a traumatic procedure with risks of epistaxis and periorbital haematoma. However, the decision to perform septorhinoplasty in a day case setting may be made on the basis of its cost-effectiveness and rapid post-operative recovery in suitable operative candidates. Through the NHS Plan, the Department of Health has stated its aim of three-quarters of operations will being performed on a day case basis within the next decade [2]. It states that in these procedures there will be no overnight stay required so that traditional waiting lists for surgery will 'become a thing of the past'.
A King's College Hospital Study commissioned by the Department of Health has suggested that day-case surgery may not be saving the NHS money [3]. However, previous reports have advocated day case septal surgery as a safe and effective practice [4-6]. Furthermore, a subjective patient based study showed that septoplasty is generally acceptable to the patient in terms of pain and overall satisfaction parameters [5,7].
Studies have shown that day-case septorhinoplasty is associated with a low complication rate and is a safe and acceptable procedure provided that stringent patient selection criteria are adhered to [8]. A specific area of dissatisfaction previously identified is inadequate pain control following discharge and this may lead to higher costs for the general practitioner. However, a recent study investigating parental satisfaction with 100 paediatric otorhinolaryngology day-case procedures concluded that with careful patient selection the degree of satisfaction with day surgery is high for a wide variety of procedures [9,10].
There have been no studies performed, which have directly investigated the 'patient demographic factors' which predict the likelihood readmission or overstaying the electively planned period in hospital following day-case ENT operations. This is of importance as it would be beneficial to know if certain groups of patients require more rigorous screening pre-operatively in an attempt to reduce readmission and 'overstay' rates. By identifying this cohort of patients, the NHS could save substantial amounts of acute and primary care expenditure in the backdrop of ever-rising day-case ENT procedure numbers. Many studies have suggested that ENT procedures currently performed as overnight cases could be performed as day-cases provided strict criteria are applied in the selection of patients [1,11]. However, none of these studies provide a clear description as to which patient groups are potentially unsuitable for undergoing day-case procedures based upon their predisposition to require readmission or stay in hospital longer than electively planned.
Previous data fails to distinguish between readmissions and overstays following nasal day-case surgery [11]. They also fail to investigate demographic indices such as age, gender, and ethnicity [5]. The present study has been conducted in the climate of an ever-advancing quality-driven clinical environment in which meticulous patient selection is vital in optimal patient post-operative care and recovery. This study of 312 elective day-case nasal operations selected from a cohort of 4274 ENT patients investigated a range of patient variables that influenced readmission and overstay frequency and duration.
This study investigates departmental day-case nasal surgery at Guy's hospital and aims to determine:
a) If it has readmission rates below that of the accepted standard for day-case surgery stipulated by the Royal College of Surgeons of England.
b) If readmission rate and unplanned overnight stay are related to epidemiological characteristics of the sample such as age, gender, and ethnicity.
and to report results in the context of clinical management by making recommendations to improve the readmission rates in each category proposed.
Method
This is a retrospective study in which the total number of ENT operations performed between the period of 3rd January 2002 and 28th June 2003 were investigated. All ENT patients operated on during this period were selected from the Guy's & St. Thomas' NHS Trust database. The hospital database has a limited range of broad categories and this can limit interpretation of results thus all available patient notes were also reviewed.
All planned day-case procedures were selected from the total cohort of elective ENT operative procedures. The day-case procedures were then further narrowed down to 'nasal day-case procedures'.
Firstly the total number of readmission episodes, overstay frequency and duration in the 'day-case nasal procedures' were calculated. A readmission is classified as a patient necessitating readmission to hospital due to a post-operative complication following discharge. An overstay however, is classified as a patient having to stay longer than the planned duration in hospital (not having been discharged in the interim) due to a post-operative complication. The groups of nasal operations of interest in this study in the light of previous studies are (septo)rhinoplasty, excision of lesion, polypectomy, sinus operation and other (which includes procedures such as intranasal ethmoidectomy, and division of adhesions of turbinate of nose etc).
A range of demographic factors including, age, gender and ethnicity were investigated with regards to their relationship to readmission rates and overstay frequency and duration. Statistical analysis including Chi-square tests, t-tests and ANOVA were performed on the above variables.
Results
The total number of ENT operations performed between the period of 3rd January 2002 and 28th June 2003 were 4274. From this cohort 501 operations (11.72%) were planned as elective ENT day-case procedures. This selected sub-sample of 501 day-cases was further narrowed down to those, which pertained to 'nasal procedures' and this group numbered 312 (62.28%). There were a total of 9 readmission episodes following 'day-case nasal procedures' during the 17-month period, which equates to a readmission rate of 2.88%. The total number of days spent in hospital as a result of readmission was 27. All patients readmitted following day-case nasal procedures were male (Fischer Exact Test = 4.41, p < 0.05). The nasal operations were grouped into (septo)rhinoplasty, excision of lesion, polypectomy, sinus operation and other.
Those patients that were required to stay longer than electively planned i.e. one day, are termed by the Guy's & St. Thomas' NHS Trust and indeed in this study as 'over-stays'. The minimum duration of overstay in our sample was one day and the maximum was 2 days. The total incidence of patients overstaying was 30 (overstay rate = 9.62%) during the timeframe studied and this equates to a total of 48 overstayed days.
Cause of readmission and overstay
Epistaxis was the leading cause for readmission/overstay n = 11/38 (28.9%) followed by levels of post-operative pain unacceptable to the patient and thus preventing them from being discharged (23.7%).
Type of procedure
The readmission rate for (septo)rhinoplasty is 1.38% and the overstay rate is 9.22%. From the analysis of days overstayed expressed as a fraction of the number of procedures performed, polypectomy and sinus operations have the highest number of days overstayed per procedure. Although the (septo)rhinoplasty overstay rate is 9.22% the number of days overstayed expressed in the context of the number of actual (septo)rhinoplaties performed is equal to the average (see Figure 1).
Figure 1 Days overstayed as a fraction of the procedures performed.
Gender
Women overstayed significantly longer (t = 1.65, p < 0.05) than men.
Age
The frequency of readmission is highest in those patients aged <20 followed by those in the 60–70 year age category. Although the patients aged above 70 years have the lowest readmission frequency, the sample size is low (n = 5) and thus the 30–40 year age category is more reliable a sample representative of a low readmission frequency. Analysis of the age of patients and frequency of day-case procedures revealed that the group containing those patients aged between 30–40 underwent the greatest number of nasal day-case procedures. However, when considering the number of overstays in terms of the numbers of procedures performed, the 30–40 age category has the lowest overstay duration as opposed to the >70 which has the highest.
Ethnicity
Analysis of variance (ANOVA) shows that the difference in overstay rates between ethnic groups did not reach statistical significance (F = 1.22, p = 0.40).
Discussion
In July 2000, the Department of Health published that 75% of surgical procedures carried out by the NHS should be performed in a day-case setting by 2010. This initiative was supported by the both The Royal College of Surgeons of England and The Royal College of Physicians of England.
Within the period of the current study there were a total of 4274 ENT operations performed at Guy's during the time-frame of investigation. Of this group 501 (11.72%) were planned as elective ambulatory procedures with the intention of same day discharge. Primarily the government target was ambiguous with regard to the types of procedure for which it aimed to conduct on a routine day-case basis. Whilst it is conceivable that some ENT procedures such as myringotomy could be performed safely as a day-case procedure, more complex and involved procedures such as deep exploration of the neck and tumour excision would be unsuitable for such rapid discharge. With no clear guidelines as to which procedures are suitable for the day-case setting, the responsibility of advancing the boundaries of ambulatory surgery rests on the clinicians, with the patients' wellbeing being at the forefront of interest.
The readmission in this study is 2.88%. These readmission rates are lower than those reported in the contemporary literature [4,8,11]. The readmission rate for (septo)rhinoplasty is 1.38% and the overstay rate is 9.22%. These rates are lower than those stipulated by the Royal College of Surgeons of England which pertain to day-case surgery in general (2–3%) as opposed to ENT cases per se.
Epistaxis was the leading cause for readmission/overstay (28.9%) followed by levels of post-operative pain unacceptable to the patient and thus preventing them from being discharged (23.7%). The cause of epistaxis was not recorded in the notes nor was the reason behind why some patients experience more pain than others. These results are in contrast to previous subjective patient based studies, which show that septorhinoplasty is generally acceptable to the patient in terms of pain and overall satisfaction parameters [5]. Women overstayed significantly longer (t = 1.65, p < 0.05) than men. A similar phenomenon has been reported in a study of cardiothoracic patient readmissions [12]. In this study the female predilection to increased in-hospital recovery time was attributed to various gender-associated factors.
Age is not a contraindication for ENT day-case surgery. The low readmission rate of patients over 70 years of age could be attributed to a number of factors including, heightened caution of the clinician in patient discharge, a low sample number and age-related variation of procedure type are dominant.
There have been no previous studies, which have investigated the effects of ethnicity on readmission rates or overstay duration. The current study shows that mixed race patients followed by black patients have the highest number of days overstayed per procedure. Analysis of variance (ANOVA) shows that there is no significant difference between ethnic groups and the length of overstay (F = 1.22, p = 0.40).
Conclusions
Day-case procedures should be performed on suitable candidates on a sound clinical basis. This includes meticulous patient selection, both on the part of the surgeon and the health care professionals in the preoperative assessment. Suitable candidates for day-case ENT surgery highlighted by this study include healthy individuals between the ages of 20 and 60. A protocol for day-case surgery does exist and this may need revision.
In the clinical setting however, epistaxis needs to be made a clinical priority by ensuring that all levels of healthcare professionals are aware of its causes and its effective immediate management. We suggest from experience that operating in the morning would increase the immediate post-operative recovery time, which may reduce the numbers of patients who complain of high levels of pain at the time of discharge. Procedures such as septorhinoplasty being performed routinely in the ambulatory setting require additional research into more effective methods of pain control. Clinical administration standards need to be improved so that the causes of overstay and readmission are clearly identifiable in patient records.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
Mr. Gurminder Singh (first and corresponding author) performed the data collection and wrote the paper with assistance from Mr. David McCormack (second author). Mr. David Roberts supervised the study and reviewed the completed manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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Hogg RP Prior MJ Johnson AP Admission rates, early readmission rates and patient acceptability of 142 cases of day case septoplasty Clin Otolaryngol 1999 24 213 5 10384847 10.1046/j.1365-2273.1999.00247.x
The NHS Plan – A plan for investment, A plan for reform (2000). HMSO, The Copyright Unit, St Clements House, 2-16 Colegate, Norwich NR3 1BQ. Fax: 01603 723000 or e-mail: [email protected]
King's College Hospital Study
Benson-Mitchell R Kenyon G Gatland D Septoplasty as a day-case procedure – a two centre study J Laryngol Otol 1996 110 129 31 8729494
Nieminen P Silvola J Aust R Stenfors LE Nasal septal surgery as an out-patient procedure J Laryngol Otol 1997 111 1034 7 9472571
Srinivasan V Arasaratnam RB Jankelowitz GA Day-case septal surgery under general anaesthesia and local anaesthesia with sedation J Laryngol Otol 1995 109 614 7 7561467
Agha RA Heaton SR Roberts DR Patient satisfaction with day case septoplasty and septorhinoplasty The Journal of One-Day Surgery 2003
Georgalas C Paun S Zainal A Patel NN Mochloulis G Assessing day-case septorhinoplasty: a prospective audit study using patient-based indices J Laryngol Otol 2002 116 707 710 12437806 10.1258/002221502760238000
Hicklin L Tostevin PM Wyatt ME Parental satisfaction with paediatric day-case ENT surgery J Laryngol Otol 1999 113 1072 5 10767918
Fitzpatrick R Surveys of patient satisfaction: important general considerations B M J 1991 302 887 9
Ganesan S Prior AJ Rubin JS Unexpected overnight admissions following day-case surgery: an analysis of a dedicated ENT day care unit Annals of the Royal College of Surgeons of England 2000 82 327 30 11041031
Steuer J Blomqvist P Granath F Rydh B Ekbom A de Faire U Stahle E Hospital readmission after coronary artery bypass grafting: are women doing worse? Ann Thorac Surg 2002 73 1380 6 12022521 10.1016/S0003-4975(02)03467-7
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BMC Med Inform Decis MakBMC Medical Informatics and Decision Making1472-6947BioMed Central London 1472-6947-4-181548815010.1186/1472-6947-4-18Research ArticleUse of and attitudes to a hospital information system by medical secretaries, nurses and physicians deprived of the paper-based medical record: a case report Lærum Hallvard [email protected] Tom H [email protected] Arild [email protected] INM, Faculty of Medicine, NTNU, Trondheim, Norway2 Sørlandet Sykehus HF Arendal, Arendal, Norway2004 16 10 2004 4 18 18 12 9 2003 16 10 2004 Copyright © 2004 Lærum 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
Most hospitals keep and update their paper-based medical records after introducing an electronic medical record or a hospital information system (HIS). This case report describes a HIS in a hospital where the paper-based medical records are scanned and eliminated. To evaluate the HIS comprehensively, the perspectives of medical secretaries and nurses are described as well as that of physicians.
Methods
We have used questionnaires and interviews to assess and compare frequency of use of the HIS for essential tasks, task performance and user satisfaction among medical secretaries, nurses and physicians.
Results
The medical secretaries use the HIS much more than the nurses and the physicians, and they consider that the electronic HIS greatly has simplified their work. The work of nurses and physicians has also become simplified, but they find less satisfaction with the system, particularly with the use of scanned document images.
Conclusions
Although the basis for reference is limited, the results support the assertion that replacing the paper-based medical record primarily benefits the medical secretaries, and to a lesser degree the nurses and the physicians. The varying results in the different employee groups emphasize the need for a multidisciplinary approach when evaluating a HIS.
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Background
Hospital information systems (HIS) and Electronic Medical Records (EMRs) are considered prerequisites for the efficient delivery of high quality health care in hospitals. However, a large number of legal and practical constraints influence on the design and introduction of such systems [1]. Hence, many EMR implementation projects do not aim at introducing the EMR and eliminating the paper-based counterpart in one step [2]. As a start, the EMR is introduced along with its paper-based counterpart, and both are kept updated. In such environments, health care workers have to deal with a hybrid electronic and paper-based solution. This probably limits the use of EMR [2]. Furthermore, errors are prone to develop due to cumbersome maintenance of the medical record information in dual storage media [3]. In Norway and in other countries, most hospital EMR projects have not passed beyond this phase [1]
Aust-Agder Hospital is the first hospital in Norway to eliminate the paper-based medical record, using a widespread [2] and commercially available HIS in combination with scanning technology. In a recent report, we have evaluated the EMR part of the HIS in this hospital [4], discussing the views of the physicians only. However, to get a more complete picture of the impact of the system, its use by employees other than physicians needs to be evaluated. Both medical secretaries and nurses are important users of a HIS, utilizing both the EMR and the administrative part of the system. The medical secretaries work as transcriptionists, receptionists and coordinators of patient logistics and communication, and the nurses have their own documentation and administrative routines. The elimination of the paper-based medical records is a radical change in the work routines in the hospital organization. To assess the impact of this change on the organization, the EMR system may be described from the perspectives of three important employee groups separately. In this report, we have used questionnaires and interviews to assess how often medical secretaries, nurses and physicians use the HIS system for essential tasks, how easily these tasks are performed using the system, and how satisfied the hospital employees are with it.
Methods
The hospital
The investigation was performed in a 410-bed community hospital in Aust-Agder county, Norway. The hospital serves a population of 102,000, caring for 18,600 inpatients and 74,000 outpatients per year (1998). The patients are admitted by primary care physicians external to the hospital and followed up by the hospital physicians. The hospital comprises of departments for psychiatry, general surgery, internal medicine, orthopaedics, gynecology, ear, nose and throat and ophthalmology. Well funded, and with a strong commitment by the hospital administration, the hospital staff began implementation of DIPS 2000® , a commercially available combined EMR and hospital administrative system in March 2000. In April 2001, all except the psychiatric department started to scan documents. From this date, all new patient data was channeled into the EMR in these departments, either as electronic text and data or as scanned documents. The HIS was available in 1100 terminals throughout the hospital, except for the inpatients' rooms. The transition to HIS was administered by a project group, which had been recruited from the hospital staff. The group worked in conjunction with the IT department and the HIS vendor, and was also responsible for communicating with and training the users. The group regularly held series of mandatory hands-on training classes adapted to each profession (3–8 h in total). However, a substantial proportion of the users never attended the classes, particularly the physicians. To reach these users, a task force of medical secretaries was trained and employed during the first month after implementation of the HIS for ambulant training in the wards. Further support was provided by a network of super users (the most experienced users) among the ward staff.
The EMR
The patient data in the EMR part of the HIS is either stored as searchable text and numbers or as document images. The former, called "regular electronic data", essentially consists of the chronological, text-based medical record integrated with lab data in numerical form and textual radiology reports (fig 1). The latter is divided by structure into two categories, as follows: Upon admittance or consultation, the documents in the old paper-based medical records are scanned into the system as digital images in TIFF format. Each image contains all the sheets of one main section of the paper-based record, and hence corresponds to a whole document group (groups A-J in fig 1). These images are called "scanned multiple documents". Upon patient discharge, various paper sheets accumulated during the stay (e.g. the medical treatment form, printouts from diagnostic devices) are scanned, dated and labeled by document type singularly (fig 1). The resulting images are called "scanned single documents". In summary, the patient data is stored as regular electronic data, scanned multiple documents and scanned single documents. They all appear in the hierarchical list in the "medical record explorer" window (fig 2), but are treated separately in this paper, due to their difference in structure, indexation and functionality. The user interface of the HIS system is identical to all types of users, although medical secretaries, nurses and physicians often utilize different parts of system.
The survey
A questionnaire previously used in a national survey of hospital physicians [2] was modified for this study. The original questionnaire contained sections regarding frequency of use of an EMR system or HIS for specified tasks, user satisfaction with the system as a whole [5] as well as detailed aspects of it [6], and availability of computers. To make the questionnaire applicable to medical secretaries and nurses, new versions of the section regarding frequency of use of the HIS were developed. In collaboration with the authors, 3–6 representatives from the medical secretaries and the nurses identified work tasks for the questionnaire each in two 2-hour group sessions, using recently developed detailed work-flow charts as templates (not shown). The identified tasks were then reduced to 23 and 19 tasks supported by the HIS, respectively (see appendix A). The questionnaire was reviewed in similar sessions by representatives from the physicians. As a result, one new task was added to the physicians' questionnaire, and four tasks not supported by the HIS were removed. For all professions, a new section was added, containing questions about ease of performing each task using the system.
The survey was conducted during February–April 2002, and 85 medical secretaries, 235 nurses and 80 physicians in the medical, surgical and other somatic wards received the questionnaire. Of these, 79 medical secretaries (93%), 172 nurses (73%) and 70 physicians (88%) responded, giving a total response rate of 81% (321/400). We used Teleform™ for data acquisition and SPSS 11.0 for Windows™ for statistical analysis.
In addition to the survey, one of the authors interviewed 8–12 representatives of each profession for 0.5–2 hours. Comments on advantages and disadvantages of the system in all relevant work tasks were noted and summarized
Results
The medical secretaries used the HIS routinely for most of their tasks defined in the questionnaire. This stands in contrast to the nurses and the physicians (fig 3). The number of tasks with a median response of "always or almost always" was highest for the medical secretaries (15 out of 23 tasks, 65%), and lowest for the nurses (4 out of 19 tasks, 21%).
The medical secretaries reported that all of the defined tasks were performed more easily than before the HIS was introduced (i.e. median response for ease of performing the task was "increased" or "significantly increased", in 23 out of 23 tasks, fig 4). In comparison, the number of tasks more easily performed was much lower for the nurses and the physicians (respectively 9 [47%] and 7 [37%] out of 19 individual tasks).
The medical secretaries were much more satisfied with the use of the HIS than the nurses and physicians, both when assessing the detailed aspects of it and the system as a whole. The detailed aspects of the HIS was assessed in twelve questions related to the factors content, accuracy, format, user friendliness and timeliness [6]. The parts of the HIS that contained scanned document images and regular electronic data were assessed separately. The medical secretaries were equally satisfied with both parts of the HIS (fig 5). This stands in contrast to nurses and in particular the physicians, who were less satisfied, particularly with the part containing the scanned document images. The difference between the professions was significant in all factors regarding the scanned document images (ANOVA p < 0.001), and in all factors except accuracy regarding the regular electronic data (fig 5, (ANOVA p = 0.001 to 0.04, p = 0.07 for factor 'accuracy').
In addition to the detailed aspects, the user satisfaction with the HIS as a whole was assessed (fig 6). The medical secretaries gave significantly more positive responses than the nurses and the physicians in all of the five questions in this section (Kruskall-Wallis p = 0.05 in question 2, p < 0.001 in the remaining four questions). However, the majority of each profession gave positive answers in all of these questions. To summarize all results regarding user satisfaction, the system seems to be well adapted to the work of medical secretaries but leave nurses and physicians less satisfied.
Partly explaining the differences in user satisfaction, the physicians reported more frequent problems related to availability of the HIS than the medical secretaries and the nurses (fig 7, Kruskall-Wallis p < 0.001 in all questions). The most frequently reported problems among the physicians occurred daily or weekly, and consisted of various software and hardware-related problems, the system working too slowly, and lack of computers where the clinical work was being done. Such problems were not frequently reported among the medical secretaries, except problems with the systems working too slowly (42% daily or weekly, 32/77).
In the interviews, the perceived advantages and disadvantages of the HIS were discussed. Both nurses and physicians in the medical ward found that patient data were more accessible when stored electronically than when stored on paper, in particular regarding lab test data. However, the nurses were still using pen and paper when documenting their activities. The medical secretaries found that generation, handling, fetching and delivery of paper documents and logistics of paper-based patient records had diminished dramatically. The generation of written text had become considerably easier. On the other hand, the scanning process had become an additional burden and was considered time consuming. Overall, handling of paper documents was considered additional work whenever the documents appeared.
Discussion
In this hospital, we have found that the medical secretaries use the HIS more extensively for their tasks than the nurses and the physicians. Also, they are much more satisfied with the HIS.
Medical secretaries reported that they use the HIS routinely for most of the tasks defined in the questionnaire (fig 3). A simple explanation is that their tasks generally are smaller in scope and have a smaller and more easily defined range of needed information types than that of the nurses and physicians (See appendix A). Hence, the medical secretaries' tasks should be more easily supported by computers than the nurses' and the physicians' tasks. The particular inefficiencies of certain paper-based routines (e.g. regarding task 6, 15, 18 and 19) readily demonstrates the usefulness of computer support [7]. Unlike the work of nurses and physicians, the work of medical secretaries is stationary, avoiding the difficulties in providing an efficient mobile work environment. In addition, each medical secretary typically is assigned a computer, while nurses and physicians usually have to share a limited number of them (fig 7, question C). Another possible reason for the difference in usage pattern could be difference in computer literacy. However, the usage patterns were not consistent with the limited differences found in self-reported computer literacy (data not shown), and the amount of in-house training of medical secretaries and physicians was principally equal.
The medical secretaries reported that all of the tasks in their questionnaire are more easily performed (fig 4). The results from the interviews identify the elimination of the paper-based medical record as a major contributor to this, as several manual paper routines have disappeared (e.g. searching for a lost paper-based medial record or sorting the contents of a medical record) or are replaced by more efficient computer functions (e.g. transferring new lab data to doctors for review). Furthermore, having the administrative functions integrated with the EMR means that a substantial selection of structured demographic, clinical and administrative data is concurrently available to the users of the HIS. This makes several tasks more efficient for the medical secretaries (e.g. sending standard letters to patients in waiting lists). The results are supported by the fact that the number of medical secretaries in the hospital has been reduced by 15 since the onset of the HIS project (Bjørn Engum, personal communication Sept 2003).
Not surprisingly, the medical secretaries were more satisfied with the system than the nurses and the physicians (figs 3 and 4). This agrees with the results of Sittig [8] and Lee [9], who both found that user satisfaction was strongest correlated to questions regarding how easily the work was done. On the other hand, when comparing the user satisfaction scores to the reference data of Doll & Torkzadeh [6], the median user satisfaction score of the medical secretaries lies between the 20th and 30th percentile of the reference data set. This suggests that there is room for improvement of the EMR system regarding the medical secretaries as well as the others. Unlike the nurses and the physicians, the medical secretaries were equally satisfied with the scanned document images as that of the regular electronic medical record. The most likely reason is that the document images are not very often used by the medical secretaries, particularly the document images scanned in sections (data not shown). The disadvantages of the document images, for instance that they can not be searched, therefore seem to affect the user satisfaction of nurses and physicians to a stronger degree than that of the medical secretaries.
The use of the HIS by medical secretaries, nurses and physicians may to some degree be compared at a task-by-task level when the tasks are equally worded. In these tasks, work roles seem to explain the differences. For instance, the tasks "Reviewing the patient's problems" (tasks 1) and "Seek out specific information from patient records" (task 2) appeared in all questionnaires. Of all the respondents, only the physicians had a significant proportion finding that these tasks were more difficult to perform than before (figure 4). A possible reason is that the physicians, in order to perform these tasks as they saw fit for their work role, more often needed to search the scanned document images extensively. When examining the task "Order clinical biochemical laboratory analyses" (task 6 for nurses, task 7 for physicians), the nurses both use the HIS more frequently for this task and find the task more easily to perform than the physicians. However, many Norwegian physicians find that order entry is a task better performed by others [10], reducing the motivation for learning the new system. This way, understanding work roles in the given context appears necessary to interpret the results.
A secondary finding in this study was that the physicians reported frequent computer-related problems, much more frequent than that of medical secretaries and nurses (fig 7). This may be due to escalated demands on computing power, system stability and availability. Without the paper-based medical record, the EMR is taken into full use and the real demands of supporting the physicians' information processing are revealed. The high reported frequency of computer-related problems may partly explain the overall lower user satisfaction of the physicians, as well as the relatively high proportion of physicians finding certain tasks more difficult to perform (task 1 and 2, fig 4). An observational study could elaborate on these relationships, focusing on what kinds of computer problems are the least tolerable to the physicians.
Limitations of the study
In the questionnaire, we do not know how often each task is carried out (using the HIS or not) or how long it takes, which means that demanding tasks might be outnumbered by the less demanding ones. Furthermore, the list of tasks supported in some way by the system may not be complete, and the list does not cover the full range of conceivable tasks suited for support by any given HIS. However, given that the tasks defined for each group cover important parts of their information-related work, a cautious comparison of general patterns of use between groups of hospital employees is possible.
Conclusion
Evaluation of a HIS in a hospital that has eliminated the paper-based medical record reveals considerable differences in user satisfaction and reported use of the system among medical secretaries, nurses and physicians. Although the basis for reference is limited, the results seem to support the claim that replacing the paper-based medical record primarily benefits the medical secretaries, and to a lesser degree the nurses and the physicians. Inspired by Aust-Agder Hospital, two of 22 other Norwegian hospitals using the same system (as of Aug 2002) are about to eliminate the paper-based medical record, making a future comparison between hospitals possible. When assessing the effects of a HIS on a hospital organization by asking users, the multidisciplinary nature of health care provision should be reflected in the selection of hospital employees that participate in the evaluation.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HL, THK and AF planned the investigation. HL and THK developed the questionnaires for the medical secretaries and nurses. THK organized the administration of the questionnaires, and HL scanned and analysed the data. HL, THK and AF wrote the manuscript jointly.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Appendix A: Task lists The three lists of tasks as they appear in the questionnaire developed for the medical secretaries, nurses and physicians, respectively.
Click here for file
Additional File 2
An English translation of the questionnaire used for the physicians in the survey. It is meant for review purposes.
Click here for file
Acknowledgments
We thank Gerd Gulstad, Bjørn Engum, Tom Schulz, Anne-Brit Riiser and Astrid Norberg for their continued help and support.
This investigation is funded by the Norwegian Ministry of Health, and the Research Council of Norway through the Kvalis project at the Norwegian University of Science and Technology, Trondheim.
Figures and Tables
Figure 1 Contents of the EMR. Document and information types found in the EMR part of the HIS. Most documents created prior to the implementation of the HIS appear as scanned multiple documents, but some old data has been imported from existing systems and hence appears as electronic text and data. Adapted from Laerum et al [4].
Figure 2 Navigation of the EMR. The medical record explorer and the multi-page viewer. Adapted from Laerum et al [4] and reproduced with permission from DIPS ASA, Norway.
Figure 3 Use of the Hospital Information System. Frequency of use of HIS for tasks specific to each profession. Within each profession, the tasks are sorted in descending order by frequency of use. High and low frequency of use is represented by blue and red color tones, respectively. The definitions of the tasks for each profession are given in appendix A. The error bars show the confidence interval of the proportion of respondents answering "Always or almost always".
Figure 4 Task performance using the HIS. Change in ease of performing individual tasks for each profession when using the HIS. The tasks appear in the same sequence as that of figure 3, i.e. the frequency with which the HIS is used for the task. The responses indicating a task to be easier to perform appear in blue tones, and those indicating it to be more difficult appear in red. The error bars show the confidence interval of the proportion of respondents answering "Significantly increased". For definitions of the individual tasks, see appendix A. (The data for the physicians[4] is included for comparison)
Figure 5 Detailed user satisfaction. User satisfaction with detailed aspects of the HIS in various professions. The mean scores of each factor (content, accuracy, format, user friendliness and timeliness) are shown in percent of maximum obtainable score. The error bars show the confidence interval of the mean. (The data for the physicians[4] is included for comparison)
Figure 6 User satisfaction. User satisfaction with the HIS as a whole in various professions. The responses colored in red tones represent low satisfaction; those colored in blue tones represent high satisfaction. The error bars show the confidence interval of the combined proportion of all positive responses (The data for the physicians[4] is included for comparison).
Figure 7 Problems related to the availability of the HIS. Reported frequency of problems related to the availability of the HIS. The questions are sorted in descending order by the physicians' frequency of problems. Red tones represent frequent problems, and blue tones represent infrequent problems. The error bars show the confidence interval of the proportion of respondents reporting frequent problems (i.e. weekly or daily).
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Dick RS Steen EB The Computer-based Patient Record - An Essential Technology for Health Care, Revised Edition 1997 Washington D.C., Institute of Medicine, National Academy Press
Lærum H Ellingsen G Faxvaag A Doctors' use of electronic medical records systems in hospitals: cross sectional survey BMJ 2001 323 1344 1348 11739222 10.1136/bmj.323.7325.1344
Mikkelsen G Aasly J Concordance of information in parallel electronic and paper based patient records Int J Med Inf 2001 63 123 131 10.1016/S1386-5056(01)00152-6
Laerum H Karlsen TH Faxvaag A Impacts of scanning and eliminating paper-based medical records on hospital physicians' clinical work practice J Am Med Inform Assoc 2003 10 588 595 12925550 10.1197/jamia.M1337
Aydin CE Rice RE Social worlds, individual-differences, and implementation - predicting attitudes toward a medical information-system Information & Management 1991 20 119 136 10.1016/0378-7206(91)90049-8
Doll WJ Torkzadeh G The measurement of end-user computing satisfaction - theoretical and Methodological issues MIS Quarterly 1991 15 5 10
Landauer Thomas K. The trouble with computers : usefulness, usability, and productivity / Thomas K Landauer 1995 Cambridge, Mass., MIT Press.
Sittig DF Kuperman GJ Fiskio J Evaluating physician satisfaction regarding user interactions with an electronic medical record system Proc AMIA Symp 1999 Bethesda, Maryland USA, American Medical Informatics Association 400 404 10566389
Lee F Teich JM Spurr CD Bates DW Implementation of physician order entry: user satisfaction and self- reported usage patterns J Am Med Inform Assoc 1996 3 42 55 8750389
H Laerum Faxvaag A Task-oriented evaluation of electronic medical records systems: development and validation of a questionnaire for physicians BMC Med Inform Decis Mak 2004 4
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BMC Med Inform Decis MakBMC Medical Informatics and Decision Making1472-6947BioMed Central London 1472-6947-4-181548815010.1186/1472-6947-4-18Research ArticleUse of and attitudes to a hospital information system by medical secretaries, nurses and physicians deprived of the paper-based medical record: a case report Lærum Hallvard [email protected] Tom H [email protected] Arild [email protected] INM, Faculty of Medicine, NTNU, Trondheim, Norway2 Sørlandet Sykehus HF Arendal, Arendal, Norway2004 16 10 2004 4 18 18 12 9 2003 16 10 2004 Copyright © 2004 Lærum 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
Most hospitals keep and update their paper-based medical records after introducing an electronic medical record or a hospital information system (HIS). This case report describes a HIS in a hospital where the paper-based medical records are scanned and eliminated. To evaluate the HIS comprehensively, the perspectives of medical secretaries and nurses are described as well as that of physicians.
Methods
We have used questionnaires and interviews to assess and compare frequency of use of the HIS for essential tasks, task performance and user satisfaction among medical secretaries, nurses and physicians.
Results
The medical secretaries use the HIS much more than the nurses and the physicians, and they consider that the electronic HIS greatly has simplified their work. The work of nurses and physicians has also become simplified, but they find less satisfaction with the system, particularly with the use of scanned document images.
Conclusions
Although the basis for reference is limited, the results support the assertion that replacing the paper-based medical record primarily benefits the medical secretaries, and to a lesser degree the nurses and the physicians. The varying results in the different employee groups emphasize the need for a multidisciplinary approach when evaluating a HIS.
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Background
Hospital information systems (HIS) and Electronic Medical Records (EMRs) are considered prerequisites for the efficient delivery of high quality health care in hospitals. However, a large number of legal and practical constraints influence on the design and introduction of such systems [1]. Hence, many EMR implementation projects do not aim at introducing the EMR and eliminating the paper-based counterpart in one step [2]. As a start, the EMR is introduced along with its paper-based counterpart, and both are kept updated. In such environments, health care workers have to deal with a hybrid electronic and paper-based solution. This probably limits the use of EMR [2]. Furthermore, errors are prone to develop due to cumbersome maintenance of the medical record information in dual storage media [3]. In Norway and in other countries, most hospital EMR projects have not passed beyond this phase [1]
Aust-Agder Hospital is the first hospital in Norway to eliminate the paper-based medical record, using a widespread [2] and commercially available HIS in combination with scanning technology. In a recent report, we have evaluated the EMR part of the HIS in this hospital [4], discussing the views of the physicians only. However, to get a more complete picture of the impact of the system, its use by employees other than physicians needs to be evaluated. Both medical secretaries and nurses are important users of a HIS, utilizing both the EMR and the administrative part of the system. The medical secretaries work as transcriptionists, receptionists and coordinators of patient logistics and communication, and the nurses have their own documentation and administrative routines. The elimination of the paper-based medical records is a radical change in the work routines in the hospital organization. To assess the impact of this change on the organization, the EMR system may be described from the perspectives of three important employee groups separately. In this report, we have used questionnaires and interviews to assess how often medical secretaries, nurses and physicians use the HIS system for essential tasks, how easily these tasks are performed using the system, and how satisfied the hospital employees are with it.
Methods
The hospital
The investigation was performed in a 410-bed community hospital in Aust-Agder county, Norway. The hospital serves a population of 102,000, caring for 18,600 inpatients and 74,000 outpatients per year (1998). The patients are admitted by primary care physicians external to the hospital and followed up by the hospital physicians. The hospital comprises of departments for psychiatry, general surgery, internal medicine, orthopaedics, gynecology, ear, nose and throat and ophthalmology. Well funded, and with a strong commitment by the hospital administration, the hospital staff began implementation of DIPS 2000® , a commercially available combined EMR and hospital administrative system in March 2000. In April 2001, all except the psychiatric department started to scan documents. From this date, all new patient data was channeled into the EMR in these departments, either as electronic text and data or as scanned documents. The HIS was available in 1100 terminals throughout the hospital, except for the inpatients' rooms. The transition to HIS was administered by a project group, which had been recruited from the hospital staff. The group worked in conjunction with the IT department and the HIS vendor, and was also responsible for communicating with and training the users. The group regularly held series of mandatory hands-on training classes adapted to each profession (3–8 h in total). However, a substantial proportion of the users never attended the classes, particularly the physicians. To reach these users, a task force of medical secretaries was trained and employed during the first month after implementation of the HIS for ambulant training in the wards. Further support was provided by a network of super users (the most experienced users) among the ward staff.
The EMR
The patient data in the EMR part of the HIS is either stored as searchable text and numbers or as document images. The former, called "regular electronic data", essentially consists of the chronological, text-based medical record integrated with lab data in numerical form and textual radiology reports (fig 1). The latter is divided by structure into two categories, as follows: Upon admittance or consultation, the documents in the old paper-based medical records are scanned into the system as digital images in TIFF format. Each image contains all the sheets of one main section of the paper-based record, and hence corresponds to a whole document group (groups A-J in fig 1). These images are called "scanned multiple documents". Upon patient discharge, various paper sheets accumulated during the stay (e.g. the medical treatment form, printouts from diagnostic devices) are scanned, dated and labeled by document type singularly (fig 1). The resulting images are called "scanned single documents". In summary, the patient data is stored as regular electronic data, scanned multiple documents and scanned single documents. They all appear in the hierarchical list in the "medical record explorer" window (fig 2), but are treated separately in this paper, due to their difference in structure, indexation and functionality. The user interface of the HIS system is identical to all types of users, although medical secretaries, nurses and physicians often utilize different parts of system.
The survey
A questionnaire previously used in a national survey of hospital physicians [2] was modified for this study. The original questionnaire contained sections regarding frequency of use of an EMR system or HIS for specified tasks, user satisfaction with the system as a whole [5] as well as detailed aspects of it [6], and availability of computers. To make the questionnaire applicable to medical secretaries and nurses, new versions of the section regarding frequency of use of the HIS were developed. In collaboration with the authors, 3–6 representatives from the medical secretaries and the nurses identified work tasks for the questionnaire each in two 2-hour group sessions, using recently developed detailed work-flow charts as templates (not shown). The identified tasks were then reduced to 23 and 19 tasks supported by the HIS, respectively (see appendix A). The questionnaire was reviewed in similar sessions by representatives from the physicians. As a result, one new task was added to the physicians' questionnaire, and four tasks not supported by the HIS were removed. For all professions, a new section was added, containing questions about ease of performing each task using the system.
The survey was conducted during February–April 2002, and 85 medical secretaries, 235 nurses and 80 physicians in the medical, surgical and other somatic wards received the questionnaire. Of these, 79 medical secretaries (93%), 172 nurses (73%) and 70 physicians (88%) responded, giving a total response rate of 81% (321/400). We used Teleform™ for data acquisition and SPSS 11.0 for Windows™ for statistical analysis.
In addition to the survey, one of the authors interviewed 8–12 representatives of each profession for 0.5–2 hours. Comments on advantages and disadvantages of the system in all relevant work tasks were noted and summarized
Results
The medical secretaries used the HIS routinely for most of their tasks defined in the questionnaire. This stands in contrast to the nurses and the physicians (fig 3). The number of tasks with a median response of "always or almost always" was highest for the medical secretaries (15 out of 23 tasks, 65%), and lowest for the nurses (4 out of 19 tasks, 21%).
The medical secretaries reported that all of the defined tasks were performed more easily than before the HIS was introduced (i.e. median response for ease of performing the task was "increased" or "significantly increased", in 23 out of 23 tasks, fig 4). In comparison, the number of tasks more easily performed was much lower for the nurses and the physicians (respectively 9 [47%] and 7 [37%] out of 19 individual tasks).
The medical secretaries were much more satisfied with the use of the HIS than the nurses and physicians, both when assessing the detailed aspects of it and the system as a whole. The detailed aspects of the HIS was assessed in twelve questions related to the factors content, accuracy, format, user friendliness and timeliness [6]. The parts of the HIS that contained scanned document images and regular electronic data were assessed separately. The medical secretaries were equally satisfied with both parts of the HIS (fig 5). This stands in contrast to nurses and in particular the physicians, who were less satisfied, particularly with the part containing the scanned document images. The difference between the professions was significant in all factors regarding the scanned document images (ANOVA p < 0.001), and in all factors except accuracy regarding the regular electronic data (fig 5, (ANOVA p = 0.001 to 0.04, p = 0.07 for factor 'accuracy').
In addition to the detailed aspects, the user satisfaction with the HIS as a whole was assessed (fig 6). The medical secretaries gave significantly more positive responses than the nurses and the physicians in all of the five questions in this section (Kruskall-Wallis p = 0.05 in question 2, p < 0.001 in the remaining four questions). However, the majority of each profession gave positive answers in all of these questions. To summarize all results regarding user satisfaction, the system seems to be well adapted to the work of medical secretaries but leave nurses and physicians less satisfied.
Partly explaining the differences in user satisfaction, the physicians reported more frequent problems related to availability of the HIS than the medical secretaries and the nurses (fig 7, Kruskall-Wallis p < 0.001 in all questions). The most frequently reported problems among the physicians occurred daily or weekly, and consisted of various software and hardware-related problems, the system working too slowly, and lack of computers where the clinical work was being done. Such problems were not frequently reported among the medical secretaries, except problems with the systems working too slowly (42% daily or weekly, 32/77).
In the interviews, the perceived advantages and disadvantages of the HIS were discussed. Both nurses and physicians in the medical ward found that patient data were more accessible when stored electronically than when stored on paper, in particular regarding lab test data. However, the nurses were still using pen and paper when documenting their activities. The medical secretaries found that generation, handling, fetching and delivery of paper documents and logistics of paper-based patient records had diminished dramatically. The generation of written text had become considerably easier. On the other hand, the scanning process had become an additional burden and was considered time consuming. Overall, handling of paper documents was considered additional work whenever the documents appeared.
Discussion
In this hospital, we have found that the medical secretaries use the HIS more extensively for their tasks than the nurses and the physicians. Also, they are much more satisfied with the HIS.
Medical secretaries reported that they use the HIS routinely for most of the tasks defined in the questionnaire (fig 3). A simple explanation is that their tasks generally are smaller in scope and have a smaller and more easily defined range of needed information types than that of the nurses and physicians (See appendix A). Hence, the medical secretaries' tasks should be more easily supported by computers than the nurses' and the physicians' tasks. The particular inefficiencies of certain paper-based routines (e.g. regarding task 6, 15, 18 and 19) readily demonstrates the usefulness of computer support [7]. Unlike the work of nurses and physicians, the work of medical secretaries is stationary, avoiding the difficulties in providing an efficient mobile work environment. In addition, each medical secretary typically is assigned a computer, while nurses and physicians usually have to share a limited number of them (fig 7, question C). Another possible reason for the difference in usage pattern could be difference in computer literacy. However, the usage patterns were not consistent with the limited differences found in self-reported computer literacy (data not shown), and the amount of in-house training of medical secretaries and physicians was principally equal.
The medical secretaries reported that all of the tasks in their questionnaire are more easily performed (fig 4). The results from the interviews identify the elimination of the paper-based medical record as a major contributor to this, as several manual paper routines have disappeared (e.g. searching for a lost paper-based medial record or sorting the contents of a medical record) or are replaced by more efficient computer functions (e.g. transferring new lab data to doctors for review). Furthermore, having the administrative functions integrated with the EMR means that a substantial selection of structured demographic, clinical and administrative data is concurrently available to the users of the HIS. This makes several tasks more efficient for the medical secretaries (e.g. sending standard letters to patients in waiting lists). The results are supported by the fact that the number of medical secretaries in the hospital has been reduced by 15 since the onset of the HIS project (Bjørn Engum, personal communication Sept 2003).
Not surprisingly, the medical secretaries were more satisfied with the system than the nurses and the physicians (figs 3 and 4). This agrees with the results of Sittig [8] and Lee [9], who both found that user satisfaction was strongest correlated to questions regarding how easily the work was done. On the other hand, when comparing the user satisfaction scores to the reference data of Doll & Torkzadeh [6], the median user satisfaction score of the medical secretaries lies between the 20th and 30th percentile of the reference data set. This suggests that there is room for improvement of the EMR system regarding the medical secretaries as well as the others. Unlike the nurses and the physicians, the medical secretaries were equally satisfied with the scanned document images as that of the regular electronic medical record. The most likely reason is that the document images are not very often used by the medical secretaries, particularly the document images scanned in sections (data not shown). The disadvantages of the document images, for instance that they can not be searched, therefore seem to affect the user satisfaction of nurses and physicians to a stronger degree than that of the medical secretaries.
The use of the HIS by medical secretaries, nurses and physicians may to some degree be compared at a task-by-task level when the tasks are equally worded. In these tasks, work roles seem to explain the differences. For instance, the tasks "Reviewing the patient's problems" (tasks 1) and "Seek out specific information from patient records" (task 2) appeared in all questionnaires. Of all the respondents, only the physicians had a significant proportion finding that these tasks were more difficult to perform than before (figure 4). A possible reason is that the physicians, in order to perform these tasks as they saw fit for their work role, more often needed to search the scanned document images extensively. When examining the task "Order clinical biochemical laboratory analyses" (task 6 for nurses, task 7 for physicians), the nurses both use the HIS more frequently for this task and find the task more easily to perform than the physicians. However, many Norwegian physicians find that order entry is a task better performed by others [10], reducing the motivation for learning the new system. This way, understanding work roles in the given context appears necessary to interpret the results.
A secondary finding in this study was that the physicians reported frequent computer-related problems, much more frequent than that of medical secretaries and nurses (fig 7). This may be due to escalated demands on computing power, system stability and availability. Without the paper-based medical record, the EMR is taken into full use and the real demands of supporting the physicians' information processing are revealed. The high reported frequency of computer-related problems may partly explain the overall lower user satisfaction of the physicians, as well as the relatively high proportion of physicians finding certain tasks more difficult to perform (task 1 and 2, fig 4). An observational study could elaborate on these relationships, focusing on what kinds of computer problems are the least tolerable to the physicians.
Limitations of the study
In the questionnaire, we do not know how often each task is carried out (using the HIS or not) or how long it takes, which means that demanding tasks might be outnumbered by the less demanding ones. Furthermore, the list of tasks supported in some way by the system may not be complete, and the list does not cover the full range of conceivable tasks suited for support by any given HIS. However, given that the tasks defined for each group cover important parts of their information-related work, a cautious comparison of general patterns of use between groups of hospital employees is possible.
Conclusion
Evaluation of a HIS in a hospital that has eliminated the paper-based medical record reveals considerable differences in user satisfaction and reported use of the system among medical secretaries, nurses and physicians. Although the basis for reference is limited, the results seem to support the claim that replacing the paper-based medical record primarily benefits the medical secretaries, and to a lesser degree the nurses and the physicians. Inspired by Aust-Agder Hospital, two of 22 other Norwegian hospitals using the same system (as of Aug 2002) are about to eliminate the paper-based medical record, making a future comparison between hospitals possible. When assessing the effects of a HIS on a hospital organization by asking users, the multidisciplinary nature of health care provision should be reflected in the selection of hospital employees that participate in the evaluation.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HL, THK and AF planned the investigation. HL and THK developed the questionnaires for the medical secretaries and nurses. THK organized the administration of the questionnaires, and HL scanned and analysed the data. HL, THK and AF wrote the manuscript jointly.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Appendix A: Task lists The three lists of tasks as they appear in the questionnaire developed for the medical secretaries, nurses and physicians, respectively.
Click here for file
Additional File 2
An English translation of the questionnaire used for the physicians in the survey. It is meant for review purposes.
Click here for file
Acknowledgments
We thank Gerd Gulstad, Bjørn Engum, Tom Schulz, Anne-Brit Riiser and Astrid Norberg for their continued help and support.
This investigation is funded by the Norwegian Ministry of Health, and the Research Council of Norway through the Kvalis project at the Norwegian University of Science and Technology, Trondheim.
Figures and Tables
Figure 1 Contents of the EMR. Document and information types found in the EMR part of the HIS. Most documents created prior to the implementation of the HIS appear as scanned multiple documents, but some old data has been imported from existing systems and hence appears as electronic text and data. Adapted from Laerum et al [4].
Figure 2 Navigation of the EMR. The medical record explorer and the multi-page viewer. Adapted from Laerum et al [4] and reproduced with permission from DIPS ASA, Norway.
Figure 3 Use of the Hospital Information System. Frequency of use of HIS for tasks specific to each profession. Within each profession, the tasks are sorted in descending order by frequency of use. High and low frequency of use is represented by blue and red color tones, respectively. The definitions of the tasks for each profession are given in appendix A. The error bars show the confidence interval of the proportion of respondents answering "Always or almost always".
Figure 4 Task performance using the HIS. Change in ease of performing individual tasks for each profession when using the HIS. The tasks appear in the same sequence as that of figure 3, i.e. the frequency with which the HIS is used for the task. The responses indicating a task to be easier to perform appear in blue tones, and those indicating it to be more difficult appear in red. The error bars show the confidence interval of the proportion of respondents answering "Significantly increased". For definitions of the individual tasks, see appendix A. (The data for the physicians[4] is included for comparison)
Figure 5 Detailed user satisfaction. User satisfaction with detailed aspects of the HIS in various professions. The mean scores of each factor (content, accuracy, format, user friendliness and timeliness) are shown in percent of maximum obtainable score. The error bars show the confidence interval of the mean. (The data for the physicians[4] is included for comparison)
Figure 6 User satisfaction. User satisfaction with the HIS as a whole in various professions. The responses colored in red tones represent low satisfaction; those colored in blue tones represent high satisfaction. The error bars show the confidence interval of the combined proportion of all positive responses (The data for the physicians[4] is included for comparison).
Figure 7 Problems related to the availability of the HIS. Reported frequency of problems related to the availability of the HIS. The questions are sorted in descending order by the physicians' frequency of problems. Red tones represent frequent problems, and blue tones represent infrequent problems. The error bars show the confidence interval of the proportion of respondents reporting frequent problems (i.e. weekly or daily).
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Lee F Teich JM Spurr CD Bates DW Implementation of physician order entry: user satisfaction and self- reported usage patterns J Am Med Inform Assoc 1996 3 42 55 8750389
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| 15485581 | PMC526260 | CC BY | 2021-01-04 16:29:15 | no | BMC Dermatol. 2004 Oct 14; 4:14 | latin-1 | BMC Dermatol | 2,004 | 10.1186/1471-5945-4-14 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-271545651510.1186/1472-6963-4-27Study ProtocolPersonalized versus non-personalized computerized decision support system to increase therapeutic quality control of oral anticoagulant therapy: an alternating time series analysis Colombet Isabelle [email protected]ère Alessandra [email protected] Rémy [email protected] Gilles [email protected] Pierre [email protected] the PHRC-OAT study group [email protected] Public Health and Medical Informatics, Georges Pompidou European Hospital, Paris, France2 Clinical Investigations Center, Georges Pompidou European Hospital, Paris, France2004 29 9 2004 4 27 27 29 7 2004 29 9 2004 Copyright © 2004 Colombet et al; licensee BioMed Central Ltd.2004Colombet 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 quality control of oral anticoagulant therapy (OAT) during the initiation and maintenance treatment is generally poor. Physicians' ordering of OAT (especially fluindione and warfarin) can be improved by dose adjustment algorithms, taking into account the results of International Normalized Ratio (INR). Reminders at the point of care, computerized or not, have been demonstrated to be effective in changing physicians prescription behavior.
However, few studies have addressed the benefit of personalized reminders versus non personalized reminders, whereas the personalized reminders require more development to access patient record data and integrate with the computerized physician order entry system.
The Hospital Information System of George Pompidou European Hospital integrates an electronic medical record, lab test and drugs order entry system. This system allows to evaluate such reminders and to consider their implementation for routine use as well as the continuous evaluation of their impact on medical practice quality indicators.
The objective of this study is to evaluate the impact of two types of reminders on overtreatment by oral anticoagulant: a simple reminder of text formatted dose adjustment table and a personalized recommendation for oral anticoagulant dose and next date of INR control, adapted to patient data. Both types of reminders appear to the physician at the moment of drug ordering.
Methods
The study is an alternating time series experiment with three 6 months periods, each one including every 2 months according to a Latin square scheme: a control period without any reminder, a period with the simple non personalized reminder, a period with personalized reminder. All patients hospitalized in departments using the computerized physician order entry system and ordered fluindione or warfarin, will be included in the study between November 2004 and May 2006.
Main outcome will be the proportion of overcoagulation, as expressed by the proportion of observation time with INR over 4.5, assuming INR change linearly. Secondary outcome is the incidence of major haemorrhagic events. Data will be collected thanks to Hospital Information Systems databases.
Data will be analyzed taking into account patient and physician clustering effect.
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Background
Iatrogenic effects of oral anticoagulants therapy
According to a study carried out by French pharmacovigilance centres, haemorrhage subsequent to oral anticoagulant treatment (OAT) is the most common drug-related side effect resulting in hospitalisation in public hospitals in France (13% of such admissions, and 0.41% of the 3,137 admissions analysed). On the basis of these findings, the AFSSAPS (French Agency for Health Product Safety) has made the prevention of iatrogenic effects related to OAT one of its priorities. Many of these events are consequences of interactions between different drugs, resulting in inappropriate doses [1-3].
Implementation of a system of support when prescriptions are made out is likely to improve prescription practices and to decrease the frequency of side effects. It should be possible to integrate a support tool into the drug prescription system, by using nomograms to adjust OAT doses.
Decision-making tools and appropriate practice
The efficiency of reminders issued at the time of prescription has been demonstrated by a various studies [4-8]. These reminders can come in a paper, telephone [9] or computer form. Hunt et al. [10] reviewed all studies (randomised and quasi-randomised) evaluating the effect of computer-based clinical decision support systems on medical practice and treatment outcome. They showed that these systems are effective for drug prescription purposes and for the implementation of various medical strategies [10]. We have demonstrated the efficiency of reminders in the French situation, both in the form of "paper" reminders [11] and in the form of computer-generated reminders [12]. Computer-generated reminders appear the most promising given the development of computer programs including prescription aids for hospital usage.
Several types of reminders can be issued at the time of prescription [13]:
• simple, general information concerning the recommendations that should be taken into account (non-specific or non-personalised reminders),
• "check list": includes questions or a precise list of practices that the doctor must tick to show that it has been done,
• reminders including clinical data concerning a specific patient that must be taken into account for a given procedure (personalised reminders).
The advantage of personalised reminders over non-personalised reminders has not been demonstrated in the literature. However, the production of personalised reminders necessitates better integration of existing information and is thus more expensive to develop. It is important to determine whether this personalised tool results in a better quality of care than non-personalised tools.
Several randomised clinical trials have tried to evaluate decision support systems for the prescription of OAT, but failed to draw any conclusions about their efficiency for several reasons: heterogeneity and complexity of the systems evaluated, experimental designs difficult to apply and not necessarily adapted, and too few patients included [14-17].
Reminders issued at the time of prescription have been shown to be effective by experimental studies, but the difficulties of maintaining the effectiveness of interventions designed to improve clinical practices remains a major problem. We evaluated the effect of an active decision support system for the prescription of low molecular weight heparin as prophylaxis for venal thrombosis in an orthopaedic surgery department. In this study, the system was and was not used during alternate periods. It showed that such programs affect practices without affecting learning [12]. Other authors have looked at the problem of the routine use of computerised systems [18,19]. The long-term use of a computer program to modify medical practices necessitates a hospital information system that ties in prescriptions and the results of medical tests, and the continuous collection of indicators making it possible to evaluate use and effect.
Georges Pompidou European Hospital (GPEH)
Organisation of the information system at GPEH
The hospital information system currently collates prescriptions and results of biological tests and imaging procedures. Eight hundred computers, both laptops and fixed posts, are used to in care procedures (in care departments and medical offices).
The Dx-Care® program is at the centre of care delivery. It is used by doctors and nurses:
• to prescribe laboratory examinations and imaging tests for a patient,
• to visualise the results of laboratory tests,
• to establish and to consult nursing schedules,
• to archive a structured observation,
• to prescribe drugs.
Dx-Care® is integrated with other applications to allow circulation of information between departments, laboratories and the pharmacy. Prescriptions for laboratory tests are transmitted to the Netlab® program which manages such tests (this program is used by all biochemistry, haemostasis and immunology laboratories, etc.) The laboratories return the results using this same program, which retransmits them to Dx-Care®. Furthermore, prescriptions of drugs are transmitted to the Phedra program, which is used by the pharmacy to manage prescriptions. The prescription is validated by the pharmacy and this validation is then transferred to Dx-Care®. The lab test prescription facility has been available in the hospital information system since 2000 and is used by all departments of the hospital. The drug prescription facility has been implemented later on, since January 2003, and its use is still increasing (currently by half of the hospital departments).
The hospital information system thus allows to install decision support systems that are activated whenever a prescription is issued and routinely to collect evaluation criteria of prescription practices. If possible and validated, the use of the hospital information system to evaluate care procedures will make it possible to collect data regularly, and routinely to assess methods for the improvement of care practices.
Organisation of quality assurance and system for recording undesirable events
With the aim of quality of care and preventing risks, the hospital is developing a system, based on the Intranet network, of declaration of undesirable events. This system must be able to record all undesirable events and incidents linked to the use of health products (drugs, medical equipment, blood products, etc.) and care (e.g. falls, lost files, bedsores, long waiting times) as well as those due to the patient environment (building, security, malevolence, etc.) and the job of health care professionals (accidental exposure to blood, chemicals, radiation, etc.).
When a health care professional decides to report an undesirable event, he or she must complete a dedicated form available on the Intranet with all relevant information. This incident form includes an item entitled "complications associated with anti-coagulants". When the doctor clicks on this item, a form specific to haemorrhagic accidents following anti-coagulant treatment appears (see form in appendix).
Prevention of thrombosis at GPEH
Among departments which already started to use the computerised drug order entry system, several (cardiology and vascular medicine departments) are heavy "consumers" of anti-thrombosis drugs (see Table 1).
In 2001, a working group representative of all the hospital departments described a group of procedures. This was done by critically reading articles published in the literature. The procedures concerned the preventive and curative indications of anticoagulants (OAT and heparins) in arterial thrombosis and venous thrombosis, and the way in which patients receiving anticoagulants are monitored and handled in cases of overdose. The procedure concerning curative OAT included a nomogram for adjusting doses of fluindione and warfarin based on previous doses and the INR (see Tables 2 and 3). These nomograms are currently available on the hospital's intranet, although they are not widely used. Their inclusion in a computerised prescription aid could increase their impact.
Study aims
Main aims
1. To evaluate the effect on the frequency of overanticoagulation of the implementation in the computerised physician order entry system of two types of tool to adjust OAT doses (one personalised and one non-personalised).
2. To assess any advantages of using the personalised tool rather than the non-personalised tool.
Secondary aims
1. To evaluate the frequency of haemorrhagic accidents in the context of the study.
2. To evaluate the feasibility of long-term implementation of the intervention.
Methods
Experimental design
The study is an alternate time series experiment which consists of three successive six-month periods (timed to change with the changing of medical residents even though they will not be the only prescribers). Each phase will consist of:
• a two-month period without active support during which evaluation criteria will be collected (period A),
• a two-month period with non-personalised active support (period B),
• a two-month period with personalised active support (period C).
To limit the impact of a learning effect on appropriate OAT management practice within the department over time (possible for medical residents), the order of these three periods was determined by using a Latin square plan (see Figure). This experimental design can be considered valid for an impact study in this context whereas a randomised controlled design is difficult to apply with just one hospital [20].
Participants
Patients
This study will include all the patients who are prescribed OAT for any indication and are hospitalised in clinical care departments where physicians are using the hospital information system to prescribe drugs.
The following table shows the number of INR examinations prescribed by these departments in a three-month period. It provides an estimation of the approximate proportion of overdoses among the INR that exceeded 2, which is supposed to be found in patients treated with OAT in these units.
Physicians
All doctors authorised to prescribe drugs in the participating departments will be included in the study: residents and fellows, registered and non registered university hospital doctors. Each six-month period will coincide with an internship semester.
Intervention
To prescribe a drug using Dx-Care®, the doctor selects the required drug from an exhaustive list. This opens up a dialogue box in which the doctor types the dose, the frequency of intake and the mode of administration. From this window, it is possible to add a text comment or to consult particular protocols that have been defined by the departments.
It is planned to integrate two types of decision support systems into the computerised prescription program:
1) non-personalised active system: when the drug is selected a window automatically opens giving the prescribing the nomogram for the adjustment of OAT doses in the form of a table (see Tables 2 and 3).
2) personalised active system: when the drug is selected a window automatically opens suggesting a dose recommended according to the nomogram (taking into account the doses previously received by the patient and the patient's INR), together with a date for next INR control and an explication.
Definition of endpoints
Overanticoagulation
Proportion of patient observation time with INR results > 4.5, assuming linear change of INRs.
Major haemorrhagic accidents
Intra-cranial haemorrhage or spontaneous haemorrhage necessitating surgery or a transfusion or decreasing haemoglobin concentration by more than 2 g/dl.
Assessment of evaluation criteria
OAT overdose
The Netlab® application allows biological laboratories to receive prescriptions and to return results. All of the INR results can be extracted from the Netlab® database accompanied by information making it possible to identify the patient, the treatment and dose received, the prescribing doctor, the hospitalisation unit, the date the test was prescribed. Data about overdoses can therefore be collected systematically by regular database searches.
Furthermore, the storage of the information in a computerised tool will make it possible to determine previous doses and INR results each time a drug is prescribed.
Haemorrhagic accidents
When a health care professional decides to declare an undesirable event, he or she fills in a specific, pre-formatted form available on the Intranet. This form includes a list of events that must be declared at the GPEH.
The declaration form includes an item entitled "complication of haemorrhagic accidents". When the doctor clicks on this item, a specific form for the declaration of a haemorrhagic accident associated with anti-coagulant treatment appears (see form in appendix).
Determination of sample size
The determination of the number of participants necessary requires the definition of the statistical unit of interest, information about the incidence of the evaluation criteria in the study population and a hypothesis about the efficiency of the intervention.
Statistical unit
In this study, the main aims are to guide each prescription and to reduce the number of anti-coagulant overdoses: the simplest statistical unit to study is therefore the INR result. This unit will be used to calculate the sample size.
This choice is not, however, perfect and the efficacy results will be presented using other indicators of the quality control of anti-coagulant treatments:
Given the low incidence of major haemorrhagic accidents (not currently measured at the GPEH but probably below 1%), it is not possible in this study to estimate the number of subjects necessary to demonstrate an effect of intervention on the "haemorrhagic accident" endpoint. Recording haemorrhagic accidents will give the frequency of such accidents, which will then be used for realistic estimates of power and sample size if further studies are carried out.
In previous studies evaluating the efficacy of tools to aid the prescription of OAT, the unit considered was not always the same, taking into account the number of INR per patient and the time between INR measurements to greater or lesser extents. The most recent studies considered the number of patient-days according to the method described by Rosendaal [15,16,21]. This method can also be used to calculate the rate of haemorrhagic events as a function of the number of patient-days for a given range of INR values.
We may also carry out an analysis for each prescribing doctor given that the intervention targets doctors directly. This will involve adjusting the effect of the intervention to the fact that intra-physician variability is a priori lower than inter-physician variability.
Number of INR measurements and predicted frequency of overdoses
During a six-month period (January to June 2004), 4 920 INRs were requested by the six departments which already routinely use the computerized physician order entry system. The frequency of overtreatment can approximately be estimated from the percentage of INR > 4.5 among INR >2. Among the 2620 INR > 2, 330 (12%) were higher than 4.5 (see Table 4). This frequency has been stable during these six months but differed considerably between departments (10% to 23%).
Hypothesis about the efficacy of the intervention
The number of INR tests necessary for a six-month period, with an α risk of 5% and a power of 80%, for the comparison of two percentages by classical methods (untreated group half the size of the treated group), for a basal incidence of the judgement criterion of 12% are and for the following hypotheses on relative reduction of the risk (RRR) of overdose, are:
• RRR 30%: 2500
• RRR 40%: 1300
• RRR 50%: 800
• RRR 60%: 500
Carrying out approximately 5000 tests over six months will make it possible to detect an intervention effect of less than 30% in this period. The experimental design includes three six-month periods and should thus ensure adequate power.
Statistical analyses
Statistical analyses will be performed with the STATA statistical software (release 8, STATA corp, College station, Tex, USA)
Standard statistical tests will be used to compare the baseline characteristics of the departments and patients.
The main analysis concerns the effect of the intervention on the number of dangerously high INRs. The analysis will be carried out using a mixed effect analysis of variance model, in which the effect linked to the period will be considered fixed and that linked to the prescription tool will be considered random [22].
Rosendaal's method will be used to analyse the number of patient-days with INR over the target [21].
Regulatory aspects
According to French policy, this study was exempt from medical ethics committee approval. The anti-coagulants being evaluated are prescribed as recommended by clinical studies validated within the GPEH. These recommendations are available on the hospital's Intranet and are thus accessible to all doctors. They conform to standard practices. Neither the patients nor the doctors will be randomised. The interventions are simply different means of giving valid information to physicians. Using funding from the PHRC (Hospital Clinical Research Program), we carried out two research studies related to this project. In the first (PHRC 95), an intervention aimed at modifying the way in which emergency department doctors handle ankle injuries, the study design was a randomised controlled study and the randomisation unit was the hospital [11]. In the second (PHRC 98), a computer-based decision-support system for the prevention of venous thromboses in orthopaedics, the experimental design was identical to that of our present study [12]. In both cases, the Ile de France branch of the Clinical Research Delegation considered that the project was exempt from medical ethics committee approval.
List of abbreviations
OAT: Oral Anticoagulant Therapy
INR: International Normalized Ratio
GPEH: Georges Pompidou European Hospital
PHRC: Hospital Clinical Research Program
Authors' contributions
IC and PD, conceived, wrote the protocol and prepared the manuscript.
GC is the statistical expert and performed the power calculations
GC and ABR revised the protocol and the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank the PHRC-OAT Study group for their comments and advice: Lise Marin (Medical Informatics Department), Martine Alhenc-Gelas (Haemostasis Laboratory), Eric Durand (Cardiology Department), François Ledru (Cardiology Department), Agnès Lillo-Le-Louët (Pharmacovigilance), Brigitte Sabatier (Pharmacy)
Figures and Tables
Figure 1 Study Design. A: Period without any decision support system; B: Period with non-personalised decision support system; C: Period with personalised decision support system
Table 1 Number of orders of fluindione during first semester 2004 in departments using computerized physician order entry system
Department January February March April May June Total
Internal Medicine* 45 50 58 53 21 227
Immunology 17 11 2 1 31
Nephrology 16 32 35 32 2 34 151
Vascular Medicine – Hypertension 71 23 46 23 46 16 225
Cardiovascular Surgery 2 129 143 140 90 64 568
Cardiology (1 unit)* 6 55 38 28 66 193
Total 106 246 331 291 219 202 1395
*Department which started to use the computerised physicians' drug order entry system in February 2004. Only one unit of the three in the Cardiology department started to use it.
Table 2 Nomogram for adjustment of fluindione from [23]
Day INR DOSE
D0 < 1,2 20 mg
D2 1–1,4 30 mg
1,5–1,7 25 mg
1,8–2,3 20 mg
2,4–3 15 mg
>3 10 mg
D4 <1,8 +10 mg
1,8–2,0 +4 mg
2,1–2,5 unchanged
2,6–3 dose > 20 mg: -5 mg
dose ≤ 20 mg: inchangée
>3 dose > 15 mg: -10 mg
dose ≤ 15 mg: -5 mg
D6 <2,3 +5 mg
2,3–3,5 unchanged
>3,5 -5 mg
Table 3 Nomogram for adjustment of warfarin from [24]
Day INR dose (mg)
≤50 years 51–65 years 66–80 years >80 years
D0 <1,4 10 9 7,5 6
D1 ≤1,5 10 9 7,5 6
≥1,6 0,5 0,5 0,5 0,5
D2 ≤1,7 10 9 7,5 6
1,8–2,3 5 4,5 4 3
2,4–2,7 4 3,5 3 2
2,8–3,1 3 2,5 2 1
3,2–3,3 2 2 1,5 1
3,4 1,5 1,5 1 1
3,5 1 1 1 0,5
3,6–4,0 0,5 0,5 0,5 0,5
>4 0 0 0 0
D3 ≤1,5 10–15 9–14 7,5–11 6–9
1,6 8 7 6 5
1,7–1,8 7 6 5 4
1,9 6 5 4,5 3,5
2,0–2,6 5 4,5 4 3
2,7–3,0 4 3,5 3 2,5
3,1–3,5 3,5 3 2,5 2
3,6–4,0 3 2,5 2 1,5
4,1–4,5 No pill, then 1–2 No pill, then 0,5–1,5 No pill, then 0,5–1,5 No pill, then 0,5–1,0
Table 4 Results of INR ordered during first semester 2004 in six departments
Number of valid results Number of INR > 2 (%) No. of INR > 4.5 (% of INR > 2)
Internal Medicine* 525 294 45 (15.3%)
Immunology 28 17 4 (23.5%)
Nephrology 182 109 18 (16.5%)
Vascular Medicine – Hypertension 560 273 28 (10.3%)
Cardiovascular Surgery 1224 633 98 (15.5%)
Cardiology (1 unit)* 2401 1294 137 (10.3%)
Total 4920 2620 330 (12.6%)
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| 15456515 | PMC526261 | CC BY | 2021-01-04 16:19:32 | no | BMC Health Serv Res. 2004 Sep 29; 4:27 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-27 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-281546268210.1186/1472-6963-4-28Research ArticlePatients' perspectives on high-tech home care: a qualitative inquiry into the user-friendliness of four technologies Lehoux Pascale [email protected] Department of Health Administration, Interdisciplinary Health Research Group (GRIS), University of Montreal, P.O. Box 6128, Branch "Centre-ville", Montreal, Quebec H3C 3J7 Canada2004 5 10 2004 4 28 28 6 4 2004 5 10 2004 Copyright © 2004 Lehoux; 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 delivery of technology-enhanced home care is growing in most industrialized countries. The objective of our study was to document, from the patient's perspective, how the level of user-friendliness of medical technology influences its integration into the private and social lives of patients. Understanding what makes a technology user-friendly should help improve the design of home care services.
Methods
Four home care interventions that are frequently used and vary in their technical and clinical features were selected: Antibiotic intravenous therapy, parenteral nutrition, peritoneal dialysis and oxygen therapy. Our qualitative study relied on the triangulation of three sources of data: 1) interviews with patients (n = 16); 2) interviews with carers (n = 6); and 3) direct observation of nursing visits of a different set of patients (n = 16). Participants of varying socioeconomic status were recruited through primary care organizations and hospitals that deliver home care within 100 km of Montreal, the largest urban area in the province of Quebec, Canada.
Results
The four interventions have both a negative and positive effect on patients' lives. These technologies were rarely perceived as user-friendly, and user-acceptance was closely linked to user-competence. Compared with acute I.V. patients, who tended to be passive, chronic patients seemed keener to master technical aspects. While some of the technical and human barriers were managed well in the home setting, engaging in the social world was more problematic. Most patients found it difficult to maintain a regular job because of the high frequency of treatment, while some carers found their autonomy and social lives restricted. Patients also tended to withdraw from social activities because of social stigmatization and technical barriers.
Conclusions
While technology contributes to improving the patients' health, it also imposes significant constraints on their lives. Policies aimed at developing home care must clearly integrate principles and resources supporting the appropriate use of technology. Close monitoring of patients should be part of all technology-enhanced home care programs.
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Background
The possibility of managing patients outside of the hospital has rarely been so widely considered. Indeed, home care is often seen as less costly and more patient-friendly [1]. However, the effectiveness and safety of home care have yet to be subjected to rigorous study [2,3]. The transfer of care from the hospital to the home raises economic and organizational issues [2]. For example, low levels of physician involvement [4] and significant increases in private spending [5] have been observed. Furthermore, the impact of the increasing use of technology in home care has not been examined [6]. A recent survey showed that 98% of Quebec (Canada) primary care organizations are using programmable pumps to deliver intravenous antibiotics therapy at home, and 84% are providing home oxygen therapy [7]. The growth of technology in home care means that lay people with varying levels of technical skills and education become direct users of health technology [6]. Therefore, the objective of our study was to document, from the patient's perspective, how the level of user-friendliness of medical technology influences its integration into the private and social lives of patients. More specifically, we adopted a "technology-in-practice perspective", which relies on qualitative in-depth investigation, by looking at what technologies do and help accomplish in the daily practices of technology users and in the organization of health care [8].
Sullivan [9] showed how growing interest in the patient's perspective is the result of convergent trends in health and social scientific research. There is a growing appreciation of how the patients' values affect their experience of a chronic health state. Medical sociologists have shown that shifting family and social relationships shape patients' perceptions and coping strategies [10]. For instance, Lowton and Gabe [11] observed that adults with cystic fibrosis, who were not expected to live for long, deployed diverse strategies to downplay the importance of their illness and compare themselves favourably to "normal, healthy" people. Although clinicians are often concerned about patient compliance, the technical and human dimensions at the root of this problem remain understudied [10,12]. Observers of high-tech home care have stressed that certain health technologies are, by their very design, unfriendly [6]. Nevertheless, very little is known about the characteristics that facilitate or impede the use of medical devices and whether patients perceive them as user-friendly or not.
The term user-friendly is used to characterize an object – often a computer system – as "easy to operate or understand; not needing special training" [13]. This notion has gained impetus over the last 20 years with the growth of information technology and research into Human-Computer Interface (HCI). In general, the human-machine interface is seen as key in enabling a smooth fit between the user, the task and the technology [14,15]. The need to design interfaces that users can rapidly understand and interact with was recognized several decades ago as it impacted workers' efficiency. Although the "overriding ethos within the community of system designers has been to try to ensure that the system is user-friendly," it remains difficult for designers to grasp all of the subtleties that shape users' needs and practices [[15]: p.126]. According to Norman, "There is a big difference between the expertise required to be a designer and that required to be a user. In their work, designers often become expert with the device they are designing. Users are often expert at the task they are trying to perform with the device" [[16]: p.156].
In the field of health care, this specialization often adds to the complexity of the work of designers, who, in addition to not being end users of the device, are simply unfamiliar with the complicated tasks health care providers are achieving through the use of technology. When moving health technologies away from the hospital and into the patient's home, the design characteristics of medical devices become even more salient, since patients have to learn how to operate them safely and with confidence. According to Norman [16], the use of any device is learned more readily if the user has a good conceptual model. "This requires that the principles of operation be observable, that all actions be consistent with the conceptual model, and that the visible parts of the device reflect the current state of the device in a way consistent with that model" [[16]: p.189]. Hence, the designer must create a conceptual model that is understandable for the user and that captures the important steps of the operation of the device. In a similar perspective, when assessing the level of user-friendliness, Lun [14] has suggested paying attention to two components: 1) user-acceptance – the extent to which the user is favourable to using the technology; and 2) user-competence – the abilities required to use the technology effectively. These two components interact with each other – the more technically complex a technology, the more elaborate the user training required. This author also underlined three principles for designing user-friendly interfaces: 1) human-machine interaction is pivotal; 2) this interaction evolves through use; and 3) user should be the key informant. Methodologically speaking, these principles imply that designers should compile users' perspectives, directly observe how technology is being used, and identify the learning curve by which technology is appropriated by users.
Because most computer interfaces are used in a somewhat confined environment, the work of scholars who have studied technical aids for the disabled and the elderly brings another dimension to the definition of user-friendliness. Conceptualizing disability as a social phenomenon, the user-friendliness of technical aids has to be gauged with respect to their ability to assist users' move freely in their social environment [17]. From this perspective, autonomy and mobility become prominent, as well as the impact of the technology on users' social identity. For instance, Pippin and Fernie [18] conducted focus groups and interviews with elderly patients in order to explore the following issues: Acceptance of dependence, experience of social stigma, recognition of one's own physical loss, appearance of technical aids, autonomy and perceptions of alternatives. This work shows that users have to cope not only with the technology but, more importantly, with the limitations they themselves experience (i.e., architectural barriers, growing old) and that may become the object of others' gazes as soon as they engage in environments outside of their private sphere. Thus, the importance of user-acceptance and user-competence will vary depending on where the technology is used. The level of user-friendliness would then be a function the type of settings in which users circulate, which affects how they succeed in (re)constructing their identity as a medical technology-user.
Social scientific work on patients learning how to cope with chronic illnesses and life-sustaining technology has offered similar observations, highlighting that each patient tends to go through a personal trajectory, or engage in "biographical work" whose aim is to give meaning to a constellation of unfolding events [10]. Here also, the setting where services are provided is likely to influence patients' perceptions and coping strategies. Although the hospital enables the patient to adopt the sick role rather straightforwardly, the home setting may force him/her to be more active and show optimism [5,6]. Family members and caregivers are also affected by the use of high-tech home care. They might be asked to provide technical and moral assistance, while coping with a profoundly modified family dynamic. In certain cases, providing assistance implies inflicting pain and discomfort [6].
To summarize, the literature underscores the many technical (e.g. weight, functionality, complexity) and human (e.g. self-image, cognitive resources, social stigma, pain, etc.) variables that influence the use of technology and which are affected by the setting (institutional, private or public) where technology use takes place. The user-friendliness of a technology therefore results from a smooth fit between human and technical features, with the fit varying between and within settings and individuals. Figure 1 illustrates the relationships between these variables. This framework posits that technical dimensions largely influence user-acceptance, and human dimensions will affect user-competence. In addition, the level of autonomy that a technology can provide in private and social settings is both shaped by its technical features and the human factors associated with its use. Accordingly, this study sought to define, from the patient's perspective, the extent to which different home care interventions could be considered user-friendly and how they were integrated into patients' private and social lives.
Methods
We selected four interventions: Antibiotic intravenous therapy (IV), parenteral nutrition (PN), oxygen therapy (O2) and peritoneal dialysis (PD). These were chosen because they are frequently used [7] and vary in their technical and clinical features, and so are likely to differentially influence how users interact on a daily basis with them (see Table 1).
Our study relied on the triangulation of three sources of data. We conducted semi-directed, individual interviews with patients (n = 16) and carers (n = 6), and directly observed nursing visits, involving a different set of patients (n = 16). This strategy enabled us to gather data on a broader set of patients. The carers we interviewed were not necessarily related to a patient participating to our study (to reduce pressures on interviewees) and were spouses or family members (often mother or daughter) of a person receiving high-tech home care. Our purposeful sampling strategy was to diversify viewpoints, by including participants of varying socioeconomic status, gender and age (Table 2). These variables are all likely to affect how patients and their carers adapt to the use of technology (e.g. contracting out private home care services, adapting the home, understanding written instructions, etc.). All participants were recruited through primary care organizations (for IV and O2) or hospital-based home care programs (for PN and PD) located within 100 km of Montreal, the largest urban centre in the province of Quebec (Canada). A member of the nursing staff in these organizations was asked to give a brochure to all eligible patients explaining the objectives and procedures of the study. After contact had been established between patients and our research team, we constructed the sample according to our diversification variables. We obtained approval from the organizations' ethics committees.
Our approach was structured according to symbolic interactionism, which focuses on how individuals, through regular interactions, develop shared meanings and conceptualize, perceive and understand the role of technology [[19]: p.201]. This approach was particularly helpful in identifying how patients, formal caregivers and informal carers, through their experience in interacting together, anticipated and defined the contributions and responsibilities of each other. Interviews were biographical, relying on Lafaille and Lebeer's technique for examining coping strategies [20]. Interview questionnaires were structured to systematically explore the themes highlighted by our framework while allowing the interviewee to develop or introduce issues he/she felt were important (questionnaire available upon request). Carers' interviews were key to eliciting how they, themselves, perceive the technology – they often have to intervene when the patient is tired or not feeling well – and how the patients' social lives were transformed because of the use of technology. Interviews lasted 60–120 minutes, and were audio-taped with the written consent of the interviewee, then transcribed into electronic format. Direct observations enabled a better understanding of how patients were educated about, and supported in the use of technology [21]. An observation guide was used to rapidly record descriptive notes during the visit, while a structured summary of key events was written up subsequent to the visit [22]. The NUD*IST software was used to code and selectively retrieve verbatim extracts [23]. A mixed strategy was applied: Codes were either derived from our framework or created when their recurrence across interviews became significant. Our analyses were designed to compare and contrast the participants' experiences with using technology, both inside and outside the patient's home. We drew up tables [24], summarizing the observations stemming from the three sources of data, to identify the main technical and human factors at play in private and social settings for each of the four technologies. Most verbatim extracts were translated from French to English, then slightly edited for the purposes of this paper.
Results
Home care technology transforms the patient's life both inside and outside the home
In the patient's home
User-acceptance was shaped by different types of anxiety (see Table 3). In the case of IV and PN, the catheter access site must be protected to avoid potential infections and dislodging of the catheter. The alarm system of the programmable pump, used for both interventions, tends to go off too easily (e.g. occlusions when the tube gets twisted). These false alarms were initially perceived as very stressful but, over time, they became a "normal" disturbance: "I don't really sleep at night. I'm afraid the catheter will get dislodged and the alarm will go off" (Interview, PN, w3). O2 patients were concerned about the risk of fire when cooking over a gas stove or being in a room with smokers. PD patients were preoccupied by a demanding regimen that required them to balance treatment with meals and other daily activities. Nevertheless, some enjoyed being empowered through greater involvement: "You do all the follow-up yourself: Why is my blood pressure high today? Do I have edema?" (Interview, PD, m2). User-competence was affected by the relationship between patient and carer. One PD patient felt annoyed by his wife who, at the beginning, was checking whether he was applying the aseptic procedures rigorously (Obs., PD, m1). The opposite was observed for a PN patient, whose partner felt useless and avoided her during the treatments (Obs., PN, w3).
The IV patients' perceptions of the technical aspects of their technology were striking. With use of this technology being, in most cases, temporary, patients were generally passive or even submissive: "You're always a slave to it, having to carry it everywhere" (Interview, IV therapy, w2). User-acceptance is in fact closely linked to competence. Older patients on IV did not feel comfortable with the electronic components of the programmable pump, which they associated with the "computer age" and about which they felt ignorant. Chronic patients seemed, in general, keener to master the technical aspects. "You can't live without air! So you have to be careful and do it right" (Interview, O2, carer, m1). One PN patient was technically confident and had developed her own technique for preventing air bubbles from forming in the tube (Obs., PN, w1). A similar confidence was shown by the carer of a PD patient: "When you see all that stuff – the reservoir, the wires – you wonder if you'll be able to do it! But, once you know how, it's easy" (Interview, PD, carer, w1). For all four interventions, manual dexterity was required to properly manipulate the different components. "If my eyes were okay, I'd have been able to do it. But I was frightened of not doing it properly, of not seeing the needle, which is so tiny" (Interview, IV, carer, w1). We also observed patients who were not able to read messages on the digital screen due to poor eyesight (due to old age or co-morbidity), limited English linguistic skills, or illiteracy. They relied on their memory or made informed guesses when operating the device.
Finally, the technology did not always fit neatly in the home setting: "Well, you wouldn't believe how hot [the room gets] when the door is closed!" (Interview, O2, caregiver, m3). Some O2 patients liked to have an extra set of tubing so they could use a second floor or sit outside on a patio (Obs., O2, w1). One PD patient planned to have an evacuation system installed so he would not have to dispose of the solution exiting the tubing from his peritoneal cavity through the toilet anymore (Obs., DP, m1).
In the patient's social life
While some of the barriers described above can be managed fairly well in the home setting, problems arise in the unpredictable "outside world": "It's great when you're at home where you're all set up. But when you're out, you're always worried about people lighting up a cigarette" (Interview, O2, carer, m3) (see Table 4). In addition, O2 patients did not like to be seen with nasal tubes, and less often invited friends over or ate in restaurants (Obs., O2, w1). In the case of PN patients, who rarely or never eat food, it was relatives who felt uncomfortable and tended to invite them over less often. Carers sometimes curtailed their social activities because they felt needed by the patient: "I didn't dare go out, absolutely not" (Interview, IV, carer, w1). The mother of a woman with PN was "always worried" and "always available" (Interview, PN, carer, w1). A wife "found the manual PD a burden – four times a day... It's like being in jail, you can't go anywhere" (Interview, PD, carer, w1).
The non-retired patients experienced major obstacles in continuing with employment because of the frequency and/or duration of treatments. Few of these patients had a full-time job. Being "hooked up" to a fixed O2 concentrator for up to 18 hours/day is not compatible with many types of work, and portable cylinders also restrict autonomy (2–4 hours). One PN patient said it was "impossible to work because of being connected for 12 hours", and receiving lots of fluid at night compromised her sleep (Interview, PN, w2). On the other hand, one PD patient chose a nocturnal exchange regulator, refusing hospital-based hemodialysis, in order to work: "You can't work if you go to the hospital three times a week, and work is very important to me" (Interview, PD, m2). In the latter case, the comparison with an alternative makes PD more acceptable.
More crucial was the "black-and-white" definition of ability to work that governs disability pension plans, which is incompatible with what patients experience: "You know, sometimes you feel okay, and sometimes you don't; sometimes you're disabled and sometimes you're not..." (Interview, PN, w3). Overall, due to compromised health and technical barriers, most patients agreed to apply for a disability plan (according to which they cannot accept paid work). A number of patients did volunteer work, such as helping neighbourhood kids with their homework (Obs., PN, w1), doing clerical work for the family business (Obs., O2, w1), or volunteer work for a patient association (Obs., O2, w3).
Discussion
This study, adopting a "technology-in-practice" perspective [8], shows that the four interventions have both a negative and a positive effect on the lives of patients and carers. Indeed, our analyses sought to provide more detail on how technology simultaneously improves and constrains patients' lives. This is compatible with Pierret's observation that "Medicine gives the chronically ill reason to hope, even as it produces limitations with which these persons have to live by making adjustments to meet everyday requirements" [[12]: p.14]. Although each technology provided patients with relative autonomy from the hospital, none of them were seen by patients as truly user-friendly. IV patients remained passive and accepting, knowing that the constraints were temporary, and although O2 does not require a high level of competence, user-acceptance remained very low, especially in public places. Patients seemed more likely to develop competence in using both PN and PD because alternatives in these cases are limited (e.g. hospital-based services or death) and acceptance becomes the only way to make sense of this whole (life-long) experience. Such findings highlight the need to increase the fit between users and technology through a better design of high-tech home care devices and through effective patient education strategies. Indeed, competence and acceptance are likely to be mutually reinforced, especially if patients are supported and their know-how re-assessed over time. Although this is already part of the nursing staff duties, the experiences shared by our interviewees and the literature [1,2] indicate that the level of support they receive may be insufficient.
As observed in a recent U.K. survey on the quality of primary health care provision, crucial factors include educational training, patient education programs and improved communication and teamwork [25]. In the case of home care, manifesting a greater concern with supporting patient education is particularly relevant in the current health policy context [1,5], where high-tech home care is increasingly seen as an "easy solution" to budgetary constraints and a growing elderly population. However, this study pinpoints the possibility that using high-tech home care without a proper patient support system might create more problems than it would solve. As stressed by Sinding, "It is only in more collectively oriented social action that higher standards of care can be established (or re-established) as within the purview of health professionals' duties, and thus confirmed as patients', and carers', entitlements" [[26]: p.1384]. It is in this perspective that this study's results take on significance.
In particular, two issues related to policy-making and clinical practice require prompt consideration. First, technology is often designed as though patients all possess similar abilities, and are neither ageing or incapacitated by other illnesses or physical disabilities [18]. Since the development of home care is largely industry-driven [6], technology designers should be asked to gather and more explicitly integrate feedback from different groups of users with varying cognitive and physical capacities [27,28]. Norman [16] suggests paying attention to two components that affect the use of technology – the design model and the user's model. Both are more or less implicit explanations (or "road maps") about how to operate the device. When the gap between these two models becomes too significant, misuse – which can lead to ineffective treatments or harmful consequences – is likely to happen. Since the biomedical equipment designer and the patient rarely, if ever, interact, it is critical that the proper use of the technology be "communicated" through its physical appearance, by the way it responds (visual and/or audio feedback), and by its fit with the private and social settings where patients are evolving.
Second, competence of patients and their carers should be reinforced through adequate education and support from physicians and nurses. Medical specialists who manage hospital-based home care programs for O2, PN, PD patients could emphasize, when enrolling patients, the need for a smooth fit between the technical and human barriers that affect patient compliance. Training programs for nurses could also focus on skills and routines that help increase the user-friendliness of technology [29,30]. Finally, both patients and clinicians need to be involved in redesigning home care services so they meet the diverse and changing needs of chronic patients [[28]: p.877].
This study has sought to better understand how the level of user-friendliness of medical technology influences its integration into the private and social lives of patients. In this regard, qualitative methods are particularly well suited for uncovering patients' views. Nevertheless, the limitations of our study should be acknowledged [21,31]. The reason for including four different interventions was to define which technical and human dimensions make health technology user-friendly (or not). This study design characteristic increased the complexity of the sampling strategy. For instance, we could not explore the specific role of variables such as gender, age and ethnicity [28]. The study design, however, put a broader perspective on the research problem. Indeed, redundancy from one interview to another and the growing saturation of our analytical categories suggest that we have captured the key elements associated with the introduction of high-tech home care into patients' lives. Overall, the triangulation of three data sources increased the credibility (or internal validity) of our findings by sharpening our understanding of how technologies are integrated into patients' lives. Our findings should, therefore, be applicable in countries and regions where similar devices are used and in similar home settings [21,31]. Finally, although lower levels of criticism were to be expected, thereby reflecting positive functional avoidance [26], overall, the participants we interviewed expressed several grievances. This is similar to the findings of a recent qualitative study where patients, in retrospect, "regretted accepting, in hope, the offer of 'active treatment' because of reduced quality of life" [[28]: p.4]. Because home care patients may, over time, adopt less than optimal routines, the concept of "acceptance" thus requires careful analysis and further investigation. There is a danger of forgetting about the "social and political factors that sustain perceptions of health system constraints" as being unavoidable, and therefore acceptable [[26]: p.1384].
Conclusion
This paper shows that the barriers facing home care patients can easily be "taken for granted," as though nothing can be done to improve the situation. With a growing elderly population and limited health care resources, this will become a major issue in most industrialized countries. According to McGarry, "The home environment as a location of care provision is largely beyond the public-professional gaze, and therefore, remains potentially hidden from scrutiny" [[32]: p.429]. In the future, the delivery of high-tech home care is likely to grow [1]. Nonetheless, this study indicates that patients who are asked to become users of medical technology face major challenges. As stressed by Sullivan, moving beyond a discussion of the benefits of technology to patients' health, to a consideration of both the positive and negative impact of technology on patients' lives, not only brings medicine closer to issues that really matter to patients, but also generates "greater scientific, ethical, and social complexity" [[9]: p.1602]. Home care involves more than simply transferring a particular technology from the hospital to the home – it requires transferring knowledge and skills to lay people, and making sure that the home and social environments enable a safe, effective, appropriate and personally satisfying use of technology. Otherwise, ineffective, potentially hazardous and socially compromising treatments may be disseminated. Policies aimed at increasing the provision of home care must carefully integrate principles and resources that support the appropriate use of technology, and close monitoring of patients must be part of all technology-enhanced home care programs [3].
Competing interests
The author declares that she has no competing interests.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
This research was funded by an operating grant from the Canadian Institutes of Health Research (CIHR; #15472). When the study was conducted, the author was a National Scholar with the National Health Research and Development Program (1998–2003; #6605-5359-48). She recently obtained a career award from the CIHR Institute for Health Policy and Services Research (IHPSR, 2003–2008; #SNI-65571).
I am grateful to the nurses, patients and carers who participated in the study for sharing their experience and opinions. I also wish to acknowledge the contribution of my co-investigators who participated in the broader study from which this paper stems: Raynald Pineault, Lucie Richard and Jocelyne Saint-Arnaud. Two research assistants collaborated in the study: Carole Charland conducted the interviews and Murielle Vergnhes helped with the preliminary analyses.
Figures and Tables
Figure 1 Technical and human dimensions that shape the user-friendliness of a technology (drawing on 12 and 13)
Table 1 Description of the four interventions
IV therapy: Used on a short-term basis to treat severe infections. The antibiotic drug can be delivered via a catheter in the patient's vein using different mechanical or electronic devices. Programmable pumps, which are battery-powered and can be carried in a shoulder bag, are seen as more reliable. Pumps are equipped with an alarm that warns of an occlusion, inadequate connection of tubes, or low battery.
Parenteral nutrition (PN): Used when oral nutrition is no longer feasible due to a disease (e.g. Crohn's disease, cancer of the digestive tract). Fluids and nutritive solutions are delivered via the patient's vein with the help of a programmable pump. A catheter is surgically inserted on a long-term basis. Patients are required to comply with very strict aseptic procedures. Patients generally use the device every day, and it may take up to 8 hours (overnight) to deliver the required amount of solution.
Peritoneal dialysis (PD): An alternative to hospital-based hemodialysis that is designed to remove urea from the blood. It also involves the insertion of a permanent catheter, in this case through the peritoneal cavity, which requires compliance with aseptic procedures. A liquid is inserted in the peritoneal cavity and flushed out. Using an electronic device, patients can set automated exchanges to occur overnight, but when using a gravity system, patients have to manually perform 3 to 5 exchanges per day.
Oxygen therapy (O2): Prescribed to patients with severe hypoxemia (due to pulmonary dysfunction). Oxygen is delivered by a fixed concentrator, via a 15-meter tube and nasal device. Patients normally use it up to 18 hours a day. Portable cylinders may be used for short periods (2–4 hours). A small oxygen-saving device allows oxygen to be delivered only when the patient is inspiring.
Table 2 Details about interviewees
Patients Carers
Intervention Gender Age Length of use (months) Gender Age
IV therapy 2 women 38/80 24/1 1 woman 67
3 men 65/65/68 2/1.5/1.5
Parenteral nutrition 5 women 29/45/45/
50/62 120/24/36
84/18 1 woman 72
Oxygen-therapy 3 women 48/62/82 96/24/36 1 man 83
Peritoneal dialysis 3 men 25/48/51 24/12/12 2 women
1 man 35/45
70
Summary 10 women + 6 men = 16 53.9 (25–81) IV: 6 Others: 44.2 4 women + 2 men = 6 62
Table 3 Technical and human factors that affect how patients use health technology at home
IV therapy (IV) Parenteral nutrition (PN) Oxygen therapy (O2) Peritoneal dialysis (PD)
Technical factors Relatively few manipulations are required, but these may become problematic for older people lacking fine manual dexterity
The alarm system of the programmable pump frequently disturbs sleep Technical tasks are numerous and require dexterity
The alarm system of the programmable pump frequently disturbs sleep
Aseptic procedures are crucial
Storage space is required for feeding solutions (extra fridge) and durables
The gravity pole bumps against carpet edges and door ledges, creating bubbles in the bag Technical tasks are simple (changing filters, connecting tubes to cylinders)
Concentrator generates noise and heat, while 15-metre long tubes run along the floor Technical tasks are numerous and require dexterity
The bedroom resembles an hospital room, and waste solutions have to be emptied into the toilet
Human factors Anxiety is triggered by the possibility of the catheter becoming dislodged
Daily activities such as cooking or taking care of a child are compromised Anxiety is triggered by the possibility of the catheter becoming dislodged
Women feel their body is not attractive anymore because of the catheter
Patients attempt to hide medical equipment from the eyes of visitors Anxiety is triggered by the dangers associated with getting too close to smokers or flames
Daily activities such as cooking or taking care of a child are compromised Patients are struggling to control their health by balancing their dietary regimen and treatments
The permanent catheter alters the patient's body image
Table 4 Technical and human factors that affect how patients use health technology in broader social life
IV therapy (IV) Parenteral nutrition (PN) Oxygen therapy (O2) Peritoneal dialysis (PD)
Technical factors Portable systems may be heavy and limit mobility, while the gravity pole confines the patient to the home Enables patients to be independent of the hospital and clinical staff
Treatment frequency is a major constraint Patients are confined to a restricted space ("hooked up" to tubes)
The portable cylinders provide short periods of autonomy (2–4 hours) The nocturnal automated exchange regulator enables a certain level of autonomy
Treatment frequency is a major constraint
Human factors Professional and social life is slightly limited, albeit for a short period Professional life is limited because of treatment frequency, the disease itself and the occasional-to-frequent re-hospitalisations
Social life is limited because so much of social life revolves around the sharing of meals Professional life is limited because of the disease and being "hooked up" to the concentrator for up to 18 hours/day
Social life is limited because of the compromised self-image associated with wearing nasal tubes and portable cylinders
The oxygen-saving device generates noise similar to that of a ventilator Professional life is still possible (and is less restricted than for patients on hospital-based hemodialysis)
Social life is still possible when using the nocturnal exchange regulator, but complicated when one wants to travel abroad or make short trips
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| 15462682 | PMC526262 | CC BY | 2021-01-04 16:03:28 | no | BMC Health Serv Res. 2004 Oct 5; 4:28 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-28 | oa_comm |
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BMC MedBMC Medicine1741-7015BioMed Central London 1741-7015-2-381550069010.1186/1741-7015-2-38Research ArticleAcetylcysteine for prevention of contrast-induced nephropathy after intravascular angiography: A systematic review and meta-analysis Bagshaw Sean M [email protected] William A [email protected] Department of Critical Care Medicine, Calgary Health Region, Calgary, Canada2 Department of Medicine, University of Calgary, Calgary, Canada3 Department of Community Health Sciences, University of Calgary, Calgary, Canada4 Centre for Health and Policy Studies, University of Calgary, Calgary, Canada2004 22 10 2004 2 38 38 16 4 2004 22 10 2004 Copyright © 2004 Bagshaw and Ghali; 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
Contrast-induced nephropathy is an important cause of acute renal failure. We assess the efficacy of acetylcysteine for prevention of contrast-induced nephropathy among patients undergoing intravascular angiography.
Methods
We conducted a systematic review and meta-analysis of randomized controlled trials comparing prophylactic acetylcysteine plus hydration versus hydration alone in patients undergoing intravascular angiography. Studies were identified by searching MEDLINE, EMBASE, and CENTRAL databases. Our main outcome measures were the risk of contrast-induced nephropathy and the difference in serum creatinine between acetylcysteine and control groups at 48 h.
Results
Fourteen studies involving 1261 patients were identified and included for analysis, and findings were heterogeneous across studies. Acetylcysteine was associated with a significantly reduced incidence of contrast-induced nephropathy in five studies, and no difference in the other nine (with a trend toward a higher incidence in six of the latter studies). The pooled odds ratio for contrast-induced nephropathy with acetylcysteine relative to control was 0.54 (95% CI, 0.32–0.91, p = 0.02) and the pooled estimate of difference in 48-h serum creatinine for acetylcysteine relative to control was -7.2 μmol/L (95% CI -19.7 to 5.3, p = 0.26). These pooled values need to be interpreted cautiously because of the heterogeneity across studies, and due to evidence of publication bias. Meta-regression suggested that the heterogeneity might be partially explained by whether the angiography was performed electively or as emergency.
Conclusion
These findings indicate that published studies of acetylcysteine for prevention of contrast-induced nephropathy yield inconsistent results. The efficacy of acetylcysteine will remain uncertain unless a large well-designed multi-center trial is performed.
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Background
Contrast-induced nephropathy is a leading cause for acquired acute reductions in kidney function [1,2]. Despite advances in supportive therapy, the incidence of contrast-induced nephropathy may continue to increase significantly with the broader utilization of radiocontrast media for diagnostic and interventional procedures.[3] Furthermore, contrast-induced nephropathy is associated with a greater risk of in-hospital morbidity, mortality, prolonged hospitalization, increased health care costs and potentially irreversible reduction in kidney function [4-8].
The pathophysiology of contrast-induced nephropathy remains incompletely understood. However, current evidence suggests that contrast media induce prolonged vasoconstriction and medullary ischemia coupled with generation of free radicals and oxidative injury to tubular cells [9-11].
Acetylcysteine, a thiol-containing anti-oxidant, has been hypothesized to prevent contrast-induced nephropathy. The potential benefit of acetylcysteine is believed to be mediated by its properties as a scavenger of free-radical species and by increasing the synthesis of nitric oxide, a potent vasodilator, in response to ischemic or other toxic injury in the kidney [12]. Given the recent publication of a series of randomized controlled trials assessing the efficacy of acetylcysteine in preventing the decline in kidney function following contrast exposure associated with intravascular angiography, we sought to conduct a systematic review and meta-analysis of these trials. The specific objectives of our meta-analysis were to assess the effect of acetycysteine on 1) the dichotomous endpoint of contrast-induced nephropathy (yes/no) and 2) serum creatinine levels following the administration of contrast media. We also conduct a meta-regression analysis to determine whether particular clinical or study quality factors influence the apparent effect of acetylcysteine on risk of contrast-induced nephropathy.
Methods
Search strategy
We identified published randomized controlled trials of acetylcysteine for prevention of contrast-induced nephropathy during intravascular angiography using both electronic and manual search strategies. We supplemented this by scanning the reference lists of all identified articles, reviewing selected conference proceedings, and by contacting experts in the field. All languages and types of publications were considered eligible. The comprehensive literature search was initially performed in April 2003 and updated in June 2004 to identify any potential new studies that may have appeared.
MEDLINE (1966 through April, 2003), EMBASE (1980 through April, 2003) and CENTRAL (Cochrane Controlled Clinical Trials Register 1996 through April, 2003) databases were searched via OVID using an approach recommended for systematic reviews of randomized trials [13]. PubMed was also searched [14]. We derived three comprehensive search themes that were then combined using the Boolean operator 'and'. The first theme used a recommended highly sensitive randomized controlled trial filter and systematic review filter method [15]. The second theme, contrast-induced nephropathy, was created by using the Boolean search term 'or' to search for the following terms appearing as both exploded medical subject headings (MeSH) or text words: 'contrast media' or 'radiocontrast' or 'kidney failure' or 'acute renal failure' or ' chronic renal failure' or 'contrast nephropathy' or 'dialysis'. The third theme, acetylcysteine, was created by a search using an exploded MeSH heading and textword search for: 'N-acetylcysteine' or 'NAC' or 'acetylcysteine' or 'Mucomyst'.
Study selection criteria
Two individuals (SMB and WAG) independently evaluated identified articles for eligibility on the basis of four inclusion criteria: 1) study design (randomized controlled trials), 2) target population (patients undergoing intravascular angiography), 3) intervention (trials of acetylcysteine plus hydration versus control) and 4) outcome (trials with explicit definition of contrast-induced nephropathy).
Data extraction
Two reviewers (SMB and WAG) independently extracted data from all primary studies fulfilling eligibility criteria. Any discrepancies in extracted data were resolved by consensus. Data extracted included identifying information, focus of the study, details of study protocol and demographic data. The primary outcome measures were the incidence of contrast-induced nephropathy and change in serum creatinine. The secondary outcome measure was requirement for renal replacement therapy. Authors of the studies were contacted for additional information when applicable.
Assessment of methodological quality
Two reviewers (SMB and WAG) independently assessed methodological quality of individual studies. Any disagreements were resolved by consensus. Items used to assess study quality were methods of randomization, any blinding, use of a placebo, reporting of losses to follow-up or missing outcome assessments, and evidence of important baseline differences between the groups [16-18]. An overall quality score was determined for each study as described by Jadad et al [16].
Prior hypotheses regarding sources of heterogeneity
The presence of heterogeneity can compromise the interpretation and validity of meta-analyses and can result from significant differences in methodology, study populations, interventions, outcomes, or chance [19]. A priori consideration of potential factors contributing to heterogeneity for acetylcysteine in prevention of contrast-induced nephropathy included baseline serum creatinine levels, volume of contrast media, volume of hydration, age, diabetes mellitus, elective or emergency procedure, and a number of trial methodology factors.
Statistical methods
Data from all of the selected randomized controlled trials were combined to estimate the pooled odds ratio (OR) with 95% confidence intervals (CIs) using a random-effects model as described by Der Simonian and Laird [20,21]. The presence of heterogeneity across trials was evaluated using a chi-square test for homogeneity [22]. Meta-regression was performed to analyze for potential clinical and study quality factors that may influence treatment effects. We tested for potential publication bias using both a Begg's test for asymmetry and an Egger's test [23,24]. All statistical analyses were performed with Stata version 8.0 (StataCorp, College Station, TX).
Results
Identification of studies
A total of 66 unique citations were identified by our initial search strategy (Figure 1). After the initial screen, 22 citations warranted further review. Among these, 15 citations were excluded: 8 were clinical reviews, 3 were prospective cohort studies, 2 were substudies of previously published randomized controlled trials, one did not include a control group, and one did not involve intravascular angiography. Therefore, we had identified 7 studies for inclusion. A repeat search of the literature conducted in June 2004 yielded seven additional eligible studies. Overall, 14 studies thus fulfilled our inclusion criteria [25-38]. All of these citations were identified by the electronic search strategy and are published in peer-reviewed journals [39].
Study characteristics
All the randomized controlled trials were published in the years 2002 through 2004. Tables 1 and 2 present the characteristics of the 14 randomized controlled trials. A total of 1261 patients were studied in these 14 randomized controlled trials, among whom 631 received acetylcysteine and 630 were in control groups. There were 563 (44.6%) patients with diabetes mellitus, of whom 284 were assigned to receive acetylcysteine and 279 were assigned to a control group. The dosing and schedule of administration of acetylcysteine was variable across studies; however, in the majority of studies, acetylcysteine was initiated 12–24 h prior to angiography. In two trials, large doses of acetylcysteine were administered immediately prior to (within 1 h) and shortly following (within 3–4 h) angiography [26,29]. All patients were administered a hydration protocol around their procedure and all received low or iso-osmolar non-ionic contrast media.
The definition of contrast-induced nephropathy was variable across studies. Four studies defined contrast-induced nephropathy as a > 44.2 μmol/L increase in serum creatinine from baseline [25,29,32,37], four used a > 25% increase in serum creatinine from baseline [26,30,33,35], four used either a > 44.2 μmol/L or a > 25% increase in serum creatinine from baseline [28,31,34,36], one used either a > 44.2 μmol/L or a > 33% increase in serum creatinine from baseline[38] and one study combined either a > 25% increase in serum creatinine from baseline or dialysis [27]. Generally, the time for ascertaining contrast-induced nephropathy for all studies was 48 h after the exposure to contrast media, with the exception of four studies, where presence or absence of contrast-induced nephropathy was determined at 24, 72 and 96 h [26,30,34,35].
Meta-analysis of incidence of contrast-induced nephropathy
The reported incidence of contrast-induced nephropathy was variable across studies. Table 3 and Figure 2 present information on the incidence of contrast-induced nephropathy for all studies. Five studies provided evidence of a risk reduction for development of contrast-induced nephropathy with acetylcysteine [26,28,33,35,37], whereas nine studies reported no evidence of benefit [25,27,29-32,34,36,38]. Furthermore, six of the latter studies yielded an odds ratio > 1.0, suggesting a trend towards an increased risk of contrast-induced nephropathy [25,29,31,32,36,38]. The overall pooled odds ratio for development of contrast-induced nephropathy using a random-effects model was 0.54 (95% CI, 0.32–0.91, p = 0.022), suggesting a significant reduction in CIN with acetylcysteine (Figure 2). However, this pooled odds ratio should be interpreted with caution because the analysis comparing the occurrence of contrast-induced nephropathy across all studies revealed significant heterogeneity (chi-square = 23.96, p = 0.032). In total, six patients required dialysis, among whom two received acetylcysteine and two were in control groups. Group assignment was not reported for the other two patients who required dialysis.
Meta-analysis of change in serum creatinine with acetylcysteine
Table 3 shows a summary of the changes in serum creatinine across studies. The pooled estimate (using a random effects model) for the difference in 48 h serum creatinine between the acetylcysteine and control groups was -7.2 μmol/L (95% CI -19.7 to 5.3, p = 0.26) based on data available from eight studies [25,27,28,31,32,35,37,38]. This suggests no significant absolute change in serum creatinine with the administration of acetylcysteine (Figure 3). Again, this pooled estimate requires cautious interpretation owing to the availability of data from only eight studies and to the presence of significant heterogeneity across studies (Q = 50.9, p < 0.0005). The change in serum creatinine at 96 h was assessed in two studies as a primary outcome [26,30]. The pooled estimate for the difference in 96-h serum creatinine for these two studies was similarly non-significant [-1.8 μmol/L (95% CI -8.9 to 5.2, p = 0.61)].
Meta-regression
Meta-regression was performed to assess a number of clinical and study quality factors that may have led to heterogeneity across studies. Interestingly, these analyses suggest that the heterogeneity may be partially explained by whether the angiography procedures were performed electively or as emergency, because studies where all enrolled patients were undergoing elective procedures had significantly lower odds ratios than did studies where emergency cases were included (coefficient for "elective-only" studies, -0.6, 95% CI, -1.24 to 0.03, p = 0.06).
Other meta-regression analyses demonstrated that the heterogeneity could not be accounted for by differences in patient age (coefficient -0.04, 95% CI, -0.2 to 0.1, p = 0.6), baseline serum creatinine (coefficient -0.001, 95% CI, -0.01 to 0.01, p = 0.9), volume of contrast media (- 0.006, 95% CI, -0.02 to 0.07, p = 0.4) or diabetes mellitus (coefficient -0.01, 95% CI, -0.03 to 0.02, p = 0.6). Likewise, heterogeneity was not accounted for by differences in study quality including use of blinding (coefficient -0.6, 95% CI, -1.7 to 0.5, p = 0.3), concealment of randomization (coefficient -0.8, 95% CI, -3.8 to 2.1, p = 0.6), use of placebo (coefficient -0.6, 95% CI, -1.7 to 0.5, p = 0.30, consecutive patient enrollment (coefficient 0.5, 95% CI, -1.5 to 2.4, p = 0.6) or overall Jadad score (coefficient 0.05, 95% CI, -0.4 to 0.5, p = 0.8).
There was some evidence to suggest possible publication bias according to Begg's test (p = 0.03, with continuity correction) and a trend with Egger's test (coefficient -3.03, 95% CI, -6.71 to 0.65, p = 0.09). Figure 4 demonstrates this graphically, as there is asymmetry in the funnel plot with a predominance of studies with large standard errors (i.e., usually small studies) showing benefit associated with acetylcysteine and a paucity of small negative studies.
Discussion
Our meta-analysis of 14 peer-reviewed studies of patients undergoing intravascular angiography may lead some to conclude that the administration of acetylcysteine causes a reduced incidence of contrast-induced nephropathy. However, such a conclusion may be premature based on data published to date because our systematic review reveals considerable heterogeneity of findings across trials. Furthermore, our meta-analysis of post-treatment creatinine values does not reveal any truly meaningful difference in serum creatinine levels at 48 h between the acetylcysteine and control groups. Finally, insufficient data are available to allow inferences to be drawn about the efficacy of acetylcysteine on clinically meaningful endpoints such as dialysis, length of hospitalization or mortality.
This meta-analysis has several features that distinguish it from a similar meta-analysis by Birck et al that recently received considerable attention, and that rather firmly concluded that acetylcysteine is beneficial [40]. First, though our meta-analysis yielded a similar overall reduction in the incidence of contrast-induced nephropathy, we have included seven additional studies. Second, we have focused primarily on patients undergoing intravascular angiography. Third, we have used the pooled odds ratio across studies as a summary statistic because of its theoretical advantage to the use of relative risks in meta-analysis [21]. Fourth, we have included an analysis of differences in serum creatinine to complement the dichotomous endpoint of contrast-induced nephropathy. Fifth, we pointedly draw attention to the fact that there is some evidence to suggest publication bias, or at the very least funnel plot asymmetry. And finally, perhaps most importantly, we have explored the heterogeneity in results across studies in much greater detail than do Birck et al, and more directly address the relevance of this heterogeneity in the overall interpretation of study results. Two other meta-analyses have also recently been published, and similarly concluded that acetylcysteine is beneficial; however, these studies also failed to adequately address the issue of the considerable heterogeneity across studies [41,42]. Collectively, these three previously published meta-analyses unfortunately send a misleading bottom-line message to the medical community – that the evidence in favor of acetylcysteine is firm [43]. Many will correspondingly infer from these three 'positive' meta-analyses that there is no longer a need for primary research into the efficacy of acetylcysteine. Our global conclusion, meanwhile, is rather different, in that we more cautiously conclude that further data may be needed before a firm conclusion can be made regarding the efficacy of acetylcysteine. Two other very recent meta-analyses [44,45] make a similar conclusion to ours, though those meta-analyses do not include as many peer-reviewed and published studies as does our updated systematic review.
The presence of heterogeneity and/or publication bias can compromise the interpretation of meta-analyses and result in erroneous and potentially misleading conclusions [19,43]. A striking example of early meta-analysis producing misleading results is that of intravenous magnesium in the treatment of acute myocardial infarction. The results of two meta-analyses of several small clinical trials on this treatment suggested a reduction in arrhythmias and mortality [46,47]. Furthermore, an argument was made at the time for the use of magnesium therapy because of ease of use, favorable side effect profile and low cost [47,48]. However, the subsequent publication of ISIS-4, a large multi-center trial involving over 58,000 patients, showed not only the absence of significant reduction in arrhythmias or mortality with magnesium, but in fact a trend towards an increased risk of heart failure [49,50], results that have since been further validated by publication of the MAGIC trial [51]. The early meta-analyses on intravenous magnesium were perhaps influenced by publication bias and the combination of data from several small randomized controlled trials [52].
There are many parallels between the intravenous magnesium story and our meta-analysis findings for acetylcysteine. The marked heterogeneity of findings across studies, and the finding of funnel plot asymmetry (indicating possible publication bias), ought to be viewed as strong cautionary points against making firm conclusions about the efficacy of acetylcysteine. And while it is true that acetylcysteine is inexpensive, easy to use and has a favorable side-effect profile, it is probably premature to conclude scientifically that it is definitely efficacious based on data published to date. Our firm conclusion based on this meta-analysis of published trails is that although the data seem quite promising, the efficacy of acetylcysteine has not been definitively proven.
To isolate potential sources of heterogeneity we performed a meta-regression analysis exploring several clinical and study quality factors. There was no evidence of association between effect size and baseline serum creatinine, volume of contrast media, or diabetes mellitus, all independently identified risk factors for development of contrast-induced nephropathy [8,53]. However, whether the angiographic procedure was performed electively or as emergency showed a significant relation with the size of the acetylcysteine effect. The need to perform emergency cardiac angiography is common in patients presenting with suspected acute coronary syndromes. Patients undergoing emergency coronary angiography have been shown to have increased mortality and poor long-term survival, independent of the development of contrast-induced nephropathy [6,54].
Funnel plot asymmetry is often interpreted to indicate publication bias. However, it is important to consider that this asymmetry may also be due to other sources of bias that deserve further examination. In particular, fundamental disparities in study design, inconsistencies in methodological quality and differences in the definition of primary outcomes may have contributed to funnel plot asymmetry. Our meta-regression analysis explored the potential role of several study quality factors, and none were identified as statistically significant predictors of apparent acetylcysteine efficacy across trials. Nonetheless, it is quite possible that other unmeasured study quality factors may have contributed to biased results and accompanying funnel plot asymmetry.
Contrast-induced nephropathy continues to be an active subject matter for clinical investigation [55,56]. A definitive randomized clinical trial comparing fenoldopam, a selective type 1 dopamine receptor agonist, with placebo recently demonstrated no significant difference in the incidence of contrast-induced nephropathy or any secondary outcomes including 30 day mortality, need for dialysis, or re-hospitalization rates [56]. Another, recent randomized trial of 192 patients undergoing intravascular angiography compared prophylactic acetylcysteine with fenoldopam [57]. The results demonstrated a 9.6% absolute risk reduction in patients randomized to acetylcysteine (4.1% vs 13.7%, respectively). Although the authors conclude that acetylcysteine is superior to fenoldopam for prevention of contrast-induced nephropathy, there was notably no significant difference in serum creatinine at 48 h. Of interest, in subgroup analysis, the authors speculate that patients with low ejection fractions (<40%) may attain additional benefit with acetylcysteine.
Conclusion
All of the above leads us to conclude that while acetylcysteine appears to be safe and inexpensive, its efficacy for the prevention of contrast-induced nephropathy remains unproven. The results of the trials that we reviewed to date should be viewed as early promising evidence of benefit, and suggest that it is now perhaps reasonable to use acetylcysteine in routine care because of its relative ease of use and safety. However, its true efficacy will remain uncertain unless a definitive well-designed multi-center trial is performed. Such a clinical trial will be most relevant if it addresses a priori clinically meaningful endpoints of renal insufficiency, rather than surrogate endpoints based on changes in creatinine levels alone, and further considers stratification on hypothesized important subgroups that may benefit such as those with a low ejection fraction [58].
Competing interests
The authors declare that they have no competing interests.
Authors' Contributions
SMB developed the study protocol, conducted literature search, screened articles for eligibility, extracted data, analyzed data, interpreted results, wrote and revised the manuscript. WAG contributed to protocol development, screened articles for eligibility, extracted data, analyzed data, interpreted results, and provided critique of successive drafts of the manuscript. Both authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Dr. Ghali is supported by a Government of Canada Research Chair in Health Services Research, and by a Health Scholar Award from the Alberta Heritage Foundation for Medical Research.
Figures and Tables
Figure 1 Flow diagram of study selection process.
Figure 2 Forest plot of odds ratios for development of contrast-induced nephropathy from 14 trials.
Figure 3 Forest plot of differences in serum creatinine between acetylcysteine and control at 48 h after contrast media administration from eight trials.
Figure 4 Evidence of publication bias by Funnel plot. Funnel plot asymmetry is demonstrated by evidence of a cluster of small studies with low-protective odds ratio and the paucity of small negative studies in the lower right of the funnel plot.
Table 1 Characteristics of studies reporting on the use of acetylcysteine for prevention of contrast-induced nephropathy.
First Author Patients Diabetes Elective Procedure Primary Outcome Acetylcysteine Protocol* Hydration Protocol Contrast Media Contrast Media Volume (mL)**
NAC Control
Allaqaband, 200225 85 41 (48%) Unclear > 44.2 μmol/L increase SCr at 48 hrs 600 mg PO bid pre/post 0.45%NS 1 mL/kg/hr 12 hr pre/post Ioversol or Iodixanol 121.6 122
Baker, 200326 80 34 (43%) Unclear > 25% increase SCr at 48 or 96 hrs 150 mg/kg IV 30 min pre & 50 mg/kg IV infusion 4 hrs post Control 0.9%NS 1 mL/kg/hr 12 hr pre/post Iodixanol 238 222
Briguori, 200227 183 69 (38%) Yes > 25% increase SCr at 48 hrs or dialysis 600 mg PO bid pre/post 0.45%NS 1 mL/kg/hr 12 hr pre/post Iopromide 194 200
Diaz-Sandoval, 200228 54 21 (39%) Yes > 44.2 μmol/L or 25% SCr increase at 48 hrs 600 mg PO bid pre/post 0.45%NS 1 mL/kg/hr 2–12 hr pre & 12 hr post Ioxilan 179 189
Durham, 200229 79 38 (48%) No > 44.2 μmol/L increase SCr at 48 hrs 1200 mg PO 1 hr pre and 3 hr post 0.45%NS 1 mL/kg/hr 12 hr pre/post Iohexol 77.4 84.7
Efrati, 200330 49 26 (53%) Yes >25% increase SCr at 24 or 96 hrs 1000 mg PO bid day pre/post 0.45%NS 1 mL/kg/hr 12 hr pre/12 post Iopromide 142 138
Fung, 200431 91 48 (53%) Yes >44.2 μmol/L or 25% decrease in GFR 400 mg PO tid day prior/post 0.9%NS 100 ml/hr 12 hr pre/12 post Iopromide 135.8 121
Goldenberg, 200432 80 43 (54%) No >44.2 μmol/L 600 mg PO bid day prior/post 0.45%NS 1 mL/kg/hr 12 hr pre/12 post Iopamidol 111 138
Kay, 200333 200 75 (38%) Yes > 25% increase SCr at 48 hrs 600 mg PO bid × 4 (3 pre) 0.9%NS 1 mL/kg/hr 12 hr pre & 6 hr post Iopamidol 130 120
Kefer, 200334 104 13 (13%) Unclear > 44.2 μmol/L increase SCr at 24 hrs 1200 mg IV 12 hr pre & immediately post D5W 20 mL/hr 12 hr pre & 24 hr post Iopromide or Iohexol NR NR
MacNeill, 200335 43 20 (46%) Yes > 25% increase SCr at 72 hrs 600 mg PO × 5 (2 pre) Inpatient: 0.45%NS 1 mL/kg/hr 12 hr pre Outpatient: 0.45%NS 2 mL/kg/hr 4 hr pre & both 12 hr post Iopromide or Ioxilan 103 116
Oldemeyer, 200336 96 43 (45%) Yes > 44.2 μmol/L or 25% SCr increase at 48 hrs 1500 mg bid × 4 (1 pre) 0.45%NS 1 mL/kg/hr 12 hr pre/post Iopamidol 134 127
Shyu, 200237 121 77 (64%) Yes > 44.2 μmol/L increase SCr at 48 hrs 400 mg PO bid pre/post 0.45%NS 1 mL/kg/hr 12 hr pre/post Iopamidol 119 115
Vallero, 200238 100 23 (23%) Unclear > 44.2 μmol/L or 33% increase SCr at 48 hrs 600 mg PO bid pre/post 0.45%NS 1 mL/kg/hr 1–2 hr pre & 24 hr post Iodixanol 187.8 219
Legend: NR = not recorded or available; NAC=acetycysteine; SCr=serum creatinine (for conversion to mg/dL divide by 88.4). *NAC administered with hydration protocol. **Data presented as means.
Table 2 Summary of quality indicators for studies of acetylcysteine for prevention of contrast-induced nephropathy.
First Author Jadad score Inclusion/exclusion criteria specified Randomization process described Use of any blinding Placebo-controlled Reported loss to follow-up Intention-to treat analysis Potential important baseline differences Power calculation
Allaqaband25 3 yes/no yes yes yes no no no yes
Baker26 2 yes/yes yes no yes no yes no yes
Briguori27 1 yes/no yes no no no unclear yes no
Diaz-Sandoval28 4 yes/yes yes yes no yes unclear yes no
Durham29 5 yes/yes yes yes yes yes unclear no yes
Efrati30 4 yes/yes no yes yes yes unclear no no
Fung31 4 yes/yes yes no no no yes no yes
Goldenberg32 5 yes/yes yes yes yes no unclear no yes
Kay33 5 yes/yes yes yes yes yes yes no yes
Kefer34 4 yes/yes yes yes yes yes unclear yes no
MacNeill35 4 yes/yes yes no yes no unclear no yes
Oldemeyer36 4 yes/yes yes yes no yes unclear no no
Shyu37 3 yes/yes yes yes no yes unclear no no
Vallero38 2 yes/yes yes no no no unclear yes no
Legend: NR = not recorded or available; Jadad score range 0–5.
Table 3 Summary of outcomes of studies of acetylcysteine for prevention of contrast-induced nephropathy.
First Author Contrast-induced nephropathy* Acetylcysteine Serum Creatinine (μmol/L) Control Serum Creatinine (μmol/L) Dialysis (N)
Acetylcysteine Control Baseline Second SCr Baseline Second SCr NAC Control
Allaqaband25 8/45 (18%) 6/40 (15%) 194.5 196.3 179.5 179.5 2 0
Baker26 2/41 (5%) 8/39 (21%) 163.6 156.5 154.7 159.1 0 0
Briguori27 6/92 (7%) 10/91 (11%) 134.4 130.8 136.1 135.3 0 1
Diaz-Sandoval28 2/25 (8%) 13/29 (45%) 146.7 135.5 137.9 166.2 0 0
Durham29 10/38 (26%) 9/41 (22%) 194.5 NR 203.3 NR NR NR
Efrati30 0/24 (0%) 2/25 (8%) 135.3 143.2 131.7 143.2 0 0
Fung31 8/46 (17%) 6/45 (13%) 200.7 216.6 209.5 212.2 NR NR
Goldenberg32 4/41 (10%) 3/39 (8%) 176.8 176.8 168.0 165.3 0 0
Kay33 4/102 (4%) 12/98 (12%) 119.3 107.8 120.2 122.0 0 0
Kefer34 2/53 (8%) 3/51 (6%) 91.9 91.1 102.5 93.7 0 0
MacNeill35 1/21 (5%) 7/22 (32%) 167.1 168.0 168.0 210.4 NR NR
Oldemeyer36 4/49 (8%) 3/47 (6%) 144.1 NR 146.7 NR 0 0
Shyu37 2/60 (3%) 15/61 (25%) 247.5 221.0 247.5 274.0 0 1
Vallero38 4/47 (9%) 4/53 (8%) 87.5 93.7 84 86.6 NR NR
Legend: SCr = serum creatinine (for conversion to mg/dL divide by 88.4); NR = not recorded or available. *Values are numbers of patients with contrast-induced nephropathy/total number of patients in treatment group (%).
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| 15500690 | PMC526263 | CC BY | 2021-01-04 16:03:34 | no | BMC Med. 2004 Oct 22; 2:38 | utf-8 | BMC Med | 2,004 | 10.1186/1741-7015-2-38 | oa_comm |
==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-781547390810.1186/1471-2164-5-78Databasei-Genome: A database to summarize oligonucleotide data in genomes Lin Feng-Mao [email protected] Hsien-Da [email protected] Yu-Chung [email protected] Jorng-Tzong [email protected] Department of Computer Science and Information Engineering
National Central University, Chung-Li 320, Taiwan2 Department of Biological Science and Technology, Institute of Bioinformatics
National Chiao Tung University, Hsin-Chu 300, Taiwan3 Department of Biotechnology, Ming Chuan University, Taipei 111, Taiwan4 Department of Life Science, National Central University, Chung-Li 320, Taiwan2004 9 10 2004 5 78 78 25 3 2004 9 10 2004 Copyright © 2004 Lin et al; licensee BioMed Central Ltd.2004Lin 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
Information on the occurrence of sequence features in genomes is crucial to comparative genomics, evolutionary analysis, the analyses of regulatory sequences and the quantitative evaluation of sequences. Computing the frequencies and the occurrences of a pattern in complete genomes is time-consuming.
Results
The proposed database provides information about sequence features generated by exhaustively computing the sequences of the complete genome. The repetitive elements in the eukaryotic genomes, such as LINEs, SINEs, Alu and LTR, are obtained from Repbase. The database supports various complete genomes including human, yeast, worm, and 128 microbial genomes.
Conclusions
This investigation presents and implements an efficiently computational approach to accumulate the occurrences of the oligonucleotides or patterns in complete genomes. A database is established to maintain the information of the sequence features, including the distributions of oligonucleotide, the gene distribution, the distribution of repetitive elements in genomes and the occurrences of the oligonucleotides. The database can provide more effective and efficient way to access the repetitive features in genomes.
repeatgenome indexoligonucleotidedatabase
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Background
During the last decade, many genomes have been successfully and completely sequenced. Summarized information about the oligonucleotides in genomes provides biologists, who interests in the evolution and growth of genomes, to work in comparative genomics, oligonucleotide probe design, primer design and the analyses of genomic repetitive features.
The computation of the occurrences and the frequency of all oligonucleotides in a complete genome is very elaborate and time-consuming, especially when the genome size is very large, such as the human and mouse genomes. A database that summarizes the occurrences and the frequencies of oligonucleotides in complete genomes can facilitate the biological and the statistical analyses of genomes.
The contents of the database can be used in many biological applications, such as comparative genomics and evolution analyses [1,2], the prediction of regulatory sequences by detecting the over-represented oligonucleotides [3-8] and primer/probe design based on the uniqueness of oligonucleotides [9]. Table 1 shows the biological applications of the database entries. The entries in the database are divided into two types, namely, the occurrence positions of oligonucleotides and the frequencies of oligonucleotides. The oligonucleotide occurrences and the oligonucleotide frequencies in both the coding regions and the non-coding regions are summarized. For instance, these information can be used in statistical analyses to study the over-representation of the regulatory sequences in upstream promoter regions in genes. van Helden et al. systematically searched the promoter regions of potentially co-regulated genes for over-represented oligonucleotides which may be transcription factor binding sites [3]. They presented a simple and fast method for isolating DNA binding sites for transcription factors from families of co-regulated genes, illustrating their results using Saccharomyces cerevisiae. Although conceptually simple, the algorithm efficiently extracted the upstream regulatory sequences that had been previously been determined experimentally for most of the yeast regulatory families already analyzed. Other studies [4-8,10-12] on the prediction of gene regulatory sequences have been based on oligonucleotide analysis.
Table 1 Applications and the relevant data in the database.
Database entries Entry types Biological applications
Oligonucleotide occurrences Positions 1. Oligonucleotide analysis for regulatory sequences
2. Oligonucleotide probe design
3. Primer design
Oligonucleotide frequencies Counts 1. Oligonucleotide analysis for regulatory sequences
2. Evolutionary analysis
3. Oligonucleotide probe design
4. Primer design
Gene coding regions Positions 1. Oligonucleotide analysis for regulatory sequences
2. Evolutionary analysis
3. Oligonucleotide probe design
4. Primer design
Repetitive element frequencies (LINE, SINE, Alu, and so on) Counts Evolutionary analysis
Repetitive element occurrences Positions Evolutionary analysis
Tandem repeats Positions Prediction for genetic disease marker
Hsieh et al. [1] investigated the oligonucleotide distributions of typical microbial genomes and found that the microbial genomes have the statistical characteristics of a much shorter DNA sequence. This peculiar property supports an evolutionary model in which a genome evolves by random mutation but grows primarily by random segmental duplication.
Repetitive elements, including LINEs, SINEs, LTR and Alu, can be investigated in evolution analysis [2]. It has been estimated that at least 43% of the human genome is occupied by four major classes of interspersed repetitive elements – LINEs, SINEs, LTR elements and DNA transposons [2]. Their analysis has yielded some insights into the evolution of the human genome. The tandem repeats provided in our database can be used for forensic analysis and the study of genetic diseases [13-16].
Another application of the established database is to facilitate the design of primers to amplify specific regions of the genomic sequence. The basic concept is that the sequences of primers from 15 to 40 bps should be unique, unlike the repetitive oligonucleotides in our database. Additionally, our database maintains the repetitive oligonucleotides that facilitate the design of oligonucleotide probes to allow the selection of signature oligonucleotides when identifying different organisms using DNA arrays [9]. The user can query oligonucleotides whose lengths exceed a threshold, such as 15 bps, to determine whether the oligonucleotides are repetitive. The non-repetitive regions of the target sequences without repetitive oligonucleotides can be used as the signatures for the target genomes.
Construction and content
Data sources and contents
The proposed database provides information about sequence features generated by exhaustively computing the sequences of the complete genome. The data sources including the complete genomes and the gene annotation information are obtained from GenBank [17]. The repetitive elements in the eukaryotic genomes, such as LINEs, SINEs, Alu and LTR, are obtained from Repbase [18]. The database supports a range of complete genomes including human, yeast, worm, and 128 microbial genomes. The Appendix lists the organisms supported in the database [see additional file 1].
The occurrences and the frequencies of oligonucleotides from one to 50 base pairs are generated and accumulated from each of the complete genome sequence. Inputting the sequence of the oligonucleotide returns the positions of the oligonucleotides. Additionally, both the occurrences and the frequencies of the repetitive elements such as LINEs, SINEs, Alu and LTR are provided by computationally scanning whole genome sequences. The tandem repeats are computationally detected by the tandem repeat finder [19]. The database also provides the gene annotation information. For instance, Table 2 presents the number of occurrences of repetitive oligonucleotides in yeast. The oligonucleotide "ACCCTA" occurs 2,724 times in the yeast genome, 822 times upstream of a gene (-600 ~-1 bp, +1 bp denotes the gene translational start postion) and 793 times in the coding regions. The counts of the occurrence of ecah oligonucleotide between one and 50 bps are present in the database.
Table 2 Number of occurrences of the repetitive oligonucleotides in yeast genome
Repetitive oligonucleotide Amount of occurrences Repetitive oligonucleotide Amount of occurrences
Genome Up-streams Coding region Genome Up-streams Coding region
ACCCTA 2,724 822 793 CAATCCA 1,895 655 343
ACCCTC 2,917 881 795 CGTCTCC 592 199 148
AGTACT 3,073 933 879 CGTCTGA 652 196 165
AGTAGA 6,673 1,970 1,798 ACAAACTA 594 179 183
AGTAGC 4,912 1,545 1,299 ACAAACTC 514 175 112
GATACC 4,829 1,638 1,005 CACAGAAAC 146 38 46
GATAGA 7,030 2,163 1,807 CACAGAAGA 164 57 39
TGGTAA 10,513 3,493 2,214 ACATATAAAAA 54 9 29
TGTAAA 11,364 3,439 3,418 ACATATAAAAC 139 34 56
AAGGGGA 1,172 299 421 ACATATAAAAG 36 7 22
AAGGGGC 626 142 256 ACTTATGTCATC 57 17 23
AGAGTGG 983 310 271 ACTTCTAGTATA 159 44 67
AGAGTTA 1,859 610 441 ACTTTTTTTTCT 32 5 21
CAATCAG 1,358 445 320 ACTTTTTTTTTC 50 6 33
Data Generation
A software is implemented to index systematically a complete genome sequence into a suffix-array using a perfect match approach [20]. This index is only able to find the perfect match for any oligonucleotide. The user can thus use it to find the positions of a designated oligonucleotide in a genome sequence. For each genome, the occurrences of all oligonucleotides shorter than 50 bps can be efficiently searched for. The occurrence is the position of the oligonucleotide in genome. The frequency is the count of oligonucleotide occurrences in a region. The regions are the complete genome, the coding regions and the non-coding regions. Frequencies of oligonucleotides with different lengths are stored in different flat-files. For example, the two chromosomes of the Vibrio cholerae genome are processed separately to allow the computation of the occurrences of oligonucleotides in each chromosomal sequence. RepeatMasker [21] and the repetitive element database, Repbase [18], are used to search the instances of the repetitive elements in eukaryotic genomes. The tandem repeat finder (TRF) is used to find the tandem repeats in genomes [19]. The TRF and RepeatMasker can find the instances of repetitive elements with imperfect matches. The settings used here for each software is described in below.
The Tandem Repeat Finder uses seven parameters. These are match score, mismatch score, indel score, probability of match and insertion, minimum score of alignment and the maximum of tandem repeat pattern size. The corresponding values used here are 2, 7, 7, 80, 10, 20 and 500. The values are the default suggestions found in Tandem Repeat Finder documentation.
The transposable elements (TEs) are detected by RepeatMasker. TEs in each genome are identified using the complete dataset available from REPbase Updates [Please add the citation]. The sensitivity and the speed of RepeatMasker are set as the default values.
Utility
Table 3 presents the two output formats – flat-files and the web query interface with a filtering function. In the flat-file format, the fields of each oligonucleotide (in a chromosome) include the sequences, the number of occurrences in the chromosome, the number of occurrences in the coding regions and the number of occurrences in the non-coding regions. The user that requires a large amount of such data can download them in this format [1].
Table 3 Output styles of the database.
Database entries Entry types Output formats
Oligonucleotide occurrences Positions Web interface
Oligonucleotide frequencies Counts Web interface and flat-file
Gene coding regions Positions Web interface
Repetitive element frequencies (LINE, SINE, Alu, and so on) Counts Web interface and flat-file
Repetitive element occurrences Positions Web interface
Tandem repeats Positions Web interface
The web interface enables users to query the occurrences of an oligonucleotide in a genome and the number of occurrences in each chromosome. Figure 1 shows this approach. The occurrences of the repetitive elements and the tandem repeats in the established database can also be queried via the web interface, as in the example given in Fig 2. Figure 3 depicts the flat-file format of oligonucleotide frequencies. The first row in the flat-file presents the basic information for the oligonucleotide frequencies and the fields are the chromosome sequence/NCBI accession number, the length of the chromosome sequence, the size of coding regions, the size of non-coding regions, the length of the oligonucleotides and the minimum number of copies of the oligonucleotide. The directories labeled C10 are the files that contain the counts of oligonucleotides with at least ten occurrences in genome. Each file name includes the sequence/NCBI accession number, the length of oligonucleotides and the minimum occurrences of oligonucleotides. For example, "NC 000913 L30 C10" is the oligonucleotides, which are 30 nucleotides in length and have at least 10 occurrences in the genome.
Figure 1 Web query interface (1/2).
Figure 2 Web query interface (2/2).
Figure 3 Database entries in flat-file format.
Figure 4 shows the web interface for the occurrences of a specific oligonucleotide. The user submits the query oligonucleotide and selects particular species; the positions of the oligonucleotide are then shown. The first line is the user submitted data. Following this information are the positions of oligonucleotides in forward strand. This is followed by the same information for the reverse strand.
Figure 4 The occurrence positions of the oligonucleotide are found by Oligos Locator.
Conclusions
We have constructed the databases of both the oligonucleotide occurrence locations and their frequencies in the coding and the non-coding regions in complete genomes. The data in flat-file format can be downloaded directly for further analyses in several biological applications. The user may also use the web interface to query and access the database contents. The database also provides a filtering function for retrieving the information about oligonucleotides under search conditions specified by the users. Furthermore, the database provides the occurrences and the frequencies of other repetitive elements, such as LINE, SINE, Alu and tandem repeats in genomes.
Availability and requirements
The database is now available at
Authors' contributions
FML implements the software and refinements the system. HDH conceives of the study and drafted the manuscripts. YCC and JTH participates the design and coordination. All authors read and approved the final manuscripts.
Supplementary Material
Additional File 1
Appendix listing the organisms supported in the database.
Click here for file
Acknowledgement
The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC93-2213-E-008-024.
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RepeatMasker
| 15473908 | PMC526275 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Oct 9; 5:78 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-78 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-241549407910.1186/1471-2210-4-24Research ArticleReduced inhibitory action of a GABAB receptor agonist on [3H]-dopamine release from rat ventral tegmental area in vitro after chronic nicotine administration Amantea Diana [email protected] Norman G [email protected] Department of Pharmacology, Medical School, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK2 Department of Pharmacobiology, University of Calabria, 87036 Rende, Cosenza, Italy2004 20 10 2004 4 24 24 2 4 2004 20 10 2004 Copyright © 2004 Amantea and Bowery; licensee BioMed Central Ltd.2004Amantea and Bowery; 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 activation of GABAB receptors in the ventral tegmental area (VTA) has been suggested to attenuate the rewarding properties of psychostimulants, including nicotine. However, the neurochemical mechanism that underlie this effect remains unknown. Since GABAB receptors modulate the release of several neurotransmitters in the mammalian brain, we have characterised the effect of the GABAB receptor agonist baclofen on the release of [3H]-dopamine ([3H]-DA) from VTA slices of naïve rats and of rats pre-treated with nicotine.
Results
In naïve rats, baclofen concentration-dependently inhibited the electrically evoked release of [3H]-DA from the isolated VTA (EC50 = 0.103 μM, 95% CI = 0.043–0.249), without affecting the basal [3H]-monoamine overflow. This effect was mediated by activation of GABAB receptors as it was blocked by the selective receptor antagonist CGP55845A. Chronic administration of nicotine (0.4 mg kg-1, s.c., for 14 days) affected neither the basal nor the electrically evoked release of [3H]-DA from VTA slices. However, the inhibitory effect of baclofen (10 μM) on the stimulated [3H]-monoamine overflow was abolished in rats pre-treated with nicotine as compared to saline-injected controls.
Conclusions
Our results demonstrate that GABAB receptor activation reduces the release of DA from the rat VTA. In addition, a reduced sensitivity of VTA GABAB receptors appears to develop after chronic exposure to nicotine. The resulting disinhibition of VTA DA neurones might therefore contribute to the sensitised dopaminergic responses observed in the rat mesocorticolimbic system following repeated administration of nicotine.
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Background
The ventral tegmental area (VTA) represents the site of origin of the mesocorticolimbic dopaminergic pathway that has been implicated in mediating the reinforcing properties of drugs of abuse, including nicotine [1-3].
The majority of cells within the ventral tegmental area consist of dopaminergic, tyrosine-hydroxylase containing neurones, which send axon projections to forebrain structures such as the nucleus accumbens and the prefrontal cortex. The non-tyrosine hydroxylase containing neurones are mainly GABAergic and function either as local interneurones to modulate the activity of the principal dopaminergic cells or as projection neurones providing inhibitory input to the cortex and the nucleus accumbens [4-8].
In addition to classical release from the axon terminals located in the forebrain, midbrain dopaminergic neurones release dopamine (DA) from their soma and dendrites [9-12]. The somatodendritic release of DA provides a primary modulation of dopamine cell function. Activation of D2 autoreceptors inhibits excitability and firing rate of VTA dopaminergic neurones [13,14] and decreases the release of dopamine from their axon terminals in the forebrain [15,16]. Furthermore, somatodendritic dopamine can indirectly modulate the activity of midbrain dopamine cells by acting on D1 receptors, which are found on GABA- and excitatory amino acids-containing terminals in the VTA [17-20].
Besides the short-loop feedback inhibition exerted by dopamine, the activity of VTA dopaminergic neurones is strongly modulated by glutamatergic and GABAergic inputs. Activation of NMDA receptors by excitatory afferents arising from the medial prefrontal cortex [21,22] induces burst firing in VTA DA neurones [23-25], which is associated with increased dopamine release from the nerve terminals in the nucleus accumbens [26,27]. The VTA also receives an extensive inhibitory influence arising from GABA interneurones and descending projections from the basal forebrain, innervating GABAA and GABAB receptors, respectively [4]. Activation of GABAB receptors has been reported to inhibit the spontaneous pacemaker-like activity of VTA DA neurones in slice preparations [4,28,29] and to decrease the firing rate and burst firing of these cells in vivo [30,31].
In addition, in vivo microdialysis studies have demonstrated that intra-VTA administration of the GABAB receptor agonist baclofen decreases extracellular dopamine levels in both the somatodendritic [32,33] and the axon-terminal regions of the mesocorticolimbic system [15,16]. The GABAB receptor-mediated inhibition of the activity of the mesocorticolimbic neurones might explain the effectiveness of baclofen to suppress nicotine self-administration when microinjected into the VTA [34]. In fact, the rewarding properties of nicotine have been ascribed to its ability to stimulate VTA dopamine neurones that project to the nucleus accumbens [2,35]. Acute administration of nicotine to drug-naïve rats increases extracellular levels of dopamine in the nucleus accumbens shell, while repeated exposure to the drug results in sensitisation of its effect on dopamine overflow in the nucleus accumbens core [36]. Sensitisation of the mesoaccumbens dopamine response to nicotine appears to be closely related to the dependence-liability of the drug and has been suggested to reflect an altered control of DA release, including reduced inhibitory influence by DA autoreceptors, and co-stimulation of NMDA receptors [37-39].
Since GABAB receptors have a prominent role in regulating the activity of VTA DA neurones and they appear to be involved in the modulation of nicotine reinforcing properties, we have characterised the effect GABAB receptor stimulation on the somatodendritic release of [3H]-DA from VTA slices of naïve rats and used this model to determine whether chronic exposure to nicotine results in altered GABAB receptor-mediated modulation of VTA DA cells.
Results
Tetrodotoxin and calcium dependence of the stimulated [3H]-DA release from VTA slices
The influence of tetrodotoxin (TTX) and calcium on the electrically evoked release of [3H]-DA from ventral tegmental slices of naïve rats was evaluated in order to determine its physiological significance under the experimental conditions used here. Removal of calcium with the inclusion of EGTA (1 mM), or addition of TTX (1 μM) to the superfusion buffer abolished the stimulated release of [3H]-DA from the tissue, without significantly affecting the spontaneous monoamine overflow. Recovery of the release occurred during the second stimulation (S2), after removal of TTX or replacement of calcium to the superfusion buffer (Figure 2).
Figure 2 Calcium-dependence and tetrodotoxin (TTX) sensitivity of the electrically evoked release of [3H]-DA from ventral tegmental area slices. Electrical stimulation (20 mA, 2 Hz, 4 min) occurred 14 min (S1) and 59 min (S2) after the beginning of sample collection. Slices were perfused with buffer alone (control), calcium-free medium containing 1 mM EGTA (Ca2+-free), or buffer containing 1 μM TTX for 15 min before and during S1 (black bar). After 18 min from the beginning of sample collection, all samples were superfused with normal buffer. Values represent mean ± s.e.mean (n = 3 rats).
Effect of GABAB receptor activation on VTA [3H]-DA release
Baclofen (0.1–100 μM), added to the superfusion buffer 36 min before the second stimulation, dose-dependently reduced the electrically evoked [3H]-DA release from VTA slices (EC50 = 0.103 μM, 95% CI = 0.043–0.249, n = 4–7 rats, Figure 3), without having any significant effect on the basal monoamine overflow (data not shown).
Figure 3 Baclofen-mediated inhibition of the electrically evoked [3H]-DA release from ventral tegmental area slices of naïve rats (EC50 = 0.103 μM, 95% CI = 0.043–0.249). Baclofen (0.01–100 μM) was added to the superfusion buffer 36 min before and during S2. Values represent mean ± s.e.mean (n = 4–7 rats).
The inhibitory effect of baclofen (10 μM) on the release of [3H]-DA was abolished when the selective GABAB receptor antagonist CGP55845A (1 μM), which had no effect on its own, was added to the superfusion buffer concomitantly with baclofen (Figure 4). Release of dopamine, expressed as S2/S1 ratio, was 1.09 ± 0.10 for control slices perfused with buffer alone; 0.73 ± 0.08 for baclofen added before S2 (P < 0.05 vs control, one-way ANOVA, Dunnet's post hoc test, n = 6–10 rats); 0.99 ± 0.13 for CGP55845A alone; and 0.93 ± 0.14 for slices perfused with both baclofen and CGP55845A.
Figure 4 Reversal of baclofen-mediated inhibition of the evoked [3H]-DA release from VTA slices by the GABAB antagonist CGP55845A. Baclofen (Bacl, 10 μM) and/or CGP55845A (CGP, 1 μM) were added to the superfusion buffer 36 min before and during S2. Drug effects were assessed by comparing the ratio S2/S1 in the presence and absence of the drug, respectively. Values represent mean ± s.e.mean (n = 6–10 rats). *p < 0.05 vs control (one-way ANOVA, Dunnett's post hoc test).
Effect of nicotine pre-treatment on the release of [3H]-DA from VTA
The release of [3H]-DA from the VTA slices of rats that received a chronic nicotine treatment was compared with the overflow observed in saline-control animals. Neither the basal nor the electrically stimulated release of [3H]-DA was significantly affected by the drug pre-treatment (Figure 5).
Figure 5 Effect of nicotine pre-treatment on basal and electrically evoked release of [3H]-DA from VTA slices. The rats received daily subcutaneous injections of nicotine (0.4 mg kg-1, n = 6) or saline (n = 6) for 14 consecutive days and the experiments were performed 24 hours after the last injection. An electrical stimulation (20 mA, 2 Hz, 4 min, black bar) was applied to the VTA slices 14 min after the beginning of sample collection. The data are expressed as fractional [3H]-DA release and represented as mean ± s.e.mean.
Effect of baclofen on the release of [3H]-DA from VTA of nicotine-treated rats
The effect of baclofen on the VTA [3H]-DA release in rats pre-treated with nicotine was assessed and compared with the effect of the drug in saline-control animals (Figure 6). The addition of baclofen (10 μM) to the superfusion buffer for 36 min before and during S2, significantly reduced the evoked release of [3H]-DA from VTA slices of rats pre-treated with saline (S2/S1: control, 1.06 ± 0.08; baclofen, 0.73 ± 0.06; P < 0.01, ANOVA for repeated measures, Bonferroni post-test, n = 5 rats). By contrast, in the rats pre-treated with nicotine, the addition of baclofen to the superfusion buffer did not produce any significant effect on the evoked release of [3H]-DA (S2/S1: control, 1.02 ± 0.06; baclofen, 0.92 ± 0.05) (Figure 6).
Figure 6 Effect of baclofen on the electrically evoked release of [3H]-DA from VTA slices of rats pretreated with nicotine or saline. The rats received daily subcutaneous injections of nicotine (0.4 mg kg-1, n = 5) or saline (n = 5) for 14 consecutive days and the experiments were performed 24 hours after the last injection. Two electrical stimulations (20 mA, 2 Hz, 4 min) were applied to the slices 14 min (S1) and 59 min (S2) after the beginning of sample collection. Baclofen (10 μM) was added to the superfusion buffer 36 min before and during S2. Control tissue was perfused with buffer alone during both S1 and S2. Data are expressed as mean ± s.e.mean and compared by ANOVA for repeated measures. **p < 0.01 vs saline-control (Bonferroni post-test).
Discussion
In the present study we have characterised the effect of baclofen on the release of [3H]-DA from ventral tegmental area slices of naïve rats, and used this model for studying the functional status of local GABAB receptors after chronic exposure to nicotine.
The electrically induced [3H]-DA release from VTA somatodendrites was calcium-dependent and tetrodotoxin-sensitive. This suggests that the release is tightly coupled with voltage-sensitive calcium influx and that it depends on the propagation of action potentials by voltage-dependent sodium channels. Our findings are consistent with the calcium sensitivity and the partial block by tetrodotoxin of the release of endogenous dopamine observed in DA cell body areas using microdialysis [10,40]. They are also consistent with the calcium and tetrodotoxin dependency of the electrically evoked [3H]-DA release from slices of the ventral tegmentum [41]. Therefore, under the experimental conditions used in the present study, release of preloaded [3H]-dopamine appears to be of neuronal origin and to have physiological relevance.
Although the radioactivity measured in the collected effluent may consist of a mixture of neurotransmitters and metabolites, the amount of tritium released from rat brain slices after electrical stimulation has been previously shown to represent a close estimation of the release of labelled or endogenous DA release [42,43]. Furthermore, the release of metabolites during the superfusion was inhibited by the presence of the monoamine oxidase inhibitor pargyline in the superfusion buffer [44]. Therefore, under our experimental conditions, the overflow of tritium from VTA slices seems likely to represent, predominantly, [3H]-DA and closely resembles exocytotic release of DA, as it was dependent on the presence of calcium in the superfusion buffer.
The VTA receives GABAergic input from both interneurones and descending projections from the basal forebrain, innervating GABAA and GABAB receptors, respectively [4,45]. Activation of GABAB receptors, located both postsynaptically on dopaminergic neurones and presynaptically on glutamatergic nerve terminals [29], decreases the spontaneous pacemaker-like activity and the burst firing of VTA DA cells [4,31]. In addition, baclofen microinjected into the VTA has been shown to decrease somatodendritic release of dopamine in this midbrain region as monitored by in vivo microdialysis [32,33]. The activation of GABAB receptors has also been reported to inhibit the potassium- or electrically evoked release of dopamine from various regions of the mammalian brain in vitro [46,47]. However, to date, there is a lack of information regarding the effect of GABAB receptor activation on the release of somatodendritic dopamine from the isolated VTA. Therefore, in the present study we have demonstrated that baclofen dose-dependently reduces the electrically evoked release of [3H]-DA from ventral tegmental area slices of naïve rats. This effect appears to be mediated by activation of GABAB receptors, since it was abolished by superfusion of the tissue with the selective receptor antagonist CGP55845A [48].
The existence of a tonic GABAB receptor-mediated inhibition of somatodendritic DA release has been suggested by the evidence that, in vivo, the administration of CGP55845A into the ventral tegmental area produces a dose-dependent increase in VTA dopamine levels [49]. By contrast, our results demonstrate that the GABAB receptor antagonist CGP55845A does not have any significant effect on the release of [3H]-DA when applied on its own to the VTA slices. The apparent discrepancy between the two studies may be ascribed to the fact that we have used isolated tissue, which is deprived of the tonic GABA input arising from the forebrain, whereas in the work of Giorgetti et al. [49] the long-loop projections to the VTA are indeed intact. In fact, the innervation of VTA GABAB receptors originates primarily from projection neurones located in the nucleus accumbens and the ventral pallidum, while the GABA interneurones appear to innervate mainly GABAA receptors [4,50].
Interestingly, previous neurochemical studies have shown that activation of GABAB receptors in the ventral tegmental area of naïve rats inhibits the release of dopamine not only in the cell body area, but also in mesocorticolimbic terminal regions, such as the nucleus accumbens [33] and the prefrontal cortex [15]. The reduction of mesocorticolimbic dopamine release might represent a likely mechanism by which baclofen attenuates nicotine self-administration in rats when microinjected into the ventral tegmental area [34]. However, to date, this hypothesis has not been confirmed and little information is known about the neurochemical mechanisms underlying the GABAB receptor-mediated modulation of nicotine reinforcement, as well as about the pharmacological interaction between nicotine and GABAB receptors. With the present study we demonstrate that while baclofen significantly reduces the electrically induced release of [3H]-DA from the VTA of saline-control rats, it has no effect on the evoked monoamine release from VTA slices of nicotine pre-treated rats. This finding represents the first demonstration that chronic exposure to nicotine might result in reduced GABAB-mediated inhibition of VTA dopaminergic neurones. This hypothesis is also consistent with preliminary in vivo studies performed in our laboratory showing that, after chronic pre-treatment of the rats with nicotine, microinfusions of baclofen into the VTA failed to reduce both the spontaneous and the nicotine-evoked overflow of dopamine in this midbrain region (D. Amantea, unpublished observation). Taken together, our findings suggest that after chronic nicotine the GABAB receptor may be desensitised, and this would result in a reduced inhibitory control of VTA dopaminergic cells, thereby facilitating a more sustained increase in the responses of mesolimbic neurones to nicotine. Interestingly, the inhibitory action of baclofen on VTA DA cells may be accounted for by stimulation of GABAB receptors located on dopaminergic and/or glutamatergic neurones [28,29]. This suggests that the effect observed after chronic administration of nicotine may be ascribed to a reduced sensitivity of GABAB receptors located either on DA cell bodies or, presynaptically, on glutamatergic terminals impinging onto DA neurones. The latter hypothesis would lead to the speculation that a chronic treatment with nicotine might result in increased excitatory input to the VTA, due to a reduced GABAB-mediated inhibitory control. Therefore, disinhibition or increased excitatory input to midbrain DA cells might contribute to the augmented dopamine output observed in the nucleus accumbens after a challenge injection of nicotine in rats pretreated with the drug [36,37]. Desensitisation of GABAB receptors located in the VTA has also been reported to occur after chronic cocaine administration, as the drug treatment resulted in reduced functional coupling of the receptor to G-proteins [51]. However, we have previously demonstrated that GABAB receptor expression and coupling to G-proteins in the ventral tegmental area of the rat are not altered after chronic exposure to nicotine [52], suggesting that desensitisation might occur at other levels, perhaps on downstream effector mechanisms. Nevertheless, it cannot be ignored that the use of in vitro autoradiography did not allow us to discriminate between receptor subpopulations, including receptor subtypes involved in different functions. Thus, if the GABAB receptors directly implicated in the control of dopamine release from the VTA represent only a small fraction of the overall receptor population in this area, autoradiographic analysis would not be sufficiently sensitive to evaluate receptor modifications occurring after chronic exposure to nicotine. Further studies aimed at characterising the mechanisms involved in GABAB receptor desensitisation following chronic nicotine treatment are required to clarify this issue.
Not surprisingly, basal and evoked release of dopamine from VTA slices of rats chronically injected with nicotine did not differ from release obtained from saline-control tissue. This confirms that sensitised dopaminergic responses to the drug depend on the activation of nicotinic receptors (nAChRs) and are not the result of a generic increase in neuronal activity. Similar results have been obtained in striatal synaptosomes from rats pretreated with the nAChR agonist anatoxin-a for 7 days: while the drug pre-exposure increased the nicotine-stimulated release of [3H]-DA from the in vitro preparation, no difference was found in the K+-evoked release between the drug pretreated animals and the saline-injected controls [53]. Thus, although the electrical stimulation used in the present study does not provide information about desensitisation or up-regulation of nAChRs, it nevertheless served as a reliable model to study the functional status of the GABAB receptor in the ventral tegmental area of rats pretreated with nicotine.
Conclusions
In conclusion, we have demonstrated that activation of GABAB receptors inhibits the release of preloaded [3H]-DA from somatodendritic fields of VTA neurones in naïve rats. This effect appears to be reduced in rats chronically treated with nicotine, suggesting that subsensitivity of GABAB receptors in the VTA might occur as a result of the drug treatment. This, in turn, would lead to disinhibition of VTA dopaminergic cells, which might contribute to the increased activity of mesocorticolimbic neurones following repeated exposure to nicotine.
Methods
Drugs
[3H]-Dopamine (specific activity 47 Ci/mmol) was obtained from Amersham (Buckinghamshire, UK). (-)-Baclofen (CGP11973A) and CGP55845A were generous gifts from Novartis Pharma (Basel, Switzerland). (-)-Nicotine hydrogen tartrate salt, nomifensine, pargyline, tetrodotoxin and ethylene glycol-bis (b-amino ethyl ether) tetraacetic acid (EGTA) were purchased from Sigma-Aldrich (Dorset, UK). All the other chemicals used in this study were obtained from Fisher Scientific (Leicestershire, UK).
Subjects
Male Wistar rats (weight 250–280 g) were maintained on a 12-h light/dark schedule (on 6:00–18:00), with free access to food and water.
For the drug treatments, rats, initially weighing 150–180 g, received subcutaneous injections of nicotine (0.4 mg kg-1, expressed as free-base) or vehicle (0.9% NaCl, 1 ml kg-1) for 14 consecutive days, once a day, between 9:30 and 10:30 a.m. Experiments were performed 24 hours after the last injection. All procedures were carried out in accordance to the UK Animals (Scientific Procedures) Act, 1986.
Tissue preparation
Animals were sacrificed by stunning followed by decapitation. The brains were rapidly removed from the skull and cooled for 2–3 min in ice-cold superfusion buffer of the following composition (in mM): NaCl, 118; KCl, 4.7; CaCl2, 1.3; MgCl2, 1.2; NaH2PO4, 1; NaHCO3, 25; glucose, 11.1; Na2EDTA, 0.004 and ascorbic acid, 0.3 (pH 7.4), saturated with 95% O2/5% CO2.
Each brain was dissected according to visual anatomical landmarks and the atlas of Paxinos & Watson [54]. To obtain the coronal section containing the VTA, the brain was placed ventral side up and two parallel cuts were made at the level of the mammillary body and the medial interpeduncular nucleus (approximately 5–6 mm posterior to bregma), using as landmark the basal cerebral peduncle. Two transversal cuts were made in correspondence of the medial lemniscus, to remove the substantia nigra from the slice. Finally the ventral part of the section was dissected out by making a horizontal cut 1.5 mm dorsal to the ventral edge of the section and deprived of the interpeduncular and mammillary nuclei by cutting 0.5 mm above the same edge. The procedure used for the dissection of the VTA is illustrated in Figure 1, which shows a photomicrogaph of a representative coronal slice immunohistochemically stained for tyrosine hydroxylase.
Figure 1 Photomicrograph of a representative coronal section of rat brain labelled with a polyclonal antibody directed against tyrosine hydroxylase. The picture shows the orientation of the cuts (dark lines) for the dissection of the ventral tegmental area (VTA). The basal cerebral peduncle (Cp) was used as a visual landmark to obtain the coronal section containing the VTA. To remove the substantia nigra (pars compacta, SNC, and reticulata, SNR) from this slice, two transversal cuts were made along the medial lemniscus (Ml) on either side. Finally, the VTA was dissected out by making a horizontal cut 1.5 mm dorsal to the ventral edge of the section and stripped of the interpeduncular nucleus (IP) by cutting 0.5 mm above the same edge. Scale bar is 1 mm.
The dissected VTA was cross-chopped (250 μm × 250 μm) with a McIlwain tissue chopper and the tissue slices were washed and resuspended in ice-cold superfusion buffer.
Tissue superfusion
The VTA slices were pre-incubated in oxygenated superfusion medium at 37°C for 15 min, followed by incubation with 0.1 μM [3H]-DA for 30 min, in the dark and in the presence of 10 μM pargyline. The incubation was terminated by washing the slices three times with buffer containing 2.5 μM nomifensine.
150 μl aliquots of slice suspension (1.5 to 2.0 mg of tissue) were transferred to each chamber of a Brandel 2000 superfusion apparatus. The tissue was superfused at a rate of 0.5 ml min-1 with oxygenated buffer maintained at 35°C and containing 2.5 μM nomifensine, to block dopamine uptake, and 10 μM pargyline to ensure that [3H] overflow represented primarily [3H]-DA rather than its metabolites [44].
After 36-min pre-superfusion, the effluent was collected in consecutive fractions of 4 min 30 sec each. The release of [3H]-DA was induced by electrical field stimulation (20 mA, 2 Hz for 4 min) using a Brandel constant current stimulator. Two stimulations occurred 14 min (S1) and 59 min (S2) after the beginning of sample collection.
Test drugs were added to the superfusion medium 36 min before the second stimulation. To determine the effects of calcium and tetrodotoxin (TTX), slices were perfused with Ca2+-free buffer (in the presence of 1 mM EGTA), or buffer containing 1 μM TTX, for 15 min before and during S1. The superfusion medium was then replaced with normal buffer until the end of superfusion and during the second stimulation.
Liquid scintillation counting
At the end of the superfusion, the slices with their filters were removed from the chamber, suspended in 1 ml of buffer, and sonicated. OptiPhase 'HiSafe' 3 scintillation fluid (Perkin Elmer, UK) was added to each vial and the radioactivity content in the superfusion samples and in the tissue slices was assayed by liquid scintillation counting using a Tri-Carb® 1500 liquid scintillation analyser (Packard Bioscience Company), programmed to count for tritium, 3 minutes per vial, at an efficiency of 60%. The number of disintegrations per minute (d.p.m.) was measured in order to determine the concentration of tritium in each sample.
Data analysis
For each time point (4 min 30 sec), release of radioactivity was expressed as fractional release, i.e., as a percentage of the amount of radioactivity in the tissue at the beginning of that collection. The electrically evoked release was expressed as the mean of the increased fractional release above baseline in the two fractions after the beginning of stimulations. Basal release, in turn, was calculated as the mean of the amount of radioactivity present in the three samples just before each period of electrical stimulation.
Results were expressed as mean ± s.e.mean of n independent experiments conducted in either triplicate or quadruplicate. For statistical analysis one-way ANOVA with Dunnett's post hoc test was used to compare values of S2/S1 in the presence of a drug versus values of S2/S1 from control slices superfused with buffer alone. When nicotine or saline were administered to the animals, values of S2/S1 were analysed by ANOVA with repeated measures (with or without baclofen) with pre-treatment as factor analysed, and post hoc comparisons were made using the Bonferroni test. The accepted level of significance was p < 0.05.
Histology
Immunohistochemistry was performed on naïve rat brain during preliminary experiments aimed at characterising the exact orientation of the cutting for the dissection of the VTA (Figure 1). Animals were sacrificed by stunning and decapitation and their brains rapidly removed from the skull. A coronal section containing the VTA was obtained as described above and the tissue was post-fixed in 4% paraformaldehyde (BDH) for 48 hours at 4°C. Serial coronal sections (30 μm thick) were cut using a vibratome and washed in 0.01 M phosphate buffered saline (PBS, Sigma-Aldrich), pH 7.4. Slices were incubated with 3% hydrogen peroxide (H2O2) for 30 min, followed by incubation with 0.2% Triton X-100 (Sigma-Aldrich) for 20 min at room temperature.
After a 1-hour pre-incubation in 10% normal goat serum (NGS, Vector), the primary antibody (rabbit polyclonal antibody to tyrosine-hydroxylase, Affiniti) was applied at a final concentration of 1:1000 (in 0.01 M PBS) and the sections were allowed to incubate overnight at 4°C. After three washes with fresh buffer, the slices were incubated for 90 minutes at room temperature with a biotinylated secondary goat anti-rabbit antibody (1:200 dilution, Chemicon).
Immunoreactivity was visualised by the avidin-biotin complex method of detection (Vectastain Elite ABC Kit, Vector) using 3,3'diaminobenzidine (DAB, peroxidase substrate kit, Vector) as the chromogen.
Authors' contributions
DA carried out the experiments, participated in the design of the study and drafted the manuscript.
NGB conceived the study, and participated in its design and coordination.
All authors read and approved the final manuscript.
Acknowledgements
We wish to express our gratitude to Dr W. Froestl for providing (-)-Baclofen and CGP55845A, and to GlaxoSmithKline (Verona, Italy) for providing financial support for this study.
==== Refs
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| 15494079 | PMC526276 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 20; 4:24 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-24 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-5-221548259710.1186/1471-2296-5-22Research ArticleGeneral practitioners believe that hypnotherapy could be a useful treatment for irritable bowel syndrome in primary care Cox Stephen [email protected] Lusignan Simon [email protected] Tom [email protected] Gillets Surgery, Deanland Road, Balcome, West Sussex, RH17 6PH, UK2 Department of Community Health Sciences, St. George's Hospital Medical School, LONDON, SW17 0RE, UK3 Surrey and Hampshire Borders NHS Trust, Ridgewood Centre, Old Bisley Road, Camberley, Surrey, GU16 5QE, UK2004 13 10 2004 5 22 22 18 3 2004 13 10 2004 Copyright © 2004 Cox et al; licensee BioMed Central Ltd.2004Cox 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
Irritable bowel syndrome is a common condition in general practice. It occurs in 10 to 20% of the population, but less than half seek medical assistance with the complaint.
Methods
A questionnaire was sent to the 406 GPs listed on the West Sussex Health Authority Medical List to investigate their views of this condition and whether they felt hypnotherapy had a place in its management
Results
38% of general practitioners responded. The achieved sample shared the characteristics of target sample.
Nearly half thought that irritable bowel syndrome (IBS) was a "nervous complaint" and used a combination of "the placebo effect of personal care," therapeutic, and dietary advice. There is considerable divergence in the perceived effectiveness of current approaches. Over 70% thought that hypnotherapy may have a role in the management of patients with IBS; though the majority (68%) felt that this should not be offered by general practitioners. 84% felt that this should be offered by qualified hypnotherapist, with 40% feeling that this should be offered outside the health service.
Conclusions
General practitioners vary in their perceptions of what constitutes effective therapy in IBS. They are willing to consider referral to a qualified hypnotherapist.
==== Body
Background
This study explores general practitioners' beliefs about irritable bowel syndrome (IBS), and whether they see hypnotherapy as an appropriate complementary therapy for its management; and if so, who should deliver it.
IBS is estimated to occur in 10–20% of the population in most countries [1]. It is known that less than half of subjects seek medical help for their complaints [2] but it is a common cause for referral to secondary care (referral is advised in patients over 50 years old with changing symptoms) [1].
Over a period of time of between 2 and 5 years, it is thought that there is a 30% turnover of patients having IBS [3]. It therefore uses significant primary care resources over many years.
IBS is typical of many conditions seen in general practice. There is a risk that some of its symptoms may actually represent serious underlying illness and there are a number of conventional medical interventions, but for many patients the interest of their practitioner is paramount [4]. The mainstays of medical management are a high fibre diet and pharmacotherapy [5]. These help to some extent but do not appear to offer cure or permanent remission. There is also little evidence of the effectiveness of dietary advice. Many general practitioners see IBS as a complex bio-psychosocial problem where an appropriate consultation style can be as important as the therapeutic interventions themselves [6].
UK general practitioners are having a greater say in the management of the local health services through the organisation of local health services into Primary Care Trusts (PCT). The latter are responsible for commissioning the health services for their resident population. Via these, general practitioners have the potential to influence the development of services for the treatment of this condition. Most PCTs are financially constrained and cannot meet all the needs of every patient. Services have to be prioritised according to need. The study investigated general practitioners' opinions as to whether hypnotherapy should be provided by Primary Care Trusts, or provided privately.
The possibility of hypnotherapy being used as a primary care level intervention for IBS has developed from the realisation in the 1980's that hypnotherapy was an effective treatment for intractable IBS in a hospital context [7,8]. These trials are good evidence that this therapy is effective in secondary care. The concept of shortening a long-term illness that often requires secondary referral, long-term drug therapy and repeated primary care attendance is an attractive one. No large-scale primary care trial of this sort of treatment has been performed. If a large RCT were to be undertaken it would be important to assess whether the findings would be perceived as relevant and likely to be implemented in a primary care setting.
There is a lack of literature about general practitioners' opinions and few validated questionnaires. A literature search identified two recent studies about IBS in primary care [9,10] which comment on the lack of confidence in the diagnosis of IBS in primary care and the over-dependence on treatment using drugs. An interview study explores medical and lay views of IBS [11]; concluding that patients are affected by medical beliefs about the nature of IBS and suggest that better explanations could be given for the disorder. A questionnaire study of consultants' and GPs' attitudes to functional bowel disorders showed marked differences in the perception of the psychological as opposed to the physical basis for the condition between the two groups – GPs favoured a psychological explanation. However, the study did not investigate alternative therapeutic intervention [12].
There are few studies describing the experiences and attitudes of GPs towards IBS management in primary care, especially pertaining to the use of alternative or psychological treatments. Although it is very likely that treatments such as hypnotherapy and cognitive behaviour therapy would have a prolonged impact on IBS if they were commonly employed at an early stage in the illness, little is known about the attitudes of primary care teams in encouraging this sort of approach.
We therefore conducted this study to discover how general practitioners perceive IBS and if they consider it to be managed effectively by current conventional therapy. We wanted to know if GPs consider hypnotherapy to be an appropriate intervention and whether GPs would refer there patients for it. Finally, we wanted to know whether such a service would be provided within general practice or another setting and if it should be funded by the NHS or be provided from the private sector.
Methods
Study setting
A survey was conducted of all general practitioners in West Sussex. The setting for the study is a mixed area. There are market towns and no large cities. Much of the population lives in sub-urban and rural areas. The area has a lower than average deprivation level than the regional norm, but in common with most of the south east of England, income is higher than the national average. As it is a rural area a larger number of practices dispense their own medicines, rather than send patients with a prescription to a local pharmacy [13].
Questionnaire design and testing
A questionnaire was designed and pre-tested with a pilot group for readability, validity, reliability, acceptability of layout and time taken to complete [14]. The questionnaire is attached as an appendix [see Additional file 1].
The sample was the 406 general practitioners listed as unrestricted principals on the Health Authority's list in 1997. This list included both full and part time general practitioners. The questionnaires were mailed to GPs using the Heath Authority's internal mail system and were returned via the same Health Authority post. At that time, the use of this service was free to the investigator. Once the first group of questionnaires had been returned, the questionnaire was repeated once to those practices that had not responded.
The responses were mostly in the form of a Likert scale. This scale enables measurement of degrees of opinion, so increasing the sensitivity of the analysis. A central category was provided for a neutral response. The design purposefully did not force a polarised choice because it was thought that the treatment would be unlikely to be in common use. It was possible that many general practitioners would genuinely not have an opinion about some of the questions. To prevent acquiescence bias, the questions were worded so that expected responses varied unpredictably according to the direction of the scale [14]. Demographic data were requested, to enable assessment of possibly biased responses due to factors such as age, sex or practice size.
Questionnaire themes included GP perception of IBS, its management, and hypnotherapy as a treatment. It also included questions related to the funding of treatment and the acceptability of hypnotherapy as part of the management of IBS in primary care. The questions were grouped together using these themes. Piloting enabled assessment of face validity and content validity [15]. The constructs being tested were not formally defined, as the purpose of the instrument was to obtain opinion about the subjects of the questions, not to form hypotheses.
Data analysis
We analysed the data using SPSS (Statistical Package for Social Sciences). The target and achieved samples were compared using the Chi-square test of proportion [16]. Where there is a statistically significant difference between the samples it is included. The results of the main questionnaire are reported as proportions rather than as a score. The purpose of this approach is to ascertain determine general practitioner opinion. Preliminary analysis found that the proportions for the 'strongly agree' and 'strongly disagree' categories are generally small. For clarity of interpretation and reporting, the 5 point-scale was collapsed into 3 where applicable, amalgamating the 'strongly agree' category with the 'agree' category, and 'strongly disagree' with 'disagree'.
Results
Response bias
155 (38%) general practitioners of the target sample returned questionnaires after the initial request and one further follow-up reminder.
The characteristics of general practitioners in achieved sample were compared with that of the target sample to assess sample bias. No statistically significant differences were found between the achieved and target samples other than fewer GPs from non-training practices and from single-handed practices responded to the survey. With the exception of these factors, the characteristics of the achieved sample are not very different from that of the population of GPs in West Sussex. A comparison of the achieved and target sample are shown in Table 1. Nine general practitioners (6%) had previously used hypnotherapy.
Table 1 Comparison of responders (achieved sample) with all West Sussex GPs (target sample)
Characteristic Achieved sample Target sample Test
% n % n
Aged 35 & under 17.4% 27 16.5% 67
Aged 36–45 43.9% 68 41.6% 169
Aged 46–55 31.0% 48 28.3% 115
Aged 56 & over 6.5% 10 12.3% 50 p = 0.17 X2
Male 68.4% 106 73.4% 298
Female 31.0% 48 26.6% 108 p = 0.20 X2
Full time 83.9% 130 88.7% 360
Part time 15.5% 24 11.3% 46 p = 0.09 X2
Research active % (95%CI) 12.9% (7.1–18.6) 20 5.7% (3.4–7.9) 23 n.s. Test of Proportion
Training 53.50% 83 16.05% 65
Non training 43.90% 68 83.95% 341 p < 0.001 X2
Single handed 5.2% 8 10.2% 41
Group 92.3% 143 89.8% 365 p < 0.05 X2
Total 155 406
(Source of GP data in West Sussex: NHS Executive Oct 1997)
How do general practitioners perceive IBS
45.2% of the respondents agreed with the statement that "IBS is mainly a nervous complaint", and 40% felt that IBS "responds mainly to the placebo effect of personal care and attention". Although many respondents seem to categorise IBS as a 'nervous complaint', conventional medical management with drugs and dietary advice is seen to have a role to play with 45.2% agreeing with the statement that drug therapy works effectively, and 38.7% dietary advice works effectively. A striking finding is that a sizeable minority of GPs were unsure if IBS patients respond to placebo effect (at 37.4%) or to medical therapy (at 41.3%). Combining the 'unsure' and the 'disagree' categories show that the majority of GPs in this survey are uncertain if existing treatment regimes for IBS (drug, dietary advice and placebo effect) are efficacious. These results are set out in Table 2.
Table 2 How general practitioners perceive irritable bowel syndrome.
Agree Unsure Disagree n=
Irritable Bowel Syndrome is mainly a 'nervous complaint' 45.2 29.7 23.2 155
Irritable Bowel Syndrome responds mainly to the placebo effect of personal care and attention 40.0 37.4 22.6 155
Drug therapy works effectively in my Irritable Bowel Syndrome patients 45.2 32.9 20.6 155
Dietary advice works effectively in my Irritable Bowel Syndrome patients 38.7 38.7 20.6 155
Irritable Bowel Syndrome responds mainly to medical/therapeutic interventions 39.4 41.3 19.4 155
N.B. the rows do not always sum to 100% as missing responses are included in the analysis but not shown.
Is care of patients with IBS adequate
Just under half of the respondents (45.2%) agreed that IBS requires more attention in primary care and less than a quarter (22.6%) disagreed with the statement that IBS required more attention. The majority (56.8%) of the respondents felt that it would be possible to manage IBS better in their practices. Only 12.9% disagreed with this statement. The vast majority (84.5%) felt that the present management of IBS is variable with less than 10% of practitioners believing that their care is effective. These results are summarised in Table 3.
Table 3 How general practitioners perceive the effectiveness of irritable bowel syndrome management
Agree Unsure Disagree n=
Irritable Bowel Syndrome requires more attention in Primary care 45.2 29.7 22.6 155
It would be practically possible to manage Irritable Bowel Syndrome better in our practice 56.8 28.4 12.9 155
Is your present management of Irritable Bowel Syndrome effective, ineffective or variable? Effective Variable Ineffective n=
9.7 84.5 4.5 155
N.B. the rows do not always sum to 100% as missing responses are included in the analysis but not shown.
General practitioners views about hypnotherapy and its role in IBS
Three quarters of general practitioners (75.5%) saw hypnotherapy as an alternative therapy. Notwithstanding this, a large majority of general practitioners in this survey agreed that hypnotherapy could help patients who suffer from both physical and psychological problems, 72.9% and 77.4% respectively. However just over a third of general practitioners (34.8%) saw hypnotherapy as potentially dangerous, with just under half feeling unsure (40.6%).
Nearly 84% felt that hypnotherapy was the province of accredited therapists, and only 20% felt that they would be willing to receive training to provide hypnotherapy themselves. The statement that hypnotherapy should be available through an accredited hypnotherapist (83.9% agreed) and not to take this on as a general practitioner (68.4% with 10.3 unsure) were two of the most polarised responses.
If a course of 8 × 30 minutes hypnotherapy were shown to be effective, there would be a willingness amongst most respondents (78.1%) to advise hypnotherapy for some, but definitely not all, IBS patients. 83% of those questioned felt that it was not something that should be offered to every patient with IBS. These results are set out in Table 4.
Table 4 What general practitioners think about hypnotherapy and their willingness to refer irritable bowel syndrome patients for hypnotherapy.
Agree Unsure Disagree n=
Is Hypnotherapy an "alternative" not a mainstream therapy? 75.5 17.4 6.5 155
Hypnotherapy could help a sufferer from a physical illness 72.9 22.6 4.5 155
Hypnotherapy could help a sufferer of a Psychological disorder 77.4 18.7 3.9 155
Hypnotherapy could be dangerous 34.8 40.6 24.5 155
Is Hypnotherapy a treatment that you might advise for your patients? Yes Neutral No n=
38.7 32.9 28.4 155
Hypnotherapy should be available through an accredited Hypnotherapist. 83.9 13.5 1.9 155
Would you be willing to provide Hypnotherapy personally (after training)? 20.6 10.3 68.4 155
If Hypnotherapy took 8 × 30 minutes sessions to ensure long-lasting remission in Irritable Bowel Syndrome: Yes No n=
Would this be a cost-effective measure to provide for all Irritable Bowel Syndrome patients? 12.9 83.2 155
Would this be a cost-effective measure to provide for some Irritable Bowel Syndrome patients? 78.1 13.5 155
Would you refer Irritable Bowel Syndrome sufferers to these sessions elsewhere 56.1 28.4 14.8 155
N.B. the rows do not always sum to 100% as missing responses are included in the analysis but not shown.
How should hypnotherapy for IBS be resourced
There is considerable uncertainty and divergence of opinions amongst the respondents on how hypnotherapy should be funded. Only 36.1% of general practitioners thought that more NHS resources should be used to give IBS sufferers better treatment, yet a slightly larger percentage (44.5%) thought that the money could be better spent elsewhere. The proportion of those answered 'unsure' in response to the above 2 statements are at large at 45.8% and 38.7% respectively.
Whilst just under half (49%) would support their primary care organisation investing in hypnotherapy if it were shown to work, there was less support for providing it within the NHS, at 29%. Most general practitioners (56.8%) thought that hypnotherapy should be provided through private hospitals, with a substantial minority (40%) thinking that insurance companies should pay for it. Details of these responses are shown in Table 5.
Table 5 Should hypnotherapy be funded by the National Health Service.
Agree Unsure Disagree n=
National Health Service resources should be used to give Irritable Bowel Syndrome sufferers better treatment 36.1 45.8 18.1 155
National Health Service resources could be better spent on other illnesses 44.5 38.7 16.8 155
I would support my primary care trust investing in Hypnotherapy (if shown to work) 49.0 29.7 21.3 155
Hypnotherapy should be available through the National Health Service 29.0 41.9 28.4 155
Hypnotherapy should be available through private hospitals 56.8 34.2 9.0 155
Medical insurance companies should pay for Hypnotherapy for their clients 40.0 46.5 12.9 155
N.B. the rows do not always sum to 100% as missing responses are included in the analysis but not shown.
Finally despite the divergence and uncertainty on hypnotherapy for IBS sufferers, over three quarters of respondents, 76.3% (table not shown), said that they would be willing to refer patients into a study of hypnotherapy for the treatment of IBS in primary care if it were conducted.
Discussion
The principal findings of the study are that many general practitioners see IBS as a "nervous condition" to be treated with care and attention, as well as drugs and dietary change. There is considerable divergence in the perception of the effectiveness of current approaches, and a willingness amongst many GPs to refer to qualified hypnotherapists if it can be shown to be effective, even though this treatment is considered "alternative" and potentially dangerous. It is also acknowledged that this could not be made a priority for the NHS, but should be provided privately outside it.
Unfortunately there were not sufficient resources available to perform telephone reminders, and this may have in part accounted for the low response rate. Comparison of the achieved sample with the target sample shows that fewer single-handed practices and more training practices took part in the survey, which may reflect a greater willingness of academically oriented practitioners to participate in research. It is beyond the scope of the present study to estimate how representative the sample is of other areas, especially inner cities and other more deprived areas where private hospital and insurance are simply not an option for most patients.
Existing literature suggests that around 40% of patients in the UK have access to complementary therapy [17]. This study indicated that that only 6% of general practitioners had ever provided hypnotherapy as a treatment, although 38% said that they might advise hypnotherapy under some circumstances.
Other studies show that doctors hold different 'public' and 'private' attitudes to IBS as an illness, and may classify patients informally into 'good' and 'bad' patients [11]. This study seems to show general practitioners responding in a 'public' way, saying that IBS needs more resources and that it could be managed more effectively than it is at present.
Another study concluded that 'Drug usage in the IBS is more than is justified and should, in our view, be minimised [10]. The present study seems to show that just under half of the general practitioners felt that drugs were effective with about a third unsure (whether through pharmacological or placebo effects was not shown).
More research is needed into the spectrum of views of general practitioners about IBS. A qualitative study might have provided more information about general practitioner beliefs about IBS and what they feel should be done to improve services. Additional quantitative work might identify whether there are age-sex differences in practitioners attitudes.
The general practitioners were clearly open to hypnotherapy to help manage IBS. It is unclear whether this was a specific attitude towards hypnotherapy, or a general willingness to promote any psychological or supportive therapy for this illness.
It would also be interesting to investigate the opinions of gastroenterologists or psychosomatic therapists.
Conclusions
The vast majority of general practitioners think that current management options for IBS are variable in their effectiveness. Many agreed that this condition needs improved treatment options in primary care; and general practitioners seem willing to consider hypnotherapy as one such treatment option if it can be shown to be effective.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SC conceived the study and developed the questionnaire, TC and S de L analysed the data, S de L with contributions from the other authors wrote the paper.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Appendix 1: The questionnaire Questionnaire used in the study
Click here for file
Acknowledgements
General practitioner colleagues who completed the questionnaire, and to my partners for help in its piloting. West Sussex Health Authority for the distribution of the questionnaire. The questionnaire was designed with the help of Sarah Clements of the South Thames research network (STaRNet.) The study was performed as part of an MSc study year in Evidence Based Healthcare at Rewley College, Oxford, and funded by an Enterprise award from Anglia and Oxford Region. This paper has been written with the support of KSSnet (Kent, Surrey and Sussex primary care research network.)
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| 15482597 | PMC526280 | CC BY | 2021-01-04 16:28:59 | no | BMC Fam Pract. 2004 Oct 13; 5:22 | utf-8 | BMC Fam Pract | 2,004 | 10.1186/1471-2296-5-22 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-5-341546960910.1186/1471-2474-5-34Study ProtocolEffectiveness of behavioural graded activity compared with physiotherapy treatment in chronic neck pain: design of a randomised clinical trial [ISRCTN88733332] Vonk Frieke [email protected] Arianne P [email protected] Mario [email protected] Cees J [email protected] Bart W [email protected] Department of General Practice, Erasmus MC, University Medical Centre Rotterdam, the Netherlands2 Department of Rheumatic diseases and Chronic pain, Hoensbroek Rehabilitation Centre, the Netherlands2004 6 10 2004 5 34 34 6 8 2004 6 10 2004 Copyright © 2004 Vonk 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 neck pain is a common complaint in the Netherlands with a point prevalence of 14.3%. Patients with chronic neck pain are often referred to a physiotherapist and, although many treatments are available, it remains unclear which type of treatment is to be preferred.
The objective of this article is to present the design of a randomised clinical trial, Ephysion, which examines the clinical and cost effectiveness of behavioural graded activity compared with a physiotherapy treatment for patients with chronic non-specific neck pain.
Methods
Eligible patients with non-specific neck pain persisting longer than 3 months will be randomly allocated to either the behavioural graded activity programme or to the physiotherapy treatment. The graded activity programme is based on an operant approach, which uses a time-contingent method to increase the patient's activity level. This treatment is compared with physiotherapy treatment using a pain-contingent method.
Primary treatment outcome is the patient's global perceived effect concerning recovery from the complaint. Global perceived effect on daily functioning is also explored as primary outcome to establish the impact of treatment on daily activity. Direct and indirect costs will also be assessed. Secondary outcomes include the patient's main complaints, pain intensity, medical consumption, functional status, quality of life, and psychological variables. Recruitment of patients will take place up to the end of the year 2004 and follow-up measurement will continue until end 2005.
==== Body
Background
Prevalence and incidence
Neck pain is a common complaint that causes substantial morbidity in western countries with a reported prevalence ranging from 9.5 to 22% [1,2]. Of all musculoskeletal pains in the Netherlands, neck pain is one of the three most reported with a point prevalence of 21%; it is more often reported by women than men [3]. In 1996 total related costs were estimated to be US $686.2 million, which is about 1% of the total Dutch health care expenditures [4]. Most neck complaints are continuous or recurrent [3]. When the neck pain persists for more than 3 months it is defined as chronic, and the related prevalence is 14.3% [3,5]. Although the prevalence of neck pain is stable over different age groups, the incidence of chronic neck pain increases with age [3,6].
There are many potential causes of neck pain, but mostly no specific underlying pathology is found so that it is designated as non-specific [7]. Although not a life- threatening disease, neck pain can negatively affect patients' quality of life, cause pain and stiffness, and may result in substantial medical consumption, absenteeism and disability [4,8].
In the Netherlands, patients with neck pain are often referred for physiotherapy. Moreover, physiotherapy accounted for 84% of the total direct medical neck pain costs in 1996 [4]. Although physiotherapists can apply various treatments, no formal guidelines are yet available.
Treatment models
Two treatment models have been described in the literature, both of which are applicable within the field of physiotherapy. The first, a biomedical model, considers pain to be a sign of physiological damages and treatment according to this model aims to remove the pathologic condition so that the pain will no longer occur [9,10]. Moreover, treatment is guided by the amount of pain a patient experiences, leading to a pain-contingent approach [11]. According to the second, a biopsychosocial model, pain is not necessarily caused by underlying pathology or impairment but can persist long after the initial pathology has healed; psychological and social factors may be important in the development and maintenance of complaints [12,13]. According to the principles of this biopsychosocial model, behavioural therapies assume that maladaptive behaviours are learned and, therefore, can be modified through new learning experiences [10,14]. Three different approaches are known: respondent, operant and, cognitive behavioural therapy [9,15,16]. The present study mainly employs an operant behavioural approach, as described by Fordyce and applied by Lindström et al [11,17]. According to this approach, the treatment focuses on decreasing pain behaviour (operants) and increasing healthy behaviour, and consists of behavioural graded activity on a time-contingent basis [11,18].
Available evidence
Many conservative physiotherapeutic treatments are available for treating neck pain, but there is insufficient evidence to allow to conclude that one type of treatment is more effective then others [19,20].
In a review on chronic pain, operant behavioural therapy was found to be beneficial to waiting list control groups on outcomes such as pain experience, mood effect other than depression, social role, and for the expression of pain behaviour [21]. Compared to other treatments, operant behavioural therapy is only beneficial for the expression of pain behaviour and role functioning [21]. Another review showed little evidence that biopsychosocial multidisciplinary rehabilitation is more effective than other rehabilitation methods for neck and shoulder pain, but the authors found only two relevant studies that satisfied the criteria for their review [22]. When examining the effectiveness of behavioural treatment for chronic pain another difficulty is that no standard protocol exists for the application of these treatments. As a result, a wide range of techniques described in the literature has been labelled as behavioural [23].
In summary, it remains unclear which type of conservative, including behavioural, treatment is to be preferred in the management of chronic neck pain. Therefore, this study, Ephysion (Effectiveness physiotherapy in neck pain), aims to evaluate the clinical and cost effectiveness of an operant behavioural programme (i.e. behavioural graded activity) compared with a physiotherapy treatment in patients with chronic non-specific neck pain. In addition, we aim to identify subgroups of patients who benefit most from one of the two treatments, and to identify the most important determinants for recovery from chronic non-specific neck pain.
Why a design article
Because a biased study design can produce incorrect conclusions, the design of a trial should be carefully examined before adopting its conclusions [24]. A design article allows to examine the design objectively without being influenced by the study results, to check any resulting articles for protocol deviations, and may also reduce the temptation to search for associations during data analysis rather then presenting hypotheses in advance [25]. Further, a published protocol informs others about which studies are in process thus reducing duplication of research effort [25]. Finally, a design article prevents publication bias in the case that future articles are not published, because study results can be retrieved from the author and the study can therefore still be included in future reviews [25,26].
Methods
Study design
A randomised clinical trial (RCT) has been designed to assess the effectiveness of behavioural graded activity compared with physiotherapy treatment in patients with chronic non-specific neck pain. The study design has been approved by the Medical Ethics Technical Commission of the Erasmus MC, University Medical Centre in Rotterdam and is in compliance with the Helsinki Declaration.
Selection of patients and informed consent
Forty general practitioners (GP) in region West Brabant in the Netherlands will select the patients. Patients are eligible if they are aged between 18 and 70 years old, have suffered from neck pain for over three months, and have an adequate knowledge of the Dutch language. Excluded are patients diagnosed with a specific disorder (e.g. a slipped disc, a tumour or a lesion in the cervical spine), those who have had physical/manual therapy during the previous six months, those with a chronic disease (e.g. rheumatoid arthritis or coronary artery disease), or those who have to undergo surgery in the near future. Eligible patients will receive an information leaflet from their GP and the GP then informs the research department.
Thereafter, the research assistant contacts the patient, provides additional information about the implications of participation, re-checks the eligibility of the patient, and completes the informed consent procedure.
Sample size
The sample size for this study is calculated according to the global perceived effect (GPE). Based on previous studies, a 20% difference in GPE is expected after completion of either treatment (9 weeks) and is considered to be clinically relevant; 160 patients are needed to detect this difference. In this calculation a power (1 - β) of 80% is taken into account. Thus, the inclusion of 80 patients per treatment group is planned.
Randomisation
An independent examiner using a computer-generated randomisation schema performs randomisation. To prevent unequal distribution, patients are pre-stratified based on three important prognostic factors: gender, age and the severity of the complaint, which are recorded at baseline [27]. Further, unequal group sizes are prevented by using a 6-block randomisation that equalizes allocation to the two treatment groups per stratum after every sixth patient [28]. After randomisation, patients choose a physiotherapist within the allocated treatment group. Then, to ensure that the treatment starts as soon as possible, the research assistant makes the first appointment for treatment.
Blinding
Patients are told to receive physiotherapy but are blinded to allocation of the two treatments; the content of the treatments is not described in the information leaflet. This enhances the quality of the study, because the patients themselves measure the effect of treatment. GPs are also blinded for allocation to prevent accidentally informing the patients of the allocated treatment. The physiotherapists are not blinded for allocation, but the physiotherapists from each treatment group are kept strictly separate and are not involved in the outcome measurement. Finally, the primary investigator is blinded for patients' allocation but the research assistant is not; neither is involved in the outcome measurement.
Physiotherapists and Interventions
After receiving written information, 34 physiotherapists in region West Brabant will participate in either the physiotherapy treatment (PT) or the graded activity programme (GAP). To optimise the contrast between the two treatments, both groups are strictly separated throughout the study. The PT group consists of 16 physiotherapists and the GAP group of 18 physiotherapists. The PT physiotherapists participate in a meeting to standardize the physiotherapy treatment. The GAP physiotherapists are instructed on the behavioural graded activity approach during a two-day theoretical and practical training course.
Both interventions are performed in an outpatient setting. A maximum of 18 treatments per patient is set and each treatment takes about 30 minutes, which is in accordance with medical insurance policy in the Netherlands. Before treatment starts, physiotherapists receive a completed questionnaire about the patient's main complaints [29]; this questionnaire reveals the three daily activities which are considered the most important complaints to the patient. Physiotherapists can use these three activities in the process of formulating the patient's primary therapy aim. In both treatments, the physiotherapist starts with a physical examination of the patient and an anamnesis. Then an individually tailored program will be applied and the process recorded after each treatment session using a specially designed form.
The physiotherapy treatment
The content of the physiotherapy treatment is decided by consensus among the participating PT physiotherapists. Treatment is according to a biomedical model, which implies guidance based on the amount and severity of pain that the patient experiences.
By consensus, the physiotherapy treatment is divided into the patient's primary therapy aim, three general treatment goals, and several techniques to attain those goals. The primary therapy aim is defined as the result the patient wants to achieve by the end of therapy. A general treatment goal is a goal for each single treatment and could, therefore, differ per treatment session. Table 1 shows the three general treatment goals, together with the techniques physiotherapists can choose to attain them. In daily practice a broad spectrum of treatment techniques are available, but in this study the techniques to be used consist of physiotherapy techniques with a strong focus on exercises. Moreover, manipulative techniques, acupuncture and other (alternative) techniques are excluded, as are physiotherapeutic applications such as ultrasound or diathermy.
Behavioural graded activity
An operant approach was the basis of the behavioural graded activity programme as used in this study. The treatment is according to a biopsychosocial model, which implies that it is guided by the patients' functional abilities and that time-contingent methods are used to increase the activity level of the patient [11]. The behavioural graded activity programme has three phases; a baseline phase, a treatment phase, and a generalization phase. These phases are not bound to strict time limits but can gradually merge into each other.
Before starting the baseline phase, the treatment vision and the patient's ideas about pain and its causes are discussed. The development and maintenance of pain will be explained and patients are reassured that it is safe to move and to increase their level of activity [11,13,30]. Both are explained by means of a pain model, which has been derived from the fear-avoiding-model of Vlaeyen et al. [13]. Thereafter primary therapy aims are formulated based on the patient's main complaints, which are described as three daily activities and were revealed in the baseline questionnaire. For each of these activities, a baseline level of intensity is determined based on a pain-contingent measure. This means that patients perform each activity at least three times, each time until they have to stop because of their pain. Afterwards, patient and physiotherapist together set a start quota and time-contingent treatment quotas for each activity. The quotas will be based on the patient's mean baseline scores, primary therapy aims [17], and on the behaviour that can be derived from the baseline measure. If necessary, facilitating disorder-oriented exercises can be added to the treatment as preparation for the activities that were pointed out as main complaints. The same approach as used for the main complaint is used for these exercises.
During the treatment phase, patients systematically increase the time-contingent quotas to enable them to reach their personal aims within a pre-set therapy time period. To ensure a successful experience during the first exercise, the start quota is below the mean baseline score. The pre-set exercise quotas have to be strictly followed; neither over-performance nor under-performance is allowed. During this phase the patient has to practice at home and document every activity or exercise on a performance chart. These charts will be discussed in the following treatment session and achievements will be reinforced while disregarding pain behaviours. Positive reinforcements of healthy behaviour and the patient's experiences of success are considered to be important to enhance the patient's motivations.
The generalization phase takes place at the end of the treatment phase. In this phase generalization of learned behaviour and management of relapses will be discussed.
Outcome measurement
Baseline questionnaires are sent after inclusion, which is as soon as possible after patients have consulted their GP. Outcome of intervention will be assessed at 4 and 9 weeks after randomisation; however, if the treatment is not finished at 9 weeks, the patients will receive an additional questionnaire (Ts) after finishing the treatment. Follow-up assessments are planned at 26 and 52 weeks after randomisation.
All outcome measures are reported by means of mailed questionnaires. Table 2 presents the outcome variables, the instruments used and the moments at which they are measured.
Primary treatment outcome of this study is the global perceived effect, which is used to assess recovery from the complaint [31]. In addition, the global perceived effect in daily functioning was explored in order to also establish impact of treatment on daily activity. Both treatment outcomes (recovery of complaint and functioning in daily activity), are assessed on a 7-point Likert-scale, ranging from completely recovered (1) to worse than ever (7).
Costs are measured using a combination of questionnaires to collect data on direct medical costs (e.g. the amount of received treatment and additional therapy received), and indirect costs due to sick leave and disability.
Secondary outcome measures include main complaints, pain intensity, medical consumption, coping, functional status, quality of life, and psychological variables. Prognostic factors are measured including demographic variables, the baseline variables and the psychological variables (table 2).
Analyses
Descriptive statistics will be used to examine comparability of baseline data between PT and GAP, and to check if randomisation was successful. Before this analysis, decisions about differences considered to be clinically relevant are made and, if necessary, adjustment will be made for these differences in multivariate analysis. Further, all outcome data will be screened for normality and, if necessary, logarithmic transformations or non-parametric methods of analysis will be applied.
The first aim is to evaluate the clinical and cost effectiveness of GAP compared to PT. Clinical effectiveness will be examined with a Student's t-test (continuous), a Chi-square test (dichotomised) or a Wilcoxon test (not normally distributed) according to the intention-to-treat principle. This means that patients will be analysed in the treatment group to which they are randomly allocated. For missing data, imputation techniques will be used. When the dropout rate is 10% or more, or loss to follow-up is 20% or more, per-protocol analysis will be performed. The results on primary outcome will be dichotomised into improved versus not improved. Improved implies completely recovered and much improved, whereas not recovered implies slightly improved, not changed, slightly worsened, much worsened, and worse than ever [31].
Cost effectiveness will be calculated from a societal perspective. Costs (direct as well as indirect) will be related to the treatment effects, based on the primary outcome measure, by calculating cost-effectiveness ratios.
The second aim is to identify subgroups of patients that benefit most from one of the two treatments. The following subgroups will be investigated: duration and severity of the complaint, depression, and fear of movement.
The third aim is to identify important variables for recovery. For this purpose multivariate analysis will be performed to investigate the influence of prognostic variables and patient characteristics on the outcome. Separate analyses will be conducted to investigate prognostic factors for short-term (3 months) and long-term (12 months) recovery.
Discussion
This study is designed to evaluate the clinical and cost effectiveness of a behavioural graded activity programme compared with a physiotherapy treatment in patients with chronic non-specific neck pain. Since physiotherapists perform both treatments in this study, contrast between the two treatments is a very important issue. There are contrasts both in the composition of the treatment and the way the physiotherapists approach the patient. With regard to the composition, the graded activity programme (GAP) starts with a systematically performed baseline measurement; this is in contrast to the physiotherapy treatment (PT), where treatment is based on history taking and physical examination. In GAP quotas are set based on the patient's behaviour, whereas in PT they are set based on pain levels and training principles. After quotas are set GAP uses a time-contingent treatment approach, which involves a pre-set systematic increase in activities. In contrast, PT uses a pain-contingent approach, which means that treatment is adapted to the patient's reaction to previous treatment sessions. Furthermore, GAP uses a hands-off approach, whereas PT may contain hands-on techniques, such as massage, traction etc (Table 1).
This study addresses an important question because chronic neck pain is a common complaint and it remains unclear which type of physiotherapeutic treatment is most effective. Recruitment of patients will take place until up to the end of 2004; follow-up measurement will continue up to end 2005.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
APV and BWK conceived the study, developed the design of the randomised clinical trial and participated in writing the article. MG is an expert in the field of graded activity and contributed to the content of the article. CJV advised on the content of the article. FV conducts the research, participated in the completion of the study design and wrote the article. All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Table 1 Treatment goals and techniques that can be used to reach the primary treatment aim.
Treatment goals Techniques
relaxation and preperation for exercise - Massage
- Relaxation exercise
- Thoracal treatment up to thoracal 9.
- Localized 3-d mobilization within physiological boundary of the joint capsule
- Mobilization in al directions within physiological boundaries.
- Traction within physiological boundaries.
- Techniques of Mulliken excluding manipulation
- Techniques of McKenzie excluding manipulation
Education Can take place at the same time as the first treatment target. Education includes patient reassurance; explanation of (physiological) load and capability of carrying a load; and encouragement of physical activity
Exercise - Passive exercise, guided active exercise, and active exercise
- Exercise at the physiotherapist
- Assign homework
Table 2 Overview of variables measured in this study
Variable Time Measured Range of unit
T0 T4 T9 Ts T26 T52
Inclusion and exclusion variables x
Demographic variables x
Baseline variables
Specific complaint characteristics x
Experience of the neck complaint and functioning in daily activities x 1–7 (Likert scale)
Co-morbidity x
Additional complaints x
Primary outcome
'Global perceived effect' (neck complaint and functioning in daily activities) [31] x x x x x 1–7 (Likert scale)
Secondary outcomes
Main complaint [29] x x x x x x 0–10 (Likert scale)
Pain (VAS) [31] x x x x x x
Medical consumption x x x x x x Dose per day
Coping with Multi-dimensional pain (MPI) Part I-II [32] x x x x 0–6 (Likert scale)
Activity (MPI, part III) x x x x x x 0–6 (likert scale)
Specific functional status (NDI) [33] x x x x x x
Quality of life (SF-36) [34, 35] x x x x
(EQ-5d) [35, 36] x x x x x x
Work activities x x x x Hours/week
Satisfaction about treatment x x x x 1–5 (Likert scale)
Compliance with treatment exercise x x x x x Number and time per week
Additional treatments x x x x x Discipline and number of treatments
Side-effects x x x x x Yes – No and any additional elucidation
Psychological (prognostic) variables
Fear of movement (TSK) [37] x x x 1–4 (likert scale)
Catastrophizing (PCS) [38] x x x 1–5 (likert scale)
Depression (CES-D) [39] x x x 1–4 (likert scale)
Self-efficacy (PSEQ) [40] x x x x x x 10–100% (very unsure – very sure)
Stages of change (PSOCQ) [41] x x 1–5 (likert scale)
Note: T0 = baseline measurement, T4, T9, (TS), T26, T52 are follow-up measurements at 4, 9, 26 and 52 weeks, respectively, after randomisation. Ts was received at the end of treatment, when treatment lasted longer than 9 weeks. MPI, Multidimensional Pain inventory; NDI, Neck Disability Index; SF-36, Short Form 36; EQ-5d, Euroquol 5-Dutch language version; TSK, Tampa Scale for Kinesiophobia; PCS, Pain Catastrophizing Scale; CES-D, Centre for Epidemiologic Studies – Depression; PSEQ, Pain Self-Efficacy Scale; PSOCQ, Pain Stages Of Change Questionnaire.
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| 15469609 | PMC526281 | CC BY | 2021-01-04 16:03:42 | no | BMC Musculoskelet Disord. 2004 Oct 6; 5:34 | utf-8 | BMC Musculoskelet Disord | 2,004 | 10.1186/1471-2474-5-34 | oa_comm |
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BMC Pulm MedBMC Pulmonary Medicine1471-2466BioMed Central London 1471-2466-4-91545856610.1186/1471-2466-4-9Research ArticleIs nebulized saline a placebo in COPD? Khan Shahina Y [email protected]'Driscoll B Ronan [email protected] Dept of Respiratory Medicine, Hope Hospital, Salford, M6 8HD UK2004 30 9 2004 4 9 9 1 7 2004 30 9 2004 Copyright © 2004 Khan and O'Driscoll; licensee BioMed Central Ltd.2004Khan and O'Driscoll; 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
Many trials of nebulized therapy have used nebulized saline as a "placebo". However, nebulized isotonic saline is sometimes used to assist sputum expectoration and relieve breathlessness in COPD patients. We designed this study to establish if nebulized saline had a placebo effect or a clinical effect.
Methods
40 patients were studied following hospital admission for exacerbated COPD (mean FEV1 30% predicted). Patients were randomised to single-blind administration of either 4 mls of nebulized isotonic saline using an efficient nebulizer (active group n = 20) or an inefficient nebulizer (placebo group n = 20). Spirometry and subjective breathlessness scores (Modified Likert Scale) were measured before nebulized treatment and 10 minutes after treatment.
Results
There was no significant change in FEV1 after active or placebo nebulized saline treatment. Patients reported a 4% improvement in mean breathlessness score following placebo (Wilcoxon test; p = 0.37) compared with 23% improvement following active nebulized saline (p = 0.0001). 65% of patients given active nebulized saline but only 5% of the placebo group reported that mucus expectoration was easier after the treatment.
Conclusions
This study lends support to the current use of nebulized saline to relieve breathlessness (possibly by facilitating sputum clearance) in COPD patients. Lung function was not affected. Nebulized saline can therefore be used as a placebo in bronchodilator studies involving COPD patients but it cannot be used as a placebo in trials assessing symptom relief.
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Background
Nebulized saline is used by some doctors and physiotherapists to assist mucus clearance and to relieve breathlessness in patients with COPD, bronchiectasis and Cystic Fibrosis. This practice is justified by a small number of studies which have demonstrated enhanced sputum expectoration or improved breathlessness after nebulized saline or humidified oxygen [1-4]. Nebulized hypertonic or isotonic saline has been used to obtain induced sputum specimens from patients with asthma and COPD for diagnostic and experimental purposes [5-9]. For example, Vlachos-Mayer and colleagues [5] used increasing strengths of nebulized saline (from isotonic up to 5%) to induce sputum in 304 patients with asthma and 25 patients with COPD. Satisfactory specimens were obtained in 93% of cases, 17% of asthmatic patients and 56% of COPD patients required only isotonic saline to achieve sputum induction.
However, nebulized saline has also been used as a placebo in several trials involving nebulized bronchodilator therapy [10,11]. For example, Jenkins et al [10] found in a double blind study that patients reported clinical benefit from nebulized saline (with MDI bronchodilator therapy) which was similar to the subjective response to nebulized bronchodilator therapy given with placebo MDI therapy. It was assumed that these patients had a placebo response to nebulized saline but it is also possible that they may have experienced a non-bronchodilator benefit from nebulized saline. We have designed a trial to determine whether the symptomatic benefit associated with nebulized saline use in clinical trials is a placebo effect or a non-bronchodilator therapeutic effect.
Methods
40 patients were studied during a hospital admission for an exacerbation of COPD. Patients were recruited at a time when their condition had stabilized prior to their planned discharge from hospital. Clinical details of the patients are summarised in table 1. Six patients undertook both limbs of the study (partial crossover design).
Table 1 Patient Characteristics
Active group Placebo group
Gender 13 Male, 7 Female 12 Male, 8 Female
Age (Mean, SD) 68.1 +/- 7.2
Range 54–79 67.3 +/- 7.4
Range 58–79
Mean FEV1
95% CI 0.77 Litres
0.65–0.89 0.78 Litres
0.61–0.95
FEV1 as % predicted (Mean and SD) 29.9 (10.0) 30.6 (14.8)
Patients were randomised to receive 4 mls of 0.9% saline using an efficient nebulizer system (active group) or an inefficient nebulizer system (placebo group). The active nebulizer was a System 22 Acorn nebulizer (Medic-Aid, Bognor Regis UK Ltd) driven by the hospital's piped oxygen supply at a flow rate of 9 l/min for 10 minutes. This nebulizer system was found to deliver 95% of particles in the size range 2.5 to 2.8 microns using a Malvern laser system. (Measurement courtesy of Dr Steve Newman, Principal Physicist, Royal Free Hospital, London, UK). This small particle size was selected to achieve effective delivery to the airways. The placebo nebulizer was an old model (1980s) Bard Inspiron nebulizer (no longer manufactured) driven by oxygen at a flow rate of 3 l/min. This nebulizer system delivered 95% of particles in the size range 9.5 to 9.9 microns. This particle size was selected to achieve a placebo effect with deposition in the tubing of the system and in the pharynx but little penetration to the airways [12]. Both nebulized treatments were administered by mouthpiece to avoid nasal deposition of saline droplets and to make it less likely that patients would notice that the output from the placebo system was different to previous nebulized treatment which they had received.
The trial was conducted in a single-blind manner. 40 slips of paper were labelled "Treatment A" or "Treatment B" and placed in opaque brown envelopes. These were shuffled in random order and each patient was asked to select one envelope. This was then opened by the investigator and the appropriate treatment was administered (A active, B Placebo). For the six patients who took part in the study twice, the second treatment consisted of whichever treatment they had not received previously.
Patients were told that we wished "to observe the effects of a nebulized treatment which is not a new or experimental drug". They were not informed of the exact nature of the nebulized treatments as this might have led patients to try to guess if the treatment which they received was a "placebo". The Ethics Committee agreed that it would not have been possible to measure a true placebo effect if patients were made aware that both treatments were saline (not a bronchodilator) and one of the nebulizers was deliberately made to run inefficiently.
Patients were recruited on the Respiratory Wards of a University hospital. We recruited patients who had diagnosis of COPD confirmed by a respiratory consultant (patients with asthma or bronchiectasis were excluded from the study). Patients were approached by one of the investigators whilst in a relatively stable phase prior to discharge from hospital following an admission for exacerbated COPD. All testing was undertaken between 12.00 and 16.00, at least four hours after bronchodilator treatment.
Prior to participating in the study, patients gave informed consent and undertook baseline measurement of FEV1 and FVC using the best of 3 blows on a Microlab 3300 Spirometer (Micro-Medical LTD, Rochester, UK. Peak Expiratory Flow (PEF) was measured using a Wright's Peak Flow meter. Each patient also recorded an assessment of their perceived level of breathlessness using a seven point modified Likert scale (1 = Not breathless, 2 = Very mild breathlessness, 3 = Mild breathlessness, 4 = Moderate breathlessness, 5 = Severe breathlessness, 6 = Very severe breathlessness, 7 = Worst possible breathlessness).
Ten minutes after completion of nebulized therapy, FEV1, FVC and PEF measurements and subjective breathlessness scores were repeated. Patients also recorded a subjective assessment of benefit using the following modified Likert scale. (1 = No benefit from this treatment, 2 = Very slight benefit, 3 = Slight benefit, 4 = Moderate benefit, 5 = Good benefit,, 6 = Very good benefit, 7 = Best possible benefit).
Patients then received 4 puffs of salbutamol (400 mcg) using a Metered Dose Inhaler and 750 ml Volumatic spacer (Glaxo Smith Kleine UK).
Fifteen minutes later, FEV1, FVC and PFR measurements and subjective breathlessness scores and symptom relief scores were repeated.
All data was entered on a SPSS version 9 statistical package. Mann Whitney U-test was used to compare lung function tests and symptom relief scores. Wilcoxon Signed Rank test was used to compare the change in breathlessness scores for matched pairs before and after nebulized saline.
The study was approved by Salford and Trafford Research Ethics Committee. All patients gave written informed consent to partake in the study and to receive a single dose of nebulized treatment (in addition to all usual treatment).
Results
34 patients completed the study; patient details are summarised in table 1. 6 patients took part in the study twice (once in each limb). This allowed 20 treatments with each nebulizer system to be compared.
The baseline FEV1 of the two treatment groups was well matched. Both groups had a non-significant fall in FEV1 after nebulized saline therapy and a small rise in FEV1 after 400 mcg salbutamol from MDI-spacer (Table 2). FVC, and PEF changes (not shown in table) were similar to FEV1 changes.
Table 2 Results All results expressed as Medians in top line and Mean (and 95% CI) in second line.
Active group Placebo group P value (Mann Whitney)
FEV1 0.77 0.80 0.84
Pre-treatment 0.77 (0.65–0.89) 0.78 (0.61–0.95)
FEV1 0.75 0.73 0.74
Post nebulized saline 0.73 (0.62–0.84) 0.75 (0.59–0.91)
FEV1 0.80 0.77 0.63
Post salbutamol MDI 0.81 (0.67–0.94) 0.79 (0.62–0.96)
Breathlessness 4 4 0.34
Score 1 (Pre-treatment). 3.9 (3.6–4.3) (1 = Not breathless, 7 = Worst possible breathlessness) 3.5 (3.0–4.0)
Breathlessness 3 4
Score 2 (Post nebulized saline) 3.0 (2.6–3.5) 3.3 (2.8–3.9) 0.85
Wilcoxon test Score 1 V Score 2 <0.0001 0.37
Breathlessness 3 3 0.35
Score 3 (Post salbutamol MDI) 2.9 (2.5–3.3) 3.0 (2.6–3.5)
Wilcoxon test Score 2 V Score 3 0.43 0.014
Symptom Relief 3 1 0.0002
Score Post nebulized saline 3.1 (2.7–3.6) (1 = No benefit, 7 = Best possible benefit) 1.7 (1.2–2.3)
Symptom Relief 3 3 0.37
Score Post salbutamol MDI 3.2 (2.7–3.7) 2.9 (2.4–3.4)
The placebo group had a 4% improvement in breathlessness after treatment (Wilcoxon p = 0.37) compared with a 23% improvement after active nebulized saline (Wilcoxon p = 0.0001). This corresponded to a reduction from 4/7 (moderate breathlessness) before treatment to 3/7 (mild breathlessness) after treatment in the active treatment group.
The mean symptom relief score (patient's assessment of benefit) for the active treatment was 3/7, (slight benefit) almost identical to the response to 400 mcg salbutamol from MDI. The placebo group had a score of 2/7(very slight benefit) after nebulized placebo and 3/7(Slight benefit) after 400 mcg salbutamol from MDI.
15 patients in the active group felt better after nebulized saline, 5 felt the same and no patient felt worse. Six patients in the placebo group felt better after nebulized treatment, 12 felt the same and 2 felt worse (Chi Squared test p = 0.013).
Patients were asked if the nebulized treatments had any effect other than relief of breathlessness. 13 patients in the active group (65%) said that the nebulized treatment assisted sputum expectoration. Only 1 patient in the placebo group reported this effect (Difference between groups: -Fisher exact test, p = 0.0001). No patient reported any adverse effects from either nebulized treatment.
Discussion
This is the first study which has compared active nebulized saline with placebo nebulized saline. The results suggest that nebulized saline has non-bronchodilator therapeutic effects that are possibly explained by airway-moistening and sputum-inducing effects of nebulized saline. Sputum volume was not measured in the present study but two thirds of patients who were given nebulized saline through an efficient nebulizer system reported that it helped them to expectorate sputum. This finding is consistent with the results of previous studies which have shown improved sputum clearance and decreased breathlessness following the open administration of nebulized saline [1,3] The results of the present study may be explained by airway-moistening and sputum-inducing effects of nebulized saline, both isotonic and hypertonic [1-9] The study of Vlachos-Mayer and colleagues [5] showed that most asthmatic patients required hypertonic saline to achieve sputum induction but more than half of COPD patients achieved sputum induction with nebulized isotonic saline (similar to the finding that 65% of COPD patients in the present study reported enhanced sputum clearance following nebulized saline).
Previous studies have shown that nebulized saline can have a bronchoconstrictor effect in some patients which is greater with hypertonic saline than with isotonic saline and greater in asthma patients than COPD patients [5,6,8,9] Nebulized isotonic saline had no significant effect on FEV1 in the study of Poole et al [3] or in the present study.
The main strength of the present study is inclusion of a placebo limb using an inefficient nebulizer system. Patients in the placebo group believed that they were receiving a nebulized treatment because a placebo effect could have been abolished if patients were told that both treatments involved no active medication and one of the patients involved an inefficient nebulizer system. This issue was discussed fully with the ethics committee and found to be acceptable because the patients did not miss any of their regular medication and they did not receive any pharmacological treatment. The use of a mouthpiece ensured that patients could not see or feel that the output from the experimental system was different to the nebulized bronchodilator therapy which they had received during their hospital admission (usually via a facemask). Furthermore, only 6 patients took part in both limbs of the study so most patients could not have tried to guess which treatment was more effective.
The 23% improvement in breathlessness in the active group was equivalent to the subjective benefit following 400 mcg of salbutamol from MDI-spacer. This improvement in breathlessness occurred without bronchodilation, mirroring the findings of Poole et al [3]. Based on the patients' observations and the results of previous studies, we believe that the therapeutic effect of nebulized saline may be produced by enhanced sputum clearance. A previous study at this hospital showed a similar subjective response to nebulized saline (given at 7 am) but the previous study also reported an improvement in FEV1 and PEF [13]. Patients in the previous study received nebulized saline on awakening, prior to their first bronchodilator treatment of he day. In these circumstances, it is likely that the nebulized saline assisted the expectoration of copious overnight secretions in the airways with some subsequent improvement in airflow. Patients in the present study were treated at about mid-day, having had bronchodilator therapy on awakening. It is therefore not surprising that the beneficial effects of nebulized saline were more modest in the present study. However, this study lends support to the common clinical practice of allowing patients with COPD to have nebulized saline "as required" as a supplement to regular nebulized bronchodilator therapy. This may assists sputum expectoration and relieve breathlessness without the side-effects that would occur if additional beta agonist treatment were given.
This study is in agreement with previous studies which have shown no bronchodilator effect (or a small bronchoconstrictor effect) when nebulized saline is given to patients with COPD [3]. This justifies the continuing use of nebulized saline as a placebo treatment in clinical trials of bronchodilator therapy which measure rise in FEV1 or PEF as the primary outcome measure.
However, as nebulized saline has non-bronchodilator therapeutic effects, it cannot be used as an inert placebo treatment in clinical studies where breathlessness or quality of life are to be measured. For example, Jenkins et al concluded that nebulized treatment had a strong placebo effect because patients expressed a preference for nebulized treatment even though the same bronchodilator effect could be achieved for most of their patients when nebulized saline was given with active bronchodilator therapy from a MDI device [10]. It is likely that many of these patients experienced a non-bronchodilator therapeutic benefit such as enhanced mucus clearance during nebulized saline therapy. It would be possible to co-administer nebulized saline with MDI bronchodilator therapy as an alternative to nebulized bronchodilator therapy for some patients with COPD who report difficulties with mucus clearance. However, this would be more inconvenient than nebulized bronchodilator therapy (and at least as expensive). For future clinical trials it would be possible to have two control groups, one receiving nebulized saline using an efficient system and one group using an inefficient system such as that used in the present study. This would allow investigators to assess whether nebulized saline had any therapeutic effect on their patients and it would also assess the true placebo response rate.
British and European nebulizer guidelines state that most patients with airflow obstruction should be treated with hand-held devices unless they have demonstrated clear additional benefit from the use of nebulized treatment in carefully monitored domiciliary studies [12,14]. The present study supports these recommendations, especially the provision that some patients may be commenced on nebulized treatment on the basis of substantial subjective benefit even if an additional bronchodilator response cannot be demonstrated. This study also supports the present practice of many physiotherapists and doctors who use nebulized isotonic saline to assist sputum clearance for patients with COPD who have difficulty in expectorating sputum.
Abbreviations
FEV1 Forced Expiratory Volume in 1 second
FVC Forced Vital Capacity
PEF Peak Expiratory Flow
COPD Chronic Obstructive Pulmonary Disease
MDI Metered dose inhaler
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BROD developed the concept for the study and both authors designed the study protocol.
SYK recruited patients and performed all study measurements.
Both authors assisted in analysis of the data and preparation of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors would like to thank Dr Steve Newman of the Royal Free Hospital, London, UK for testing the two nebulizer systems used in the trial.
==== Refs
Sutton PP Gemmell HG Innes N Davidson J Smith FW Legge JS Friend JA Use of nebulized saline and nebulized terbutaline as an adjunct to chest physiotherapy Thorax 1988 43 57 60 3353875
Eng PA Morton J Douglass JA Riedler J Wilson J Cobertson CF Short-term efficacy of ultrasonically nebulized hypertonic saline in cystic fibrosis Paediatr Pulmonol 1996 21 77 83 10.1002/(SICI)1099-0496(199602)21:2<77::AID-PPUL3>3.3.CO;2-R
Poole PJ Brodie SM Stewart JM Black PN The effects of nebulized isotonic saline and terbutaline on breathlessness in severe chronic obstructive pulmonary disease (COPD) Aust NZ J Med 1998 28 322 326
Conway JH Fleming JS Perring S Holgate ST Humidification as an adjunct to chest physiotherapy in aiding tracheo-bronchial clearance in patients with bronchiectasis Respir Med 1992 86 109 114 1615175
Vlachos-Mayer H Leigh R Sharon RF Hussack P Hargreave FE Success and safety of sputum induction in the clinical setting Eur Respir J 2000 16 997 1000 11153606 10.1183/09031936.00.16599700
Rytila PH Lindqvist AE Laitinen LA Safety of sputum induction in chronic obstructive pulmonary disease Eur Respir J 2000 15 1116 1119 10885433 10.1034/j.1399-3003.2000.01522.x
Wark AB Simpson JL Hensley MJ Gibson PG Safety of sputum induction with isotonic saline in adults with acute severe asthma Clin Exp Allergy 2001 31 1745 1753 11696051 10.1046/j.1365-2222.2001.01230.x
Sutherland ER Pak J Langmack EL Silkoff PE Martin RJ Safety of sputum induction in moderate-to-severe chronic obstructive pulmonary disease Respiratory Medicine 2002 96 482 486 12194630 10.1053/rmed.2002.1342
Cataldo D Foidart JM Lau L Bartsch P Djukanovic R Louis R Induced Sputum: Comparison between isotonic and hypertonic saline solutions in patients with asthma Chest 2001 120 1815 1821 11742907 10.1378/chest.120.6.1815
Jenkins SC Heaton RW Fulton TJ Moxham J Comparison of domiciliary nebulized salbutamol and salbutamol from a metered dose inhaler in stable chronic airflow limitation Chest 1987 91 804 07 3556051
Goldman JM Teale C Muers MF Simplifying the assessment of patients with chronic airflow limitation for home nebulizer therapy Respiratory Medicine 1992 86 33 38 1533047
Boe J Dennis JH O'Driscoll BR European Respiratory Society Guidelines on the use of nebulizers European Respiratory Journal 2001 18 228 242 11510796 10.1183/09031936.01.00220001
O'Driscoll BR Kay EA Taylor RJ Bernstein A Home nebulizers: can optimal therapy be predicted by laboratory studies? Respiratory Medicine 1990 84 471 477 2148827
O'Driscoll BR Nebulizers for chronic obstructive pulmonary disease Thorax 1997 52 S49 52 9155851
| 15458566 | PMC526282 | CC BY | 2021-01-04 16:30:10 | no | BMC Pulm Med. 2004 Sep 30; 4:9 | utf-8 | BMC Pulm Med | 2,004 | 10.1186/1471-2466-4-9 | oa_comm |
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BMC OphthalmolBMC Ophthalmology1471-2415BioMed Central London 1471-2415-4-141547390210.1186/1471-2415-4-14Research ArticleErytrocyte membrane anionic charge in type 2 diabetic patients with retinopathy Budak Yasemin [email protected] Hakan [email protected] Muberra [email protected] Dilek [email protected] SSK Sevket Yilmaz Hospital, Section of Clinical Chemistry, Turkey2 SSK Sevket Yilmaz Hospital, Section of Family Medicine, Turkey3 SSK Sevket Yilmaz Hospital, Section of Ophthalmology, Turkey4 Marmara University Medical School, Section of Endocrinology and Metabolism, Turkey2004 8 10 2004 4 14 14 26 5 2004 8 10 2004 Copyright © 2004 Budak 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 Steno hypothesis states that changes in basement membrane anionic charge leads to diabetic microvascular complications. In diabetic nephropathy, loss of basement membrane glycosaminoglycans and the association between glomerular basement membrane heparan sulphate and proteinuria has been documented. A correlation between erythrocyte surface and the glomerular capillary wall charges has also been observed.
The aim of this study is to evaluate the relationship between retinopathy and erythrocyte anionic charge and urinary glycosaminoglycan excretion in type 2 diabetic patients.
Methods
49 subjects (58 ± 7 yrs, M/F 27/22) with type 2 diabetes with proliferative retinopathy (n = 13), nonproliferative retinopathy (n = 13) and without retinopathy (n = 23) were included in the study. 38 healthy subjects were selected as control group (57 ± 5 yrs, M/F 19/19). Erythrocyte anionic charge (EAC) was determined by the binding of the cationic dye, alcian blue. Urinary glycosaminoglycan and microalbumin excretion were measured.
Results
EAC was significantly decreased in diabetic patients with retinopathy (255 ± 30 ng alcian blue/106 RBC, 312 ± 30 ng alcian blue/106 RBC for diabetic and control groups respectively, p < 0.001). We did not observe an association between urinary GAG and microalbumin excretion and diabetic retinopathy. EAC is found to be negatively corralated with microalbuminuria in all groups.
Conclusions
We conclude that type 2 diabetic patients with low erythrocyte anionic charge are associated with diabetic retinopathy. Reduction of negative charge of basement membranes may indicate general changes in microvasculature rather than retinopathy. More prospective and large studies needs to clarify the role of glycosaminoglycans on progression of retinopathy in type 2 diabetic patients.
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Background
Diabetic retinopathy is the leading cause of blindness in diabetic adults [1,2]. During the first two decades of the disease, nearly all patients with type 1 diabetes and > 60 % of type 2 diabetes have retinopathy. The duration of the diabetes is probably the strongest predictor for development and progression of retinopathy [3]. The incidence of retinopathy positively correlates with HbA1c [4].
Diabetic patients with evidence of nephropathy are characterized by a 5 to 10 times higher incidence of proliferative retinopathy [5]. Albuminuria not only is associated with kidney disease but, is also a strong predictor of cardiovascular disease and proliferative retinopathy, suggesting it reflects a generalized vascular disease [6]. Thus, the coincidence of generalized vascular dysfunction, albuminuria, mesangial expansion, proliferative retinopathy and accelerated development of atherosclerosis suggests a common cause of abnormalities in susceptible diabetic patients.
The Steno hypothesis proposed that increased loss of anionic charge from basement membranes leads to diabetic microangiopathy [7]. This hypothesis has been proven for diabetic nephropathy but it's not clear for diabetic retinopathy.
Anionic content of basement membranes predominantly produced by glycosaminoglycan molecules (GAG). They constitute the charge selective barrier of the glomerul basement membrane [8]. Several clinical studies indicate that loss of the GAG is associated with diabetic albuminuria in diabetic and experimental models [9,10].
Although abnormal GAG metabolism is a well known phenomen in diabetic nephropathy, it is still debated whether it has a pathogenic role in diabetic retinopathy. The aim of this study was to evaluate the relationship between diabetic retinopathy and erythrocyte anionic charge as well as the urinary glycosaminoglycan excretion in type 2 diabetic patients.
Methods
49 outpatients (27 male, 22 female) with type 2 diabetes mellitus diagnosed after the age of 30, were included and divided into 3 subgroups according to severety of retinopathy. Diabetes was diagnosed according to American Diabetes Association criteria. The onset of diabetes was after the age of 30 in these patients.
The study was approved by the institutional ethics committee and patients gave written informed consent. Exclusion criteria included hypertension, myocardial infarction, cerebrovascular disease, heart failure, treatment with antiaggregants, steroids or other drugs that effect blood pressure and glucose, serum creatinine > 200 μmol/l, connective tissue disorders and other systemic disease.
Renal and bladder infection diseases of patients and controls were excluded biochemically and microbiologically.
The ocular fundus was examined by an ophthalmologist after dilation of the pupils and classified. The patients were divided into three groups according to the severity of retinopathy. Group 1 consisted of 23 patients without retinopathy (R0). Group 2 consisted of 13 patients with nonproliferative retinopathy (R1-hemorrhages, microaneurysms, cotton-wool exudates). The 13 patients in group 3 (R2) had proliferative retinopathy (R2-neovascularisations, vitreous hemorrhages, ablatio of the retina).
Age and sex matched 38 healthy volunteers were included as healthy controls. Demographic characteristics of the groups are shown in Table 1.
Venous samples were collected after an overnight fast and 24 hour urine samples collected.
The charge on erythrocytes (RBC) was measured by means of cationic dye alcian blue 8GX (Sigma catalogue no: A 5268) according to the method of Levin with minor modifications as follows: [11]. From citrated venous blood samples, platelets and leukocytes were removed using the method of Beutler et al. [12]. After removal of platelets and leucocytes erythrocytes were washed 3 times in phosphate buffered saline (PBS), and subsequently a fraction of RBC was resuspended in PBS containing alcian blue at a final concentration of 250 mg/l. After a 30 min incubation at 37°C, the RBC suspension was centrifuged, and the remaining alcian blue concentration was measured in the supernatant with a Shimadzu UV 1201 V spectrophotometer (Shimadzu, Japan) at a wavelength of 650 nm. Each determination represents the mean of 2 assays. The quantity of alcian blue bound to RBCs was expressed as nanograms of alcian blue per 106 RBCs. In our experimental conditions the intraassay and interassay coefficients of variation were 5.8 and 7.6 % for 100 nanograms of alcian blue per 106 RBCs.
Urinary glycosaminoglycan (UGAG) excretion was determined in 24 hr urine samples spectrophotometrically at 520 nm by the addition of dimethylmethylene blue (Aldrich Chem Co., USA) and bovine kidney heparan sulfate as standard (Sigma catalogue no H 7640) [13]. In our experimental conditions the intraassay and interassay coefficients of variation were 1.5 and 2.4 % for 10 mg/l glycosaminoglycan, respectively.
Serum sialic acid was measured in serum with a colorimetric method described by Svennerholm with a Shimadzu UV-1201 spectrophotometer (Shimadzu, Japan) with a wavelength of 525 nm. Using N-acetyl neuraminic acid (Sigma catalogue no: A 3007) as standard [14]. Intra assay and interassay coefficients of variations were 6.6 and 9.2 % for 1 mmol/l sialic acid respectively.
Urinary albumin excretion was measured by an immunoturbidemetric assay (Roche diagnostics, USA) on automated clinical chemistry analyzer (Hitachi 902). In all patients, the annual level was determined as the mean of urinary albumin excretions in three 24 h urine collections taken at home during normal physical activity. The intraassay and interassay coefficients of variation were 1.3 and 4.3 % for a mean value of 25 mg/l albumin concentration.
Serum glucose, cholesterol, triglyceride and HDL (Dade Behring, USA) were evaluated with enzymatic methods (Dade Behring, USA) on an automated clinical chemistry analyzer (Dimension RxL). Creatinin was assayed by the Jaffe reaction on an automated clinical chemistry analyzer (Dimension RxL).
HbA1C (reference range 4.4–6.0 %) was measured by a turbidimetric inhibition immunoassay technique (Roche Diagnostics, USA) on automated clinical chemistry analyzer (Hitachi 902). The intraassay and interassay coefficients of variation were 1.8 and 3.0 % for a mean value of 10.5 % HbA1C concentration.
The analysis of the data was performed with a PC compatible Instat-II programme. Paired t test and ANOVA were used where appropriate. Correlation analysis was performed with Spearman rank test. The differences were considered significant when the probability was p < 0.05. The results were given as mean ± SD.
Results
The clinical characteristics of the study groups are shown in Table 1. There were no difference in age and BMI between the groups. Duration of diabetes mellitus was slightly longer, although not significantly different than that of the patients with proliferative retinopathy.
Results of laboratory parameters are shown in Table 2. Patients with proliferative retinopathy had higher concentrations of triglyceride and lower concentrations of HDL cholesterol as compared to diabetics without evidence of retinopathy and control group. Serum cholesterol level was the same in all study groups. Blood glucose and HbA1C levels were not different in subgroups of our diabetic population.
Distribution of erythrocyte anionic charges of all study groups are demonstrated in figure 1. EAC was 312 ± 30 ng alcian blue/106 RBC in healthy controls. Diabetic patients both with nonproliferative and proliferative retinopathy had a significantly lower alcian blue binding to RBCs compared with diabetic patients without retinopathy (p < 0.001).
A marked (p < 0.05) difference was found between type 2 diabetic patients (8.3 ± 4.1, 8.7 ± 4.8, 8.4 ± 3.3 mg/24 h, Table 2) and controls (5.0 ± 2.4 mg/24 h) regarding urinary GAG excretion. We did not observe a correlation between urinary GAG excretion and diabetic retinopathy.
A significant difference (p < 0.05) was observed in serum sialic acid values in diabetic patients compared with healthy controls. Serum sialic acid level was higher in the nonproliferative retinopathy group, but the difference was statistically insignificant (p > 0.05).
Microalbumin level was significantly p < 0.05 higher in diabetic patients (73 ± 40 [35–250], 75 ± 21 [50–202], 85 ± 45 [56–224] mg/24 h, Table 2) compared to healthy controls (10 ± 6 mg/24 h). But there wasn't a correlation between the severity of retinopathy and microalbuminuria (p > 0.05).
Results of the Spearman rank correlation test are shown on table 3. EAC is found to be negatively corralated with microalbuminuria in all groups.
Discussion
Our study demonstrates a statistically significant quantitative reduction of alcian blue binding to RBCs in diabetic patients than healthy subjects, which is in accordance with literature [15,16]. Previously we have shown a statistically significant quantitative reduction of alcian blue binding to RBCs in streptozosin diabetic rats [17]. Alcian blue is a complex amphoteric molecule that binds to acidic glycoproteins which represent a large class of cell surface anionic molecules [18]. As alcian blue binding is an expression of the anionic charge on the cell surface, this result indicates that RBC anionic charge is reduced in diabetic patients.
Erythrocyte anionic charge by itself is unlikely to be important in the pathogenesis of diabetic retinopathy but reduced RBC alcian blue binding was found to be associated with the loss of glomerular basement membrane anionic charges in diabetic rats and patients [19]. Changes in the composition of basement membranes are likely to be responsible for functional disturbances and hence for the development of capillary disease. Chakrabarti et al. reported a decreased density of anionic sites in the retinal basement membrane of BB rats [20]. A similar decrease in anionic density was also demonstrated in the Bruch's membrane of BB and streptozosin diabetic rats [21].
Several authors have questioned the validity of the alcian blue binding assay. The major objections concern the incomplete alcian blue dissolution in PBS buffer and its tendency to precipitate with time [22,23]. We prepared a fresh alcian blue solution for each experiment. Each experiment was considered valid when no change in the optical density of the blank alcian blue solution occurred over the duration of the experiment. In addition, the experiments were done in batches with cells from a healthy person. Therefore any flaw in the methodology would not affect only one group and thus distort the results.
Diabetic retinopathy is characterized by gradually progressive alterations in the retinal microvasculature leading to vascular hyperpermeability, capillary occlusion and ultimately neovascularization. Thickening of the retinal microvascular basement membrane and loss of anionic content are well documented morphological features of diabetic retinopathy [20,21,24]. These changes causes breakdown of the blood-retinal barrier resulting in capillary hyperpermeability and leakage of proteins into the deep and superficial layers of the retina [1,2].
Glycosaminoglycan primarily heparan sulfate has been implicated in permeability properties of basement membrane of glomerular basement membrane as well as the retinal basement membrane [25]. Kahaly et al. reported that urinary GAG excretion is increased in diabetic patients with microangiopathy [26]. Williamson et al and Ginn et al. have shown that albumin permeation is increased into the diabetic compared to normal retina [27,28]. Loss of heparan sulfate from the retinal basement membrane in addition to its thickening may cause increased vascular permeation of albumin and other substances into the retina and possibly into the optic nerve.
Systemic treatment of IDDM patients with proteinuria with danaparoid sodium, a glycosaminoglycan, not only reduced proteinuria, but also decreased the number of hard exudates in the retina [29]. Studies in diabetic rats have shown reduced activity of the key enzyme in the biosynthesis of heparan sulfate, N-deacetylase, which results in impaired heparan sulfate biosynthesis in experimental diabetes [30,31].
In this study we found that urinary GAG excretion is increased in diabetic patients than healthy controls, but there was no difference in GAG excretion between nonproliferative and proliferative retinopathy patients. We couldn't find a correlation between GAG excretion and EAC but we found a correlation between albumin excretion and EAC. Although our working hypothesis is that the reduction in anionic charge in basement membranes may be due to an abnormal GAG metabolism, we couldn't confirm it with this study.
Our study did not directly address the pathophysiology of diabetic retinopathy but a reduction in erythrocyte charge perhaps connected with the properties of retinal basement membrane.
Several reports suggest that elevated serum sialic acid levels in type 1 and type 2 diabetic patients indicate microvascular damage [32,33]. In our study, diabetic patients were found to have elevated serum sialic acid levels in comparison with healthy controls. Therefore, the increase in serum sialic acid level may indicate a coincidence between the structural alterations in the retina and general vascular damage.
Conclusions
We conclude that type 2 diabetic patients with low erythrocyte anionic charge are associated with diabetic retinopathy. Reduction of negative charge of basement membranes may indicate general changes in microvasculature rather than retinopathy. More prospective and large studies needs to clarify the role of glycosaminoglycans on progression of retinopathy in type 2 diabetic patients.
List of abbrevations
BMI: Body mass index
EAC: Erythrocyte anionic charge
GAG: Glycosaminoglycan
PBS: Phosphate buffered saline
RBC: Erythrocytes
UGAG : Urinary glycosaminoglycan
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
YB : Evaluated laboratory parameters. Designed the study. Wrote the paper.
HD: Diagnosed outpatients according to American Diabetes Association criteria.
MA: Examined ocular fundus after dilation of the pupils and classified according to the severity of retinopathy.
DY: Performed the analysis of the data with a PC compatible Instat-II programme. Improved the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Distribution of erythrocyte anionic charges of all study groups.
Table 1 Demographic characteristics of the study group
Control group Group R0 GroupR1 Group R2
Age(yrs) 57 ± 5 59 ± 8 56 ± 5 57 ± 5
M/F 19/19 13/10 7/6 7/6
BMI(kg/m2 ) 28 ± 5 27.8 ± 6 29.6 ± 5 28.5 ± 8
OAD/insulin - 22/1 12/1 12/1
Duration of diabetes (yrs) - 10 ± 5 11 ± 6 12 ± 8
Table 2 Laboratory parameters of the study groups
Control group Group R0 GroupR1 Group R2
Fasting glucose (mg/dl) 100 ± 26 229 ± 70 234 ± 89 236 ± 120
HbA1c (%) 7.6 ± 0.7 8.0 ± 0.7 8.0 ± 0.8
Cholesterol (mg/dl) 192 ± 44 187 ± 33 197 ± 41 193 ± 40
Triglycerides (mg/dl) 131 ± 55 153 ± 84 199 ± 71 228 ± 143
HDL (mg/dl) 45 ± 9 39 ± 9 37 ± 7 36 ± 10
Serum sialic acid (mmol/l) 1.81 ± 0.3 2.0 ± 0.5 2.1 ± 0.3 2.3 ± 0.5
EAC (ng alcian blue 106 erythrocyte) 312 ± 30 281 ± 30 259 ± 23 252 ± 37
UGAG (mg/24 h) 5.0 ± 2.4 8.3 ± 4.1 8.7 ± 4.8 8.4 ± 3.3
Microalbumin (mg/24 h) 10 ± 6 73 ± 40 75 ± 21 85 ± 45
Table 3 Correlation analysis between erythrocyte anionic charge (EAC) and other parameters.
EAC (ng AB/ 106 erythrocyte)
r P
HbA1c (%) -0.251 0.073
Microalbumin (mg/24 h) -0.393 0.032*
UGAG (mg/24 h) -0.226 0.094
Serum sialic acid (mmol/L) 0.095 0.546
Duration of diabetes (yrs) 0.097 0.478
* Statistically significant at 0.05 level.
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| 15473902 | PMC526283 | CC BY | 2021-01-04 16:03:49 | no | BMC Ophthalmol. 2004 Oct 8; 4:14 | utf-8 | BMC Ophthalmol | 2,004 | 10.1186/1471-2415-4-14 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-751548814210.1186/1471-2407-4-75Research ArticleFully human IgG and IgM antibodies directed against the carcinoembryonic antigen (CEA) Gold 4 epitope and designed for radioimmunotherapy (RIT) of colorectal cancers Garambois Véronique [email protected] Fabienne [email protected] Elodie [email protected] Marc [email protected]ère Martine [email protected] Robin X [email protected] Binyam [email protected]èlegrin André [email protected] INSERM, EMI0227 Centre de Recherche en Cancérologie, CRLC Val d'Aurelle-Paul Lamarque, F-34298 Cedex 5 Montpellier, France2 CNRS UMR 5160, Montpellier, France3 Abgenix, Inc., Fremont, CA, USA2004 15 10 2004 4 75 75 8 7 2004 15 10 2004 Copyright © 2004 Garambois 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
Human monoclonal antibodies (MAbs) are needed for colon cancer radioimmunotherapy (RIT) to allow for repeated injections. Carcinoembryonic antigen (CEA) being the reference antigen for immunotargeting of these tumors, we developed human anti-CEA MAbs.
Methods
XenoMouse®-G2 animals were immunized with CEA. Among all the antibodies produced, two of them, VG-IgG2κ and VG-IgM, were selected for characterization in vitro in comparison with the human-mouse chimeric anti-CEA MAb X4 using flow cytometry, surface plasmon resonance, and binding to radiolabeled soluble CEA and in vivo in human colon carcinoma LS174T bearing nude mice.
Results
Flow cytometry analysis demonstrated binding of MAbs on CEA-expressing cells without any binding on NCA-expressing human granulocytes. In a competitive binding assay using five reference MAbs, directed against the five Gold CEA epitopes, VG-IgG2κ and VG-IgM were shown to be directed against the Gold 4 epitope. The affinities of purified VG-IgG2κ and VG-IgM were determined to be 0.19 ± 0.06 × 108 M-1 and 1.30 ± 0.06 × 108 M-1, respectively, as compared with 0.61 ± 0.05 × 108 M-1 for the reference MAb X4. In a soluble phase assay, the binding capacities of VG-IgG2κ and VG-IgM to soluble CEA were clearly lower than that of the control chimeric MAb X4. A human MAb concentration of about 10-7 M was needed to precipitate approximatively 1 ng 125I-rhCEA as compared with 10-9 M for MAb X4, suggesting a preferential binding of the human MAbs to solid phase CEA. In vivo, 24 h post-injection, 125I-VG-IgG2κ demonstrated a high tumor uptake (25.4 ± 7.3%ID/g), close to that of 131I-X4 (21.7 ± 7.2%ID/g). At 72 h post-injection, 125I-VG-IgG2κ was still concentrated in the tumor (28.4 ± 11.0%ID/g) whereas the tumor concentration of 131I-X4 was significantly reduced (12.5 ± 4.8%ID/g). At no time after injection was there any accumulation of the radiolabeled MAbs in normal tissues. A pertinent analysis of VG-IgM biodistribution was not possible in this mouse model in which IgM displays a very short half-life due to poly-Ig receptor expression in the liver.
Conclusion
Our human anti-CEA IgG2κ is a promising candidate for radioimmunotherapy in intact form, as F(ab')2 fragments, or as a bispecific antibody.
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Background
During the last few years, radioimmunotherapy (RIT) using MAbs to specifically target therapeutic radiation doses to tumors has led to objective responses in radiosensitive hematological cancers, particularly, in non-Hodgkin's lymphoma (NHL) [1,2]. On the basis of these clinical results, ibritumomab tiuxetan (90Y-Zevalin; IDEC Pharmaceuticals) was registered for treatment of relapsed, indolent, and transformed CD20+ NHL and, more recently, tositumomab (131I-Bexxar; Corixa) received regulatory approval; development of other promising products is in the pipeline [3].
Although targeting of solid tumors with radiolabeled antibodies was first reported years ago [4,5], RIT success in such tumors has been limited to patients with stable disease, occasional mixed responses, and serological responses [6-8]. Different parameters can be considered as responsible for these results: (i) the decreased radiosensitivity of solid tumors as compared with hematological cancers [9,10], (ii) the difficult penetration of MAbs in solid tumors [11], and (iii) consequently, the limited radiation dose that can be delivered to the tumor [12,13]. However, recent studies have reported a therapeutic window for RIT in solid tumors in small-volume and minimal residual disease [8] and in combination with chemotherapy [14]. The authors of all the recent pertinent clinical studies agree with the need of repeated injections for RIT of solid tumors and, consequently, with the need of humanized or, preferentially, human MAbs [14,15].
Colorectal cancers represent a high percentage of solid tumors and are dramatically in need of therapeutic progress. Surgery is the only potentially curative treatment. Despite recent developments in chemotherapy protocols, the overall median survival in metastatic colorectal cancer remains inferior to two years, and the recurrence rate after resection of a stage III tumor is up to 50% [16-18]. For RIT of colorectal cancers, carcinoembryonic antigen (CEA) is a preferential target antigen since (i) it is expressed in almost all tumors (>95%), (ii) it is available at high antigenic density on the cell surface, and (iii) many clinical studies have demonstrated a low MAb uptake in normal intestine despite CEA expression on these tissues. The only limitation of CEA as target antigen in RIT is the possible presence of circulating CEA in the serum of cancer patients, but this is without consequence in small-volume and minimal residual disease in which its level is generally low [19].
Different chimeric or humanized anti-CEA MAbs have been described and evaluated in experimental and clinical studies [8,14,15,19,20]. In the present study using the XenoMouse® technology, we describe the generation and the characterization of two fully human anti-CEA antibodies, one IgG2κ and one IgM, designed for RIT of colorectal cancers.
Methods
Generation of fully human MAbs from XenoMouse® strains
Generation and characterization of the XenoMouse®-G2 strain, engineered to produce fully Human IgG2κ antibodies, was described by Mendez et al. [21]. XenoMouse®-G2 animals were immunized i.p. with 20 μg of human recombinant CEA (rhCEA) [22] emulsified in complete Freund's adjuvant for the primary immunization and in incomplete Freund's adjuvant for additional immunizations carried out at one month intervals. Immunization was repeated three to five times. Two days before fusion, mice were boosted i.v. with 100 μg rhCEA in phosphate buffered saline (PBS). Spleen cells from immunized mice were fused with the non-secretory myeloma P3-X63-Ag.8.653 by addition of polyethylene glycol (PEG) and were subjected to HAT selection. Wells containing growing cells were evaluated for the production of the desired antibody, and if positive, the cultures were cloned. The hybridomas described in this report were subcloned at least five times.
Reference anti-CEA MAbs and control MAb
The mouse-human chimeric MAb X4 was used as positive control in all the experiments. MAb X4 was constructed using the variable domains from the murine MAb CE25 and the constant domains from a human IgG4κ subclass [23,24]. It is specific for the CEA epitope Gold 4 [24] and does not cross-react with NCA or other granulocyte proteins [25]. Chimeric MAb X4 was produced in Sp2/0 cells transfected with a single vector containing both the chimeric heavy and light chains [24]. Murine MAbs F6, 35A7, B17, CE25, and 192, which are specific for the CEA epitopes Gold 1 to 5, respectively, were used for epitope determination [26]. MAb F6 was kindly provided in purified form by Schering-CIS Biointernational (Gif-sur-Yvette, France). MAbs 35A7, B17, CE25, and 192 were produced from mouse hybridoma ascites fluid by ammonium sulfate precipitation and ion-exchange chromatography. The human IgG MonoD, kindly provided by MAbgène (Alès, France), was used as irrelevant human IgG1 [27].
Cell lines and human granulocytes
The CEA-positive human colon carcinoma LS174T cell line [28] was obtained from the Cell Distribution Center, American Type Culture Collection (Rockville, MD). The CO115-5F12 clone, obtained by transfection of the full-length CEA-cDNA in a CEA negative clone of the CO115 human colon carcinoma cell line, has been described [29]. Cells were grown in RPMI 1640 medium containing 10% heat-inactivated fetal bovine serum, streptomycin (0.1 mg/ml), penicillin (0.1 IU/ml), and amphotericin B (0.25 μg/ml). The neomycin analogue G418 was added at a concentration of 200 μg/ml to the CO115-5F12 cell culture. All culture medium supplements were purchased from Life Technologies, Inc. (Gibco BRL, Gaithersburg, MD). For flow cytometry analysis, cells were harvested after incubation for a few minutes in trypsin-EDTA (0.5 mg/ml and 0.2 mg/ml, respectively).
Granulocytes were obtained from heparin-treated human peripheral blood by using gradient density centrifugation methods. A double gradient was formed by layering an equal volume of HISTOPAQUE®-1077 and HISTOPAQUE®-1119. Following centrifugation at 700 g for 30 minutes, cells of the granulocytic series were found at the 1077/1119 interphase.
Screening by ELISA and flow cytometry
The specificity of the antibodies in hybridoma supernatants was determined by ELISA using rhCEA to capture the antibodies (coating overnight at 2 μg/ml rhCEA at room temperature). Horse radish peroxidase (HRP)-conjugated goat anti-human IgGκ (Sigma, Lyon, France) and HRP-conjugated sheep anti-human IgG (γ chain) (Silenius, Hawthorn, Australia) were used as detection antibodies.
Determination of CEA or NCA specific antibodies in hybridoma supernatants was carried out by flow cytometry using CEA positive cells (CO115-5F12) and NCA positive cells (human granulocytes). About 5 × 105 cells were incubated for 1.5 h at 4°C with hybridoma supernatants or controls (RPMI medium for background measurement and RPMI containing 20 μg/ml MAb X4 for positive control). After washing, the cells were incubated with an FITC conjugated goat anti-human IgG kappa light chain (Sigma) or with the murine anti-human μ chain DA4-4 (ATCC HB-57), FITC labelled in our laboratory, for 1 h at 4°C; then they were washed twice before analysis on a FACScanII (Becton-Dickinson, Le-Pont-De-Claix, France). Each figure represents data obtained from analysis of 10000 cells.
Human MAb production, purification, and molecular characterization
The percentage of fetal calf serum was gradually reduced in the culture medium before MAb purification (10, 5, 2.5 and 0%). MAbs were purified from large volumes of hybridoma supernatants or ascites produced in nude mice using Hitrap® NHS-anti-human κ chain MAb HP6053 (ATCC CRLC-1758).
Proteins separated on SDS-PAGE 6% polyacrylmamide gels were transferred to a nitrocellulose membrane (Protran BA85, Schleicher and Schuell, Dassel, Germany). Non-specific binding sites were blocked overnight at 4°C by incubation with 5% (w/v) non-fat dry milk in TBS. The membrane was probed for 2 h at room temperature with serum diluted 1:1000 or the following antibodies: anti-human κ light chain-HRP conjugate (A7164, Sigma), anti-human λ light chain-HRP conjugate (A5175, Sigma), anti-human μ chain-HRP conjugate (A0420, Sigma), rabbit anti-human J chain-specific antiserum [30]. Bound serum antibodies were detected with a goat anti-rabbit whole molecule-alkaline phosphatase (AP) conjugate (A8025, Sigma). HRP was detected by addition of a chloronaphthol (Sigma) solution containing 0.05% of hydrogen peroxide and AP by addition of BCIP-NBT (Sigma). A human pentameric IgM anti-Rhesus D including a J chain, kindly provided by MAbgène (Alès, France), was used as positive control[27].
Antibody VH and Vκ cDNAs were recovered from hybridomas by RT-PCR and sequenced using the ABI-PRISM Big Dye Terminator Cycle Sequencing Kit (Perkin Elmer, Boston, MA). Determination of V, D, and J gene usage was performed using in silico methods.
Measurement of antibody affinity to CEA
The affinities of the antibodies for CEA were determined by using surface plasmon resonance (SPR) technology (Biacore AB, Uppsala, Sweden). rhCEA [22] was immobilized on a CM5 sensor chip by the method of thiol ligation according to the manufacturer's instructions (BIACORE Methods Manual Supplement 5a). Each MAb was injected at a concentration of 50 μg/ml in HBS buffer (Hepes-buffered saline, pH 7.4, 3 mM EDTA ; 0.05% BIACORE surfactant) at a flow rate of 20 μl/min. Dissociation was carried out in running buffer (HBS). Regeneration of the sensor chip was performed by using 15 μl of 100 mM HCl. The kinetic parameters were determined by using BIAevaluation 3.2 software.
MAbs and CEA radioiodination
Batches of 50 μg or 100 μg of MAb or rhCEA were labelled with 4.6 MBq or 11.5 MBq, respectively, of 125I or 131I, kindly provided by Schering-CIS Biointernational by the iodogen method (1,3,4,6-tetrachloro-3α, 6α-diphenylglycoluryl, Sigma). Free radioiodine was separated from the protein on a Sephadex G-25 column (Pharmacia) equilibrated in PBS, pH 7.4.
Binding of MAbs to CEA in a soluble phase assay
About 1 ng 125I-rhCEA (final concentration 16.7 × 10-12 M) was incubated with increasing concentrations of antibody (68.3 × 10-12 to 33.3 × 10-15 M) for 2 h in 0.15 M phosphate buffer, pH 7.4. CEA-antibody complexes were precipitated at 4°C with 53.5% (v/v) saturated ammonium sulfate. Background binding to an irrelevant human IgG, MonoD, was subtracted. The radioactivity precipitated with a rabbit polyclonal anti-CEA serum was taken as 100%.
Epitope mapping by RIA
A competitive binding assay of radiolabeled human MAb and unlabeled anti-CEA MAb was used to determine the CEA epitope recognized by the human MAbs. RhCEA (100 ng/well) in TBS was coated on microtiter wells overnight at room temperature. An excess (500 ng/well) of each of the different reference anti-CEA MAbs, specific for the Gold epitopes 1 to 5, was then added to the wells and incubated for 1.5 h at 37°C. Then, without washing, each 125I-human MAb (15 ng) was added to the wells and incubated for 1 h 30 at 37°C. The percentage of binding was determined by measuring the radioactivity bound to the rhCEA after two washings.
Biodistribution studies
Two million LS174T cells were grafted s.c. into the right flank of female Swiss nude mice (nu/nu, Iffa Credo, l'Arbresle, France). When the tumors had reached a volume of about 150 mm3 (100 to 300 mm3), mice were grouped according to tumor volume. Lugol iodine solution (10% solution) was added to the drinking water one day before the injection of radiolabeled MAbs. Groups of four mice were injected with a 125I-labeled human MAb (VG-IgG2κ or VG-IgM) together with 131I-labeled chimeric MAb X4 as positive control. The total amount of each injected antibody was adjusted to 8 μg protein by adding unlabeled MAb. To determine the biodistribution of the MAbs, mice were sacrificed 24 or 72 h after injection. The blood, tumor, and all normal organs were weighed, and the differential radioactivity was measured in a dual channel scintillation counter. The results are expressed as the percentage of the injected dose of radioactivity present per gram of tissue (% ID/g).
Results
Human anti-CEA MAb characterization
In order to develop human anti-CEA MAbs, XenoMouse®-G2 animals were immunized with rhCEA. The XenoMouse®-G2 strain produces both fully human IgG2κ and fully human IgMκ antibodies as part of the normal immune response, as described by Mendez et al. [21]. Fusion of splenic B cells from immunized mice with mouse myeloma cells yielded a panel of hybridomas that secreted human anti-CEA antibodies as determined by ELISA and flow cytometry analysis (data not shown). These MAbs were produced for in vivo tumor targeting purposes. For this reason, (i) hybridoma supernatants were screened by flow cytometry because, by this technique, the selection is made on cell membrane-bound CEA, which is in a conformation as close as possible to that observed in vivo; and (ii) hybridoma supernatants were screened on human granulocytes to eliminate all hybridomas producing anti-NCA antibodies.
Among the 52 antibodies produced, two, VG-IgG2κ and VG-IgM, were selected for further characterization based on hybridoma stability and flow cytometry analysis results. VH and VL domains sequencing confirmed the VG-IgG2κ and VG-IgM monoclonality and the isotype (data not shown). Flow cytometry analysis demonstrated strong binding of purified MAbs on CEA-expressing cells CO115-5F12 (Figure 1A) without any binding on NCA-expressing human granulocytes (Figure 1B).
MAb VG-IgG2κ and VG-IgM epitope specificities were determined in a competitive binding assay using five reference MAbs directed against the Gold 1 to 5 CEA epitopes [26]. As demonstrated by this assay (Table 1), the two human MAbs were directed against the CEA Gold 4 epitope since they were only inhibited by the murine MAb CE25 and the chimeric MAb X4. A partial inhibition, attributed to steric hindrance, was observed with MAb B17 (Gold 3) for VG-IgG2κ (31%) and X4 (13%).
VG-IgM was further analyzed for the presence of J chain using western blot. The J chain is a ligand for the poly-Ig receptor expressed on mouse hepatocytes. Figure 2 confirms that VG-IgM is constituted of human κ light chain, human μ heavy chain, and J chain as compared with an anti-Rhesus D IgM which also contains a J chain but is composed of a λ light chain [27].
MAb binding to solid phase CEA and to soluble phase CEA
Using surface plasmon resonance technology, the affinities of purified VG-IgG2κ and VG-IgM were determined to be 0.19 ± 0.06 × 108 M-1 and 1.30 ± 0.06 × 108 M-1, respectively, as compared with 0.61 ± 0.05 × 108 M-1 for the reference MAb X4.
MAb binding to soluble phase CEA was measured using trace amounts of 125I-CEA incubated with increasing concentrations of antibodies (Figure 3). The binding capacities of VG-IgG2κ and VG-IgM to soluble CEA were found to be clearly lower than that of the control chimeric MAb X4. A human MAb concentration of about 10-7 M was needed to precipitate around 1 ng 125I-rhCEA (final concentration 16.7 × 10-12 M) as compared with 10-9 M for MAb X4 (Figure 3).
Biodistribution studies
The human MAbs were compared to the chimeric MAb X4 in nude mice bearing human colon carcinoma LS174T xenografts. Two groups of four mice were co-injected with 125I-VG-IgG2κ and 131I-X4. In the first group of mice dissected 24 h post-injection, 125I-VG-IgG2κ demonstrated a high tumor uptake (25.4 ± 7.3% ID/g), very close to that of 131I-X4 (21.7 ± 7.2% ID/g) (Figure 4). At 72 h post-injection, the 125I-VG-IgG2κ was still concentrated in the tumor with 28.4 ± 11.0% ID/g whereas the tumor concentration of 131I-X4 was significantly reduced, with only 12.5 ± 4.8% ID/g (Figure 4). This difference was attributed to a higher in vivo stability of VG-IgG2κ than X4 since the %ID recovered in the whole mouse at that time was 74.1 ± 1.5 and 55.6 ± 1.6 for the human and the chimeric MAbs, respectively. At no time after injection was there any accumulation of the radiolabeled MAbs in normal tissues. At 24 h, the tumor-to-normal tissue ratios of the antibodies were in the same range for 131I-X4, with representatives values of 10.98 ± 0.35, 28.78 ± 0.36, and 2.24 ± 0.38 for liver, muscle and blood, respectively, and for 125I-VG-IgG2κ, which gave values of 8.63 ± 0.30, 28.30 ± 0.31, and 1.74 ± 0.32 for the same organs, respectively (Table 2). At 72 h, the increase in the tumor-to-normal tissue ratios was very similar for the two MAbs (Table 2).
In two other groups of mice, 125I-VG-IgM was co-injected with 131I-X4. The reference chimeric MAb X4 gave tumor localization results comparable to that obtained when it was co-injected with 125I-VG-IgG2κ (19.1 ± 1.9 and 11.9 ± 3.1% ID/g tumor at 24 h and 72 h, respectively). Due to the mouse poly-Ig receptor expression in liver, 125I-VG-IgM was eliminated very rapidly, giving only 3.7 ± 1.0 and 0.3 ± 0.1% ID/g blood at 24 h and 72 h, respectively. This short clearance was responsible for a low tumor uptake (7.4 ± 2.8 and 1.8 ± 2.4% ID/g tumor at 24 h and 72 h, respectively). The percentage of injected MAb recovered in the whole mouse at 24 h was only 20.5% for 125I-VG-IgM as compared with 53.7% for 131I-X4.
Discussion
Monoclonal antibodies are now routinely used in the clinic. Whereas the first generation of MAbs were murine or chimeric antibodies, it is now clear that the best clinical results are obtained with humanized or fully human MAbs [31]. Fully human antibodies such as ABX-EGF are anticipated to exhibit a long serum half-life and minimal immunogenicity with repeated administration, even in immunocompetent patients [32].
In some situations, naked MAbs have demonstrated their therapeutic efficacy, particularly, when the target antigen is a receptor implicated in cell proliferation processes [33,34]. However, the addition of radioactive isotopes on already efficient MAbs can lead to improved therapeutic results like that obtained with the anti-CD20 antibodies [35]. RIT remains attractive for solid tumors where the antibody penetration is limited and where the cross-fire phenomenon could lead to the destruction of cells which were not targeted by the radiolabeled MAb [13]. According to all the published or ongoing clinical studies, RIT could be applied to micrometastases from solid tumors or solid tumors in the minimal residual disease states [6,8,14,15]. One potential limitation of intact human MAbs for RIT could be their long serum half-life, which could lead to bone marrow suppression. The use of human MAb fragments for RIT would reduce their serum half-life, and possibly circumvent this limitation.
Among solid tumors, colorectal cancers represent one of the main causes of death, and CEA is well known as an ideal target antigen for RIT of these cancers [12]. Up to now, only a few human anti-CEA antibodies have been described. A human MAb directed against the carbohydrate moiety of CEA was described by Tsukazaki et al [36]. However, it was poorly characterized, and homology between the carbohydrate moieties of CEA and related molecules such as NCA would certainly lead to limited specificity of this anti-CEA MAb. Very recently, Imakiire et al. described human antibodies generated using the KM-Mouse [37]. They demonstrated complement- and cell-dependent cytotoxicity in vitro and presented preliminary data on tumor growth inhibition using MKN-45 cells grafted into SCID mice. However, they did not give any results on the biodistribution of their antibodies in radiolabeled form nor indications on how they could be used in RIT. Anti-CEA MAb PR1A3, which exhibits preferential binding to cell-bound CEA, was recently humanized, but to our knowledge, this MAb has not yet been evaluated in experimental or clinical studies [20]. A few clinical phase I or II trials suggest a certain degree of efficiency of humanized or chimeric anti-CEA mAbs, radiolabeled with either 131Iodine or 90Ytrium, in heavily pre-treated patients with metastatic colorectal cancer (MCRC) CT84.66 [8,14,15]. One of these trials showed a few objective tumor responses in MCRC of small-volume disease and provided some arguments in favor of this kind of therapy in an adjuvant setting [8]. The development of such chimeric, humanized, or human anti-CEA MAbs by different academic groups and industrial companies underlines the interest to generate a fully human MAb for RIT of colorectal cancers.
In the present study, the characterization of our fully human anti-CEA MAbs was conducted in comparison with the chimerized anti-CEA MAb X4, which has been shown to be clinically relevant [19,24]. Furthermore, the newly developed human MAbs were found to be directed against the same CEA epitope, namely Gold 4 (Table 1). That makes X4 an even better positive control, in particular, for the affinity measurements; although no difference has been reported between the different CEA epitopes for tumor immunotargeting [26]. VG-IgG2κ and VG-IgM are CEA-specific, i.e., NCA negative. This is particularly important for VG-IgM since the avidity generated by the pentameric molecule could have resulted in enhanced non-specific binding to CEA-related molecules.
The VG-IgG2κ and VG-IgM affinity constants were found to be similar to that of the control MAb X4 when determined using the BIACORE technology, but the binding of the human MAbs to soluble CEA (Figure 3) was clearly weaker than that of X4. This is particularly interesting for in vivo use in patients where some circulating CEA can be found. The molecular basis of this observation is not clear. The only possible comparison is with MAb PR1A3, which preferentially binds to cell bound CEA, and to a recombinant chimeric protein containing only the CEA B3 domain [20,38]. This reduced binding to the whole soluble CEA was attributed by the authors to be due to a conformational change supposed to occur when the CEA is shed into the circulation, resulting in steric blocking of antibody access to the B3 domain [20].
Using human colon carcinoma bearing nude mice, we obtained high tumor uptakes with VG-IgG2κ, making this antibody a good candidate for future clinical studies (Figure 4). In addition to the comparison with chimeric MAb X4, we also performed biodistribution studies comparing VG-IgG2κ and the murine MAb 35A7, with which we obtained the highest tumor uptakes in tumor bearing nude mice [39,40]. Seventy-two hours post injection, 125I-VG-IgG2κ localized in the tumor up to 26.2 ± 1.7% ID/g as compared with 28.5 ± 2.8% ID/g for 131I-35A7, suggesting a tumor residence time as long as that observed for MAb 35A7 (data not shown). These results could seem contradictory, given the fact that the affinity of VG-IgG2κ for CEA is not as high as that of Mab 35A7. This could be explained by the "affinity barrier" effect described in solid tumors by several authors who demonstrated that very high affinity MAbs localized at the periphery of tumor nodules and that lower affinity MAbs are able to distribute homogenously in these nodules [41-43].
Since, up to now, there are no data available on the biodistribution of any anti-CEA IgM in mice, we decided to analyze the biodistribution of our VG-IgM in LS174T tumor bearing nude mice. The disappointing tumor uptakes (7.4 ± 2.8 and 1.8 ± 2.4%ID/g tumor at 24 h and 72 h, respectively) could be attributed to a very short half-life due to poly-Ig receptor expression in the mouse liver which induces a rapid hepatobiliary transport of poly-IgA and IgM [44,45]. Based on the results obtained by Borchardt et al., we compared the tumor uptakes following i.p. and i.v. injection of the VG-IgM [46]. The tumor uptakes observed after i.p. injection were even lower (0.25%ID/g tumor at 24 h). These results are in contradiction with those obtained by Borchardt et al. [46]. In SK-Ov3 peritoneal carcinomatosis bearing nude mice, these authors showed a marked difference in tumor uptake between i.v. and i.p. injections of AC6C3-2B12 human IgM: 39% vs. 0.9%ID/g tumor for i.p. vs. i.v. injection, respectively, at 24 h and 28% vs. 1.4%ID/g at 48 h [46]. Liver and spleen uptakes were reduced following the i.p. injection as compared with the i.v. injection, but these uptakes in normal tissues could be related to this particular IgM and not relevant for our VG-IgM [46]. Indeed, the precise nature of the target antigen of their AC6C3-2B12 human IgM is unclear, but the discrepancy between our respective results could be due to the lack of J chain in their IgM. Without the J chain, IgM is unable to bind to the poly-Ig receptor, and as such it is not transported into the bile nor eliminated rapidly. The presence of the J chain in our VG-IgM makes it a fully functional IgM but limits its uptake by the human tumor grafted in nude mice.
Conclusions
In the present study, we described two fully human anti-CEA MAbs. Even though the results obtained in tumor bearing nude mice cannot be extrapolated directly to humans, VG-IgM remains attractive for RIT of CEA-positive peritoneal carcinomatosis in man [47]since humans lack hepatic expression of poly-Ig receptor [48]. A first step toward this aim will be to study the biodistribution and tumor uptakes of low doses of radiolabeled VG-IgM. VG-IgG2κ is obviously a candidate for radioimmunotherapy in intact form, as F(ab')2 fragments, or as a bispecific antibody to be used in the affinity enhancement system (AES) approach [49]. Furthermore, we intend to test it in a model for immunophotodetection of cancer [50], and it could be the basis for preparing different anti-CEA immunoconjugates [51,52] and fusion proteins [53].
Competing interests
No competing interest for VG, FG, EF, MY, MP and AP. RXL and BB are employees of Abgenix, Inc.
Authors' contributions
VG participated in the design of the study, performed all the cell fusions, cell culture experiments, antibody characterization assays and in vivo experiments. FG and EF participated in the cell culture experiments and antibody characterization assays. MP performed the affinity measurements using BIACORE. MY participated in the design of the study. RXL and BB sequenced the VH and VL domains. AP conceived the study, participated in its design and coordination. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Mr Michel Brissac and Ms Geneviève Heintz for expert technical assistance, Dr Blaise Corthésy for providing the rabbit anti-human J chain-specific antiserum, and Dr Isabelle Navarro-Teulon for fruitful discussions. We are indebted to Dr Sharon Lynn Salhi for editing the manuscript.
Figures and Tables
Figure 1 Flow cytometry analysis. Flow cytometry analysis of MAbs VG-IgG2κ, VG-IgM, and X4 reactivity against the CEA-expressing CO115-5F12 human colon carcinoma cell line (A) and NCA-expressing human granulocytes (B). 12A11 and 16B10 are two human anti-CEA MAbs that cross-react with NCA. 192 is a murine anti-CEA MAb that cross-reacts with NCA. Binding of the different primary antibodies was detected using either anti-human κ chain, anti-human μ chain, or anti-mouse γ chain as indicated.
Figure 2 Presence of J chain in VG-IgM. Western blot analysis of VG-IgM (lanes 1, 3, 5, and 7) as compared with control IgM (lanes 2, 4, 6, and 8). MAbs transferred to a nitrocellulose membrane were analyzed for the presence of human μ heavy chain, human κ light chain, human λ light chain, and J chain as described in Materials and Methods.
Figure 3 Binding of MAbs to soluble phase CEA. Precipitation of 125I-CEA with MAbs VG-IgG2κ (■), VG-IgM (▲), and X4 (●) following incubation in a soluble phase assay.
Figure 4 Biodistribution studies in LS174T tumor bearing nude mice. Biodistribution study of 125I-VG-IgG2κ (■) as compared with 131I-X4 (○) in LS174T tumor bearing nude mice dissected 24 and 72 h after i.v. co-injection. The tissues shown are (from left to right) tumor, liver, kidneys, lung, spleen, heart, muscle, bone, skin, stomach, low bowel, colon, carcass and blood. Results are expressed in terms of %ID/g ± SD.
Table 1 VG-IgG2κ and VG-IgM epitope mapping
Tracer MAb
Gold epitope Inhibitor Mab VG-IgG2κ VG-IgM X4
1 F6 1a 2 <1
2 35A7 <1 9 3
3 B17 31 <1 13
4 CE25 99 95 83
4 X4 98 89 68
5 192 3 11 <1
aResults are expressed as a percentage of inhibition of the binding of radiolabeled tracer MAbs by an excess of the reference MAbs directed against the five Gold CEA epitopes.
Table 2 Tumor to non-tumor tissue ratios of antibody concentrations
24 h 72 h
Tissue VG-IgG2κ X4 VG-IgG2κ X4
Liver 8.63 ± 0.30a 10.98 ± 0.35 13.41 ± 0.41 15.80 ± 0.41
Kidneys 9.00 ± 0.30 7.13 ± 0.37 14.14 ± 0.42 10.33 ± 0.43
Lungs 4.23 ± 0.32 5.13 ± 0.36 6.03 ± 0.40 6.74 ± 0.41
Spleen 13.32 ± 0.33 13.48 ± 0.37 16.54 ± 0.42 17.53 ± 0.42
Heart 6.00 ± 0.30 7.34 ± 0.34 8.80 ± 0.41 10.00 ± 0.43
Muscle 28.30 ± 0.31 28.78 ± 0.36 36.48 ± 0.44 36.18 ± 0.45
Bone 31.15 ± 0.64 24.50 ± 0.68 26.14 ± 0.43 23.95 ± 0.42
Skin 5.74 ± 0.29 7.01 ± 0.33 9.10 ± 0.42 10.05 ± 0.43
Stomach 19.13 ± 0.31 12.64 ± 0.38 59.76 ± 0.46 40.04 ± 0.49
Small bowel 23.73 ± 0.30 26.12 ± 0.34 37.84 ± 0.42 41.97 ± 0.43
Large bowel 33.92 ± 0.29 33.57 ± 0.35 67.68 ± 0.42 68.45 ± 0.45
Carcass 10.39 ± 0.30 12.39 ± 0.34 15.39 ± 0.40 17.22 ± 0.42
Blood 1.74 ± 0.32 2.24 ± 0.38 2.81 ± 0.41 3.15 ± 0.42
aRatios were calculated by dividing the %ID/g of the tumor by that obtained in each individual organ.
==== Refs
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| 15488142 | PMC526287 | CC BY | 2021-01-04 16:03:01 | no | BMC Cancer. 2004 Oct 15; 4:75 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-75 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-691545652010.1186/1471-2407-4-69Research ArticleChemotherapy with cisplatin and vinorelbine for elderly patients with locally advanced or metastatic non-small-cell lung cancer (NSCLC) Pereira José Rodrigues [email protected] Sandro J [email protected] Sueli M [email protected] Flora K [email protected] Lung Cancer Division, Instituto do Câncer Arnaldo Vieira de Carvalho, R. Martinico Prado 26 cj. 101, 01224-010 São Paulo, Brazil2 Pulmonary Division, University of São Paulo Medical School, Av. Dr. Arnaldo 455, 01246-903 São Paulo, Brazil3 Current address: Oncology Unit, Hospital Santa Izabel, Pca Almeida Couto 500, 40050-410 Salvador, Brazil2004 29 9 2004 4 69 69 1 8 2003 29 9 2004 Copyright © 2004 Pereira 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 modest improvements in the survival of patients with non-small cell lung cancer (NSCLC) can be achieved with cisplatin-based chemotherapy (CT), its value is disputed in the geriatric setting. In this study, we evaluate the feasibility of vinorelbine/cisplatin CT for elderly NSCLC patients.
Methods
In this pilot phase I/II trial, all patients received CT with vinorelbine 25 mg/m2, on day 1 and 8, and cisplatin on day 1, in 28 days-cycles. After stratification for age (up to 75 years), younger patients were sequentially allocated to moderate cisplatin doses (80 mg/m2 or 90 mg/m2), and older patients were allocated to lower cisplatin doses (60 mg/m2 or 70 mg/m2). We recruited patients aged over 70 years with newly diagnosed NSCLC, clinical stage III or IV, Karnofsky performance status ≥ 70%, normal serum creatinine, peripheral neuropathy ≤ grade 1, and no prior cancer therapy.
Results
Analysis was by intention to treat. Main toxicities (grade 3–4) was as follows: neutropenia, 20%; anemia, 11%; and thrombocytopenia, 2%; alopecia, 55%; fatigue, 11%; and peripheral neurotoxicity, 2%. No grade 3–4 emesis or renal toxicity occurred. Global median time to progression (TTP) and overall survival (OS) were 27.0 (95% CI: 10.1 to 43.7) weeks and 30.1 (95% CI: 24.4 to 35.8) weeks; 1- and 2-year survival rates were 36.3% and 13.2%, respectively. Overall response rate was 50.0% (95% CI: 35.4% to 64.5%), with 1 complete response; no difference on response rate was noticed according to cisplatin dose. Median overall survival was 30.1 weeks, with 1- and 2-year survival rates of 36.3% and 13.2%, respectively.
Conclusion
Age does not preclude assessment on the role of cisplatin-vinorelbine CT for elderly NSCLC patients with good performance status and adequate bodily functions.
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Background
Lung cancer is a disease with a great incidence in older people. In Brazil, its incidence rate in male sex was expected to reach 18.8:100.000 in 1999, and aged patients may have accounted for 57% of all lung cancer deaths. Unfortunately, up to two thirds will present at diagnosis with advanced disease, requiring chemotherapy (CT) [1].
Vinorelbine, a semi synthetic vinca alkaloid, is a highly active drug for NSCLC and its association with cisplatin is worthwhile. European and Southwest Oncology Group trials demonstrated that vinorelbine/cisplatin (VP) offer therapeutic advantage over both drugs alone [2-4], previous cisplatin-based schedules [3]; comparing to taxane combinations, VP is therapeutically equivalent to carboplatin/paclitaxel [5] or carboplatin/docetaxel [6] but inferior to cisplatin/docetaxel [6].
Although modest improvements in the survival of patients with non-small cell lung cancer (NSCLC) can be achieved with cisplatin-based CT [7], its value is disputed in the geriatric context. The simultaneous presence of several diseases and homeostenosis, an age-related physiologic process that change the way that the body handles drugs, can shift therapeutic index, allowing harm outweigh any survival gain. On the other hand, underdiagnosis and undertreatment of lung cancer in the elderly is a fact, often explained in terms of ageism in medical oncology staff [8] and people's beliefs and fears about the disease and its treatment [8,9]. Whether we should treat or not aged patients with cisplatin-based CT surely is an unsolved issue. In this study, we evaluated the feasibleness and activity of vinorelbine/cisplatin CT for elderly NSCLC patients.
Methods
This study enrolled patients older than 70 years with unresectable locally advanced or metastatic NSCLC. Informed consent was obtained from patients and their relatives, as approved by the Institutional Ethical Committee. Inclusion criteria: all patients had to have histologically confirmed NSCLC; Karnofsky performance status ≥ 70; measurable disease; adequate bone marrow reserve (neutrophils ≥ 2 × 109/L and platelets ≥ 100 × 109/L), bilirubin under 1.25 times upper normal value (UNV), aspartate aminotransferase/alanine aminotransferase (AST/ALT) under 2 times UNV, and renal function (creatinine level under 120 μmol/L); no symptomatic brain metastasis; no prior cancer therapy; no indication for palliative radiation therapy; no previous or concomitant malignancy; and adequate social support. Exclusion criteria were symptomatic peripheral neuropathy and comorbidity regarded as an impediment for CT, such as renal disease, heart failure, coronary heart disease, uncontrolled infection, and cognitive impairment.
Baseline work up included a medical history and physical examination; whole blood count (WBC) and biochemistry; chest x-ray; bone scintigraphy scan; chest, abdominal and brain computed axial tomography scans; and electrocardiogram. Although pretreatment bone scan and brain CAT scan are recommended only when signs or symptoms of disease are present, they were added here due to Institutional Protocol Reviewers recommendation.
Treatment consisted of vinorelbine 25 mg/m2 on days 1 and 8, administered intravenously in bolus, followed by intravenous cisplatin over 1 hour. CT was administered every 4 weeks. Prophylactic anti-emetic drugs (intra venous dexamethasone 20 mg and ondansetron 16 mg) and fluid hydration (0.9% saline, 1 L/m2; and magnesium sulfate, 20 mmol) was used to minimize renal toxicity. Dexamethasone 4 mg PO BID plus metoclopramide 10 mg PO QID for 4 days was used to prevent delayed emesis. Patients were time-sequentially assigned to one of two groups, from lower to higher doses, according to age strata. Study doses were defined based on previous reports on renal tolerance of cisplatin in geriatric patients [10,11]. Age stratification was arbitrary. Those aged up to 75 years received cisplatin 60 or 70 mg/m2 and those aged 70 to 75 received cisplatin 80 or 90 mg/m2. Assignation to high dose groups (70 mg/m2 and 90 mg/m2) occurred after evaluation of toxicity at inferior doses, evaluated according to National Cancer Institute criteria. The protocol required a minimum of 6 and a maximum of 18 patients per group after the 1st cycle for safety analysis.
Chemotherapy doses were reduced for haematological, neurological, hepatic and renal toxicities. Toxicity was graded according to National Cancer Institute (NCI) common toxicity criteria guidelines. Changes in dosage were based on WBC results obtained on day 1 of treatment; if neutrophils were <1.5 × 109/L and platelets were <100 × 109/L, treatment was delayed by 1 week. Treament on days 8 had to be cancelled if neutrophil counts were <1.0 × 109/L and platelets were <100 × 109/L. If treatment could not be given after a 2-week interval because of haematological toxicity, it had to be discontinued and the patient withdrawn from the study. Concomitant use of hematopoietic growth factors were not allowed in the first treatment cycle but were administered subsequently on individual basis. Neurological toxicity above grade 2 resulted in suspension of treatment; ototoxicity grade 2 or 3 resulted in a 50% dose reduction of cisplatin.
The following dose modifications of vinorelbine were set based on AST/ALT (aspartate aminotransferase/alanine aminotransferase) and bilirubin values on day 1 or day 8 of treatment: if AST/ALT were between 5.1 and 20.0 × UNV or bilirubin was between 1.5 and 3.0 × UNV, dosing was cancelled and the patient was reassessed 1 week later. If AST/ALT were >20.0 × UNV or bilirubin was >3.0 × UNV, vinorelbine was discontinued. If serum creatinine was grade >1, the dose was delayed by 1 week and the test repeated. After a 2-week delay, the patient was taken off the study. WBC and biochemistry were also performed on day + 14 of treatment.
We intended to administer a maximum of four CT cycles followed by radiation therapy in responding patients with stage III disease and six CT cycles in patients with wet IIIB or stage IV disease. Notwithstanding radical radiation therapy (RT) should deliver a total dose down to 66 Gy, covering tumor site and regional lymph nodes, and palliative therapy could use doses under to 45 Gy, the final choice of dose, fractioning, irradiated volume, and energies of radiation was at the radiation oncologist's discretion. Treatment interruption was allowed in case of disease progression, severe adverse events, or patient preference.
Chest x-ray was performed before each cycle, and CAT scans every two cycle for response evaluation. Tumor response was recorded according to World Health Organization criteria and measured by the same observer (JRP). All responses had to be confirmed 3–4 weeks from initial evaluation. We reported here the best response designation recorded from the start of treatment until disease progression. Patients stopping treatment with an unconfirmed response, or only short stabilisation were considered as inevaluable, unless the response or stabilisation was further confirmed in the absence of any treatment. Patients were monitored for the first month off-study then followed up every 2–3 months.
The dose intensity was calculated for both drugs by dividing the actual dose delivered by the length of therapy. Toxic death was defined as death occurring during the chemotherapeutic phase (including four weeks after its end) and due to drug toxicity. Early death was defined as death within four weeks after a chemotherapy cycle without severe toxicity and not related to the malignant disease. Response and survival were calculated by intention-to-treat. Progression was defined in relation to the best response obtained. The time to tumor progression lasted from the first day of treatment to the date of the first observation of progressive disease. Survival was defined as the time elapsed from the beginning of CT until death or last follow-up visit. Time-to-event analysis was performed using the Kaplan-Meier product-limit estimator. All analyses were carried out using a computer program (SPSS version 8.0, Chicago, USA).
Results
Forty-four patients were recruited from July 1996 to June 1998; twenty-nine aged 70–75 year and fifteen older than 75 years. Cisplatin doses were as follows: in the older cohort, seven patients received 60 mg/m2 and eight received 70 mg/m2; in the former, fifteen patients received 80 mg/m2 and fourteen received 90 mg/m2. Patient characteristics are given in Table 1. Most of the patients presented stage III disease (56.8%) and squamous cell carcinoma (52.3%).
Treatment results are shown in Table 2. A total of 125 CT cycles were administered and the median was 3 (range: 1 to 6). No difference was noticed on the dose intensity achieved across the four groups (Kruskal-Wallis test, p = 0.13). Objective response could not be evaluated in six patients due to treatment discontinuation before cycle 2: early death (1), withdrawal of consent (2) and toxicity (3). Twelve out of 25 patients with stage III disease responded to CT and received radical radiation therapy (median delivered dose: 50 Gy; range: 40 Gy to 66 Gy). Fifteen patients (34.0%) received maximum allowed CT cycles; excessive toxicity (n = 8), progressive disease (n = 3), progressive disease after initial response (n = 13), and patient choice (n = 5) were reasons for protocol withdrawal. At a median follow-up time of 77.2 weeks, six patients were alive. Response rate (RR) was 50.0% (95% CI: 35.4% to 64.5%), with 1 complete response. Global median time to progression (TTP) and overall survival (OS) were 27.0 (95% CI: 10.1 to 43.7) weeks and 30.1 (95% CI: 24.4 to 35.8) weeks; 1- and 2-year survival rates were 36.3% and 13.2%, respectively. No significant difference was noticed in RR (p = 0.65), TTP (p = 0.62), and OS (p = 0.44) across study groups. There was no difference according to stage (III vs. IV) in RR (48% vs. 53%, p = 0.76), TTP (32.6 vs. 25.0 weeks, p = 0.73), or OS (31.7 vs. 28.6 weeks, p = 0.33). Likewise there was no difference according to age groups (70–74 vs. ≥ 75 years) in RR (55% vs. 40%, p = 0.34), TTP (31.7 vs. 28.6 weeks, p = 0.33), or OS (30.1 vs. 31.7 weeks, p = 0.76).
Toxicity data are presented in Table 3. Hematological toxicity (grade 3–4) was as follows: neutropenia, 20%; anemia, 11%; and thrombocytopenia, 2%. Common nonhematologic grade 3–4 side effects were alopecia (18%) and fatigue (11%); severe peripheral neurotoxicity occurred in one patient; neither severe emesis nor renal toxicity was noticed. At the highest cisplatin dose (90 mg/m2) there were two early deaths and one toxic death due to neutropenic sepsis. No case of febrile neutropenia was noticed.
Discussion
Treatment of elderly NSCLC patients with cisplatin-based regimens has been a less contentious matter nowadays but toxicity remains a major issue. In our pilot study, chemotherapy with cisplatin 70–80 mg/m2 on day 1 plus vinorelbine 25 mg/m2 on days 1 and 8, repeated each 28 days per in the maximum four cycles, was feasible for elderly NSCLC patients. Neurological and renal tolerance was particularly good. At the time we developed the protocol, no quality-of-life instrument had been validated for use in Brazil. Thus, a drawback in our study is the absence of quality-of-life analysis, which precludes evaluation of key dimensions in geriatric oncology.
Cisplatin induces a sensory neuropathy due to axonal damage that is dependent on the total-dose and single-dose intensity [12], and it is also time-dependent [13], making histological lesions more common than clinical toxicity. Although in this trial most patients received moderate cisplatin doses, only one had grade 3 peripheral neuropathy. Our data may reflect the low median of cycles actually administered rather than inaccuracy of clinical signs to evidence tissue lesion. Similarly, Ohe et al. [14] delivered a median of three cycle of cisplatin-containing CT and reported no case of severe neuropathy.
Cisplatin renal toxicity has been attributed to drug-protein interactions and the inactivation of specific brush border enzymes, resulting in damage of the loops of Henle, the distal tubules, and collecting ducts. Patients aged above 70 or even 80 are regarded as susceptible to cisplatin-induced renal damage as the younger counterparts [15,16] and current studies have reported a low incidence of renal toxicity in elderly patients [14,17]. No case of severe renal toxicity was noticed in our patients, as estimated by serum creatinine and its clearance (Cockroft-Gault method), but this finding may be artifactual due to small sample sizes, selection bias, and low sensitivity of estimated creatinine clearance to predict actual glomerular filtration rate (GFR) [18].
The observed response rate here was in the usually range of NSCLC phase II trials, but the absence of external review of radiological data and the widened confidence intervals expected because of small sample sizes in each group limit assertions that could otherwise be drawn. The 1-year survival rate (36%) was good but essentially equivalent to the reported elsewhere [19] for vinorelbine alone (32%) and inferior to the observed for weekly cisplatin-docetaxel (64%) [14]. Nonetheless, survival figures should be cautiously considered in this underpowered, heterogeneous, non-randomized pilot study.
Vinorelbine is a cytotoxic agent that clearly has expanded the therapeutic options for elderly NSCLC patients [20,21]. The next logical step to improve therapeutic indexes was to combine it with other active drugs. Recently, relevant results emerged from European phase III trials addressing the role of novel drugs in the treatment of elderly NSCLC patients [19,22-25]. The Elderly Lung Cancer Vinorelbine Italian Study (ELVIS) [19], interrupted for slow recruitment, evidenced an improvement of some lung cancer-related symptoms (pain and dyspnea), worsening of toxicity-related symptoms (cognitive function, constipation, and peripheral neuropathy), and a limited survival advantage (28 vs. 21 weeks) for single-agent vinorelbine as compared to supportive care, a survival gain that resembles the benefit reported by meta-analysis for nowadays considered substandard cisplatin-based regimens in advanced NSCLC.
Although sequential administration of drugs is an attractive option for aged or frail patients, a setting where minimal treatment-related toxicity should be pursued, research on the role of non-platinum combinations for elderly NSCLC patients aroused attention. The Southern Italy Cooperative Oncology Group (SICOG) evaluated whether the association of vinorelbine and gemcitabine would be better than vinorelbine alone. To date, final results of this trial have not to come. Despite an article focusing on the interim analysis of 120 patients (60 at each group) claimed a survival advantage for the combined arm [22], further intent-to-treat analysis including 18 patients more reached a less optimistic conclusion: median survival in the combined and single-agent arms were nearly the same (25 weeks and 23 weeks, respectively) and both values were deemed comparable to the observed in the supportive care arm of the ELVIS (21 weeks) [23]. Since that at least 152 patients was treated in the SICOG trial [24], a definite report of mature survival data is awaited. In addition, investigators of the Multicenter Italian Lung Cancer in the Elderly Study (MILES) [25] reported no survival benefit for the combination of vinorelbine plus gemcitabine in comparison to single-agent vinorelbine or gemcitabine in the treatment of elderly NSCLC patients.
The question whether or not cisplatin-containing regimens should be used to treat aged patients remains an important, still open, issue. As observed for paclitaxel-carboplatin [26], gemcitabine-cisplatin [27], and docetaxel-cisplatin [14] associations, the role of vinorelbine-cisplatin regimens deserve to be investigated. Until the outcome of large clinical trials addressing this issue proves at least the equivalence of newer drug associations to platinum-based regimens, as seems to be true for the combination of paclitaxel and gemcitabine [28], there are few reasons to preclude the evaluation of current combined regimens in the chemotherapy of elderly NSCLC patients with normal bodily functions and good performance status.
Competing interests
This study was supported by Institutional funds only. Authors did hot 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.
Abbreviations (order of appearance)
NSCLC: non-small cell lung cancerCT: chemotherapy
NCI: National Cancer Institute
ELVIS: Elderly Lung Cancer Vinorelbine Italian Study
SICOG: Southern Italy Cooperative Oncology Group
GFR: glomerular filtration rate
Authors' contributions
JRP was responsible for study conception, protocol conduction, and results interpretation. SJM carried out the data analysis and results discussion. SMN and FKI were responsible for patient care and data gathering
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors are grateful to Dr. Olavo Feher for a critical review of this manuscript.
Figures and Tables
Table 1 Patient characteristics
Study Group Total
70 to 75 years > 75 years
80 mg/m2 (n = 15) 90 mg/m2 (n = 14) 60 mg/m2 (n = 7) 70 mg/m2 (n = 8) (n = 44)
Median age (range, in years) 73 (71 to 74) 73 (71 to 74) 79 (76 to 84) 77 (76 to 85) 74 (71 to 75)
Gender
Male 12 12 5 5 34 (77%)
Female 3 2 2 3 10 (23%)
Karnofsky performance status
70 8 7 4 3 22 (50%)
80 – 90 7 7 3 5 22 (50%)
Stage:
IIIA 5 1 - 2 8 (18%)
IIIB 5 8 3 1 17 (39%)
IV 5 5 4 5 19 (43%)
Histology:
Squamous cell carcinoma 9 5 2 7 23 (52%)
Adenocarcinoma 3 7 3 - 13 (30%)
Others 3 2 2 1 8 (18%)
Table 2 Therapy results
Study Group Total
70 to 75 years > 75 years
80 mg/m2 (n = 15) 90 mg/m2 (n = 14) 60 mg/m2 (n = 7) 70 mg/m2 (n = 8) (n = 44)
Treatment administered
Chemotherapy cycles
No. 46 42 14 23 125
Median (range) 2 (1 to 4) 2 (1 to 5) 3 (1 to 5) 3 (1 to 6) 3 (1 to 6)
Vinorelbine (median, mg/m2/wk) 8.6 8.8 9.4 10.3 8.9
Cisplatin (median, mg/m2/wk) 13.6 15.9 11.2 14.4 14.5
Efficacy
Complete response - - - 1 1
Partial response 8 8 2 3 21
Stable disease 4 4 3 2 13
Progressive disease 2 1 - - 3
Not evaluated 1 1 2 2 6
Overall response rate (%) 53 57 29 50 50
Time to progression (median, in weeks) 25.0 23.0 15.7 18.4 27.0
Overall Survival
Median (weeks) 31.0 17.3 26.6 31.7 30.1
1-year (%) 40.0 35.7 28.5 37.5 36.3
Table 3 Patient tolerance
70 to 75 years > 75 years
80 mg/m2 90 mg/m2 60 mg/m2 70 mg/m2 Grade ≥ 3
3 4 3 4 3 4 3 4 n %
Haematological
Neutrophils 2 3 1 2 - - 1 - 9 20
Haemoglobin 2 - 2 - - - 1 - 5 11
Platelets - - - - - - 1 - 1 2
Clinical
Alopecia (grade 2) (3) (2) (1) (2) 8 18
Fatigue 1 - 2 - - - 1 - 4 9
Nausea and vomiting - - - - - - - - -
Neurosensory 1 - - - - - - - 1 2
Renal - - - - - - - - -
Infection - - 1 - - - - - 1 2
Early death - 2 - - 2 4
Toxic death - 1 - - 1 2
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| 15456520 | PMC526296 | CC BY | 2021-01-04 16:03:01 | no | BMC Cancer. 2004 Sep 29; 4:69 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-69 | oa_comm |
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BMC Med Inform Decis MakBMC Medical Informatics and Decision Making1472-6947BioMed Central London 1472-6947-4-171547947110.1186/1472-6947-4-17Research ArticleProspective study of clinician-entered research data in the Emergency Department using an Internet-based system after the HIPAA Privacy Rule Kline Jeffrey A [email protected] Charles L [email protected] William B [email protected] Michael S [email protected] Department of Emergency Medicine, Carolinas Medical Center, Charlotte, NC, USA2 BreathQuant Medical Systems Inc, Charlotte NC, USA2004 12 10 2004 4 17 17 22 6 2004 12 10 2004 Copyright © 2004 Kline 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
Design and test the reliability of a web-based system for multicenter, real-time collection of data in the emergency department (ED), under waiver of authorization, in compliance with HIPAA.
Methods
This was a phase I, two-hospital study of patients undergoing evaluation for possible pulmonary embolism. Data were collected by on-duty clinicians on an HTML data collection form (prospective e-form), populated using either a personal digital assistant (PDA) or personal computer (PC). Data forms were uploaded to a central, offsite server using secure socket protocol transfer. Each form was assigned a unique identifier, and all PHI data were encrypted, but were password-accessible by authorized research personnel to complete a follow-up e-form.
Results
From April 15, 2003-April 15 2004, 1022 prospective e-forms and 605 follow-up e-forms were uploaded. Complexities of PDA use compelled clinicians to use PCs in the ED for data entry for most forms. No data were lost and server log query revealed no unauthorized entry. Prospectively obtained PHI data, encrypted upon server upload, were successfully decrypted using password-protected access to allow follow-up without difficulty in 605 cases. Non-PHI data from prospective and follow-up forms were available to the study investigators via standard file transfer protocol.
Conclusions
Data can be accurately collected from on-duty clinicians in the ED using real-time, PC-Internet data entry in compliance with the Privacy Rule. Deidentification-reidentification of PHI was successfully accomplished by a password-protected encryption-deencryption mechanism to permit follow-up by approved research personnel.
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Background
The ability of medical researchers to obtain and store electronic clinical data was complicated by requirements of the Patient Privacy Rule of the Health Insurance Portability and Accountability Act ("HIPAA") of 1996 in title 45 of the Federal Register, parts 160, subparts A and E of part 164 [1-3]. The HIPAA creates a conflict for investigators. The law specifies that 18 data elements, known as protected health identifiers (PHI), that could be used to identify the patient, must be adequately protected from disclosure. However, to allow follow-up, the investigator usually must collect PHI. In the most conservative interpretation of the Privacy Rule, investigators must obtain written informed consent and written authorization to collect PHI. In the hectic setting of the emergency department, the step of obtaining written authorization can bias the data sample [4]. The Privacy Rule does allow PHI to be collected without written authorization if the institutional Privacy Board grants waiver of authorization. Waiver of authorization requires special handling of PHI.
Existing electronic data collection methods are limited in their ability to centralize data in a fashion that expedites data sharing while remaining in compliance with HIPAA. For example, commercial spreadsheets that run on Windows® do not mandate user identification, do not partition and encrypt sensitive data, and do not maintain a record and audit trail of use. Accordingly, we developed a comprehensive electronic system for clinicians to capture clinical research data from the bedside using commercially available hardware and data upload over the Internet. The system was programmed with multiple security steps and authentication procedures to maintain data security and privacy.
We tested the hypothesis that real-time clinical data can be obtained from clinicians in multiple hospitals using electronic data collection stored in an off-site server, under waiver of Authorization, while remaining in compliance with the Privacy Rule. This study represents the development and implementation phase of an ongoing multicenter study to collect prospective and follow-up clinical data from patients undergoing evaluation for pulmonary embolism in the emergency department. The specific aims of this study were to: 1. Test the feasibility of real-time, electronic data collection on personal digital assistants and personal computers in the emergency department setting in two hospitals. 2. Test if the system would correctly upload protected health information (PHI) in a secure and encrypted fashion, but allow follow-up to be performed by selected individuals using password-protected access to PHI.
Methods
Human subjects and Institutional approval
Patients were enrolled from two hospitals in Charlotte, NC: Carolinas Medical Center Main and Carolinas Medical Center University. The clinical protocol was approved under waiver of informed consent and waiver of authorization by the Carolinas HealthCare Institutional Review Board and the Institutional Privacy Board in accordance with the published guidelines of the Department of Health and Human Services (DHHS) [5], which were reviewed by Annas [6].
Because of institutional sensitivity about maintaining compliance with the privacy rule, this project required intensive planning and due diligence. Over a 6-month period, the authors scheduled several meetings with the Director of Privacy in Clinical Research, the hospital's Assistant Vice President of Privacy, and the Director of Information Security to discuss the protocol and methods. These individuals had oversight for privacy issues for both hospitals. Then, to facilitate the process of gaining assistance and approval from the Information Systems Department in implementing the technical aspects (software deployment and firewall access) at both hospitals, we obtained a letter of approval from each of these individuals to physically show to technical support personnel.
All patients in this study underwent evaluation for pulmonary embolism. The method of selection and diagnosis have been described previously [7]. For each patient, two electronic data forms ("e-forms") were (or will be) completed. The first was a prospective e-form that was completed in real-time in the emergency department by the clinician in charge of the patient's care. The second e-form encoded follow-up data, and was completed 45 days or more after the prospective form. The follow-up e-form was completed by one of two research associates. This study was non-interventional.
System overview
This system was designed to allow data to be transferred from multiple sites and stored on one server using technical requirements described in part 160 and 164 of the Privacy Rule. Figure 1 shows a schematic of the overall system structure with the hypothetical participation of four sites. According to published recommendations of the DHHS, the overarching requirement for collection of databases under waiver of authorization is to ensure de-identification of data. The DHHS specifies that this can be done either by the "safe harbor" approach, which entails removal, or the "statistical probability" method, which for practical purposes, incorporates data encryption/de-encryption techniques. The present system uses the statistical probability method, whereby the PHI data are subjected to 128-key bit encryption prior to upload on the server, but are linked to a non-PHI unique identifier (e.g., CMC0001) that allows joining of non-PHI data with PHI data for the purpose of conducting follow-up (see the star in the middle of the schematic in Figure 1). This step allows a research coordinator with the appropriate login and password to access de-encrypted (re-identified) data. In the final step, an FTP protocol was used to download the non-PHI follow-up data together with the correct prospective data for each patient. Both the prospective and follow-up data were exported in table form, one row per patient. In summary, authorized research personnel from each site had password-protected access to PHI of patients from their hospital only, while unauthorized personnel could access non-PHI study data via an FTP. An example of the latter would occur in Figure 1 if the site PI from hospital 1 were interested in viewing research data collected at hospital 3. The description of the individual elements of the system that follows is presented in the order that the study was conducted.
Data entry form structure
The trigger for data entry was the decision to order a diagnostic test to rule out pulmonary embolism in a symptomatic emergency department patient. Patient data were entered on the prospective e-form. The e-forms were programmed using hypertext markup language (HTML) in conjunction with active server pages (ASP) and Standard Query Language (SQL). The prospective and follow-up e-forms are shown in Figures 2 and 3. The prospective e-form contained a total of 70 fields for data entry including text strings, pull-downs, and click portals. The explicit definition of each field was provided by embedded text that could be viewed by mouse click over an adjacent question mark. These terms are defined in the Table 1. When the user executed an e-form upload, the server-side ASP code queried data fields for presence of an entry and validity of the entry. For parametric data, such as heart rate, the side-code query interrogated whether numerals had been entered and whether the number fell within a defined range. For example, the heart rate entry had to fall within 21 and 200 beats per minute. (If the investigator encountered a patient with a parameter outside of the allowed range, he or she could click an email link to notify the study administrator, who could override the system to make the entry.) Likewise, if the form contained a missing field, or a nonsensical entry from keystroke error (e.g. a heart rate of "t3"), the server would not load the form, and an error message directed the clinician to the field requiring correction and highlighted the erroneous entry in red shading. Once the field was corrected, the form could be uploaded.
To test for data validity in the prospective forms uploaded by clinicians, two authors independently examined each of 70 fields for all patients uploaded. We evaluated for blank cells, nonsensical character entry, or numeric entry that fell outside the predefined ranges.
Real-time data entry
Forms were completed by attending physicians (N = 22), resident physicians (N = 20), and physician assistants (N = 6) in two emergency departments while the patient of interest was still in the emergency department. Prior to study deployment, each clinician was individually trained in a 10-minute session by the study principal investigator using a pre-defined protocol, and each clinician received a follow-up letter that summarized the training session. Data forms could be accessed in the emergency department using designated Internet-connected personal computers within patient care areas, or could be completed using individually owned personal digital assistants using the Pocket PC® operating system followed by synchronized upload to the hosted server. All clinicians owned a compatible PDA. The authors and staff assistants provided technical support to assist clinicians in the process of downloading the prospective e-form to their PDAs and uploading completed e-forms to the study's central server using commercial software (Microsoft ActiveSynch® v. 3.5). The clinicians were shown that prospective data entry forms could also be accessed through a URL hyperlink that was posted on the desktop of all Internet-connected computers in both emergency departments. When the user clicked the hyperlink, this action routed the user through the firewall directly to the hosted server for this study. All computers ran Windows 95 or higher, with ethernet connection to a T3 44.736 Mbps channel.
Upon opening the first web page, the user viewed a list of clinician names (Figure 4). The clinician then chose his or her name and opened a new, blank prospective e-form. No login was required to access or upload the form, but the central server was programmed to accept uploaded e-forms only from Internet provider addresses of the computers in the two emergency departments. When each new, complete prospective e-form was uploaded to the hosted server, the server encoded the e-form with a unique identification number bearing the initials of the hospital where data originated, the sequence number and clinician who entered the data. (e.g., CMC023JAK).
Privacy controls
Multiple methods were used to ensure that protected health information would not be subject to unauthorized access, viewing or hijacking. When clinicians entered data in the emergency department, the server polled the form for inactivity exceeding 30 minutes, at which time the page would automatically close without being uploaded. We anticipated scenarios where a clinician would enter data on a prospective form that would need to be revised as a result of updated information (e.g., access to additional medical records, or arrival of family). To allow for such editing, the clinician could re-access any prospective form for a period of 60 minutes after initial upload, provided that the Internet provider address of the computer was the same as the computer from which the form originated. After 60 minutes, the prospective e-form could be altered only by a study administrator. All data were transferred using secure socket link (SSL) protocol. The central server (Win2000 OS) was located off-site at a large commercial web hosting facility (NTT/Verio Inc). Upon upload, all fields containing any of the 18 elements of that constitute PHI were subjected to 128 key-bit encryption. Data were stored in relational tables. To allow data analysis for research purposes, the study PI could access stored by file transfer protocol and exported into a format compatible with commercial software (e.g., Microsoft Access®, Seattle WA). However, all PHI data fields were remained encrypted.
Follow-up data entry
Patients were then followed prospectively to determine outcome at 45 days. The follow-up data were entered by an IRB and privacy board approved, designated research coordinator. Because follow-up mandated access to PHI data, a separate web page was developed to allow the study coordinator to have administrative access to the necessary data. The research coordinator would type the appropriate URL address and then view a login page (Figure 5). The research coordinator could obtain password-protected access to the list of all uploaded prospective data forms (Figure 6), and upon mouse click of the "Follow-up Patient" button, the follow-up form was displayed with the required data to assist in follow-up, including patient name, medical record number, social security number, and telephone number (see top of Figure 2). This system thus allowed upload of prospective and follow-up e-forms from multiple hospitals, while research coordinators with IRB and privacy board approval could view the minimum PHI required to perform follow-up at their hospital. Research coordinators could not view PHI from other hospitals. However, using a password-protected file transfer protocol, the central study PI could view the non-PHI clinical data input by all participating hospitals, without access to PHI from any hospital.
The information required to populate the follow-up e-form required the research coordinator to perform a standardized review of a comprehensive medical record database maintained by the hospital. The first database was a central electronic record storing system where laboratory and radiology results and any transcriptions of dictated clinician notes and optical scanned images could be found for the entire hospital network. This allowed the evaluation of any return visit to the hospital system, (inpatient, ED, clinic or other outpatient visit) to determine if the patient had any of the outcomes of interest in the follow-up form. If no follow-up data were available within the hospital system to prove the patient was alive at 45 days, then the follow-up protocol required query of the public social security master death index to determine if a death certificate had been filed for the patient. Finally, if no valid follow-up were documented by electronic database search, we then attempted to contact the patient through a previously described, stepwise procedure, consisting of a mailed questionnaire, followed by a telephone call, if necessary [7].
When all of the data required for the follow-up form were obtained and input into the form, the research coordinator would complete the form and press the "check form for completeness" button. This would activate a system to ensure that all necessary follow-up data were entered. For example, every patient had to have a valid 45-day follow-up, either in the form of a documented follow-up to a clinic, telephone follow-up with the patient, or confirmation of patient death within 45 days.
Results
The system was fully implemented on April 15, 2003. As of April 15, 2004, prospective data forms were uploaded from 1022 patients evaluated for acute pulmonary embolism. Prospective data have been entered by 42 clinicians and 6 physician assistants from two hospitals in Charlotte, NC. The primary technical barrier to implementation was the process of loading and using the prospective e-forms on a personal digital assistant. All 48 clinicians required individual help and training, of over one hour each to show them how to install the e-form on their PDAs. This impedance was compounded by real-time difficulties associated with stylus use on a small PDA screen, followed by difficulties with uploading to the website from the PDA led to abandonment of this method of data entry. Out of 48 clinicians, only 6 successfully uploaded more than one e-form from the PDA. Only 12 of 1022 uploaded e-forms originated from a PDA.
The primary technical barrier to implementation on the personal computers included maintaining the URL icon on desktops in the ED (it was occasionally removed by unknown persons). This problem was solved by the permanent link on the hospital's intranet home page. In two separate instances, clinicians reported that they had populated the e-form, attempted to submit, and for unknown reasons, were unable to upload the e-form, and they had to reenter the data and resubmit the e-form. The server has maintained a log of all successive e-forms uploaded by each clinician. No uploaded prospective forms have been lost or deleted. The side-server system was designed to prevent e-form upload with missing or erroneous data.
To examine if this system properly, two observers reviewed the eight parametric field entries (age, heart rate, respiratory rate, systolic blood pressure, pulse oximetry, height, weight and temperature) that were key-entered by clinicians for 1022 patients. Validity required that both observers agree that the entry was a real number within the prespecified range of the parameter. In 44/8176 fields of 12 patients (0.6%), two observers deemed the parametric field entry to be useless for statistical analysis. Stated another way, these data would be coded as missing after data cleaning were completed. However, no categorical data were missing or erroneous. As a result, 1010/1022 (98.8%) of prospective e-forms had usable data in all 70 fields.
Ninety-four percent of all 1022 patients have reported the site hospital to be their hospital of choice. Follow-up forms (Figure 3) have been completed and uploaded on 605 of 1022 patients. Using existing hospital-approved login and authentication procedures, research personnel were able to access necessary databases from their home computers. Thus, using their personal Internet connection and private telephone line, the research associates were able to complete follow-up forms from home. Follow-up forms were completed in an average of 20 ± 12 minutes.
Follow-up has revealed that all prospective e-forms were authentic, and each was completed on an emergency department patient who underwent at least one clinical test for pulmonary embolism. No bogus forms were detected during follow-up to date. This demonstrated a low likelihood of an unauthorized person generating a spoofed form on one of the designated computers in the ED treatment area, given that the automatic control system would not allow a form to be uploaded until all 70 fields are completed.
All uploaded prospective and follow-up data were obtained by the central PI using file transfer protocol and were inputted into a spreadsheet without difficulty. Figure 7 shows a screenshot illustrating a partial view of the downloaded study data, including the appearance of the PHI fields after encryption as well as unencrypted data. The purpose of this figure is to demonstrate that the central PI could have access to necessary study data from all sites while remaining blinded to PHI data. The "study ID" field represents the unique identifier used to re-identify PHI data.
Query of the server log revealed no evidence of website hijacking or other intrusion. The server computer which houses the study database and runs the web application uses the Windows® Server 2003 operating system. The only means of electronic access to the server is via hypertext transfer protocol (HTTP) and file transfer protocol (FTP). Both of these system services log all requests made to their ports. An example log entry is shown in the appendix.
Discussion
The step of obtaining written Authorization to comply with HIPAA can impart a selection bias in registries intended to study acute disease processes [4]. In section 164.512(i), the Privacy Rule allows for waiver of Authorization when the "research could not practicably be conducted without the waiver" and the "use of the PHI involves no more than a minimal risk to the privacy of individuals." The present report tests a system designed to collect clinical data in real-time from patients with acute diseases at multiple hospitals, including a mechanism to facilitate follow-up, while protecting the privacy of the participants.
The first research objective was to determine if PDAs would represent an efficient and secure mechanism for clinicians to record real-time data at the bedside in the emergency department setting. Our experience in this phase I, two-hospital study demonstrated that PDAs presented unexpected complexities that eroded our enthusiasm. The clients were clinicians with variable levels of technical sophistication. Despite our use of a relatively standard process, clinicians found it difficult to download the e-form from the website onto their PDAs, and many needed help from the study authors. Clinicians frequently forgot to bring their PDA devices to work, and during the one-year course of this study, 10 of 48 clinicians bought new PDA devices. Clinicians consistently reported difficulty with the small screen size and data entry with a stylus. Unfortunately, we did not quantify this opinion using a structured survey. We believe this represents the first published experience at using PDAs to collect research data in the emergency department setting. Our results are somewhat less positive than other studies that have reported the use of hand-held computers to maintain clinical databases [8,9]. However, Lu and colleagues previously recognized similar barriers to physician use of PDAs [10]. We emphasize that our protocol was preplanned, adequately budgeted, and technically supported to disseminate the e-form via the PDA. Unfortunately, we did not perform preplanned measurements to explain this failure. We cannot conclude inferiority of the PDA versus other methods (e.g., paper forms or PC platform) for data collection accuracy inasmuch as we did not compare key quality index data (e.g., comparison rate of compliance, missed data, key errors, lost forms) between methods. Thus, we can only explain the failure of the PDA mechanism in the broad terminology of "it lacked feasibility."
The second objective was a relatively complex task intended to determine if the system would allow prospective and follow-up data collection over the Internet in compliance with the requirements of the Privacy Rule. From a functional standpoint, we sought to determine if the system would allow us to protect the data fields that needed to be protected, but allow the non-sensitive data to be accessed by study personnel who did not have local IRB approval. This was accomplished while maintaining strict security standards at each step of data transfer (see Figure 1). Data were uploaded from designated Internet provider addresses via secure socket link protocol and stored in a database on an offsite hosted server that was protected by several layers. No study data could be accessed without a password. Further, the system mandated specific password-protected access to PHI only by IRB- and privacy board-approved individuals at each hospital. This mechanism was designed facilitate the acquisition of patient follow-up data at participating hospitals. However, the central study PI could download the study data of interest via a separate password-protected file transfer protocol, but the PHI data were encrypted (see Figure 7). Because the PHI data were stored on the server after 128 keybit encryption, even in the event of unauthorized data access (hacking), the hijacker would be unable to view the PHI.
Although a large number of commercial systems are available for storing clinical data, most are designed and marketed explicitly for the billing process. In contrast, from the perspective of research, relatively little has been published on the design and implementation of a web-based system to allow collection of clinical data in a multicenter trial design [11,12]. We believe this is the first report of successful web-based clinical data collection under waiver of Authorization and in compliance with CFR 45, parts 160 and 164. This phase I project was limited to two hospitals in the same city, both covered under the same IRB. However, we submit that the system is ready to be expanded to other hospitals in the second phase of the study.
This system was designed to be a reasonably comprehensive tool to obtain key information about the beliefs of a clinician at the time of test ordering. Here, we refer to the clinician's beliefs as what they thought were the values of certain specific clinical data that are commonly used to estimate the pretest probability of pulmonary embolism. To capture these beliefs in real time, the system cannot default to a retrospective review of the patient's chart, or having the clinician complete the form after a shift. Within the emergency department setting, the flow of knowledge is dynamic for each patient. As a consequence of time urgency, emergency clinicians often must decide to order expensive imaging tests based upon limited, changing, and sometimes erroneous information. Occasionally, clinical information becomes updated after an expensive radiological test has been done (e.g., a family member arrives with new information, or medical records arrive from another facility by facsimile). Accordingly, the data collection instrument must accurately capture the information that the clinician uses to motivate his or her test ordering behavior, rather than to collect data after the test results have returned, and more complete medical records may have arrived.
This report represents a phase I study. For the second phase, we will deploy this system to 10 US hospitals to allow collection of data from 5000 patients. The ultimate goal of this project is to collect a large, multicenter database, as the substrate for a mathematical model to generate a pretest probability of pulmonary embolism based upon beliefs of many clinicians.
Conclusions
Research data can be successfully collected, entered and uploaded to a hosted server by emergency physicians working in different emergency departments, and in compliance with the Privacy Rule. Use of server side controls to test for data validity ensured that 98.8% of uploaded forms contained complete data usable for statistical analysis. The PHI data were successfully encrypted and deencrypted using password access to allow follow-up at a later date. Server log query demonstrated no evidence of intrusion or data loss, suggesting that data were securely stored.
Abbreviations
ASP – Active server pages
ED – Emergency department
FTP – File transfer protocol
DHHS – Department of Health and Human Services
HIPAA – Health Insurance Portability and Accountablity Act
HTML – Hypertext markup language
HTTP – Hypertext transfer protocol
IRB – Institutional Review Board
PDA – Personal digital assistant
PHI – Protected health identifiers
PI – Principal investigator
SQL – Standard query language
RUL – Uniform resource locator
Competing interests
Jeffrey A. Kline is primary author on a US copyright certificate "Electronic Form ("e-form") For Secure Collection of Clinical Data Via the Internet" and inventor on a US patent (pending) that describes the present work. Jeffrey A. Kline and Charles L. Johnson own stock in BreathQuant Medical Systems.
Appendix
Example of a log entry for the HTTP system service
208.10.156.69 – [02/Mar/2004:23:55:55 +0000] "POST /pestudy/peadmin.asp HTTP/1.1" 302 1349 "" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)"
The IP address of the HTTP requesting browser is 208.10.156.69 and the resource requested is the ASP file . For the FTP system service this would be a typical log entry
208.10.156.69 – generic [06/Aug/2003:17:00:14 +0000] " [22786]USER generic FTP" 331 0 "-" "-"
The IP address of the HTTP requesting browser is 208.10.156.69 and the USER identity that was logged in was "generic". Both these log files are scanned once a week to look for any suspicious requests. To date there have been no identified intrusion or hijack attempts on the specific study database. As an HTTP responding server on the public Internet, the web (HTTP) server program does receive many well identified "virus spreading" requests which it denies and whose denials are logged. One such "virus related" request common to all web logs today is
61.100.6.181 – [01/May/2004:11:30:52 +0000] "GET /scripts/nsiislog.dll HTTP/1.1" 404 3806
This example virus was developed to attack a vulnerability which existed in Microsoft Web Servers but was eliminated by a security update for their web (HTTP) server. The highlighted 404 code denotes that the request was denied. As security updates from Microsoft become available the study web server is updated. At present there are no known HTTP vulnerabilities which would allow an unauthorized user to gain access to files on the server.
Authors' contributions
JAK conceived the study, obtained funding and participated in the system design, data collection and drafted the manuscript. CLJ carried out all technical programming, participated in system design and drafting the manuscript. WBW participated in idea conception, system design, data collection and drafting the manuscript. MSR participated in data collection and drafting the manuscript.
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 funded by grant R41HL074415-01 from the National Institutes of Health/NHLBI
Presented at the Annual Meeting of the Society for Academic Emergency Medicine, Orlando FL, May 2004
Figures and Tables
Figure 1 Schematic of system structure.
Figure 2 Reproduction of the prospective e-form as seen by the user.
Figure 3 Reproduction of the follow-up e-form as seen by the user.
Figure 4 Login web page for clinicians to access prospective e-forms.
Figure 5 Login web page to allow secure authenticated access to follow-up e-forms.
Figure 6 User view after successful authentication. From this list, the desired follow-up e-forms can be opened and populated.
Figure 7 Reproduction of sample data as viewed by the central study PI after file transfer protocol. The non-encrypted patient data, such as age, race and vital signs were obscured by pixellation by the author.
Table 1 Dictionary of variables visible to the user via hover buttons
Hospital of Choice – The hospital where the patient says he or she receives his or her care. If the patient agrees that the study hospital is his or her hospital of choice, this means that in the patient's opinion, he or she agrees to return to the study hospital for persistent symptoms in the next 45 days.
Vital signs – Measured in the ED using local standards and FDA-approved devices. SaO2% must be made with the patient breathing room air or the lowest tolerable rate of oxygen administration.
Dyspnea – Patient perception of difficulty breathing at the time of evaluation. Includes sensation described as shortness of breath, breathlessness, labored breathing, trouble breathing, not breathing right.
Pleuritic Chest Pain – Focal pain in the thorax located inferior to the transverse axis through the clavicles extending to the costal margin. The pain must change with breathing and must not be reproduced by palpation.
Substernal Chest Pain – Pain located behind the sternum that is not reproduced with palpation.
Chest pain Reproduced with Palpation – Thoracic pain that increases when palpated gently and the patient agrees that the elicited pain feels similar to the pain that he or she is experiencing.
Leg or Arm Swelling – Asymmetry observed on gross inspection. Does not require measurement or presence of edema.
Wheezing – Determined by auscultation by any healthcare provider either in prehospital or ED setting.
Alternative diagnosis – Another specific disease process for which an ICD-9 code or set of codes exist, including pneumonia, cardiac ischemia, CHF, bronchospasm, muscle strain, acute bronchitis, biliary disease, pneumothorax, aortic dissection, pericarditis, costochondritis. Descriptive diagnoses of atypical chest pain or chest pain of unknown etiology, or Chest pain NOS are not sufficient to be considered alternative diagnosis. Likewise, purely psychiatric diagnoses such as anxiety or fibromyalgia are not to be considered as alternative diagnoses.
Physician estimate of PTP – The probability that the patient has PE on the day of evaluation.
Past Medical History terms. All terms are based upon the clinician's best estimate of the presence or absence of each condition based upon data available during the ED shift. Data sources may include patient perception, family perception, oral or facsimile communication with other clinicians, evaluation of written or electronic medical records.
COPD – In the appropriate clinical setting (eg, history of smoking) a patient affirms yes to the question, "have you been told you have emphysema or damage to your lungs from smoking that has required medical treatment" This diagnosis does require spirometry results.
Surgery in past 4 weeks: Includes any surgery requiring general anesthesia.
Trauma in past 4 weeks: Any traumatic injury requiring hospitalization for >24 hours or causing fracture requiring stabilization in an ED. Does not include minor laceration repair, scrapes, bruises, concussion, whiplash or soft tissue injuries that did not warrant admission
Coronary Artery Disease – Coronary stenoses diagnosed either by clinical grounds if drug therapy is given, or by coronary imaging. Includes prior MI, prior stenting or CABG.
CHF – Systolic or diastolic heart failure. Can be based upon clinical diagnosis if drug therapy is given. Does not include diagnosis of CHF made for the first time on the day of enrollment.
DVT or PE. Requires that the patient was treated with either caval interruption, or warfarin or fractionated heparin anticoagulation for more than 11 weeks.
Malignancy – History of cancer. Decision to term the presence of cancer as "treated and inactive" means the patient perceives that the disease is in remission and the clinician has no evidence to the contrary. If evidence of previously unknown metastases is found in the ED, then the clinician uses the information at hand to code the malignancy status.
Immobility – Pathological restriction in body movement. Includes bed-bound patients who cannot or do not walk for periods exceeding 48 hours. Also includes any patient in a cast or with an external fixator in place.
End stage disease – Any disease process, such as AIDs, cancer, advanced incurable lung or heart disease with life expectancy < 6 months.
Follow-up e-form terms
Sepsis – As defined by American College of Chest Physicians
Discharge Diagnosis – Based upon written diagnoses and ICD 10 coding
Troponin – pull down box allows multiple choices of results for either troponin I or C testing.
Pressor support – >5 micrograms of dopamine per kg per min or any use of norepinephrine or epinephrine infusion
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| 15479471 | PMC526297 | CC BY | 2021-01-04 16:03:41 | no | BMC Med Inform Decis Mak. 2004 Oct 12; 4:17 | utf-8 | BMC Med Inform Decis Mak | 2,004 | 10.1186/1472-6947-4-17 | oa_comm |
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BMC NursBMC Nursing1472-6955BioMed Central London 1472-6955-3-51548814410.1186/1472-6955-3-5Research ArticlePhysical restraint use among nursing home residents: A comparison of two data collection methods Laurin Danielle [email protected] Philippe [email protected] René [email protected] Pierre J [email protected] Laval University Geriatric Research Unit, Quebec, CANADA2 Faculty of Pharmacy, Laval University, Quebec, CANADA3 Faculty of Nursing Sciences, Laval University, Quebec, CANADA4 Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, Quebec, CANADA2004 15 10 2004 3 5 5 13 4 2004 15 10 2004 Copyright © 2004 Laurin et al; licensee BioMed Central Ltd.2004Laurin 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 view of the issues surrounding physical restraint use, it is important to have a method of measurement as valid and reliable as possible. We determined the sensitivity and specificity of physical restraint use a) reported by nursing staff and b) reviewed from medical and nursing records in nursing home settings, by comparing these methods with direct observation.
Methods
We sampled eight care units in skilled nursing homes, seven care units in nursing homes and one long-term care unit in a hospital, from eight facilities which included 28 nurses and 377 residents. Physical restraint use was assessed the day following three periods of direct observation by two different means: interview with one or several members of the regular nursing staff, and review of medical and nursing records. Sensitivity and specificity values were calculated according to 2-by-2 contingency tables. Differences between the methods were assessed using the phi coefficient. Other information collected included: demographic characteristics, disruptive behaviors, body alignment problems, cognitive and functional skills.
Results
Compared to direct observation (gold standard), reported restraint use by nursing staff yielded a sensitivity of 87.4% at a specificity of 93.7% (phi = 0.84). When data was reviewed from subjects' medical and nursing records, sensitivity was reduced to 74.8%, and specificity to 86.3% (phi = 0.54). Justifications for restraint use including risk for falls, agitation, body alignment problems and aggressiveness were associated with the use of physical restraints.
Conclusions
The interview of nursing staff and the review of medical and nursing records are both valid and reliable techniques for measuring physical restraint use among nursing home residents. Higher sensitivity and specificity values were achieved when nursing staff was interviewed as compared to reviewing medical records. This study suggests that the interview of nursing staff is a more reliable method of data collection.
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Background
Nursing homes have the mandate to offer care settings to frail dependent older individuals. However, a renewed emphasis has emerged over the past decades to become more than just a home for older people [1]. A growing number of facilities are actually striving to preserve residents' sense of control and dignity in order to achieve the highest level of well-being [2,3]. This new way of thinking is based upon values of respect of autonomy and freedom for older persons in various ways such as the resident's right to take risks or to make his/her own choices [4].
Although predominantly intended as protective devices, physical restraints in nursing homes are being denunciated as measures that go conversely with the aforementioned principles [5]. Justifications for controlling confusion, agitated and aggressive behaviors are being questioned [6-8] and beneficial effects of physical restraints on falls and injuries, incontinence, muscle atrophy and quality of life challenged [8-15]. Moreover, physical restraints have been associated with cognitive impairment, nosocomial infections, pressure sores and death [10,16-19].
According to the literature, the overall prevalence of restraint use in nursing homes ranges between 4 and 68% [5,20]. This wide variation may be explained by definitions of physical restraints used, study sample sizes, characteristics of care settings, and residents' characteristics and cognitive status. Another explanation could be the choice of techniques of data collection. Several methods have been used alone or in combination for the measurement of physical restraint use [20-22]: direct observation, survey or interview of nursing staff, review of medical and nursing records and, when the cognitive status allows it, interview with residents themselves. In view of the consequential issues surrounding the use of physical restraints, it is important to have a method of measurement as valid and reliable as possible. While direct observation is undoubtedly the most valid and reliable method of measurement, it is also the most expensive means to measure physical restraint use. On the other hand, abstracting data from medical records and interviewing nursing staff have the potential to reduce the cost associated with data collection, but their sensitivity and specificity values need to be demonstrated. In addition, apart from the USA, data sources such as Minimum Data Set (MDS) have not been widely implemented in nursing home facilities throughout the world.
The objective of this study was to determine the sensitivity and specificity of the measurement of physical restraint use reported by members of the nursing staff and reviewed from medical and nursing records among nursing home residents, compared to direct observation. Since underreporting is much more susceptible to be problematic than overreporting, another objective of this study was to compare the sensitivity of the information reported by one nurse with that reported by two nurses or more questioned together. Our research hypothesis was that sensitivity of the interview is highest when the information is collected from more than one nurse.
Methods
The study was conducted in eight facilities representing a convenience sample of the long-term care facilities in the Quebec City area, Canada. These institutions were carefully selected in order to include a mix of characteristics in size (small and large), geographic location (urban and rural), university affiliation and vocation (units associated with psychiatric or rehabilitation team). Selection was made after discussion with nursing direction of each setting to gather units of different practice such as regular units and specialized units for residents with dementia or severe behavioral problems. Twenty-five subjects were randomly chosen from each unit; if an unit comprised less than 25 residents, all of its residents were included. This study was approved by the ethics committee at Laval University. Data collection took place between January and June 1992.
Definition of physical restraint
A physical restraint was defined as a mechanical means applied on a resident in order to interfere with his/her mobility, including: vest, waist, wrist or ankle restraints, geriatric chair or wheelchair with fixed tray table, or any other type of locally designed devices [23]. Restrictive siderails, defined as two raised full-length siderails [24], were considered as an intermediate measure and analyzed separately because they are frequently used to prevent bed-related falls during nighttime in long-term care settings [25].
Physical restraint measurements
Physical restraint use was measured according to three methods: direct observation, interview with members of the nursing staff including licensed practical as well as registered nurses (one or more than one nurse, generally two, questioned together), and review of medical and nursing notes.
Direct observation
Direct observation of restraints on care units were made independently by two trained research assistants using a pre-tested questionnaire. For practical reasons, observations were made before the chart reviews and the nurses' interviews on three occasions (7h00 AM, 11h00 AM and 3h30 PM) on one day. These specific times were selected as being representative of periods of different nurse staffing, and of overloaded periods during morning and afternoon.
Interview with nursing staff
In order to reduce the occurrence of an information bias, the nursing staff was blinded to the main objective of the research project. Structured interviews were carried out the day following direct observation by one of the authors (PJD), who was unaware of the observations. Interviews with the nurse in charge of each unit were scheduled, although he/she had the liberty to be represented or assisted by other members of the nursing staff. Physical restraint use on each subject was identified for every hour during the last 24 hours, without knowledge of the times that direct observation was made, by means of a pre-tested questionnaire. The questionnaire covered questions about types of physical restraints (belt, vest, wrist, ankle, fixed tray table, siderails), reasons for use (risk of falls, agitation, wandering, aggressive behaviors, body alignment problems) and the duration including hours and minutes. Other information collected during the interview included: gross cognitive and functional information, risks for falls, history of falls during the last month, agitation, wandering, aggressive behavior and body alignment problems. Cognitive status was evaluated according to five items: recall, speech, and orientation to time, space and people. Three aspects of the functional status were assessed: urinary incontinence, fecal incontinence, and ability to transfer. Respondents could refer to subjects' clinical records at any time during the interview.
Review of medical files
Restraint use from subjects' medical charts and nursing orders for the last six months was reviewed with a pre-tested questionnaire by a research assistant who was blinded to the observations. Additional information taken into consideration comprised: demographic characteristics, prescriptions for restraints, methods of resident supervision, and psychotropic medications administered in the last 48 hours.
Statistical analysis
Sociodemographic characteristics of the study sample as well as physical restraint use by methods of data collection were examined using descriptive analysis. Interrater reliability between the two research assistants was tested using the kappa statistic. Direct observation served as the gold standard [26]. To be declared concordant, an observation had to agree with the nurses' interviews on the type of restraint, and on the time of use within one hour. This time frame was set to allow a margin of error of 30 minutes for a reported information and because assessment of restraint use per care unit took an average of another 30 minutes. Each observation was considered as an event independent from one another which may produce a slight overestimation of the precision but no bias. Sensitivity (probability that a person with restraints will be classified as such) and specificity (probability that a person without restraints will be classified as such) values were calculated according to 2-by-2 contingency tables. Differences between methods of measurement were assessed using the phi coefficient. The relationships between potential determinants of restraint use including residents' characteristics and other specific variables reported by nursing staff, and sensitivity values measured by comparing the use reported by nursing staff to direct observation, were examined using chi square tests. Stratification according to these variables allowed to identify specific reasons of underreporting restraint use in the context of a descriptive study.
Results
Data collection was carried out in 16 nursing units. Of these units, eight depicted skilled nursing home care units, seven nursing home care units, and one long-term care unit within a short-term care hospital. Information was collected for 377 residents with the help of 28 nurses. Residents' age ranged from 32 to 102 years, with a median of 80 years. The sample was 62% female, and median length of stay was 45 months (0 to 720 months). Benzodiazepines and neuroleptics were administered to 35% and 25% of the subjects, respectively.
A total of 6,744 observations over a possibility of 6,786 were made (377 residents by three direct observations and six types of restraints). Prevalence results on physical restraint use according to direct observations (interrater reliability = 92.7%; kappa coefficient = 0.86 (95% confidence interval (CI): 0.73–0.97)), interviews with nursing staff and reviews of clinical records are summarized in Table 1. Fixed tray tables were observed in 23.6% of residents, belts in 12.7% and vests in 4.0% whereas wrist, ankle or other restraints (including locally designed devices, straps or blankets) were used marginally. The nursing staff reported the use of lapboards, belts and vests in 27.6, 17.2 and 5.6% of residents, respectively. Medical and nursing records specified the use of lapboards in 17.2% of residents, the use of belts in 19.4% and the use of vests in 8%. Overall, one third (33.7%) of residents were observed restrained, 32.4% of residents were reported as such by members of the nursing staff, and 38.2% of residents in medical records. Siderails were observed in 62.9% of residents while they were reported by nursing staff in 63.7% of residents, and were mentioned in 72.1% of residents' clinical records.
Table 1 Physical restraint use according to a) direct observation, b) interviews with the nursing staff, and c) reviews of medical and nursing records, among 377 nursing home residents
Physical restraint use
Direct observation Interview with nursing staff Review of clinical records
Physical restraint N (%) N (%) N (%)
Fixed tray table 89 (23.6) 104 (27.6) 65 (17.2)
Belt 48 (12.7) 65 (17.2) 73 (19.4)
Vest 15 (4.0) 21 (5.6) 30 (8.0)
Wrist 2 (0.5) 1 (0.3) 2 (0.5)
Ankle 0 (0) 1 (0.3) 0 (0)
Others 3 (0.8) 5 (1.3) 14 (3.7)
Any physical restraints 127 (33.7) 122 (32.4) 144 (38.2)
Siderails 237 (62.9) 240 (63.7) 272 (72.1)
The interview with nursing staff and the review of medical and nursing orders were both highly associated with the observation data (Table 2). The interview of nursing staff showed a somewhat stronger relationship with direct observation compared to the chart review (phi = 0.84 vs. 0.54). Sensitivity and specificity values of the information were highest when data was measured with the assistance of the nursing staff compared to chart reviews. Reported restraint use according to nursing staff (one nurse or more) gave a sensitivity value of 87.4% at a specificity of 93.7%. When data was reviewed from subjects' medical and nursing notes, sensitivity was reduced to 74.8%, and specificity to 86.3%. Restraint use was underreported in 12.6% (16/127) of interviews with nursing staff, and in 25.2% (32/127) of clinical records whereas it was over reported in 4.4% of interviews, and in 19.6% of clinical records.
Table 2 Observed physical restraint use compared to restraint use reported a) by interview with the nursing staff, and b) by review of medical and nursing records, among 377 nursing home residents
Direct observation Direct observation
Yes No Total Yes No Total
a) Interview with nursing staff* b) Review of medical and nursing records†
Yes 111 11 122 Yes 95 49 144
No 16 239 255 No 32 201 233
Total 127 250 377 Total 127 250 377
* Sensitivity = 87.4%; specificity = 93.7%; phi = 0.84.
† Sensitivity = 74.8%; specificity = 86.3%; phi = 0.54.
Sensitivity values according to specific residents' characteristics and other reported variables are given in Table 3. Increased sensitivity values by 10% or over were observed for perceived risk for falls, agitated behaviors, body alignment problems, aggressive behaviors, urinary incontinence, fecal incontinence, and incapacity to transfer. Sensitivity of the measurement was similar when two or more nurses were interviewed compared to one nurse, although a higher value was noticed when two nurses were questioned (94.1% vs. 85.1%). Significant relationships between perceived risk for falls (p = 0.03), agitated behavior (p = 0.04), body alignment problems (p < 0.001) and aggressive behavior (p = 0.01), and reported restraint use by nursing staff were observed. No association was observed for residents' age and sex, number of nurses interviewed, history of falls, wandering problem, disorientation to time, space or people, recall troubles, speech troubles, urinary and fecal incontinence, and ability to transfer.
Table 3 Sensitivity values of specific variables reported by nursing staff, among 377 nursing home residents
Variable N Sensitivity
%
Demographic Characteristic
Sex
Male 142 86.4
Female 235 88.0
Age (years)
< 65 41 96.0
65 – 74 83 89.3
75 – 84 127 83.8
> 85 126 83.8
Reported restraint use
Nurses interviewed
One nurse 256 85.1
Two or more nurses 121 94.1
Justification for restraint use
Perceived risk for falls
Yes 249 91.3
No 127 77.1
History of falls
Yes 47 92.9
No 327 86.6
Agitated behaviors
Yes 92 95.6
No 285 82.9
Wandering
Yes 53 81.8
No 324 87.9
Body alignment problems
Yes 131 97.4
No 246 72.6
Aggressive behaviors
Yes 123 97.8
No 254 81.7
Functional characteristics
Disorientation to space
Yes 202 89.9
No 169 82.2
Disorientation to time
Yes 223 88.5
No 141 81.8
Disorientation to people
Yes 166 91.1
No 208 82.5
Recall troubles
Yes 220 88.9
No 144 81.6
Speech troubles
Yes 184 89.0
No 192 84.1
Urinary incontinence
Yes 249 88.8
No 128 72.7
Fecal incontinence
Yes 226 89.2
No 151 75.0
Unable to transfer
Yes 224 89.2
No 153 75.0
Discussion
The measurement of physical restraint use according to interview with members of the nursing staff and review of medical charts and nursing orders both reflect accurately the reality observed in long-term care setting residents. Our study has also shown that sensitivity and specificity values of the reported measurement are higher than those calculated from medical charts and nursing orders. This phenomenon is not surprising considering that the keeping of medical and nursing orders in nursing homes isn't usually done on a daily basis [27], as opposed to acute care settings.
The current investigation was carried out in units of diverse facilities. The selection of these facilities was intended to allow the participation of subjects and care units of various characteristics as compared to other studies usually designed [28]. The sample of nursing home residents included in this study corresponded well to the physically and cognitively impaired residents generally housing in long-term care institutions.
Limitations of the current study must be taken into account when interpreting these findings. First, data were collected in 1992. Due to the implementation of the OBRA act, it is probable that the prevalence figures given in the current study are overestimations of those that would be observed in 2004. On the other hand, the province of Quebec just recently launched its first comprehensive policy on physical restraint use [23]. Furthermore, the purpose of this study was to compare the sensitivity values of two reporting techniques with direct observation. This comparison should not be affected by the prevalence of physical restraint use. In addition, although a higher proportion of restrained residents might seem more difficult for the nurses to remember as compared to a lower proportion, the nurses didn't show any hesitation when recalling the use of physical restraints as the majority of residents had been living there for a long period of time. Second, we used a convenience sample of long-term care facilities rather than one drawn randomly. We wanted to determine differences and similarities in various practice facilities regarding physical restrain use. The chosen sample provided a relatively broad range of clinical settings. Also, the assessment by nurses was performed the day after direct observation. This time period was chosen in order to reduce recall bias as much as possible, and therefore increase the sensitivity of the reporting technique although this may not be practical in many situations. Another limitation for the interpretation is the use of a descriptive study design. This design is useful to measure the frequency in which a situation occurs or collect data on possible risk factors, but does not allow to infer causal relationships.
It is well known that the prevalence of residents with physical restraints is usually underreported since a social desirability bias tends to affect the validity of the information when the nursing staff has to declare the use of restraints [29]. Despite that restraints are generally applied for safety reasons, nurses nevertheless experience inner struggle when they have to apply them [11,22]. These feelings could influence the nurses' answer when they are interviewed individually and could introduce subsequently a differential misclassification error. In our study, although the sensitivity value improved when interviews were done with two nurses instead of one, the difference was not considered clinically significant. Reported use of physical restraints by two nurses reduced but did not eliminate the presence of an information bias, since the underreporting went down from 14.9 to 5.9%. On the other hand, we do not think that the resulting effect on the prevalence estimates is of consequence. This phenomenon is equally present but to a much lesser extent in the over reporting data since from 6.2% with one nurse, the prevalence of over reported restraint use was reduced to 1.1% when two nurses were interviewed.
Even though other studies have observed an association between residents' characteristics and the risk of being restrained [8,10,17,30,31], these characteristics were not related to the use of physical restraints. Rather, justifications for the use of physical restraints such as disruptive behaviors (e.g. aggressiveness, agitation, and body alignment problems) were associated with their use. For example, it is noteworthy that the risk for falls as perceived by nursing staff was associated with restraint use whereas a history of falls was not. This means that physical restraints were used as preventive devices for a large proportion of subjects, in spite of numerous studies that do not support such practices [13-15,22].
Conclusions
Compared to review of clinical records, reported physical restraint use by interviewing nursing staff is a simple, efficient, and valid technique of collection of data regarding their use in nursing homes. No severe information bias was observed even though the use of physical restraints may be associated with poor quality of care. This method of measurement appears to be reliable and valid for research purposes. Moreover, our study provides support to the American initiative in regard to the monitoring of several outcomes in nursing homes through nursing staff reports [32-35]. According to our results, interviewing nursing staff is a sensitive and specific method of eliciting information on physical restraint use. Finally, these results have implications for future research in the field. Interviewing nurses on different aspects of medical and nursing care seemed to be a reliable method.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
DL participated in the second line of statistical analyses, and drafted the manuscript. PV drafted parts of the document and contributed to the editing. RV contributed to the editing of the manuscript. PJD served as the Principal Investigator, designed the study, participated and oversaw field activity, revised and edited the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We are grateful to Diane Richard, RN, for expert advice and assistance during data collection. We also recognize the work of Josée Bourdages, PhD, for assistance during data collection, and of Claude Lapointe, MSc, and Zohra Benounissa, MScA, for statistical assistance.
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| 15488144 | PMC526298 | CC BY | 2021-01-04 16:30:13 | no | BMC Nurs. 2004 Oct 15; 3:5 | utf-8 | BMC Nurs | 2,004 | 10.1186/1472-6955-3-5 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-791547947010.1186/1471-2164-5-79Research ArticleProtein kinases of the human malaria parasite Plasmodium falciparum: the kinome of a divergent eukaryote Ward Pauline [email protected] Leila [email protected] Jeremy [email protected] Christian [email protected] Wellcome Centre for Molecular Parasitology, University of Glasgow, 56 Dumbarton Road, Glasgow G11 6NU, Scotland, UK2 INSERM U609, Wellcome Centre for Molecular Parasitology, University of Glasgow, 56 Dumbarton Road, Glasgow G11 6NU, Scotland, UK3 Division of Advanced Technologies, Abbott Laboratories, 100 Abbott Park Road, Abbott Park, IL 60064, USA2004 12 10 2004 5 79 79 5 7 2004 12 10 2004 Copyright © 2004 Ward et al; licensee BioMed Central Ltd.2004Ward 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, caused by the parasitic protist Plasmodium falciparum, represents a major public health problem in the developing world. The P. falciparum genome has been sequenced, which provides new opportunities for the identification of novel drug targets. Eukaryotic protein kinases (ePKs) form a large family of enzymes with crucial roles in most cellular processes; hence malarial ePKS represent potential drug targets. We report an exhaustive analysis of the P. falciparum genomic database (PlasmoDB) aimed at identifying and classifying all ePKs in this organism.
Results
Using a variety of bioinformatics tools, we identified 65 malarial ePK sequences and constructed a phylogenetic tree to position these sequences relative to the seven established ePK groups. Predominant features of the tree were: (i) that several malarial sequences did not cluster within any of the known ePK groups; (ii) that the CMGC group, whose members are usually involved in the control of cell proliferation, had the highest number of malarial ePKs; and (iii) that no malarial ePK clustered with the tyrosine kinase (TyrK) or STE groups, pointing to the absence of three-component MAPK modules in the parasite. A novel family of 20 ePK-related sequences was identified and called FIKK, on the basis of a conserved amino acid motif. The FIKK family seems restricted to Apicomplexa, with 20 members in P. falciparum and just one member in some other Apicomplexan species.
Conclusion
The considerable phylogenetic distance between Apicomplexa and other Eukaryotes is reflected by profound divergences between the kinome of malaria parasites and that of yeast or mammalian cells.
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Background
Modulation of protein phosphorylation through the antagonistic effects of protein kinases and protein phosphatases is a major regulatory mechanism of most cellular processes. Dysregulation of protein phosphorylation in human cells plays a major role in many diseases such as cancers and neurodegenerative disorders [1]. This has prompted the search for drugs targeting protein kinases, an endeavour which led in 2002 to the commercialisation of Gleevec, the first protein kinase inhibitor used as a drug for human disease [2]. Additional molecules targeting protein kinases are in clinical trial [3,4], and significant developments in this field are expected in the next few years. Some of the most devastating infectious diseases are caused by protists such as malaria parasites and trypanosomatids: hence, about half the global population lives in malarious areas, with 10% of the world population contracting the disease each year, which results in 1–3 million annual deaths. The essential role played by eukaryotic protein kinases (ePKs) in crucial cellular functions makes them attractive potential targets for drugs against such eukaryotic infectious agents [5].
Malaria parasites have a complex life cycle. Infection of human beings by Plasmodium falciparum, the species responsible for the lethal form of human malaria, begins with the bite of an infected Anopheles mosquito, which delivers sporozoites into the bloodstream. These cells establish an infection inside hepatocytes, where they undergo an intense multiplication generating several thousand merozoites, a process called exo-erythrocytic schizogony. The merozoites invade erythrocytes, where they also undergo schizogony, the process that is responsible for malaria pathogenesis. Some merozoites, however, arrest the cell cycle and differentiate into male or female gametocytes, which are infective to the mosquito. Once ingested by the insect, the gametocytes develop into gametes (which for the male cells involves three rapid rounds of cell division) and fuse into a zygote. Further development in the mosquito involves a process of sporogony, producing sporozoites that accumulate in the salivary glands and are now ready to infect a new human host (see for information on malaria).
The observation that many parasitic ePKs display profound structural and functional divergences from their counterparts in their vertebrate hosts [5-7] suggests that specific inhibition is an attainable goal. The availability of PlasmoDB, a genomic database for Plasmodium falciparum [8], now permits a systematic analysis of the entire complement of ePKs encoded in the genome (the "kinome") of this pathogen, an important milestone both in our understanding of Plasmodium biology and in the definition of potential novel drug targets.
Prior to the genomic era, the initial classification system of Hanks and Quinn [9] distributed ePKs into four major groups:
• the cyclic-nucleotide- and calcium/phospholipid-dependent kinases (the AGC group);
• the CMGC group, constituted of the cyclin-dependent- (CDK), mitogen-activated- (MAPK), glycogen-synthase- (GSK) and CDK-like kinases;
• the calmodulin-dependent kinases (CaMK), and
• the tyrosine kinases (TyrK).
ePKs that did not clearly fit into any of these groups were placed into the OPK ("other protein kinases") group. The primary structure of all enzymes in these groups conform to the model described by Hanks, in which the catalytic domain is subdivided into eleven subdomains, which can be aligned across all groups. In addition to the "typical" ePKs, several enzymes possessing protein kinase activity, but which are unrelated (or only distantly related) to ePKs at the primary structure level, have been identified and termed "atypical protein kinases" (aPKs). These include phosphatidyl-inositol 3' kinase (PI3K), DNA-dependent protein kinase, and members of pyruvate dehydrogenase kinase family.
Exhaustive analyses of the kinome of some model organisms have now been published. The kinome of S. cerevisiae contains 115 ePKs [10], and the genomes of D. melanogaster, C. elegans and H. sapiens comprise 239, 454 and 510–520 ePK-coding genes, respectively [11-14]. On the basis of this wealth of new data, three additional major ePK groups were recognized (reviewed in [15]:
• the casein kinase 1 (CK1) group;
• the STE group, which includes many enzymes functioning in MAPK pathways, although the MAPKs themselves belong to the CMGC group (STE stands for "sterile", referring to the fact that enzymes belonging to this group were first identified in genetic analysis of yeast sterile mutants);
• the tyrosine kinase-like (TKL) group, which, as its name indicates, includes enzymes that are related to those in the TyrK group, although they are serine-threonine protein kinases.
Furthermore, a description of the 369 non-receptor serine/threonine protein kinases of the plant Arabidopsis thaliana has recently been published [16]. Comparative examination of this and previously available kinomes has demonstrated that members of all major ePK groups can be found in yeast, worms, insects, mammals and plants, with the exception of TyrKs, which are not found in yeast. That most TyrKs function in hormone-response receptor-linked pathways suggests that this family arose as an adaptation to the needs for intercellular communication in multicellular organisms. It has however been reported recently that a few unicellular eukaryotes (Chlamydomonoas, Entamoeba and Phytophtora) possess putative TyrK family members [17]. Despite the fact that most serine-threonine ePKs groups are found in all eukaryotes, indicating that their appearance occurred early in evolution, each of the kinomes has nevertheless its specificities. A striking feature in this respect is the considerable extension of some ePK families in some organisms but not in others. For example, yeast and Drosophila have 4 and 10 members of the casein kinase 1 (CK1) group respectively, whereas the C. elegans genome encodes 85 CK1-related genes.
With the exception of the plant A. thaliana, all eukaryotes whose kinome has been characterised, from yeast to man, belong to the Opisthokonta phylogenetic group. As depicted in Fig. 1, this lineage represents only one small branch of the eukaryotic tree. Several eukaryotes of high medical importance, such as malaria parasites (Alveolates) or trypanosomes (Discicristates), belong to phylogenetic groups that are vastly distant from both the Opisthokonta and Planta branches [18]. This is reflected by many profound peculiarities in their basic biology (see [5] for a review). Divergences from model eukaryotes can also be expected not only at the level of individual protein kinases of these organisms (as has been previously documented in a number of instances – see [5,6] for reviews), but at the level of their kinome as well. As is documented below, our analysis of the P. falciparum kinome confirms this prediction.
Figure 1 Phylogenetic distance between malaria parasites and the organisms used as model Eukaryotes. With the exception of the plant Arabidopsis, the organisms whose kinome has been characterised (yeast, worms, Drosophila and human), all belong to the Opisthokonta lineage, which is vastly distant from the Alveolata branch which include the Apicomplexa. Adapted from Badlauf, S (2003), with permission (Copyright 2003 AAAS).
Results and discussion
Overview of the tree
65 sequences related to ePKs were retrieved from PlasmoDB and used to construct a phylogenetic tree as described in the Methods section (see Additional file 1 for the alignment). The tree of the P. falciparum kinome (Fig. 2) indicates that although the parasite possesses enzymes belonging to most of the major serine/threonine kinase groups, as described in the following paragraphs, several enzymes do not cluster with any of these groups.
Figure 2 The P. falciparum kinome. Phylogenetic tree of ePKs from P. falciparum. The tree was compiled using conserved portions of aligned sequences using a protein distance matrix method (see Additional file 1 for the alignment). All major groupings discussed were observed in the 100 replicate bootstrap tree (not shown). Branches with bootstrap values >70 are shown in red and >40 in blue. The scale bar represents 0.1 mutational changes per residue (10 PAM units). 65 sequences from P. falciparum are shown (in red characters), together with representative members of major subgroups of human kinases (in black characters). The P. falciparum sequences are labelled with their identifier in the PlasmoDB database and, where applicable, with the published name of the enzymes. The human sequences are labeled with HUGO gene names.
CK1 group
Only one malarial kinase, the previously described PfCK1 [PF11_0377] [19], clearly falls within this group, which is vastly expanded in some other kinomes (e.g. 85 genes in C. elegans, see above).
AGC group
Five malarial kinases cluster within this group, three of which have been characterized: the cAMP-dependent PfPKA [PFI1685w] [20], the cGMP-dependent PfPKG [PF14_0346] [21], and PfPKB [PFL2250c] [22], an enzyme displaying maximal similarity to AKT/PKB. In other eukaryotes, PKB functions in the PI3K-dependent pathway; a PI3K kinase homologue is present in the P. falciparum genome (see below). Two additional sequences [PFC0385c and PF11_0464] form a separate cluster attached to the base of the AGC branch. There appears to be no clear member of the PKC subfamily.
CamK group
The main branch of the tree that contains the human CamKs also contains 13 PfePKs, which underlines the importance of calcium signalling in the parasite [23]. A tight cluster is formed by five of these enzymes, which share the overall structure of the calcium-dependent protein kinases (CDPKs) found in plants and ciliates but not in Metazoans. CDPKs are characterised by the presence of a kinase catalytic domain located on the same polypeptide as four EF-hand calcium-binding domains. Four of these enzymes have been described previously: PfCDPK1 [PFB0815w] [24], PfCDPK2 [MAL6P1.108] [25], PfCDKP3 [PFC0420w] [26] and more recently PfCDPK4 [PF07_0072]. The latter enzyme is expressed in sexual stages and was shown to be essential for development of the parasite in the mosquito, through mediating cell cycle resumption during male gametocyte exflagellation [27]. A fifth CDPK [PF13_0211], which like the four cited above possesses four EF-hand motifs, has been discovered in the present study. PF11_0242 appears to be related to CDPKs, but contains only one EF-hand motif. PfPK2 [Pfl1885c] constitutes a sister branch to the CDPK group. This enzyme was previously characterized as being related to the CamK family [28], and has no EF-hand domain. No malarial kinase clusters closely with the mammalian CamKs used to anchor the tree. Six other sequences, however, form a sister branch to the cluster that contains the CDPKs; only one of these six sequences (PF11_0239) possesses an EF-hand domain. The CamK activity described [29] as crucial for ookinete development in the mosquito vector (see below) is likely to be associated with one of the enzymes in this group.
CMGC group
Eighteen malarial kinases cluster within this group, which makes it the most prominent group in the Plasmodium kinome. Interestingly, in other eukaryotic systems a majority of CMGC kinases are involved in the control of cell proliferation and development, and their relative abundance in the P. falciparum kinome may reflect the variety of successive proliferative and non-proliferative stages which constitute the life cycle of malaria parasites. Six enzymes are related to the cyclin-dependent kinase family, 5 of which were identified previously (reviewed in [30]), the last one (Pfcrk-5, [MAL6P1.271]) having been discovered during the present analysis. Two previously characterised mitogen-activated protein kinases (MAPKs), Pfmap-1 [PF14_0294] [31-33] and Pfmap-2 [PF11_0147] [34], cluster together with a member of the MAPK family, as expected. Two enzymes, PfPK6 [PF13_0206] [35] and Pfcrk-4 [PFC0755c] (Equinet, Le Roch and Doerig, unpublished), display features of both CDKs and MAPKs according to BLASTP analysis. Their position either in a cluster (composed of PfPK6 and Pfcrk-5) that is intermediate between the CDK and the MAPK groups, or in a cluster (composed of Pfcrk-4 and uncharacterized MAL13P1.196) at the base of the CDK/MAPK/GSK3 branch, is consistent with these early observations. Three GSK3-related kinases, two of which [PF08_0044 and PFC0525c] have been characterised previously [36,37], form a cluster within the CMGC group. Four additional enzymes form another cluster that includes human Clk1, one of which is a previously described LAMMER-related kinase [PF14_0431] [38]. The complexity of the CMGC group, its relative importance in the P. falciparum kinome, and our long-standing interest in the control of cell proliferation and differentiation in the parasite, prompted us to produce a three-species comparative tree of this group (see below and Fig. 3).
Figure 3 A three-species tree of the CMGC group. Phylogenetic tree showing members of the CMGC group of protein kinases from P. falciparum, yeast and human. The tree was compiled using conserved portions of aligned sequences using a protein distance matrix method; the tree shown is a consensus tree built from 100 bootstrap replicates. Branches with bootstrap values >70 are shown in red and >40 in blue. The scale bar represents 0.1 mutational changes per residue (10 PAM units). The P. falciparum sequences are identified by with their identifier in the PlasmoDB database and, where applicable, with the published name of the enzymes. The human sequences are labeled (black) with HUGO gene names (except for sk466, which is a numerical designation taken from Manning et al. (2002), and the yeast sequences (blue) identified according to the catalogue in Hunter and Plowman (1997).
TKL group
Five malarial enzymes appear in the vicinity of the TyrK-like group, including two [MAL6P1.191 and PFB0520w] that display maximal homology to MAPKKK-related or MLK (mixed-lineage kinases) enzymes upon BLASTP analysis. PFB0520w clusters with the TGFβ receptor (TGFβ1). The malarial sequence is much more similar to TGFβ receptors than to mammalian Raf, and furthermore, in common with TGFβ receptors, the malarial enzyme has a predicted transmembrane sequence N-terminal to the kinase domain. Mammalian TGFβ receptors assemble as heterodimers, and it remains to be seen whether the malarial enzyme forms a homodimer or has the capacity to coassemble with a mammalian subunit.
Absence of members of the STE and TyrK groups
No malarial protein kinase clusters with the STE7/11/20 group, which is consistent with the lack of success of earlier in vitro and in silico attempts at identifying MAPKK malarial homologues [39,40] and points to a divergent organisation of the MAPK pathways in malaria parasites (see below). It is relevant to mention here that one of the P. falciparum NIMA-related enzymes (see below) possesses an activation site that closely mimics that of MEK1/2. This enzyme, Pfnek-1 [PFL1370w], is able to specifically phosphorylate Pfmap-2 (but neither Pfmap-1 nor mammalian ERK2) in vitro, and to act in synergy with Pfmap-2 towards the phosphorylation of exogenous substrates [39]. This suggests that Pfmap-2 activity may be regulated by Pfnek-1. However, the physiological relevance of these finding remains to be demonstrated. Our tree indicates that members of the TyrK family are absent, as is the case in yeast and most (but not all) unicellular eukaryotes [17].
Other clusters and "orphan" kinases
Five Plasmodium genes form a cluster of NIMA-related sequences that includes the NIMA-related kinase Nek1. Of these five, four are recognised by BLASTP analysis as being related to the NIMA/Nek family [41], including the well characterised Pfnek-1 enzyme [39]. The fifth enzyme, MAL6P1.56, does not cluster with the NIMA-like kinases in other analyses (not shown).
Several protein kinases appear not to cluster clearly with any defined group, or to constitute small "satellite" clusters. Examples of such "orphan" kinases are (i) the cluster formed by PfKIN [PF140516], an enzyme previously described as related to the SNF1 family [42], with two uncharacterised PfPKs [PF14_0476 and PF13_0085]. This cluster is located at the base of the CamK and AGC branches, and does not strongly associate with any established ePK group (when mammalian NIM1/SNF1-like kinases were included in the phylogenetic tree no malarial kinases clustered with them (not shown)). (ii) A group of three malarial enzymes, including PfPK4 [MAL6P1.146], a previously characterised HRI kinase homologue [43], that are similar to mammalian elongation factor kinases, and form a distinct cluster associated to the NIMA group. (iii) Several sequences that are isolated at the base of major branches of the tree, indicating an absence of relatedness to established ePK groups. These include the "P. falciparum exported protein kinase" (PfEST, MAL7P1.91) [44], which forms an isolated branch at the base of the part of the tree containing the CMGC, CamK and AGC groups, PFL2280w, which is in a similar situation, and a group of two sequences at forming a sister cluster to the branch containing the AGC and CamK groups. One of these two sequences, PfPK7 [PFB0605w], displays relatedness to AGC and STE kinases in BLAST analysis (see below).
So far, four PfePKs have been described as appearing as "composite" enzymes displaying features from more than one established ePK family. As mentioned above, PfPK6 [PF130206] and Pfcrk-4 [PFC0755c] both display relatedness to CDKs and MAPKs, and this is confirmed by their position on the tree. The MAPKK-like activation site of Pfnek-1 [PFL1370w] has been discussed above. The fourth example is that of PfPK7 [PFB0605w], an enzyme whose C-terminal region carries a sequence which is conserved in MAPKKs but whose N-terminal region is more closely related to that of PKAs [40]. This sequence does not cluster with any well-defined group in the tree, although it associates with uncharacterized PFI1415w in a sister cluster to the major branch containing the AGC and CamK groups. Whether such "dual" enzymes represent common ancestors to subsequently divergent families which have been conserved in the evolution of the Apicomplexan lineage, or whether they arose from domain shuffling between existing kinase genes, remains to be elucidated. It is possible that additional such "composite" enzymes will be identified, particularly among the PfPKs which do not associate with well defined PK groups.
A three-species comparison of CMGC kinases
Because of the large number of CMGC-group kinases found in the P. falciparum genome, we carried out a more thorough analysis in which the 18 malarial kinases belonging to this group were compared with comprehensive sets of related kinases from the yeast and human genomes (Fig. 3). The phylogenetic tree was constructed in a similar way to that in Figure 2. 152 amino acid positions from the alignment were used in the construction of the tree.
Evidence for absence of typical 3-component MAPK pathways
In this analysis, both P. falciparum kinases (Pfmap-1 and Pfmap-2) previously reported as belonging to the ERK family clustered, as expected, with the MAP kinases. However, in contrast to previous suggestions brought forward before the full complement of mammalian ERKs had been characterised [33,45], they do not specifically cluster with ERK1/2. Rather, they lie outside the cluster of typical MAP kinases comprising the p38, JNK and ERK1/2 classes from human and yeast. Pfmap-2 lies at a basal position relative to the MAPK family, indicating no preferential relatedness to any of its subfamilies. Pfmap-1, in contrast, clearly associates with ERK8, a recently described member of the ERK family which, like Pfmap-1, has a large extension at the C-terminus [46]. In the orthologous rat enzyme ERK7, a similar extension has been shown to be involved in regulation of enzymatic activity [47,48]. It has hence been proposed that ERK8/7 may not be part of typical three-component (MEKK-MEK-MAPK) modules which are the hallmark of the ERK1/2, p38 and JNK pathways. Formal demonstration that ERK8/7 is not regulated by classical MEKs in mammalian cells is difficult because of the numerous MEK homologues present in the genome. The situation in P. falciparum therefore provides a first clear example that in vivo regulation of a kinase related to ERK8/7 does not require a typical MEK, since no member of the latter family is present in the parasite's genome (see above). It is perhaps unsurprising that P. falciparum lacks MAP kinases of the ERK1/ERK2, p38 of JNK subfamilies, given the absence of MAPKKs and STE-like MAPKKKs in the genome. In summary, our data indicate that although the malaria parasite uses MAPK homologues, these are not part of three-component modules – to our knowledge, P. falciparum is the first eukaryote demonstrated to lack such modules.
Cell cycle control kinases
Three P. falciparum kinases cluster with the cell division kinase group that includes the human cell cycle CDKs. PfPK5 [MAL13P1.279] appears orthologous to yeast cdc28 and to human CDK1-3. PfPK5 displays similar levels (60% identity) of overall homology to both mammalian CDK1 and CDK5; however, in our analysis this enzyme clearly clusters with the former, lending support to the idea that this enzyme is a functional homologue of the major cell cycle control kinases, as previously suggested [49,50]. The other two malarial enzymes that clearly cluster within the CDK group, Pfcrk-3 [PFD0740w] and Pfcrk-1 [PFD0865c], cannot be assigned an orthology with any yeast kinase. However, Pfcrk-1 appears to be related to human CDKs such as CDK10 and CDK11 that are involved in transcriptional control, consistent with earlier reports [51] that this enzyme shares primary structure features with the human PITSLRE (CDK11) kinases. Pfmrk [PFL00141] was initially described [52] as a putative homologue of the CDK-activating kinases (CAKs) such as mammalian CDK7, and subsequently shown to be able to undergo some activation by human cyclin H (the cognate cyclin activator of mammalian CDK7) and by Pfcyc-1, a P. falciparum protein with maximal homology to cyclin H [50,53]. However, in our tree Pfmrk appears not to be included in the CDK7 cluster, but instead lies at an intermediate position between the MAPK and the CDK groups. It is relevant to mention here that sequence-based prediction of kinase-cyclin pairs is difficult: for example, PfPK5, a clear CDK1-3 orthologue, is unexpectedly activated very efficiently in vitro by human cyclin H (a CDK7 activator) and p25 (a highly specific CDK5 activator), among other cyclin-related proteins [50]. This may be explained by structural properties making this enzyme very prone to adopt the active conformation [54]. Extreme caution must therefore be exercised in predicting precise functions for the four cyclin-related proteins which have been identified so far [55].
The positions of the clusters containing (i) PfPK6 [PF130206] [35] and Pfcrk-5 [MAL6P1.271], and (ii) Pfcrk-4 and uncharacterized MAL13P1.196, are consistent with the data in the general tree, and confirm the previously detected relatedness of two of these enzymes to both CDKs and MAPKs. Overall, the number of clear orthologues of cell division kinases in the P. falciparum genome is smaller than that in the yeast or human genomes, and may represent a minimum complement of such kinases that are necessary for the completion of a eukaryotic cell cycle. Alternatively, some cell cycle control functions assured by CDKs in human cells may be taken over, in Plasmodium, by some of the CMGC kinases with no clear relatedness to established families.
Other CMGC kinases
A number of CMGC group kinases interact with factors involved in mRNA splicing. PF11_0156 clearly is an orthologue of human PRP4, a kinase that is associated with mRNA splicing and histone deacetylation and that is conserved in most eukaryotic genomes (including Schizosaccharomyces pombe, but not Saccharomyces cerevisiae) [56,57]. Human SRPK1 phosphorylates the "Serine-Arginine-rich pre-mRNA splicing factors" called SR proteins, and homologues are conserved in all eukaryotic genomes [58,59]. Two P. falciparum kinases (PFC0105w and PFl4_0408) cluster with SPRK. Both these kinases have an insertion between domains VIb and VII that is a distinctive feature of SRPKs. Previously described PfLAMMER [PF14_0431] [38] associates with yeast kns1 [60] and the related human LAMMER kinases CLK1-4 that also phosphorylate SR proteins [61].
Other kinases clustering within the CMGC group include a single orthologue of casein kinase 2α [PF11_0096]. Other eukaryotes have at least 2 alpha subunit-encoding genes, emphasizing the relative simplicity of the P. falciparum kinome. As detected on the general tree (Fig. 2), three malarial enzyme cluster with the GSK3 family, the most closely related to human GSK3α/β being the recently characterised PfGSK3 [PFC0525c], which appears to be exported into the host erythrocyte [37].
In several instances our phylogenetic classification of individual kinases differs from the previously reported classification based on BLAST searches. There are at least two reasons for this discrepancy. Firstly, our analysis is based on a comprehensive catalogue of protein kinases from P. falciparum, and we have access to comprehensive catalogues from several other organisms. In contrast, several malarial ePKs were classified at the time of their initial identification several years ago, when the sequences could be compared only to non-comprehensive sets. As an example, both P. falciparum MAPKs were identified before the mammalian ERK8/7 enzymes were discovered, and the closest sequences available at the time were those of the ERK1/2 family. Secondly, it has been reported that BLAST performs poorly in assigning orthology between human and C. elegans genes [62]. This is because of extensive independent gene duplication on the lineages leading to the two organisms. Humans and P. falciparum are much more distantly related and there has been extensive gene duplication on the human side. Our data support the view that reliable assignments of orthology between genes in distantly related species might only be assigned through the construction of phylogenetic trees and suggest that comparisons based on BLAST must be interpreted cautiously.
FIKK, a novel, Apicomplexa-specific group of ePK-related proteins
That only 65 typical ePKs were identified in this search is somewhat surprising, as Saccharomyces cerevisiae, whose genome (12 megabases) is half the size of the P. falciparum genome (24.8 megabases), encodes approximately twice as many enzymes of this family.
In preliminary analyses, 21 sequences identified in the HMM search appeared to form a tight cluster that is relatively distantly related to the more typical ePK groups discussed above. Based on an amino acid motif corresponding to subdomain II of the ePK catalytic domain, and which is well conserved in members of this novel family, we called this group "FIKK". In addition to the ePK catalytic domain-like region, the FIKK sequences all have a highly variable N-terminal extension, and in some cases the catalytic domain itself is interrupted by large insertions (as is the case for several of the 65 "typical" malarial ePKs, see below). An alignment of the FIKK kinase-like domain with the 65 typical ePKs in the P. falciparum genome showed that they share most of the residues that are conserved in the ePK catalytic domain. Indeed, with the exception of the Glycine triad in subdomain I, all residues which are crucial for phosphotransfer or structural stability of protein kinases, and therefore well conserved throughout the family, are present in all members of this family (see Table 1 and Fig. 4). In contrast, no FIKK sequence possesses a full Glycine triad (GxGxxG) in subdomain I. This triad is present in a majority of ePKs and is involved in positioning the ATP molecule in the catalytic cleft [63]. However, one, and sometimes two glycine residues are present in subdomain I of the FIKK sequences. This is also the case in a number of enzymes with demonstrated protein kinase activity from many organisms (including P. falciparum) [40], and it is clearly established that ATP binding and phosphotransfer ability is not dependent on the presence of a Glycine triad. Although lacking the Glycine triad, all FIKK sequences possess an N-terminal extension, with a conserved tryptophan residue in the region that corresponds to subdomain I. One of the FIKK sequences is represented in PlasmoDB as two contiguous ORFs (PF14_0733 and PF14_0734) separated by a gap. This is presumably due to erroneous prediction: alignment with other FIKKs clearly shows these sequences represent two parts of a single member of the FIKK family rather than two separate genes. Furthermore, RT-PCR across the two predicted ORFs demonstrates that both sections are present on the same mRNA molecule. Interestingly, sequencing of the RT-PCR product showed that the open reading frame in the cDNA is interrupted by an in-frame stop codon, which is presumably the cause of the misprediction of the gene structure. That this sequence is cDNA than genomic is ascertained by the presence of an intron near the 3'end (see Additional file 2). Whether PF14_0733/4 is a transcribed pseudogene, or whether a protein can be produced by readthrough of the internal stop codon as has been documented for another P. falciparum gene [64], remains to be determined. In any case, it appears there are only 20 FIKK sequences in the genome, instead of the 21 that were counted originally (see above).
Table 1 Variability in key residues of the protein kinase catalytic domain. The residues indicated at the top are: G1, G2, G3, the three residues constituting the glycine triad (corresponding to G51, 53 and G56 in human PKAα), and which form a hairpin enclosing part of the ATP molecule; the lysine in subdomain II (K73), which contacts the α- and β-phosphate of ATP, anchoring and orienting the ATP; the glutamate of subdomain III (E92), which forms a salt bridge with the former residue; the aspartate and asparagine within the HRDXXXXN signature motif of ePKs in subdomain VIB (D167, N172), the former of which is thought to be the catalytic residue acting as a base acceptor; the aspartate in the DFG motif of subdomain VII (D185), which binds to the Mg2+ (or Mn2+) ion associated with the β-and γ-phosphates of ATP; the glutamate in subdomain VIII (E209), which forms a salt bond with the arginine in subdomain XI and provides structural stability of the C-terminal lobe; and the aspartate in subdomain IX (D221), which is involved in structural stability of the catalytic loop of subdomain VI through hydrogen bonding with the backbone. The conservation status of these residues in the 65 malarial typical ePKs is summarized at the top of the Table, and that of the 20 FIKK family members is presented at the bottom. It is immediately apparent that with the exception of the Glycine triad in subdomain I, all important residues are extremely well conserved in the FIKK sequences
Residue G1 G2 G3 K E D N D(FG) E D R
subdomain I I I II III VIB VIB VII VIII IX XI
"Typical" ePKs (65)
Number not conserved 16 10 27 0 4 0 0 1 5 2 2
% conserved 75 85 58 100 94 100 100 98 92 97 97
Amino-acid substitution 11S
3A I MAL7P1_18
N PF14_0408
N PFA0380A
K PFB0665w K PFB0665w Q MAL6P1_108
Y PF11_0060
Y PF11_0220
N PF11_0377
Q PFC0420w E PF11_0220
L PFI1415w N MAL7P1-18
N PF10_0160
Lacking all three Gs in subdomain I MAL7P1-18
MAL7P1_73
MAL7P1_91
PF11_0060
PF14_0408
PFA0380w
PFI1415w
PFL0080c
FIKK
Number not conserved 13 12 17 0(a) 0 0 0 0 0 1 0
All 20 FIKK have a conserved W in a [ILV][YF]W[NTS]XX[GC] motif approx 100 residues upstream of the FIKK motif E PF14_0733
% conserved 28 33 5 100 100 100 100 100 100 95 100
Note: a. This residue corresponds to the first K in the FIKK motif.
Figure 4 Comparative primary structure of FIKKs and typical ePKs. The eleven subdomains of the protein kinase catalytic domain are indicated in the central bar. The residues which are conserved in most ePKs (see legend to Table 1 for details) are indicated at the top. The corresponding residues in FIKKs are indicated under the bar, together with some of the motifs with which they are associated and which are conserved in all FIKK family members.
In addition to the residues conserved in typical ePKs, several amino-acid motifs are fully conserved in all members of the FIKK family (Fig. 4 and 5). These can be used to define signature motifs, which allowed us to perform a number of motif searches in various databases, to determine whether members of this ePK-like family are present in other organisms. Interestingly, sequences containing such motifs could be retrieved only from Apicomplexan species: 20 sequences in the P. falciparum genome, one in P. berghei (Pb75h08p1c-3-1074-4583), one in P. yoelii (chrPy1 00951-1-3319-5523), one in P. knowlesi (Pk2145b11q1c-4-8079-3688) and one in P. vivax (Pv402596-4-9942-5746). In contrast, no FIKK family member was found in the (yet incompletely sequenced) genomes of P. chabaudi or P. reichenowi. Searches of the NRprot database, which contained sequences representing all eukaryotic and prokaryotic phyla, yielded only the Plasmodium sequences mentioned above (20 in P. falciparum, and one each in P. berghei, yoelii, knowlesi and vivax). In agreement with the motif searches, BLAST analysis of the NRprot database with PF10_0160 finds 20 Plasmodium falciparum and one yoelii sequences among the top hits (E < 10-37). Weaker hits (E > 10-5) are mostly MAPKs from a variety of organisms. Further investigations using Apicomplexan genome project databases (Sanger and TIGR) allowed us to identify one such sequence in Toxoplasma gondii and one in Cryptosporidium parvum. Taken together, these data strongly suggest that the FIKK group is specific to Apicomplexa, and has undergone a dramatic expansion in P. falciparum. Interestingly, of the 20 FIKK sequences in the P. falciparum genome, 7 are located on chromosome 9, where they are arranged in a contiguous subtelomeric tandem array, a common location for genes involved in antigenic variation such as those of the var/PfEMP1 [65] or Rifin [66] families (Fig. 6). On the tree depicted in Fig. 6, these sequences (PFI0095c to PFI0125c) tend to cluster together. The structure of the tree (no major subgroups, with most of the branch points very close to each other and a fairly uniform branch length) suggests a rapid and presumably recent expansion of the family. This hypothesis is supported by the presence of the tandem array, an indicator of gene duplication. Furthermore, the presence of only one FIKK gene in several other apicomplexan species is consistent with the expansion in P. falciparum being a recent event. Obviously, a definite conclusion about the species distribution of this gene family will have to await the completion of additional genome sequencing projects, especially with respect to other Plasmodium species and other Apicomplexan genera.
Figure 5 Alignment of four representative sequences of the FIKK family with a typical ePK (PfPK5 [MAL13P1.279], a CDK homologue). Asterisks indicate those residues that are invariant in all 20 FIKK sequences.
Figure 6 A tree of the FIKK family. Phylogenetic tree of FIKKs from P. falciparum. The tree was compiled using conserved portions of aligned sequences (see Additional file 3) using a protein distance matrix method. The scale bar represents 0.1 mutational changes per residues (10 PAM units). Bootstrap values over 75 are shown. The bottom panel shows a map of one of the telomeric and subtelomeric regions of chromosome 9 obtained from the PlasmoDB website. The location of genes encoding proteins of the var/PfEMP1 (Duffy et al., 2003), rifin (Kyes et al., 1999) and FIKK (this study) families is indicated.
Although no experimental evidence is available that associates PK activity with any of the FIKK sequences, the fact that all residues required for phosphotransfer and ePK folding are present strongly suggests that these proteins are indeed protein kinases. Some FIKKs have a predicted signal peptide (PFD1165w, PFE0045c, MAL13P1.109, PFI0095c, PFI0105c, PFI0110c) and/or transmembrane helix (PFD1165w, PFD1175w, PF10_0160, PFI0110c, PFI0125c, PFI0100c has two) at the N-terminus. Otherwise, aside from their similarity to the kinase domain, no recognised Pfam domains are found in these proteins. Two of the FIKK sequences have been identified as P. falciparum antigens in the context of immunological studies: the R45 trophozoite antigen (PFD1175w) [67] and the 3.8 protein (PF10_0160). No function has previously been attributed to either of these proteins. R45 has a large insertion of 570 residues, comprising mostly His, Lys, Asn, Ser and Asp residues, relative to the other members of the FIKK family. The belonging of R45 to a 20-sequence family in the P. falciparum genome has been discovered independently in the context of research into the R45 antigen (Schneider and Puijalon, personal communication, to be published elsewhere).
Features of gene structure
Table 1 presents the degree of conservation, in malarial ePKs, of residues that play a crucial role in ePK enzyme function (see legend to Table 1 for details). As is the case in ePKs from other eukaryotes, the Glycine triad is not complete in many PfPKs and in all FIKKs, and none of the three glycine residues are present in 8 of the 65 "typical" ePfPKs. Other important residues are better conserved in the malarial PKs. The observation that some sequences (e.g. PF11_0060, PF14_0733 and MAL7P1.18) lack more than one of these conserved residues raises the question of their ability to function as protein kinases. These may represent kinase-dead scaffold proteins similar to those found in other eukaryotes, such as KSR [68]. In contrast, all 20 FIKKs possess essentially all these residues, despite a conservative D > E substitution in subdomain IX of PF14_0733.
Like in many other plasmodial proteins, large extensions rich in charged and/or polar residues, and in some cases repeated amino acid motifs, are found adjacent to the catalytic domain of several PfPKs. Several enzymes also carry such sequences as insertions within the catalytic domain. The function of these elements is as yet undetermined, although there is evidence in some cases [e.g. Pfmap-1, [32]] that extensions are absent from the enzymes in parasite protein extracts, presumably as a result from proteolytic cleavage. In some sequences (e.g. PFD0740w [Pfcrk-3] and PFC0755c [Pfcrk-4]), large insertions have been mapped to the hinge region between adjacent β-sheets in the N-terminal lobe; hence, it can be argued that such insertions may not interfere with proper folding of the catalytic domain (Equinet and Doerig, unpublished).
Organelle targeting
Malaria parasites possess two organelles with extra-chromosomal DNA: the apicoplast and the mitochondrion. The apicoplast is a four-membrane organelle carrying a circular 35 kb DNA whose structure is very similar to that of plastid genomes. It is specific to the Apicomplexa (hence its name), and thought to originate from secondary endosymbiosis [69]. As is the case for chloroplasts in plants [70], it appears that many genes whose products are essential for apicoplast function and survival have been transferred to the "host cell" nucleus; products of these genes must be addressed back to the organelle. A bipartite peptide has been identified and shown to be necessary and sufficient for targeting a protein to the apicoplast [71]. The 35 kb genome of the apicoplast does not encode any PK, but it is to be expected that protein phosphorylation is necessary for function and maintenance of the organelle. We used an algorithm available on PlasmoDB to determine that 5/65 typical ePfPKs (including 2 NIMA-related kinases) and 6/18 FIKKs are predicted to be addressed to the apicoplast (see Fig. 7). Likewise, four kinases (none of them of the FIKK family) possess a potential mitochondrion-targeting signal sequence, as defined by the algorithm available on PlasmoDB [72]. It is important to emphasise that presence or absence of targeting signals relies on gene structure prediction algorithms, which have been demonstrated to be erroneous in some instances (see ref. [55] for an example); therefore this must be considered with caution until the 5'end of the cDNAs has been sequenced, and targeting to the organelle has been verified experimentally by the transfection of constructs expressing GFP-tagged proteins.
Figure 7 P. falciparum ePKs and related proteins, and stage-specificity of their expression. PlasmoDB gene identifiers are indicated in the left column, followed by the published names where applicable. Identifiers of enzymes belonging to defined ePK groups appear in color (see the inset for color codes). Microarray data from the Le Roch et al. and Bozdech et al. studies available on PlasmoDB, were compiled to produce the third column. Genes were arranged in function of the timing of their expression according to Bozdech et al., to illustrate the fact essentially all of them are expressed in a regulated way during erythrocytic schizogony, and that this process involves sequential but overlapping expression of most kinases in the genome. The phaseogram (data generated by Bozdech et al. and available on the PlasmoDB website) represent the relative abundance of mRNAs throughout the erythrocytic asexual cycle, measured by two-colour competitive hybridisation between total RNA from each time point and a reference pool of total RNA from all time points (48 time points, i.e. one per hour during the 48 hours of the asexual cycle, starting one hour post invasion). The phaseogram shows the red/green colorimetric representation of the gene expression ratio for each oligonucleotide. Green: negative ratio (no expression), red: positive ratio (expression); grey or white: no data. See Bozdech et al. (2003) and PlasmoDB for details. To the right of the phaseogram, the presence or absence of mRNA in samples from merozoites (M), gametocytes (G) and sporozoites (S) is indicated by red boxes (data generated by Le Roch et al.). Where only one of the two synchronised merozoite population gave a signal, the M box is colored in orange (see Le Roch et al. 2003 for details). Columns to the right indicate those molecules which, according to the gene prediction algorithm used in PlasmoDB, possess a putative apicoplast or mitochondrion targeting sequence (see text for details).
Regulatory subunits
Proteins devoid of kinase activity but which are known to associate with, and regulate the activity of, ePKs have been identified in PlasmoDB. These include four previously characterised cyclins [PF14_0605, PF13_0022, PFL1330c and PFE0920c] which have been demonstrated to associate with histone H1 kinase activities in parasite extracts [50,55], a PKA regulatory subunit [PFL1110c], which as expected is able to down-regulate PKA in parasite extracts (Merckx and Doerig, unpublished), and two putative CK2 regulatory subunits [PF11_0048 and PF13_0232].
Genes encoding aPKs
BLASTP searches of PlasmoDB were performed using atypical protein kinases (aPKs) from Homo sapiens as queries. GeneDB was also used to look for relevant Pfam domains (ABC1, FAT, FATC, Bromodomain, RIO). Two members of the RIO kinase family were found: PFL1490w (RIOK1-like) and PFD0975w (RIOK2-like). Enzymes of this family are involved in rRNA processing in S. cerevisiae [73]. We also identified two putative members of the ABC1 family of atypical protein kinases [PF08_0098 and PF14_0143]. Some P. falciparum genes (e.g. PFD0685c and PF14_0326) display regions with low-level similarity to the histidine kinase domain (scores between 4 and 5 with Pfam entries PF00512 and PF06580), but the significance of this observation remains to be established.
No significant hits were obtained with A6 kinases, Alpha kinase, pyruvate dehydrogenase kinase, aminoglycoside phosphotransferases or DNA-dependent kinases. In contrast, we identified a malarial phosphatidyl-inositol-3 kinase homologue [PFE0765w], in agreement with experimental studies [74] and the presence of a PKB homologue (see above) demonstrating the presence of a phosphatidyl-inositol pathway in the parasite. However, the PI3K homologue, like two other sequences (PFE0485w and PFD0965w) related to PI4K, appears not to contain the FAT and FATC domains which are present in PIKs from other organism and have been associated with protein kinase activity [75]. Hence, it may be that these three enzymes function solely as phosphatidylinositol kinases, a proposition that requires experimental testing. Overall, these results on malarial aPKs contrast with those obtained from the recently-sequenced L. major, T. brucei and T. cruzi genomes, where ABC1 and RIO kinases were found, as were PIKK (with the FAT and FATC domains), PDHK and Alpha kinase family members (Parsons and Ward, unpublished).
Expression pattern of PfPKs during the P. falciparum life cycle
Data from two studies [76,77] of the P. falciparum transcriptome during development are available on the PlasmoDB database. We compiled these data to obtain a general picture of PfePK gene expression during erythrocytic development (Fig. 7). It is clear that the steady-state level of mRNA is developmentally regulated for all the PfPK genes, in accordance with the unique gene expression pattern described in this organism by Bozdech et al. [76]. Most of the PfePKs are expressed in trophozoites and schizonts, but some PK mRNAs are clearly predominantly detected in rings, the younger form following erythrocyte invasion. Data from Le Roch et al. [77] included a transcriptome analysis of additional development stages: free merozoites, gametocytes and sporozoites. Compilation of data from this study indicated that a small number of PfePKs are specific to gametocytes, including two of the NIMA-related kinases (one of which is potentially targeted to the apicoplast), one of the MAPKs (Pfmap-2), and PfKIN, an enzyme previously described as related to the SNF1 family (see above). Gametocyte-specific expression had been described in the literature for the latter two enzymes [42,45]. Overall, and despite some discrepancies, there is good agreement between the two studies with respect to PfePK genes, as illustrated by the observation that PfePKs whose expression is detected in late schizonts and segmenters by Bozdech et al. are also detected in free merozoites by Le Roch et al. At least some of these enzymes are likely to play a role in invasion of the erythrocyte by the merozoite. As expected, the PK genes that are gametocyte-specific according to Le Roch et al. (and hence likely to play a role during sexual development of the parasite) give low intensity signals in the dataset from Bozdech et al. (see for example Pfmap-2 or Pfnek-4 to illustrate this point).
Conclusion
This study has allowed us to classify the 65 typical ePKs encoded by the P. falciparum genome, and to establish the presence of a novel group of ePK-related genes, the FIKK family, which, from analysis of currently available databases, appears to be specific to Apicomplexa and considerably extended in P. falciparum. The number of genes encoding protein kinases is somewhat smaller than expected from analogy with other organisms. We cannot exclude that our study, which is based on sequence similarity with ePKs, may have missed genes encoding proteins with protein kinase activity, but with a primary structure that would be too divergent from that of known ePKs to be identified. Nevertheless, it is hoped that the present study will facilitate investigations into the regulation of many pathways and processes operating during growth and development of the parasite. In addition to the FIKKs, several malarial ePKs belong to "orphan" groups, as they do not cluster clearly with established ePK groups as defined in model organisms. Furthermore, our analysis provides evidence that elements which are usually found in eukaryotes are absent or dramatically modified in malaria parasites. Such elements include MAPK pathway components and PKC, for example. These important divergences between the malarial and human kinomes reflects the vast phylogenetic distance between Apicomplexans and Opisthokonta, and strengthen our expectations that specific interference with essential functions of the parasite can be achieved through the use of protein kinase inhibitors.
Methods
Identification of ePK genes in the P. falciparum genome
The set of predicted peptides of the Plasmodium falciparum genome 3D7 [78] was downloaded from PlasmoDB [8]. A Hidden Markov Model search [79] of the predicted proteins encoded by the genome was carried out using a eukaryotic protein kinase profile downloaded from the Pfam database [80]. In addition, PlasmoDB was searched for proteins carrying a Gene Ontology molecular function assignment [81] of 'protein kinase activity' (GO:0004672). This allowed us to constitute an initial list of 108 sequences. After inspection, 15 were removed that had high e-value (>0.01), low HMM scores (<-110) and visibly lacked a protein kinase domain. The remaining 93 sequences were aligned using our own Hidden Markov Model, trained on a complete set of human protein kinases, to check for the presence of the key kinase motifs. In addition, the genomic context of each putative kinase gene was examined to check for missing exons using GeneDB and Artemis [82]. Eight proteins, the first four of which have a PlasmoDB enzyme assignment to EC2.7.1 (phosphotransferases), lacked sufficient similarity to typical eukaryotic protein kinases to be aligned meaningfully across the kinase domain. These sequences were: PF13_0166, PFC0945w, PFE0170c, PFI1275w, MAL7P1.127, MAL7P1.132, PF11_0079 and PF14_0264; they were removed from further analysis. A further 20 sequences constituted the FIKK family (see below). This set of closely related, but atypical, sequences was analysed separately. The remaining 65 sequences represent the complement of typical protein kinases in P. falciparum. Although the Hidden Markov Model used for the alignment is based on an extensive training set, the alignment did require some manual optimisation. This is partly because of the extreme diversity of the gene family and partly because many predicted proteins from P. falciparum contain large repetitive insertions (Hidden Markov Model-based alignment protocols would be expected to cope better in these circumstances than other common methods). A full alignment of the kinase domains is shown in Additional file 1. Once a definitive set of the 65 sequences representing typical ePKs had been assembled, a phylogenetic tree was produced using Phylip [83], with the Protdist and Fitch algorithms. Human protein kinases were added to the alignment in order to improve the visualization of the main groups of protein kinases among the P. falciparum sequences. Only gap-free conserved regions of the alignment were used for the construction of the tree (164 amino acid positions). Bootstrap values supporting the branches of the tree are rather low; this is to be expected given the diversity of the protein kinase family.
Authors' contributions
PW performed most of the database searches for ePK-related sequences and constructed the FIKK phylogenetic tree; LE contributed to the searches for aPKs, compiled expression data and performed the in silico and in vitro analyses of the FIKK family. JP generated the HMM-derived alignments, constructed the ePK phylogenetic trees and contributed significantly to their description in the text. CD coordinated the study and wrote the larger part of the manuscript. All authors read and approved the manuscript.
Supplementary Material
Additional File 1
Alignment of the 65 "typical" P. falciparum ePKs used for constructing the tree in Fig. 2. Please see the Methods section for details on how the alignment was generated.
Click here for file
Additional File 2
partial sequence of the cDNA for the gene PF14_0733/PF14_0734.
Click here for file
Additional File 3
Alignement of the 20 FIKK sequences used to construct the tree in Fig. 6.
Click here for file
Acknowledgements
This work was made possible by the availability of the P. falciparum genome database PlasmoDB. We are indebted to all members of the team which contributed to the development of this database, which is proving an invaluable tool for molecular research on malaria. We thank the authors of the Le Roch et al. and Bozdech et al. microarray studies for making their raw data available for compilation on the PlasmoDB website. Financial support for the Plasmodium Genome Consortium was provided by the Burroughs Wellcome Fund, the Wellcome Trust, the National Institutes of Health (NIAID) and the U.S. Department of Defense, Military Infectious Diseases Research Program. Financial Support for PlasmoDB was provided by the Burroughs Wellcome Fund.
Work in the C.D. laboratory is supported by the UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases (TDR), by the French Ministère de la Défense (Délégation Générale pour l'Armement [DGA]), by the French-South African joint program on Science and Technology, and by INSERM. L.E. is the recipient of a studentship awarded by the French Délégation Générale pour l'Armement (DGA).
We are grateful to A. Schneider and O. Puijalon (Institut Pasteur, Paris) for freely discussing their data on the R45 antigen/FIKK family prior to publication, to O. Billker (Imperial College, London) for critical reading of the manuscript, and to T. Monteil for help with the FIKK RT-PCR experiments.
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| 15479470 | PMC526369 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Oct 12; 5:79 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-79 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-251549810510.1186/1471-2210-4-25Research ArticleInhibitory effects of proanthocyanidins from Ribes nigrum leaves on carrageenin acute inflammatory reactions induced in rats Garbacki Nancy [email protected] Monique [email protected] Luc [email protected] Jacques [email protected] Laboratoire de Physiologie humaine, CHU, Tour 3, Université de Liège, Avenue de l'Hôpital, 3, B-4000 Sart Tilman, Belgium2 Laboratoire de Pharmacognosie (C.P.S.N.S.), CHU, Tour 4, Université de Liège, avenue de l'Hôpital 1, B-4000 Sart-Tilman, Belgium2004 21 10 2004 4 25 25 14 5 2004 21 10 2004 Copyright © 2004 Garbacki et al; licensee BioMed Central Ltd.2004Garbacki 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 anti-inflammatory effects of proanthocyanidins (PACs), isolated from blackcurrant (Ribes nigrum L.) leaves, were analysed using carrageenin-induced paw oedema and carrageenin-induced pleurisy in rats.
Results
Pretreatment of the animals with PACs (10, 30, 60 and 100 mg/kg, i.p.) reduced paw oedema induced by carrageenin in a dose and time-dependent manner. PACs also inhibited dose-dependently carrageenin-induced pleurisy in rats. They reduced (A) lung injury, (B) pleural exudate formation, (C) polymorphonuclear cell infiltration, (D) pleural exudate levels of TNF-α, IL-1β and CINC-1 but did not affect IL-6 and IL-10 levels. They reduced (E) pleural exudate levels of nitrite/nitrate (NOx). In indomethacin treated rats, the volume of pleural exudate was low, its content in leukocytes and its contents in TNF-α, IL-1β, IL-6 and IL-10 but not in NOx were reduced. These data suggest that the anti-inflammatory properties of PACs are achieved through a different pattern from those of indomethacin.
Conclusion
These results suggest that the main mechanism of the anti-inflammatory effect of PACs mainly lies in an interference with the migration of the leukocytes. Moreover, PACs inhibited in vivo nitric oxide release.
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Background
Proanthocyanidins are compounds, naturally occurring in various plants, with anti-inflammatory [1,2] and anti-arthritic activities [3]. They are reported to prevent skin aging and heart diseases, they scavenge oxygen free radicals and inhibit UV radiation-induced peroxidation [4-10].
We have isolated prodelphinidins and procyanidins, proanthocyanidins (PACs) from blackcurrant (Ribes nigrum L., Grossulariaceae) leaves which are used in European traditional medicine for the treatment of inflammatory disorders such as rheumatic diseases [11]. Majority of these compounds are water soluble monomers and oligomers (2 to 3 units) consisting of flavan 3-ol monomer units linked together by mostly C-4 to C-8 (Figure 1) and to a lesser extent C-4 to C-6 bindings. Few tetramers are also found.
Figure 1 Chemical structure of proanthocyanidins. Where R = H, it is a procyanidin: catechin (R1 = H and R2 = OH) and epicatechin (R1 = OH and R2 = H); Where R = OH, it is a prodelphinidin: gallocatechin (R1 = H and R2 = OH) and epigallocatechin (R1 = OH and R2 = H).
Previously, we have observed that, in vitro, these compounds profoundly affect the metabolism of chondrocytes : they increase the secretion from these cells of type II collagen and proteoglycans while they decrease the secretion of prostaglandin E2 (PGE2) [12]. On the other hand, while these compounds inhibited purified cyclo-oxygenase-1 and cyclo-oxygenase-2, they did not reduce the release of thromboxane B2 and PGE2 from human in vitro stimulated platelets and neutrophils respectively [12]. Moreover, PACs might influence the contractile status of smooth muscles of blood vessels : intravenous and intraperitoneal injection of PACs induced a drop of the blood pressure without a significant bradycardia [13]. This effect counteracts the hypertensive activity of norepinephrine.
The present studies were designed to evaluate the potential anti-inflammatory activities of these compounds, in vivo, on carrageenin-induced paw oedema and pleurisy in rats. This latter inflammatory reaction allowed us to examine the influence of PACs not only on the exudate volume and polymorphonuclear cell accumulation but also on the release of several cytokines, IL-1β, TNF-α, IL-6, IL-10, CINC-1 and of nitric oxide (NO). These cytokines and NO are among the more important mediators involved in inflammatory processes [14-16].
Results
Influence of PACs on rat paw oedema
Carrageenin-induced oedema was significantly inhibited by PACs dose-dependently (Figure 2). This inhibitory effect was efficient from 2 h after the carrageenin injection for the two upper doses of PACs and was significative 4 h after the carrageenin administration for all doses of PACs. The maximum inhibitory effect of PACs reached 63% at 4 h after carrageenin, time of the maximum development of the oedema.
Figure 2 Time course of inflammatory reaction induced by injection of carrageenin 1% in rat hind paw and its antagonism by PACs (10, 30, 60 and 100 mg/kg-1). Inflammation is expressed as the increase of the rat paw volume (ml) from 0 to 4 h following injection of carrageenin. The volume of the paw was reduced by PACs at the four doses tested and the inhibition is time and dose-dependant. Each value is the mean ± s.e. mean of n = 6 experiments. *P < 0.05 versus carrageenin.
Influence of PACs on the carrageenin-induced pleurisy
In control rats, the volume of the exudate collected 4 h after carrageenin injection reached 0.87 ± 0.18 ml per rat (n = 12). This exudate contained a large number of cells, mostly (> 95%) polymorphonuclear leukocytes (PMNs). The total leukocytes number in the exudate was 119.71 ± 29.29 × 106 per rat (Figure 3A). PACs significantly reduced the volume of the exudate in a dose-dependent relationship, showing a maximum inhibitory effect (48%) from the dose of 30 mg/kg which was not increased by the upper doses of PACs. As expected, the volume of the exudate was reduced in indomethacin-treated rats. On the other hand, PMNs infiltration (Figure 3B) was significantly inhibited by PACs in a dose-dependent way and by indomethacin.
Figure 3 Effect of indomethacin and PACs on carrageenin-induced pleurisy. At 4 h after carrageenin injection, the volume of the exudate (A) was reduced by PACs (10, 30, 60 and 100 mg/kg) and indomethacin (10 mg/kg) administration. The accumulation of polymorphonuclear cells (PMNs, B) in the pleural cavity was inhibited by all tested drugs. Each value is the mean ± s.e. mean of n = 6 experiments. °P < 0.05 versus sham. *P < 0.05 versus carrageenin.
Effects of PACs on the release of cytokines
High levels of TNF-α, IL-1β, IL-6, IL-10 and CINC-1 were found in pleural exudates induced by carrageenin (Figure 4). Indomethacin reduced the level of the five cytokines studied while PACs lowered significatively the levels of TNF-α (Figure 4A), inhibited the release of IL-1β (Figure 4B) but did not affect IL-6 levels (Figure 4C) and IL-10 production (Figure 4D). PACs also lowered significantly CINC-1 levels (Figure 4E).
Figure 4 Effect of indomethacin and PACs on cytokines release in pleural exudate. Pleural injection of carrageenin caused by 4 h an increase in the release of the cytokines, tumor necrosis factor alpha (TNF-α, A), interleukin-1β (IL-1β, B), interleukin-6 (IL-6, C), interleukin-10 (IL-10, D) and cytokine-induced neutrophil chemoattractant-1 (CINC-1, E). TNF-α, IL-1β and CINC-1 levels were reduced by PACs, but IL-6 and IL-10 levels were not modified. Indomethacin lowered the level of all these cytokines. Each value is the mean ± s.e. mean of n = 6 experiments. °P < 0.05 versus sham. *P < 0.05 versus carrageenin.
Effect of PACs on nitrite/nitrate (NOx) levels in pleural exudate
The pleural exudate of carrageenin-treated rats contained a large amount of NOx (716 ± 32 μM; n = 6) (Figure 5). The amount of NOx in pleural exudate of rats treated with 10 mg/kg indomethacin was similar to the content found in the control group. On the other hand, PACs, at 30 mg/kg, significantly decreased the amounts of NOx in pleural exudate from 51%.
Figure 5 Effect of PACs and indomethacin on NOx formation in pleural exudate. Production of NOx release was not significantly affected by pretreatment of rats with indomethacin (10 mg/kg, intraperitoneally) while PACs caused an inhibition in NOx production. Each value is the mean ± s.e. mean of n = 6 experiments. °P < 0.05 versus sham. *P < 0.05 versus carrageenin.
Histological examination of lung sections
Histological examination of lung sections revealed significant tissue injury (Figure 6) when compared with lung sections taken from saline-treated rats (Figure 6A). Lung withdrawn from rats treated with carrageenin showed oedema, tissue injury and an extensive infiltration of the tissue by PMNs (Figure 6B). Pretreatment of rats with indomethacin (10 mg/kg, i.p.) or PACs (30 mg/kg, i.p.) showed a reduced lung injury as well as a decrease in the infiltration of PMNs (Figures 6C,6D).
Figure 6 Effect of PACs on lung injury. When compared to lung sections taken from control animals (A), lung sections from carrageenin-treated rats (B) demonstrated interstitial haemorrhage and polymorphonuclear leukocyte accumulation. Lung sections from a carrageenin-treated rat that had received PACs (30 mg/kg) (C) or indomethacin (10 mg/kg) (D) exhibited reduced interstitial haemorraghe and a lesser cellular infiltration. Original magnification: × 125.
Discussion
Proanthocyanidins (PACs) from Ribes nigrum leaves reduced the inflammatory reactions induced by carrageenin in rats : the extent of the paw oedema was halved, the volume of the pleural exudates and its content in TNF-α, IL-1β, CINC-1 and NOx were reduced, the infiltration of leukocytes into the lungs and the accumulation of leukocytes into the pleural cavity were largely diminished.
PACs have been reported to be able to scavenge free radicals and NO [17]. This property could be an explanation of the reduction of NOx level in the pleural fluid after PACs treatment. According to Ialenti et al [18], during the development of carrageenin-induced pleurisy, the main role of NO is the inhibition of leukocytes migration to the inflammatory site. However, in rats pretreated with PACs, the level of NOx and of leukocytes are simultaneously reduced. This result suggests that PACs could more or less directly affect the transmigration of leukocytes.
The development of carrageenin-induced inflammatory reactions in rats results from the activation of the kinin system, the accumulation of leukocytes and the release of several mediators such as prostanoids and cytokines [19,20]. Indeed, these inflammatory reactions are greatly reduced in kininogen-deficient rats, in animals pretreated with kinin-antagonists and in leucopenic rats [19,21]. Previous studies [22] have demonstrated that PACs can reduce other inflammatory reactions such as the oedemas induced in rats by nystatin and concanavalin-A in which the kinin system is not involved [19] but in which leukocytes play a major role [23]. The comparison of the major determinants of these three kinds of reactions, all inhibited by PACs, is another argument suggesting that the main target explaining the anti-inflammatory activity of PACs would be the involvement of leukocytes.
Pro-inflammatory cytokines TNF-α, IL-1β and IL-6 are sequentially released in the pleural exudates induced by carrageenin in rat [14]. These cytokines cause chemotaxis to attract granulocytes and monocytes and then, migrating leukocytes produce, in turn, further cytokines, such as TNF-α and IL-1β, and other pro-inflammatory mediators [15]. IL-6 has been proposed as a crucial mediator for the development of carrageenin-induced pleurisy and for the accumulation of leukocytes in the inflammatory site. Indeed, in carrageenin-induced pleurisy in IL-6 knock-out mice, the degree of plasma exudation, leukocyte migration and the release of TNF-α and IL-1β were greatly reduced. Moreover, a positive feedback plays an important part in the development of the oedema as levels of TNF-α and IL-1β are attenuated in IL-6 knock-out mice [24]. PACs did not affect the level of IL-6 and of IL-10, an anti-inflammatory cytokine, but reduced the pleural content of TNF-α, IL-1β and leukocytes. This result indicates that the release of IL-6 does not depend on the presence of leukocytes, of TNF-α and IL-1β on one hand, and, on the other hand, suggest that the main target of PACs would be the accumulation of leukocytes and the associated release of inflammatory mediators.
TNF-α plays an important role in promoting and amplifying lung inflammation through the release of chemotactic factors such as CINC-1 (rat IL-8), an important mediator that promotes the migration of neutrophils [25] and oesinophils [26]. CINC-1 can increase the expression of LFA-1 integrin on rat neutrophils [27] and because expression of leukocyte adhesion molecules such as E-selectin is dependent on CINC [28], the inhibition of CINC-1 levels in pleural exudates by PACs may exert both direct and indirect effects on neutrophil vascular adhesion and extravascular migration. PACs probably acts by disrupting TNF-α, IL-1β, CINC-1 and PMNs accumulation pathways. One of the mechanism for the anti-inflammatory effect of PACs may be attenuation of the migration of PMNs in the exudate, because CINC-1, a representative cytokine for PMNs migration in rats, is suppressed by PACs in parallel with PMNs number dose-related fashion. Although, clarification for the precise mechanism would remain in future study.
Recently, grape seed proanthocyanidins have been demonstrated to reduce the expression of soluble adhesion molecules, ICAM-1, VCAM-1 and E-selectin in the plasma of systemic sclerosis patients [29]. The same compounds have been shown to inhibit TNF-α-induced V-CAM-1 expression in human umbilical vein endothelial cells cultures [30]. A possible mechanism of the anti-inflammatory effect of PACs would be an interference with the expression or the effect of adhesion molecules. This interference would result in a reduction of polymorphonuclear cell migration and subsequently in a reduction of the release of pro-inflammatory factors such as TNF-α and IL-1β.
Injection of carrageenin into the pleural cavity induces the accumulation of leukocytes, a release of cytokines, the expression of inducible NO synthase and of cyclo-oxygenase-2, and thus the release of large amounts of nitric oxide and of prostanoids [16]. The inhibitory effect of PACs on the accumulation of leukocytes and on the release of TNF-α and IL-1β could have resulted in a decrease in the induction of inducible NO-synthase and of cyclo-oxygenase-2 and finally of plasma exudation.
Comparatively, some animals have been treated with indomethacin. The inhibitory effect of this well-known non-steroidal anti-inflammatory drug is larger than that obtained with PACs. Indomethacin greatly reduced plasma exudation, nearly suppressed the accumulation of leukocytes and decreased the levels of the cytokines while, it did not modify the pleural content of NOx. Indomethacin is known to inhibit the cyclooxygenase-1 and -2 responsible of the release of PGE2 production. The peak of cyclooxygenase-2 activity measured by prostanoid levels in carrageenin-induced pleural exudates spreads from 2 to 6 h after irritant injection [31,32]. Both IL-6 and IL-10 release are, in part, stimulated by PGE2 [33,34]. An inhibition of PGE2 production by high doses of indomethacin could result in a downregulation of IL-6 and IL-10 production [35,36]. Moreover, Cuzzocrea et al [24], using carrageenin-induced pleurisy in IL-6 knock out mice, showed that IL-1β and TNF-α production in the pleural exudates is, at least, partly IL-6 dependent. Our results showing a reduction in the levels of IL-1β, TNF-α, IL-6, IL-10 and CINC-1 by indomethacin four hours after the induction of the pleurisy, could be mainly explained through the inhibition of PGE2 and IL-6 pathways.
Conclusions
In conclusion, we have shown that proanthocyanidins isolated from Ribes nigrum leaves interfere with the accumulation of circulating leukocytes, associated with a reduction of pro-inflammatory factors such as TNF-α, IL-1β and CINC-1, a decrease of NOx level and a decrease in plasma exudation.
Methods
Animals
We used male Wistar rats, weighing 250 – 300 gm. The animals were maintained on a standard laboratory diet with free access to water. The experiments were conducted as approved by the Animal Ethics Committee of the University of Liège, Belgium.
Paw oedema
Rats were pretreated with an intraperitoneal administration of saline or PACs (10, 30, 60 and 100 mg/kg). Thirty minutes later, lambda carrageenin, (0.1 ml, 10 mg/ml) was injected into the plantar region of the right hind paw. Each experimental group contained six animals. Paw volume was measured using a water plethysmometer (Ugo Basile) before and 1 h, 2 h and 4 h after the injection of carrageenin. After 4 h, the animals were anaesthetized with a large dose of sodium pentobarbital (80 mg/kg).
Carrageenin-induced pleurisy
Rats were pretreated with an intraperitoneal injection of saline, PACs (10, 30, 60 or 100 mg/kg) or indomethacin (10 mg/kg) 30 min before the intrapleural injection of the irritant. They were then anaesthetized with ketamine HCl (75 mg/kg) and carrageenin (0.2 ml, 10 mg/ml) or saline (0.2 ml) was administered into the right pleural cavity. Each experimental group contained 6 animals. Four hours later, the animals were anaesthetized with sodium pentobarbital (80 mg/kg). The chest was carefully opened and the pleural cavity rinsed with 2.0 ml saline solution containing heparin (5 U/ml). Exudates and washing solutions were removed by aspiration and the total volume measured. Exudates with blood were rejected. Exudates were aliquoted and kept frozen at -32°C.
After removal of the exudates, lungs were withdrawn and fixed for one week under 30 cm pressure with 10% formaldehyde aqueous solution containing 0.480 M Na2HPO4 and 0.187 M KH2PO4 (pH 7.2) at room temperature. They were then dehydrated by graded ethanol and embedded in Paraplast. Tissue sections (thickness 7 μm) were deparaffinized with UltraClear, stained with hematoxylin-eosine and examined using light microscopy.
The volume of the exudates was calculated by subtracting the volume of the washing solution (2.0 ml) from the total volume recovered. A sample of each exudate was diluted in phosphate buffer and total leukocyte count was performed using a hemocytometer.
The levels of IL-1β, TNF-α, IL-6 and IL-10 in the exudates were measured using a colorimetric commercial ELISA kit (Biosource, Nivelles, Belgium) with a lower detection limit of 4, 3, 8 and 5 pg/ml, respectively. The levels of CINC-1 in the exudates were measured using a colorimetric commercial ELISA kit (Amersham Biosciences, Freiburg, Germany) with a lower detection limit of 0.49 pg/ml.
The amount of NOx (nitrite/nitrate) present in the exudates was determined using a microplate assay method (Calbiochem, Leuven, Belgium) based on Griess reaction after reduction of NO3 - to NO2 - with a lower detection limit of 1 μM.
Extraction and purification of proanthocyanidins
Proanthocyanidins from Ribes nigrum leaves were extracted and isolated according to a previously described method [37]. A voucher sample (RN 210590) has been deposited in the Pharmaceutical Institute of Liège, Belgium. Briefly, leaves were powdered separately and then extracted at room temperature with acetone (70 % v/v in water). The acetone was removed under vacuum at 40°C. The resulting aqueous solution was freeze-dried. Isolation was carried out by MPLC on reversed-phase RP8 with water-acetone (9:1) to obtain a total proanthocyanidin-enriched fraction (PACs).
Materials
We used ketamine-HCl from Pfizer (Bruxelles, Belgium), sodium pentobarbital from Ceva (Bruxelles, Belgium) and heparin from B. Braun Medicals (Diegem, Belgium). PACs and lambda carrageenin (Sigma, Bornem, Belgium) were dissolved in saline. Indomethacin (Merck, Sharp and Dohme, Leuven, Belgium) was dissolved in Tris-HCl (0.15 M, pH 7.4).
Statistical evaluation
Results are given as mean ± standard error of the mean (s.e. mean) of N observations. For the oedema paw studies, a Mixed Procedure SAS (normal distribution) was used to compare difference of least square means. For the pleurisy studies, data sets were examined by one-way analysis of variance (ANOVA) followed by a Scheffe post-hoc test. A P-value of less than 0.05 was considered significant.
Authors' contributions
NG carried out PACs isolation, animal experimentation, immunoassays, lung sections and statistical analysis. MT coordinated and participated to the PACs isolation. LA coordinated the PACs isolation. JD participated in animal experimentation, conceived of the study and participated in its design and coordination.
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| 15498105 | PMC526370 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 21; 4:25 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-25 | oa_comm |
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